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- LICENSE +201 -0
- README.md +8 -11
- app.py +230 -0
- configs/mm/faster_rcnn_r50_fpn_coco.py +182 -0
- configs/mm/hrnet_w48_coco_256x192.py +169 -0
- configs/stable-diffusion/sd-v1-inference.yaml +65 -0
- dist_util.py +91 -0
- ldm/__pycache__/inference_base.cpython-38.pyc +0 -0
- ldm/__pycache__/util.cpython-38.pyc +0 -0
- ldm/data/__init__.py +0 -0
- ldm/data/dataset_coco.py +36 -0
- ldm/data/dataset_depth.py +35 -0
- ldm/data/dataset_laion.py +130 -0
- ldm/data/dataset_wikiart.py +67 -0
- ldm/data/utils.py +60 -0
- ldm/inference_base.py +292 -0
- ldm/lr_scheduler.py +98 -0
- ldm/models/__pycache__/autoencoder.cpython-38.pyc +0 -0
- ldm/models/autoencoder.py +211 -0
- ldm/models/diffusion/__init__.py +0 -0
- ldm/models/diffusion/__pycache__/__init__.cpython-38.pyc +0 -0
- ldm/models/diffusion/__pycache__/ddim.cpython-38.pyc +0 -0
- ldm/models/diffusion/__pycache__/ddpm.cpython-38.pyc +0 -0
- ldm/models/diffusion/__pycache__/plms.cpython-38.pyc +0 -0
- ldm/models/diffusion/ddim.py +293 -0
- ldm/models/diffusion/ddpm.py +1329 -0
- ldm/models/diffusion/dpm_solver/__init__.py +1 -0
- ldm/models/diffusion/dpm_solver/dpm_solver.py +1217 -0
- ldm/models/diffusion/dpm_solver/sampler.py +87 -0
- ldm/models/diffusion/plms.py +243 -0
- ldm/modules/__pycache__/attention.cpython-38.pyc +0 -0
- ldm/modules/__pycache__/ema.cpython-38.pyc +0 -0
- ldm/modules/attention.py +344 -0
- ldm/modules/diffusionmodules/__init__.py +0 -0
- ldm/modules/diffusionmodules/__pycache__/__init__.cpython-38.pyc +0 -0
- ldm/modules/diffusionmodules/__pycache__/model.cpython-38.pyc +0 -0
- ldm/modules/diffusionmodules/__pycache__/openaimodel.cpython-38.pyc +0 -0
- ldm/modules/diffusionmodules/__pycache__/util.cpython-38.pyc +0 -0
- ldm/modules/diffusionmodules/model.py +852 -0
- ldm/modules/diffusionmodules/openaimodel.py +798 -0
- ldm/modules/diffusionmodules/util.py +270 -0
- ldm/modules/distributions/__init__.py +0 -0
- ldm/modules/distributions/__pycache__/__init__.cpython-38.pyc +0 -0
- ldm/modules/distributions/__pycache__/distributions.cpython-38.pyc +0 -0
- ldm/modules/distributions/distributions.py +92 -0
- ldm/modules/ema.py +80 -0
- ldm/modules/encoders/__init__.py +0 -0
- ldm/modules/encoders/__pycache__/__init__.cpython-38.pyc +0 -0
- ldm/modules/encoders/__pycache__/adapter.cpython-38.pyc +0 -0
- ldm/modules/encoders/__pycache__/modules.cpython-38.pyc +0 -0
LICENSE
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README.md
CHANGED
@@ -1,13 +1,10 @@
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-
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title: T2I
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-
emoji: π’
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colorFrom: green
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colorTo: gray
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sdk: gradio
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sdk_version: 3.
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pinned: false
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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license: openrail
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title: T2I-Adapter
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sdk: gradio
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sdk_version: 3.19.1
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emoji: π»
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colorFrom: pink
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colorTo: blue
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pinned: false
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python_version: 3.8.16
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app_file: app.py
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app.py
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|
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|
|
|
|
1 |
+
# demo inspired by https://huggingface.co/spaces/lambdalabs/image-mixer-demo
|
2 |
+
import argparse
|
3 |
+
import copy
|
4 |
+
import gradio as gr
|
5 |
+
import torch
|
6 |
+
from functools import partial
|
7 |
+
from itertools import chain
|
8 |
+
from torch import autocast
|
9 |
+
from pytorch_lightning import seed_everything
|
10 |
+
|
11 |
+
from basicsr.utils import tensor2img
|
12 |
+
from ldm.inference_base import DEFAULT_NEGATIVE_PROMPT, diffusion_inference, get_adapters, get_sd_models
|
13 |
+
from ldm.modules.extra_condition import api
|
14 |
+
from ldm.modules.extra_condition.api import ExtraCondition, get_cond_model
|
15 |
+
from ldm.modules.encoders.adapter import CoAdapterFuser
|
16 |
+
import os
|
17 |
+
from huggingface_hub import hf_hub_url
|
18 |
+
import subprocess
|
19 |
+
import shlex
|
20 |
+
|
21 |
+
torch.set_grad_enabled(False)
|
22 |
+
|
23 |
+
urls = {
|
24 |
+
'TencentARC/T2I-Adapter':[
|
25 |
+
'third-party-models/body_pose_model.pth', 'third-party-models/table5_pidinet.pth',
|
26 |
+
'models/coadapter-canny-sd15v1.pth',
|
27 |
+
'models/coadapter-color-sd15v1.pth',
|
28 |
+
'models/coadapter-sketch-sd15v1.pth',
|
29 |
+
'models/coadapter-style-sd15v1.pth',
|
30 |
+
'models/coadapter-depth-sd15v1.pth',
|
31 |
+
'models/coadapter-fuser-sd15v1.pth',
|
32 |
+
|
33 |
+
],
|
34 |
+
'runwayml/stable-diffusion-v1-5': ['v1-5-pruned-emaonly.ckpt'],
|
35 |
+
'andite/anything-v4.0': ['anything-v4.5-pruned.ckpt', 'anything-v4.0.vae.pt'],
|
36 |
+
}
|
37 |
+
|
38 |
+
if os.path.exists('models') == False:
|
39 |
+
os.mkdir('models')
|
40 |
+
for repo in urls:
|
41 |
+
files = urls[repo]
|
42 |
+
for file in files:
|
43 |
+
url = hf_hub_url(repo, file)
|
44 |
+
name_ckp = url.split('/')[-1]
|
45 |
+
save_path = os.path.join('models',name_ckp)
|
46 |
+
if os.path.exists(save_path) == False:
|
47 |
+
subprocess.run(shlex.split(f'wget {url} -O {save_path}'))
|
48 |
+
|
49 |
+
supported_cond = ['style', 'color', 'sketch', 'depth', 'canny']
|
50 |
+
|
51 |
+
# config
|
52 |
+
parser = argparse.ArgumentParser()
|
53 |
+
parser.add_argument(
|
54 |
+
'--sd_ckpt',
|
55 |
+
type=str,
|
56 |
+
default='models/v1-5-pruned-emaonly.ckpt',
|
57 |
+
help='path to checkpoint of stable diffusion model, both .ckpt and .safetensor are supported',
|
58 |
+
)
|
59 |
+
parser.add_argument(
|
60 |
+
'--vae_ckpt',
|
61 |
+
type=str,
|
62 |
+
default=None,
|
63 |
+
help='vae checkpoint, anime SD models usually have seperate vae ckpt that need to be loaded',
|
64 |
+
)
|
65 |
+
global_opt = parser.parse_args()
|
66 |
+
global_opt.config = 'configs/stable-diffusion/sd-v1-inference.yaml'
|
67 |
+
for cond_name in supported_cond:
|
68 |
+
setattr(global_opt, f'{cond_name}_adapter_ckpt', f'models/coadapter-{cond_name}-sd15v1.pth')
|
69 |
+
global_opt.device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
|
70 |
+
global_opt.max_resolution = 512 * 512
|
71 |
+
global_opt.sampler = 'ddim'
|
72 |
+
global_opt.cond_weight = 1.0
|
73 |
+
global_opt.C = 4
|
74 |
+
global_opt.f = 8
|
75 |
+
#TODO: expose style_cond_tau to users
|
76 |
+
global_opt.style_cond_tau = 1.0
|
77 |
+
|
78 |
+
# stable-diffusion model
|
79 |
+
sd_model, sampler = get_sd_models(global_opt)
|
80 |
+
# adapters and models to processing condition inputs
|
81 |
+
adapters = {}
|
82 |
+
cond_models = {}
|
83 |
+
|
84 |
+
torch.cuda.empty_cache()
|
85 |
+
|
86 |
+
# fuser is indispensable
|
87 |
+
coadapter_fuser = CoAdapterFuser(unet_channels=[320, 640, 1280, 1280], width=768, num_head=8, n_layes=3)
|
88 |
+
coadapter_fuser.load_state_dict(torch.load(f'models/coadapter-fuser-sd15v1.pth'))
|
89 |
+
coadapter_fuser = coadapter_fuser.to(global_opt.device)
|
90 |
+
|
91 |
+
|
92 |
+
def run(*args):
|
93 |
+
with torch.inference_mode(), \
|
94 |
+
sd_model.ema_scope(), \
|
95 |
+
autocast('cuda'):
|
96 |
+
|
97 |
+
inps = []
|
98 |
+
for i in range(0, len(args) - 8, len(supported_cond)):
|
99 |
+
inps.append(args[i:i + len(supported_cond)])
|
100 |
+
|
101 |
+
opt = copy.deepcopy(global_opt)
|
102 |
+
opt.prompt, opt.neg_prompt, opt.scale, opt.n_samples, opt.seed, opt.steps, opt.resize_short_edge, opt.cond_tau \
|
103 |
+
= args[-8:]
|
104 |
+
|
105 |
+
conds = []
|
106 |
+
activated_conds = []
|
107 |
+
for idx, (b, im1, im2, cond_weight) in enumerate(zip(*inps)):
|
108 |
+
cond_name = supported_cond[idx]
|
109 |
+
if b == 'Nothing':
|
110 |
+
if cond_name in adapters:
|
111 |
+
adapters[cond_name]['model'] = adapters[cond_name]['model'].cpu()
|
112 |
+
else:
|
113 |
+
activated_conds.append(cond_name)
|
114 |
+
if cond_name in adapters:
|
115 |
+
adapters[cond_name]['model'] = adapters[cond_name]['model'].to(opt.device)
|
116 |
+
else:
|
117 |
+
adapters[cond_name] = get_adapters(opt, getattr(ExtraCondition, cond_name))
|
118 |
+
adapters[cond_name]['cond_weight'] = cond_weight
|
119 |
+
|
120 |
+
process_cond_module = getattr(api, f'get_cond_{cond_name}')
|
121 |
+
|
122 |
+
if b == 'Image':
|
123 |
+
if cond_name not in cond_models:
|
124 |
+
cond_models[cond_name] = get_cond_model(opt, getattr(ExtraCondition, cond_name))
|
125 |
+
conds.append(process_cond_module(opt, im1, 'image', cond_models[cond_name]))
|
126 |
+
else:
|
127 |
+
conds.append(process_cond_module(opt, im2, cond_name, None))
|
128 |
+
|
129 |
+
features = dict()
|
130 |
+
for idx, cond_name in enumerate(activated_conds):
|
131 |
+
cur_feats = adapters[cond_name]['model'](conds[idx])
|
132 |
+
if isinstance(cur_feats, list):
|
133 |
+
for i in range(len(cur_feats)):
|
134 |
+
cur_feats[i] *= adapters[cond_name]['cond_weight']
|
135 |
+
else:
|
136 |
+
cur_feats *= adapters[cond_name]['cond_weight']
|
137 |
+
features[cond_name] = cur_feats
|
138 |
+
|
139 |
+
adapter_features, append_to_context = coadapter_fuser(features)
|
140 |
+
|
141 |
+
output_conds = []
|
142 |
+
for cond in conds:
|
143 |
+
output_conds.append(tensor2img(cond, rgb2bgr=False))
|
144 |
+
|
145 |
+
ims = []
|
146 |
+
seed_everything(opt.seed)
|
147 |
+
for _ in range(opt.n_samples):
|
148 |
+
result = diffusion_inference(opt, sd_model, sampler, adapter_features, append_to_context)
|
149 |
+
ims.append(tensor2img(result, rgb2bgr=False))
|
150 |
+
|
151 |
+
# Clear GPU memory cache so less likely to OOM
|
152 |
+
torch.cuda.empty_cache()
|
153 |
+
return ims, output_conds
|
154 |
+
|
155 |
+
|
156 |
+
def change_visible(im1, im2, val):
|
157 |
+
outputs = {}
|
158 |
+
if val == "Image":
|
159 |
+
outputs[im1] = gr.update(visible=True)
|
160 |
+
outputs[im2] = gr.update(visible=False)
|
161 |
+
elif val == "Nothing":
|
162 |
+
outputs[im1] = gr.update(visible=False)
|
163 |
+
outputs[im2] = gr.update(visible=False)
|
164 |
+
else:
|
165 |
+
outputs[im1] = gr.update(visible=False)
|
166 |
+
outputs[im2] = gr.update(visible=True)
|
167 |
+
return outputs
|
168 |
+
|
169 |
+
|
170 |
+
DESCRIPTION = '''# CoAdapter
|
171 |
+
[Paper](https://arxiv.org/abs/2302.08453) [GitHub](https://github.com/TencentARC/T2I-Adapter)
|
172 |
+
|
173 |
+
This gradio demo is for a simple experience of CoAdapter:
|
174 |
+
'''
|
175 |
+
with gr.Blocks(title="CoAdapter", css=".gr-box {border-color: #8136e2}") as demo:
|
176 |
+
gr.Markdown(DESCRIPTION)
|
177 |
+
|
178 |
+
btns = []
|
179 |
+
ims1 = []
|
180 |
+
ims2 = []
|
181 |
+
cond_weights = []
|
182 |
+
|
183 |
+
with gr.Row():
|
184 |
+
for cond_name in supported_cond:
|
185 |
+
with gr.Box():
|
186 |
+
with gr.Column():
|
187 |
+
btn1 = gr.Radio(
|
188 |
+
choices=["Image", cond_name, "Nothing"],
|
189 |
+
label=f"Input type for {cond_name}",
|
190 |
+
interactive=True,
|
191 |
+
value="Nothing",
|
192 |
+
)
|
193 |
+
im1 = gr.Image(source='upload', label="Image", interactive=True, visible=False, type="numpy")
|
194 |
+
im2 = gr.Image(source='upload', label=cond_name, interactive=True, visible=False, type="numpy")
|
195 |
+
cond_weight = gr.Slider(
|
196 |
+
label="Condition weight", minimum=0, maximum=5, step=0.05, value=1, interactive=True)
|
197 |
+
|
198 |
+
fn = partial(change_visible, im1, im2)
|
199 |
+
btn1.change(fn=fn, inputs=[btn1], outputs=[im1, im2], queue=False)
|
200 |
+
|
201 |
+
btns.append(btn1)
|
202 |
+
ims1.append(im1)
|
203 |
+
ims2.append(im2)
|
204 |
+
cond_weights.append(cond_weight)
|
205 |
+
|
206 |
+
with gr.Column():
|
207 |
+
prompt = gr.Textbox(label="Prompt")
|
208 |
+
neg_prompt = gr.Textbox(label="Negative Prompt", value=DEFAULT_NEGATIVE_PROMPT)
|
209 |
+
scale = gr.Slider(label="Guidance Scale (Classifier free guidance)", value=7.5, minimum=1, maximum=20, step=0.1)
|
210 |
+
n_samples = gr.Slider(label="Num samples", value=1, minimum=1, maximum=8, step=1)
|
211 |
+
seed = gr.Slider(label="Seed", value=42, minimum=0, maximum=10000, step=1)
|
212 |
+
steps = gr.Slider(label="Steps", value=50, minimum=10, maximum=100, step=1)
|
213 |
+
resize_short_edge = gr.Slider(label="Image resolution", value=512, minimum=320, maximum=1024, step=1)
|
214 |
+
cond_tau = gr.Slider(
|
215 |
+
label="timestamp parameter that determines until which step the adapter is applied",
|
216 |
+
value=1.0,
|
217 |
+
minimum=0.1,
|
218 |
+
maximum=1.0,
|
219 |
+
step=0.05)
|
220 |
+
|
221 |
+
with gr.Row():
|
222 |
+
submit = gr.Button("Generate")
|
223 |
+
output = gr.Gallery().style(grid=2, height='auto')
|
224 |
+
cond = gr.Gallery().style(grid=2, height='auto')
|
225 |
+
|
226 |
+
inps = list(chain(btns, ims1, ims2, cond_weights))
|
227 |
+
inps.extend([prompt, neg_prompt, scale, n_samples, seed, steps, resize_short_edge, cond_tau])
|
228 |
+
submit.click(fn=run, inputs=inps, outputs=[output, cond])
|
229 |
+
# demo.launch()
|
230 |
+
demo.launch(server_port=43343, server_name='0.0.0.0')
|
configs/mm/faster_rcnn_r50_fpn_coco.py
ADDED
@@ -0,0 +1,182 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
checkpoint_config = dict(interval=1)
|
2 |
+
# yapf:disable
|
3 |
+
log_config = dict(
|
4 |
+
interval=50,
|
5 |
+
hooks=[
|
6 |
+
dict(type='TextLoggerHook'),
|
7 |
+
# dict(type='TensorboardLoggerHook')
|
8 |
+
])
|
9 |
+
# yapf:enable
|
10 |
+
dist_params = dict(backend='nccl')
|
11 |
+
log_level = 'INFO'
|
12 |
+
load_from = None
|
13 |
+
resume_from = None
|
14 |
+
workflow = [('train', 1)]
|
15 |
+
# optimizer
|
16 |
+
optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)
|
17 |
+
optimizer_config = dict(grad_clip=None)
|
18 |
+
# learning policy
|
19 |
+
lr_config = dict(
|
20 |
+
policy='step',
|
21 |
+
warmup='linear',
|
22 |
+
warmup_iters=500,
|
23 |
+
warmup_ratio=0.001,
|
24 |
+
step=[8, 11])
|
25 |
+
total_epochs = 12
|
26 |
+
|
27 |
+
model = dict(
|
28 |
+
type='FasterRCNN',
|
29 |
+
pretrained='torchvision://resnet50',
|
30 |
+
backbone=dict(
|
31 |
+
type='ResNet',
|
32 |
+
depth=50,
|
33 |
+
num_stages=4,
|
34 |
+
out_indices=(0, 1, 2, 3),
|
35 |
+
frozen_stages=1,
|
36 |
+
norm_cfg=dict(type='BN', requires_grad=True),
|
37 |
+
norm_eval=True,
|
38 |
+
style='pytorch'),
|
39 |
+
neck=dict(
|
40 |
+
type='FPN',
|
41 |
+
in_channels=[256, 512, 1024, 2048],
|
42 |
+
out_channels=256,
|
43 |
+
num_outs=5),
|
44 |
+
rpn_head=dict(
|
45 |
+
type='RPNHead',
|
46 |
+
in_channels=256,
|
47 |
+
feat_channels=256,
|
48 |
+
anchor_generator=dict(
|
49 |
+
type='AnchorGenerator',
|
50 |
+
scales=[8],
|
51 |
+
ratios=[0.5, 1.0, 2.0],
|
52 |
+
strides=[4, 8, 16, 32, 64]),
|
53 |
+
bbox_coder=dict(
|
54 |
+
type='DeltaXYWHBBoxCoder',
|
55 |
+
target_means=[.0, .0, .0, .0],
|
56 |
+
target_stds=[1.0, 1.0, 1.0, 1.0]),
|
57 |
+
loss_cls=dict(
|
58 |
+
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
|
59 |
+
loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
|
60 |
+
roi_head=dict(
|
61 |
+
type='StandardRoIHead',
|
62 |
+
bbox_roi_extractor=dict(
|
63 |
+
type='SingleRoIExtractor',
|
64 |
+
roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
|
65 |
+
out_channels=256,
|
66 |
+
featmap_strides=[4, 8, 16, 32]),
|
67 |
+
bbox_head=dict(
|
68 |
+
type='Shared2FCBBoxHead',
|
69 |
+
in_channels=256,
|
70 |
+
fc_out_channels=1024,
|
71 |
+
roi_feat_size=7,
|
72 |
+
num_classes=80,
|
73 |
+
bbox_coder=dict(
|
74 |
+
type='DeltaXYWHBBoxCoder',
|
75 |
+
target_means=[0., 0., 0., 0.],
|
76 |
+
target_stds=[0.1, 0.1, 0.2, 0.2]),
|
77 |
+
reg_class_agnostic=False,
|
78 |
+
loss_cls=dict(
|
79 |
+
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
|
80 |
+
loss_bbox=dict(type='L1Loss', loss_weight=1.0))),
|
81 |
+
# model training and testing settings
|
82 |
+
train_cfg=dict(
|
83 |
+
rpn=dict(
|
84 |
+
assigner=dict(
|
85 |
+
type='MaxIoUAssigner',
|
86 |
+
pos_iou_thr=0.7,
|
87 |
+
neg_iou_thr=0.3,
|
88 |
+
min_pos_iou=0.3,
|
89 |
+
match_low_quality=True,
|
90 |
+
ignore_iof_thr=-1),
|
91 |
+
sampler=dict(
|
92 |
+
type='RandomSampler',
|
93 |
+
num=256,
|
94 |
+
pos_fraction=0.5,
|
95 |
+
neg_pos_ub=-1,
|
96 |
+
add_gt_as_proposals=False),
|
97 |
+
allowed_border=-1,
|
98 |
+
pos_weight=-1,
|
99 |
+
debug=False),
|
100 |
+
rpn_proposal=dict(
|
101 |
+
nms_pre=2000,
|
102 |
+
max_per_img=1000,
|
103 |
+
nms=dict(type='nms', iou_threshold=0.7),
|
104 |
+
min_bbox_size=0),
|
105 |
+
rcnn=dict(
|
106 |
+
assigner=dict(
|
107 |
+
type='MaxIoUAssigner',
|
108 |
+
pos_iou_thr=0.5,
|
109 |
+
neg_iou_thr=0.5,
|
110 |
+
min_pos_iou=0.5,
|
111 |
+
match_low_quality=False,
|
112 |
+
ignore_iof_thr=-1),
|
113 |
+
sampler=dict(
|
114 |
+
type='RandomSampler',
|
115 |
+
num=512,
|
116 |
+
pos_fraction=0.25,
|
117 |
+
neg_pos_ub=-1,
|
118 |
+
add_gt_as_proposals=True),
|
119 |
+
pos_weight=-1,
|
120 |
+
debug=False)),
|
121 |
+
test_cfg=dict(
|
122 |
+
rpn=dict(
|
123 |
+
nms_pre=1000,
|
124 |
+
max_per_img=1000,
|
125 |
+
nms=dict(type='nms', iou_threshold=0.7),
|
126 |
+
min_bbox_size=0),
|
127 |
+
rcnn=dict(
|
128 |
+
score_thr=0.05,
|
129 |
+
nms=dict(type='nms', iou_threshold=0.5),
|
130 |
+
max_per_img=100)
|
131 |
+
# soft-nms is also supported for rcnn testing
|
132 |
+
# e.g., nms=dict(type='soft_nms', iou_threshold=0.5, min_score=0.05)
|
133 |
+
))
|
134 |
+
|
135 |
+
dataset_type = 'CocoDataset'
|
136 |
+
data_root = 'data/coco'
|
137 |
+
img_norm_cfg = dict(
|
138 |
+
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
|
139 |
+
train_pipeline = [
|
140 |
+
dict(type='LoadImageFromFile'),
|
141 |
+
dict(type='LoadAnnotations', with_bbox=True),
|
142 |
+
dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
|
143 |
+
dict(type='RandomFlip', flip_ratio=0.5),
|
144 |
+
dict(type='Normalize', **img_norm_cfg),
|
145 |
+
dict(type='Pad', size_divisor=32),
|
146 |
+
dict(type='DefaultFormatBundle'),
|
147 |
+
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
|
148 |
+
]
|
149 |
+
test_pipeline = [
|
150 |
+
dict(type='LoadImageFromFile'),
|
151 |
+
dict(
|
152 |
+
type='MultiScaleFlipAug',
|
153 |
+
img_scale=(1333, 800),
|
154 |
+
flip=False,
|
155 |
+
transforms=[
|
156 |
+
dict(type='Resize', keep_ratio=True),
|
157 |
+
dict(type='RandomFlip'),
|
158 |
+
dict(type='Normalize', **img_norm_cfg),
|
159 |
+
dict(type='Pad', size_divisor=32),
|
160 |
+
dict(type='DefaultFormatBundle'),
|
161 |
+
dict(type='Collect', keys=['img']),
|
162 |
+
])
|
163 |
+
]
|
164 |
+
data = dict(
|
165 |
+
samples_per_gpu=2,
|
166 |
+
workers_per_gpu=2,
|
167 |
+
train=dict(
|
168 |
+
type=dataset_type,
|
169 |
+
ann_file=f'{data_root}/annotations/instances_train2017.json',
|
170 |
+
img_prefix=f'{data_root}/train2017/',
|
171 |
+
pipeline=train_pipeline),
|
172 |
+
val=dict(
|
173 |
+
type=dataset_type,
|
174 |
+
ann_file=f'{data_root}/annotations/instances_val2017.json',
|
175 |
+
img_prefix=f'{data_root}/val2017/',
|
176 |
+
pipeline=test_pipeline),
|
177 |
+
test=dict(
|
178 |
+
type=dataset_type,
|
179 |
+
ann_file=f'{data_root}/annotations/instances_val2017.json',
|
180 |
+
img_prefix=f'{data_root}/val2017/',
|
181 |
+
pipeline=test_pipeline))
|
182 |
+
evaluation = dict(interval=1, metric='bbox')
|
configs/mm/hrnet_w48_coco_256x192.py
ADDED
@@ -0,0 +1,169 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# _base_ = [
|
2 |
+
# '../../../../_base_/default_runtime.py',
|
3 |
+
# '../../../../_base_/datasets/coco.py'
|
4 |
+
# ]
|
5 |
+
evaluation = dict(interval=10, metric='mAP', save_best='AP')
|
6 |
+
|
7 |
+
optimizer = dict(
|
8 |
+
type='Adam',
|
9 |
+
lr=5e-4,
|
10 |
+
)
|
11 |
+
optimizer_config = dict(grad_clip=None)
|
12 |
+
# learning policy
|
13 |
+
lr_config = dict(
|
14 |
+
policy='step',
|
15 |
+
warmup='linear',
|
16 |
+
warmup_iters=500,
|
17 |
+
warmup_ratio=0.001,
|
18 |
+
step=[170, 200])
|
19 |
+
total_epochs = 210
|
20 |
+
channel_cfg = dict(
|
21 |
+
num_output_channels=17,
|
22 |
+
dataset_joints=17,
|
23 |
+
dataset_channel=[
|
24 |
+
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16],
|
25 |
+
],
|
26 |
+
inference_channel=[
|
27 |
+
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16
|
28 |
+
])
|
29 |
+
|
30 |
+
# model settings
|
31 |
+
model = dict(
|
32 |
+
type='TopDown',
|
33 |
+
pretrained='https://download.openmmlab.com/mmpose/'
|
34 |
+
'pretrain_models/hrnet_w48-8ef0771d.pth',
|
35 |
+
backbone=dict(
|
36 |
+
type='HRNet',
|
37 |
+
in_channels=3,
|
38 |
+
extra=dict(
|
39 |
+
stage1=dict(
|
40 |
+
num_modules=1,
|
41 |
+
num_branches=1,
|
42 |
+
block='BOTTLENECK',
|
43 |
+
num_blocks=(4, ),
|
44 |
+
num_channels=(64, )),
|
45 |
+
stage2=dict(
|
46 |
+
num_modules=1,
|
47 |
+
num_branches=2,
|
48 |
+
block='BASIC',
|
49 |
+
num_blocks=(4, 4),
|
50 |
+
num_channels=(48, 96)),
|
51 |
+
stage3=dict(
|
52 |
+
num_modules=4,
|
53 |
+
num_branches=3,
|
54 |
+
block='BASIC',
|
55 |
+
num_blocks=(4, 4, 4),
|
56 |
+
num_channels=(48, 96, 192)),
|
57 |
+
stage4=dict(
|
58 |
+
num_modules=3,
|
59 |
+
num_branches=4,
|
60 |
+
block='BASIC',
|
61 |
+
num_blocks=(4, 4, 4, 4),
|
62 |
+
num_channels=(48, 96, 192, 384))),
|
63 |
+
),
|
64 |
+
keypoint_head=dict(
|
65 |
+
type='TopdownHeatmapSimpleHead',
|
66 |
+
in_channels=48,
|
67 |
+
out_channels=channel_cfg['num_output_channels'],
|
68 |
+
num_deconv_layers=0,
|
69 |
+
extra=dict(final_conv_kernel=1, ),
|
70 |
+
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)),
|
71 |
+
train_cfg=dict(),
|
72 |
+
test_cfg=dict(
|
73 |
+
flip_test=True,
|
74 |
+
post_process='default',
|
75 |
+
shift_heatmap=True,
|
76 |
+
modulate_kernel=11))
|
77 |
+
|
78 |
+
data_cfg = dict(
|
79 |
+
image_size=[192, 256],
|
80 |
+
heatmap_size=[48, 64],
|
81 |
+
num_output_channels=channel_cfg['num_output_channels'],
|
82 |
+
num_joints=channel_cfg['dataset_joints'],
|
83 |
+
dataset_channel=channel_cfg['dataset_channel'],
|
84 |
+
inference_channel=channel_cfg['inference_channel'],
|
85 |
+
soft_nms=False,
|
86 |
+
nms_thr=1.0,
|
87 |
+
oks_thr=0.9,
|
88 |
+
vis_thr=0.2,
|
89 |
+
use_gt_bbox=False,
|
90 |
+
det_bbox_thr=0.0,
|
91 |
+
bbox_file='data/coco/person_detection_results/'
|
92 |
+
'COCO_val2017_detections_AP_H_56_person.json',
|
93 |
+
)
|
94 |
+
|
95 |
+
train_pipeline = [
|
96 |
+
dict(type='LoadImageFromFile'),
|
97 |
+
dict(type='TopDownGetBboxCenterScale', padding=1.25),
|
98 |
+
dict(type='TopDownRandomShiftBboxCenter', shift_factor=0.16, prob=0.3),
|
99 |
+
dict(type='TopDownRandomFlip', flip_prob=0.5),
|
100 |
+
dict(
|
101 |
+
type='TopDownHalfBodyTransform',
|
102 |
+
num_joints_half_body=8,
|
103 |
+
prob_half_body=0.3),
|
104 |
+
dict(
|
105 |
+
type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5),
|
106 |
+
dict(type='TopDownAffine'),
|
107 |
+
dict(type='ToTensor'),
|
108 |
+
dict(
|
109 |
+
type='NormalizeTensor',
|
110 |
+
mean=[0.485, 0.456, 0.406],
|
111 |
+
std=[0.229, 0.224, 0.225]),
|
112 |
+
dict(type='TopDownGenerateTarget', sigma=2),
|
113 |
+
dict(
|
114 |
+
type='Collect',
|
115 |
+
keys=['img', 'target', 'target_weight'],
|
116 |
+
meta_keys=[
|
117 |
+
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale',
|
118 |
+
'rotation', 'bbox_score', 'flip_pairs'
|
119 |
+
]),
|
120 |
+
]
|
121 |
+
|
122 |
+
val_pipeline = [
|
123 |
+
dict(type='LoadImageFromFile'),
|
124 |
+
dict(type='TopDownGetBboxCenterScale', padding=1.25),
|
125 |
+
dict(type='TopDownAffine'),
|
126 |
+
dict(type='ToTensor'),
|
127 |
+
dict(
|
128 |
+
type='NormalizeTensor',
|
129 |
+
mean=[0.485, 0.456, 0.406],
|
130 |
+
std=[0.229, 0.224, 0.225]),
|
131 |
+
dict(
|
132 |
+
type='Collect',
|
133 |
+
keys=['img'],
|
134 |
+
meta_keys=[
|
135 |
+
'image_file', 'center', 'scale', 'rotation', 'bbox_score',
|
136 |
+
'flip_pairs'
|
137 |
+
]),
|
138 |
+
]
|
139 |
+
|
140 |
+
test_pipeline = val_pipeline
|
141 |
+
|
142 |
+
data_root = 'data/coco'
|
143 |
+
data = dict(
|
144 |
+
samples_per_gpu=32,
|
145 |
+
workers_per_gpu=2,
|
146 |
+
val_dataloader=dict(samples_per_gpu=32),
|
147 |
+
test_dataloader=dict(samples_per_gpu=32),
|
148 |
+
train=dict(
|
149 |
+
type='TopDownCocoDataset',
|
150 |
+
ann_file=f'{data_root}/annotations/person_keypoints_train2017.json',
|
151 |
+
img_prefix=f'{data_root}/train2017/',
|
152 |
+
data_cfg=data_cfg,
|
153 |
+
pipeline=train_pipeline,
|
154 |
+
dataset_info={{_base_.dataset_info}}),
|
155 |
+
val=dict(
|
156 |
+
type='TopDownCocoDataset',
|
157 |
+
ann_file=f'{data_root}/annotations/person_keypoints_val2017.json',
|
158 |
+
img_prefix=f'{data_root}/val2017/',
|
159 |
+
data_cfg=data_cfg,
|
160 |
+
pipeline=val_pipeline,
|
161 |
+
dataset_info={{_base_.dataset_info}}),
|
162 |
+
test=dict(
|
163 |
+
type='TopDownCocoDataset',
|
164 |
+
ann_file=f'{data_root}/annotations/person_keypoints_val2017.json',
|
165 |
+
img_prefix=f'{data_root}/val2017/',
|
166 |
+
data_cfg=data_cfg,
|
167 |
+
pipeline=test_pipeline,
|
168 |
+
dataset_info={{_base_.dataset_info}}),
|
169 |
+
)
|
configs/stable-diffusion/sd-v1-inference.yaml
ADDED
@@ -0,0 +1,65 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
model:
|
2 |
+
base_learning_rate: 1.0e-04
|
3 |
+
target: ldm.models.diffusion.ddpm.LatentDiffusion
|
4 |
+
params:
|
5 |
+
linear_start: 0.00085
|
6 |
+
linear_end: 0.0120
|
7 |
+
num_timesteps_cond: 1
|
8 |
+
log_every_t: 200
|
9 |
+
timesteps: 1000
|
10 |
+
first_stage_key: "jpg"
|
11 |
+
cond_stage_key: "txt"
|
12 |
+
image_size: 64
|
13 |
+
channels: 4
|
14 |
+
cond_stage_trainable: false # Note: different from the one we trained before
|
15 |
+
conditioning_key: crossattn
|
16 |
+
monitor: val/loss_simple_ema
|
17 |
+
scale_factor: 0.18215
|
18 |
+
use_ema: False
|
19 |
+
|
20 |
+
unet_config:
|
21 |
+
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
|
22 |
+
params:
|
23 |
+
use_fp16: True
|
24 |
+
image_size: 32 # unused
|
25 |
+
in_channels: 4
|
26 |
+
out_channels: 4
|
27 |
+
model_channels: 320
|
28 |
+
attention_resolutions: [ 4, 2, 1 ]
|
29 |
+
num_res_blocks: 2
|
30 |
+
channel_mult: [ 1, 2, 4, 4 ]
|
31 |
+
num_heads: 8
|
32 |
+
use_spatial_transformer: True
|
33 |
+
transformer_depth: 1
|
34 |
+
context_dim: 768
|
35 |
+
use_checkpoint: True
|
36 |
+
legacy: False
|
37 |
+
|
38 |
+
first_stage_config:
|
39 |
+
target: ldm.models.autoencoder.AutoencoderKL
|
40 |
+
params:
|
41 |
+
embed_dim: 4
|
42 |
+
monitor: val/rec_loss
|
43 |
+
ddconfig:
|
44 |
+
double_z: true
|
45 |
+
z_channels: 4
|
46 |
+
resolution: 512
|
47 |
+
in_channels: 3
|
48 |
+
out_ch: 3
|
49 |
+
ch: 128
|
50 |
+
ch_mult:
|
51 |
+
- 1
|
52 |
+
- 2
|
53 |
+
- 4
|
54 |
+
- 4
|
55 |
+
num_res_blocks: 2
|
56 |
+
attn_resolutions: []
|
57 |
+
dropout: 0.0
|
58 |
+
lossconfig:
|
59 |
+
target: torch.nn.Identity
|
60 |
+
|
61 |
+
cond_stage_config:
|
62 |
+
target: ldm.modules.encoders.modules.WebUIFrozenCLIPEmebedder
|
63 |
+
params:
|
64 |
+
version: openai/clip-vit-large-patch14
|
65 |
+
layer: last
|
dist_util.py
ADDED
@@ -0,0 +1,91 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# Modified from https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/dist_utils.py # noqa: E501
|
2 |
+
import functools
|
3 |
+
import os
|
4 |
+
import subprocess
|
5 |
+
import torch
|
6 |
+
import torch.distributed as dist
|
7 |
+
import torch.multiprocessing as mp
|
8 |
+
from torch.nn.parallel import DataParallel, DistributedDataParallel
|
9 |
+
|
10 |
+
|
11 |
+
def init_dist(launcher, backend='nccl', **kwargs):
|
12 |
+
if mp.get_start_method(allow_none=True) is None:
|
13 |
+
mp.set_start_method('spawn')
|
14 |
+
if launcher == 'pytorch':
|
15 |
+
_init_dist_pytorch(backend, **kwargs)
|
16 |
+
elif launcher == 'slurm':
|
17 |
+
_init_dist_slurm(backend, **kwargs)
|
18 |
+
else:
|
19 |
+
raise ValueError(f'Invalid launcher type: {launcher}')
|
20 |
+
|
21 |
+
|
22 |
+
def _init_dist_pytorch(backend, **kwargs):
|
23 |
+
rank = int(os.environ['RANK'])
|
24 |
+
num_gpus = torch.cuda.device_count()
|
25 |
+
torch.cuda.set_device(rank % num_gpus)
|
26 |
+
dist.init_process_group(backend=backend, **kwargs)
|
27 |
+
|
28 |
+
|
29 |
+
def _init_dist_slurm(backend, port=None):
|
30 |
+
"""Initialize slurm distributed training environment.
|
31 |
+
|
32 |
+
If argument ``port`` is not specified, then the master port will be system
|
33 |
+
environment variable ``MASTER_PORT``. If ``MASTER_PORT`` is not in system
|
34 |
+
environment variable, then a default port ``29500`` will be used.
|
35 |
+
|
36 |
+
Args:
|
37 |
+
backend (str): Backend of torch.distributed.
|
38 |
+
port (int, optional): Master port. Defaults to None.
|
39 |
+
"""
|
40 |
+
proc_id = int(os.environ['SLURM_PROCID'])
|
41 |
+
ntasks = int(os.environ['SLURM_NTASKS'])
|
42 |
+
node_list = os.environ['SLURM_NODELIST']
|
43 |
+
num_gpus = torch.cuda.device_count()
|
44 |
+
torch.cuda.set_device(proc_id % num_gpus)
|
45 |
+
addr = subprocess.getoutput(f'scontrol show hostname {node_list} | head -n1')
|
46 |
+
# specify master port
|
47 |
+
if port is not None:
|
48 |
+
os.environ['MASTER_PORT'] = str(port)
|
49 |
+
elif 'MASTER_PORT' in os.environ:
|
50 |
+
pass # use MASTER_PORT in the environment variable
|
51 |
+
else:
|
52 |
+
# 29500 is torch.distributed default port
|
53 |
+
os.environ['MASTER_PORT'] = '29500'
|
54 |
+
os.environ['MASTER_ADDR'] = addr
|
55 |
+
os.environ['WORLD_SIZE'] = str(ntasks)
|
56 |
+
os.environ['LOCAL_RANK'] = str(proc_id % num_gpus)
|
57 |
+
os.environ['RANK'] = str(proc_id)
|
58 |
+
dist.init_process_group(backend=backend)
|
59 |
+
|
60 |
+
|
61 |
+
def get_dist_info():
|
62 |
+
if dist.is_available():
|
63 |
+
initialized = dist.is_initialized()
|
64 |
+
else:
|
65 |
+
initialized = False
|
66 |
+
if initialized:
|
67 |
+
rank = dist.get_rank()
|
68 |
+
world_size = dist.get_world_size()
|
69 |
+
else:
|
70 |
+
rank = 0
|
71 |
+
world_size = 1
|
72 |
+
return rank, world_size
|
73 |
+
|
74 |
+
|
75 |
+
def master_only(func):
|
76 |
+
|
77 |
+
@functools.wraps(func)
|
78 |
+
def wrapper(*args, **kwargs):
|
79 |
+
rank, _ = get_dist_info()
|
80 |
+
if rank == 0:
|
81 |
+
return func(*args, **kwargs)
|
82 |
+
|
83 |
+
return wrapper
|
84 |
+
|
85 |
+
def get_bare_model(net):
|
86 |
+
"""Get bare model, especially under wrapping with
|
87 |
+
DistributedDataParallel or DataParallel.
|
88 |
+
"""
|
89 |
+
if isinstance(net, (DataParallel, DistributedDataParallel)):
|
90 |
+
net = net.module
|
91 |
+
return net
|
ldm/__pycache__/inference_base.cpython-38.pyc
ADDED
Binary file (6.28 kB). View file
|
|
ldm/__pycache__/util.cpython-38.pyc
ADDED
Binary file (6.23 kB). View file
|
|
ldm/data/__init__.py
ADDED
File without changes
|
ldm/data/dataset_coco.py
ADDED
@@ -0,0 +1,36 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import json
|
2 |
+
import cv2
|
3 |
+
import os
|
4 |
+
from basicsr.utils import img2tensor
|
5 |
+
|
6 |
+
|
7 |
+
class dataset_coco_mask_color():
|
8 |
+
def __init__(self, path_json, root_path_im, root_path_mask, image_size):
|
9 |
+
super(dataset_coco_mask_color, self).__init__()
|
10 |
+
with open(path_json, 'r', encoding='utf-8') as fp:
|
11 |
+
data = json.load(fp)
|
12 |
+
data = data['annotations']
|
13 |
+
self.files = []
|
14 |
+
self.root_path_im = root_path_im
|
15 |
+
self.root_path_mask = root_path_mask
|
16 |
+
for file in data:
|
17 |
+
name = "%012d.png" % file['image_id']
|
18 |
+
self.files.append({'name': name, 'sentence': file['caption']})
|
19 |
+
|
20 |
+
def __getitem__(self, idx):
|
21 |
+
file = self.files[idx]
|
22 |
+
name = file['name']
|
23 |
+
# print(os.path.join(self.root_path_im, name))
|
24 |
+
im = cv2.imread(os.path.join(self.root_path_im, name.replace('.png', '.jpg')))
|
25 |
+
im = cv2.resize(im, (512, 512))
|
26 |
+
im = img2tensor(im, bgr2rgb=True, float32=True) / 255.
|
27 |
+
|
28 |
+
mask = cv2.imread(os.path.join(self.root_path_mask, name)) # [:,:,0]
|
29 |
+
mask = cv2.resize(mask, (512, 512))
|
30 |
+
mask = img2tensor(mask, bgr2rgb=True, float32=True) / 255. # [0].unsqueeze(0)#/255.
|
31 |
+
|
32 |
+
sentence = file['sentence']
|
33 |
+
return {'im': im, 'mask': mask, 'sentence': sentence}
|
34 |
+
|
35 |
+
def __len__(self):
|
36 |
+
return len(self.files)
|
ldm/data/dataset_depth.py
ADDED
@@ -0,0 +1,35 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import json
|
2 |
+
import cv2
|
3 |
+
import os
|
4 |
+
from basicsr.utils import img2tensor
|
5 |
+
|
6 |
+
|
7 |
+
class DepthDataset():
|
8 |
+
def __init__(self, meta_file):
|
9 |
+
super(DepthDataset, self).__init__()
|
10 |
+
|
11 |
+
self.files = []
|
12 |
+
with open(meta_file, 'r') as f:
|
13 |
+
lines = f.readlines()
|
14 |
+
for line in lines:
|
15 |
+
img_path = line.strip()
|
16 |
+
depth_img_path = img_path.rsplit('.', 1)[0] + '.depth.png'
|
17 |
+
txt_path = img_path.rsplit('.', 1)[0] + '.txt'
|
18 |
+
self.files.append({'img_path': img_path, 'depth_img_path': depth_img_path, 'txt_path': txt_path})
|
19 |
+
|
20 |
+
def __getitem__(self, idx):
|
21 |
+
file = self.files[idx]
|
22 |
+
|
23 |
+
im = cv2.imread(file['img_path'])
|
24 |
+
im = img2tensor(im, bgr2rgb=True, float32=True) / 255.
|
25 |
+
|
26 |
+
depth = cv2.imread(file['depth_img_path']) # [:,:,0]
|
27 |
+
depth = img2tensor(depth, bgr2rgb=True, float32=True) / 255. # [0].unsqueeze(0)#/255.
|
28 |
+
|
29 |
+
with open(file['txt_path'], 'r') as fs:
|
30 |
+
sentence = fs.readline().strip()
|
31 |
+
|
32 |
+
return {'im': im, 'depth': depth, 'sentence': sentence}
|
33 |
+
|
34 |
+
def __len__(self):
|
35 |
+
return len(self.files)
|
ldm/data/dataset_laion.py
ADDED
@@ -0,0 +1,130 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# -*- coding: utf-8 -*-
|
2 |
+
|
3 |
+
import numpy as np
|
4 |
+
import os
|
5 |
+
import pytorch_lightning as pl
|
6 |
+
import torch
|
7 |
+
import webdataset as wds
|
8 |
+
from torchvision.transforms import transforms
|
9 |
+
|
10 |
+
from ldm.util import instantiate_from_config
|
11 |
+
|
12 |
+
|
13 |
+
def dict_collation_fn(samples, combine_tensors=True, combine_scalars=True):
|
14 |
+
"""Take a list of samples (as dictionary) and create a batch, preserving the keys.
|
15 |
+
If `tensors` is True, `ndarray` objects are combined into
|
16 |
+
tensor batches.
|
17 |
+
:param dict samples: list of samples
|
18 |
+
:param bool tensors: whether to turn lists of ndarrays into a single ndarray
|
19 |
+
:returns: single sample consisting of a batch
|
20 |
+
:rtype: dict
|
21 |
+
"""
|
22 |
+
keys = set.intersection(*[set(sample.keys()) for sample in samples])
|
23 |
+
batched = {key: [] for key in keys}
|
24 |
+
|
25 |
+
for s in samples:
|
26 |
+
[batched[key].append(s[key]) for key in batched]
|
27 |
+
|
28 |
+
result = {}
|
29 |
+
for key in batched:
|
30 |
+
if isinstance(batched[key][0], (int, float)):
|
31 |
+
if combine_scalars:
|
32 |
+
result[key] = np.array(list(batched[key]))
|
33 |
+
elif isinstance(batched[key][0], torch.Tensor):
|
34 |
+
if combine_tensors:
|
35 |
+
result[key] = torch.stack(list(batched[key]))
|
36 |
+
elif isinstance(batched[key][0], np.ndarray):
|
37 |
+
if combine_tensors:
|
38 |
+
result[key] = np.array(list(batched[key]))
|
39 |
+
else:
|
40 |
+
result[key] = list(batched[key])
|
41 |
+
return result
|
42 |
+
|
43 |
+
|
44 |
+
class WebDataModuleFromConfig(pl.LightningDataModule):
|
45 |
+
|
46 |
+
def __init__(self,
|
47 |
+
tar_base,
|
48 |
+
batch_size,
|
49 |
+
train=None,
|
50 |
+
validation=None,
|
51 |
+
test=None,
|
52 |
+
num_workers=4,
|
53 |
+
multinode=True,
|
54 |
+
min_size=None,
|
55 |
+
max_pwatermark=1.0,
|
56 |
+
**kwargs):
|
57 |
+
super().__init__()
|
58 |
+
print(f'Setting tar base to {tar_base}')
|
59 |
+
self.tar_base = tar_base
|
60 |
+
self.batch_size = batch_size
|
61 |
+
self.num_workers = num_workers
|
62 |
+
self.train = train
|
63 |
+
self.validation = validation
|
64 |
+
self.test = test
|
65 |
+
self.multinode = multinode
|
66 |
+
self.min_size = min_size # filter out very small images
|
67 |
+
self.max_pwatermark = max_pwatermark # filter out watermarked images
|
68 |
+
|
69 |
+
def make_loader(self, dataset_config):
|
70 |
+
image_transforms = [instantiate_from_config(tt) for tt in dataset_config.image_transforms]
|
71 |
+
image_transforms = transforms.Compose(image_transforms)
|
72 |
+
|
73 |
+
process = instantiate_from_config(dataset_config['process'])
|
74 |
+
|
75 |
+
shuffle = dataset_config.get('shuffle', 0)
|
76 |
+
shardshuffle = shuffle > 0
|
77 |
+
|
78 |
+
nodesplitter = wds.shardlists.split_by_node if self.multinode else wds.shardlists.single_node_only
|
79 |
+
|
80 |
+
tars = os.path.join(self.tar_base, dataset_config.shards)
|
81 |
+
|
82 |
+
dset = wds.WebDataset(
|
83 |
+
tars, nodesplitter=nodesplitter, shardshuffle=shardshuffle,
|
84 |
+
handler=wds.warn_and_continue).repeat().shuffle(shuffle)
|
85 |
+
print(f'Loading webdataset with {len(dset.pipeline[0].urls)} shards.')
|
86 |
+
|
87 |
+
dset = (
|
88 |
+
dset.select(self.filter_keys).decode('pil',
|
89 |
+
handler=wds.warn_and_continue).select(self.filter_size).map_dict(
|
90 |
+
jpg=image_transforms, handler=wds.warn_and_continue).map(process))
|
91 |
+
dset = (dset.batched(self.batch_size, partial=False, collation_fn=dict_collation_fn))
|
92 |
+
|
93 |
+
loader = wds.WebLoader(dset, batch_size=None, shuffle=False, num_workers=self.num_workers)
|
94 |
+
|
95 |
+
return loader
|
96 |
+
|
97 |
+
def filter_size(self, x):
|
98 |
+
if self.min_size is None:
|
99 |
+
return True
|
100 |
+
try:
|
101 |
+
return x['json']['original_width'] >= self.min_size and x['json']['original_height'] >= self.min_size and x[
|
102 |
+
'json']['pwatermark'] <= self.max_pwatermark
|
103 |
+
except Exception:
|
104 |
+
return False
|
105 |
+
|
106 |
+
def filter_keys(self, x):
|
107 |
+
try:
|
108 |
+
return ("jpg" in x) and ("txt" in x)
|
109 |
+
except Exception:
|
110 |
+
return False
|
111 |
+
|
112 |
+
def train_dataloader(self):
|
113 |
+
return self.make_loader(self.train)
|
114 |
+
|
115 |
+
def val_dataloader(self):
|
116 |
+
return None
|
117 |
+
|
118 |
+
def test_dataloader(self):
|
119 |
+
return None
|
120 |
+
|
121 |
+
|
122 |
+
if __name__ == '__main__':
|
123 |
+
from omegaconf import OmegaConf
|
124 |
+
config = OmegaConf.load("configs/stable-diffusion/train_canny_sd_v1.yaml")
|
125 |
+
datamod = WebDataModuleFromConfig(**config["data"]["params"])
|
126 |
+
dataloader = datamod.train_dataloader()
|
127 |
+
|
128 |
+
for batch in dataloader:
|
129 |
+
print(batch.keys())
|
130 |
+
print(batch['jpg'].shape)
|
ldm/data/dataset_wikiart.py
ADDED
@@ -0,0 +1,67 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import json
|
2 |
+
import os.path
|
3 |
+
|
4 |
+
from PIL import Image
|
5 |
+
from torch.utils.data import DataLoader
|
6 |
+
|
7 |
+
from transformers import CLIPProcessor
|
8 |
+
from torchvision.transforms import transforms
|
9 |
+
|
10 |
+
import pytorch_lightning as pl
|
11 |
+
|
12 |
+
|
13 |
+
class WikiArtDataset():
|
14 |
+
def __init__(self, meta_file):
|
15 |
+
super(WikiArtDataset, self).__init__()
|
16 |
+
|
17 |
+
self.files = []
|
18 |
+
with open(meta_file, 'r') as f:
|
19 |
+
js = json.load(f)
|
20 |
+
for img_path in js:
|
21 |
+
img_name = os.path.splitext(os.path.basename(img_path))[0]
|
22 |
+
caption = img_name.split('_')[-1]
|
23 |
+
caption = caption.split('-')
|
24 |
+
j = len(caption) - 1
|
25 |
+
while j >= 0:
|
26 |
+
if not caption[j].isdigit():
|
27 |
+
break
|
28 |
+
j -= 1
|
29 |
+
if j < 0:
|
30 |
+
continue
|
31 |
+
sentence = ' '.join(caption[:j + 1])
|
32 |
+
self.files.append({'img_path': os.path.join('datasets/wikiart', img_path), 'sentence': sentence})
|
33 |
+
|
34 |
+
version = 'openai/clip-vit-large-patch14'
|
35 |
+
self.processor = CLIPProcessor.from_pretrained(version)
|
36 |
+
|
37 |
+
self.jpg_transform = transforms.Compose([
|
38 |
+
transforms.Resize(512),
|
39 |
+
transforms.RandomCrop(512),
|
40 |
+
transforms.ToTensor(),
|
41 |
+
])
|
42 |
+
|
43 |
+
def __getitem__(self, idx):
|
44 |
+
file = self.files[idx]
|
45 |
+
|
46 |
+
im = Image.open(file['img_path'])
|
47 |
+
|
48 |
+
im_tensor = self.jpg_transform(im)
|
49 |
+
|
50 |
+
clip_im = self.processor(images=im, return_tensors="pt")['pixel_values'][0]
|
51 |
+
|
52 |
+
return {'jpg': im_tensor, 'style': clip_im, 'txt': file['sentence']}
|
53 |
+
|
54 |
+
def __len__(self):
|
55 |
+
return len(self.files)
|
56 |
+
|
57 |
+
|
58 |
+
class WikiArtDataModule(pl.LightningDataModule):
|
59 |
+
def __init__(self, meta_file, batch_size, num_workers):
|
60 |
+
super(WikiArtDataModule, self).__init__()
|
61 |
+
self.train_dataset = WikiArtDataset(meta_file)
|
62 |
+
self.batch_size = batch_size
|
63 |
+
self.num_workers = num_workers
|
64 |
+
|
65 |
+
def train_dataloader(self):
|
66 |
+
return DataLoader(self.train_dataset, batch_size=self.batch_size, shuffle=True, num_workers=self.num_workers,
|
67 |
+
pin_memory=True)
|
ldm/data/utils.py
ADDED
@@ -0,0 +1,60 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# -*- coding: utf-8 -*-
|
2 |
+
|
3 |
+
import cv2
|
4 |
+
import numpy as np
|
5 |
+
from torchvision.transforms import transforms
|
6 |
+
from torchvision.transforms.functional import to_tensor
|
7 |
+
from transformers import CLIPProcessor
|
8 |
+
|
9 |
+
from basicsr.utils import img2tensor
|
10 |
+
|
11 |
+
|
12 |
+
class AddCannyFreezeThreshold(object):
|
13 |
+
|
14 |
+
def __init__(self, low_threshold=100, high_threshold=200):
|
15 |
+
self.low_threshold = low_threshold
|
16 |
+
self.high_threshold = high_threshold
|
17 |
+
|
18 |
+
def __call__(self, sample):
|
19 |
+
# sample['jpg'] is PIL image
|
20 |
+
x = sample['jpg']
|
21 |
+
img = cv2.cvtColor(np.array(x), cv2.COLOR_RGB2BGR)
|
22 |
+
canny = cv2.Canny(img, self.low_threshold, self.high_threshold)[..., None]
|
23 |
+
sample['canny'] = img2tensor(canny, bgr2rgb=True, float32=True) / 255.
|
24 |
+
sample['jpg'] = to_tensor(x)
|
25 |
+
return sample
|
26 |
+
|
27 |
+
|
28 |
+
class AddCannyRandomThreshold(object):
|
29 |
+
|
30 |
+
def __init__(self, low_threshold=100, high_threshold=200, shift_range=50):
|
31 |
+
self.low_threshold = low_threshold
|
32 |
+
self.high_threshold = high_threshold
|
33 |
+
self.threshold_prng = np.random.RandomState()
|
34 |
+
self.shift_range = shift_range
|
35 |
+
|
36 |
+
def __call__(self, sample):
|
37 |
+
# sample['jpg'] is PIL image
|
38 |
+
x = sample['jpg']
|
39 |
+
img = cv2.cvtColor(np.array(x), cv2.COLOR_RGB2BGR)
|
40 |
+
low_threshold = self.low_threshold + self.threshold_prng.randint(-self.shift_range, self.shift_range)
|
41 |
+
high_threshold = self.high_threshold + self.threshold_prng.randint(-self.shift_range, self.shift_range)
|
42 |
+
canny = cv2.Canny(img, low_threshold, high_threshold)[..., None]
|
43 |
+
sample['canny'] = img2tensor(canny, bgr2rgb=True, float32=True) / 255.
|
44 |
+
sample['jpg'] = to_tensor(x)
|
45 |
+
return sample
|
46 |
+
|
47 |
+
|
48 |
+
class AddStyle(object):
|
49 |
+
|
50 |
+
def __init__(self, version):
|
51 |
+
self.processor = CLIPProcessor.from_pretrained(version)
|
52 |
+
self.pil_to_tensor = transforms.ToTensor()
|
53 |
+
|
54 |
+
def __call__(self, sample):
|
55 |
+
# sample['jpg'] is PIL image
|
56 |
+
x = sample['jpg']
|
57 |
+
style = self.processor(images=x, return_tensors="pt")['pixel_values'][0]
|
58 |
+
sample['style'] = style
|
59 |
+
sample['jpg'] = to_tensor(x)
|
60 |
+
return sample
|
ldm/inference_base.py
ADDED
@@ -0,0 +1,292 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import argparse
|
2 |
+
import torch
|
3 |
+
from omegaconf import OmegaConf
|
4 |
+
|
5 |
+
from ldm.models.diffusion.ddim import DDIMSampler
|
6 |
+
from ldm.models.diffusion.plms import PLMSSampler
|
7 |
+
from ldm.modules.encoders.adapter import Adapter, StyleAdapter, Adapter_light
|
8 |
+
from ldm.modules.extra_condition.api import ExtraCondition
|
9 |
+
from ldm.util import fix_cond_shapes, load_model_from_config, read_state_dict
|
10 |
+
|
11 |
+
DEFAULT_NEGATIVE_PROMPT = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, ' \
|
12 |
+
'fewer digits, cropped, worst quality, low quality'
|
13 |
+
|
14 |
+
|
15 |
+
def get_base_argument_parser() -> argparse.ArgumentParser:
|
16 |
+
"""get the base argument parser for inference scripts"""
|
17 |
+
parser = argparse.ArgumentParser()
|
18 |
+
parser.add_argument(
|
19 |
+
'--outdir',
|
20 |
+
type=str,
|
21 |
+
help='dir to write results to',
|
22 |
+
default=None,
|
23 |
+
)
|
24 |
+
|
25 |
+
parser.add_argument(
|
26 |
+
'--prompt',
|
27 |
+
type=str,
|
28 |
+
nargs='?',
|
29 |
+
default=None,
|
30 |
+
help='positive prompt',
|
31 |
+
)
|
32 |
+
|
33 |
+
parser.add_argument(
|
34 |
+
'--neg_prompt',
|
35 |
+
type=str,
|
36 |
+
default=DEFAULT_NEGATIVE_PROMPT,
|
37 |
+
help='negative prompt',
|
38 |
+
)
|
39 |
+
|
40 |
+
parser.add_argument(
|
41 |
+
'--cond_path',
|
42 |
+
type=str,
|
43 |
+
default=None,
|
44 |
+
help='condition image path',
|
45 |
+
)
|
46 |
+
|
47 |
+
parser.add_argument(
|
48 |
+
'--cond_inp_type',
|
49 |
+
type=str,
|
50 |
+
default='image',
|
51 |
+
help='the type of the input condition image, take depth T2I as example, the input can be raw image, '
|
52 |
+
'which depth will be calculated, or the input can be a directly a depth map image',
|
53 |
+
)
|
54 |
+
|
55 |
+
parser.add_argument(
|
56 |
+
'--sampler',
|
57 |
+
type=str,
|
58 |
+
default='ddim',
|
59 |
+
choices=['ddim', 'plms'],
|
60 |
+
help='sampling algorithm, currently, only ddim and plms are supported, more are on the way',
|
61 |
+
)
|
62 |
+
|
63 |
+
parser.add_argument(
|
64 |
+
'--steps',
|
65 |
+
type=int,
|
66 |
+
default=50,
|
67 |
+
help='number of sampling steps',
|
68 |
+
)
|
69 |
+
|
70 |
+
parser.add_argument(
|
71 |
+
'--sd_ckpt',
|
72 |
+
type=str,
|
73 |
+
default='models/sd-v1-4.ckpt',
|
74 |
+
help='path to checkpoint of stable diffusion model, both .ckpt and .safetensor are supported',
|
75 |
+
)
|
76 |
+
|
77 |
+
parser.add_argument(
|
78 |
+
'--vae_ckpt',
|
79 |
+
type=str,
|
80 |
+
default=None,
|
81 |
+
help='vae checkpoint, anime SD models usually have seperate vae ckpt that need to be loaded',
|
82 |
+
)
|
83 |
+
|
84 |
+
parser.add_argument(
|
85 |
+
'--adapter_ckpt',
|
86 |
+
type=str,
|
87 |
+
default=None,
|
88 |
+
help='path to checkpoint of adapter',
|
89 |
+
)
|
90 |
+
|
91 |
+
parser.add_argument(
|
92 |
+
'--config',
|
93 |
+
type=str,
|
94 |
+
default='configs/stable-diffusion/sd-v1-inference.yaml',
|
95 |
+
help='path to config which constructs SD model',
|
96 |
+
)
|
97 |
+
|
98 |
+
parser.add_argument(
|
99 |
+
'--max_resolution',
|
100 |
+
type=float,
|
101 |
+
default=512 * 512,
|
102 |
+
help='max image height * width, only for computer with limited vram',
|
103 |
+
)
|
104 |
+
|
105 |
+
parser.add_argument(
|
106 |
+
'--resize_short_edge',
|
107 |
+
type=int,
|
108 |
+
default=None,
|
109 |
+
help='resize short edge of the input image, if this arg is set, max_resolution will not be used',
|
110 |
+
)
|
111 |
+
|
112 |
+
parser.add_argument(
|
113 |
+
'--C',
|
114 |
+
type=int,
|
115 |
+
default=4,
|
116 |
+
help='latent channels',
|
117 |
+
)
|
118 |
+
|
119 |
+
parser.add_argument(
|
120 |
+
'--f',
|
121 |
+
type=int,
|
122 |
+
default=8,
|
123 |
+
help='downsampling factor',
|
124 |
+
)
|
125 |
+
|
126 |
+
parser.add_argument(
|
127 |
+
'--scale',
|
128 |
+
type=float,
|
129 |
+
default=7.5,
|
130 |
+
help='unconditional guidance scale: eps = eps(x, empty) + scale * (eps(x, cond) - eps(x, empty))',
|
131 |
+
)
|
132 |
+
|
133 |
+
parser.add_argument(
|
134 |
+
'--cond_tau',
|
135 |
+
type=float,
|
136 |
+
default=1.0,
|
137 |
+
help='timestamp parameter that determines until which step the adapter is applied, '
|
138 |
+
'similar as Prompt-to-Prompt tau',
|
139 |
+
)
|
140 |
+
|
141 |
+
parser.add_argument(
|
142 |
+
'--style_cond_tau',
|
143 |
+
type=float,
|
144 |
+
default=1.0,
|
145 |
+
help='timestamp parameter that determines until which step the adapter is applied, '
|
146 |
+
'similar as Prompt-to-Prompt tau',
|
147 |
+
)
|
148 |
+
|
149 |
+
parser.add_argument(
|
150 |
+
'--cond_weight',
|
151 |
+
type=float,
|
152 |
+
default=1.0,
|
153 |
+
help='the adapter features are multiplied by the cond_weight. The larger the cond_weight, the more aligned '
|
154 |
+
'the generated image and condition will be, but the generated quality may be reduced',
|
155 |
+
)
|
156 |
+
|
157 |
+
parser.add_argument(
|
158 |
+
'--seed',
|
159 |
+
type=int,
|
160 |
+
default=42,
|
161 |
+
)
|
162 |
+
|
163 |
+
parser.add_argument(
|
164 |
+
'--n_samples',
|
165 |
+
type=int,
|
166 |
+
default=4,
|
167 |
+
help='# of samples to generate',
|
168 |
+
)
|
169 |
+
|
170 |
+
return parser
|
171 |
+
|
172 |
+
|
173 |
+
def get_sd_models(opt):
|
174 |
+
"""
|
175 |
+
build stable diffusion model, sampler
|
176 |
+
"""
|
177 |
+
# SD
|
178 |
+
config = OmegaConf.load(f"{opt.config}")
|
179 |
+
model = load_model_from_config(config, opt.sd_ckpt, opt.vae_ckpt)
|
180 |
+
sd_model = model.to(opt.device)
|
181 |
+
|
182 |
+
# sampler
|
183 |
+
if opt.sampler == 'plms':
|
184 |
+
sampler = PLMSSampler(model)
|
185 |
+
elif opt.sampler == 'ddim':
|
186 |
+
sampler = DDIMSampler(model)
|
187 |
+
else:
|
188 |
+
raise NotImplementedError
|
189 |
+
|
190 |
+
return sd_model, sampler
|
191 |
+
|
192 |
+
|
193 |
+
def get_t2i_adapter_models(opt):
|
194 |
+
config = OmegaConf.load(f"{opt.config}")
|
195 |
+
model = load_model_from_config(config, opt.sd_ckpt, opt.vae_ckpt)
|
196 |
+
adapter_ckpt_path = getattr(opt, f'{opt.which_cond}_adapter_ckpt', None)
|
197 |
+
if adapter_ckpt_path is None:
|
198 |
+
adapter_ckpt_path = getattr(opt, 'adapter_ckpt')
|
199 |
+
adapter_ckpt = read_state_dict(adapter_ckpt_path)
|
200 |
+
new_state_dict = {}
|
201 |
+
for k, v in adapter_ckpt.items():
|
202 |
+
if not k.startswith('adapter.'):
|
203 |
+
new_state_dict[f'adapter.{k}'] = v
|
204 |
+
else:
|
205 |
+
new_state_dict[k] = v
|
206 |
+
m, u = model.load_state_dict(new_state_dict, strict=False)
|
207 |
+
if len(u) > 0:
|
208 |
+
print(f"unexpected keys in loading adapter ckpt {adapter_ckpt_path}:")
|
209 |
+
print(u)
|
210 |
+
|
211 |
+
model = model.to(opt.device)
|
212 |
+
|
213 |
+
# sampler
|
214 |
+
if opt.sampler == 'plms':
|
215 |
+
sampler = PLMSSampler(model)
|
216 |
+
elif opt.sampler == 'ddim':
|
217 |
+
sampler = DDIMSampler(model)
|
218 |
+
else:
|
219 |
+
raise NotImplementedError
|
220 |
+
|
221 |
+
return model, sampler
|
222 |
+
|
223 |
+
|
224 |
+
def get_cond_ch(cond_type: ExtraCondition):
|
225 |
+
if cond_type == ExtraCondition.sketch or cond_type == ExtraCondition.canny:
|
226 |
+
return 1
|
227 |
+
return 3
|
228 |
+
|
229 |
+
|
230 |
+
def get_adapters(opt, cond_type: ExtraCondition):
|
231 |
+
adapter = {}
|
232 |
+
cond_weight = getattr(opt, f'{cond_type.name}_weight', None)
|
233 |
+
if cond_weight is None:
|
234 |
+
cond_weight = getattr(opt, 'cond_weight')
|
235 |
+
adapter['cond_weight'] = cond_weight
|
236 |
+
|
237 |
+
if cond_type == ExtraCondition.style:
|
238 |
+
adapter['model'] = StyleAdapter(width=1024, context_dim=768, num_head=8, n_layes=3, num_token=8).to(opt.device)
|
239 |
+
elif cond_type == ExtraCondition.color:
|
240 |
+
adapter['model'] = Adapter_light(
|
241 |
+
cin=64 * get_cond_ch(cond_type),
|
242 |
+
channels=[320, 640, 1280, 1280],
|
243 |
+
nums_rb=4).to(opt.device)
|
244 |
+
else:
|
245 |
+
adapter['model'] = Adapter(
|
246 |
+
cin=64 * get_cond_ch(cond_type),
|
247 |
+
channels=[320, 640, 1280, 1280][:4],
|
248 |
+
nums_rb=2,
|
249 |
+
ksize=1,
|
250 |
+
sk=True,
|
251 |
+
use_conv=False).to(opt.device)
|
252 |
+
ckpt_path = getattr(opt, f'{cond_type.name}_adapter_ckpt', None)
|
253 |
+
if ckpt_path is None:
|
254 |
+
ckpt_path = getattr(opt, 'adapter_ckpt')
|
255 |
+
adapter['model'].load_state_dict(torch.load(ckpt_path))
|
256 |
+
|
257 |
+
return adapter
|
258 |
+
|
259 |
+
|
260 |
+
def diffusion_inference(opt, model, sampler, adapter_features, append_to_context=None):
|
261 |
+
# get text embedding
|
262 |
+
c = model.get_learned_conditioning([opt.prompt])
|
263 |
+
if opt.scale != 1.0:
|
264 |
+
uc = model.get_learned_conditioning([opt.neg_prompt])
|
265 |
+
else:
|
266 |
+
uc = None
|
267 |
+
c, uc = fix_cond_shapes(model, c, uc)
|
268 |
+
|
269 |
+
if not hasattr(opt, 'H'):
|
270 |
+
opt.H = 512
|
271 |
+
opt.W = 512
|
272 |
+
shape = [opt.C, opt.H // opt.f, opt.W // opt.f]
|
273 |
+
|
274 |
+
samples_latents, _ = sampler.sample(
|
275 |
+
S=opt.steps,
|
276 |
+
conditioning=c,
|
277 |
+
batch_size=1,
|
278 |
+
shape=shape,
|
279 |
+
verbose=False,
|
280 |
+
unconditional_guidance_scale=opt.scale,
|
281 |
+
unconditional_conditioning=uc,
|
282 |
+
x_T=None,
|
283 |
+
features_adapter=adapter_features,
|
284 |
+
append_to_context=append_to_context,
|
285 |
+
cond_tau=opt.cond_tau,
|
286 |
+
style_cond_tau=opt.style_cond_tau,
|
287 |
+
)
|
288 |
+
|
289 |
+
x_samples = model.decode_first_stage(samples_latents)
|
290 |
+
x_samples = torch.clamp((x_samples + 1.0) / 2.0, min=0.0, max=1.0)
|
291 |
+
|
292 |
+
return x_samples
|
ldm/lr_scheduler.py
ADDED
@@ -0,0 +1,98 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
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|
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|
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|
|
|
|
|
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|
|
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|
|
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|
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|
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|
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|
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|
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|
|
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|
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|
|
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|
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|
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|
|
|
|
|
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|
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|
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|
|
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|
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|
|
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|
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|
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|
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|
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|
|
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|
|
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|
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|
|
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|
|
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|
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|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import numpy as np
|
2 |
+
|
3 |
+
|
4 |
+
class LambdaWarmUpCosineScheduler:
|
5 |
+
"""
|
6 |
+
note: use with a base_lr of 1.0
|
7 |
+
"""
|
8 |
+
def __init__(self, warm_up_steps, lr_min, lr_max, lr_start, max_decay_steps, verbosity_interval=0):
|
9 |
+
self.lr_warm_up_steps = warm_up_steps
|
10 |
+
self.lr_start = lr_start
|
11 |
+
self.lr_min = lr_min
|
12 |
+
self.lr_max = lr_max
|
13 |
+
self.lr_max_decay_steps = max_decay_steps
|
14 |
+
self.last_lr = 0.
|
15 |
+
self.verbosity_interval = verbosity_interval
|
16 |
+
|
17 |
+
def schedule(self, n, **kwargs):
|
18 |
+
if self.verbosity_interval > 0:
|
19 |
+
if n % self.verbosity_interval == 0: print(f"current step: {n}, recent lr-multiplier: {self.last_lr}")
|
20 |
+
if n < self.lr_warm_up_steps:
|
21 |
+
lr = (self.lr_max - self.lr_start) / self.lr_warm_up_steps * n + self.lr_start
|
22 |
+
self.last_lr = lr
|
23 |
+
return lr
|
24 |
+
else:
|
25 |
+
t = (n - self.lr_warm_up_steps) / (self.lr_max_decay_steps - self.lr_warm_up_steps)
|
26 |
+
t = min(t, 1.0)
|
27 |
+
lr = self.lr_min + 0.5 * (self.lr_max - self.lr_min) * (
|
28 |
+
1 + np.cos(t * np.pi))
|
29 |
+
self.last_lr = lr
|
30 |
+
return lr
|
31 |
+
|
32 |
+
def __call__(self, n, **kwargs):
|
33 |
+
return self.schedule(n,**kwargs)
|
34 |
+
|
35 |
+
|
36 |
+
class LambdaWarmUpCosineScheduler2:
|
37 |
+
"""
|
38 |
+
supports repeated iterations, configurable via lists
|
39 |
+
note: use with a base_lr of 1.0.
|
40 |
+
"""
|
41 |
+
def __init__(self, warm_up_steps, f_min, f_max, f_start, cycle_lengths, verbosity_interval=0):
|
42 |
+
assert len(warm_up_steps) == len(f_min) == len(f_max) == len(f_start) == len(cycle_lengths)
|
43 |
+
self.lr_warm_up_steps = warm_up_steps
|
44 |
+
self.f_start = f_start
|
45 |
+
self.f_min = f_min
|
46 |
+
self.f_max = f_max
|
47 |
+
self.cycle_lengths = cycle_lengths
|
48 |
+
self.cum_cycles = np.cumsum([0] + list(self.cycle_lengths))
|
49 |
+
self.last_f = 0.
|
50 |
+
self.verbosity_interval = verbosity_interval
|
51 |
+
|
52 |
+
def find_in_interval(self, n):
|
53 |
+
interval = 0
|
54 |
+
for cl in self.cum_cycles[1:]:
|
55 |
+
if n <= cl:
|
56 |
+
return interval
|
57 |
+
interval += 1
|
58 |
+
|
59 |
+
def schedule(self, n, **kwargs):
|
60 |
+
cycle = self.find_in_interval(n)
|
61 |
+
n = n - self.cum_cycles[cycle]
|
62 |
+
if self.verbosity_interval > 0:
|
63 |
+
if n % self.verbosity_interval == 0: print(f"current step: {n}, recent lr-multiplier: {self.last_f}, "
|
64 |
+
f"current cycle {cycle}")
|
65 |
+
if n < self.lr_warm_up_steps[cycle]:
|
66 |
+
f = (self.f_max[cycle] - self.f_start[cycle]) / self.lr_warm_up_steps[cycle] * n + self.f_start[cycle]
|
67 |
+
self.last_f = f
|
68 |
+
return f
|
69 |
+
else:
|
70 |
+
t = (n - self.lr_warm_up_steps[cycle]) / (self.cycle_lengths[cycle] - self.lr_warm_up_steps[cycle])
|
71 |
+
t = min(t, 1.0)
|
72 |
+
f = self.f_min[cycle] + 0.5 * (self.f_max[cycle] - self.f_min[cycle]) * (
|
73 |
+
1 + np.cos(t * np.pi))
|
74 |
+
self.last_f = f
|
75 |
+
return f
|
76 |
+
|
77 |
+
def __call__(self, n, **kwargs):
|
78 |
+
return self.schedule(n, **kwargs)
|
79 |
+
|
80 |
+
|
81 |
+
class LambdaLinearScheduler(LambdaWarmUpCosineScheduler2):
|
82 |
+
|
83 |
+
def schedule(self, n, **kwargs):
|
84 |
+
cycle = self.find_in_interval(n)
|
85 |
+
n = n - self.cum_cycles[cycle]
|
86 |
+
if self.verbosity_interval > 0:
|
87 |
+
if n % self.verbosity_interval == 0: print(f"current step: {n}, recent lr-multiplier: {self.last_f}, "
|
88 |
+
f"current cycle {cycle}")
|
89 |
+
|
90 |
+
if n < self.lr_warm_up_steps[cycle]:
|
91 |
+
f = (self.f_max[cycle] - self.f_start[cycle]) / self.lr_warm_up_steps[cycle] * n + self.f_start[cycle]
|
92 |
+
self.last_f = f
|
93 |
+
return f
|
94 |
+
else:
|
95 |
+
f = self.f_min[cycle] + (self.f_max[cycle] - self.f_min[cycle]) * (self.cycle_lengths[cycle] - n) / (self.cycle_lengths[cycle])
|
96 |
+
self.last_f = f
|
97 |
+
return f
|
98 |
+
|
ldm/models/__pycache__/autoencoder.cpython-38.pyc
ADDED
Binary file (7.48 kB). View file
|
|
ldm/models/autoencoder.py
ADDED
@@ -0,0 +1,211 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
|
|
|
|
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|
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|
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|
|
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|
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|
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|
|
|
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|
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|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
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|
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|
|
|
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|
|
|
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|
|
|
|
|
|
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|
|
|
|
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|
|
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|
|
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|
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|
|
|
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|
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|
|
|
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|
|
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|
|
|
|
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|
|
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|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import torch
|
2 |
+
import pytorch_lightning as pl
|
3 |
+
import torch.nn.functional as F
|
4 |
+
import torch.nn as nn
|
5 |
+
from contextlib import contextmanager
|
6 |
+
|
7 |
+
from ldm.modules.diffusionmodules.model import Encoder, Decoder
|
8 |
+
from ldm.modules.distributions.distributions import DiagonalGaussianDistribution
|
9 |
+
|
10 |
+
from ldm.util import instantiate_from_config
|
11 |
+
from ldm.modules.ema import LitEma
|
12 |
+
|
13 |
+
|
14 |
+
class AutoencoderKL(pl.LightningModule):
|
15 |
+
def __init__(self,
|
16 |
+
ddconfig,
|
17 |
+
lossconfig,
|
18 |
+
embed_dim,
|
19 |
+
ckpt_path=None,
|
20 |
+
ignore_keys=[],
|
21 |
+
image_key="image",
|
22 |
+
colorize_nlabels=None,
|
23 |
+
monitor=None,
|
24 |
+
ema_decay=None,
|
25 |
+
learn_logvar=False
|
26 |
+
):
|
27 |
+
super().__init__()
|
28 |
+
self.learn_logvar = learn_logvar
|
29 |
+
self.image_key = image_key
|
30 |
+
self.encoder = Encoder(**ddconfig)
|
31 |
+
self.decoder = Decoder(**ddconfig)
|
32 |
+
self.loss = instantiate_from_config(lossconfig)
|
33 |
+
assert ddconfig["double_z"]
|
34 |
+
self.quant_conv = torch.nn.Conv2d(2*ddconfig["z_channels"], 2*embed_dim, 1)
|
35 |
+
self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)
|
36 |
+
self.embed_dim = embed_dim
|
37 |
+
if colorize_nlabels is not None:
|
38 |
+
assert type(colorize_nlabels)==int
|
39 |
+
self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1))
|
40 |
+
if monitor is not None:
|
41 |
+
self.monitor = monitor
|
42 |
+
|
43 |
+
self.use_ema = ema_decay is not None
|
44 |
+
if self.use_ema:
|
45 |
+
self.ema_decay = ema_decay
|
46 |
+
assert 0. < ema_decay < 1.
|
47 |
+
self.model_ema = LitEma(self, decay=ema_decay)
|
48 |
+
print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
|
49 |
+
|
50 |
+
if ckpt_path is not None:
|
51 |
+
self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
|
52 |
+
|
53 |
+
def init_from_ckpt(self, path, ignore_keys=list()):
|
54 |
+
sd = torch.load(path, map_location="cpu")["state_dict"]
|
55 |
+
keys = list(sd.keys())
|
56 |
+
for k in keys:
|
57 |
+
for ik in ignore_keys:
|
58 |
+
if k.startswith(ik):
|
59 |
+
print("Deleting key {} from state_dict.".format(k))
|
60 |
+
del sd[k]
|
61 |
+
self.load_state_dict(sd, strict=False)
|
62 |
+
print(f"Restored from {path}")
|
63 |
+
|
64 |
+
@contextmanager
|
65 |
+
def ema_scope(self, context=None):
|
66 |
+
if self.use_ema:
|
67 |
+
self.model_ema.store(self.parameters())
|
68 |
+
self.model_ema.copy_to(self)
|
69 |
+
if context is not None:
|
70 |
+
print(f"{context}: Switched to EMA weights")
|
71 |
+
try:
|
72 |
+
yield None
|
73 |
+
finally:
|
74 |
+
if self.use_ema:
|
75 |
+
self.model_ema.restore(self.parameters())
|
76 |
+
if context is not None:
|
77 |
+
print(f"{context}: Restored training weights")
|
78 |
+
|
79 |
+
def on_train_batch_end(self, *args, **kwargs):
|
80 |
+
if self.use_ema:
|
81 |
+
self.model_ema(self)
|
82 |
+
|
83 |
+
def encode(self, x):
|
84 |
+
h = self.encoder(x)
|
85 |
+
moments = self.quant_conv(h)
|
86 |
+
posterior = DiagonalGaussianDistribution(moments)
|
87 |
+
return posterior
|
88 |
+
|
89 |
+
def decode(self, z):
|
90 |
+
z = self.post_quant_conv(z)
|
91 |
+
dec = self.decoder(z)
|
92 |
+
return dec
|
93 |
+
|
94 |
+
def forward(self, input, sample_posterior=True):
|
95 |
+
posterior = self.encode(input)
|
96 |
+
if sample_posterior:
|
97 |
+
z = posterior.sample()
|
98 |
+
else:
|
99 |
+
z = posterior.mode()
|
100 |
+
dec = self.decode(z)
|
101 |
+
return dec, posterior
|
102 |
+
|
103 |
+
def get_input(self, batch, k):
|
104 |
+
x = batch[k]
|
105 |
+
if len(x.shape) == 3:
|
106 |
+
x = x[..., None]
|
107 |
+
x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float()
|
108 |
+
return x
|
109 |
+
|
110 |
+
def training_step(self, batch, batch_idx, optimizer_idx):
|
111 |
+
inputs = self.get_input(batch, self.image_key)
|
112 |
+
reconstructions, posterior = self(inputs)
|
113 |
+
|
114 |
+
if optimizer_idx == 0:
|
115 |
+
# train encoder+decoder+logvar
|
116 |
+
aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step,
|
117 |
+
last_layer=self.get_last_layer(), split="train")
|
118 |
+
self.log("aeloss", aeloss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
|
119 |
+
self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=False)
|
120 |
+
return aeloss
|
121 |
+
|
122 |
+
if optimizer_idx == 1:
|
123 |
+
# train the discriminator
|
124 |
+
discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step,
|
125 |
+
last_layer=self.get_last_layer(), split="train")
|
126 |
+
|
127 |
+
self.log("discloss", discloss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
|
128 |
+
self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=False)
|
129 |
+
return discloss
|
130 |
+
|
131 |
+
def validation_step(self, batch, batch_idx):
|
132 |
+
log_dict = self._validation_step(batch, batch_idx)
|
133 |
+
with self.ema_scope():
|
134 |
+
log_dict_ema = self._validation_step(batch, batch_idx, postfix="_ema")
|
135 |
+
return log_dict
|
136 |
+
|
137 |
+
def _validation_step(self, batch, batch_idx, postfix=""):
|
138 |
+
inputs = self.get_input(batch, self.image_key)
|
139 |
+
reconstructions, posterior = self(inputs)
|
140 |
+
aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, 0, self.global_step,
|
141 |
+
last_layer=self.get_last_layer(), split="val"+postfix)
|
142 |
+
|
143 |
+
discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, 1, self.global_step,
|
144 |
+
last_layer=self.get_last_layer(), split="val"+postfix)
|
145 |
+
|
146 |
+
self.log(f"val{postfix}/rec_loss", log_dict_ae[f"val{postfix}/rec_loss"])
|
147 |
+
self.log_dict(log_dict_ae)
|
148 |
+
self.log_dict(log_dict_disc)
|
149 |
+
return self.log_dict
|
150 |
+
|
151 |
+
def configure_optimizers(self):
|
152 |
+
lr = self.learning_rate
|
153 |
+
ae_params_list = list(self.encoder.parameters()) + list(self.decoder.parameters()) + list(
|
154 |
+
self.quant_conv.parameters()) + list(self.post_quant_conv.parameters())
|
155 |
+
if self.learn_logvar:
|
156 |
+
print(f"{self.__class__.__name__}: Learning logvar")
|
157 |
+
ae_params_list.append(self.loss.logvar)
|
158 |
+
opt_ae = torch.optim.Adam(ae_params_list,
|
159 |
+
lr=lr, betas=(0.5, 0.9))
|
160 |
+
opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(),
|
161 |
+
lr=lr, betas=(0.5, 0.9))
|
162 |
+
return [opt_ae, opt_disc], []
|
163 |
+
|
164 |
+
def get_last_layer(self):
|
165 |
+
return self.decoder.conv_out.weight
|
166 |
+
|
167 |
+
@torch.no_grad()
|
168 |
+
def log_images(self, batch, only_inputs=False, log_ema=False, **kwargs):
|
169 |
+
log = dict()
|
170 |
+
x = self.get_input(batch, self.image_key)
|
171 |
+
x = x.to(self.device)
|
172 |
+
if not only_inputs:
|
173 |
+
xrec, posterior = self(x)
|
174 |
+
if x.shape[1] > 3:
|
175 |
+
# colorize with random projection
|
176 |
+
assert xrec.shape[1] > 3
|
177 |
+
x = self.to_rgb(x)
|
178 |
+
xrec = self.to_rgb(xrec)
|
179 |
+
log["samples"] = self.decode(torch.randn_like(posterior.sample()))
|
180 |
+
log["reconstructions"] = xrec
|
181 |
+
log["inputs"] = x
|
182 |
+
return log
|
183 |
+
|
184 |
+
def to_rgb(self, x):
|
185 |
+
assert self.image_key == "segmentation"
|
186 |
+
if not hasattr(self, "colorize"):
|
187 |
+
self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x))
|
188 |
+
x = F.conv2d(x, weight=self.colorize)
|
189 |
+
x = 2.*(x-x.min())/(x.max()-x.min()) - 1.
|
190 |
+
return x
|
191 |
+
|
192 |
+
|
193 |
+
class IdentityFirstStage(nn.Module):
|
194 |
+
def __init__(self, *args, vq_interface=False, **kwargs):
|
195 |
+
self.vq_interface = vq_interface
|
196 |
+
super().__init__()
|
197 |
+
|
198 |
+
def encode(self, x, *args, **kwargs):
|
199 |
+
return x
|
200 |
+
|
201 |
+
def decode(self, x, *args, **kwargs):
|
202 |
+
return x
|
203 |
+
|
204 |
+
def quantize(self, x, *args, **kwargs):
|
205 |
+
if self.vq_interface:
|
206 |
+
return x, None, [None, None, None]
|
207 |
+
return x
|
208 |
+
|
209 |
+
def forward(self, x, *args, **kwargs):
|
210 |
+
return x
|
211 |
+
|
ldm/models/diffusion/__init__.py
ADDED
File without changes
|
ldm/models/diffusion/__pycache__/__init__.cpython-38.pyc
ADDED
Binary file (206 Bytes). View file
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|
ldm/models/diffusion/__pycache__/ddim.cpython-38.pyc
ADDED
Binary file (8.35 kB). View file
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|
ldm/models/diffusion/__pycache__/ddpm.cpython-38.pyc
ADDED
Binary file (39.5 kB). View file
|
|
ldm/models/diffusion/__pycache__/plms.cpython-38.pyc
ADDED
Binary file (7.53 kB). View file
|
|
ldm/models/diffusion/ddim.py
ADDED
@@ -0,0 +1,293 @@
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|
|
1 |
+
"""SAMPLING ONLY."""
|
2 |
+
|
3 |
+
import torch
|
4 |
+
import numpy as np
|
5 |
+
from tqdm import tqdm
|
6 |
+
|
7 |
+
from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like, \
|
8 |
+
extract_into_tensor
|
9 |
+
|
10 |
+
|
11 |
+
class DDIMSampler(object):
|
12 |
+
def __init__(self, model, schedule="linear", **kwargs):
|
13 |
+
super().__init__()
|
14 |
+
self.model = model
|
15 |
+
self.ddpm_num_timesteps = model.num_timesteps
|
16 |
+
self.schedule = schedule
|
17 |
+
|
18 |
+
def register_buffer(self, name, attr):
|
19 |
+
if type(attr) == torch.Tensor:
|
20 |
+
if attr.device != torch.device("cuda"):
|
21 |
+
attr = attr.to(torch.device("cuda"))
|
22 |
+
setattr(self, name, attr)
|
23 |
+
|
24 |
+
def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True):
|
25 |
+
self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps,
|
26 |
+
num_ddpm_timesteps=self.ddpm_num_timesteps, verbose=verbose)
|
27 |
+
alphas_cumprod = self.model.alphas_cumprod
|
28 |
+
assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep'
|
29 |
+
to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device)
|
30 |
+
|
31 |
+
self.register_buffer('betas', to_torch(self.model.betas))
|
32 |
+
self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
|
33 |
+
self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev))
|
34 |
+
|
35 |
+
# calculations for diffusion q(x_t | x_{t-1}) and others
|
36 |
+
self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu())))
|
37 |
+
self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu())))
|
38 |
+
self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu())))
|
39 |
+
self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu())))
|
40 |
+
self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1)))
|
41 |
+
|
42 |
+
# ddim sampling parameters
|
43 |
+
ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(),
|
44 |
+
ddim_timesteps=self.ddim_timesteps,
|
45 |
+
eta=ddim_eta, verbose=verbose)
|
46 |
+
self.register_buffer('ddim_sigmas', ddim_sigmas)
|
47 |
+
self.register_buffer('ddim_alphas', ddim_alphas)
|
48 |
+
self.register_buffer('ddim_alphas_prev', ddim_alphas_prev)
|
49 |
+
self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas))
|
50 |
+
sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt(
|
51 |
+
(1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * (
|
52 |
+
1 - self.alphas_cumprod / self.alphas_cumprod_prev))
|
53 |
+
self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps)
|
54 |
+
|
55 |
+
@torch.no_grad()
|
56 |
+
def sample(self,
|
57 |
+
S,
|
58 |
+
batch_size,
|
59 |
+
shape,
|
60 |
+
conditioning=None,
|
61 |
+
callback=None,
|
62 |
+
normals_sequence=None,
|
63 |
+
img_callback=None,
|
64 |
+
quantize_x0=False,
|
65 |
+
eta=0.,
|
66 |
+
mask=None,
|
67 |
+
x0=None,
|
68 |
+
temperature=1.,
|
69 |
+
noise_dropout=0.,
|
70 |
+
score_corrector=None,
|
71 |
+
corrector_kwargs=None,
|
72 |
+
verbose=True,
|
73 |
+
x_T=None,
|
74 |
+
log_every_t=100,
|
75 |
+
unconditional_guidance_scale=1.,
|
76 |
+
unconditional_conditioning=None,
|
77 |
+
features_adapter=None,
|
78 |
+
append_to_context=None,
|
79 |
+
cond_tau=0.4,
|
80 |
+
style_cond_tau=1.0,
|
81 |
+
# this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
|
82 |
+
**kwargs
|
83 |
+
):
|
84 |
+
if conditioning is not None:
|
85 |
+
if isinstance(conditioning, dict):
|
86 |
+
cbs = conditioning[list(conditioning.keys())[0]].shape[0]
|
87 |
+
if cbs != batch_size:
|
88 |
+
print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
|
89 |
+
else:
|
90 |
+
if conditioning.shape[0] != batch_size:
|
91 |
+
print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
|
92 |
+
|
93 |
+
self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)
|
94 |
+
# sampling
|
95 |
+
C, H, W = shape
|
96 |
+
size = (batch_size, C, H, W)
|
97 |
+
print(f'Data shape for DDIM sampling is {size}, eta {eta}')
|
98 |
+
|
99 |
+
samples, intermediates = self.ddim_sampling(conditioning, size,
|
100 |
+
callback=callback,
|
101 |
+
img_callback=img_callback,
|
102 |
+
quantize_denoised=quantize_x0,
|
103 |
+
mask=mask, x0=x0,
|
104 |
+
ddim_use_original_steps=False,
|
105 |
+
noise_dropout=noise_dropout,
|
106 |
+
temperature=temperature,
|
107 |
+
score_corrector=score_corrector,
|
108 |
+
corrector_kwargs=corrector_kwargs,
|
109 |
+
x_T=x_T,
|
110 |
+
log_every_t=log_every_t,
|
111 |
+
unconditional_guidance_scale=unconditional_guidance_scale,
|
112 |
+
unconditional_conditioning=unconditional_conditioning,
|
113 |
+
features_adapter=features_adapter,
|
114 |
+
append_to_context=append_to_context,
|
115 |
+
cond_tau=cond_tau,
|
116 |
+
style_cond_tau=style_cond_tau,
|
117 |
+
)
|
118 |
+
return samples, intermediates
|
119 |
+
|
120 |
+
@torch.no_grad()
|
121 |
+
def ddim_sampling(self, cond, shape,
|
122 |
+
x_T=None, ddim_use_original_steps=False,
|
123 |
+
callback=None, timesteps=None, quantize_denoised=False,
|
124 |
+
mask=None, x0=None, img_callback=None, log_every_t=100,
|
125 |
+
temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
|
126 |
+
unconditional_guidance_scale=1., unconditional_conditioning=None, features_adapter=None,
|
127 |
+
append_to_context=None, cond_tau=0.4, style_cond_tau=1.0):
|
128 |
+
device = self.model.betas.device
|
129 |
+
b = shape[0]
|
130 |
+
if x_T is None:
|
131 |
+
img = torch.randn(shape, device=device)
|
132 |
+
else:
|
133 |
+
img = x_T
|
134 |
+
|
135 |
+
if timesteps is None:
|
136 |
+
timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps
|
137 |
+
elif timesteps is not None and not ddim_use_original_steps:
|
138 |
+
subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1
|
139 |
+
timesteps = self.ddim_timesteps[:subset_end]
|
140 |
+
|
141 |
+
intermediates = {'x_inter': [img], 'pred_x0': [img]}
|
142 |
+
time_range = reversed(range(0, timesteps)) if ddim_use_original_steps else np.flip(timesteps)
|
143 |
+
total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0]
|
144 |
+
print(f"Running DDIM Sampling with {total_steps} timesteps")
|
145 |
+
|
146 |
+
iterator = tqdm(time_range, desc='DDIM Sampler', total=total_steps)
|
147 |
+
|
148 |
+
for i, step in enumerate(iterator):
|
149 |
+
index = total_steps - i - 1
|
150 |
+
ts = torch.full((b,), step, device=device, dtype=torch.long)
|
151 |
+
|
152 |
+
if mask is not None:
|
153 |
+
assert x0 is not None
|
154 |
+
img_orig = self.model.q_sample(x0, ts) # TODO: deterministic forward pass?
|
155 |
+
img = img_orig * mask + (1. - mask) * img
|
156 |
+
|
157 |
+
outs = self.p_sample_ddim(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps,
|
158 |
+
quantize_denoised=quantize_denoised, temperature=temperature,
|
159 |
+
noise_dropout=noise_dropout, score_corrector=score_corrector,
|
160 |
+
corrector_kwargs=corrector_kwargs,
|
161 |
+
unconditional_guidance_scale=unconditional_guidance_scale,
|
162 |
+
unconditional_conditioning=unconditional_conditioning,
|
163 |
+
features_adapter=None if index < int(
|
164 |
+
(1 - cond_tau) * total_steps) else features_adapter,
|
165 |
+
append_to_context=None if index < int(
|
166 |
+
(1 - style_cond_tau) * total_steps) else append_to_context,
|
167 |
+
)
|
168 |
+
img, pred_x0 = outs
|
169 |
+
if callback: callback(i)
|
170 |
+
if img_callback: img_callback(pred_x0, i)
|
171 |
+
|
172 |
+
if index % log_every_t == 0 or index == total_steps - 1:
|
173 |
+
intermediates['x_inter'].append(img)
|
174 |
+
intermediates['pred_x0'].append(pred_x0)
|
175 |
+
|
176 |
+
return img, intermediates
|
177 |
+
|
178 |
+
@torch.no_grad()
|
179 |
+
def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
|
180 |
+
temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
|
181 |
+
unconditional_guidance_scale=1., unconditional_conditioning=None, features_adapter=None,
|
182 |
+
append_to_context=None):
|
183 |
+
b, *_, device = *x.shape, x.device
|
184 |
+
|
185 |
+
if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
|
186 |
+
if append_to_context is not None:
|
187 |
+
model_output = self.model.apply_model(x, t, torch.cat([c, append_to_context], dim=1),
|
188 |
+
features_adapter=features_adapter)
|
189 |
+
else:
|
190 |
+
model_output = self.model.apply_model(x, t, c, features_adapter=features_adapter)
|
191 |
+
else:
|
192 |
+
x_in = torch.cat([x] * 2)
|
193 |
+
t_in = torch.cat([t] * 2)
|
194 |
+
if isinstance(c, dict):
|
195 |
+
assert isinstance(unconditional_conditioning, dict)
|
196 |
+
c_in = dict()
|
197 |
+
for k in c:
|
198 |
+
if isinstance(c[k], list):
|
199 |
+
c_in[k] = [torch.cat([
|
200 |
+
unconditional_conditioning[k][i],
|
201 |
+
c[k][i]]) for i in range(len(c[k]))]
|
202 |
+
else:
|
203 |
+
c_in[k] = torch.cat([
|
204 |
+
unconditional_conditioning[k],
|
205 |
+
c[k]])
|
206 |
+
elif isinstance(c, list):
|
207 |
+
c_in = list()
|
208 |
+
assert isinstance(unconditional_conditioning, list)
|
209 |
+
for i in range(len(c)):
|
210 |
+
c_in.append(torch.cat([unconditional_conditioning[i], c[i]]))
|
211 |
+
else:
|
212 |
+
if append_to_context is not None:
|
213 |
+
pad_len = append_to_context.size(1)
|
214 |
+
new_unconditional_conditioning = torch.cat(
|
215 |
+
[unconditional_conditioning, unconditional_conditioning[:, -pad_len:, :]], dim=1)
|
216 |
+
new_c = torch.cat([c, append_to_context], dim=1)
|
217 |
+
c_in = torch.cat([new_unconditional_conditioning, new_c])
|
218 |
+
else:
|
219 |
+
c_in = torch.cat([unconditional_conditioning, c])
|
220 |
+
model_uncond, model_t = self.model.apply_model(x_in, t_in, c_in, features_adapter=features_adapter).chunk(2)
|
221 |
+
model_output = model_uncond + unconditional_guidance_scale * (model_t - model_uncond)
|
222 |
+
|
223 |
+
if self.model.parameterization == "v":
|
224 |
+
e_t = self.model.predict_eps_from_z_and_v(x, t, model_output)
|
225 |
+
else:
|
226 |
+
e_t = model_output
|
227 |
+
|
228 |
+
if score_corrector is not None:
|
229 |
+
assert self.model.parameterization == "eps", 'not implemented'
|
230 |
+
e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
|
231 |
+
|
232 |
+
alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
|
233 |
+
alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
|
234 |
+
sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
|
235 |
+
sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
|
236 |
+
# select parameters corresponding to the currently considered timestep
|
237 |
+
a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
|
238 |
+
a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
|
239 |
+
sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
|
240 |
+
sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index], device=device)
|
241 |
+
|
242 |
+
# current prediction for x_0
|
243 |
+
if self.model.parameterization != "v":
|
244 |
+
pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
|
245 |
+
else:
|
246 |
+
pred_x0 = self.model.predict_start_from_z_and_v(x, t, model_output)
|
247 |
+
|
248 |
+
if quantize_denoised:
|
249 |
+
pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
|
250 |
+
# direction pointing to x_t
|
251 |
+
dir_xt = (1. - a_prev - sigma_t ** 2).sqrt() * e_t
|
252 |
+
noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
|
253 |
+
if noise_dropout > 0.:
|
254 |
+
noise = torch.nn.functional.dropout(noise, p=noise_dropout)
|
255 |
+
x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
|
256 |
+
return x_prev, pred_x0
|
257 |
+
|
258 |
+
@torch.no_grad()
|
259 |
+
def stochastic_encode(self, x0, t, use_original_steps=False, noise=None):
|
260 |
+
# fast, but does not allow for exact reconstruction
|
261 |
+
# t serves as an index to gather the correct alphas
|
262 |
+
if use_original_steps:
|
263 |
+
sqrt_alphas_cumprod = self.sqrt_alphas_cumprod
|
264 |
+
sqrt_one_minus_alphas_cumprod = self.sqrt_one_minus_alphas_cumprod
|
265 |
+
else:
|
266 |
+
sqrt_alphas_cumprod = torch.sqrt(self.ddim_alphas)
|
267 |
+
sqrt_one_minus_alphas_cumprod = self.ddim_sqrt_one_minus_alphas
|
268 |
+
|
269 |
+
if noise is None:
|
270 |
+
noise = torch.randn_like(x0)
|
271 |
+
return (extract_into_tensor(sqrt_alphas_cumprod, t, x0.shape) * x0 +
|
272 |
+
extract_into_tensor(sqrt_one_minus_alphas_cumprod, t, x0.shape) * noise)
|
273 |
+
|
274 |
+
@torch.no_grad()
|
275 |
+
def decode(self, x_latent, cond, t_start, unconditional_guidance_scale=1.0, unconditional_conditioning=None,
|
276 |
+
use_original_steps=False):
|
277 |
+
|
278 |
+
timesteps = np.arange(self.ddpm_num_timesteps) if use_original_steps else self.ddim_timesteps
|
279 |
+
timesteps = timesteps[:t_start]
|
280 |
+
|
281 |
+
time_range = np.flip(timesteps)
|
282 |
+
total_steps = timesteps.shape[0]
|
283 |
+
print(f"Running DDIM Sampling with {total_steps} timesteps")
|
284 |
+
|
285 |
+
iterator = tqdm(time_range, desc='Decoding image', total=total_steps)
|
286 |
+
x_dec = x_latent
|
287 |
+
for i, step in enumerate(iterator):
|
288 |
+
index = total_steps - i - 1
|
289 |
+
ts = torch.full((x_latent.shape[0],), step, device=x_latent.device, dtype=torch.long)
|
290 |
+
x_dec, _ = self.p_sample_ddim(x_dec, cond, ts, index=index, use_original_steps=use_original_steps,
|
291 |
+
unconditional_guidance_scale=unconditional_guidance_scale,
|
292 |
+
unconditional_conditioning=unconditional_conditioning)
|
293 |
+
return x_dec
|
ldm/models/diffusion/ddpm.py
ADDED
@@ -0,0 +1,1329 @@
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|
1 |
+
"""
|
2 |
+
wild mixture of
|
3 |
+
https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
|
4 |
+
https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py
|
5 |
+
https://github.com/CompVis/taming-transformers
|
6 |
+
-- merci
|
7 |
+
"""
|
8 |
+
|
9 |
+
import torch
|
10 |
+
import torch.nn as nn
|
11 |
+
import numpy as np
|
12 |
+
import pytorch_lightning as pl
|
13 |
+
from torch.optim.lr_scheduler import LambdaLR
|
14 |
+
from einops import rearrange, repeat
|
15 |
+
from contextlib import contextmanager, nullcontext
|
16 |
+
from functools import partial
|
17 |
+
import itertools
|
18 |
+
from tqdm import tqdm
|
19 |
+
from torchvision.utils import make_grid
|
20 |
+
from pytorch_lightning.utilities.distributed import rank_zero_only
|
21 |
+
from omegaconf import ListConfig
|
22 |
+
|
23 |
+
from ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config
|
24 |
+
from ldm.modules.ema import LitEma
|
25 |
+
from ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution
|
26 |
+
from ldm.models.autoencoder import IdentityFirstStage, AutoencoderKL
|
27 |
+
from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like
|
28 |
+
from ldm.models.diffusion.ddim import DDIMSampler
|
29 |
+
|
30 |
+
|
31 |
+
__conditioning_keys__ = {'concat': 'c_concat',
|
32 |
+
'crossattn': 'c_crossattn',
|
33 |
+
'adm': 'y'}
|
34 |
+
|
35 |
+
|
36 |
+
def disabled_train(self, mode=True):
|
37 |
+
"""Overwrite model.train with this function to make sure train/eval mode
|
38 |
+
does not change anymore."""
|
39 |
+
return self
|
40 |
+
|
41 |
+
|
42 |
+
def uniform_on_device(r1, r2, shape, device):
|
43 |
+
return (r1 - r2) * torch.rand(*shape, device=device) + r2
|
44 |
+
|
45 |
+
|
46 |
+
class DDPM(pl.LightningModule):
|
47 |
+
# classic DDPM with Gaussian diffusion, in image space
|
48 |
+
def __init__(self,
|
49 |
+
unet_config,
|
50 |
+
timesteps=1000,
|
51 |
+
beta_schedule="linear",
|
52 |
+
loss_type="l2",
|
53 |
+
ckpt_path=None,
|
54 |
+
ignore_keys=[],
|
55 |
+
load_only_unet=False,
|
56 |
+
monitor="val/loss",
|
57 |
+
use_ema=True,
|
58 |
+
first_stage_key="image",
|
59 |
+
image_size=256,
|
60 |
+
channels=3,
|
61 |
+
log_every_t=100,
|
62 |
+
clip_denoised=True,
|
63 |
+
linear_start=1e-4,
|
64 |
+
linear_end=2e-2,
|
65 |
+
cosine_s=8e-3,
|
66 |
+
given_betas=None,
|
67 |
+
original_elbo_weight=0.,
|
68 |
+
v_posterior=0., # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta
|
69 |
+
l_simple_weight=1.,
|
70 |
+
conditioning_key=None,
|
71 |
+
parameterization="eps", # all assuming fixed variance schedules
|
72 |
+
scheduler_config=None,
|
73 |
+
use_positional_encodings=False,
|
74 |
+
learn_logvar=False,
|
75 |
+
logvar_init=0.,
|
76 |
+
make_it_fit=False,
|
77 |
+
ucg_training=None,
|
78 |
+
reset_ema=False,
|
79 |
+
reset_num_ema_updates=False,
|
80 |
+
):
|
81 |
+
super().__init__()
|
82 |
+
assert parameterization in ["eps", "x0", "v"], 'currently only supporting "eps" and "x0" and "v"'
|
83 |
+
self.parameterization = parameterization
|
84 |
+
print(f"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode")
|
85 |
+
self.cond_stage_model = None
|
86 |
+
self.clip_denoised = clip_denoised
|
87 |
+
self.log_every_t = log_every_t
|
88 |
+
self.first_stage_key = first_stage_key
|
89 |
+
self.image_size = image_size # try conv?
|
90 |
+
self.channels = channels
|
91 |
+
self.use_positional_encodings = use_positional_encodings
|
92 |
+
self.model = DiffusionWrapper(unet_config, conditioning_key)
|
93 |
+
count_params(self.model, verbose=True)
|
94 |
+
self.use_ema = use_ema
|
95 |
+
if self.use_ema:
|
96 |
+
self.model_ema = LitEma(self.model)
|
97 |
+
print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
|
98 |
+
|
99 |
+
self.use_scheduler = scheduler_config is not None
|
100 |
+
if self.use_scheduler:
|
101 |
+
self.scheduler_config = scheduler_config
|
102 |
+
|
103 |
+
self.v_posterior = v_posterior
|
104 |
+
self.original_elbo_weight = original_elbo_weight
|
105 |
+
self.l_simple_weight = l_simple_weight
|
106 |
+
|
107 |
+
if monitor is not None:
|
108 |
+
self.monitor = monitor
|
109 |
+
self.make_it_fit = make_it_fit
|
110 |
+
if reset_ema: assert exists(ckpt_path)
|
111 |
+
if ckpt_path is not None:
|
112 |
+
self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet)
|
113 |
+
if reset_ema:
|
114 |
+
assert self.use_ema
|
115 |
+
print(f"Resetting ema to pure model weights. This is useful when restoring from an ema-only checkpoint.")
|
116 |
+
self.model_ema = LitEma(self.model)
|
117 |
+
if reset_num_ema_updates:
|
118 |
+
print(" +++++++++++ WARNING: RESETTING NUM_EMA UPDATES TO ZERO +++++++++++ ")
|
119 |
+
assert self.use_ema
|
120 |
+
self.model_ema.reset_num_updates()
|
121 |
+
|
122 |
+
self.register_schedule(given_betas=given_betas, beta_schedule=beta_schedule, timesteps=timesteps,
|
123 |
+
linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s)
|
124 |
+
|
125 |
+
self.loss_type = loss_type
|
126 |
+
|
127 |
+
self.learn_logvar = learn_logvar
|
128 |
+
self.logvar = torch.full(fill_value=logvar_init, size=(self.num_timesteps,))
|
129 |
+
if self.learn_logvar:
|
130 |
+
self.logvar = nn.Parameter(self.logvar, requires_grad=True)
|
131 |
+
|
132 |
+
self.ucg_training = ucg_training or dict()
|
133 |
+
if self.ucg_training:
|
134 |
+
self.ucg_prng = np.random.RandomState()
|
135 |
+
|
136 |
+
def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000,
|
137 |
+
linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
|
138 |
+
if exists(given_betas):
|
139 |
+
betas = given_betas
|
140 |
+
else:
|
141 |
+
betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end,
|
142 |
+
cosine_s=cosine_s)
|
143 |
+
alphas = 1. - betas
|
144 |
+
alphas_cumprod = np.cumprod(alphas, axis=0)
|
145 |
+
alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
|
146 |
+
|
147 |
+
timesteps, = betas.shape
|
148 |
+
self.num_timesteps = int(timesteps)
|
149 |
+
self.linear_start = linear_start
|
150 |
+
self.linear_end = linear_end
|
151 |
+
assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep'
|
152 |
+
|
153 |
+
to_torch = partial(torch.tensor, dtype=torch.float32)
|
154 |
+
|
155 |
+
self.register_buffer('betas', to_torch(betas))
|
156 |
+
self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
|
157 |
+
self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev))
|
158 |
+
|
159 |
+
# calculations for diffusion q(x_t | x_{t-1}) and others
|
160 |
+
self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
|
161 |
+
self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
|
162 |
+
self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod)))
|
163 |
+
self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod)))
|
164 |
+
self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1)))
|
165 |
+
|
166 |
+
# calculations for posterior q(x_{t-1} | x_t, x_0)
|
167 |
+
posterior_variance = (1 - self.v_posterior) * betas * (1. - alphas_cumprod_prev) / (
|
168 |
+
1. - alphas_cumprod) + self.v_posterior * betas
|
169 |
+
# above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
|
170 |
+
self.register_buffer('posterior_variance', to_torch(posterior_variance))
|
171 |
+
# below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
|
172 |
+
self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20))))
|
173 |
+
self.register_buffer('posterior_mean_coef1', to_torch(
|
174 |
+
betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod)))
|
175 |
+
self.register_buffer('posterior_mean_coef2', to_torch(
|
176 |
+
(1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod)))
|
177 |
+
|
178 |
+
if self.parameterization == "eps":
|
179 |
+
lvlb_weights = self.betas ** 2 / (
|
180 |
+
2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod))
|
181 |
+
elif self.parameterization == "x0":
|
182 |
+
lvlb_weights = 0.5 * np.sqrt(torch.Tensor(alphas_cumprod)) / (2. * 1 - torch.Tensor(alphas_cumprod))
|
183 |
+
elif self.parameterization == "v":
|
184 |
+
lvlb_weights = torch.ones_like(self.betas ** 2 / (
|
185 |
+
2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod)))
|
186 |
+
else:
|
187 |
+
raise NotImplementedError("mu not supported")
|
188 |
+
lvlb_weights[0] = lvlb_weights[1]
|
189 |
+
self.register_buffer('lvlb_weights', lvlb_weights, persistent=False)
|
190 |
+
assert not torch.isnan(self.lvlb_weights).all()
|
191 |
+
|
192 |
+
@contextmanager
|
193 |
+
def ema_scope(self, context=None):
|
194 |
+
if self.use_ema:
|
195 |
+
self.model_ema.store(self.model.parameters())
|
196 |
+
self.model_ema.copy_to(self.model)
|
197 |
+
if context is not None:
|
198 |
+
print(f"{context}: Switched to EMA weights")
|
199 |
+
try:
|
200 |
+
yield None
|
201 |
+
finally:
|
202 |
+
if self.use_ema:
|
203 |
+
self.model_ema.restore(self.model.parameters())
|
204 |
+
if context is not None:
|
205 |
+
print(f"{context}: Restored training weights")
|
206 |
+
|
207 |
+
@torch.no_grad()
|
208 |
+
def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
|
209 |
+
sd = torch.load(path, map_location="cpu")
|
210 |
+
if "state_dict" in list(sd.keys()):
|
211 |
+
sd = sd["state_dict"]
|
212 |
+
keys = list(sd.keys())
|
213 |
+
for k in keys:
|
214 |
+
for ik in ignore_keys:
|
215 |
+
if k.startswith(ik):
|
216 |
+
print("Deleting key {} from state_dict.".format(k))
|
217 |
+
del sd[k]
|
218 |
+
if self.make_it_fit:
|
219 |
+
n_params = len([name for name, _ in
|
220 |
+
itertools.chain(self.named_parameters(),
|
221 |
+
self.named_buffers())])
|
222 |
+
for name, param in tqdm(
|
223 |
+
itertools.chain(self.named_parameters(),
|
224 |
+
self.named_buffers()),
|
225 |
+
desc="Fitting old weights to new weights",
|
226 |
+
total=n_params
|
227 |
+
):
|
228 |
+
if not name in sd:
|
229 |
+
continue
|
230 |
+
old_shape = sd[name].shape
|
231 |
+
new_shape = param.shape
|
232 |
+
assert len(old_shape) == len(new_shape)
|
233 |
+
if len(new_shape) > 2:
|
234 |
+
# we only modify first two axes
|
235 |
+
assert new_shape[2:] == old_shape[2:]
|
236 |
+
# assumes first axis corresponds to output dim
|
237 |
+
if not new_shape == old_shape:
|
238 |
+
new_param = param.clone()
|
239 |
+
old_param = sd[name]
|
240 |
+
if len(new_shape) == 1:
|
241 |
+
for i in range(new_param.shape[0]):
|
242 |
+
new_param[i] = old_param[i % old_shape[0]]
|
243 |
+
elif len(new_shape) >= 2:
|
244 |
+
for i in range(new_param.shape[0]):
|
245 |
+
for j in range(new_param.shape[1]):
|
246 |
+
new_param[i, j] = old_param[i % old_shape[0], j % old_shape[1]]
|
247 |
+
|
248 |
+
n_used_old = torch.ones(old_shape[1])
|
249 |
+
for j in range(new_param.shape[1]):
|
250 |
+
n_used_old[j % old_shape[1]] += 1
|
251 |
+
n_used_new = torch.zeros(new_shape[1])
|
252 |
+
for j in range(new_param.shape[1]):
|
253 |
+
n_used_new[j] = n_used_old[j % old_shape[1]]
|
254 |
+
|
255 |
+
n_used_new = n_used_new[None, :]
|
256 |
+
while len(n_used_new.shape) < len(new_shape):
|
257 |
+
n_used_new = n_used_new.unsqueeze(-1)
|
258 |
+
new_param /= n_used_new
|
259 |
+
|
260 |
+
sd[name] = new_param
|
261 |
+
|
262 |
+
missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
|
263 |
+
sd, strict=False)
|
264 |
+
print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
|
265 |
+
if len(missing) > 0:
|
266 |
+
print(f"Missing Keys:\n {missing}")
|
267 |
+
if len(unexpected) > 0:
|
268 |
+
print(f"\nUnexpected Keys:\n {unexpected}")
|
269 |
+
|
270 |
+
def q_mean_variance(self, x_start, t):
|
271 |
+
"""
|
272 |
+
Get the distribution q(x_t | x_0).
|
273 |
+
:param x_start: the [N x C x ...] tensor of noiseless inputs.
|
274 |
+
:param t: the number of diffusion steps (minus 1). Here, 0 means one step.
|
275 |
+
:return: A tuple (mean, variance, log_variance), all of x_start's shape.
|
276 |
+
"""
|
277 |
+
mean = (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start)
|
278 |
+
variance = extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape)
|
279 |
+
log_variance = extract_into_tensor(self.log_one_minus_alphas_cumprod, t, x_start.shape)
|
280 |
+
return mean, variance, log_variance
|
281 |
+
|
282 |
+
def predict_start_from_noise(self, x_t, t, noise):
|
283 |
+
return (
|
284 |
+
extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t -
|
285 |
+
extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise
|
286 |
+
)
|
287 |
+
|
288 |
+
def predict_start_from_z_and_v(self, x_t, t, v):
|
289 |
+
# self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
|
290 |
+
# self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
|
291 |
+
return (
|
292 |
+
extract_into_tensor(self.sqrt_alphas_cumprod, t, x_t.shape) * x_t -
|
293 |
+
extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_t.shape) * v
|
294 |
+
)
|
295 |
+
|
296 |
+
def predict_eps_from_z_and_v(self, x_t, t, v):
|
297 |
+
return (
|
298 |
+
extract_into_tensor(self.sqrt_alphas_cumprod, t, x_t.shape) * v +
|
299 |
+
extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_t.shape) * x_t
|
300 |
+
)
|
301 |
+
|
302 |
+
def q_posterior(self, x_start, x_t, t):
|
303 |
+
posterior_mean = (
|
304 |
+
extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start +
|
305 |
+
extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t
|
306 |
+
)
|
307 |
+
posterior_variance = extract_into_tensor(self.posterior_variance, t, x_t.shape)
|
308 |
+
posterior_log_variance_clipped = extract_into_tensor(self.posterior_log_variance_clipped, t, x_t.shape)
|
309 |
+
return posterior_mean, posterior_variance, posterior_log_variance_clipped
|
310 |
+
|
311 |
+
def p_mean_variance(self, x, t, clip_denoised: bool):
|
312 |
+
model_out = self.model(x, t)
|
313 |
+
if self.parameterization == "eps":
|
314 |
+
x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
|
315 |
+
elif self.parameterization == "x0":
|
316 |
+
x_recon = model_out
|
317 |
+
if clip_denoised:
|
318 |
+
x_recon.clamp_(-1., 1.)
|
319 |
+
|
320 |
+
model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
|
321 |
+
return model_mean, posterior_variance, posterior_log_variance
|
322 |
+
|
323 |
+
@torch.no_grad()
|
324 |
+
def p_sample(self, x, t, clip_denoised=True, repeat_noise=False):
|
325 |
+
b, *_, device = *x.shape, x.device
|
326 |
+
model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, clip_denoised=clip_denoised)
|
327 |
+
noise = noise_like(x.shape, device, repeat_noise)
|
328 |
+
# no noise when t == 0
|
329 |
+
nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
|
330 |
+
return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
|
331 |
+
|
332 |
+
@torch.no_grad()
|
333 |
+
def p_sample_loop(self, shape, return_intermediates=False):
|
334 |
+
device = self.betas.device
|
335 |
+
b = shape[0]
|
336 |
+
img = torch.randn(shape, device=device)
|
337 |
+
intermediates = [img]
|
338 |
+
for i in tqdm(reversed(range(0, self.num_timesteps)), desc='Sampling t', total=self.num_timesteps):
|
339 |
+
img = self.p_sample(img, torch.full((b,), i, device=device, dtype=torch.long),
|
340 |
+
clip_denoised=self.clip_denoised)
|
341 |
+
if i % self.log_every_t == 0 or i == self.num_timesteps - 1:
|
342 |
+
intermediates.append(img)
|
343 |
+
if return_intermediates:
|
344 |
+
return img, intermediates
|
345 |
+
return img
|
346 |
+
|
347 |
+
@torch.no_grad()
|
348 |
+
def sample(self, batch_size=16, return_intermediates=False):
|
349 |
+
image_size = self.image_size
|
350 |
+
channels = self.channels
|
351 |
+
return self.p_sample_loop((batch_size, channels, image_size, image_size),
|
352 |
+
return_intermediates=return_intermediates)
|
353 |
+
|
354 |
+
def q_sample(self, x_start, t, noise=None):
|
355 |
+
noise = default(noise, lambda: torch.randn_like(x_start))
|
356 |
+
return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
|
357 |
+
extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise)
|
358 |
+
|
359 |
+
def get_v(self, x, noise, t):
|
360 |
+
return (
|
361 |
+
extract_into_tensor(self.sqrt_alphas_cumprod, t, x.shape) * noise -
|
362 |
+
extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x.shape) * x
|
363 |
+
)
|
364 |
+
|
365 |
+
def get_loss(self, pred, target, mean=True):
|
366 |
+
if self.loss_type == 'l1':
|
367 |
+
loss = (target - pred).abs()
|
368 |
+
if mean:
|
369 |
+
loss = loss.mean()
|
370 |
+
elif self.loss_type == 'l2':
|
371 |
+
if mean:
|
372 |
+
loss = torch.nn.functional.mse_loss(target, pred)
|
373 |
+
else:
|
374 |
+
loss = torch.nn.functional.mse_loss(target, pred, reduction='none')
|
375 |
+
else:
|
376 |
+
raise NotImplementedError("unknown loss type '{loss_type}'")
|
377 |
+
|
378 |
+
return loss
|
379 |
+
|
380 |
+
def p_losses(self, x_start, t, noise=None):
|
381 |
+
noise = default(noise, lambda: torch.randn_like(x_start))
|
382 |
+
x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
|
383 |
+
model_out = self.model(x_noisy, t)
|
384 |
+
|
385 |
+
loss_dict = {}
|
386 |
+
if self.parameterization == "eps":
|
387 |
+
target = noise
|
388 |
+
elif self.parameterization == "x0":
|
389 |
+
target = x_start
|
390 |
+
elif self.parameterization == "v":
|
391 |
+
target = self.get_v(x_start, noise, t)
|
392 |
+
else:
|
393 |
+
raise NotImplementedError(f"Parameterization {self.parameterization} not yet supported")
|
394 |
+
|
395 |
+
loss = self.get_loss(model_out, target, mean=False).mean(dim=[1, 2, 3])
|
396 |
+
|
397 |
+
log_prefix = 'train' if self.training else 'val'
|
398 |
+
|
399 |
+
loss_dict.update({f'{log_prefix}/loss_simple': loss.mean()})
|
400 |
+
loss_simple = loss.mean() * self.l_simple_weight
|
401 |
+
|
402 |
+
loss_vlb = (self.lvlb_weights[t] * loss).mean()
|
403 |
+
loss_dict.update({f'{log_prefix}/loss_vlb': loss_vlb})
|
404 |
+
|
405 |
+
loss = loss_simple + self.original_elbo_weight * loss_vlb
|
406 |
+
|
407 |
+
loss_dict.update({f'{log_prefix}/loss': loss})
|
408 |
+
|
409 |
+
return loss, loss_dict
|
410 |
+
|
411 |
+
def forward(self, x, *args, **kwargs):
|
412 |
+
# b, c, h, w, device, img_size, = *x.shape, x.device, self.image_size
|
413 |
+
# assert h == img_size and w == img_size, f'height and width of image must be {img_size}'
|
414 |
+
t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
|
415 |
+
return self.p_losses(x, t, *args, **kwargs)
|
416 |
+
|
417 |
+
def get_input(self, batch, k):
|
418 |
+
x = batch[k]
|
419 |
+
# if len(x.shape) == 3:
|
420 |
+
# x = x[..., None]
|
421 |
+
# x = rearrange(x, 'b h w c -> b c h w')
|
422 |
+
# x = x.to(memory_format=torch.contiguous_format).float()
|
423 |
+
return x
|
424 |
+
|
425 |
+
def shared_step(self, batch):
|
426 |
+
x = self.get_input(batch, self.first_stage_key)
|
427 |
+
loss, loss_dict = self(x)
|
428 |
+
return loss, loss_dict
|
429 |
+
|
430 |
+
def training_step(self, batch, batch_idx):
|
431 |
+
loss, loss_dict = self.shared_step(batch)
|
432 |
+
|
433 |
+
self.log_dict(loss_dict, prog_bar=True,
|
434 |
+
logger=True, on_step=True, on_epoch=True)
|
435 |
+
|
436 |
+
self.log("global_step", self.global_step,
|
437 |
+
prog_bar=True, logger=True, on_step=True, on_epoch=False)
|
438 |
+
|
439 |
+
if self.use_scheduler:
|
440 |
+
lr = self.optimizers().param_groups[0]['lr']
|
441 |
+
self.log('lr_abs', lr, prog_bar=True, logger=True, on_step=True, on_epoch=False)
|
442 |
+
|
443 |
+
return loss
|
444 |
+
|
445 |
+
@torch.no_grad()
|
446 |
+
def validation_step(self, batch, batch_idx):
|
447 |
+
_, loss_dict_no_ema = self.shared_step(batch)
|
448 |
+
with self.ema_scope():
|
449 |
+
_, loss_dict_ema = self.shared_step(batch)
|
450 |
+
loss_dict_ema = {key + '_ema': loss_dict_ema[key] for key in loss_dict_ema}
|
451 |
+
self.log_dict(loss_dict_no_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
|
452 |
+
self.log_dict(loss_dict_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
|
453 |
+
|
454 |
+
def on_train_batch_end(self, *args, **kwargs):
|
455 |
+
if self.use_ema:
|
456 |
+
self.model_ema(self.model)
|
457 |
+
|
458 |
+
def _get_rows_from_list(self, samples):
|
459 |
+
n_imgs_per_row = len(samples)
|
460 |
+
denoise_grid = rearrange(samples, 'n b c h w -> b n c h w')
|
461 |
+
denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
|
462 |
+
denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
|
463 |
+
return denoise_grid
|
464 |
+
|
465 |
+
@torch.no_grad()
|
466 |
+
def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=None, **kwargs):
|
467 |
+
log = dict()
|
468 |
+
x = self.get_input(batch, self.first_stage_key)
|
469 |
+
N = min(x.shape[0], N)
|
470 |
+
n_row = min(x.shape[0], n_row)
|
471 |
+
x = x.to(self.device)[:N]
|
472 |
+
log["inputs"] = x
|
473 |
+
|
474 |
+
# get diffusion row
|
475 |
+
diffusion_row = list()
|
476 |
+
x_start = x[:n_row]
|
477 |
+
|
478 |
+
for t in range(self.num_timesteps):
|
479 |
+
if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
|
480 |
+
t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
|
481 |
+
t = t.to(self.device).long()
|
482 |
+
noise = torch.randn_like(x_start)
|
483 |
+
x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
|
484 |
+
diffusion_row.append(x_noisy)
|
485 |
+
|
486 |
+
log["diffusion_row"] = self._get_rows_from_list(diffusion_row)
|
487 |
+
|
488 |
+
if sample:
|
489 |
+
# get denoise row
|
490 |
+
with self.ema_scope("Plotting"):
|
491 |
+
samples, denoise_row = self.sample(batch_size=N, return_intermediates=True)
|
492 |
+
|
493 |
+
log["samples"] = samples
|
494 |
+
log["denoise_row"] = self._get_rows_from_list(denoise_row)
|
495 |
+
|
496 |
+
if return_keys:
|
497 |
+
if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
|
498 |
+
return log
|
499 |
+
else:
|
500 |
+
return {key: log[key] for key in return_keys}
|
501 |
+
return log
|
502 |
+
|
503 |
+
def configure_optimizers(self):
|
504 |
+
lr = self.learning_rate
|
505 |
+
params = list(self.model.parameters())
|
506 |
+
if self.learn_logvar:
|
507 |
+
params = params + [self.logvar]
|
508 |
+
opt = torch.optim.AdamW(params, lr=lr)
|
509 |
+
return opt
|
510 |
+
|
511 |
+
|
512 |
+
class LatentDiffusion(DDPM):
|
513 |
+
"""main class"""
|
514 |
+
|
515 |
+
def __init__(self,
|
516 |
+
first_stage_config,
|
517 |
+
cond_stage_config,
|
518 |
+
num_timesteps_cond=None,
|
519 |
+
cond_stage_key="image",
|
520 |
+
cond_stage_trainable=False,
|
521 |
+
concat_mode=True,
|
522 |
+
cond_stage_forward=None,
|
523 |
+
conditioning_key=None,
|
524 |
+
scale_factor=1.0,
|
525 |
+
scale_by_std=False,
|
526 |
+
*args, **kwargs):
|
527 |
+
self.num_timesteps_cond = default(num_timesteps_cond, 1)
|
528 |
+
self.scale_by_std = scale_by_std
|
529 |
+
assert self.num_timesteps_cond <= kwargs['timesteps']
|
530 |
+
# for backwards compatibility after implementation of DiffusionWrapper
|
531 |
+
if conditioning_key is None:
|
532 |
+
conditioning_key = 'concat' if concat_mode else 'crossattn'
|
533 |
+
if cond_stage_config == '__is_unconditional__':
|
534 |
+
conditioning_key = None
|
535 |
+
ckpt_path = kwargs.pop("ckpt_path", None)
|
536 |
+
reset_ema = kwargs.pop("reset_ema", False)
|
537 |
+
reset_num_ema_updates = kwargs.pop("reset_num_ema_updates", False)
|
538 |
+
ignore_keys = kwargs.pop("ignore_keys", [])
|
539 |
+
super().__init__(conditioning_key=conditioning_key, *args, **kwargs)
|
540 |
+
self.concat_mode = concat_mode
|
541 |
+
self.cond_stage_trainable = cond_stage_trainable
|
542 |
+
self.cond_stage_key = cond_stage_key
|
543 |
+
try:
|
544 |
+
self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1
|
545 |
+
except:
|
546 |
+
self.num_downs = 0
|
547 |
+
if not scale_by_std:
|
548 |
+
self.scale_factor = scale_factor
|
549 |
+
else:
|
550 |
+
self.register_buffer('scale_factor', torch.tensor(scale_factor))
|
551 |
+
self.instantiate_first_stage(first_stage_config)
|
552 |
+
self.instantiate_cond_stage(cond_stage_config)
|
553 |
+
self.cond_stage_forward = cond_stage_forward
|
554 |
+
self.clip_denoised = False
|
555 |
+
self.bbox_tokenizer = None
|
556 |
+
|
557 |
+
self.restarted_from_ckpt = False
|
558 |
+
if ckpt_path is not None:
|
559 |
+
self.init_from_ckpt(ckpt_path, ignore_keys)
|
560 |
+
self.restarted_from_ckpt = True
|
561 |
+
if reset_ema:
|
562 |
+
assert self.use_ema
|
563 |
+
print(
|
564 |
+
f"Resetting ema to pure model weights. This is useful when restoring from an ema-only checkpoint.")
|
565 |
+
self.model_ema = LitEma(self.model)
|
566 |
+
if reset_num_ema_updates:
|
567 |
+
print(" +++++++++++ WARNING: RESETTING NUM_EMA UPDATES TO ZERO +++++++++++ ")
|
568 |
+
assert self.use_ema
|
569 |
+
self.model_ema.reset_num_updates()
|
570 |
+
|
571 |
+
def make_cond_schedule(self, ):
|
572 |
+
self.cond_ids = torch.full(size=(self.num_timesteps,), fill_value=self.num_timesteps - 1, dtype=torch.long)
|
573 |
+
ids = torch.round(torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)).long()
|
574 |
+
self.cond_ids[:self.num_timesteps_cond] = ids
|
575 |
+
|
576 |
+
def register_schedule(self,
|
577 |
+
given_betas=None, beta_schedule="linear", timesteps=1000,
|
578 |
+
linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
|
579 |
+
super().register_schedule(given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s)
|
580 |
+
|
581 |
+
self.shorten_cond_schedule = self.num_timesteps_cond > 1
|
582 |
+
if self.shorten_cond_schedule:
|
583 |
+
self.make_cond_schedule()
|
584 |
+
|
585 |
+
def instantiate_first_stage(self, config):
|
586 |
+
model = instantiate_from_config(config)
|
587 |
+
self.first_stage_model = model.eval()
|
588 |
+
self.first_stage_model.train = disabled_train
|
589 |
+
for param in self.first_stage_model.parameters():
|
590 |
+
param.requires_grad = False
|
591 |
+
|
592 |
+
def instantiate_cond_stage(self, config):
|
593 |
+
if not self.cond_stage_trainable:
|
594 |
+
if config == "__is_first_stage__":
|
595 |
+
print("Using first stage also as cond stage.")
|
596 |
+
self.cond_stage_model = self.first_stage_model
|
597 |
+
elif config == "__is_unconditional__":
|
598 |
+
print(f"Training {self.__class__.__name__} as an unconditional model.")
|
599 |
+
self.cond_stage_model = None
|
600 |
+
# self.be_unconditional = True
|
601 |
+
else:
|
602 |
+
model = instantiate_from_config(config)
|
603 |
+
self.cond_stage_model = model.eval()
|
604 |
+
self.cond_stage_model.train = disabled_train
|
605 |
+
for param in self.cond_stage_model.parameters():
|
606 |
+
param.requires_grad = False
|
607 |
+
else:
|
608 |
+
assert config != '__is_first_stage__'
|
609 |
+
assert config != '__is_unconditional__'
|
610 |
+
model = instantiate_from_config(config)
|
611 |
+
self.cond_stage_model = model
|
612 |
+
|
613 |
+
def _get_denoise_row_from_list(self, samples, desc='', force_no_decoder_quantization=False):
|
614 |
+
denoise_row = []
|
615 |
+
for zd in tqdm(samples, desc=desc):
|
616 |
+
denoise_row.append(self.decode_first_stage(zd.to(self.device),
|
617 |
+
force_not_quantize=force_no_decoder_quantization))
|
618 |
+
n_imgs_per_row = len(denoise_row)
|
619 |
+
denoise_row = torch.stack(denoise_row) # n_log_step, n_row, C, H, W
|
620 |
+
denoise_grid = rearrange(denoise_row, 'n b c h w -> b n c h w')
|
621 |
+
denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
|
622 |
+
denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
|
623 |
+
return denoise_grid
|
624 |
+
|
625 |
+
def get_first_stage_encoding(self, encoder_posterior):
|
626 |
+
if isinstance(encoder_posterior, DiagonalGaussianDistribution):
|
627 |
+
z = encoder_posterior.sample()
|
628 |
+
elif isinstance(encoder_posterior, torch.Tensor):
|
629 |
+
z = encoder_posterior
|
630 |
+
else:
|
631 |
+
raise NotImplementedError(f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented")
|
632 |
+
return self.scale_factor * z
|
633 |
+
|
634 |
+
def get_learned_conditioning(self, c):
|
635 |
+
if self.cond_stage_forward is None:
|
636 |
+
if hasattr(self.cond_stage_model, 'encode') and callable(self.cond_stage_model.encode):
|
637 |
+
c = self.cond_stage_model.encode(c)
|
638 |
+
if isinstance(c, DiagonalGaussianDistribution):
|
639 |
+
c = c.mode()
|
640 |
+
else:
|
641 |
+
c = self.cond_stage_model(c)
|
642 |
+
else:
|
643 |
+
assert hasattr(self.cond_stage_model, self.cond_stage_forward)
|
644 |
+
c = getattr(self.cond_stage_model, self.cond_stage_forward)(c)
|
645 |
+
return c
|
646 |
+
|
647 |
+
def meshgrid(self, h, w):
|
648 |
+
y = torch.arange(0, h).view(h, 1, 1).repeat(1, w, 1)
|
649 |
+
x = torch.arange(0, w).view(1, w, 1).repeat(h, 1, 1)
|
650 |
+
|
651 |
+
arr = torch.cat([y, x], dim=-1)
|
652 |
+
return arr
|
653 |
+
|
654 |
+
def delta_border(self, h, w):
|
655 |
+
"""
|
656 |
+
:param h: height
|
657 |
+
:param w: width
|
658 |
+
:return: normalized distance to image border,
|
659 |
+
wtith min distance = 0 at border and max dist = 0.5 at image center
|
660 |
+
"""
|
661 |
+
lower_right_corner = torch.tensor([h - 1, w - 1]).view(1, 1, 2)
|
662 |
+
arr = self.meshgrid(h, w) / lower_right_corner
|
663 |
+
dist_left_up = torch.min(arr, dim=-1, keepdims=True)[0]
|
664 |
+
dist_right_down = torch.min(1 - arr, dim=-1, keepdims=True)[0]
|
665 |
+
edge_dist = torch.min(torch.cat([dist_left_up, dist_right_down], dim=-1), dim=-1)[0]
|
666 |
+
return edge_dist
|
667 |
+
|
668 |
+
def get_weighting(self, h, w, Ly, Lx, device):
|
669 |
+
weighting = self.delta_border(h, w)
|
670 |
+
weighting = torch.clip(weighting, self.split_input_params["clip_min_weight"],
|
671 |
+
self.split_input_params["clip_max_weight"], )
|
672 |
+
weighting = weighting.view(1, h * w, 1).repeat(1, 1, Ly * Lx).to(device)
|
673 |
+
|
674 |
+
if self.split_input_params["tie_braker"]:
|
675 |
+
L_weighting = self.delta_border(Ly, Lx)
|
676 |
+
L_weighting = torch.clip(L_weighting,
|
677 |
+
self.split_input_params["clip_min_tie_weight"],
|
678 |
+
self.split_input_params["clip_max_tie_weight"])
|
679 |
+
|
680 |
+
L_weighting = L_weighting.view(1, 1, Ly * Lx).to(device)
|
681 |
+
weighting = weighting * L_weighting
|
682 |
+
return weighting
|
683 |
+
|
684 |
+
def get_fold_unfold(self, x, kernel_size, stride, uf=1, df=1): # todo load once not every time, shorten code
|
685 |
+
"""
|
686 |
+
:param x: img of size (bs, c, h, w)
|
687 |
+
:return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1])
|
688 |
+
"""
|
689 |
+
bs, nc, h, w = x.shape
|
690 |
+
|
691 |
+
# number of crops in image
|
692 |
+
Ly = (h - kernel_size[0]) // stride[0] + 1
|
693 |
+
Lx = (w - kernel_size[1]) // stride[1] + 1
|
694 |
+
|
695 |
+
if uf == 1 and df == 1:
|
696 |
+
fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
|
697 |
+
unfold = torch.nn.Unfold(**fold_params)
|
698 |
+
|
699 |
+
fold = torch.nn.Fold(output_size=x.shape[2:], **fold_params)
|
700 |
+
|
701 |
+
weighting = self.get_weighting(kernel_size[0], kernel_size[1], Ly, Lx, x.device).to(x.dtype)
|
702 |
+
normalization = fold(weighting).view(1, 1, h, w) # normalizes the overlap
|
703 |
+
weighting = weighting.view((1, 1, kernel_size[0], kernel_size[1], Ly * Lx))
|
704 |
+
|
705 |
+
elif uf > 1 and df == 1:
|
706 |
+
fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
|
707 |
+
unfold = torch.nn.Unfold(**fold_params)
|
708 |
+
|
709 |
+
fold_params2 = dict(kernel_size=(kernel_size[0] * uf, kernel_size[0] * uf),
|
710 |
+
dilation=1, padding=0,
|
711 |
+
stride=(stride[0] * uf, stride[1] * uf))
|
712 |
+
fold = torch.nn.Fold(output_size=(x.shape[2] * uf, x.shape[3] * uf), **fold_params2)
|
713 |
+
|
714 |
+
weighting = self.get_weighting(kernel_size[0] * uf, kernel_size[1] * uf, Ly, Lx, x.device).to(x.dtype)
|
715 |
+
normalization = fold(weighting).view(1, 1, h * uf, w * uf) # normalizes the overlap
|
716 |
+
weighting = weighting.view((1, 1, kernel_size[0] * uf, kernel_size[1] * uf, Ly * Lx))
|
717 |
+
|
718 |
+
elif df > 1 and uf == 1:
|
719 |
+
fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
|
720 |
+
unfold = torch.nn.Unfold(**fold_params)
|
721 |
+
|
722 |
+
fold_params2 = dict(kernel_size=(kernel_size[0] // df, kernel_size[0] // df),
|
723 |
+
dilation=1, padding=0,
|
724 |
+
stride=(stride[0] // df, stride[1] // df))
|
725 |
+
fold = torch.nn.Fold(output_size=(x.shape[2] // df, x.shape[3] // df), **fold_params2)
|
726 |
+
|
727 |
+
weighting = self.get_weighting(kernel_size[0] // df, kernel_size[1] // df, Ly, Lx, x.device).to(x.dtype)
|
728 |
+
normalization = fold(weighting).view(1, 1, h // df, w // df) # normalizes the overlap
|
729 |
+
weighting = weighting.view((1, 1, kernel_size[0] // df, kernel_size[1] // df, Ly * Lx))
|
730 |
+
|
731 |
+
else:
|
732 |
+
raise NotImplementedError
|
733 |
+
|
734 |
+
return fold, unfold, normalization, weighting
|
735 |
+
|
736 |
+
@torch.no_grad()
|
737 |
+
def get_input(self, batch, k, return_first_stage_outputs=False, force_c_encode=False,
|
738 |
+
cond_key=None, return_original_cond=False, bs=None):
|
739 |
+
x = super().get_input(batch, k)
|
740 |
+
if bs is not None:
|
741 |
+
x = x[:bs]
|
742 |
+
x = x.to(self.device)
|
743 |
+
encoder_posterior = self.encode_first_stage(x)
|
744 |
+
z = self.get_first_stage_encoding(encoder_posterior).detach()
|
745 |
+
|
746 |
+
if self.model.conditioning_key is not None:
|
747 |
+
if cond_key is None:
|
748 |
+
cond_key = self.cond_stage_key
|
749 |
+
if cond_key != self.first_stage_key:
|
750 |
+
if cond_key in ['caption', 'coordinates_bbox', "txt"]:
|
751 |
+
xc = batch[cond_key]
|
752 |
+
elif cond_key in ['class_label', 'cls']:
|
753 |
+
xc = batch
|
754 |
+
else:
|
755 |
+
xc = super().get_input(batch, cond_key).to(self.device)
|
756 |
+
else:
|
757 |
+
xc = x
|
758 |
+
if not self.cond_stage_trainable or force_c_encode:
|
759 |
+
if isinstance(xc, dict) or isinstance(xc, list):
|
760 |
+
# import pudb; pudb.set_trace()
|
761 |
+
c = self.get_learned_conditioning(xc)
|
762 |
+
else:
|
763 |
+
c = self.get_learned_conditioning(xc.to(self.device))
|
764 |
+
else:
|
765 |
+
c = xc
|
766 |
+
if bs is not None:
|
767 |
+
c = c[:bs]
|
768 |
+
|
769 |
+
if self.use_positional_encodings:
|
770 |
+
pos_x, pos_y = self.compute_latent_shifts(batch)
|
771 |
+
ckey = __conditioning_keys__[self.model.conditioning_key]
|
772 |
+
c = {ckey: c, 'pos_x': pos_x, 'pos_y': pos_y}
|
773 |
+
|
774 |
+
else:
|
775 |
+
c = None
|
776 |
+
xc = None
|
777 |
+
if self.use_positional_encodings:
|
778 |
+
pos_x, pos_y = self.compute_latent_shifts(batch)
|
779 |
+
c = {'pos_x': pos_x, 'pos_y': pos_y}
|
780 |
+
out = [z, c]
|
781 |
+
if return_first_stage_outputs:
|
782 |
+
xrec = self.decode_first_stage(z)
|
783 |
+
out.extend([x, xrec])
|
784 |
+
if return_original_cond:
|
785 |
+
out.append(xc)
|
786 |
+
return out
|
787 |
+
|
788 |
+
@torch.no_grad()
|
789 |
+
def decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):
|
790 |
+
if predict_cids:
|
791 |
+
if z.dim() == 4:
|
792 |
+
z = torch.argmax(z.exp(), dim=1).long()
|
793 |
+
z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
|
794 |
+
z = rearrange(z, 'b h w c -> b c h w').contiguous()
|
795 |
+
|
796 |
+
z = 1. / self.scale_factor * z
|
797 |
+
return self.first_stage_model.decode(z)
|
798 |
+
|
799 |
+
@torch.no_grad()
|
800 |
+
def encode_first_stage(self, x):
|
801 |
+
return self.first_stage_model.encode(x)
|
802 |
+
|
803 |
+
def shared_step(self, batch, **kwargs):
|
804 |
+
x, c = self.get_input(batch, self.first_stage_key)
|
805 |
+
loss = self(x, c, **kwargs)
|
806 |
+
return loss
|
807 |
+
|
808 |
+
def get_time_with_schedule(self, scheduler, bs):
|
809 |
+
if scheduler == 'linear':
|
810 |
+
t = torch.randint(0, self.num_timesteps, (bs,), device=self.device).long()
|
811 |
+
elif scheduler == 'cosine':
|
812 |
+
t = torch.rand((bs, ), device=self.device)
|
813 |
+
t = torch.cos(torch.pi / 2. * t) * self.num_timesteps
|
814 |
+
t = t.long()
|
815 |
+
elif scheduler == 'cubic':
|
816 |
+
t = torch.rand((bs,), device=self.device)
|
817 |
+
t = (1 - t ** 3) * self.num_timesteps
|
818 |
+
t = t.long()
|
819 |
+
else:
|
820 |
+
raise NotImplementedError
|
821 |
+
t = torch.clamp(t, min=0, max=self.num_timesteps-1)
|
822 |
+
return t
|
823 |
+
|
824 |
+
def forward(self, x, c, *args, **kwargs):
|
825 |
+
if 't' not in kwargs:
|
826 |
+
t = torch.randint(0, self.num_timesteps, (x.shape[0], ), device=self.device).long()
|
827 |
+
else:
|
828 |
+
t = kwargs.pop('t')
|
829 |
+
|
830 |
+
return self.p_losses(x, c, t, *args, **kwargs)
|
831 |
+
|
832 |
+
def apply_model(self, x_noisy, t, cond, return_ids=False, **kwargs):
|
833 |
+
if isinstance(cond, dict):
|
834 |
+
# hybrid case, cond is expected to be a dict
|
835 |
+
pass
|
836 |
+
else:
|
837 |
+
if not isinstance(cond, list):
|
838 |
+
cond = [cond]
|
839 |
+
key = 'c_concat' if self.model.conditioning_key == 'concat' else 'c_crossattn'
|
840 |
+
cond = {key: cond}
|
841 |
+
|
842 |
+
x_recon = self.model(x_noisy, t, **cond, **kwargs)
|
843 |
+
|
844 |
+
if isinstance(x_recon, tuple) and not return_ids:
|
845 |
+
return x_recon[0]
|
846 |
+
else:
|
847 |
+
return x_recon
|
848 |
+
|
849 |
+
def _predict_eps_from_xstart(self, x_t, t, pred_xstart):
|
850 |
+
return (extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - pred_xstart) / \
|
851 |
+
extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)
|
852 |
+
|
853 |
+
def _prior_bpd(self, x_start):
|
854 |
+
"""
|
855 |
+
Get the prior KL term for the variational lower-bound, measured in
|
856 |
+
bits-per-dim.
|
857 |
+
This term can't be optimized, as it only depends on the encoder.
|
858 |
+
:param x_start: the [N x C x ...] tensor of inputs.
|
859 |
+
:return: a batch of [N] KL values (in bits), one per batch element.
|
860 |
+
"""
|
861 |
+
batch_size = x_start.shape[0]
|
862 |
+
t = torch.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device)
|
863 |
+
qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t)
|
864 |
+
kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0)
|
865 |
+
return mean_flat(kl_prior) / np.log(2.0)
|
866 |
+
|
867 |
+
def p_losses(self, x_start, cond, t, noise=None, **kwargs):
|
868 |
+
noise = default(noise, lambda: torch.randn_like(x_start))
|
869 |
+
x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
|
870 |
+
model_output = self.apply_model(x_noisy, t, cond, **kwargs)
|
871 |
+
|
872 |
+
loss_dict = {}
|
873 |
+
prefix = 'train' if self.training else 'val'
|
874 |
+
|
875 |
+
if self.parameterization == "x0":
|
876 |
+
target = x_start
|
877 |
+
elif self.parameterization == "eps":
|
878 |
+
target = noise
|
879 |
+
elif self.parameterization == "v":
|
880 |
+
target = self.get_v(x_start, noise, t)
|
881 |
+
else:
|
882 |
+
raise NotImplementedError()
|
883 |
+
|
884 |
+
loss_simple = self.get_loss(model_output, target, mean=False).mean([1, 2, 3])
|
885 |
+
loss_dict.update({f'{prefix}/loss_simple': loss_simple.mean()})
|
886 |
+
|
887 |
+
logvar_t = self.logvar[t].to(self.device)
|
888 |
+
loss = loss_simple / torch.exp(logvar_t) + logvar_t
|
889 |
+
# loss = loss_simple / torch.exp(self.logvar) + self.logvar
|
890 |
+
if self.learn_logvar:
|
891 |
+
loss_dict.update({f'{prefix}/loss_gamma': loss.mean()})
|
892 |
+
loss_dict.update({'logvar': self.logvar.data.mean()})
|
893 |
+
|
894 |
+
loss = self.l_simple_weight * loss.mean()
|
895 |
+
|
896 |
+
loss_vlb = self.get_loss(model_output, target, mean=False).mean(dim=(1, 2, 3))
|
897 |
+
loss_vlb = (self.lvlb_weights[t] * loss_vlb).mean()
|
898 |
+
loss_dict.update({f'{prefix}/loss_vlb': loss_vlb})
|
899 |
+
loss += (self.original_elbo_weight * loss_vlb)
|
900 |
+
loss_dict.update({f'{prefix}/loss': loss})
|
901 |
+
|
902 |
+
return loss, loss_dict
|
903 |
+
|
904 |
+
def p_mean_variance(self, x, c, t, clip_denoised: bool, return_codebook_ids=False, quantize_denoised=False,
|
905 |
+
return_x0=False, score_corrector=None, corrector_kwargs=None):
|
906 |
+
t_in = t
|
907 |
+
model_out = self.apply_model(x, t_in, c, return_ids=return_codebook_ids)
|
908 |
+
|
909 |
+
if score_corrector is not None:
|
910 |
+
assert self.parameterization == "eps"
|
911 |
+
model_out = score_corrector.modify_score(self, model_out, x, t, c, **corrector_kwargs)
|
912 |
+
|
913 |
+
if return_codebook_ids:
|
914 |
+
model_out, logits = model_out
|
915 |
+
|
916 |
+
if self.parameterization == "eps":
|
917 |
+
x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
|
918 |
+
elif self.parameterization == "x0":
|
919 |
+
x_recon = model_out
|
920 |
+
else:
|
921 |
+
raise NotImplementedError()
|
922 |
+
|
923 |
+
if clip_denoised:
|
924 |
+
x_recon.clamp_(-1., 1.)
|
925 |
+
if quantize_denoised:
|
926 |
+
x_recon, _, [_, _, indices] = self.first_stage_model.quantize(x_recon)
|
927 |
+
model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
|
928 |
+
if return_codebook_ids:
|
929 |
+
return model_mean, posterior_variance, posterior_log_variance, logits
|
930 |
+
elif return_x0:
|
931 |
+
return model_mean, posterior_variance, posterior_log_variance, x_recon
|
932 |
+
else:
|
933 |
+
return model_mean, posterior_variance, posterior_log_variance
|
934 |
+
|
935 |
+
@torch.no_grad()
|
936 |
+
def p_sample(self, x, c, t, clip_denoised=False, repeat_noise=False,
|
937 |
+
return_codebook_ids=False, quantize_denoised=False, return_x0=False,
|
938 |
+
temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None):
|
939 |
+
b, *_, device = *x.shape, x.device
|
940 |
+
outputs = self.p_mean_variance(x=x, c=c, t=t, clip_denoised=clip_denoised,
|
941 |
+
return_codebook_ids=return_codebook_ids,
|
942 |
+
quantize_denoised=quantize_denoised,
|
943 |
+
return_x0=return_x0,
|
944 |
+
score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
|
945 |
+
if return_codebook_ids:
|
946 |
+
raise DeprecationWarning("Support dropped.")
|
947 |
+
model_mean, _, model_log_variance, logits = outputs
|
948 |
+
elif return_x0:
|
949 |
+
model_mean, _, model_log_variance, x0 = outputs
|
950 |
+
else:
|
951 |
+
model_mean, _, model_log_variance = outputs
|
952 |
+
|
953 |
+
noise = noise_like(x.shape, device, repeat_noise) * temperature
|
954 |
+
if noise_dropout > 0.:
|
955 |
+
noise = torch.nn.functional.dropout(noise, p=noise_dropout)
|
956 |
+
# no noise when t == 0
|
957 |
+
nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
|
958 |
+
|
959 |
+
if return_codebook_ids:
|
960 |
+
return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, logits.argmax(dim=1)
|
961 |
+
if return_x0:
|
962 |
+
return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, x0
|
963 |
+
else:
|
964 |
+
return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
|
965 |
+
|
966 |
+
@torch.no_grad()
|
967 |
+
def progressive_denoising(self, cond, shape, verbose=True, callback=None, quantize_denoised=False,
|
968 |
+
img_callback=None, mask=None, x0=None, temperature=1., noise_dropout=0.,
|
969 |
+
score_corrector=None, corrector_kwargs=None, batch_size=None, x_T=None, start_T=None,
|
970 |
+
log_every_t=None):
|
971 |
+
if not log_every_t:
|
972 |
+
log_every_t = self.log_every_t
|
973 |
+
timesteps = self.num_timesteps
|
974 |
+
if batch_size is not None:
|
975 |
+
b = batch_size if batch_size is not None else shape[0]
|
976 |
+
shape = [batch_size] + list(shape)
|
977 |
+
else:
|
978 |
+
b = batch_size = shape[0]
|
979 |
+
if x_T is None:
|
980 |
+
img = torch.randn(shape, device=self.device)
|
981 |
+
else:
|
982 |
+
img = x_T
|
983 |
+
intermediates = []
|
984 |
+
if cond is not None:
|
985 |
+
if isinstance(cond, dict):
|
986 |
+
cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
|
987 |
+
list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
|
988 |
+
else:
|
989 |
+
cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
|
990 |
+
|
991 |
+
if start_T is not None:
|
992 |
+
timesteps = min(timesteps, start_T)
|
993 |
+
iterator = tqdm(reversed(range(0, timesteps)), desc='Progressive Generation',
|
994 |
+
total=timesteps) if verbose else reversed(
|
995 |
+
range(0, timesteps))
|
996 |
+
if type(temperature) == float:
|
997 |
+
temperature = [temperature] * timesteps
|
998 |
+
|
999 |
+
for i in iterator:
|
1000 |
+
ts = torch.full((b,), i, device=self.device, dtype=torch.long)
|
1001 |
+
if self.shorten_cond_schedule:
|
1002 |
+
assert self.model.conditioning_key != 'hybrid'
|
1003 |
+
tc = self.cond_ids[ts].to(cond.device)
|
1004 |
+
cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
|
1005 |
+
|
1006 |
+
img, x0_partial = self.p_sample(img, cond, ts,
|
1007 |
+
clip_denoised=self.clip_denoised,
|
1008 |
+
quantize_denoised=quantize_denoised, return_x0=True,
|
1009 |
+
temperature=temperature[i], noise_dropout=noise_dropout,
|
1010 |
+
score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
|
1011 |
+
if mask is not None:
|
1012 |
+
assert x0 is not None
|
1013 |
+
img_orig = self.q_sample(x0, ts)
|
1014 |
+
img = img_orig * mask + (1. - mask) * img
|
1015 |
+
|
1016 |
+
if i % log_every_t == 0 or i == timesteps - 1:
|
1017 |
+
intermediates.append(x0_partial)
|
1018 |
+
if callback: callback(i)
|
1019 |
+
if img_callback: img_callback(img, i)
|
1020 |
+
return img, intermediates
|
1021 |
+
|
1022 |
+
@torch.no_grad()
|
1023 |
+
def p_sample_loop(self, cond, shape, return_intermediates=False,
|
1024 |
+
x_T=None, verbose=True, callback=None, timesteps=None, quantize_denoised=False,
|
1025 |
+
mask=None, x0=None, img_callback=None, start_T=None,
|
1026 |
+
log_every_t=None):
|
1027 |
+
|
1028 |
+
if not log_every_t:
|
1029 |
+
log_every_t = self.log_every_t
|
1030 |
+
device = self.betas.device
|
1031 |
+
b = shape[0]
|
1032 |
+
if x_T is None:
|
1033 |
+
img = torch.randn(shape, device=device)
|
1034 |
+
else:
|
1035 |
+
img = x_T
|
1036 |
+
|
1037 |
+
intermediates = [img]
|
1038 |
+
if timesteps is None:
|
1039 |
+
timesteps = self.num_timesteps
|
1040 |
+
|
1041 |
+
if start_T is not None:
|
1042 |
+
timesteps = min(timesteps, start_T)
|
1043 |
+
iterator = tqdm(reversed(range(0, timesteps)), desc='Sampling t', total=timesteps) if verbose else reversed(
|
1044 |
+
range(0, timesteps))
|
1045 |
+
|
1046 |
+
if mask is not None:
|
1047 |
+
assert x0 is not None
|
1048 |
+
assert x0.shape[2:3] == mask.shape[2:3] # spatial size has to match
|
1049 |
+
|
1050 |
+
for i in iterator:
|
1051 |
+
ts = torch.full((b,), i, device=device, dtype=torch.long)
|
1052 |
+
if self.shorten_cond_schedule:
|
1053 |
+
assert self.model.conditioning_key != 'hybrid'
|
1054 |
+
tc = self.cond_ids[ts].to(cond.device)
|
1055 |
+
cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
|
1056 |
+
|
1057 |
+
img = self.p_sample(img, cond, ts,
|
1058 |
+
clip_denoised=self.clip_denoised,
|
1059 |
+
quantize_denoised=quantize_denoised)
|
1060 |
+
if mask is not None:
|
1061 |
+
img_orig = self.q_sample(x0, ts)
|
1062 |
+
img = img_orig * mask + (1. - mask) * img
|
1063 |
+
|
1064 |
+
if i % log_every_t == 0 or i == timesteps - 1:
|
1065 |
+
intermediates.append(img)
|
1066 |
+
if callback: callback(i)
|
1067 |
+
if img_callback: img_callback(img, i)
|
1068 |
+
|
1069 |
+
if return_intermediates:
|
1070 |
+
return img, intermediates
|
1071 |
+
return img
|
1072 |
+
|
1073 |
+
@torch.no_grad()
|
1074 |
+
def sample(self, cond, batch_size=16, return_intermediates=False, x_T=None,
|
1075 |
+
verbose=True, timesteps=None, quantize_denoised=False,
|
1076 |
+
mask=None, x0=None, shape=None, **kwargs):
|
1077 |
+
if shape is None:
|
1078 |
+
shape = (batch_size, self.channels, self.image_size, self.image_size)
|
1079 |
+
if cond is not None:
|
1080 |
+
if isinstance(cond, dict):
|
1081 |
+
cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
|
1082 |
+
list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
|
1083 |
+
else:
|
1084 |
+
cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
|
1085 |
+
return self.p_sample_loop(cond,
|
1086 |
+
shape,
|
1087 |
+
return_intermediates=return_intermediates, x_T=x_T,
|
1088 |
+
verbose=verbose, timesteps=timesteps, quantize_denoised=quantize_denoised,
|
1089 |
+
mask=mask, x0=x0)
|
1090 |
+
|
1091 |
+
@torch.no_grad()
|
1092 |
+
def sample_log(self, cond, batch_size, ddim, ddim_steps, **kwargs):
|
1093 |
+
if ddim:
|
1094 |
+
ddim_sampler = DDIMSampler(self)
|
1095 |
+
shape = (self.channels, self.image_size, self.image_size)
|
1096 |
+
samples, intermediates = ddim_sampler.sample(ddim_steps, batch_size,
|
1097 |
+
shape, cond, verbose=False, **kwargs)
|
1098 |
+
|
1099 |
+
else:
|
1100 |
+
samples, intermediates = self.sample(cond=cond, batch_size=batch_size,
|
1101 |
+
return_intermediates=True, **kwargs)
|
1102 |
+
|
1103 |
+
return samples, intermediates
|
1104 |
+
|
1105 |
+
@torch.no_grad()
|
1106 |
+
def get_unconditional_conditioning(self, batch_size, null_label=None):
|
1107 |
+
if null_label is not None:
|
1108 |
+
xc = null_label
|
1109 |
+
if isinstance(xc, ListConfig):
|
1110 |
+
xc = list(xc)
|
1111 |
+
if isinstance(xc, dict) or isinstance(xc, list):
|
1112 |
+
c = self.get_learned_conditioning(xc)
|
1113 |
+
else:
|
1114 |
+
if hasattr(xc, "to"):
|
1115 |
+
xc = xc.to(self.device)
|
1116 |
+
c = self.get_learned_conditioning(xc)
|
1117 |
+
else:
|
1118 |
+
if self.cond_stage_key in ["class_label", "cls"]:
|
1119 |
+
xc = self.cond_stage_model.get_unconditional_conditioning(batch_size, device=self.device)
|
1120 |
+
return self.get_learned_conditioning(xc)
|
1121 |
+
else:
|
1122 |
+
raise NotImplementedError("todo")
|
1123 |
+
if isinstance(c, list): # in case the encoder gives us a list
|
1124 |
+
for i in range(len(c)):
|
1125 |
+
c[i] = repeat(c[i], '1 ... -> b ...', b=batch_size).to(self.device)
|
1126 |
+
else:
|
1127 |
+
c = repeat(c, '1 ... -> b ...', b=batch_size).to(self.device)
|
1128 |
+
return c
|
1129 |
+
|
1130 |
+
@torch.no_grad()
|
1131 |
+
def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=50, ddim_eta=0., return_keys=None,
|
1132 |
+
quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True,
|
1133 |
+
plot_diffusion_rows=True, unconditional_guidance_scale=1., unconditional_guidance_label=None,
|
1134 |
+
use_ema_scope=True,
|
1135 |
+
**kwargs):
|
1136 |
+
ema_scope = self.ema_scope if use_ema_scope else nullcontext
|
1137 |
+
use_ddim = ddim_steps is not None
|
1138 |
+
|
1139 |
+
log = dict()
|
1140 |
+
z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key,
|
1141 |
+
return_first_stage_outputs=True,
|
1142 |
+
force_c_encode=True,
|
1143 |
+
return_original_cond=True,
|
1144 |
+
bs=N)
|
1145 |
+
N = min(x.shape[0], N)
|
1146 |
+
n_row = min(x.shape[0], n_row)
|
1147 |
+
log["inputs"] = x
|
1148 |
+
log["reconstruction"] = xrec
|
1149 |
+
if self.model.conditioning_key is not None:
|
1150 |
+
if hasattr(self.cond_stage_model, "decode"):
|
1151 |
+
xc = self.cond_stage_model.decode(c)
|
1152 |
+
log["conditioning"] = xc
|
1153 |
+
elif self.cond_stage_key in ["caption", "txt"]:
|
1154 |
+
xc = log_txt_as_img((x.shape[2], x.shape[3]), batch[self.cond_stage_key], size=x.shape[2] // 25)
|
1155 |
+
log["conditioning"] = xc
|
1156 |
+
elif self.cond_stage_key in ['class_label', "cls"]:
|
1157 |
+
try:
|
1158 |
+
xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"], size=x.shape[2] // 25)
|
1159 |
+
log['conditioning'] = xc
|
1160 |
+
except KeyError:
|
1161 |
+
# probably no "human_label" in batch
|
1162 |
+
pass
|
1163 |
+
elif isimage(xc):
|
1164 |
+
log["conditioning"] = xc
|
1165 |
+
if ismap(xc):
|
1166 |
+
log["original_conditioning"] = self.to_rgb(xc)
|
1167 |
+
|
1168 |
+
if plot_diffusion_rows:
|
1169 |
+
# get diffusion row
|
1170 |
+
diffusion_row = list()
|
1171 |
+
z_start = z[:n_row]
|
1172 |
+
for t in range(self.num_timesteps):
|
1173 |
+
if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
|
1174 |
+
t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
|
1175 |
+
t = t.to(self.device).long()
|
1176 |
+
noise = torch.randn_like(z_start)
|
1177 |
+
z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise)
|
1178 |
+
diffusion_row.append(self.decode_first_stage(z_noisy))
|
1179 |
+
|
1180 |
+
diffusion_row = torch.stack(diffusion_row) # n_log_step, n_row, C, H, W
|
1181 |
+
diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w')
|
1182 |
+
diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w')
|
1183 |
+
diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0])
|
1184 |
+
log["diffusion_row"] = diffusion_grid
|
1185 |
+
|
1186 |
+
if sample:
|
1187 |
+
# get denoise row
|
1188 |
+
with ema_scope("Sampling"):
|
1189 |
+
samples, z_denoise_row = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,
|
1190 |
+
ddim_steps=ddim_steps, eta=ddim_eta)
|
1191 |
+
# samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True)
|
1192 |
+
x_samples = self.decode_first_stage(samples)
|
1193 |
+
log["samples"] = x_samples
|
1194 |
+
if plot_denoise_rows:
|
1195 |
+
denoise_grid = self._get_denoise_row_from_list(z_denoise_row)
|
1196 |
+
log["denoise_row"] = denoise_grid
|
1197 |
+
|
1198 |
+
if quantize_denoised and not isinstance(self.first_stage_model, AutoencoderKL) and not isinstance(
|
1199 |
+
self.first_stage_model, IdentityFirstStage):
|
1200 |
+
# also display when quantizing x0 while sampling
|
1201 |
+
with ema_scope("Plotting Quantized Denoised"):
|
1202 |
+
samples, z_denoise_row = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,
|
1203 |
+
ddim_steps=ddim_steps, eta=ddim_eta,
|
1204 |
+
quantize_denoised=True)
|
1205 |
+
# samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True,
|
1206 |
+
# quantize_denoised=True)
|
1207 |
+
x_samples = self.decode_first_stage(samples.to(self.device))
|
1208 |
+
log["samples_x0_quantized"] = x_samples
|
1209 |
+
|
1210 |
+
if unconditional_guidance_scale > 1.0:
|
1211 |
+
uc = self.get_unconditional_conditioning(N, unconditional_guidance_label)
|
1212 |
+
if self.model.conditioning_key == "crossattn-adm":
|
1213 |
+
uc = {"c_crossattn": [uc], "c_adm": c["c_adm"]}
|
1214 |
+
with ema_scope("Sampling with classifier-free guidance"):
|
1215 |
+
samples_cfg, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,
|
1216 |
+
ddim_steps=ddim_steps, eta=ddim_eta,
|
1217 |
+
unconditional_guidance_scale=unconditional_guidance_scale,
|
1218 |
+
unconditional_conditioning=uc,
|
1219 |
+
)
|
1220 |
+
x_samples_cfg = self.decode_first_stage(samples_cfg)
|
1221 |
+
log[f"samples_cfg_scale_{unconditional_guidance_scale:.2f}"] = x_samples_cfg
|
1222 |
+
|
1223 |
+
if inpaint:
|
1224 |
+
# make a simple center square
|
1225 |
+
b, h, w = z.shape[0], z.shape[2], z.shape[3]
|
1226 |
+
mask = torch.ones(N, h, w).to(self.device)
|
1227 |
+
# zeros will be filled in
|
1228 |
+
mask[:, h // 4:3 * h // 4, w // 4:3 * w // 4] = 0.
|
1229 |
+
mask = mask[:, None, ...]
|
1230 |
+
with ema_scope("Plotting Inpaint"):
|
1231 |
+
samples, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim, eta=ddim_eta,
|
1232 |
+
ddim_steps=ddim_steps, x0=z[:N], mask=mask)
|
1233 |
+
x_samples = self.decode_first_stage(samples.to(self.device))
|
1234 |
+
log["samples_inpainting"] = x_samples
|
1235 |
+
log["mask"] = mask
|
1236 |
+
|
1237 |
+
# outpaint
|
1238 |
+
mask = 1. - mask
|
1239 |
+
with ema_scope("Plotting Outpaint"):
|
1240 |
+
samples, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim, eta=ddim_eta,
|
1241 |
+
ddim_steps=ddim_steps, x0=z[:N], mask=mask)
|
1242 |
+
x_samples = self.decode_first_stage(samples.to(self.device))
|
1243 |
+
log["samples_outpainting"] = x_samples
|
1244 |
+
|
1245 |
+
if plot_progressive_rows:
|
1246 |
+
with ema_scope("Plotting Progressives"):
|
1247 |
+
img, progressives = self.progressive_denoising(c,
|
1248 |
+
shape=(self.channels, self.image_size, self.image_size),
|
1249 |
+
batch_size=N)
|
1250 |
+
prog_row = self._get_denoise_row_from_list(progressives, desc="Progressive Generation")
|
1251 |
+
log["progressive_row"] = prog_row
|
1252 |
+
|
1253 |
+
if return_keys:
|
1254 |
+
if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
|
1255 |
+
return log
|
1256 |
+
else:
|
1257 |
+
return {key: log[key] for key in return_keys}
|
1258 |
+
return log
|
1259 |
+
|
1260 |
+
def configure_optimizers(self):
|
1261 |
+
lr = self.learning_rate
|
1262 |
+
params = list(self.model.parameters())
|
1263 |
+
if self.cond_stage_trainable:
|
1264 |
+
print(f"{self.__class__.__name__}: Also optimizing conditioner params!")
|
1265 |
+
params = params + list(self.cond_stage_model.parameters())
|
1266 |
+
if self.learn_logvar:
|
1267 |
+
print('Diffusion model optimizing logvar')
|
1268 |
+
params.append(self.logvar)
|
1269 |
+
opt = torch.optim.AdamW(params, lr=lr)
|
1270 |
+
if self.use_scheduler:
|
1271 |
+
assert 'target' in self.scheduler_config
|
1272 |
+
scheduler = instantiate_from_config(self.scheduler_config)
|
1273 |
+
|
1274 |
+
print("Setting up LambdaLR scheduler...")
|
1275 |
+
scheduler = [
|
1276 |
+
{
|
1277 |
+
'scheduler': LambdaLR(opt, lr_lambda=scheduler.schedule),
|
1278 |
+
'interval': 'step',
|
1279 |
+
'frequency': 1
|
1280 |
+
}]
|
1281 |
+
return [opt], scheduler
|
1282 |
+
return opt
|
1283 |
+
|
1284 |
+
@torch.no_grad()
|
1285 |
+
def to_rgb(self, x):
|
1286 |
+
x = x.float()
|
1287 |
+
if not hasattr(self, "colorize"):
|
1288 |
+
self.colorize = torch.randn(3, x.shape[1], 1, 1).to(x)
|
1289 |
+
x = nn.functional.conv2d(x, weight=self.colorize)
|
1290 |
+
x = 2. * (x - x.min()) / (x.max() - x.min()) - 1.
|
1291 |
+
return x
|
1292 |
+
|
1293 |
+
|
1294 |
+
class DiffusionWrapper(pl.LightningModule):
|
1295 |
+
def __init__(self, diff_model_config, conditioning_key):
|
1296 |
+
super().__init__()
|
1297 |
+
self.diffusion_model = instantiate_from_config(diff_model_config)
|
1298 |
+
self.conditioning_key = conditioning_key
|
1299 |
+
assert self.conditioning_key in [None, 'concat', 'crossattn', 'hybrid', 'adm', 'hybrid-adm', 'crossattn-adm']
|
1300 |
+
|
1301 |
+
def forward(self, x, t, c_concat: list = None, c_crossattn: list = None, c_adm=None, **kwargs):
|
1302 |
+
if self.conditioning_key is None:
|
1303 |
+
out = self.diffusion_model(x, t, **kwargs)
|
1304 |
+
elif self.conditioning_key == 'concat':
|
1305 |
+
xc = torch.cat([x] + c_concat, dim=1)
|
1306 |
+
out = self.diffusion_model(xc, t, **kwargs)
|
1307 |
+
elif self.conditioning_key == 'crossattn':
|
1308 |
+
cc = torch.cat(c_crossattn, 1)
|
1309 |
+
out = self.diffusion_model(x, t, context=cc, **kwargs)
|
1310 |
+
elif self.conditioning_key == 'hybrid':
|
1311 |
+
xc = torch.cat([x] + c_concat, dim=1)
|
1312 |
+
cc = torch.cat(c_crossattn, 1)
|
1313 |
+
out = self.diffusion_model(xc, t, context=cc, **kwargs)
|
1314 |
+
elif self.conditioning_key == 'hybrid-adm':
|
1315 |
+
assert c_adm is not None
|
1316 |
+
xc = torch.cat([x] + c_concat, dim=1)
|
1317 |
+
cc = torch.cat(c_crossattn, 1)
|
1318 |
+
out = self.diffusion_model(xc, t, context=cc, y=c_adm, **kwargs)
|
1319 |
+
elif self.conditioning_key == 'crossattn-adm':
|
1320 |
+
assert c_adm is not None
|
1321 |
+
cc = torch.cat(c_crossattn, 1)
|
1322 |
+
out = self.diffusion_model(x, t, context=cc, y=c_adm, **kwargs)
|
1323 |
+
elif self.conditioning_key == 'adm':
|
1324 |
+
cc = c_crossattn[0]
|
1325 |
+
out = self.diffusion_model(x, t, y=cc, **kwargs)
|
1326 |
+
else:
|
1327 |
+
raise NotImplementedError()
|
1328 |
+
|
1329 |
+
return out
|
ldm/models/diffusion/dpm_solver/__init__.py
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
from .sampler import DPMSolverSampler
|
ldm/models/diffusion/dpm_solver/dpm_solver.py
ADDED
@@ -0,0 +1,1217 @@
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|
1 |
+
import torch
|
2 |
+
import torch.nn.functional as F
|
3 |
+
import math
|
4 |
+
from tqdm import tqdm
|
5 |
+
|
6 |
+
|
7 |
+
class NoiseScheduleVP:
|
8 |
+
def __init__(
|
9 |
+
self,
|
10 |
+
schedule='discrete',
|
11 |
+
betas=None,
|
12 |
+
alphas_cumprod=None,
|
13 |
+
continuous_beta_0=0.1,
|
14 |
+
continuous_beta_1=20.,
|
15 |
+
):
|
16 |
+
"""Create a wrapper class for the forward SDE (VP type).
|
17 |
+
|
18 |
+
***
|
19 |
+
Update: We support discrete-time diffusion models by implementing a picewise linear interpolation for log_alpha_t.
|
20 |
+
We recommend to use schedule='discrete' for the discrete-time diffusion models, especially for high-resolution images.
|
21 |
+
***
|
22 |
+
|
23 |
+
The forward SDE ensures that the condition distribution q_{t|0}(x_t | x_0) = N ( alpha_t * x_0, sigma_t^2 * I ).
|
24 |
+
We further define lambda_t = log(alpha_t) - log(sigma_t), which is the half-logSNR (described in the DPM-Solver paper).
|
25 |
+
Therefore, we implement the functions for computing alpha_t, sigma_t and lambda_t. For t in [0, T], we have:
|
26 |
+
|
27 |
+
log_alpha_t = self.marginal_log_mean_coeff(t)
|
28 |
+
sigma_t = self.marginal_std(t)
|
29 |
+
lambda_t = self.marginal_lambda(t)
|
30 |
+
|
31 |
+
Moreover, as lambda(t) is an invertible function, we also support its inverse function:
|
32 |
+
|
33 |
+
t = self.inverse_lambda(lambda_t)
|
34 |
+
|
35 |
+
===============================================================
|
36 |
+
|
37 |
+
We support both discrete-time DPMs (trained on n = 0, 1, ..., N-1) and continuous-time DPMs (trained on t in [t_0, T]).
|
38 |
+
|
39 |
+
1. For discrete-time DPMs:
|
40 |
+
|
41 |
+
For discrete-time DPMs trained on n = 0, 1, ..., N-1, we convert the discrete steps to continuous time steps by:
|
42 |
+
t_i = (i + 1) / N
|
43 |
+
e.g. for N = 1000, we have t_0 = 1e-3 and T = t_{N-1} = 1.
|
44 |
+
We solve the corresponding diffusion ODE from time T = 1 to time t_0 = 1e-3.
|
45 |
+
|
46 |
+
Args:
|
47 |
+
betas: A `torch.Tensor`. The beta array for the discrete-time DPM. (See the original DDPM paper for details)
|
48 |
+
alphas_cumprod: A `torch.Tensor`. The cumprod alphas for the discrete-time DPM. (See the original DDPM paper for details)
|
49 |
+
|
50 |
+
Note that we always have alphas_cumprod = cumprod(betas). Therefore, we only need to set one of `betas` and `alphas_cumprod`.
|
51 |
+
|
52 |
+
**Important**: Please pay special attention for the args for `alphas_cumprod`:
|
53 |
+
The `alphas_cumprod` is the \hat{alpha_n} arrays in the notations of DDPM. Specifically, DDPMs assume that
|
54 |
+
q_{t_n | 0}(x_{t_n} | x_0) = N ( \sqrt{\hat{alpha_n}} * x_0, (1 - \hat{alpha_n}) * I ).
|
55 |
+
Therefore, the notation \hat{alpha_n} is different from the notation alpha_t in DPM-Solver. In fact, we have
|
56 |
+
alpha_{t_n} = \sqrt{\hat{alpha_n}},
|
57 |
+
and
|
58 |
+
log(alpha_{t_n}) = 0.5 * log(\hat{alpha_n}).
|
59 |
+
|
60 |
+
|
61 |
+
2. For continuous-time DPMs:
|
62 |
+
|
63 |
+
We support two types of VPSDEs: linear (DDPM) and cosine (improved-DDPM). The hyperparameters for the noise
|
64 |
+
schedule are the default settings in DDPM and improved-DDPM:
|
65 |
+
|
66 |
+
Args:
|
67 |
+
beta_min: A `float` number. The smallest beta for the linear schedule.
|
68 |
+
beta_max: A `float` number. The largest beta for the linear schedule.
|
69 |
+
cosine_s: A `float` number. The hyperparameter in the cosine schedule.
|
70 |
+
cosine_beta_max: A `float` number. The hyperparameter in the cosine schedule.
|
71 |
+
T: A `float` number. The ending time of the forward process.
|
72 |
+
|
73 |
+
===============================================================
|
74 |
+
|
75 |
+
Args:
|
76 |
+
schedule: A `str`. The noise schedule of the forward SDE. 'discrete' for discrete-time DPMs,
|
77 |
+
'linear' or 'cosine' for continuous-time DPMs.
|
78 |
+
Returns:
|
79 |
+
A wrapper object of the forward SDE (VP type).
|
80 |
+
|
81 |
+
===============================================================
|
82 |
+
|
83 |
+
Example:
|
84 |
+
|
85 |
+
# For discrete-time DPMs, given betas (the beta array for n = 0, 1, ..., N - 1):
|
86 |
+
>>> ns = NoiseScheduleVP('discrete', betas=betas)
|
87 |
+
|
88 |
+
# For discrete-time DPMs, given alphas_cumprod (the \hat{alpha_n} array for n = 0, 1, ..., N - 1):
|
89 |
+
>>> ns = NoiseScheduleVP('discrete', alphas_cumprod=alphas_cumprod)
|
90 |
+
|
91 |
+
# For continuous-time DPMs (VPSDE), linear schedule:
|
92 |
+
>>> ns = NoiseScheduleVP('linear', continuous_beta_0=0.1, continuous_beta_1=20.)
|
93 |
+
|
94 |
+
"""
|
95 |
+
|
96 |
+
if schedule not in ['discrete', 'linear', 'cosine']:
|
97 |
+
raise ValueError(
|
98 |
+
"Unsupported noise schedule {}. The schedule needs to be 'discrete' or 'linear' or 'cosine'".format(
|
99 |
+
schedule))
|
100 |
+
|
101 |
+
self.schedule = schedule
|
102 |
+
if schedule == 'discrete':
|
103 |
+
if betas is not None:
|
104 |
+
log_alphas = 0.5 * torch.log(1 - betas).cumsum(dim=0)
|
105 |
+
else:
|
106 |
+
assert alphas_cumprod is not None
|
107 |
+
log_alphas = 0.5 * torch.log(alphas_cumprod)
|
108 |
+
self.total_N = len(log_alphas)
|
109 |
+
self.T = 1.
|
110 |
+
self.t_array = torch.linspace(0., 1., self.total_N + 1)[1:].reshape((1, -1))
|
111 |
+
self.log_alpha_array = log_alphas.reshape((1, -1,))
|
112 |
+
else:
|
113 |
+
self.total_N = 1000
|
114 |
+
self.beta_0 = continuous_beta_0
|
115 |
+
self.beta_1 = continuous_beta_1
|
116 |
+
self.cosine_s = 0.008
|
117 |
+
self.cosine_beta_max = 999.
|
118 |
+
self.cosine_t_max = math.atan(self.cosine_beta_max * (1. + self.cosine_s) / math.pi) * 2. * (
|
119 |
+
1. + self.cosine_s) / math.pi - self.cosine_s
|
120 |
+
self.cosine_log_alpha_0 = math.log(math.cos(self.cosine_s / (1. + self.cosine_s) * math.pi / 2.))
|
121 |
+
self.schedule = schedule
|
122 |
+
if schedule == 'cosine':
|
123 |
+
# For the cosine schedule, T = 1 will have numerical issues. So we manually set the ending time T.
|
124 |
+
# Note that T = 0.9946 may be not the optimal setting. However, we find it works well.
|
125 |
+
self.T = 0.9946
|
126 |
+
else:
|
127 |
+
self.T = 1.
|
128 |
+
|
129 |
+
def marginal_log_mean_coeff(self, t):
|
130 |
+
"""
|
131 |
+
Compute log(alpha_t) of a given continuous-time label t in [0, T].
|
132 |
+
"""
|
133 |
+
if self.schedule == 'discrete':
|
134 |
+
return interpolate_fn(t.reshape((-1, 1)), self.t_array.to(t.device),
|
135 |
+
self.log_alpha_array.to(t.device)).reshape((-1))
|
136 |
+
elif self.schedule == 'linear':
|
137 |
+
return -0.25 * t ** 2 * (self.beta_1 - self.beta_0) - 0.5 * t * self.beta_0
|
138 |
+
elif self.schedule == 'cosine':
|
139 |
+
log_alpha_fn = lambda s: torch.log(torch.cos((s + self.cosine_s) / (1. + self.cosine_s) * math.pi / 2.))
|
140 |
+
log_alpha_t = log_alpha_fn(t) - self.cosine_log_alpha_0
|
141 |
+
return log_alpha_t
|
142 |
+
|
143 |
+
def marginal_alpha(self, t):
|
144 |
+
"""
|
145 |
+
Compute alpha_t of a given continuous-time label t in [0, T].
|
146 |
+
"""
|
147 |
+
return torch.exp(self.marginal_log_mean_coeff(t))
|
148 |
+
|
149 |
+
def marginal_std(self, t):
|
150 |
+
"""
|
151 |
+
Compute sigma_t of a given continuous-time label t in [0, T].
|
152 |
+
"""
|
153 |
+
return torch.sqrt(1. - torch.exp(2. * self.marginal_log_mean_coeff(t)))
|
154 |
+
|
155 |
+
def marginal_lambda(self, t):
|
156 |
+
"""
|
157 |
+
Compute lambda_t = log(alpha_t) - log(sigma_t) of a given continuous-time label t in [0, T].
|
158 |
+
"""
|
159 |
+
log_mean_coeff = self.marginal_log_mean_coeff(t)
|
160 |
+
log_std = 0.5 * torch.log(1. - torch.exp(2. * log_mean_coeff))
|
161 |
+
return log_mean_coeff - log_std
|
162 |
+
|
163 |
+
def inverse_lambda(self, lamb):
|
164 |
+
"""
|
165 |
+
Compute the continuous-time label t in [0, T] of a given half-logSNR lambda_t.
|
166 |
+
"""
|
167 |
+
if self.schedule == 'linear':
|
168 |
+
tmp = 2. * (self.beta_1 - self.beta_0) * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb))
|
169 |
+
Delta = self.beta_0 ** 2 + tmp
|
170 |
+
return tmp / (torch.sqrt(Delta) + self.beta_0) / (self.beta_1 - self.beta_0)
|
171 |
+
elif self.schedule == 'discrete':
|
172 |
+
log_alpha = -0.5 * torch.logaddexp(torch.zeros((1,)).to(lamb.device), -2. * lamb)
|
173 |
+
t = interpolate_fn(log_alpha.reshape((-1, 1)), torch.flip(self.log_alpha_array.to(lamb.device), [1]),
|
174 |
+
torch.flip(self.t_array.to(lamb.device), [1]))
|
175 |
+
return t.reshape((-1,))
|
176 |
+
else:
|
177 |
+
log_alpha = -0.5 * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb))
|
178 |
+
t_fn = lambda log_alpha_t: torch.arccos(torch.exp(log_alpha_t + self.cosine_log_alpha_0)) * 2. * (
|
179 |
+
1. + self.cosine_s) / math.pi - self.cosine_s
|
180 |
+
t = t_fn(log_alpha)
|
181 |
+
return t
|
182 |
+
|
183 |
+
|
184 |
+
def model_wrapper(
|
185 |
+
model,
|
186 |
+
noise_schedule,
|
187 |
+
model_type="noise",
|
188 |
+
model_kwargs={},
|
189 |
+
guidance_type="uncond",
|
190 |
+
condition=None,
|
191 |
+
unconditional_condition=None,
|
192 |
+
guidance_scale=1.,
|
193 |
+
classifier_fn=None,
|
194 |
+
classifier_kwargs={},
|
195 |
+
):
|
196 |
+
"""Create a wrapper function for the noise prediction model.
|
197 |
+
|
198 |
+
DPM-Solver needs to solve the continuous-time diffusion ODEs. For DPMs trained on discrete-time labels, we need to
|
199 |
+
firstly wrap the model function to a noise prediction model that accepts the continuous time as the input.
|
200 |
+
|
201 |
+
We support four types of the diffusion model by setting `model_type`:
|
202 |
+
|
203 |
+
1. "noise": noise prediction model. (Trained by predicting noise).
|
204 |
+
|
205 |
+
2. "x_start": data prediction model. (Trained by predicting the data x_0 at time 0).
|
206 |
+
|
207 |
+
3. "v": velocity prediction model. (Trained by predicting the velocity).
|
208 |
+
The "v" prediction is derivation detailed in Appendix D of [1], and is used in Imagen-Video [2].
|
209 |
+
|
210 |
+
[1] Salimans, Tim, and Jonathan Ho. "Progressive distillation for fast sampling of diffusion models."
|
211 |
+
arXiv preprint arXiv:2202.00512 (2022).
|
212 |
+
[2] Ho, Jonathan, et al. "Imagen Video: High Definition Video Generation with Diffusion Models."
|
213 |
+
arXiv preprint arXiv:2210.02303 (2022).
|
214 |
+
|
215 |
+
4. "score": marginal score function. (Trained by denoising score matching).
|
216 |
+
Note that the score function and the noise prediction model follows a simple relationship:
|
217 |
+
```
|
218 |
+
noise(x_t, t) = -sigma_t * score(x_t, t)
|
219 |
+
```
|
220 |
+
|
221 |
+
We support three types of guided sampling by DPMs by setting `guidance_type`:
|
222 |
+
1. "uncond": unconditional sampling by DPMs.
|
223 |
+
The input `model` has the following format:
|
224 |
+
``
|
225 |
+
model(x, t_input, **model_kwargs) -> noise | x_start | v | score
|
226 |
+
``
|
227 |
+
|
228 |
+
2. "classifier": classifier guidance sampling [3] by DPMs and another classifier.
|
229 |
+
The input `model` has the following format:
|
230 |
+
``
|
231 |
+
model(x, t_input, **model_kwargs) -> noise | x_start | v | score
|
232 |
+
``
|
233 |
+
|
234 |
+
The input `classifier_fn` has the following format:
|
235 |
+
``
|
236 |
+
classifier_fn(x, t_input, cond, **classifier_kwargs) -> logits(x, t_input, cond)
|
237 |
+
``
|
238 |
+
|
239 |
+
[3] P. Dhariwal and A. Q. Nichol, "Diffusion models beat GANs on image synthesis,"
|
240 |
+
in Advances in Neural Information Processing Systems, vol. 34, 2021, pp. 8780-8794.
|
241 |
+
|
242 |
+
3. "classifier-free": classifier-free guidance sampling by conditional DPMs.
|
243 |
+
The input `model` has the following format:
|
244 |
+
``
|
245 |
+
model(x, t_input, cond, **model_kwargs) -> noise | x_start | v | score
|
246 |
+
``
|
247 |
+
And if cond == `unconditional_condition`, the model output is the unconditional DPM output.
|
248 |
+
|
249 |
+
[4] Ho, Jonathan, and Tim Salimans. "Classifier-free diffusion guidance."
|
250 |
+
arXiv preprint arXiv:2207.12598 (2022).
|
251 |
+
|
252 |
+
|
253 |
+
The `t_input` is the time label of the model, which may be discrete-time labels (i.e. 0 to 999)
|
254 |
+
or continuous-time labels (i.e. epsilon to T).
|
255 |
+
|
256 |
+
We wrap the model function to accept only `x` and `t_continuous` as inputs, and outputs the predicted noise:
|
257 |
+
``
|
258 |
+
def model_fn(x, t_continuous) -> noise:
|
259 |
+
t_input = get_model_input_time(t_continuous)
|
260 |
+
return noise_pred(model, x, t_input, **model_kwargs)
|
261 |
+
``
|
262 |
+
where `t_continuous` is the continuous time labels (i.e. epsilon to T). And we use `model_fn` for DPM-Solver.
|
263 |
+
|
264 |
+
===============================================================
|
265 |
+
|
266 |
+
Args:
|
267 |
+
model: A diffusion model with the corresponding format described above.
|
268 |
+
noise_schedule: A noise schedule object, such as NoiseScheduleVP.
|
269 |
+
model_type: A `str`. The parameterization type of the diffusion model.
|
270 |
+
"noise" or "x_start" or "v" or "score".
|
271 |
+
model_kwargs: A `dict`. A dict for the other inputs of the model function.
|
272 |
+
guidance_type: A `str`. The type of the guidance for sampling.
|
273 |
+
"uncond" or "classifier" or "classifier-free".
|
274 |
+
condition: A pytorch tensor. The condition for the guided sampling.
|
275 |
+
Only used for "classifier" or "classifier-free" guidance type.
|
276 |
+
unconditional_condition: A pytorch tensor. The condition for the unconditional sampling.
|
277 |
+
Only used for "classifier-free" guidance type.
|
278 |
+
guidance_scale: A `float`. The scale for the guided sampling.
|
279 |
+
classifier_fn: A classifier function. Only used for the classifier guidance.
|
280 |
+
classifier_kwargs: A `dict`. A dict for the other inputs of the classifier function.
|
281 |
+
Returns:
|
282 |
+
A noise prediction model that accepts the noised data and the continuous time as the inputs.
|
283 |
+
"""
|
284 |
+
|
285 |
+
def get_model_input_time(t_continuous):
|
286 |
+
"""
|
287 |
+
Convert the continuous-time `t_continuous` (in [epsilon, T]) to the model input time.
|
288 |
+
For discrete-time DPMs, we convert `t_continuous` in [1 / N, 1] to `t_input` in [0, 1000 * (N - 1) / N].
|
289 |
+
For continuous-time DPMs, we just use `t_continuous`.
|
290 |
+
"""
|
291 |
+
if noise_schedule.schedule == 'discrete':
|
292 |
+
return (t_continuous - 1. / noise_schedule.total_N) * 1000.
|
293 |
+
else:
|
294 |
+
return t_continuous
|
295 |
+
|
296 |
+
def noise_pred_fn(x, t_continuous, cond=None):
|
297 |
+
if t_continuous.reshape((-1,)).shape[0] == 1:
|
298 |
+
t_continuous = t_continuous.expand((x.shape[0]))
|
299 |
+
t_input = get_model_input_time(t_continuous)
|
300 |
+
if cond is None:
|
301 |
+
output = model(x, t_input, **model_kwargs)
|
302 |
+
else:
|
303 |
+
output = model(x, t_input, cond, **model_kwargs)
|
304 |
+
if model_type == "noise":
|
305 |
+
return output
|
306 |
+
elif model_type == "x_start":
|
307 |
+
alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous)
|
308 |
+
dims = x.dim()
|
309 |
+
return (x - expand_dims(alpha_t, dims) * output) / expand_dims(sigma_t, dims)
|
310 |
+
elif model_type == "v":
|
311 |
+
alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous)
|
312 |
+
dims = x.dim()
|
313 |
+
return expand_dims(alpha_t, dims) * output + expand_dims(sigma_t, dims) * x
|
314 |
+
elif model_type == "score":
|
315 |
+
sigma_t = noise_schedule.marginal_std(t_continuous)
|
316 |
+
dims = x.dim()
|
317 |
+
return -expand_dims(sigma_t, dims) * output
|
318 |
+
|
319 |
+
def cond_grad_fn(x, t_input):
|
320 |
+
"""
|
321 |
+
Compute the gradient of the classifier, i.e. nabla_{x} log p_t(cond | x_t).
|
322 |
+
"""
|
323 |
+
with torch.enable_grad():
|
324 |
+
x_in = x.detach().requires_grad_(True)
|
325 |
+
log_prob = classifier_fn(x_in, t_input, condition, **classifier_kwargs)
|
326 |
+
return torch.autograd.grad(log_prob.sum(), x_in)[0]
|
327 |
+
|
328 |
+
def model_fn(x, t_continuous):
|
329 |
+
"""
|
330 |
+
The noise predicition model function that is used for DPM-Solver.
|
331 |
+
"""
|
332 |
+
if t_continuous.reshape((-1,)).shape[0] == 1:
|
333 |
+
t_continuous = t_continuous.expand((x.shape[0]))
|
334 |
+
if guidance_type == "uncond":
|
335 |
+
return noise_pred_fn(x, t_continuous)
|
336 |
+
elif guidance_type == "classifier":
|
337 |
+
assert classifier_fn is not None
|
338 |
+
t_input = get_model_input_time(t_continuous)
|
339 |
+
cond_grad = cond_grad_fn(x, t_input)
|
340 |
+
sigma_t = noise_schedule.marginal_std(t_continuous)
|
341 |
+
noise = noise_pred_fn(x, t_continuous)
|
342 |
+
return noise - guidance_scale * expand_dims(sigma_t, dims=cond_grad.dim()) * cond_grad
|
343 |
+
elif guidance_type == "classifier-free":
|
344 |
+
if guidance_scale == 1. or unconditional_condition is None:
|
345 |
+
return noise_pred_fn(x, t_continuous, cond=condition)
|
346 |
+
else:
|
347 |
+
x_in = torch.cat([x] * 2)
|
348 |
+
t_in = torch.cat([t_continuous] * 2)
|
349 |
+
c_in = torch.cat([unconditional_condition, condition])
|
350 |
+
noise_uncond, noise = noise_pred_fn(x_in, t_in, cond=c_in).chunk(2)
|
351 |
+
return noise_uncond + guidance_scale * (noise - noise_uncond)
|
352 |
+
|
353 |
+
assert model_type in ["noise", "x_start", "v"]
|
354 |
+
assert guidance_type in ["uncond", "classifier", "classifier-free"]
|
355 |
+
return model_fn
|
356 |
+
|
357 |
+
|
358 |
+
class DPM_Solver:
|
359 |
+
def __init__(self, model_fn, noise_schedule, predict_x0=False, thresholding=False, max_val=1.):
|
360 |
+
"""Construct a DPM-Solver.
|
361 |
+
|
362 |
+
We support both the noise prediction model ("predicting epsilon") and the data prediction model ("predicting x0").
|
363 |
+
If `predict_x0` is False, we use the solver for the noise prediction model (DPM-Solver).
|
364 |
+
If `predict_x0` is True, we use the solver for the data prediction model (DPM-Solver++).
|
365 |
+
In such case, we further support the "dynamic thresholding" in [1] when `thresholding` is True.
|
366 |
+
The "dynamic thresholding" can greatly improve the sample quality for pixel-space DPMs with large guidance scales.
|
367 |
+
|
368 |
+
Args:
|
369 |
+
model_fn: A noise prediction model function which accepts the continuous-time input (t in [epsilon, T]):
|
370 |
+
``
|
371 |
+
def model_fn(x, t_continuous):
|
372 |
+
return noise
|
373 |
+
``
|
374 |
+
noise_schedule: A noise schedule object, such as NoiseScheduleVP.
|
375 |
+
predict_x0: A `bool`. If true, use the data prediction model; else, use the noise prediction model.
|
376 |
+
thresholding: A `bool`. Valid when `predict_x0` is True. Whether to use the "dynamic thresholding" in [1].
|
377 |
+
max_val: A `float`. Valid when both `predict_x0` and `thresholding` are True. The max value for thresholding.
|
378 |
+
|
379 |
+
[1] Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S Sara Mahdavi, Rapha Gontijo Lopes, et al. Photorealistic text-to-image diffusion models with deep language understanding. arXiv preprint arXiv:2205.11487, 2022b.
|
380 |
+
"""
|
381 |
+
self.model = model_fn
|
382 |
+
self.noise_schedule = noise_schedule
|
383 |
+
self.predict_x0 = predict_x0
|
384 |
+
self.thresholding = thresholding
|
385 |
+
self.max_val = max_val
|
386 |
+
|
387 |
+
def noise_prediction_fn(self, x, t):
|
388 |
+
"""
|
389 |
+
Return the noise prediction model.
|
390 |
+
"""
|
391 |
+
return self.model(x, t)
|
392 |
+
|
393 |
+
def data_prediction_fn(self, x, t):
|
394 |
+
"""
|
395 |
+
Return the data prediction model (with thresholding).
|
396 |
+
"""
|
397 |
+
noise = self.noise_prediction_fn(x, t)
|
398 |
+
dims = x.dim()
|
399 |
+
alpha_t, sigma_t = self.noise_schedule.marginal_alpha(t), self.noise_schedule.marginal_std(t)
|
400 |
+
x0 = (x - expand_dims(sigma_t, dims) * noise) / expand_dims(alpha_t, dims)
|
401 |
+
if self.thresholding:
|
402 |
+
p = 0.995 # A hyperparameter in the paper of "Imagen" [1].
|
403 |
+
s = torch.quantile(torch.abs(x0).reshape((x0.shape[0], -1)), p, dim=1)
|
404 |
+
s = expand_dims(torch.maximum(s, self.max_val * torch.ones_like(s).to(s.device)), dims)
|
405 |
+
x0 = torch.clamp(x0, -s, s) / s
|
406 |
+
return x0
|
407 |
+
|
408 |
+
def model_fn(self, x, t):
|
409 |
+
"""
|
410 |
+
Convert the model to the noise prediction model or the data prediction model.
|
411 |
+
"""
|
412 |
+
if self.predict_x0:
|
413 |
+
return self.data_prediction_fn(x, t)
|
414 |
+
else:
|
415 |
+
return self.noise_prediction_fn(x, t)
|
416 |
+
|
417 |
+
def get_time_steps(self, skip_type, t_T, t_0, N, device):
|
418 |
+
"""Compute the intermediate time steps for sampling.
|
419 |
+
|
420 |
+
Args:
|
421 |
+
skip_type: A `str`. The type for the spacing of the time steps. We support three types:
|
422 |
+
- 'logSNR': uniform logSNR for the time steps.
|
423 |
+
- 'time_uniform': uniform time for the time steps. (**Recommended for high-resolutional data**.)
|
424 |
+
- 'time_quadratic': quadratic time for the time steps. (Used in DDIM for low-resolutional data.)
|
425 |
+
t_T: A `float`. The starting time of the sampling (default is T).
|
426 |
+
t_0: A `float`. The ending time of the sampling (default is epsilon).
|
427 |
+
N: A `int`. The total number of the spacing of the time steps.
|
428 |
+
device: A torch device.
|
429 |
+
Returns:
|
430 |
+
A pytorch tensor of the time steps, with the shape (N + 1,).
|
431 |
+
"""
|
432 |
+
if skip_type == 'logSNR':
|
433 |
+
lambda_T = self.noise_schedule.marginal_lambda(torch.tensor(t_T).to(device))
|
434 |
+
lambda_0 = self.noise_schedule.marginal_lambda(torch.tensor(t_0).to(device))
|
435 |
+
logSNR_steps = torch.linspace(lambda_T.cpu().item(), lambda_0.cpu().item(), N + 1).to(device)
|
436 |
+
return self.noise_schedule.inverse_lambda(logSNR_steps)
|
437 |
+
elif skip_type == 'time_uniform':
|
438 |
+
return torch.linspace(t_T, t_0, N + 1).to(device)
|
439 |
+
elif skip_type == 'time_quadratic':
|
440 |
+
t_order = 2
|
441 |
+
t = torch.linspace(t_T ** (1. / t_order), t_0 ** (1. / t_order), N + 1).pow(t_order).to(device)
|
442 |
+
return t
|
443 |
+
else:
|
444 |
+
raise ValueError(
|
445 |
+
"Unsupported skip_type {}, need to be 'logSNR' or 'time_uniform' or 'time_quadratic'".format(skip_type))
|
446 |
+
|
447 |
+
def get_orders_and_timesteps_for_singlestep_solver(self, steps, order, skip_type, t_T, t_0, device):
|
448 |
+
"""
|
449 |
+
Get the order of each step for sampling by the singlestep DPM-Solver.
|
450 |
+
|
451 |
+
We combine both DPM-Solver-1,2,3 to use all the function evaluations, which is named as "DPM-Solver-fast".
|
452 |
+
Given a fixed number of function evaluations by `steps`, the sampling procedure by DPM-Solver-fast is:
|
453 |
+
- If order == 1:
|
454 |
+
We take `steps` of DPM-Solver-1 (i.e. DDIM).
|
455 |
+
- If order == 2:
|
456 |
+
- Denote K = (steps // 2). We take K or (K + 1) intermediate time steps for sampling.
|
457 |
+
- If steps % 2 == 0, we use K steps of DPM-Solver-2.
|
458 |
+
- If steps % 2 == 1, we use K steps of DPM-Solver-2 and 1 step of DPM-Solver-1.
|
459 |
+
- If order == 3:
|
460 |
+
- Denote K = (steps // 3 + 1). We take K intermediate time steps for sampling.
|
461 |
+
- If steps % 3 == 0, we use (K - 2) steps of DPM-Solver-3, and 1 step of DPM-Solver-2 and 1 step of DPM-Solver-1.
|
462 |
+
- If steps % 3 == 1, we use (K - 1) steps of DPM-Solver-3 and 1 step of DPM-Solver-1.
|
463 |
+
- If steps % 3 == 2, we use (K - 1) steps of DPM-Solver-3 and 1 step of DPM-Solver-2.
|
464 |
+
|
465 |
+
============================================
|
466 |
+
Args:
|
467 |
+
order: A `int`. The max order for the solver (2 or 3).
|
468 |
+
steps: A `int`. The total number of function evaluations (NFE).
|
469 |
+
skip_type: A `str`. The type for the spacing of the time steps. We support three types:
|
470 |
+
- 'logSNR': uniform logSNR for the time steps.
|
471 |
+
- 'time_uniform': uniform time for the time steps. (**Recommended for high-resolutional data**.)
|
472 |
+
- 'time_quadratic': quadratic time for the time steps. (Used in DDIM for low-resolutional data.)
|
473 |
+
t_T: A `float`. The starting time of the sampling (default is T).
|
474 |
+
t_0: A `float`. The ending time of the sampling (default is epsilon).
|
475 |
+
device: A torch device.
|
476 |
+
Returns:
|
477 |
+
orders: A list of the solver order of each step.
|
478 |
+
"""
|
479 |
+
if order == 3:
|
480 |
+
K = steps // 3 + 1
|
481 |
+
if steps % 3 == 0:
|
482 |
+
orders = [3, ] * (K - 2) + [2, 1]
|
483 |
+
elif steps % 3 == 1:
|
484 |
+
orders = [3, ] * (K - 1) + [1]
|
485 |
+
else:
|
486 |
+
orders = [3, ] * (K - 1) + [2]
|
487 |
+
elif order == 2:
|
488 |
+
if steps % 2 == 0:
|
489 |
+
K = steps // 2
|
490 |
+
orders = [2, ] * K
|
491 |
+
else:
|
492 |
+
K = steps // 2 + 1
|
493 |
+
orders = [2, ] * (K - 1) + [1]
|
494 |
+
elif order == 1:
|
495 |
+
K = 1
|
496 |
+
orders = [1, ] * steps
|
497 |
+
else:
|
498 |
+
raise ValueError("'order' must be '1' or '2' or '3'.")
|
499 |
+
if skip_type == 'logSNR':
|
500 |
+
# To reproduce the results in DPM-Solver paper
|
501 |
+
timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, K, device)
|
502 |
+
else:
|
503 |
+
timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, steps, device)[
|
504 |
+
torch.cumsum(torch.tensor([0, ] + orders)).to(device)]
|
505 |
+
return timesteps_outer, orders
|
506 |
+
|
507 |
+
def denoise_to_zero_fn(self, x, s):
|
508 |
+
"""
|
509 |
+
Denoise at the final step, which is equivalent to solve the ODE from lambda_s to infty by first-order discretization.
|
510 |
+
"""
|
511 |
+
return self.data_prediction_fn(x, s)
|
512 |
+
|
513 |
+
def dpm_solver_first_update(self, x, s, t, model_s=None, return_intermediate=False):
|
514 |
+
"""
|
515 |
+
DPM-Solver-1 (equivalent to DDIM) from time `s` to time `t`.
|
516 |
+
|
517 |
+
Args:
|
518 |
+
x: A pytorch tensor. The initial value at time `s`.
|
519 |
+
s: A pytorch tensor. The starting time, with the shape (x.shape[0],).
|
520 |
+
t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
|
521 |
+
model_s: A pytorch tensor. The model function evaluated at time `s`.
|
522 |
+
If `model_s` is None, we evaluate the model by `x` and `s`; otherwise we directly use it.
|
523 |
+
return_intermediate: A `bool`. If true, also return the model value at time `s`.
|
524 |
+
Returns:
|
525 |
+
x_t: A pytorch tensor. The approximated solution at time `t`.
|
526 |
+
"""
|
527 |
+
ns = self.noise_schedule
|
528 |
+
dims = x.dim()
|
529 |
+
lambda_s, lambda_t = ns.marginal_lambda(s), ns.marginal_lambda(t)
|
530 |
+
h = lambda_t - lambda_s
|
531 |
+
log_alpha_s, log_alpha_t = ns.marginal_log_mean_coeff(s), ns.marginal_log_mean_coeff(t)
|
532 |
+
sigma_s, sigma_t = ns.marginal_std(s), ns.marginal_std(t)
|
533 |
+
alpha_t = torch.exp(log_alpha_t)
|
534 |
+
|
535 |
+
if self.predict_x0:
|
536 |
+
phi_1 = torch.expm1(-h)
|
537 |
+
if model_s is None:
|
538 |
+
model_s = self.model_fn(x, s)
|
539 |
+
x_t = (
|
540 |
+
expand_dims(sigma_t / sigma_s, dims) * x
|
541 |
+
- expand_dims(alpha_t * phi_1, dims) * model_s
|
542 |
+
)
|
543 |
+
if return_intermediate:
|
544 |
+
return x_t, {'model_s': model_s}
|
545 |
+
else:
|
546 |
+
return x_t
|
547 |
+
else:
|
548 |
+
phi_1 = torch.expm1(h)
|
549 |
+
if model_s is None:
|
550 |
+
model_s = self.model_fn(x, s)
|
551 |
+
x_t = (
|
552 |
+
expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
|
553 |
+
- expand_dims(sigma_t * phi_1, dims) * model_s
|
554 |
+
)
|
555 |
+
if return_intermediate:
|
556 |
+
return x_t, {'model_s': model_s}
|
557 |
+
else:
|
558 |
+
return x_t
|
559 |
+
|
560 |
+
def singlestep_dpm_solver_second_update(self, x, s, t, r1=0.5, model_s=None, return_intermediate=False,
|
561 |
+
solver_type='dpm_solver'):
|
562 |
+
"""
|
563 |
+
Singlestep solver DPM-Solver-2 from time `s` to time `t`.
|
564 |
+
|
565 |
+
Args:
|
566 |
+
x: A pytorch tensor. The initial value at time `s`.
|
567 |
+
s: A pytorch tensor. The starting time, with the shape (x.shape[0],).
|
568 |
+
t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
|
569 |
+
r1: A `float`. The hyperparameter of the second-order solver.
|
570 |
+
model_s: A pytorch tensor. The model function evaluated at time `s`.
|
571 |
+
If `model_s` is None, we evaluate the model by `x` and `s`; otherwise we directly use it.
|
572 |
+
return_intermediate: A `bool`. If true, also return the model value at time `s` and `s1` (the intermediate time).
|
573 |
+
solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
|
574 |
+
The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
|
575 |
+
Returns:
|
576 |
+
x_t: A pytorch tensor. The approximated solution at time `t`.
|
577 |
+
"""
|
578 |
+
if solver_type not in ['dpm_solver', 'taylor']:
|
579 |
+
raise ValueError("'solver_type' must be either 'dpm_solver' or 'taylor', got {}".format(solver_type))
|
580 |
+
if r1 is None:
|
581 |
+
r1 = 0.5
|
582 |
+
ns = self.noise_schedule
|
583 |
+
dims = x.dim()
|
584 |
+
lambda_s, lambda_t = ns.marginal_lambda(s), ns.marginal_lambda(t)
|
585 |
+
h = lambda_t - lambda_s
|
586 |
+
lambda_s1 = lambda_s + r1 * h
|
587 |
+
s1 = ns.inverse_lambda(lambda_s1)
|
588 |
+
log_alpha_s, log_alpha_s1, log_alpha_t = ns.marginal_log_mean_coeff(s), ns.marginal_log_mean_coeff(
|
589 |
+
s1), ns.marginal_log_mean_coeff(t)
|
590 |
+
sigma_s, sigma_s1, sigma_t = ns.marginal_std(s), ns.marginal_std(s1), ns.marginal_std(t)
|
591 |
+
alpha_s1, alpha_t = torch.exp(log_alpha_s1), torch.exp(log_alpha_t)
|
592 |
+
|
593 |
+
if self.predict_x0:
|
594 |
+
phi_11 = torch.expm1(-r1 * h)
|
595 |
+
phi_1 = torch.expm1(-h)
|
596 |
+
|
597 |
+
if model_s is None:
|
598 |
+
model_s = self.model_fn(x, s)
|
599 |
+
x_s1 = (
|
600 |
+
expand_dims(sigma_s1 / sigma_s, dims) * x
|
601 |
+
- expand_dims(alpha_s1 * phi_11, dims) * model_s
|
602 |
+
)
|
603 |
+
model_s1 = self.model_fn(x_s1, s1)
|
604 |
+
if solver_type == 'dpm_solver':
|
605 |
+
x_t = (
|
606 |
+
expand_dims(sigma_t / sigma_s, dims) * x
|
607 |
+
- expand_dims(alpha_t * phi_1, dims) * model_s
|
608 |
+
- (0.5 / r1) * expand_dims(alpha_t * phi_1, dims) * (model_s1 - model_s)
|
609 |
+
)
|
610 |
+
elif solver_type == 'taylor':
|
611 |
+
x_t = (
|
612 |
+
expand_dims(sigma_t / sigma_s, dims) * x
|
613 |
+
- expand_dims(alpha_t * phi_1, dims) * model_s
|
614 |
+
+ (1. / r1) * expand_dims(alpha_t * ((torch.exp(-h) - 1.) / h + 1.), dims) * (
|
615 |
+
model_s1 - model_s)
|
616 |
+
)
|
617 |
+
else:
|
618 |
+
phi_11 = torch.expm1(r1 * h)
|
619 |
+
phi_1 = torch.expm1(h)
|
620 |
+
|
621 |
+
if model_s is None:
|
622 |
+
model_s = self.model_fn(x, s)
|
623 |
+
x_s1 = (
|
624 |
+
expand_dims(torch.exp(log_alpha_s1 - log_alpha_s), dims) * x
|
625 |
+
- expand_dims(sigma_s1 * phi_11, dims) * model_s
|
626 |
+
)
|
627 |
+
model_s1 = self.model_fn(x_s1, s1)
|
628 |
+
if solver_type == 'dpm_solver':
|
629 |
+
x_t = (
|
630 |
+
expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
|
631 |
+
- expand_dims(sigma_t * phi_1, dims) * model_s
|
632 |
+
- (0.5 / r1) * expand_dims(sigma_t * phi_1, dims) * (model_s1 - model_s)
|
633 |
+
)
|
634 |
+
elif solver_type == 'taylor':
|
635 |
+
x_t = (
|
636 |
+
expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
|
637 |
+
- expand_dims(sigma_t * phi_1, dims) * model_s
|
638 |
+
- (1. / r1) * expand_dims(sigma_t * ((torch.exp(h) - 1.) / h - 1.), dims) * (model_s1 - model_s)
|
639 |
+
)
|
640 |
+
if return_intermediate:
|
641 |
+
return x_t, {'model_s': model_s, 'model_s1': model_s1}
|
642 |
+
else:
|
643 |
+
return x_t
|
644 |
+
|
645 |
+
def singlestep_dpm_solver_third_update(self, x, s, t, r1=1. / 3., r2=2. / 3., model_s=None, model_s1=None,
|
646 |
+
return_intermediate=False, solver_type='dpm_solver'):
|
647 |
+
"""
|
648 |
+
Singlestep solver DPM-Solver-3 from time `s` to time `t`.
|
649 |
+
|
650 |
+
Args:
|
651 |
+
x: A pytorch tensor. The initial value at time `s`.
|
652 |
+
s: A pytorch tensor. The starting time, with the shape (x.shape[0],).
|
653 |
+
t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
|
654 |
+
r1: A `float`. The hyperparameter of the third-order solver.
|
655 |
+
r2: A `float`. The hyperparameter of the third-order solver.
|
656 |
+
model_s: A pytorch tensor. The model function evaluated at time `s`.
|
657 |
+
If `model_s` is None, we evaluate the model by `x` and `s`; otherwise we directly use it.
|
658 |
+
model_s1: A pytorch tensor. The model function evaluated at time `s1` (the intermediate time given by `r1`).
|
659 |
+
If `model_s1` is None, we evaluate the model at `s1`; otherwise we directly use it.
|
660 |
+
return_intermediate: A `bool`. If true, also return the model value at time `s`, `s1` and `s2` (the intermediate times).
|
661 |
+
solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
|
662 |
+
The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
|
663 |
+
Returns:
|
664 |
+
x_t: A pytorch tensor. The approximated solution at time `t`.
|
665 |
+
"""
|
666 |
+
if solver_type not in ['dpm_solver', 'taylor']:
|
667 |
+
raise ValueError("'solver_type' must be either 'dpm_solver' or 'taylor', got {}".format(solver_type))
|
668 |
+
if r1 is None:
|
669 |
+
r1 = 1. / 3.
|
670 |
+
if r2 is None:
|
671 |
+
r2 = 2. / 3.
|
672 |
+
ns = self.noise_schedule
|
673 |
+
dims = x.dim()
|
674 |
+
lambda_s, lambda_t = ns.marginal_lambda(s), ns.marginal_lambda(t)
|
675 |
+
h = lambda_t - lambda_s
|
676 |
+
lambda_s1 = lambda_s + r1 * h
|
677 |
+
lambda_s2 = lambda_s + r2 * h
|
678 |
+
s1 = ns.inverse_lambda(lambda_s1)
|
679 |
+
s2 = ns.inverse_lambda(lambda_s2)
|
680 |
+
log_alpha_s, log_alpha_s1, log_alpha_s2, log_alpha_t = ns.marginal_log_mean_coeff(
|
681 |
+
s), ns.marginal_log_mean_coeff(s1), ns.marginal_log_mean_coeff(s2), ns.marginal_log_mean_coeff(t)
|
682 |
+
sigma_s, sigma_s1, sigma_s2, sigma_t = ns.marginal_std(s), ns.marginal_std(s1), ns.marginal_std(
|
683 |
+
s2), ns.marginal_std(t)
|
684 |
+
alpha_s1, alpha_s2, alpha_t = torch.exp(log_alpha_s1), torch.exp(log_alpha_s2), torch.exp(log_alpha_t)
|
685 |
+
|
686 |
+
if self.predict_x0:
|
687 |
+
phi_11 = torch.expm1(-r1 * h)
|
688 |
+
phi_12 = torch.expm1(-r2 * h)
|
689 |
+
phi_1 = torch.expm1(-h)
|
690 |
+
phi_22 = torch.expm1(-r2 * h) / (r2 * h) + 1.
|
691 |
+
phi_2 = phi_1 / h + 1.
|
692 |
+
phi_3 = phi_2 / h - 0.5
|
693 |
+
|
694 |
+
if model_s is None:
|
695 |
+
model_s = self.model_fn(x, s)
|
696 |
+
if model_s1 is None:
|
697 |
+
x_s1 = (
|
698 |
+
expand_dims(sigma_s1 / sigma_s, dims) * x
|
699 |
+
- expand_dims(alpha_s1 * phi_11, dims) * model_s
|
700 |
+
)
|
701 |
+
model_s1 = self.model_fn(x_s1, s1)
|
702 |
+
x_s2 = (
|
703 |
+
expand_dims(sigma_s2 / sigma_s, dims) * x
|
704 |
+
- expand_dims(alpha_s2 * phi_12, dims) * model_s
|
705 |
+
+ r2 / r1 * expand_dims(alpha_s2 * phi_22, dims) * (model_s1 - model_s)
|
706 |
+
)
|
707 |
+
model_s2 = self.model_fn(x_s2, s2)
|
708 |
+
if solver_type == 'dpm_solver':
|
709 |
+
x_t = (
|
710 |
+
expand_dims(sigma_t / sigma_s, dims) * x
|
711 |
+
- expand_dims(alpha_t * phi_1, dims) * model_s
|
712 |
+
+ (1. / r2) * expand_dims(alpha_t * phi_2, dims) * (model_s2 - model_s)
|
713 |
+
)
|
714 |
+
elif solver_type == 'taylor':
|
715 |
+
D1_0 = (1. / r1) * (model_s1 - model_s)
|
716 |
+
D1_1 = (1. / r2) * (model_s2 - model_s)
|
717 |
+
D1 = (r2 * D1_0 - r1 * D1_1) / (r2 - r1)
|
718 |
+
D2 = 2. * (D1_1 - D1_0) / (r2 - r1)
|
719 |
+
x_t = (
|
720 |
+
expand_dims(sigma_t / sigma_s, dims) * x
|
721 |
+
- expand_dims(alpha_t * phi_1, dims) * model_s
|
722 |
+
+ expand_dims(alpha_t * phi_2, dims) * D1
|
723 |
+
- expand_dims(alpha_t * phi_3, dims) * D2
|
724 |
+
)
|
725 |
+
else:
|
726 |
+
phi_11 = torch.expm1(r1 * h)
|
727 |
+
phi_12 = torch.expm1(r2 * h)
|
728 |
+
phi_1 = torch.expm1(h)
|
729 |
+
phi_22 = torch.expm1(r2 * h) / (r2 * h) - 1.
|
730 |
+
phi_2 = phi_1 / h - 1.
|
731 |
+
phi_3 = phi_2 / h - 0.5
|
732 |
+
|
733 |
+
if model_s is None:
|
734 |
+
model_s = self.model_fn(x, s)
|
735 |
+
if model_s1 is None:
|
736 |
+
x_s1 = (
|
737 |
+
expand_dims(torch.exp(log_alpha_s1 - log_alpha_s), dims) * x
|
738 |
+
- expand_dims(sigma_s1 * phi_11, dims) * model_s
|
739 |
+
)
|
740 |
+
model_s1 = self.model_fn(x_s1, s1)
|
741 |
+
x_s2 = (
|
742 |
+
expand_dims(torch.exp(log_alpha_s2 - log_alpha_s), dims) * x
|
743 |
+
- expand_dims(sigma_s2 * phi_12, dims) * model_s
|
744 |
+
- r2 / r1 * expand_dims(sigma_s2 * phi_22, dims) * (model_s1 - model_s)
|
745 |
+
)
|
746 |
+
model_s2 = self.model_fn(x_s2, s2)
|
747 |
+
if solver_type == 'dpm_solver':
|
748 |
+
x_t = (
|
749 |
+
expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
|
750 |
+
- expand_dims(sigma_t * phi_1, dims) * model_s
|
751 |
+
- (1. / r2) * expand_dims(sigma_t * phi_2, dims) * (model_s2 - model_s)
|
752 |
+
)
|
753 |
+
elif solver_type == 'taylor':
|
754 |
+
D1_0 = (1. / r1) * (model_s1 - model_s)
|
755 |
+
D1_1 = (1. / r2) * (model_s2 - model_s)
|
756 |
+
D1 = (r2 * D1_0 - r1 * D1_1) / (r2 - r1)
|
757 |
+
D2 = 2. * (D1_1 - D1_0) / (r2 - r1)
|
758 |
+
x_t = (
|
759 |
+
expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
|
760 |
+
- expand_dims(sigma_t * phi_1, dims) * model_s
|
761 |
+
- expand_dims(sigma_t * phi_2, dims) * D1
|
762 |
+
- expand_dims(sigma_t * phi_3, dims) * D2
|
763 |
+
)
|
764 |
+
|
765 |
+
if return_intermediate:
|
766 |
+
return x_t, {'model_s': model_s, 'model_s1': model_s1, 'model_s2': model_s2}
|
767 |
+
else:
|
768 |
+
return x_t
|
769 |
+
|
770 |
+
def multistep_dpm_solver_second_update(self, x, model_prev_list, t_prev_list, t, solver_type="dpm_solver"):
|
771 |
+
"""
|
772 |
+
Multistep solver DPM-Solver-2 from time `t_prev_list[-1]` to time `t`.
|
773 |
+
|
774 |
+
Args:
|
775 |
+
x: A pytorch tensor. The initial value at time `s`.
|
776 |
+
model_prev_list: A list of pytorch tensor. The previous computed model values.
|
777 |
+
t_prev_list: A list of pytorch tensor. The previous times, each time has the shape (x.shape[0],)
|
778 |
+
t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
|
779 |
+
solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
|
780 |
+
The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
|
781 |
+
Returns:
|
782 |
+
x_t: A pytorch tensor. The approximated solution at time `t`.
|
783 |
+
"""
|
784 |
+
if solver_type not in ['dpm_solver', 'taylor']:
|
785 |
+
raise ValueError("'solver_type' must be either 'dpm_solver' or 'taylor', got {}".format(solver_type))
|
786 |
+
ns = self.noise_schedule
|
787 |
+
dims = x.dim()
|
788 |
+
model_prev_1, model_prev_0 = model_prev_list
|
789 |
+
t_prev_1, t_prev_0 = t_prev_list
|
790 |
+
lambda_prev_1, lambda_prev_0, lambda_t = ns.marginal_lambda(t_prev_1), ns.marginal_lambda(
|
791 |
+
t_prev_0), ns.marginal_lambda(t)
|
792 |
+
log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t)
|
793 |
+
sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t)
|
794 |
+
alpha_t = torch.exp(log_alpha_t)
|
795 |
+
|
796 |
+
h_0 = lambda_prev_0 - lambda_prev_1
|
797 |
+
h = lambda_t - lambda_prev_0
|
798 |
+
r0 = h_0 / h
|
799 |
+
D1_0 = expand_dims(1. / r0, dims) * (model_prev_0 - model_prev_1)
|
800 |
+
if self.predict_x0:
|
801 |
+
if solver_type == 'dpm_solver':
|
802 |
+
x_t = (
|
803 |
+
expand_dims(sigma_t / sigma_prev_0, dims) * x
|
804 |
+
- expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * model_prev_0
|
805 |
+
- 0.5 * expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * D1_0
|
806 |
+
)
|
807 |
+
elif solver_type == 'taylor':
|
808 |
+
x_t = (
|
809 |
+
expand_dims(sigma_t / sigma_prev_0, dims) * x
|
810 |
+
- expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * model_prev_0
|
811 |
+
+ expand_dims(alpha_t * ((torch.exp(-h) - 1.) / h + 1.), dims) * D1_0
|
812 |
+
)
|
813 |
+
else:
|
814 |
+
if solver_type == 'dpm_solver':
|
815 |
+
x_t = (
|
816 |
+
expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x
|
817 |
+
- expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * model_prev_0
|
818 |
+
- 0.5 * expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * D1_0
|
819 |
+
)
|
820 |
+
elif solver_type == 'taylor':
|
821 |
+
x_t = (
|
822 |
+
expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x
|
823 |
+
- expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * model_prev_0
|
824 |
+
- expand_dims(sigma_t * ((torch.exp(h) - 1.) / h - 1.), dims) * D1_0
|
825 |
+
)
|
826 |
+
return x_t
|
827 |
+
|
828 |
+
def multistep_dpm_solver_third_update(self, x, model_prev_list, t_prev_list, t, solver_type='dpm_solver'):
|
829 |
+
"""
|
830 |
+
Multistep solver DPM-Solver-3 from time `t_prev_list[-1]` to time `t`.
|
831 |
+
|
832 |
+
Args:
|
833 |
+
x: A pytorch tensor. The initial value at time `s`.
|
834 |
+
model_prev_list: A list of pytorch tensor. The previous computed model values.
|
835 |
+
t_prev_list: A list of pytorch tensor. The previous times, each time has the shape (x.shape[0],)
|
836 |
+
t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
|
837 |
+
solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
|
838 |
+
The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
|
839 |
+
Returns:
|
840 |
+
x_t: A pytorch tensor. The approximated solution at time `t`.
|
841 |
+
"""
|
842 |
+
ns = self.noise_schedule
|
843 |
+
dims = x.dim()
|
844 |
+
model_prev_2, model_prev_1, model_prev_0 = model_prev_list
|
845 |
+
t_prev_2, t_prev_1, t_prev_0 = t_prev_list
|
846 |
+
lambda_prev_2, lambda_prev_1, lambda_prev_0, lambda_t = ns.marginal_lambda(t_prev_2), ns.marginal_lambda(
|
847 |
+
t_prev_1), ns.marginal_lambda(t_prev_0), ns.marginal_lambda(t)
|
848 |
+
log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t)
|
849 |
+
sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t)
|
850 |
+
alpha_t = torch.exp(log_alpha_t)
|
851 |
+
|
852 |
+
h_1 = lambda_prev_1 - lambda_prev_2
|
853 |
+
h_0 = lambda_prev_0 - lambda_prev_1
|
854 |
+
h = lambda_t - lambda_prev_0
|
855 |
+
r0, r1 = h_0 / h, h_1 / h
|
856 |
+
D1_0 = expand_dims(1. / r0, dims) * (model_prev_0 - model_prev_1)
|
857 |
+
D1_1 = expand_dims(1. / r1, dims) * (model_prev_1 - model_prev_2)
|
858 |
+
D1 = D1_0 + expand_dims(r0 / (r0 + r1), dims) * (D1_0 - D1_1)
|
859 |
+
D2 = expand_dims(1. / (r0 + r1), dims) * (D1_0 - D1_1)
|
860 |
+
if self.predict_x0:
|
861 |
+
x_t = (
|
862 |
+
expand_dims(sigma_t / sigma_prev_0, dims) * x
|
863 |
+
- expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * model_prev_0
|
864 |
+
+ expand_dims(alpha_t * ((torch.exp(-h) - 1.) / h + 1.), dims) * D1
|
865 |
+
- expand_dims(alpha_t * ((torch.exp(-h) - 1. + h) / h ** 2 - 0.5), dims) * D2
|
866 |
+
)
|
867 |
+
else:
|
868 |
+
x_t = (
|
869 |
+
expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x
|
870 |
+
- expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * model_prev_0
|
871 |
+
- expand_dims(sigma_t * ((torch.exp(h) - 1.) / h - 1.), dims) * D1
|
872 |
+
- expand_dims(sigma_t * ((torch.exp(h) - 1. - h) / h ** 2 - 0.5), dims) * D2
|
873 |
+
)
|
874 |
+
return x_t
|
875 |
+
|
876 |
+
def singlestep_dpm_solver_update(self, x, s, t, order, return_intermediate=False, solver_type='dpm_solver', r1=None,
|
877 |
+
r2=None):
|
878 |
+
"""
|
879 |
+
Singlestep DPM-Solver with the order `order` from time `s` to time `t`.
|
880 |
+
|
881 |
+
Args:
|
882 |
+
x: A pytorch tensor. The initial value at time `s`.
|
883 |
+
s: A pytorch tensor. The starting time, with the shape (x.shape[0],).
|
884 |
+
t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
|
885 |
+
order: A `int`. The order of DPM-Solver. We only support order == 1 or 2 or 3.
|
886 |
+
return_intermediate: A `bool`. If true, also return the model value at time `s`, `s1` and `s2` (the intermediate times).
|
887 |
+
solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
|
888 |
+
The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
|
889 |
+
r1: A `float`. The hyperparameter of the second-order or third-order solver.
|
890 |
+
r2: A `float`. The hyperparameter of the third-order solver.
|
891 |
+
Returns:
|
892 |
+
x_t: A pytorch tensor. The approximated solution at time `t`.
|
893 |
+
"""
|
894 |
+
if order == 1:
|
895 |
+
return self.dpm_solver_first_update(x, s, t, return_intermediate=return_intermediate)
|
896 |
+
elif order == 2:
|
897 |
+
return self.singlestep_dpm_solver_second_update(x, s, t, return_intermediate=return_intermediate,
|
898 |
+
solver_type=solver_type, r1=r1)
|
899 |
+
elif order == 3:
|
900 |
+
return self.singlestep_dpm_solver_third_update(x, s, t, return_intermediate=return_intermediate,
|
901 |
+
solver_type=solver_type, r1=r1, r2=r2)
|
902 |
+
else:
|
903 |
+
raise ValueError("Solver order must be 1 or 2 or 3, got {}".format(order))
|
904 |
+
|
905 |
+
def multistep_dpm_solver_update(self, x, model_prev_list, t_prev_list, t, order, solver_type='dpm_solver'):
|
906 |
+
"""
|
907 |
+
Multistep DPM-Solver with the order `order` from time `t_prev_list[-1]` to time `t`.
|
908 |
+
|
909 |
+
Args:
|
910 |
+
x: A pytorch tensor. The initial value at time `s`.
|
911 |
+
model_prev_list: A list of pytorch tensor. The previous computed model values.
|
912 |
+
t_prev_list: A list of pytorch tensor. The previous times, each time has the shape (x.shape[0],)
|
913 |
+
t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
|
914 |
+
order: A `int`. The order of DPM-Solver. We only support order == 1 or 2 or 3.
|
915 |
+
solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
|
916 |
+
The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
|
917 |
+
Returns:
|
918 |
+
x_t: A pytorch tensor. The approximated solution at time `t`.
|
919 |
+
"""
|
920 |
+
if order == 1:
|
921 |
+
return self.dpm_solver_first_update(x, t_prev_list[-1], t, model_s=model_prev_list[-1])
|
922 |
+
elif order == 2:
|
923 |
+
return self.multistep_dpm_solver_second_update(x, model_prev_list, t_prev_list, t, solver_type=solver_type)
|
924 |
+
elif order == 3:
|
925 |
+
return self.multistep_dpm_solver_third_update(x, model_prev_list, t_prev_list, t, solver_type=solver_type)
|
926 |
+
else:
|
927 |
+
raise ValueError("Solver order must be 1 or 2 or 3, got {}".format(order))
|
928 |
+
|
929 |
+
def dpm_solver_adaptive(self, x, order, t_T, t_0, h_init=0.05, atol=0.0078, rtol=0.05, theta=0.9, t_err=1e-5,
|
930 |
+
solver_type='dpm_solver'):
|
931 |
+
"""
|
932 |
+
The adaptive step size solver based on singlestep DPM-Solver.
|
933 |
+
|
934 |
+
Args:
|
935 |
+
x: A pytorch tensor. The initial value at time `t_T`.
|
936 |
+
order: A `int`. The (higher) order of the solver. We only support order == 2 or 3.
|
937 |
+
t_T: A `float`. The starting time of the sampling (default is T).
|
938 |
+
t_0: A `float`. The ending time of the sampling (default is epsilon).
|
939 |
+
h_init: A `float`. The initial step size (for logSNR).
|
940 |
+
atol: A `float`. The absolute tolerance of the solver. For image data, the default setting is 0.0078, followed [1].
|
941 |
+
rtol: A `float`. The relative tolerance of the solver. The default setting is 0.05.
|
942 |
+
theta: A `float`. The safety hyperparameter for adapting the step size. The default setting is 0.9, followed [1].
|
943 |
+
t_err: A `float`. The tolerance for the time. We solve the diffusion ODE until the absolute error between the
|
944 |
+
current time and `t_0` is less than `t_err`. The default setting is 1e-5.
|
945 |
+
solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
|
946 |
+
The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
|
947 |
+
Returns:
|
948 |
+
x_0: A pytorch tensor. The approximated solution at time `t_0`.
|
949 |
+
|
950 |
+
[1] A. Jolicoeur-Martineau, K. Li, R. PichΓ©-Taillefer, T. Kachman, and I. Mitliagkas, "Gotta go fast when generating data with score-based models," arXiv preprint arXiv:2105.14080, 2021.
|
951 |
+
"""
|
952 |
+
ns = self.noise_schedule
|
953 |
+
s = t_T * torch.ones((x.shape[0],)).to(x)
|
954 |
+
lambda_s = ns.marginal_lambda(s)
|
955 |
+
lambda_0 = ns.marginal_lambda(t_0 * torch.ones_like(s).to(x))
|
956 |
+
h = h_init * torch.ones_like(s).to(x)
|
957 |
+
x_prev = x
|
958 |
+
nfe = 0
|
959 |
+
if order == 2:
|
960 |
+
r1 = 0.5
|
961 |
+
lower_update = lambda x, s, t: self.dpm_solver_first_update(x, s, t, return_intermediate=True)
|
962 |
+
higher_update = lambda x, s, t, **kwargs: self.singlestep_dpm_solver_second_update(x, s, t, r1=r1,
|
963 |
+
solver_type=solver_type,
|
964 |
+
**kwargs)
|
965 |
+
elif order == 3:
|
966 |
+
r1, r2 = 1. / 3., 2. / 3.
|
967 |
+
lower_update = lambda x, s, t: self.singlestep_dpm_solver_second_update(x, s, t, r1=r1,
|
968 |
+
return_intermediate=True,
|
969 |
+
solver_type=solver_type)
|
970 |
+
higher_update = lambda x, s, t, **kwargs: self.singlestep_dpm_solver_third_update(x, s, t, r1=r1, r2=r2,
|
971 |
+
solver_type=solver_type,
|
972 |
+
**kwargs)
|
973 |
+
else:
|
974 |
+
raise ValueError("For adaptive step size solver, order must be 2 or 3, got {}".format(order))
|
975 |
+
while torch.abs((s - t_0)).mean() > t_err:
|
976 |
+
t = ns.inverse_lambda(lambda_s + h)
|
977 |
+
x_lower, lower_noise_kwargs = lower_update(x, s, t)
|
978 |
+
x_higher = higher_update(x, s, t, **lower_noise_kwargs)
|
979 |
+
delta = torch.max(torch.ones_like(x).to(x) * atol, rtol * torch.max(torch.abs(x_lower), torch.abs(x_prev)))
|
980 |
+
norm_fn = lambda v: torch.sqrt(torch.square(v.reshape((v.shape[0], -1))).mean(dim=-1, keepdim=True))
|
981 |
+
E = norm_fn((x_higher - x_lower) / delta).max()
|
982 |
+
if torch.all(E <= 1.):
|
983 |
+
x = x_higher
|
984 |
+
s = t
|
985 |
+
x_prev = x_lower
|
986 |
+
lambda_s = ns.marginal_lambda(s)
|
987 |
+
h = torch.min(theta * h * torch.float_power(E, -1. / order).float(), lambda_0 - lambda_s)
|
988 |
+
nfe += order
|
989 |
+
print('adaptive solver nfe', nfe)
|
990 |
+
return x
|
991 |
+
|
992 |
+
def sample(self, x, steps=20, t_start=None, t_end=None, order=3, skip_type='time_uniform',
|
993 |
+
method='singlestep', lower_order_final=True, denoise_to_zero=False, solver_type='dpm_solver',
|
994 |
+
atol=0.0078, rtol=0.05,
|
995 |
+
):
|
996 |
+
"""
|
997 |
+
Compute the sample at time `t_end` by DPM-Solver, given the initial `x` at time `t_start`.
|
998 |
+
|
999 |
+
=====================================================
|
1000 |
+
|
1001 |
+
We support the following algorithms for both noise prediction model and data prediction model:
|
1002 |
+
- 'singlestep':
|
1003 |
+
Singlestep DPM-Solver (i.e. "DPM-Solver-fast" in the paper), which combines different orders of singlestep DPM-Solver.
|
1004 |
+
We combine all the singlestep solvers with order <= `order` to use up all the function evaluations (steps).
|
1005 |
+
The total number of function evaluations (NFE) == `steps`.
|
1006 |
+
Given a fixed NFE == `steps`, the sampling procedure is:
|
1007 |
+
- If `order` == 1:
|
1008 |
+
- Denote K = steps. We use K steps of DPM-Solver-1 (i.e. DDIM).
|
1009 |
+
- If `order` == 2:
|
1010 |
+
- Denote K = (steps // 2) + (steps % 2). We take K intermediate time steps for sampling.
|
1011 |
+
- If steps % 2 == 0, we use K steps of singlestep DPM-Solver-2.
|
1012 |
+
- If steps % 2 == 1, we use (K - 1) steps of singlestep DPM-Solver-2 and 1 step of DPM-Solver-1.
|
1013 |
+
- If `order` == 3:
|
1014 |
+
- Denote K = (steps // 3 + 1). We take K intermediate time steps for sampling.
|
1015 |
+
- If steps % 3 == 0, we use (K - 2) steps of singlestep DPM-Solver-3, and 1 step of singlestep DPM-Solver-2 and 1 step of DPM-Solver-1.
|
1016 |
+
- If steps % 3 == 1, we use (K - 1) steps of singlestep DPM-Solver-3 and 1 step of DPM-Solver-1.
|
1017 |
+
- If steps % 3 == 2, we use (K - 1) steps of singlestep DPM-Solver-3 and 1 step of singlestep DPM-Solver-2.
|
1018 |
+
- 'multistep':
|
1019 |
+
Multistep DPM-Solver with the order of `order`. The total number of function evaluations (NFE) == `steps`.
|
1020 |
+
We initialize the first `order` values by lower order multistep solvers.
|
1021 |
+
Given a fixed NFE == `steps`, the sampling procedure is:
|
1022 |
+
Denote K = steps.
|
1023 |
+
- If `order` == 1:
|
1024 |
+
- We use K steps of DPM-Solver-1 (i.e. DDIM).
|
1025 |
+
- If `order` == 2:
|
1026 |
+
- We firstly use 1 step of DPM-Solver-1, then use (K - 1) step of multistep DPM-Solver-2.
|
1027 |
+
- If `order` == 3:
|
1028 |
+
- We firstly use 1 step of DPM-Solver-1, then 1 step of multistep DPM-Solver-2, then (K - 2) step of multistep DPM-Solver-3.
|
1029 |
+
- 'singlestep_fixed':
|
1030 |
+
Fixed order singlestep DPM-Solver (i.e. DPM-Solver-1 or singlestep DPM-Solver-2 or singlestep DPM-Solver-3).
|
1031 |
+
We use singlestep DPM-Solver-`order` for `order`=1 or 2 or 3, with total [`steps` // `order`] * `order` NFE.
|
1032 |
+
- 'adaptive':
|
1033 |
+
Adaptive step size DPM-Solver (i.e. "DPM-Solver-12" and "DPM-Solver-23" in the paper).
|
1034 |
+
We ignore `steps` and use adaptive step size DPM-Solver with a higher order of `order`.
|
1035 |
+
You can adjust the absolute tolerance `atol` and the relative tolerance `rtol` to balance the computatation costs
|
1036 |
+
(NFE) and the sample quality.
|
1037 |
+
- If `order` == 2, we use DPM-Solver-12 which combines DPM-Solver-1 and singlestep DPM-Solver-2.
|
1038 |
+
- If `order` == 3, we use DPM-Solver-23 which combines singlestep DPM-Solver-2 and singlestep DPM-Solver-3.
|
1039 |
+
|
1040 |
+
=====================================================
|
1041 |
+
|
1042 |
+
Some advices for choosing the algorithm:
|
1043 |
+
- For **unconditional sampling** or **guided sampling with small guidance scale** by DPMs:
|
1044 |
+
Use singlestep DPM-Solver ("DPM-Solver-fast" in the paper) with `order = 3`.
|
1045 |
+
e.g.
|
1046 |
+
>>> dpm_solver = DPM_Solver(model_fn, noise_schedule, predict_x0=False)
|
1047 |
+
>>> x_sample = dpm_solver.sample(x, steps=steps, t_start=t_start, t_end=t_end, order=3,
|
1048 |
+
skip_type='time_uniform', method='singlestep')
|
1049 |
+
- For **guided sampling with large guidance scale** by DPMs:
|
1050 |
+
Use multistep DPM-Solver with `predict_x0 = True` and `order = 2`.
|
1051 |
+
e.g.
|
1052 |
+
>>> dpm_solver = DPM_Solver(model_fn, noise_schedule, predict_x0=True)
|
1053 |
+
>>> x_sample = dpm_solver.sample(x, steps=steps, t_start=t_start, t_end=t_end, order=2,
|
1054 |
+
skip_type='time_uniform', method='multistep')
|
1055 |
+
|
1056 |
+
We support three types of `skip_type`:
|
1057 |
+
- 'logSNR': uniform logSNR for the time steps. **Recommended for low-resolutional images**
|
1058 |
+
- 'time_uniform': uniform time for the time steps. **Recommended for high-resolutional images**.
|
1059 |
+
- 'time_quadratic': quadratic time for the time steps.
|
1060 |
+
|
1061 |
+
=====================================================
|
1062 |
+
Args:
|
1063 |
+
x: A pytorch tensor. The initial value at time `t_start`
|
1064 |
+
e.g. if `t_start` == T, then `x` is a sample from the standard normal distribution.
|
1065 |
+
steps: A `int`. The total number of function evaluations (NFE).
|
1066 |
+
t_start: A `float`. The starting time of the sampling.
|
1067 |
+
If `T` is None, we use self.noise_schedule.T (default is 1.0).
|
1068 |
+
t_end: A `float`. The ending time of the sampling.
|
1069 |
+
If `t_end` is None, we use 1. / self.noise_schedule.total_N.
|
1070 |
+
e.g. if total_N == 1000, we have `t_end` == 1e-3.
|
1071 |
+
For discrete-time DPMs:
|
1072 |
+
- We recommend `t_end` == 1. / self.noise_schedule.total_N.
|
1073 |
+
For continuous-time DPMs:
|
1074 |
+
- We recommend `t_end` == 1e-3 when `steps` <= 15; and `t_end` == 1e-4 when `steps` > 15.
|
1075 |
+
order: A `int`. The order of DPM-Solver.
|
1076 |
+
skip_type: A `str`. The type for the spacing of the time steps. 'time_uniform' or 'logSNR' or 'time_quadratic'.
|
1077 |
+
method: A `str`. The method for sampling. 'singlestep' or 'multistep' or 'singlestep_fixed' or 'adaptive'.
|
1078 |
+
denoise_to_zero: A `bool`. Whether to denoise to time 0 at the final step.
|
1079 |
+
Default is `False`. If `denoise_to_zero` is `True`, the total NFE is (`steps` + 1).
|
1080 |
+
|
1081 |
+
This trick is firstly proposed by DDPM (https://arxiv.org/abs/2006.11239) and
|
1082 |
+
score_sde (https://arxiv.org/abs/2011.13456). Such trick can improve the FID
|
1083 |
+
for diffusion models sampling by diffusion SDEs for low-resolutional images
|
1084 |
+
(such as CIFAR-10). However, we observed that such trick does not matter for
|
1085 |
+
high-resolutional images. As it needs an additional NFE, we do not recommend
|
1086 |
+
it for high-resolutional images.
|
1087 |
+
lower_order_final: A `bool`. Whether to use lower order solvers at the final steps.
|
1088 |
+
Only valid for `method=multistep` and `steps < 15`. We empirically find that
|
1089 |
+
this trick is a key to stabilizing the sampling by DPM-Solver with very few steps
|
1090 |
+
(especially for steps <= 10). So we recommend to set it to be `True`.
|
1091 |
+
solver_type: A `str`. The taylor expansion type for the solver. `dpm_solver` or `taylor`. We recommend `dpm_solver`.
|
1092 |
+
atol: A `float`. The absolute tolerance of the adaptive step size solver. Valid when `method` == 'adaptive'.
|
1093 |
+
rtol: A `float`. The relative tolerance of the adaptive step size solver. Valid when `method` == 'adaptive'.
|
1094 |
+
Returns:
|
1095 |
+
x_end: A pytorch tensor. The approximated solution at time `t_end`.
|
1096 |
+
|
1097 |
+
"""
|
1098 |
+
t_0 = 1. / self.noise_schedule.total_N if t_end is None else t_end
|
1099 |
+
t_T = self.noise_schedule.T if t_start is None else t_start
|
1100 |
+
device = x.device
|
1101 |
+
if method == 'adaptive':
|
1102 |
+
with torch.no_grad():
|
1103 |
+
x = self.dpm_solver_adaptive(x, order=order, t_T=t_T, t_0=t_0, atol=atol, rtol=rtol,
|
1104 |
+
solver_type=solver_type)
|
1105 |
+
elif method == 'multistep':
|
1106 |
+
assert steps >= order
|
1107 |
+
timesteps = self.get_time_steps(skip_type=skip_type, t_T=t_T, t_0=t_0, N=steps, device=device)
|
1108 |
+
assert timesteps.shape[0] - 1 == steps
|
1109 |
+
with torch.no_grad():
|
1110 |
+
vec_t = timesteps[0].expand((x.shape[0]))
|
1111 |
+
model_prev_list = [self.model_fn(x, vec_t)]
|
1112 |
+
t_prev_list = [vec_t]
|
1113 |
+
# Init the first `order` values by lower order multistep DPM-Solver.
|
1114 |
+
for init_order in tqdm(range(1, order), desc="DPM init order"):
|
1115 |
+
vec_t = timesteps[init_order].expand(x.shape[0])
|
1116 |
+
x = self.multistep_dpm_solver_update(x, model_prev_list, t_prev_list, vec_t, init_order,
|
1117 |
+
solver_type=solver_type)
|
1118 |
+
model_prev_list.append(self.model_fn(x, vec_t))
|
1119 |
+
t_prev_list.append(vec_t)
|
1120 |
+
# Compute the remaining values by `order`-th order multistep DPM-Solver.
|
1121 |
+
for step in tqdm(range(order, steps + 1), desc="DPM multistep"):
|
1122 |
+
vec_t = timesteps[step].expand(x.shape[0])
|
1123 |
+
if lower_order_final and steps < 15:
|
1124 |
+
step_order = min(order, steps + 1 - step)
|
1125 |
+
else:
|
1126 |
+
step_order = order
|
1127 |
+
x = self.multistep_dpm_solver_update(x, model_prev_list, t_prev_list, vec_t, step_order,
|
1128 |
+
solver_type=solver_type)
|
1129 |
+
for i in range(order - 1):
|
1130 |
+
t_prev_list[i] = t_prev_list[i + 1]
|
1131 |
+
model_prev_list[i] = model_prev_list[i + 1]
|
1132 |
+
t_prev_list[-1] = vec_t
|
1133 |
+
# We do not need to evaluate the final model value.
|
1134 |
+
if step < steps:
|
1135 |
+
model_prev_list[-1] = self.model_fn(x, vec_t)
|
1136 |
+
elif method in ['singlestep', 'singlestep_fixed']:
|
1137 |
+
if method == 'singlestep':
|
1138 |
+
timesteps_outer, orders = self.get_orders_and_timesteps_for_singlestep_solver(steps=steps, order=order,
|
1139 |
+
skip_type=skip_type,
|
1140 |
+
t_T=t_T, t_0=t_0,
|
1141 |
+
device=device)
|
1142 |
+
elif method == 'singlestep_fixed':
|
1143 |
+
K = steps // order
|
1144 |
+
orders = [order, ] * K
|
1145 |
+
timesteps_outer = self.get_time_steps(skip_type=skip_type, t_T=t_T, t_0=t_0, N=K, device=device)
|
1146 |
+
for i, order in enumerate(orders):
|
1147 |
+
t_T_inner, t_0_inner = timesteps_outer[i], timesteps_outer[i + 1]
|
1148 |
+
timesteps_inner = self.get_time_steps(skip_type=skip_type, t_T=t_T_inner.item(), t_0=t_0_inner.item(),
|
1149 |
+
N=order, device=device)
|
1150 |
+
lambda_inner = self.noise_schedule.marginal_lambda(timesteps_inner)
|
1151 |
+
vec_s, vec_t = t_T_inner.tile(x.shape[0]), t_0_inner.tile(x.shape[0])
|
1152 |
+
h = lambda_inner[-1] - lambda_inner[0]
|
1153 |
+
r1 = None if order <= 1 else (lambda_inner[1] - lambda_inner[0]) / h
|
1154 |
+
r2 = None if order <= 2 else (lambda_inner[2] - lambda_inner[0]) / h
|
1155 |
+
x = self.singlestep_dpm_solver_update(x, vec_s, vec_t, order, solver_type=solver_type, r1=r1, r2=r2)
|
1156 |
+
if denoise_to_zero:
|
1157 |
+
x = self.denoise_to_zero_fn(x, torch.ones((x.shape[0],)).to(device) * t_0)
|
1158 |
+
return x
|
1159 |
+
|
1160 |
+
|
1161 |
+
#############################################################
|
1162 |
+
# other utility functions
|
1163 |
+
#############################################################
|
1164 |
+
|
1165 |
+
def interpolate_fn(x, xp, yp):
|
1166 |
+
"""
|
1167 |
+
A piecewise linear function y = f(x), using xp and yp as keypoints.
|
1168 |
+
We implement f(x) in a differentiable way (i.e. applicable for autograd).
|
1169 |
+
The function f(x) is well-defined for all x-axis. (For x beyond the bounds of xp, we use the outmost points of xp to define the linear function.)
|
1170 |
+
|
1171 |
+
Args:
|
1172 |
+
x: PyTorch tensor with shape [N, C], where N is the batch size, C is the number of channels (we use C = 1 for DPM-Solver).
|
1173 |
+
xp: PyTorch tensor with shape [C, K], where K is the number of keypoints.
|
1174 |
+
yp: PyTorch tensor with shape [C, K].
|
1175 |
+
Returns:
|
1176 |
+
The function values f(x), with shape [N, C].
|
1177 |
+
"""
|
1178 |
+
N, K = x.shape[0], xp.shape[1]
|
1179 |
+
all_x = torch.cat([x.unsqueeze(2), xp.unsqueeze(0).repeat((N, 1, 1))], dim=2)
|
1180 |
+
sorted_all_x, x_indices = torch.sort(all_x, dim=2)
|
1181 |
+
x_idx = torch.argmin(x_indices, dim=2)
|
1182 |
+
cand_start_idx = x_idx - 1
|
1183 |
+
start_idx = torch.where(
|
1184 |
+
torch.eq(x_idx, 0),
|
1185 |
+
torch.tensor(1, device=x.device),
|
1186 |
+
torch.where(
|
1187 |
+
torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx,
|
1188 |
+
),
|
1189 |
+
)
|
1190 |
+
end_idx = torch.where(torch.eq(start_idx, cand_start_idx), start_idx + 2, start_idx + 1)
|
1191 |
+
start_x = torch.gather(sorted_all_x, dim=2, index=start_idx.unsqueeze(2)).squeeze(2)
|
1192 |
+
end_x = torch.gather(sorted_all_x, dim=2, index=end_idx.unsqueeze(2)).squeeze(2)
|
1193 |
+
start_idx2 = torch.where(
|
1194 |
+
torch.eq(x_idx, 0),
|
1195 |
+
torch.tensor(0, device=x.device),
|
1196 |
+
torch.where(
|
1197 |
+
torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx,
|
1198 |
+
),
|
1199 |
+
)
|
1200 |
+
y_positions_expanded = yp.unsqueeze(0).expand(N, -1, -1)
|
1201 |
+
start_y = torch.gather(y_positions_expanded, dim=2, index=start_idx2.unsqueeze(2)).squeeze(2)
|
1202 |
+
end_y = torch.gather(y_positions_expanded, dim=2, index=(start_idx2 + 1).unsqueeze(2)).squeeze(2)
|
1203 |
+
cand = start_y + (x - start_x) * (end_y - start_y) / (end_x - start_x)
|
1204 |
+
return cand
|
1205 |
+
|
1206 |
+
|
1207 |
+
def expand_dims(v, dims):
|
1208 |
+
"""
|
1209 |
+
Expand the tensor `v` to the dim `dims`.
|
1210 |
+
|
1211 |
+
Args:
|
1212 |
+
`v`: a PyTorch tensor with shape [N].
|
1213 |
+
`dim`: a `int`.
|
1214 |
+
Returns:
|
1215 |
+
a PyTorch tensor with shape [N, 1, 1, ..., 1] and the total dimension is `dims`.
|
1216 |
+
"""
|
1217 |
+
return v[(...,) + (None,) * (dims - 1)]
|
ldm/models/diffusion/dpm_solver/sampler.py
ADDED
@@ -0,0 +1,87 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
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|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
"""SAMPLING ONLY."""
|
2 |
+
import torch
|
3 |
+
|
4 |
+
from .dpm_solver import NoiseScheduleVP, model_wrapper, DPM_Solver
|
5 |
+
|
6 |
+
|
7 |
+
MODEL_TYPES = {
|
8 |
+
"eps": "noise",
|
9 |
+
"v": "v"
|
10 |
+
}
|
11 |
+
|
12 |
+
|
13 |
+
class DPMSolverSampler(object):
|
14 |
+
def __init__(self, model, **kwargs):
|
15 |
+
super().__init__()
|
16 |
+
self.model = model
|
17 |
+
to_torch = lambda x: x.clone().detach().to(torch.float32).to(model.device)
|
18 |
+
self.register_buffer('alphas_cumprod', to_torch(model.alphas_cumprod))
|
19 |
+
|
20 |
+
def register_buffer(self, name, attr):
|
21 |
+
if type(attr) == torch.Tensor:
|
22 |
+
if attr.device != torch.device("cuda"):
|
23 |
+
attr = attr.to(torch.device("cuda"))
|
24 |
+
setattr(self, name, attr)
|
25 |
+
|
26 |
+
@torch.no_grad()
|
27 |
+
def sample(self,
|
28 |
+
S,
|
29 |
+
batch_size,
|
30 |
+
shape,
|
31 |
+
conditioning=None,
|
32 |
+
callback=None,
|
33 |
+
normals_sequence=None,
|
34 |
+
img_callback=None,
|
35 |
+
quantize_x0=False,
|
36 |
+
eta=0.,
|
37 |
+
mask=None,
|
38 |
+
x0=None,
|
39 |
+
temperature=1.,
|
40 |
+
noise_dropout=0.,
|
41 |
+
score_corrector=None,
|
42 |
+
corrector_kwargs=None,
|
43 |
+
verbose=True,
|
44 |
+
x_T=None,
|
45 |
+
log_every_t=100,
|
46 |
+
unconditional_guidance_scale=1.,
|
47 |
+
unconditional_conditioning=None,
|
48 |
+
# this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
|
49 |
+
**kwargs
|
50 |
+
):
|
51 |
+
if conditioning is not None:
|
52 |
+
if isinstance(conditioning, dict):
|
53 |
+
cbs = conditioning[list(conditioning.keys())[0]].shape[0]
|
54 |
+
if cbs != batch_size:
|
55 |
+
print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
|
56 |
+
else:
|
57 |
+
if conditioning.shape[0] != batch_size:
|
58 |
+
print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
|
59 |
+
|
60 |
+
# sampling
|
61 |
+
C, H, W = shape
|
62 |
+
size = (batch_size, C, H, W)
|
63 |
+
|
64 |
+
print(f'Data shape for DPM-Solver sampling is {size}, sampling steps {S}')
|
65 |
+
|
66 |
+
device = self.model.betas.device
|
67 |
+
if x_T is None:
|
68 |
+
img = torch.randn(size, device=device)
|
69 |
+
else:
|
70 |
+
img = x_T
|
71 |
+
|
72 |
+
ns = NoiseScheduleVP('discrete', alphas_cumprod=self.alphas_cumprod)
|
73 |
+
|
74 |
+
model_fn = model_wrapper(
|
75 |
+
lambda x, t, c: self.model.apply_model(x, t, c),
|
76 |
+
ns,
|
77 |
+
model_type=MODEL_TYPES[self.model.parameterization],
|
78 |
+
guidance_type="classifier-free",
|
79 |
+
condition=conditioning,
|
80 |
+
unconditional_condition=unconditional_conditioning,
|
81 |
+
guidance_scale=unconditional_guidance_scale,
|
82 |
+
)
|
83 |
+
|
84 |
+
dpm_solver = DPM_Solver(model_fn, ns, predict_x0=True, thresholding=False)
|
85 |
+
x = dpm_solver.sample(img, steps=S, skip_type="time_uniform", method="multistep", order=2, lower_order_final=True)
|
86 |
+
|
87 |
+
return x.to(device), None
|
ldm/models/diffusion/plms.py
ADDED
@@ -0,0 +1,243 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
|
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|
|
|
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|
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|
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|
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|
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|
|
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|
|
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|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
"""SAMPLING ONLY."""
|
2 |
+
|
3 |
+
import torch
|
4 |
+
import numpy as np
|
5 |
+
from tqdm import tqdm
|
6 |
+
from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like
|
7 |
+
|
8 |
+
|
9 |
+
class PLMSSampler(object):
|
10 |
+
def __init__(self, model, schedule="linear", **kwargs):
|
11 |
+
super().__init__()
|
12 |
+
self.model = model
|
13 |
+
self.ddpm_num_timesteps = model.num_timesteps
|
14 |
+
self.schedule = schedule
|
15 |
+
|
16 |
+
def register_buffer(self, name, attr):
|
17 |
+
if type(attr) == torch.Tensor:
|
18 |
+
if attr.device != torch.device("cuda"):
|
19 |
+
attr = attr.to(torch.device("cuda"))
|
20 |
+
setattr(self, name, attr)
|
21 |
+
|
22 |
+
def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True):
|
23 |
+
if ddim_eta != 0:
|
24 |
+
raise ValueError('ddim_eta must be 0 for PLMS')
|
25 |
+
self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps,
|
26 |
+
num_ddpm_timesteps=self.ddpm_num_timesteps, verbose=verbose)
|
27 |
+
alphas_cumprod = self.model.alphas_cumprod
|
28 |
+
assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep'
|
29 |
+
to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device)
|
30 |
+
|
31 |
+
self.register_buffer('betas', to_torch(self.model.betas))
|
32 |
+
self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
|
33 |
+
self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev))
|
34 |
+
|
35 |
+
# calculations for diffusion q(x_t | x_{t-1}) and others
|
36 |
+
self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu())))
|
37 |
+
self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu())))
|
38 |
+
self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu())))
|
39 |
+
self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu())))
|
40 |
+
self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1)))
|
41 |
+
|
42 |
+
# ddim sampling parameters
|
43 |
+
ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(),
|
44 |
+
ddim_timesteps=self.ddim_timesteps,
|
45 |
+
eta=ddim_eta, verbose=verbose)
|
46 |
+
self.register_buffer('ddim_sigmas', ddim_sigmas)
|
47 |
+
self.register_buffer('ddim_alphas', ddim_alphas)
|
48 |
+
self.register_buffer('ddim_alphas_prev', ddim_alphas_prev)
|
49 |
+
self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas))
|
50 |
+
sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt(
|
51 |
+
(1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * (
|
52 |
+
1 - self.alphas_cumprod / self.alphas_cumprod_prev))
|
53 |
+
self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps)
|
54 |
+
|
55 |
+
@torch.no_grad()
|
56 |
+
def sample(self,
|
57 |
+
S,
|
58 |
+
batch_size,
|
59 |
+
shape,
|
60 |
+
conditioning=None,
|
61 |
+
callback=None,
|
62 |
+
normals_sequence=None,
|
63 |
+
img_callback=None,
|
64 |
+
quantize_x0=False,
|
65 |
+
eta=0.,
|
66 |
+
mask=None,
|
67 |
+
x0=None,
|
68 |
+
temperature=1.,
|
69 |
+
noise_dropout=0.,
|
70 |
+
score_corrector=None,
|
71 |
+
corrector_kwargs=None,
|
72 |
+
verbose=True,
|
73 |
+
x_T=None,
|
74 |
+
log_every_t=100,
|
75 |
+
unconditional_guidance_scale=1.,
|
76 |
+
unconditional_conditioning=None,
|
77 |
+
features_adapter=None,
|
78 |
+
cond_tau=0.4,
|
79 |
+
# this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
|
80 |
+
**kwargs
|
81 |
+
):
|
82 |
+
# print('*'*20,x_T)
|
83 |
+
# exit(0)
|
84 |
+
if conditioning is not None:
|
85 |
+
if isinstance(conditioning, dict):
|
86 |
+
cbs = conditioning[list(conditioning.keys())[0]].shape[0]
|
87 |
+
if cbs != batch_size:
|
88 |
+
print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
|
89 |
+
else:
|
90 |
+
if conditioning.shape[0] != batch_size:
|
91 |
+
print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
|
92 |
+
|
93 |
+
self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)
|
94 |
+
C, H, W = shape
|
95 |
+
size = (batch_size, C, H, W)
|
96 |
+
print(f'Data shape for PLMS sampling is {size}')
|
97 |
+
|
98 |
+
samples, intermediates = self.plms_sampling(conditioning, size,
|
99 |
+
callback=callback,
|
100 |
+
img_callback=img_callback,
|
101 |
+
quantize_denoised=quantize_x0,
|
102 |
+
mask=mask, x0=x0,
|
103 |
+
ddim_use_original_steps=False,
|
104 |
+
noise_dropout=noise_dropout,
|
105 |
+
temperature=temperature,
|
106 |
+
score_corrector=score_corrector,
|
107 |
+
corrector_kwargs=corrector_kwargs,
|
108 |
+
x_T=x_T,
|
109 |
+
log_every_t=log_every_t,
|
110 |
+
unconditional_guidance_scale=unconditional_guidance_scale,
|
111 |
+
unconditional_conditioning=unconditional_conditioning,
|
112 |
+
features_adapter=features_adapter,
|
113 |
+
cond_tau=cond_tau
|
114 |
+
)
|
115 |
+
return samples, intermediates
|
116 |
+
|
117 |
+
@torch.no_grad()
|
118 |
+
def plms_sampling(self, cond, shape,
|
119 |
+
x_T=None, ddim_use_original_steps=False,
|
120 |
+
callback=None, timesteps=None, quantize_denoised=False,
|
121 |
+
mask=None, x0=None, img_callback=None, log_every_t=100,
|
122 |
+
temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
|
123 |
+
unconditional_guidance_scale=1., unconditional_conditioning=None, features_adapter=None,
|
124 |
+
cond_tau=0.4):
|
125 |
+
device = self.model.betas.device
|
126 |
+
b = shape[0]
|
127 |
+
if x_T is None:
|
128 |
+
img = torch.randn(shape, device=device)
|
129 |
+
else:
|
130 |
+
img = x_T
|
131 |
+
if timesteps is None:
|
132 |
+
timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps
|
133 |
+
elif timesteps is not None and not ddim_use_original_steps:
|
134 |
+
subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1
|
135 |
+
timesteps = self.ddim_timesteps[:subset_end]
|
136 |
+
|
137 |
+
intermediates = {'x_inter': [img], 'pred_x0': [img]}
|
138 |
+
time_range = list(reversed(range(0, timesteps))) if ddim_use_original_steps else np.flip(timesteps)
|
139 |
+
total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0]
|
140 |
+
print(f"Running PLMS Sampling with {total_steps} timesteps")
|
141 |
+
|
142 |
+
iterator = tqdm(time_range, desc='PLMS Sampler', total=total_steps)
|
143 |
+
old_eps = []
|
144 |
+
|
145 |
+
for i, step in enumerate(iterator):
|
146 |
+
index = total_steps - i - 1
|
147 |
+
ts = torch.full((b,), step, device=device, dtype=torch.long)
|
148 |
+
ts_next = torch.full((b,), time_range[min(i + 1, len(time_range) - 1)], device=device, dtype=torch.long)
|
149 |
+
|
150 |
+
if mask is not None: # and index>=10:
|
151 |
+
assert x0 is not None
|
152 |
+
img_orig = self.model.q_sample(x0, ts) # TODO: deterministic forward pass?
|
153 |
+
img = img_orig * mask + (1. - mask) * img
|
154 |
+
|
155 |
+
outs = self.p_sample_plms(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps,
|
156 |
+
quantize_denoised=quantize_denoised, temperature=temperature,
|
157 |
+
noise_dropout=noise_dropout, score_corrector=score_corrector,
|
158 |
+
corrector_kwargs=corrector_kwargs,
|
159 |
+
unconditional_guidance_scale=unconditional_guidance_scale,
|
160 |
+
unconditional_conditioning=unconditional_conditioning,
|
161 |
+
old_eps=old_eps, t_next=ts_next,
|
162 |
+
features_adapter=None if index < int(
|
163 |
+
(1 - cond_tau) * total_steps) else features_adapter)
|
164 |
+
|
165 |
+
img, pred_x0, e_t = outs
|
166 |
+
old_eps.append(e_t)
|
167 |
+
if len(old_eps) >= 4:
|
168 |
+
old_eps.pop(0)
|
169 |
+
if callback: callback(i)
|
170 |
+
if img_callback: img_callback(pred_x0, i)
|
171 |
+
|
172 |
+
if index % log_every_t == 0 or index == total_steps - 1:
|
173 |
+
intermediates['x_inter'].append(img)
|
174 |
+
intermediates['pred_x0'].append(pred_x0)
|
175 |
+
|
176 |
+
return img, intermediates
|
177 |
+
|
178 |
+
@torch.no_grad()
|
179 |
+
def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
|
180 |
+
temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
|
181 |
+
unconditional_guidance_scale=1., unconditional_conditioning=None, old_eps=None, t_next=None,
|
182 |
+
features_adapter=None):
|
183 |
+
b, *_, device = *x.shape, x.device
|
184 |
+
|
185 |
+
def get_model_output(x, t):
|
186 |
+
if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
|
187 |
+
e_t = self.model.apply_model(x, t, c, features_adapter=features_adapter)
|
188 |
+
else:
|
189 |
+
x_in = torch.cat([x] * 2)
|
190 |
+
t_in = torch.cat([t] * 2)
|
191 |
+
c_in = torch.cat([unconditional_conditioning, c])
|
192 |
+
e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in, features_adapter=features_adapter).chunk(2)
|
193 |
+
e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
|
194 |
+
|
195 |
+
if score_corrector is not None:
|
196 |
+
assert self.model.parameterization == "eps"
|
197 |
+
e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
|
198 |
+
|
199 |
+
return e_t
|
200 |
+
|
201 |
+
alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
|
202 |
+
alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
|
203 |
+
sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
|
204 |
+
sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
|
205 |
+
|
206 |
+
def get_x_prev_and_pred_x0(e_t, index):
|
207 |
+
# select parameters corresponding to the currently considered timestep
|
208 |
+
a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
|
209 |
+
a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
|
210 |
+
sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
|
211 |
+
sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index], device=device)
|
212 |
+
|
213 |
+
# current prediction for x_0
|
214 |
+
pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
|
215 |
+
if quantize_denoised:
|
216 |
+
pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
|
217 |
+
# direction pointing to x_t
|
218 |
+
dir_xt = (1. - a_prev - sigma_t ** 2).sqrt() * e_t
|
219 |
+
noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
|
220 |
+
if noise_dropout > 0.:
|
221 |
+
noise = torch.nn.functional.dropout(noise, p=noise_dropout)
|
222 |
+
x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
|
223 |
+
return x_prev, pred_x0
|
224 |
+
|
225 |
+
e_t = get_model_output(x, t)
|
226 |
+
if len(old_eps) == 0:
|
227 |
+
# Pseudo Improved Euler (2nd order)
|
228 |
+
x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t, index)
|
229 |
+
e_t_next = get_model_output(x_prev, t_next)
|
230 |
+
e_t_prime = (e_t + e_t_next) / 2
|
231 |
+
elif len(old_eps) == 1:
|
232 |
+
# 2nd order Pseudo Linear Multistep (Adams-Bashforth)
|
233 |
+
e_t_prime = (3 * e_t - old_eps[-1]) / 2
|
234 |
+
elif len(old_eps) == 2:
|
235 |
+
# 3nd order Pseudo Linear Multistep (Adams-Bashforth)
|
236 |
+
e_t_prime = (23 * e_t - 16 * old_eps[-1] + 5 * old_eps[-2]) / 12
|
237 |
+
elif len(old_eps) >= 3:
|
238 |
+
# 4nd order Pseudo Linear Multistep (Adams-Bashforth)
|
239 |
+
e_t_prime = (55 * e_t - 59 * old_eps[-1] + 37 * old_eps[-2] - 9 * old_eps[-3]) / 24
|
240 |
+
|
241 |
+
x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t_prime, index)
|
242 |
+
|
243 |
+
return x_prev, pred_x0, e_t
|
ldm/modules/__pycache__/attention.cpython-38.pyc
ADDED
Binary file (10.6 kB). View file
|
|
ldm/modules/__pycache__/ema.cpython-38.pyc
ADDED
Binary file (3.23 kB). View file
|
|
ldm/modules/attention.py
ADDED
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|
1 |
+
from inspect import isfunction
|
2 |
+
import math
|
3 |
+
import torch
|
4 |
+
import torch.nn.functional as F
|
5 |
+
from torch import nn, einsum
|
6 |
+
from einops import rearrange, repeat
|
7 |
+
from typing import Optional, Any
|
8 |
+
|
9 |
+
from ldm.modules.diffusionmodules.util import checkpoint
|
10 |
+
|
11 |
+
|
12 |
+
try:
|
13 |
+
import xformers
|
14 |
+
import xformers.ops
|
15 |
+
XFORMERS_IS_AVAILBLE = True
|
16 |
+
except:
|
17 |
+
XFORMERS_IS_AVAILBLE = False
|
18 |
+
|
19 |
+
# CrossAttn precision handling
|
20 |
+
import os
|
21 |
+
_ATTN_PRECISION = os.environ.get("ATTN_PRECISION", "fp32")
|
22 |
+
|
23 |
+
if os.environ.get("DISABLE_XFORMERS", "false").lower() == 'true':
|
24 |
+
XFORMERS_IS_AVAILBLE = False
|
25 |
+
|
26 |
+
|
27 |
+
def exists(val):
|
28 |
+
return val is not None
|
29 |
+
|
30 |
+
|
31 |
+
def uniq(arr):
|
32 |
+
return{el: True for el in arr}.keys()
|
33 |
+
|
34 |
+
|
35 |
+
def default(val, d):
|
36 |
+
if exists(val):
|
37 |
+
return val
|
38 |
+
return d() if isfunction(d) else d
|
39 |
+
|
40 |
+
|
41 |
+
def max_neg_value(t):
|
42 |
+
return -torch.finfo(t.dtype).max
|
43 |
+
|
44 |
+
|
45 |
+
def init_(tensor):
|
46 |
+
dim = tensor.shape[-1]
|
47 |
+
std = 1 / math.sqrt(dim)
|
48 |
+
tensor.uniform_(-std, std)
|
49 |
+
return tensor
|
50 |
+
|
51 |
+
|
52 |
+
# feedforward
|
53 |
+
class GEGLU(nn.Module):
|
54 |
+
def __init__(self, dim_in, dim_out):
|
55 |
+
super().__init__()
|
56 |
+
self.proj = nn.Linear(dim_in, dim_out * 2)
|
57 |
+
|
58 |
+
def forward(self, x):
|
59 |
+
x, gate = self.proj(x).chunk(2, dim=-1)
|
60 |
+
return x * F.gelu(gate)
|
61 |
+
|
62 |
+
|
63 |
+
class FeedForward(nn.Module):
|
64 |
+
def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.):
|
65 |
+
super().__init__()
|
66 |
+
inner_dim = int(dim * mult)
|
67 |
+
dim_out = default(dim_out, dim)
|
68 |
+
project_in = nn.Sequential(
|
69 |
+
nn.Linear(dim, inner_dim),
|
70 |
+
nn.GELU()
|
71 |
+
) if not glu else GEGLU(dim, inner_dim)
|
72 |
+
|
73 |
+
self.net = nn.Sequential(
|
74 |
+
project_in,
|
75 |
+
nn.Dropout(dropout),
|
76 |
+
nn.Linear(inner_dim, dim_out)
|
77 |
+
)
|
78 |
+
|
79 |
+
def forward(self, x):
|
80 |
+
return self.net(x)
|
81 |
+
|
82 |
+
|
83 |
+
def zero_module(module):
|
84 |
+
"""
|
85 |
+
Zero out the parameters of a module and return it.
|
86 |
+
"""
|
87 |
+
for p in module.parameters():
|
88 |
+
p.detach().zero_()
|
89 |
+
return module
|
90 |
+
|
91 |
+
|
92 |
+
def Normalize(in_channels):
|
93 |
+
return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
|
94 |
+
|
95 |
+
|
96 |
+
class SpatialSelfAttention(nn.Module):
|
97 |
+
def __init__(self, in_channels):
|
98 |
+
super().__init__()
|
99 |
+
self.in_channels = in_channels
|
100 |
+
|
101 |
+
self.norm = Normalize(in_channels)
|
102 |
+
self.q = torch.nn.Conv2d(in_channels,
|
103 |
+
in_channels,
|
104 |
+
kernel_size=1,
|
105 |
+
stride=1,
|
106 |
+
padding=0)
|
107 |
+
self.k = torch.nn.Conv2d(in_channels,
|
108 |
+
in_channels,
|
109 |
+
kernel_size=1,
|
110 |
+
stride=1,
|
111 |
+
padding=0)
|
112 |
+
self.v = torch.nn.Conv2d(in_channels,
|
113 |
+
in_channels,
|
114 |
+
kernel_size=1,
|
115 |
+
stride=1,
|
116 |
+
padding=0)
|
117 |
+
self.proj_out = torch.nn.Conv2d(in_channels,
|
118 |
+
in_channels,
|
119 |
+
kernel_size=1,
|
120 |
+
stride=1,
|
121 |
+
padding=0)
|
122 |
+
|
123 |
+
def forward(self, x):
|
124 |
+
h_ = x
|
125 |
+
h_ = self.norm(h_)
|
126 |
+
q = self.q(h_)
|
127 |
+
k = self.k(h_)
|
128 |
+
v = self.v(h_)
|
129 |
+
|
130 |
+
# compute attention
|
131 |
+
b,c,h,w = q.shape
|
132 |
+
q = rearrange(q, 'b c h w -> b (h w) c')
|
133 |
+
k = rearrange(k, 'b c h w -> b c (h w)')
|
134 |
+
w_ = torch.einsum('bij,bjk->bik', q, k)
|
135 |
+
|
136 |
+
w_ = w_ * (int(c)**(-0.5))
|
137 |
+
w_ = torch.nn.functional.softmax(w_, dim=2)
|
138 |
+
|
139 |
+
# attend to values
|
140 |
+
v = rearrange(v, 'b c h w -> b c (h w)')
|
141 |
+
w_ = rearrange(w_, 'b i j -> b j i')
|
142 |
+
h_ = torch.einsum('bij,bjk->bik', v, w_)
|
143 |
+
h_ = rearrange(h_, 'b c (h w) -> b c h w', h=h)
|
144 |
+
h_ = self.proj_out(h_)
|
145 |
+
|
146 |
+
return x+h_
|
147 |
+
|
148 |
+
|
149 |
+
class CrossAttention(nn.Module):
|
150 |
+
def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.):
|
151 |
+
super().__init__()
|
152 |
+
inner_dim = dim_head * heads
|
153 |
+
context_dim = default(context_dim, query_dim)
|
154 |
+
|
155 |
+
self.scale = dim_head ** -0.5
|
156 |
+
self.heads = heads
|
157 |
+
|
158 |
+
self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
|
159 |
+
self.to_k = nn.Linear(context_dim, inner_dim, bias=False)
|
160 |
+
self.to_v = nn.Linear(context_dim, inner_dim, bias=False)
|
161 |
+
|
162 |
+
self.to_out = nn.Sequential(
|
163 |
+
nn.Linear(inner_dim, query_dim),
|
164 |
+
nn.Dropout(dropout)
|
165 |
+
)
|
166 |
+
|
167 |
+
def forward(self, x, context=None, mask=None):
|
168 |
+
h = self.heads
|
169 |
+
|
170 |
+
q = self.to_q(x)
|
171 |
+
context = default(context, x)
|
172 |
+
k = self.to_k(context)
|
173 |
+
v = self.to_v(context)
|
174 |
+
|
175 |
+
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
|
176 |
+
|
177 |
+
# force cast to fp32 to avoid overflowing
|
178 |
+
if _ATTN_PRECISION =="fp32":
|
179 |
+
with torch.autocast(enabled=False, device_type = 'cuda'):
|
180 |
+
q, k = q.float(), k.float()
|
181 |
+
sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
|
182 |
+
else:
|
183 |
+
sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
|
184 |
+
|
185 |
+
del q, k
|
186 |
+
|
187 |
+
if exists(mask):
|
188 |
+
mask = rearrange(mask, 'b ... -> b (...)')
|
189 |
+
max_neg_value = -torch.finfo(sim.dtype).max
|
190 |
+
mask = repeat(mask, 'b j -> (b h) () j', h=h)
|
191 |
+
sim.masked_fill_(~mask, max_neg_value)
|
192 |
+
|
193 |
+
# attention, what we cannot get enough of
|
194 |
+
sim = sim.softmax(dim=-1)
|
195 |
+
|
196 |
+
out = einsum('b i j, b j d -> b i d', sim, v)
|
197 |
+
out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
|
198 |
+
return self.to_out(out)
|
199 |
+
|
200 |
+
|
201 |
+
class MemoryEfficientCrossAttention(nn.Module):
|
202 |
+
# https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223
|
203 |
+
def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.0):
|
204 |
+
super().__init__()
|
205 |
+
print(f"Setting up {self.__class__.__name__}. Query dim is {query_dim}, context_dim is {context_dim} and using "
|
206 |
+
f"{heads} heads.")
|
207 |
+
inner_dim = dim_head * heads
|
208 |
+
context_dim = default(context_dim, query_dim)
|
209 |
+
|
210 |
+
self.heads = heads
|
211 |
+
self.dim_head = dim_head
|
212 |
+
|
213 |
+
self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
|
214 |
+
self.to_k = nn.Linear(context_dim, inner_dim, bias=False)
|
215 |
+
self.to_v = nn.Linear(context_dim, inner_dim, bias=False)
|
216 |
+
|
217 |
+
self.to_out = nn.Sequential(nn.Linear(inner_dim, query_dim), nn.Dropout(dropout))
|
218 |
+
self.attention_op: Optional[Any] = None
|
219 |
+
|
220 |
+
def forward(self, x, context=None, mask=None):
|
221 |
+
q = self.to_q(x)
|
222 |
+
context = default(context, x)
|
223 |
+
k = self.to_k(context)
|
224 |
+
v = self.to_v(context)
|
225 |
+
|
226 |
+
b, _, _ = q.shape
|
227 |
+
q, k, v = map(
|
228 |
+
lambda t: t.unsqueeze(3)
|
229 |
+
.reshape(b, t.shape[1], self.heads, self.dim_head)
|
230 |
+
.permute(0, 2, 1, 3)
|
231 |
+
.reshape(b * self.heads, t.shape[1], self.dim_head)
|
232 |
+
.contiguous(),
|
233 |
+
(q, k, v),
|
234 |
+
)
|
235 |
+
|
236 |
+
# actually compute the attention, what we cannot get enough of
|
237 |
+
out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=self.attention_op)
|
238 |
+
|
239 |
+
if exists(mask):
|
240 |
+
raise NotImplementedError
|
241 |
+
out = (
|
242 |
+
out.unsqueeze(0)
|
243 |
+
.reshape(b, self.heads, out.shape[1], self.dim_head)
|
244 |
+
.permute(0, 2, 1, 3)
|
245 |
+
.reshape(b, out.shape[1], self.heads * self.dim_head)
|
246 |
+
)
|
247 |
+
return self.to_out(out)
|
248 |
+
|
249 |
+
|
250 |
+
class BasicTransformerBlock(nn.Module):
|
251 |
+
ATTENTION_MODES = {
|
252 |
+
"softmax": CrossAttention, # vanilla attention
|
253 |
+
"softmax-xformers": MemoryEfficientCrossAttention
|
254 |
+
}
|
255 |
+
def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True,
|
256 |
+
disable_self_attn=False):
|
257 |
+
super().__init__()
|
258 |
+
attn_mode = "softmax-xformers" if XFORMERS_IS_AVAILBLE else "softmax"
|
259 |
+
assert attn_mode in self.ATTENTION_MODES
|
260 |
+
attn_cls = self.ATTENTION_MODES[attn_mode]
|
261 |
+
self.disable_self_attn = disable_self_attn
|
262 |
+
self.attn1 = attn_cls(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout,
|
263 |
+
context_dim=context_dim if self.disable_self_attn else None) # is a self-attention if not self.disable_self_attn
|
264 |
+
self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff)
|
265 |
+
self.attn2 = attn_cls(query_dim=dim, context_dim=context_dim,
|
266 |
+
heads=n_heads, dim_head=d_head, dropout=dropout) # is self-attn if context is none
|
267 |
+
self.norm1 = nn.LayerNorm(dim)
|
268 |
+
self.norm2 = nn.LayerNorm(dim)
|
269 |
+
self.norm3 = nn.LayerNorm(dim)
|
270 |
+
self.checkpoint = checkpoint
|
271 |
+
|
272 |
+
def forward(self, x, context=None):
|
273 |
+
return checkpoint(self._forward, (x, context), self.parameters(), self.checkpoint)
|
274 |
+
|
275 |
+
def _forward(self, x, context=None):
|
276 |
+
x = self.attn1(self.norm1(x), context=context if self.disable_self_attn else None) + x
|
277 |
+
x = self.attn2(self.norm2(x), context=context) + x
|
278 |
+
x = self.ff(self.norm3(x)) + x
|
279 |
+
return x
|
280 |
+
|
281 |
+
|
282 |
+
class SpatialTransformer(nn.Module):
|
283 |
+
"""
|
284 |
+
Transformer block for image-like data.
|
285 |
+
First, project the input (aka embedding)
|
286 |
+
and reshape to b, t, d.
|
287 |
+
Then apply standard transformer action.
|
288 |
+
Finally, reshape to image
|
289 |
+
NEW: use_linear for more efficiency instead of the 1x1 convs
|
290 |
+
"""
|
291 |
+
def __init__(self, in_channels, n_heads, d_head,
|
292 |
+
depth=1, dropout=0., context_dim=None,
|
293 |
+
disable_self_attn=False, use_linear=False,
|
294 |
+
use_checkpoint=True):
|
295 |
+
super().__init__()
|
296 |
+
if exists(context_dim) and not isinstance(context_dim, list):
|
297 |
+
context_dim = [context_dim]
|
298 |
+
self.in_channels = in_channels
|
299 |
+
inner_dim = n_heads * d_head
|
300 |
+
self.norm = Normalize(in_channels)
|
301 |
+
if not use_linear:
|
302 |
+
self.proj_in = nn.Conv2d(in_channels,
|
303 |
+
inner_dim,
|
304 |
+
kernel_size=1,
|
305 |
+
stride=1,
|
306 |
+
padding=0)
|
307 |
+
else:
|
308 |
+
self.proj_in = nn.Linear(in_channels, inner_dim)
|
309 |
+
|
310 |
+
self.transformer_blocks = nn.ModuleList(
|
311 |
+
[BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim[d],
|
312 |
+
disable_self_attn=disable_self_attn, checkpoint=use_checkpoint)
|
313 |
+
for d in range(depth)]
|
314 |
+
)
|
315 |
+
if not use_linear:
|
316 |
+
self.proj_out = zero_module(nn.Conv2d(inner_dim,
|
317 |
+
in_channels,
|
318 |
+
kernel_size=1,
|
319 |
+
stride=1,
|
320 |
+
padding=0))
|
321 |
+
else:
|
322 |
+
self.proj_out = zero_module(nn.Linear(in_channels, inner_dim))
|
323 |
+
self.use_linear = use_linear
|
324 |
+
|
325 |
+
def forward(self, x, context=None):
|
326 |
+
# note: if no context is given, cross-attention defaults to self-attention
|
327 |
+
if not isinstance(context, list):
|
328 |
+
context = [context]
|
329 |
+
b, c, h, w = x.shape
|
330 |
+
x_in = x
|
331 |
+
x = self.norm(x)
|
332 |
+
if not self.use_linear:
|
333 |
+
x = self.proj_in(x)
|
334 |
+
x = rearrange(x, 'b c h w -> b (h w) c').contiguous()
|
335 |
+
if self.use_linear:
|
336 |
+
x = self.proj_in(x)
|
337 |
+
for i, block in enumerate(self.transformer_blocks):
|
338 |
+
x = block(x, context=context[i])
|
339 |
+
if self.use_linear:
|
340 |
+
x = self.proj_out(x)
|
341 |
+
x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w).contiguous()
|
342 |
+
if not self.use_linear:
|
343 |
+
x = self.proj_out(x)
|
344 |
+
return x + x_in
|
ldm/modules/diffusionmodules/__init__.py
ADDED
File without changes
|
ldm/modules/diffusionmodules/__pycache__/__init__.cpython-38.pyc
ADDED
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ldm/modules/diffusionmodules/__pycache__/model.cpython-38.pyc
ADDED
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ldm/modules/diffusionmodules/__pycache__/openaimodel.cpython-38.pyc
ADDED
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ldm/modules/diffusionmodules/__pycache__/util.cpython-38.pyc
ADDED
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|
ldm/modules/diffusionmodules/model.py
ADDED
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|
1 |
+
# pytorch_diffusion + derived encoder decoder
|
2 |
+
import math
|
3 |
+
import torch
|
4 |
+
import torch.nn as nn
|
5 |
+
import numpy as np
|
6 |
+
from einops import rearrange
|
7 |
+
from typing import Optional, Any
|
8 |
+
|
9 |
+
from ldm.modules.attention import MemoryEfficientCrossAttention
|
10 |
+
|
11 |
+
try:
|
12 |
+
import xformers
|
13 |
+
import xformers.ops
|
14 |
+
XFORMERS_IS_AVAILBLE = True
|
15 |
+
except:
|
16 |
+
XFORMERS_IS_AVAILBLE = False
|
17 |
+
print("No module 'xformers'. Proceeding without it.")
|
18 |
+
|
19 |
+
|
20 |
+
def get_timestep_embedding(timesteps, embedding_dim):
|
21 |
+
"""
|
22 |
+
This matches the implementation in Denoising Diffusion Probabilistic Models:
|
23 |
+
From Fairseq.
|
24 |
+
Build sinusoidal embeddings.
|
25 |
+
This matches the implementation in tensor2tensor, but differs slightly
|
26 |
+
from the description in Section 3.5 of "Attention Is All You Need".
|
27 |
+
"""
|
28 |
+
assert len(timesteps.shape) == 1
|
29 |
+
|
30 |
+
half_dim = embedding_dim // 2
|
31 |
+
emb = math.log(10000) / (half_dim - 1)
|
32 |
+
emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb)
|
33 |
+
emb = emb.to(device=timesteps.device)
|
34 |
+
emb = timesteps.float()[:, None] * emb[None, :]
|
35 |
+
emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
|
36 |
+
if embedding_dim % 2 == 1: # zero pad
|
37 |
+
emb = torch.nn.functional.pad(emb, (0,1,0,0))
|
38 |
+
return emb
|
39 |
+
|
40 |
+
|
41 |
+
def nonlinearity(x):
|
42 |
+
# swish
|
43 |
+
return x*torch.sigmoid(x)
|
44 |
+
|
45 |
+
|
46 |
+
def Normalize(in_channels, num_groups=32):
|
47 |
+
return torch.nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True)
|
48 |
+
|
49 |
+
|
50 |
+
class Upsample(nn.Module):
|
51 |
+
def __init__(self, in_channels, with_conv):
|
52 |
+
super().__init__()
|
53 |
+
self.with_conv = with_conv
|
54 |
+
if self.with_conv:
|
55 |
+
self.conv = torch.nn.Conv2d(in_channels,
|
56 |
+
in_channels,
|
57 |
+
kernel_size=3,
|
58 |
+
stride=1,
|
59 |
+
padding=1)
|
60 |
+
|
61 |
+
def forward(self, x):
|
62 |
+
x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest")
|
63 |
+
if self.with_conv:
|
64 |
+
x = self.conv(x)
|
65 |
+
return x
|
66 |
+
|
67 |
+
|
68 |
+
class Downsample(nn.Module):
|
69 |
+
def __init__(self, in_channels, with_conv):
|
70 |
+
super().__init__()
|
71 |
+
self.with_conv = with_conv
|
72 |
+
if self.with_conv:
|
73 |
+
# no asymmetric padding in torch conv, must do it ourselves
|
74 |
+
self.conv = torch.nn.Conv2d(in_channels,
|
75 |
+
in_channels,
|
76 |
+
kernel_size=3,
|
77 |
+
stride=2,
|
78 |
+
padding=0)
|
79 |
+
|
80 |
+
def forward(self, x):
|
81 |
+
if self.with_conv:
|
82 |
+
pad = (0,1,0,1)
|
83 |
+
x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
|
84 |
+
x = self.conv(x)
|
85 |
+
else:
|
86 |
+
x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2)
|
87 |
+
return x
|
88 |
+
|
89 |
+
|
90 |
+
class ResnetBlock(nn.Module):
|
91 |
+
def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False,
|
92 |
+
dropout, temb_channels=512):
|
93 |
+
super().__init__()
|
94 |
+
self.in_channels = in_channels
|
95 |
+
out_channels = in_channels if out_channels is None else out_channels
|
96 |
+
self.out_channels = out_channels
|
97 |
+
self.use_conv_shortcut = conv_shortcut
|
98 |
+
|
99 |
+
self.norm1 = Normalize(in_channels)
|
100 |
+
self.conv1 = torch.nn.Conv2d(in_channels,
|
101 |
+
out_channels,
|
102 |
+
kernel_size=3,
|
103 |
+
stride=1,
|
104 |
+
padding=1)
|
105 |
+
if temb_channels > 0:
|
106 |
+
self.temb_proj = torch.nn.Linear(temb_channels,
|
107 |
+
out_channels)
|
108 |
+
self.norm2 = Normalize(out_channels)
|
109 |
+
self.dropout = torch.nn.Dropout(dropout)
|
110 |
+
self.conv2 = torch.nn.Conv2d(out_channels,
|
111 |
+
out_channels,
|
112 |
+
kernel_size=3,
|
113 |
+
stride=1,
|
114 |
+
padding=1)
|
115 |
+
if self.in_channels != self.out_channels:
|
116 |
+
if self.use_conv_shortcut:
|
117 |
+
self.conv_shortcut = torch.nn.Conv2d(in_channels,
|
118 |
+
out_channels,
|
119 |
+
kernel_size=3,
|
120 |
+
stride=1,
|
121 |
+
padding=1)
|
122 |
+
else:
|
123 |
+
self.nin_shortcut = torch.nn.Conv2d(in_channels,
|
124 |
+
out_channels,
|
125 |
+
kernel_size=1,
|
126 |
+
stride=1,
|
127 |
+
padding=0)
|
128 |
+
|
129 |
+
def forward(self, x, temb):
|
130 |
+
h = x
|
131 |
+
h = self.norm1(h)
|
132 |
+
h = nonlinearity(h)
|
133 |
+
h = self.conv1(h)
|
134 |
+
|
135 |
+
if temb is not None:
|
136 |
+
h = h + self.temb_proj(nonlinearity(temb))[:,:,None,None]
|
137 |
+
|
138 |
+
h = self.norm2(h)
|
139 |
+
h = nonlinearity(h)
|
140 |
+
h = self.dropout(h)
|
141 |
+
h = self.conv2(h)
|
142 |
+
|
143 |
+
if self.in_channels != self.out_channels:
|
144 |
+
if self.use_conv_shortcut:
|
145 |
+
x = self.conv_shortcut(x)
|
146 |
+
else:
|
147 |
+
x = self.nin_shortcut(x)
|
148 |
+
|
149 |
+
return x+h
|
150 |
+
|
151 |
+
|
152 |
+
class AttnBlock(nn.Module):
|
153 |
+
def __init__(self, in_channels):
|
154 |
+
super().__init__()
|
155 |
+
self.in_channels = in_channels
|
156 |
+
|
157 |
+
self.norm = Normalize(in_channels)
|
158 |
+
self.q = torch.nn.Conv2d(in_channels,
|
159 |
+
in_channels,
|
160 |
+
kernel_size=1,
|
161 |
+
stride=1,
|
162 |
+
padding=0)
|
163 |
+
self.k = torch.nn.Conv2d(in_channels,
|
164 |
+
in_channels,
|
165 |
+
kernel_size=1,
|
166 |
+
stride=1,
|
167 |
+
padding=0)
|
168 |
+
self.v = torch.nn.Conv2d(in_channels,
|
169 |
+
in_channels,
|
170 |
+
kernel_size=1,
|
171 |
+
stride=1,
|
172 |
+
padding=0)
|
173 |
+
self.proj_out = torch.nn.Conv2d(in_channels,
|
174 |
+
in_channels,
|
175 |
+
kernel_size=1,
|
176 |
+
stride=1,
|
177 |
+
padding=0)
|
178 |
+
|
179 |
+
def forward(self, x):
|
180 |
+
h_ = x
|
181 |
+
h_ = self.norm(h_)
|
182 |
+
q = self.q(h_)
|
183 |
+
k = self.k(h_)
|
184 |
+
v = self.v(h_)
|
185 |
+
|
186 |
+
# compute attention
|
187 |
+
b,c,h,w = q.shape
|
188 |
+
q = q.reshape(b,c,h*w)
|
189 |
+
q = q.permute(0,2,1) # b,hw,c
|
190 |
+
k = k.reshape(b,c,h*w) # b,c,hw
|
191 |
+
w_ = torch.bmm(q,k) # b,hw,hw w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
|
192 |
+
w_ = w_ * (int(c)**(-0.5))
|
193 |
+
w_ = torch.nn.functional.softmax(w_, dim=2)
|
194 |
+
|
195 |
+
# attend to values
|
196 |
+
v = v.reshape(b,c,h*w)
|
197 |
+
w_ = w_.permute(0,2,1) # b,hw,hw (first hw of k, second of q)
|
198 |
+
h_ = torch.bmm(v,w_) # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
|
199 |
+
h_ = h_.reshape(b,c,h,w)
|
200 |
+
|
201 |
+
h_ = self.proj_out(h_)
|
202 |
+
|
203 |
+
return x+h_
|
204 |
+
|
205 |
+
class MemoryEfficientAttnBlock(nn.Module):
|
206 |
+
"""
|
207 |
+
Uses xformers efficient implementation,
|
208 |
+
see https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223
|
209 |
+
Note: this is a single-head self-attention operation
|
210 |
+
"""
|
211 |
+
#
|
212 |
+
def __init__(self, in_channels):
|
213 |
+
super().__init__()
|
214 |
+
self.in_channels = in_channels
|
215 |
+
|
216 |
+
self.norm = Normalize(in_channels)
|
217 |
+
self.q = torch.nn.Conv2d(in_channels,
|
218 |
+
in_channels,
|
219 |
+
kernel_size=1,
|
220 |
+
stride=1,
|
221 |
+
padding=0)
|
222 |
+
self.k = torch.nn.Conv2d(in_channels,
|
223 |
+
in_channels,
|
224 |
+
kernel_size=1,
|
225 |
+
stride=1,
|
226 |
+
padding=0)
|
227 |
+
self.v = torch.nn.Conv2d(in_channels,
|
228 |
+
in_channels,
|
229 |
+
kernel_size=1,
|
230 |
+
stride=1,
|
231 |
+
padding=0)
|
232 |
+
self.proj_out = torch.nn.Conv2d(in_channels,
|
233 |
+
in_channels,
|
234 |
+
kernel_size=1,
|
235 |
+
stride=1,
|
236 |
+
padding=0)
|
237 |
+
self.attention_op: Optional[Any] = None
|
238 |
+
|
239 |
+
def forward(self, x):
|
240 |
+
h_ = x
|
241 |
+
h_ = self.norm(h_)
|
242 |
+
q = self.q(h_)
|
243 |
+
k = self.k(h_)
|
244 |
+
v = self.v(h_)
|
245 |
+
|
246 |
+
# compute attention
|
247 |
+
B, C, H, W = q.shape
|
248 |
+
q, k, v = map(lambda x: rearrange(x, 'b c h w -> b (h w) c'), (q, k, v))
|
249 |
+
|
250 |
+
q, k, v = map(
|
251 |
+
lambda t: t.unsqueeze(3)
|
252 |
+
.reshape(B, t.shape[1], 1, C)
|
253 |
+
.permute(0, 2, 1, 3)
|
254 |
+
.reshape(B * 1, t.shape[1], C)
|
255 |
+
.contiguous(),
|
256 |
+
(q, k, v),
|
257 |
+
)
|
258 |
+
out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=self.attention_op)
|
259 |
+
|
260 |
+
out = (
|
261 |
+
out.unsqueeze(0)
|
262 |
+
.reshape(B, 1, out.shape[1], C)
|
263 |
+
.permute(0, 2, 1, 3)
|
264 |
+
.reshape(B, out.shape[1], C)
|
265 |
+
)
|
266 |
+
out = rearrange(out, 'b (h w) c -> b c h w', b=B, h=H, w=W, c=C)
|
267 |
+
out = self.proj_out(out)
|
268 |
+
return x+out
|
269 |
+
|
270 |
+
|
271 |
+
class MemoryEfficientCrossAttentionWrapper(MemoryEfficientCrossAttention):
|
272 |
+
def forward(self, x, context=None, mask=None):
|
273 |
+
b, c, h, w = x.shape
|
274 |
+
x = rearrange(x, 'b c h w -> b (h w) c')
|
275 |
+
out = super().forward(x, context=context, mask=mask)
|
276 |
+
out = rearrange(out, 'b (h w) c -> b c h w', h=h, w=w, c=c)
|
277 |
+
return x + out
|
278 |
+
|
279 |
+
|
280 |
+
def make_attn(in_channels, attn_type="vanilla", attn_kwargs=None):
|
281 |
+
assert attn_type in ["vanilla", "vanilla-xformers", "memory-efficient-cross-attn", "linear", "none"], f'attn_type {attn_type} unknown'
|
282 |
+
if XFORMERS_IS_AVAILBLE and attn_type == "vanilla":
|
283 |
+
attn_type = "vanilla-xformers"
|
284 |
+
print(f"making attention of type '{attn_type}' with {in_channels} in_channels")
|
285 |
+
if attn_type == "vanilla":
|
286 |
+
assert attn_kwargs is None
|
287 |
+
return AttnBlock(in_channels)
|
288 |
+
elif attn_type == "vanilla-xformers":
|
289 |
+
print(f"building MemoryEfficientAttnBlock with {in_channels} in_channels...")
|
290 |
+
return MemoryEfficientAttnBlock(in_channels)
|
291 |
+
elif type == "memory-efficient-cross-attn":
|
292 |
+
attn_kwargs["query_dim"] = in_channels
|
293 |
+
return MemoryEfficientCrossAttentionWrapper(**attn_kwargs)
|
294 |
+
elif attn_type == "none":
|
295 |
+
return nn.Identity(in_channels)
|
296 |
+
else:
|
297 |
+
raise NotImplementedError()
|
298 |
+
|
299 |
+
|
300 |
+
class Model(nn.Module):
|
301 |
+
def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
|
302 |
+
attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
|
303 |
+
resolution, use_timestep=True, use_linear_attn=False, attn_type="vanilla"):
|
304 |
+
super().__init__()
|
305 |
+
if use_linear_attn: attn_type = "linear"
|
306 |
+
self.ch = ch
|
307 |
+
self.temb_ch = self.ch*4
|
308 |
+
self.num_resolutions = len(ch_mult)
|
309 |
+
self.num_res_blocks = num_res_blocks
|
310 |
+
self.resolution = resolution
|
311 |
+
self.in_channels = in_channels
|
312 |
+
|
313 |
+
self.use_timestep = use_timestep
|
314 |
+
if self.use_timestep:
|
315 |
+
# timestep embedding
|
316 |
+
self.temb = nn.Module()
|
317 |
+
self.temb.dense = nn.ModuleList([
|
318 |
+
torch.nn.Linear(self.ch,
|
319 |
+
self.temb_ch),
|
320 |
+
torch.nn.Linear(self.temb_ch,
|
321 |
+
self.temb_ch),
|
322 |
+
])
|
323 |
+
|
324 |
+
# downsampling
|
325 |
+
self.conv_in = torch.nn.Conv2d(in_channels,
|
326 |
+
self.ch,
|
327 |
+
kernel_size=3,
|
328 |
+
stride=1,
|
329 |
+
padding=1)
|
330 |
+
|
331 |
+
curr_res = resolution
|
332 |
+
in_ch_mult = (1,)+tuple(ch_mult)
|
333 |
+
self.down = nn.ModuleList()
|
334 |
+
for i_level in range(self.num_resolutions):
|
335 |
+
block = nn.ModuleList()
|
336 |
+
attn = nn.ModuleList()
|
337 |
+
block_in = ch*in_ch_mult[i_level]
|
338 |
+
block_out = ch*ch_mult[i_level]
|
339 |
+
for i_block in range(self.num_res_blocks):
|
340 |
+
block.append(ResnetBlock(in_channels=block_in,
|
341 |
+
out_channels=block_out,
|
342 |
+
temb_channels=self.temb_ch,
|
343 |
+
dropout=dropout))
|
344 |
+
block_in = block_out
|
345 |
+
if curr_res in attn_resolutions:
|
346 |
+
attn.append(make_attn(block_in, attn_type=attn_type))
|
347 |
+
down = nn.Module()
|
348 |
+
down.block = block
|
349 |
+
down.attn = attn
|
350 |
+
if i_level != self.num_resolutions-1:
|
351 |
+
down.downsample = Downsample(block_in, resamp_with_conv)
|
352 |
+
curr_res = curr_res // 2
|
353 |
+
self.down.append(down)
|
354 |
+
|
355 |
+
# middle
|
356 |
+
self.mid = nn.Module()
|
357 |
+
self.mid.block_1 = ResnetBlock(in_channels=block_in,
|
358 |
+
out_channels=block_in,
|
359 |
+
temb_channels=self.temb_ch,
|
360 |
+
dropout=dropout)
|
361 |
+
self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
|
362 |
+
self.mid.block_2 = ResnetBlock(in_channels=block_in,
|
363 |
+
out_channels=block_in,
|
364 |
+
temb_channels=self.temb_ch,
|
365 |
+
dropout=dropout)
|
366 |
+
|
367 |
+
# upsampling
|
368 |
+
self.up = nn.ModuleList()
|
369 |
+
for i_level in reversed(range(self.num_resolutions)):
|
370 |
+
block = nn.ModuleList()
|
371 |
+
attn = nn.ModuleList()
|
372 |
+
block_out = ch*ch_mult[i_level]
|
373 |
+
skip_in = ch*ch_mult[i_level]
|
374 |
+
for i_block in range(self.num_res_blocks+1):
|
375 |
+
if i_block == self.num_res_blocks:
|
376 |
+
skip_in = ch*in_ch_mult[i_level]
|
377 |
+
block.append(ResnetBlock(in_channels=block_in+skip_in,
|
378 |
+
out_channels=block_out,
|
379 |
+
temb_channels=self.temb_ch,
|
380 |
+
dropout=dropout))
|
381 |
+
block_in = block_out
|
382 |
+
if curr_res in attn_resolutions:
|
383 |
+
attn.append(make_attn(block_in, attn_type=attn_type))
|
384 |
+
up = nn.Module()
|
385 |
+
up.block = block
|
386 |
+
up.attn = attn
|
387 |
+
if i_level != 0:
|
388 |
+
up.upsample = Upsample(block_in, resamp_with_conv)
|
389 |
+
curr_res = curr_res * 2
|
390 |
+
self.up.insert(0, up) # prepend to get consistent order
|
391 |
+
|
392 |
+
# end
|
393 |
+
self.norm_out = Normalize(block_in)
|
394 |
+
self.conv_out = torch.nn.Conv2d(block_in,
|
395 |
+
out_ch,
|
396 |
+
kernel_size=3,
|
397 |
+
stride=1,
|
398 |
+
padding=1)
|
399 |
+
|
400 |
+
def forward(self, x, t=None, context=None):
|
401 |
+
#assert x.shape[2] == x.shape[3] == self.resolution
|
402 |
+
if context is not None:
|
403 |
+
# assume aligned context, cat along channel axis
|
404 |
+
x = torch.cat((x, context), dim=1)
|
405 |
+
if self.use_timestep:
|
406 |
+
# timestep embedding
|
407 |
+
assert t is not None
|
408 |
+
temb = get_timestep_embedding(t, self.ch)
|
409 |
+
temb = self.temb.dense[0](temb)
|
410 |
+
temb = nonlinearity(temb)
|
411 |
+
temb = self.temb.dense[1](temb)
|
412 |
+
else:
|
413 |
+
temb = None
|
414 |
+
|
415 |
+
# downsampling
|
416 |
+
hs = [self.conv_in(x)]
|
417 |
+
for i_level in range(self.num_resolutions):
|
418 |
+
for i_block in range(self.num_res_blocks):
|
419 |
+
h = self.down[i_level].block[i_block](hs[-1], temb)
|
420 |
+
if len(self.down[i_level].attn) > 0:
|
421 |
+
h = self.down[i_level].attn[i_block](h)
|
422 |
+
hs.append(h)
|
423 |
+
if i_level != self.num_resolutions-1:
|
424 |
+
hs.append(self.down[i_level].downsample(hs[-1]))
|
425 |
+
|
426 |
+
# middle
|
427 |
+
h = hs[-1]
|
428 |
+
h = self.mid.block_1(h, temb)
|
429 |
+
h = self.mid.attn_1(h)
|
430 |
+
h = self.mid.block_2(h, temb)
|
431 |
+
|
432 |
+
# upsampling
|
433 |
+
for i_level in reversed(range(self.num_resolutions)):
|
434 |
+
for i_block in range(self.num_res_blocks+1):
|
435 |
+
h = self.up[i_level].block[i_block](
|
436 |
+
torch.cat([h, hs.pop()], dim=1), temb)
|
437 |
+
if len(self.up[i_level].attn) > 0:
|
438 |
+
h = self.up[i_level].attn[i_block](h)
|
439 |
+
if i_level != 0:
|
440 |
+
h = self.up[i_level].upsample(h)
|
441 |
+
|
442 |
+
# end
|
443 |
+
h = self.norm_out(h)
|
444 |
+
h = nonlinearity(h)
|
445 |
+
h = self.conv_out(h)
|
446 |
+
return h
|
447 |
+
|
448 |
+
def get_last_layer(self):
|
449 |
+
return self.conv_out.weight
|
450 |
+
|
451 |
+
|
452 |
+
class Encoder(nn.Module):
|
453 |
+
def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
|
454 |
+
attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
|
455 |
+
resolution, z_channels, double_z=True, use_linear_attn=False, attn_type="vanilla",
|
456 |
+
**ignore_kwargs):
|
457 |
+
super().__init__()
|
458 |
+
if use_linear_attn: attn_type = "linear"
|
459 |
+
self.ch = ch
|
460 |
+
self.temb_ch = 0
|
461 |
+
self.num_resolutions = len(ch_mult)
|
462 |
+
self.num_res_blocks = num_res_blocks
|
463 |
+
self.resolution = resolution
|
464 |
+
self.in_channels = in_channels
|
465 |
+
|
466 |
+
# downsampling
|
467 |
+
self.conv_in = torch.nn.Conv2d(in_channels,
|
468 |
+
self.ch,
|
469 |
+
kernel_size=3,
|
470 |
+
stride=1,
|
471 |
+
padding=1)
|
472 |
+
|
473 |
+
curr_res = resolution
|
474 |
+
in_ch_mult = (1,)+tuple(ch_mult)
|
475 |
+
self.in_ch_mult = in_ch_mult
|
476 |
+
self.down = nn.ModuleList()
|
477 |
+
for i_level in range(self.num_resolutions):
|
478 |
+
block = nn.ModuleList()
|
479 |
+
attn = nn.ModuleList()
|
480 |
+
block_in = ch*in_ch_mult[i_level]
|
481 |
+
block_out = ch*ch_mult[i_level]
|
482 |
+
for i_block in range(self.num_res_blocks):
|
483 |
+
block.append(ResnetBlock(in_channels=block_in,
|
484 |
+
out_channels=block_out,
|
485 |
+
temb_channels=self.temb_ch,
|
486 |
+
dropout=dropout))
|
487 |
+
block_in = block_out
|
488 |
+
if curr_res in attn_resolutions:
|
489 |
+
attn.append(make_attn(block_in, attn_type=attn_type))
|
490 |
+
down = nn.Module()
|
491 |
+
down.block = block
|
492 |
+
down.attn = attn
|
493 |
+
if i_level != self.num_resolutions-1:
|
494 |
+
down.downsample = Downsample(block_in, resamp_with_conv)
|
495 |
+
curr_res = curr_res // 2
|
496 |
+
self.down.append(down)
|
497 |
+
|
498 |
+
# middle
|
499 |
+
self.mid = nn.Module()
|
500 |
+
self.mid.block_1 = ResnetBlock(in_channels=block_in,
|
501 |
+
out_channels=block_in,
|
502 |
+
temb_channels=self.temb_ch,
|
503 |
+
dropout=dropout)
|
504 |
+
self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
|
505 |
+
self.mid.block_2 = ResnetBlock(in_channels=block_in,
|
506 |
+
out_channels=block_in,
|
507 |
+
temb_channels=self.temb_ch,
|
508 |
+
dropout=dropout)
|
509 |
+
|
510 |
+
# end
|
511 |
+
self.norm_out = Normalize(block_in)
|
512 |
+
self.conv_out = torch.nn.Conv2d(block_in,
|
513 |
+
2*z_channels if double_z else z_channels,
|
514 |
+
kernel_size=3,
|
515 |
+
stride=1,
|
516 |
+
padding=1)
|
517 |
+
|
518 |
+
def forward(self, x):
|
519 |
+
# timestep embedding
|
520 |
+
temb = None
|
521 |
+
|
522 |
+
# downsampling
|
523 |
+
hs = [self.conv_in(x)]
|
524 |
+
for i_level in range(self.num_resolutions):
|
525 |
+
for i_block in range(self.num_res_blocks):
|
526 |
+
h = self.down[i_level].block[i_block](hs[-1], temb)
|
527 |
+
if len(self.down[i_level].attn) > 0:
|
528 |
+
h = self.down[i_level].attn[i_block](h)
|
529 |
+
hs.append(h)
|
530 |
+
if i_level != self.num_resolutions-1:
|
531 |
+
hs.append(self.down[i_level].downsample(hs[-1]))
|
532 |
+
|
533 |
+
# middle
|
534 |
+
h = hs[-1]
|
535 |
+
h = self.mid.block_1(h, temb)
|
536 |
+
h = self.mid.attn_1(h)
|
537 |
+
h = self.mid.block_2(h, temb)
|
538 |
+
|
539 |
+
# end
|
540 |
+
h = self.norm_out(h)
|
541 |
+
h = nonlinearity(h)
|
542 |
+
h = self.conv_out(h)
|
543 |
+
return h
|
544 |
+
|
545 |
+
|
546 |
+
class Decoder(nn.Module):
|
547 |
+
def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
|
548 |
+
attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
|
549 |
+
resolution, z_channels, give_pre_end=False, tanh_out=False, use_linear_attn=False,
|
550 |
+
attn_type="vanilla", **ignorekwargs):
|
551 |
+
super().__init__()
|
552 |
+
if use_linear_attn: attn_type = "linear"
|
553 |
+
self.ch = ch
|
554 |
+
self.temb_ch = 0
|
555 |
+
self.num_resolutions = len(ch_mult)
|
556 |
+
self.num_res_blocks = num_res_blocks
|
557 |
+
self.resolution = resolution
|
558 |
+
self.in_channels = in_channels
|
559 |
+
self.give_pre_end = give_pre_end
|
560 |
+
self.tanh_out = tanh_out
|
561 |
+
|
562 |
+
# compute in_ch_mult, block_in and curr_res at lowest res
|
563 |
+
in_ch_mult = (1,)+tuple(ch_mult)
|
564 |
+
block_in = ch*ch_mult[self.num_resolutions-1]
|
565 |
+
curr_res = resolution // 2**(self.num_resolutions-1)
|
566 |
+
self.z_shape = (1,z_channels,curr_res,curr_res)
|
567 |
+
print("Working with z of shape {} = {} dimensions.".format(
|
568 |
+
self.z_shape, np.prod(self.z_shape)))
|
569 |
+
|
570 |
+
# z to block_in
|
571 |
+
self.conv_in = torch.nn.Conv2d(z_channels,
|
572 |
+
block_in,
|
573 |
+
kernel_size=3,
|
574 |
+
stride=1,
|
575 |
+
padding=1)
|
576 |
+
|
577 |
+
# middle
|
578 |
+
self.mid = nn.Module()
|
579 |
+
self.mid.block_1 = ResnetBlock(in_channels=block_in,
|
580 |
+
out_channels=block_in,
|
581 |
+
temb_channels=self.temb_ch,
|
582 |
+
dropout=dropout)
|
583 |
+
self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
|
584 |
+
self.mid.block_2 = ResnetBlock(in_channels=block_in,
|
585 |
+
out_channels=block_in,
|
586 |
+
temb_channels=self.temb_ch,
|
587 |
+
dropout=dropout)
|
588 |
+
|
589 |
+
# upsampling
|
590 |
+
self.up = nn.ModuleList()
|
591 |
+
for i_level in reversed(range(self.num_resolutions)):
|
592 |
+
block = nn.ModuleList()
|
593 |
+
attn = nn.ModuleList()
|
594 |
+
block_out = ch*ch_mult[i_level]
|
595 |
+
for i_block in range(self.num_res_blocks+1):
|
596 |
+
block.append(ResnetBlock(in_channels=block_in,
|
597 |
+
out_channels=block_out,
|
598 |
+
temb_channels=self.temb_ch,
|
599 |
+
dropout=dropout))
|
600 |
+
block_in = block_out
|
601 |
+
if curr_res in attn_resolutions:
|
602 |
+
attn.append(make_attn(block_in, attn_type=attn_type))
|
603 |
+
up = nn.Module()
|
604 |
+
up.block = block
|
605 |
+
up.attn = attn
|
606 |
+
if i_level != 0:
|
607 |
+
up.upsample = Upsample(block_in, resamp_with_conv)
|
608 |
+
curr_res = curr_res * 2
|
609 |
+
self.up.insert(0, up) # prepend to get consistent order
|
610 |
+
|
611 |
+
# end
|
612 |
+
self.norm_out = Normalize(block_in)
|
613 |
+
self.conv_out = torch.nn.Conv2d(block_in,
|
614 |
+
out_ch,
|
615 |
+
kernel_size=3,
|
616 |
+
stride=1,
|
617 |
+
padding=1)
|
618 |
+
|
619 |
+
def forward(self, z):
|
620 |
+
#assert z.shape[1:] == self.z_shape[1:]
|
621 |
+
self.last_z_shape = z.shape
|
622 |
+
|
623 |
+
# timestep embedding
|
624 |
+
temb = None
|
625 |
+
|
626 |
+
# z to block_in
|
627 |
+
h = self.conv_in(z)
|
628 |
+
|
629 |
+
# middle
|
630 |
+
h = self.mid.block_1(h, temb)
|
631 |
+
h = self.mid.attn_1(h)
|
632 |
+
h = self.mid.block_2(h, temb)
|
633 |
+
|
634 |
+
# upsampling
|
635 |
+
for i_level in reversed(range(self.num_resolutions)):
|
636 |
+
for i_block in range(self.num_res_blocks+1):
|
637 |
+
h = self.up[i_level].block[i_block](h, temb)
|
638 |
+
if len(self.up[i_level].attn) > 0:
|
639 |
+
h = self.up[i_level].attn[i_block](h)
|
640 |
+
if i_level != 0:
|
641 |
+
h = self.up[i_level].upsample(h)
|
642 |
+
|
643 |
+
# end
|
644 |
+
if self.give_pre_end:
|
645 |
+
return h
|
646 |
+
|
647 |
+
h = self.norm_out(h)
|
648 |
+
h = nonlinearity(h)
|
649 |
+
h = self.conv_out(h)
|
650 |
+
if self.tanh_out:
|
651 |
+
h = torch.tanh(h)
|
652 |
+
return h
|
653 |
+
|
654 |
+
|
655 |
+
class SimpleDecoder(nn.Module):
|
656 |
+
def __init__(self, in_channels, out_channels, *args, **kwargs):
|
657 |
+
super().__init__()
|
658 |
+
self.model = nn.ModuleList([nn.Conv2d(in_channels, in_channels, 1),
|
659 |
+
ResnetBlock(in_channels=in_channels,
|
660 |
+
out_channels=2 * in_channels,
|
661 |
+
temb_channels=0, dropout=0.0),
|
662 |
+
ResnetBlock(in_channels=2 * in_channels,
|
663 |
+
out_channels=4 * in_channels,
|
664 |
+
temb_channels=0, dropout=0.0),
|
665 |
+
ResnetBlock(in_channels=4 * in_channels,
|
666 |
+
out_channels=2 * in_channels,
|
667 |
+
temb_channels=0, dropout=0.0),
|
668 |
+
nn.Conv2d(2*in_channels, in_channels, 1),
|
669 |
+
Upsample(in_channels, with_conv=True)])
|
670 |
+
# end
|
671 |
+
self.norm_out = Normalize(in_channels)
|
672 |
+
self.conv_out = torch.nn.Conv2d(in_channels,
|
673 |
+
out_channels,
|
674 |
+
kernel_size=3,
|
675 |
+
stride=1,
|
676 |
+
padding=1)
|
677 |
+
|
678 |
+
def forward(self, x):
|
679 |
+
for i, layer in enumerate(self.model):
|
680 |
+
if i in [1,2,3]:
|
681 |
+
x = layer(x, None)
|
682 |
+
else:
|
683 |
+
x = layer(x)
|
684 |
+
|
685 |
+
h = self.norm_out(x)
|
686 |
+
h = nonlinearity(h)
|
687 |
+
x = self.conv_out(h)
|
688 |
+
return x
|
689 |
+
|
690 |
+
|
691 |
+
class UpsampleDecoder(nn.Module):
|
692 |
+
def __init__(self, in_channels, out_channels, ch, num_res_blocks, resolution,
|
693 |
+
ch_mult=(2,2), dropout=0.0):
|
694 |
+
super().__init__()
|
695 |
+
# upsampling
|
696 |
+
self.temb_ch = 0
|
697 |
+
self.num_resolutions = len(ch_mult)
|
698 |
+
self.num_res_blocks = num_res_blocks
|
699 |
+
block_in = in_channels
|
700 |
+
curr_res = resolution // 2 ** (self.num_resolutions - 1)
|
701 |
+
self.res_blocks = nn.ModuleList()
|
702 |
+
self.upsample_blocks = nn.ModuleList()
|
703 |
+
for i_level in range(self.num_resolutions):
|
704 |
+
res_block = []
|
705 |
+
block_out = ch * ch_mult[i_level]
|
706 |
+
for i_block in range(self.num_res_blocks + 1):
|
707 |
+
res_block.append(ResnetBlock(in_channels=block_in,
|
708 |
+
out_channels=block_out,
|
709 |
+
temb_channels=self.temb_ch,
|
710 |
+
dropout=dropout))
|
711 |
+
block_in = block_out
|
712 |
+
self.res_blocks.append(nn.ModuleList(res_block))
|
713 |
+
if i_level != self.num_resolutions - 1:
|
714 |
+
self.upsample_blocks.append(Upsample(block_in, True))
|
715 |
+
curr_res = curr_res * 2
|
716 |
+
|
717 |
+
# end
|
718 |
+
self.norm_out = Normalize(block_in)
|
719 |
+
self.conv_out = torch.nn.Conv2d(block_in,
|
720 |
+
out_channels,
|
721 |
+
kernel_size=3,
|
722 |
+
stride=1,
|
723 |
+
padding=1)
|
724 |
+
|
725 |
+
def forward(self, x):
|
726 |
+
# upsampling
|
727 |
+
h = x
|
728 |
+
for k, i_level in enumerate(range(self.num_resolutions)):
|
729 |
+
for i_block in range(self.num_res_blocks + 1):
|
730 |
+
h = self.res_blocks[i_level][i_block](h, None)
|
731 |
+
if i_level != self.num_resolutions - 1:
|
732 |
+
h = self.upsample_blocks[k](h)
|
733 |
+
h = self.norm_out(h)
|
734 |
+
h = nonlinearity(h)
|
735 |
+
h = self.conv_out(h)
|
736 |
+
return h
|
737 |
+
|
738 |
+
|
739 |
+
class LatentRescaler(nn.Module):
|
740 |
+
def __init__(self, factor, in_channels, mid_channels, out_channels, depth=2):
|
741 |
+
super().__init__()
|
742 |
+
# residual block, interpolate, residual block
|
743 |
+
self.factor = factor
|
744 |
+
self.conv_in = nn.Conv2d(in_channels,
|
745 |
+
mid_channels,
|
746 |
+
kernel_size=3,
|
747 |
+
stride=1,
|
748 |
+
padding=1)
|
749 |
+
self.res_block1 = nn.ModuleList([ResnetBlock(in_channels=mid_channels,
|
750 |
+
out_channels=mid_channels,
|
751 |
+
temb_channels=0,
|
752 |
+
dropout=0.0) for _ in range(depth)])
|
753 |
+
self.attn = AttnBlock(mid_channels)
|
754 |
+
self.res_block2 = nn.ModuleList([ResnetBlock(in_channels=mid_channels,
|
755 |
+
out_channels=mid_channels,
|
756 |
+
temb_channels=0,
|
757 |
+
dropout=0.0) for _ in range(depth)])
|
758 |
+
|
759 |
+
self.conv_out = nn.Conv2d(mid_channels,
|
760 |
+
out_channels,
|
761 |
+
kernel_size=1,
|
762 |
+
)
|
763 |
+
|
764 |
+
def forward(self, x):
|
765 |
+
x = self.conv_in(x)
|
766 |
+
for block in self.res_block1:
|
767 |
+
x = block(x, None)
|
768 |
+
x = torch.nn.functional.interpolate(x, size=(int(round(x.shape[2]*self.factor)), int(round(x.shape[3]*self.factor))))
|
769 |
+
x = self.attn(x)
|
770 |
+
for block in self.res_block2:
|
771 |
+
x = block(x, None)
|
772 |
+
x = self.conv_out(x)
|
773 |
+
return x
|
774 |
+
|
775 |
+
|
776 |
+
class MergedRescaleEncoder(nn.Module):
|
777 |
+
def __init__(self, in_channels, ch, resolution, out_ch, num_res_blocks,
|
778 |
+
attn_resolutions, dropout=0.0, resamp_with_conv=True,
|
779 |
+
ch_mult=(1,2,4,8), rescale_factor=1.0, rescale_module_depth=1):
|
780 |
+
super().__init__()
|
781 |
+
intermediate_chn = ch * ch_mult[-1]
|
782 |
+
self.encoder = Encoder(in_channels=in_channels, num_res_blocks=num_res_blocks, ch=ch, ch_mult=ch_mult,
|
783 |
+
z_channels=intermediate_chn, double_z=False, resolution=resolution,
|
784 |
+
attn_resolutions=attn_resolutions, dropout=dropout, resamp_with_conv=resamp_with_conv,
|
785 |
+
out_ch=None)
|
786 |
+
self.rescaler = LatentRescaler(factor=rescale_factor, in_channels=intermediate_chn,
|
787 |
+
mid_channels=intermediate_chn, out_channels=out_ch, depth=rescale_module_depth)
|
788 |
+
|
789 |
+
def forward(self, x):
|
790 |
+
x = self.encoder(x)
|
791 |
+
x = self.rescaler(x)
|
792 |
+
return x
|
793 |
+
|
794 |
+
|
795 |
+
class MergedRescaleDecoder(nn.Module):
|
796 |
+
def __init__(self, z_channels, out_ch, resolution, num_res_blocks, attn_resolutions, ch, ch_mult=(1,2,4,8),
|
797 |
+
dropout=0.0, resamp_with_conv=True, rescale_factor=1.0, rescale_module_depth=1):
|
798 |
+
super().__init__()
|
799 |
+
tmp_chn = z_channels*ch_mult[-1]
|
800 |
+
self.decoder = Decoder(out_ch=out_ch, z_channels=tmp_chn, attn_resolutions=attn_resolutions, dropout=dropout,
|
801 |
+
resamp_with_conv=resamp_with_conv, in_channels=None, num_res_blocks=num_res_blocks,
|
802 |
+
ch_mult=ch_mult, resolution=resolution, ch=ch)
|
803 |
+
self.rescaler = LatentRescaler(factor=rescale_factor, in_channels=z_channels, mid_channels=tmp_chn,
|
804 |
+
out_channels=tmp_chn, depth=rescale_module_depth)
|
805 |
+
|
806 |
+
def forward(self, x):
|
807 |
+
x = self.rescaler(x)
|
808 |
+
x = self.decoder(x)
|
809 |
+
return x
|
810 |
+
|
811 |
+
|
812 |
+
class Upsampler(nn.Module):
|
813 |
+
def __init__(self, in_size, out_size, in_channels, out_channels, ch_mult=2):
|
814 |
+
super().__init__()
|
815 |
+
assert out_size >= in_size
|
816 |
+
num_blocks = int(np.log2(out_size//in_size))+1
|
817 |
+
factor_up = 1.+ (out_size % in_size)
|
818 |
+
print(f"Building {self.__class__.__name__} with in_size: {in_size} --> out_size {out_size} and factor {factor_up}")
|
819 |
+
self.rescaler = LatentRescaler(factor=factor_up, in_channels=in_channels, mid_channels=2*in_channels,
|
820 |
+
out_channels=in_channels)
|
821 |
+
self.decoder = Decoder(out_ch=out_channels, resolution=out_size, z_channels=in_channels, num_res_blocks=2,
|
822 |
+
attn_resolutions=[], in_channels=None, ch=in_channels,
|
823 |
+
ch_mult=[ch_mult for _ in range(num_blocks)])
|
824 |
+
|
825 |
+
def forward(self, x):
|
826 |
+
x = self.rescaler(x)
|
827 |
+
x = self.decoder(x)
|
828 |
+
return x
|
829 |
+
|
830 |
+
|
831 |
+
class Resize(nn.Module):
|
832 |
+
def __init__(self, in_channels=None, learned=False, mode="bilinear"):
|
833 |
+
super().__init__()
|
834 |
+
self.with_conv = learned
|
835 |
+
self.mode = mode
|
836 |
+
if self.with_conv:
|
837 |
+
print(f"Note: {self.__class__.__name} uses learned downsampling and will ignore the fixed {mode} mode")
|
838 |
+
raise NotImplementedError()
|
839 |
+
assert in_channels is not None
|
840 |
+
# no asymmetric padding in torch conv, must do it ourselves
|
841 |
+
self.conv = torch.nn.Conv2d(in_channels,
|
842 |
+
in_channels,
|
843 |
+
kernel_size=4,
|
844 |
+
stride=2,
|
845 |
+
padding=1)
|
846 |
+
|
847 |
+
def forward(self, x, scale_factor=1.0):
|
848 |
+
if scale_factor==1.0:
|
849 |
+
return x
|
850 |
+
else:
|
851 |
+
x = torch.nn.functional.interpolate(x, mode=self.mode, align_corners=False, scale_factor=scale_factor)
|
852 |
+
return x
|
ldm/modules/diffusionmodules/openaimodel.py
ADDED
@@ -0,0 +1,798 @@
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|
1 |
+
from abc import abstractmethod
|
2 |
+
import math
|
3 |
+
import torch
|
4 |
+
|
5 |
+
import numpy as np
|
6 |
+
import torch as th
|
7 |
+
import torch.nn as nn
|
8 |
+
import torch.nn.functional as F
|
9 |
+
|
10 |
+
from ldm.modules.diffusionmodules.util import (
|
11 |
+
checkpoint,
|
12 |
+
conv_nd,
|
13 |
+
linear,
|
14 |
+
avg_pool_nd,
|
15 |
+
zero_module,
|
16 |
+
normalization,
|
17 |
+
timestep_embedding,
|
18 |
+
)
|
19 |
+
from ldm.modules.attention import SpatialTransformer
|
20 |
+
from ldm.util import exists
|
21 |
+
|
22 |
+
|
23 |
+
# dummy replace
|
24 |
+
def convert_module_to_f16(x):
|
25 |
+
pass
|
26 |
+
|
27 |
+
def convert_module_to_f32(x):
|
28 |
+
pass
|
29 |
+
|
30 |
+
|
31 |
+
## go
|
32 |
+
class AttentionPool2d(nn.Module):
|
33 |
+
"""
|
34 |
+
Adapted from CLIP: https://github.com/openai/CLIP/blob/main/clip/model.py
|
35 |
+
"""
|
36 |
+
|
37 |
+
def __init__(
|
38 |
+
self,
|
39 |
+
spacial_dim: int,
|
40 |
+
embed_dim: int,
|
41 |
+
num_heads_channels: int,
|
42 |
+
output_dim: int = None,
|
43 |
+
):
|
44 |
+
super().__init__()
|
45 |
+
self.positional_embedding = nn.Parameter(th.randn(embed_dim, spacial_dim ** 2 + 1) / embed_dim ** 0.5)
|
46 |
+
self.qkv_proj = conv_nd(1, embed_dim, 3 * embed_dim, 1)
|
47 |
+
self.c_proj = conv_nd(1, embed_dim, output_dim or embed_dim, 1)
|
48 |
+
self.num_heads = embed_dim // num_heads_channels
|
49 |
+
self.attention = QKVAttention(self.num_heads)
|
50 |
+
|
51 |
+
def forward(self, x):
|
52 |
+
b, c, *_spatial = x.shape
|
53 |
+
x = x.reshape(b, c, -1) # NC(HW)
|
54 |
+
x = th.cat([x.mean(dim=-1, keepdim=True), x], dim=-1) # NC(HW+1)
|
55 |
+
x = x + self.positional_embedding[None, :, :].to(x.dtype) # NC(HW+1)
|
56 |
+
x = self.qkv_proj(x)
|
57 |
+
x = self.attention(x)
|
58 |
+
x = self.c_proj(x)
|
59 |
+
return x[:, :, 0]
|
60 |
+
|
61 |
+
|
62 |
+
class TimestepBlock(nn.Module):
|
63 |
+
"""
|
64 |
+
Any module where forward() takes timestep embeddings as a second argument.
|
65 |
+
"""
|
66 |
+
|
67 |
+
@abstractmethod
|
68 |
+
def forward(self, x, emb):
|
69 |
+
"""
|
70 |
+
Apply the module to `x` given `emb` timestep embeddings.
|
71 |
+
"""
|
72 |
+
|
73 |
+
|
74 |
+
class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
|
75 |
+
"""
|
76 |
+
A sequential module that passes timestep embeddings to the children that
|
77 |
+
support it as an extra input.
|
78 |
+
"""
|
79 |
+
|
80 |
+
def forward(self, x, emb, context=None):
|
81 |
+
for layer in self:
|
82 |
+
if isinstance(layer, TimestepBlock):
|
83 |
+
x = layer(x, emb)
|
84 |
+
elif isinstance(layer, SpatialTransformer):
|
85 |
+
x = layer(x, context)
|
86 |
+
else:
|
87 |
+
x = layer(x)
|
88 |
+
return x
|
89 |
+
|
90 |
+
|
91 |
+
class Upsample(nn.Module):
|
92 |
+
"""
|
93 |
+
An upsampling layer with an optional convolution.
|
94 |
+
:param channels: channels in the inputs and outputs.
|
95 |
+
:param use_conv: a bool determining if a convolution is applied.
|
96 |
+
:param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
|
97 |
+
upsampling occurs in the inner-two dimensions.
|
98 |
+
"""
|
99 |
+
|
100 |
+
def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1):
|
101 |
+
super().__init__()
|
102 |
+
self.channels = channels
|
103 |
+
self.out_channels = out_channels or channels
|
104 |
+
self.use_conv = use_conv
|
105 |
+
self.dims = dims
|
106 |
+
if use_conv:
|
107 |
+
self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=padding)
|
108 |
+
|
109 |
+
def forward(self, x):
|
110 |
+
assert x.shape[1] == self.channels
|
111 |
+
if self.dims == 3:
|
112 |
+
x = F.interpolate(
|
113 |
+
x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest"
|
114 |
+
)
|
115 |
+
else:
|
116 |
+
x = F.interpolate(x, scale_factor=2, mode="nearest")
|
117 |
+
if self.use_conv:
|
118 |
+
x = self.conv(x)
|
119 |
+
return x
|
120 |
+
|
121 |
+
class TransposedUpsample(nn.Module):
|
122 |
+
'Learned 2x upsampling without padding'
|
123 |
+
def __init__(self, channels, out_channels=None, ks=5):
|
124 |
+
super().__init__()
|
125 |
+
self.channels = channels
|
126 |
+
self.out_channels = out_channels or channels
|
127 |
+
|
128 |
+
self.up = nn.ConvTranspose2d(self.channels,self.out_channels,kernel_size=ks,stride=2)
|
129 |
+
|
130 |
+
def forward(self,x):
|
131 |
+
return self.up(x)
|
132 |
+
|
133 |
+
|
134 |
+
class Downsample(nn.Module):
|
135 |
+
"""
|
136 |
+
A downsampling layer with an optional convolution.
|
137 |
+
:param channels: channels in the inputs and outputs.
|
138 |
+
:param use_conv: a bool determining if a convolution is applied.
|
139 |
+
:param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
|
140 |
+
downsampling occurs in the inner-two dimensions.
|
141 |
+
"""
|
142 |
+
|
143 |
+
def __init__(self, channels, use_conv, dims=2, out_channels=None,padding=1):
|
144 |
+
super().__init__()
|
145 |
+
self.channels = channels
|
146 |
+
self.out_channels = out_channels or channels
|
147 |
+
self.use_conv = use_conv
|
148 |
+
self.dims = dims
|
149 |
+
stride = 2 if dims != 3 else (1, 2, 2)
|
150 |
+
if use_conv:
|
151 |
+
self.op = conv_nd(
|
152 |
+
dims, self.channels, self.out_channels, 3, stride=stride, padding=padding
|
153 |
+
)
|
154 |
+
else:
|
155 |
+
assert self.channels == self.out_channels
|
156 |
+
self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride)
|
157 |
+
|
158 |
+
def forward(self, x):
|
159 |
+
assert x.shape[1] == self.channels
|
160 |
+
return self.op(x)
|
161 |
+
|
162 |
+
|
163 |
+
class ResBlock(TimestepBlock):
|
164 |
+
"""
|
165 |
+
A residual block that can optionally change the number of channels.
|
166 |
+
:param channels: the number of input channels.
|
167 |
+
:param emb_channels: the number of timestep embedding channels.
|
168 |
+
:param dropout: the rate of dropout.
|
169 |
+
:param out_channels: if specified, the number of out channels.
|
170 |
+
:param use_conv: if True and out_channels is specified, use a spatial
|
171 |
+
convolution instead of a smaller 1x1 convolution to change the
|
172 |
+
channels in the skip connection.
|
173 |
+
:param dims: determines if the signal is 1D, 2D, or 3D.
|
174 |
+
:param use_checkpoint: if True, use gradient checkpointing on this module.
|
175 |
+
:param up: if True, use this block for upsampling.
|
176 |
+
:param down: if True, use this block for downsampling.
|
177 |
+
"""
|
178 |
+
|
179 |
+
def __init__(
|
180 |
+
self,
|
181 |
+
channels,
|
182 |
+
emb_channels,
|
183 |
+
dropout,
|
184 |
+
out_channels=None,
|
185 |
+
use_conv=False,
|
186 |
+
use_scale_shift_norm=False,
|
187 |
+
dims=2,
|
188 |
+
use_checkpoint=False,
|
189 |
+
up=False,
|
190 |
+
down=False,
|
191 |
+
):
|
192 |
+
super().__init__()
|
193 |
+
self.channels = channels
|
194 |
+
self.emb_channels = emb_channels
|
195 |
+
self.dropout = dropout
|
196 |
+
self.out_channels = out_channels or channels
|
197 |
+
self.use_conv = use_conv
|
198 |
+
self.use_checkpoint = use_checkpoint
|
199 |
+
self.use_scale_shift_norm = use_scale_shift_norm
|
200 |
+
|
201 |
+
self.in_layers = nn.Sequential(
|
202 |
+
normalization(channels),
|
203 |
+
nn.SiLU(),
|
204 |
+
conv_nd(dims, channels, self.out_channels, 3, padding=1),
|
205 |
+
)
|
206 |
+
|
207 |
+
self.updown = up or down
|
208 |
+
|
209 |
+
if up:
|
210 |
+
self.h_upd = Upsample(channels, False, dims)
|
211 |
+
self.x_upd = Upsample(channels, False, dims)
|
212 |
+
elif down:
|
213 |
+
self.h_upd = Downsample(channels, False, dims)
|
214 |
+
self.x_upd = Downsample(channels, False, dims)
|
215 |
+
else:
|
216 |
+
self.h_upd = self.x_upd = nn.Identity()
|
217 |
+
|
218 |
+
self.emb_layers = nn.Sequential(
|
219 |
+
nn.SiLU(),
|
220 |
+
linear(
|
221 |
+
emb_channels,
|
222 |
+
2 * self.out_channels if use_scale_shift_norm else self.out_channels,
|
223 |
+
),
|
224 |
+
)
|
225 |
+
self.out_layers = nn.Sequential(
|
226 |
+
normalization(self.out_channels),
|
227 |
+
nn.SiLU(),
|
228 |
+
nn.Dropout(p=dropout),
|
229 |
+
zero_module(
|
230 |
+
conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1)
|
231 |
+
),
|
232 |
+
)
|
233 |
+
|
234 |
+
if self.out_channels == channels:
|
235 |
+
self.skip_connection = nn.Identity()
|
236 |
+
elif use_conv:
|
237 |
+
self.skip_connection = conv_nd(
|
238 |
+
dims, channels, self.out_channels, 3, padding=1
|
239 |
+
)
|
240 |
+
else:
|
241 |
+
self.skip_connection = conv_nd(dims, channels, self.out_channels, 1)
|
242 |
+
|
243 |
+
def forward(self, x, emb):
|
244 |
+
"""
|
245 |
+
Apply the block to a Tensor, conditioned on a timestep embedding.
|
246 |
+
:param x: an [N x C x ...] Tensor of features.
|
247 |
+
:param emb: an [N x emb_channels] Tensor of timestep embeddings.
|
248 |
+
:return: an [N x C x ...] Tensor of outputs.
|
249 |
+
"""
|
250 |
+
return checkpoint(
|
251 |
+
self._forward, (x, emb), self.parameters(), self.use_checkpoint
|
252 |
+
)
|
253 |
+
|
254 |
+
|
255 |
+
def _forward(self, x, emb):
|
256 |
+
if self.updown:
|
257 |
+
in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]
|
258 |
+
h = in_rest(x)
|
259 |
+
h = self.h_upd(h)
|
260 |
+
x = self.x_upd(x)
|
261 |
+
h = in_conv(h)
|
262 |
+
else:
|
263 |
+
h = self.in_layers(x)
|
264 |
+
emb_out = self.emb_layers(emb).type(h.dtype)
|
265 |
+
while len(emb_out.shape) < len(h.shape):
|
266 |
+
emb_out = emb_out[..., None]
|
267 |
+
if self.use_scale_shift_norm:
|
268 |
+
out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
|
269 |
+
scale, shift = th.chunk(emb_out, 2, dim=1)
|
270 |
+
h = out_norm(h) * (1 + scale) + shift
|
271 |
+
h = out_rest(h)
|
272 |
+
else:
|
273 |
+
h = h + emb_out
|
274 |
+
h = self.out_layers(h)
|
275 |
+
return self.skip_connection(x) + h
|
276 |
+
|
277 |
+
|
278 |
+
class AttentionBlock(nn.Module):
|
279 |
+
"""
|
280 |
+
An attention block that allows spatial positions to attend to each other.
|
281 |
+
Originally ported from here, but adapted to the N-d case.
|
282 |
+
https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66.
|
283 |
+
"""
|
284 |
+
|
285 |
+
def __init__(
|
286 |
+
self,
|
287 |
+
channels,
|
288 |
+
num_heads=1,
|
289 |
+
num_head_channels=-1,
|
290 |
+
use_checkpoint=False,
|
291 |
+
use_new_attention_order=False,
|
292 |
+
):
|
293 |
+
super().__init__()
|
294 |
+
self.channels = channels
|
295 |
+
if num_head_channels == -1:
|
296 |
+
self.num_heads = num_heads
|
297 |
+
else:
|
298 |
+
assert (
|
299 |
+
channels % num_head_channels == 0
|
300 |
+
), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}"
|
301 |
+
self.num_heads = channels // num_head_channels
|
302 |
+
self.use_checkpoint = use_checkpoint
|
303 |
+
self.norm = normalization(channels)
|
304 |
+
self.qkv = conv_nd(1, channels, channels * 3, 1)
|
305 |
+
if use_new_attention_order:
|
306 |
+
# split qkv before split heads
|
307 |
+
self.attention = QKVAttention(self.num_heads)
|
308 |
+
else:
|
309 |
+
# split heads before split qkv
|
310 |
+
self.attention = QKVAttentionLegacy(self.num_heads)
|
311 |
+
|
312 |
+
self.proj_out = zero_module(conv_nd(1, channels, channels, 1))
|
313 |
+
|
314 |
+
def forward(self, x):
|
315 |
+
return checkpoint(self._forward, (x,), self.parameters(), True) # TODO: check checkpoint usage, is True # TODO: fix the .half call!!!
|
316 |
+
#return pt_checkpoint(self._forward, x) # pytorch
|
317 |
+
|
318 |
+
def _forward(self, x):
|
319 |
+
b, c, *spatial = x.shape
|
320 |
+
x = x.reshape(b, c, -1)
|
321 |
+
qkv = self.qkv(self.norm(x))
|
322 |
+
h = self.attention(qkv)
|
323 |
+
h = self.proj_out(h)
|
324 |
+
return (x + h).reshape(b, c, *spatial)
|
325 |
+
|
326 |
+
|
327 |
+
def count_flops_attn(model, _x, y):
|
328 |
+
"""
|
329 |
+
A counter for the `thop` package to count the operations in an
|
330 |
+
attention operation.
|
331 |
+
Meant to be used like:
|
332 |
+
macs, params = thop.profile(
|
333 |
+
model,
|
334 |
+
inputs=(inputs, timestamps),
|
335 |
+
custom_ops={QKVAttention: QKVAttention.count_flops},
|
336 |
+
)
|
337 |
+
"""
|
338 |
+
b, c, *spatial = y[0].shape
|
339 |
+
num_spatial = int(np.prod(spatial))
|
340 |
+
# We perform two matmuls with the same number of ops.
|
341 |
+
# The first computes the weight matrix, the second computes
|
342 |
+
# the combination of the value vectors.
|
343 |
+
matmul_ops = 2 * b * (num_spatial ** 2) * c
|
344 |
+
model.total_ops += th.DoubleTensor([matmul_ops])
|
345 |
+
|
346 |
+
|
347 |
+
class QKVAttentionLegacy(nn.Module):
|
348 |
+
"""
|
349 |
+
A module which performs QKV attention. Matches legacy QKVAttention + input/ouput heads shaping
|
350 |
+
"""
|
351 |
+
|
352 |
+
def __init__(self, n_heads):
|
353 |
+
super().__init__()
|
354 |
+
self.n_heads = n_heads
|
355 |
+
|
356 |
+
def forward(self, qkv):
|
357 |
+
"""
|
358 |
+
Apply QKV attention.
|
359 |
+
:param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs.
|
360 |
+
:return: an [N x (H * C) x T] tensor after attention.
|
361 |
+
"""
|
362 |
+
bs, width, length = qkv.shape
|
363 |
+
assert width % (3 * self.n_heads) == 0
|
364 |
+
ch = width // (3 * self.n_heads)
|
365 |
+
q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1)
|
366 |
+
scale = 1 / math.sqrt(math.sqrt(ch))
|
367 |
+
weight = th.einsum(
|
368 |
+
"bct,bcs->bts", q * scale, k * scale
|
369 |
+
) # More stable with f16 than dividing afterwards
|
370 |
+
weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
|
371 |
+
a = th.einsum("bts,bcs->bct", weight, v)
|
372 |
+
return a.reshape(bs, -1, length)
|
373 |
+
|
374 |
+
@staticmethod
|
375 |
+
def count_flops(model, _x, y):
|
376 |
+
return count_flops_attn(model, _x, y)
|
377 |
+
|
378 |
+
|
379 |
+
class QKVAttention(nn.Module):
|
380 |
+
"""
|
381 |
+
A module which performs QKV attention and splits in a different order.
|
382 |
+
"""
|
383 |
+
|
384 |
+
def __init__(self, n_heads):
|
385 |
+
super().__init__()
|
386 |
+
self.n_heads = n_heads
|
387 |
+
|
388 |
+
def forward(self, qkv):
|
389 |
+
"""
|
390 |
+
Apply QKV attention.
|
391 |
+
:param qkv: an [N x (3 * H * C) x T] tensor of Qs, Ks, and Vs.
|
392 |
+
:return: an [N x (H * C) x T] tensor after attention.
|
393 |
+
"""
|
394 |
+
bs, width, length = qkv.shape
|
395 |
+
assert width % (3 * self.n_heads) == 0
|
396 |
+
ch = width // (3 * self.n_heads)
|
397 |
+
q, k, v = qkv.chunk(3, dim=1)
|
398 |
+
scale = 1 / math.sqrt(math.sqrt(ch))
|
399 |
+
weight = th.einsum(
|
400 |
+
"bct,bcs->bts",
|
401 |
+
(q * scale).view(bs * self.n_heads, ch, length),
|
402 |
+
(k * scale).view(bs * self.n_heads, ch, length),
|
403 |
+
) # More stable with f16 than dividing afterwards
|
404 |
+
weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
|
405 |
+
a = th.einsum("bts,bcs->bct", weight, v.reshape(bs * self.n_heads, ch, length))
|
406 |
+
return a.reshape(bs, -1, length)
|
407 |
+
|
408 |
+
@staticmethod
|
409 |
+
def count_flops(model, _x, y):
|
410 |
+
return count_flops_attn(model, _x, y)
|
411 |
+
|
412 |
+
|
413 |
+
class UNetModel(nn.Module):
|
414 |
+
"""
|
415 |
+
The full UNet model with attention and timestep embedding.
|
416 |
+
:param in_channels: channels in the input Tensor.
|
417 |
+
:param model_channels: base channel count for the model.
|
418 |
+
:param out_channels: channels in the output Tensor.
|
419 |
+
:param num_res_blocks: number of residual blocks per downsample.
|
420 |
+
:param attention_resolutions: a collection of downsample rates at which
|
421 |
+
attention will take place. May be a set, list, or tuple.
|
422 |
+
For example, if this contains 4, then at 4x downsampling, attention
|
423 |
+
will be used.
|
424 |
+
:param dropout: the dropout probability.
|
425 |
+
:param channel_mult: channel multiplier for each level of the UNet.
|
426 |
+
:param conv_resample: if True, use learned convolutions for upsampling and
|
427 |
+
downsampling.
|
428 |
+
:param dims: determines if the signal is 1D, 2D, or 3D.
|
429 |
+
:param num_classes: if specified (as an int), then this model will be
|
430 |
+
class-conditional with `num_classes` classes.
|
431 |
+
:param use_checkpoint: use gradient checkpointing to reduce memory usage.
|
432 |
+
:param num_heads: the number of attention heads in each attention layer.
|
433 |
+
:param num_heads_channels: if specified, ignore num_heads and instead use
|
434 |
+
a fixed channel width per attention head.
|
435 |
+
:param num_heads_upsample: works with num_heads to set a different number
|
436 |
+
of heads for upsampling. Deprecated.
|
437 |
+
:param use_scale_shift_norm: use a FiLM-like conditioning mechanism.
|
438 |
+
:param resblock_updown: use residual blocks for up/downsampling.
|
439 |
+
:param use_new_attention_order: use a different attention pattern for potentially
|
440 |
+
increased efficiency.
|
441 |
+
"""
|
442 |
+
|
443 |
+
def __init__(
|
444 |
+
self,
|
445 |
+
image_size,
|
446 |
+
in_channels,
|
447 |
+
model_channels,
|
448 |
+
out_channels,
|
449 |
+
num_res_blocks,
|
450 |
+
attention_resolutions,
|
451 |
+
dropout=0,
|
452 |
+
channel_mult=(1, 2, 4, 8),
|
453 |
+
conv_resample=True,
|
454 |
+
dims=2,
|
455 |
+
num_classes=None,
|
456 |
+
use_checkpoint=False,
|
457 |
+
use_fp16=False,
|
458 |
+
num_heads=-1,
|
459 |
+
num_head_channels=-1,
|
460 |
+
num_heads_upsample=-1,
|
461 |
+
use_scale_shift_norm=False,
|
462 |
+
resblock_updown=False,
|
463 |
+
use_new_attention_order=False,
|
464 |
+
use_spatial_transformer=False, # custom transformer support
|
465 |
+
transformer_depth=1, # custom transformer support
|
466 |
+
context_dim=None, # custom transformer support
|
467 |
+
n_embed=None, # custom support for prediction of discrete ids into codebook of first stage vq model
|
468 |
+
legacy=True,
|
469 |
+
disable_self_attentions=None,
|
470 |
+
num_attention_blocks=None,
|
471 |
+
disable_middle_self_attn=False,
|
472 |
+
use_linear_in_transformer=False,
|
473 |
+
):
|
474 |
+
super().__init__()
|
475 |
+
if use_spatial_transformer:
|
476 |
+
assert context_dim is not None, 'Fool!! You forgot to include the dimension of your cross-attention conditioning...'
|
477 |
+
|
478 |
+
if context_dim is not None:
|
479 |
+
assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...'
|
480 |
+
from omegaconf.listconfig import ListConfig
|
481 |
+
if type(context_dim) == ListConfig:
|
482 |
+
context_dim = list(context_dim)
|
483 |
+
|
484 |
+
if num_heads_upsample == -1:
|
485 |
+
num_heads_upsample = num_heads
|
486 |
+
|
487 |
+
if num_heads == -1:
|
488 |
+
assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set'
|
489 |
+
|
490 |
+
if num_head_channels == -1:
|
491 |
+
assert num_heads != -1, 'Either num_heads or num_head_channels has to be set'
|
492 |
+
|
493 |
+
self.image_size = image_size
|
494 |
+
self.in_channels = in_channels
|
495 |
+
self.model_channels = model_channels
|
496 |
+
self.out_channels = out_channels
|
497 |
+
if isinstance(num_res_blocks, int):
|
498 |
+
self.num_res_blocks = len(channel_mult) * [num_res_blocks]
|
499 |
+
else:
|
500 |
+
if len(num_res_blocks) != len(channel_mult):
|
501 |
+
raise ValueError("provide num_res_blocks either as an int (globally constant) or "
|
502 |
+
"as a list/tuple (per-level) with the same length as channel_mult")
|
503 |
+
self.num_res_blocks = num_res_blocks
|
504 |
+
if disable_self_attentions is not None:
|
505 |
+
# should be a list of booleans, indicating whether to disable self-attention in TransformerBlocks or not
|
506 |
+
assert len(disable_self_attentions) == len(channel_mult)
|
507 |
+
if num_attention_blocks is not None:
|
508 |
+
assert len(num_attention_blocks) == len(self.num_res_blocks)
|
509 |
+
assert all(map(lambda i: self.num_res_blocks[i] >= num_attention_blocks[i], range(len(num_attention_blocks))))
|
510 |
+
print(f"Constructor of UNetModel received num_attention_blocks={num_attention_blocks}. "
|
511 |
+
f"This option has LESS priority than attention_resolutions {attention_resolutions}, "
|
512 |
+
f"i.e., in cases where num_attention_blocks[i] > 0 but 2**i not in attention_resolutions, "
|
513 |
+
f"attention will still not be set.")
|
514 |
+
|
515 |
+
self.attention_resolutions = attention_resolutions
|
516 |
+
self.dropout = dropout
|
517 |
+
self.channel_mult = channel_mult
|
518 |
+
self.conv_resample = conv_resample
|
519 |
+
self.num_classes = num_classes
|
520 |
+
self.use_checkpoint = use_checkpoint
|
521 |
+
self.dtype = th.float16 if use_fp16 else th.float32
|
522 |
+
self.num_heads = num_heads
|
523 |
+
self.num_head_channels = num_head_channels
|
524 |
+
self.num_heads_upsample = num_heads_upsample
|
525 |
+
self.predict_codebook_ids = n_embed is not None
|
526 |
+
|
527 |
+
time_embed_dim = model_channels * 4
|
528 |
+
self.time_embed = nn.Sequential(
|
529 |
+
linear(model_channels, time_embed_dim),
|
530 |
+
nn.SiLU(),
|
531 |
+
linear(time_embed_dim, time_embed_dim),
|
532 |
+
)
|
533 |
+
|
534 |
+
if self.num_classes is not None:
|
535 |
+
if isinstance(self.num_classes, int):
|
536 |
+
self.label_emb = nn.Embedding(num_classes, time_embed_dim)
|
537 |
+
elif self.num_classes == "continuous":
|
538 |
+
print("setting up linear c_adm embedding layer")
|
539 |
+
self.label_emb = nn.Linear(1, time_embed_dim)
|
540 |
+
else:
|
541 |
+
raise ValueError()
|
542 |
+
|
543 |
+
self.input_blocks = nn.ModuleList(
|
544 |
+
[
|
545 |
+
TimestepEmbedSequential(
|
546 |
+
conv_nd(dims, in_channels, model_channels, 3, padding=1)
|
547 |
+
)
|
548 |
+
]
|
549 |
+
)
|
550 |
+
self._feature_size = model_channels
|
551 |
+
input_block_chans = [model_channels]
|
552 |
+
ch = model_channels
|
553 |
+
ds = 1
|
554 |
+
for level, mult in enumerate(channel_mult):
|
555 |
+
for nr in range(self.num_res_blocks[level]):
|
556 |
+
layers = [
|
557 |
+
ResBlock(
|
558 |
+
ch,
|
559 |
+
time_embed_dim,
|
560 |
+
dropout,
|
561 |
+
out_channels=mult * model_channels,
|
562 |
+
dims=dims,
|
563 |
+
use_checkpoint=use_checkpoint,
|
564 |
+
use_scale_shift_norm=use_scale_shift_norm,
|
565 |
+
)
|
566 |
+
]
|
567 |
+
ch = mult * model_channels
|
568 |
+
if ds in attention_resolutions:
|
569 |
+
if num_head_channels == -1:
|
570 |
+
dim_head = ch // num_heads
|
571 |
+
else:
|
572 |
+
num_heads = ch // num_head_channels
|
573 |
+
dim_head = num_head_channels
|
574 |
+
if legacy:
|
575 |
+
#num_heads = 1
|
576 |
+
dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
|
577 |
+
if exists(disable_self_attentions):
|
578 |
+
disabled_sa = disable_self_attentions[level]
|
579 |
+
else:
|
580 |
+
disabled_sa = False
|
581 |
+
|
582 |
+
if not exists(num_attention_blocks) or nr < num_attention_blocks[level]:
|
583 |
+
layers.append(
|
584 |
+
AttentionBlock(
|
585 |
+
ch,
|
586 |
+
use_checkpoint=use_checkpoint,
|
587 |
+
num_heads=num_heads,
|
588 |
+
num_head_channels=dim_head,
|
589 |
+
use_new_attention_order=use_new_attention_order,
|
590 |
+
) if not use_spatial_transformer else SpatialTransformer(
|
591 |
+
ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim,
|
592 |
+
disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer,
|
593 |
+
use_checkpoint=use_checkpoint
|
594 |
+
)
|
595 |
+
)
|
596 |
+
self.input_blocks.append(TimestepEmbedSequential(*layers))
|
597 |
+
self._feature_size += ch
|
598 |
+
input_block_chans.append(ch)
|
599 |
+
if level != len(channel_mult) - 1:
|
600 |
+
out_ch = ch
|
601 |
+
self.input_blocks.append(
|
602 |
+
TimestepEmbedSequential(
|
603 |
+
ResBlock(
|
604 |
+
ch,
|
605 |
+
time_embed_dim,
|
606 |
+
dropout,
|
607 |
+
out_channels=out_ch,
|
608 |
+
dims=dims,
|
609 |
+
use_checkpoint=use_checkpoint,
|
610 |
+
use_scale_shift_norm=use_scale_shift_norm,
|
611 |
+
down=True,
|
612 |
+
)
|
613 |
+
if resblock_updown
|
614 |
+
else Downsample(
|
615 |
+
ch, conv_resample, dims=dims, out_channels=out_ch
|
616 |
+
)
|
617 |
+
)
|
618 |
+
)
|
619 |
+
ch = out_ch
|
620 |
+
input_block_chans.append(ch)
|
621 |
+
ds *= 2
|
622 |
+
self._feature_size += ch
|
623 |
+
|
624 |
+
if num_head_channels == -1:
|
625 |
+
dim_head = ch // num_heads
|
626 |
+
else:
|
627 |
+
num_heads = ch // num_head_channels
|
628 |
+
dim_head = num_head_channels
|
629 |
+
if legacy:
|
630 |
+
#num_heads = 1
|
631 |
+
dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
|
632 |
+
self.middle_block = TimestepEmbedSequential(
|
633 |
+
ResBlock(
|
634 |
+
ch,
|
635 |
+
time_embed_dim,
|
636 |
+
dropout,
|
637 |
+
dims=dims,
|
638 |
+
use_checkpoint=use_checkpoint,
|
639 |
+
use_scale_shift_norm=use_scale_shift_norm,
|
640 |
+
),
|
641 |
+
AttentionBlock(
|
642 |
+
ch,
|
643 |
+
use_checkpoint=use_checkpoint,
|
644 |
+
num_heads=num_heads,
|
645 |
+
num_head_channels=dim_head,
|
646 |
+
use_new_attention_order=use_new_attention_order,
|
647 |
+
) if not use_spatial_transformer else SpatialTransformer( # always uses a self-attn
|
648 |
+
ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim,
|
649 |
+
disable_self_attn=disable_middle_self_attn, use_linear=use_linear_in_transformer,
|
650 |
+
use_checkpoint=use_checkpoint
|
651 |
+
),
|
652 |
+
ResBlock(
|
653 |
+
ch,
|
654 |
+
time_embed_dim,
|
655 |
+
dropout,
|
656 |
+
dims=dims,
|
657 |
+
use_checkpoint=use_checkpoint,
|
658 |
+
use_scale_shift_norm=use_scale_shift_norm,
|
659 |
+
),
|
660 |
+
)
|
661 |
+
self._feature_size += ch
|
662 |
+
|
663 |
+
self.output_blocks = nn.ModuleList([])
|
664 |
+
for level, mult in list(enumerate(channel_mult))[::-1]:
|
665 |
+
for i in range(self.num_res_blocks[level] + 1):
|
666 |
+
ich = input_block_chans.pop()
|
667 |
+
layers = [
|
668 |
+
ResBlock(
|
669 |
+
ch + ich,
|
670 |
+
time_embed_dim,
|
671 |
+
dropout,
|
672 |
+
out_channels=model_channels * mult,
|
673 |
+
dims=dims,
|
674 |
+
use_checkpoint=use_checkpoint,
|
675 |
+
use_scale_shift_norm=use_scale_shift_norm,
|
676 |
+
)
|
677 |
+
]
|
678 |
+
ch = model_channels * mult
|
679 |
+
if ds in attention_resolutions:
|
680 |
+
if num_head_channels == -1:
|
681 |
+
dim_head = ch // num_heads
|
682 |
+
else:
|
683 |
+
num_heads = ch // num_head_channels
|
684 |
+
dim_head = num_head_channels
|
685 |
+
if legacy:
|
686 |
+
#num_heads = 1
|
687 |
+
dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
|
688 |
+
if exists(disable_self_attentions):
|
689 |
+
disabled_sa = disable_self_attentions[level]
|
690 |
+
else:
|
691 |
+
disabled_sa = False
|
692 |
+
|
693 |
+
if not exists(num_attention_blocks) or i < num_attention_blocks[level]:
|
694 |
+
layers.append(
|
695 |
+
AttentionBlock(
|
696 |
+
ch,
|
697 |
+
use_checkpoint=use_checkpoint,
|
698 |
+
num_heads=num_heads_upsample,
|
699 |
+
num_head_channels=dim_head,
|
700 |
+
use_new_attention_order=use_new_attention_order,
|
701 |
+
) if not use_spatial_transformer else SpatialTransformer(
|
702 |
+
ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim,
|
703 |
+
disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer,
|
704 |
+
use_checkpoint=use_checkpoint
|
705 |
+
)
|
706 |
+
)
|
707 |
+
if level and i == self.num_res_blocks[level]:
|
708 |
+
out_ch = ch
|
709 |
+
layers.append(
|
710 |
+
ResBlock(
|
711 |
+
ch,
|
712 |
+
time_embed_dim,
|
713 |
+
dropout,
|
714 |
+
out_channels=out_ch,
|
715 |
+
dims=dims,
|
716 |
+
use_checkpoint=use_checkpoint,
|
717 |
+
use_scale_shift_norm=use_scale_shift_norm,
|
718 |
+
up=True,
|
719 |
+
)
|
720 |
+
if resblock_updown
|
721 |
+
else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch)
|
722 |
+
)
|
723 |
+
ds //= 2
|
724 |
+
self.output_blocks.append(TimestepEmbedSequential(*layers))
|
725 |
+
self._feature_size += ch
|
726 |
+
|
727 |
+
self.out = nn.Sequential(
|
728 |
+
normalization(ch),
|
729 |
+
nn.SiLU(),
|
730 |
+
zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)),
|
731 |
+
)
|
732 |
+
if self.predict_codebook_ids:
|
733 |
+
self.id_predictor = nn.Sequential(
|
734 |
+
normalization(ch),
|
735 |
+
conv_nd(dims, model_channels, n_embed, 1),
|
736 |
+
#nn.LogSoftmax(dim=1) # change to cross_entropy and produce non-normalized logits
|
737 |
+
)
|
738 |
+
|
739 |
+
def convert_to_fp16(self):
|
740 |
+
"""
|
741 |
+
Convert the torso of the model to float16.
|
742 |
+
"""
|
743 |
+
self.input_blocks.apply(convert_module_to_f16)
|
744 |
+
self.middle_block.apply(convert_module_to_f16)
|
745 |
+
self.output_blocks.apply(convert_module_to_f16)
|
746 |
+
|
747 |
+
def convert_to_fp32(self):
|
748 |
+
"""
|
749 |
+
Convert the torso of the model to float32.
|
750 |
+
"""
|
751 |
+
self.input_blocks.apply(convert_module_to_f32)
|
752 |
+
self.middle_block.apply(convert_module_to_f32)
|
753 |
+
self.output_blocks.apply(convert_module_to_f32)
|
754 |
+
|
755 |
+
def forward(self, x, timesteps=None, context=None, y=None, features_adapter=None, append_to_context=None, **kwargs):
|
756 |
+
"""
|
757 |
+
Apply the model to an input batch.
|
758 |
+
:param x: an [N x C x ...] Tensor of inputs.
|
759 |
+
:param timesteps: a 1-D batch of timesteps.
|
760 |
+
:param context: conditioning plugged in via crossattn
|
761 |
+
:param y: an [N] Tensor of labels, if class-conditional.
|
762 |
+
:return: an [N x C x ...] Tensor of outputs.
|
763 |
+
"""
|
764 |
+
assert (y is not None) == (
|
765 |
+
self.num_classes is not None
|
766 |
+
), "must specify y if and only if the model is class-conditional"
|
767 |
+
hs = []
|
768 |
+
t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)
|
769 |
+
emb = self.time_embed(t_emb)
|
770 |
+
|
771 |
+
if self.num_classes is not None:
|
772 |
+
assert y.shape[0] == x.shape[0]
|
773 |
+
emb = emb + self.label_emb(y)
|
774 |
+
|
775 |
+
h = x.type(self.dtype)
|
776 |
+
|
777 |
+
if append_to_context is not None:
|
778 |
+
context = torch.cat([context, append_to_context], dim=1)
|
779 |
+
|
780 |
+
adapter_idx = 0
|
781 |
+
for id, module in enumerate(self.input_blocks):
|
782 |
+
h = module(h, emb, context)
|
783 |
+
if ((id+1)%3 == 0) and features_adapter is not None:
|
784 |
+
h = h + features_adapter[adapter_idx]
|
785 |
+
adapter_idx += 1
|
786 |
+
hs.append(h)
|
787 |
+
if features_adapter is not None:
|
788 |
+
assert len(features_adapter)==adapter_idx, 'Wrong features_adapter'
|
789 |
+
|
790 |
+
h = self.middle_block(h, emb, context)
|
791 |
+
for module in self.output_blocks:
|
792 |
+
h = th.cat([h, hs.pop()], dim=1)
|
793 |
+
h = module(h, emb, context)
|
794 |
+
h = h.type(x.dtype)
|
795 |
+
if self.predict_codebook_ids:
|
796 |
+
return self.id_predictor(h)
|
797 |
+
else:
|
798 |
+
return self.out(h)
|
ldm/modules/diffusionmodules/util.py
ADDED
@@ -0,0 +1,270 @@
|
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|
|
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|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# adopted from
|
2 |
+
# https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py
|
3 |
+
# and
|
4 |
+
# https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
|
5 |
+
# and
|
6 |
+
# https://github.com/openai/guided-diffusion/blob/0ba878e517b276c45d1195eb29f6f5f72659a05b/guided_diffusion/nn.py
|
7 |
+
#
|
8 |
+
# thanks!
|
9 |
+
|
10 |
+
|
11 |
+
import os
|
12 |
+
import math
|
13 |
+
import torch
|
14 |
+
import torch.nn as nn
|
15 |
+
import numpy as np
|
16 |
+
from einops import repeat
|
17 |
+
|
18 |
+
from ldm.util import instantiate_from_config
|
19 |
+
|
20 |
+
|
21 |
+
def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
|
22 |
+
if schedule == "linear":
|
23 |
+
betas = (
|
24 |
+
torch.linspace(linear_start ** 0.5, linear_end ** 0.5, n_timestep, dtype=torch.float64) ** 2
|
25 |
+
)
|
26 |
+
|
27 |
+
elif schedule == "cosine":
|
28 |
+
timesteps = (
|
29 |
+
torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep + cosine_s
|
30 |
+
)
|
31 |
+
alphas = timesteps / (1 + cosine_s) * np.pi / 2
|
32 |
+
alphas = torch.cos(alphas).pow(2)
|
33 |
+
alphas = alphas / alphas[0]
|
34 |
+
betas = 1 - alphas[1:] / alphas[:-1]
|
35 |
+
betas = np.clip(betas, a_min=0, a_max=0.999)
|
36 |
+
|
37 |
+
elif schedule == "sqrt_linear":
|
38 |
+
betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64)
|
39 |
+
elif schedule == "sqrt":
|
40 |
+
betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) ** 0.5
|
41 |
+
else:
|
42 |
+
raise ValueError(f"schedule '{schedule}' unknown.")
|
43 |
+
return betas.numpy()
|
44 |
+
|
45 |
+
|
46 |
+
def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True):
|
47 |
+
if ddim_discr_method == 'uniform':
|
48 |
+
c = num_ddpm_timesteps // num_ddim_timesteps
|
49 |
+
ddim_timesteps = np.asarray(list(range(0, num_ddpm_timesteps, c)))
|
50 |
+
elif ddim_discr_method == 'quad':
|
51 |
+
ddim_timesteps = ((np.linspace(0, np.sqrt(num_ddpm_timesteps * .8), num_ddim_timesteps)) ** 2).astype(int)
|
52 |
+
else:
|
53 |
+
raise NotImplementedError(f'There is no ddim discretization method called "{ddim_discr_method}"')
|
54 |
+
|
55 |
+
# assert ddim_timesteps.shape[0] == num_ddim_timesteps
|
56 |
+
# add one to get the final alpha values right (the ones from first scale to data during sampling)
|
57 |
+
steps_out = ddim_timesteps + 1
|
58 |
+
if verbose:
|
59 |
+
print(f'Selected timesteps for ddim sampler: {steps_out}')
|
60 |
+
return steps_out
|
61 |
+
|
62 |
+
|
63 |
+
def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True):
|
64 |
+
# select alphas for computing the variance schedule
|
65 |
+
alphas = alphacums[ddim_timesteps]
|
66 |
+
alphas_prev = np.asarray([alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist())
|
67 |
+
|
68 |
+
# according the the formula provided in https://arxiv.org/abs/2010.02502
|
69 |
+
sigmas = eta * np.sqrt((1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev))
|
70 |
+
if verbose:
|
71 |
+
print(f'Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}')
|
72 |
+
print(f'For the chosen value of eta, which is {eta}, '
|
73 |
+
f'this results in the following sigma_t schedule for ddim sampler {sigmas}')
|
74 |
+
return sigmas, alphas, alphas_prev
|
75 |
+
|
76 |
+
|
77 |
+
def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999):
|
78 |
+
"""
|
79 |
+
Create a beta schedule that discretizes the given alpha_t_bar function,
|
80 |
+
which defines the cumulative product of (1-beta) over time from t = [0,1].
|
81 |
+
:param num_diffusion_timesteps: the number of betas to produce.
|
82 |
+
:param alpha_bar: a lambda that takes an argument t from 0 to 1 and
|
83 |
+
produces the cumulative product of (1-beta) up to that
|
84 |
+
part of the diffusion process.
|
85 |
+
:param max_beta: the maximum beta to use; use values lower than 1 to
|
86 |
+
prevent singularities.
|
87 |
+
"""
|
88 |
+
betas = []
|
89 |
+
for i in range(num_diffusion_timesteps):
|
90 |
+
t1 = i / num_diffusion_timesteps
|
91 |
+
t2 = (i + 1) / num_diffusion_timesteps
|
92 |
+
betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))
|
93 |
+
return np.array(betas)
|
94 |
+
|
95 |
+
|
96 |
+
def extract_into_tensor(a, t, x_shape):
|
97 |
+
b, *_ = t.shape
|
98 |
+
out = a.gather(-1, t)
|
99 |
+
return out.reshape(b, *((1,) * (len(x_shape) - 1)))
|
100 |
+
|
101 |
+
|
102 |
+
def checkpoint(func, inputs, params, flag):
|
103 |
+
"""
|
104 |
+
Evaluate a function without caching intermediate activations, allowing for
|
105 |
+
reduced memory at the expense of extra compute in the backward pass.
|
106 |
+
:param func: the function to evaluate.
|
107 |
+
:param inputs: the argument sequence to pass to `func`.
|
108 |
+
:param params: a sequence of parameters `func` depends on but does not
|
109 |
+
explicitly take as arguments.
|
110 |
+
:param flag: if False, disable gradient checkpointing.
|
111 |
+
"""
|
112 |
+
if flag:
|
113 |
+
args = tuple(inputs) + tuple(params)
|
114 |
+
return CheckpointFunction.apply(func, len(inputs), *args)
|
115 |
+
else:
|
116 |
+
return func(*inputs)
|
117 |
+
|
118 |
+
|
119 |
+
class CheckpointFunction(torch.autograd.Function):
|
120 |
+
@staticmethod
|
121 |
+
def forward(ctx, run_function, length, *args):
|
122 |
+
ctx.run_function = run_function
|
123 |
+
ctx.input_tensors = list(args[:length])
|
124 |
+
ctx.input_params = list(args[length:])
|
125 |
+
ctx.gpu_autocast_kwargs = {"enabled": torch.is_autocast_enabled(),
|
126 |
+
"dtype": torch.get_autocast_gpu_dtype(),
|
127 |
+
"cache_enabled": torch.is_autocast_cache_enabled()}
|
128 |
+
with torch.no_grad():
|
129 |
+
output_tensors = ctx.run_function(*ctx.input_tensors)
|
130 |
+
return output_tensors
|
131 |
+
|
132 |
+
@staticmethod
|
133 |
+
def backward(ctx, *output_grads):
|
134 |
+
ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors]
|
135 |
+
with torch.enable_grad(), \
|
136 |
+
torch.cuda.amp.autocast(**ctx.gpu_autocast_kwargs):
|
137 |
+
# Fixes a bug where the first op in run_function modifies the
|
138 |
+
# Tensor storage in place, which is not allowed for detach()'d
|
139 |
+
# Tensors.
|
140 |
+
shallow_copies = [x.view_as(x) for x in ctx.input_tensors]
|
141 |
+
output_tensors = ctx.run_function(*shallow_copies)
|
142 |
+
input_grads = torch.autograd.grad(
|
143 |
+
output_tensors,
|
144 |
+
ctx.input_tensors + ctx.input_params,
|
145 |
+
output_grads,
|
146 |
+
allow_unused=True,
|
147 |
+
)
|
148 |
+
del ctx.input_tensors
|
149 |
+
del ctx.input_params
|
150 |
+
del output_tensors
|
151 |
+
return (None, None) + input_grads
|
152 |
+
|
153 |
+
|
154 |
+
def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False):
|
155 |
+
"""
|
156 |
+
Create sinusoidal timestep embeddings.
|
157 |
+
:param timesteps: a 1-D Tensor of N indices, one per batch element.
|
158 |
+
These may be fractional.
|
159 |
+
:param dim: the dimension of the output.
|
160 |
+
:param max_period: controls the minimum frequency of the embeddings.
|
161 |
+
:return: an [N x dim] Tensor of positional embeddings.
|
162 |
+
"""
|
163 |
+
if not repeat_only:
|
164 |
+
half = dim // 2
|
165 |
+
freqs = torch.exp(
|
166 |
+
-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half
|
167 |
+
).to(device=timesteps.device)
|
168 |
+
args = timesteps[:, None].float() * freqs[None]
|
169 |
+
embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
|
170 |
+
if dim % 2:
|
171 |
+
embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
|
172 |
+
else:
|
173 |
+
embedding = repeat(timesteps, 'b -> b d', d=dim)
|
174 |
+
return embedding
|
175 |
+
|
176 |
+
|
177 |
+
def zero_module(module):
|
178 |
+
"""
|
179 |
+
Zero out the parameters of a module and return it.
|
180 |
+
"""
|
181 |
+
for p in module.parameters():
|
182 |
+
p.detach().zero_()
|
183 |
+
return module
|
184 |
+
|
185 |
+
|
186 |
+
def scale_module(module, scale):
|
187 |
+
"""
|
188 |
+
Scale the parameters of a module and return it.
|
189 |
+
"""
|
190 |
+
for p in module.parameters():
|
191 |
+
p.detach().mul_(scale)
|
192 |
+
return module
|
193 |
+
|
194 |
+
|
195 |
+
def mean_flat(tensor):
|
196 |
+
"""
|
197 |
+
Take the mean over all non-batch dimensions.
|
198 |
+
"""
|
199 |
+
return tensor.mean(dim=list(range(1, len(tensor.shape))))
|
200 |
+
|
201 |
+
|
202 |
+
def normalization(channels):
|
203 |
+
"""
|
204 |
+
Make a standard normalization layer.
|
205 |
+
:param channels: number of input channels.
|
206 |
+
:return: an nn.Module for normalization.
|
207 |
+
"""
|
208 |
+
return GroupNorm32(32, channels)
|
209 |
+
|
210 |
+
|
211 |
+
# PyTorch 1.7 has SiLU, but we support PyTorch 1.5.
|
212 |
+
class SiLU(nn.Module):
|
213 |
+
def forward(self, x):
|
214 |
+
return x * torch.sigmoid(x)
|
215 |
+
|
216 |
+
|
217 |
+
class GroupNorm32(nn.GroupNorm):
|
218 |
+
def forward(self, x):
|
219 |
+
return super().forward(x.float()).type(x.dtype)
|
220 |
+
|
221 |
+
def conv_nd(dims, *args, **kwargs):
|
222 |
+
"""
|
223 |
+
Create a 1D, 2D, or 3D convolution module.
|
224 |
+
"""
|
225 |
+
if dims == 1:
|
226 |
+
return nn.Conv1d(*args, **kwargs)
|
227 |
+
elif dims == 2:
|
228 |
+
return nn.Conv2d(*args, **kwargs)
|
229 |
+
elif dims == 3:
|
230 |
+
return nn.Conv3d(*args, **kwargs)
|
231 |
+
raise ValueError(f"unsupported dimensions: {dims}")
|
232 |
+
|
233 |
+
|
234 |
+
def linear(*args, **kwargs):
|
235 |
+
"""
|
236 |
+
Create a linear module.
|
237 |
+
"""
|
238 |
+
return nn.Linear(*args, **kwargs)
|
239 |
+
|
240 |
+
|
241 |
+
def avg_pool_nd(dims, *args, **kwargs):
|
242 |
+
"""
|
243 |
+
Create a 1D, 2D, or 3D average pooling module.
|
244 |
+
"""
|
245 |
+
if dims == 1:
|
246 |
+
return nn.AvgPool1d(*args, **kwargs)
|
247 |
+
elif dims == 2:
|
248 |
+
return nn.AvgPool2d(*args, **kwargs)
|
249 |
+
elif dims == 3:
|
250 |
+
return nn.AvgPool3d(*args, **kwargs)
|
251 |
+
raise ValueError(f"unsupported dimensions: {dims}")
|
252 |
+
|
253 |
+
|
254 |
+
class HybridConditioner(nn.Module):
|
255 |
+
|
256 |
+
def __init__(self, c_concat_config, c_crossattn_config):
|
257 |
+
super().__init__()
|
258 |
+
self.concat_conditioner = instantiate_from_config(c_concat_config)
|
259 |
+
self.crossattn_conditioner = instantiate_from_config(c_crossattn_config)
|
260 |
+
|
261 |
+
def forward(self, c_concat, c_crossattn):
|
262 |
+
c_concat = self.concat_conditioner(c_concat)
|
263 |
+
c_crossattn = self.crossattn_conditioner(c_crossattn)
|
264 |
+
return {'c_concat': [c_concat], 'c_crossattn': [c_crossattn]}
|
265 |
+
|
266 |
+
|
267 |
+
def noise_like(shape, device, repeat=False):
|
268 |
+
repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1)))
|
269 |
+
noise = lambda: torch.randn(shape, device=device)
|
270 |
+
return repeat_noise() if repeat else noise()
|
ldm/modules/distributions/__init__.py
ADDED
File without changes
|
ldm/modules/distributions/__pycache__/__init__.cpython-38.pyc
ADDED
Binary file (211 Bytes). View file
|
|
ldm/modules/distributions/__pycache__/distributions.cpython-38.pyc
ADDED
Binary file (3.85 kB). View file
|
|
ldm/modules/distributions/distributions.py
ADDED
@@ -0,0 +1,92 @@
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
|
|
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|
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|
|
|
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|
|
|
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|
|
|
|
|
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|
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|
|
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|
|
|
|
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|
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|
|
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|
|
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|
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|
|
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|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
|
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|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import torch
|
2 |
+
import numpy as np
|
3 |
+
|
4 |
+
|
5 |
+
class AbstractDistribution:
|
6 |
+
def sample(self):
|
7 |
+
raise NotImplementedError()
|
8 |
+
|
9 |
+
def mode(self):
|
10 |
+
raise NotImplementedError()
|
11 |
+
|
12 |
+
|
13 |
+
class DiracDistribution(AbstractDistribution):
|
14 |
+
def __init__(self, value):
|
15 |
+
self.value = value
|
16 |
+
|
17 |
+
def sample(self):
|
18 |
+
return self.value
|
19 |
+
|
20 |
+
def mode(self):
|
21 |
+
return self.value
|
22 |
+
|
23 |
+
|
24 |
+
class DiagonalGaussianDistribution(object):
|
25 |
+
def __init__(self, parameters, deterministic=False):
|
26 |
+
self.parameters = parameters
|
27 |
+
self.mean, self.logvar = torch.chunk(parameters, 2, dim=1)
|
28 |
+
self.logvar = torch.clamp(self.logvar, -30.0, 20.0)
|
29 |
+
self.deterministic = deterministic
|
30 |
+
self.std = torch.exp(0.5 * self.logvar)
|
31 |
+
self.var = torch.exp(self.logvar)
|
32 |
+
if self.deterministic:
|
33 |
+
self.var = self.std = torch.zeros_like(self.mean).to(device=self.parameters.device)
|
34 |
+
|
35 |
+
def sample(self):
|
36 |
+
x = self.mean + self.std * torch.randn(self.mean.shape).to(device=self.parameters.device)
|
37 |
+
return x
|
38 |
+
|
39 |
+
def kl(self, other=None):
|
40 |
+
if self.deterministic:
|
41 |
+
return torch.Tensor([0.])
|
42 |
+
else:
|
43 |
+
if other is None:
|
44 |
+
return 0.5 * torch.sum(torch.pow(self.mean, 2)
|
45 |
+
+ self.var - 1.0 - self.logvar,
|
46 |
+
dim=[1, 2, 3])
|
47 |
+
else:
|
48 |
+
return 0.5 * torch.sum(
|
49 |
+
torch.pow(self.mean - other.mean, 2) / other.var
|
50 |
+
+ self.var / other.var - 1.0 - self.logvar + other.logvar,
|
51 |
+
dim=[1, 2, 3])
|
52 |
+
|
53 |
+
def nll(self, sample, dims=[1,2,3]):
|
54 |
+
if self.deterministic:
|
55 |
+
return torch.Tensor([0.])
|
56 |
+
logtwopi = np.log(2.0 * np.pi)
|
57 |
+
return 0.5 * torch.sum(
|
58 |
+
logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var,
|
59 |
+
dim=dims)
|
60 |
+
|
61 |
+
def mode(self):
|
62 |
+
return self.mean
|
63 |
+
|
64 |
+
|
65 |
+
def normal_kl(mean1, logvar1, mean2, logvar2):
|
66 |
+
"""
|
67 |
+
source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12
|
68 |
+
Compute the KL divergence between two gaussians.
|
69 |
+
Shapes are automatically broadcasted, so batches can be compared to
|
70 |
+
scalars, among other use cases.
|
71 |
+
"""
|
72 |
+
tensor = None
|
73 |
+
for obj in (mean1, logvar1, mean2, logvar2):
|
74 |
+
if isinstance(obj, torch.Tensor):
|
75 |
+
tensor = obj
|
76 |
+
break
|
77 |
+
assert tensor is not None, "at least one argument must be a Tensor"
|
78 |
+
|
79 |
+
# Force variances to be Tensors. Broadcasting helps convert scalars to
|
80 |
+
# Tensors, but it does not work for torch.exp().
|
81 |
+
logvar1, logvar2 = [
|
82 |
+
x if isinstance(x, torch.Tensor) else torch.tensor(x).to(tensor)
|
83 |
+
for x in (logvar1, logvar2)
|
84 |
+
]
|
85 |
+
|
86 |
+
return 0.5 * (
|
87 |
+
-1.0
|
88 |
+
+ logvar2
|
89 |
+
- logvar1
|
90 |
+
+ torch.exp(logvar1 - logvar2)
|
91 |
+
+ ((mean1 - mean2) ** 2) * torch.exp(-logvar2)
|
92 |
+
)
|
ldm/modules/ema.py
ADDED
@@ -0,0 +1,80 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import torch
|
2 |
+
from torch import nn
|
3 |
+
|
4 |
+
|
5 |
+
class LitEma(nn.Module):
|
6 |
+
def __init__(self, model, decay=0.9999, use_num_upates=True):
|
7 |
+
super().__init__()
|
8 |
+
if decay < 0.0 or decay > 1.0:
|
9 |
+
raise ValueError('Decay must be between 0 and 1')
|
10 |
+
|
11 |
+
self.m_name2s_name = {}
|
12 |
+
self.register_buffer('decay', torch.tensor(decay, dtype=torch.float32))
|
13 |
+
self.register_buffer('num_updates', torch.tensor(0, dtype=torch.int) if use_num_upates
|
14 |
+
else torch.tensor(-1, dtype=torch.int))
|
15 |
+
|
16 |
+
for name, p in model.named_parameters():
|
17 |
+
if p.requires_grad:
|
18 |
+
# remove as '.'-character is not allowed in buffers
|
19 |
+
s_name = name.replace('.', '')
|
20 |
+
self.m_name2s_name.update({name: s_name})
|
21 |
+
self.register_buffer(s_name, p.clone().detach().data)
|
22 |
+
|
23 |
+
self.collected_params = []
|
24 |
+
|
25 |
+
def reset_num_updates(self):
|
26 |
+
del self.num_updates
|
27 |
+
self.register_buffer('num_updates', torch.tensor(0, dtype=torch.int))
|
28 |
+
|
29 |
+
def forward(self, model):
|
30 |
+
decay = self.decay
|
31 |
+
|
32 |
+
if self.num_updates >= 0:
|
33 |
+
self.num_updates += 1
|
34 |
+
decay = min(self.decay, (1 + self.num_updates) / (10 + self.num_updates))
|
35 |
+
|
36 |
+
one_minus_decay = 1.0 - decay
|
37 |
+
|
38 |
+
with torch.no_grad():
|
39 |
+
m_param = dict(model.named_parameters())
|
40 |
+
shadow_params = dict(self.named_buffers())
|
41 |
+
|
42 |
+
for key in m_param:
|
43 |
+
if m_param[key].requires_grad:
|
44 |
+
sname = self.m_name2s_name[key]
|
45 |
+
shadow_params[sname] = shadow_params[sname].type_as(m_param[key])
|
46 |
+
shadow_params[sname].sub_(one_minus_decay * (shadow_params[sname] - m_param[key]))
|
47 |
+
else:
|
48 |
+
assert not key in self.m_name2s_name
|
49 |
+
|
50 |
+
def copy_to(self, model):
|
51 |
+
m_param = dict(model.named_parameters())
|
52 |
+
shadow_params = dict(self.named_buffers())
|
53 |
+
for key in m_param:
|
54 |
+
if m_param[key].requires_grad:
|
55 |
+
m_param[key].data.copy_(shadow_params[self.m_name2s_name[key]].data)
|
56 |
+
else:
|
57 |
+
assert not key in self.m_name2s_name
|
58 |
+
|
59 |
+
def store(self, parameters):
|
60 |
+
"""
|
61 |
+
Save the current parameters for restoring later.
|
62 |
+
Args:
|
63 |
+
parameters: Iterable of `torch.nn.Parameter`; the parameters to be
|
64 |
+
temporarily stored.
|
65 |
+
"""
|
66 |
+
self.collected_params = [param.clone() for param in parameters]
|
67 |
+
|
68 |
+
def restore(self, parameters):
|
69 |
+
"""
|
70 |
+
Restore the parameters stored with the `store` method.
|
71 |
+
Useful to validate the model with EMA parameters without affecting the
|
72 |
+
original optimization process. Store the parameters before the
|
73 |
+
`copy_to` method. After validation (or model saving), use this to
|
74 |
+
restore the former parameters.
|
75 |
+
Args:
|
76 |
+
parameters: Iterable of `torch.nn.Parameter`; the parameters to be
|
77 |
+
updated with the stored parameters.
|
78 |
+
"""
|
79 |
+
for c_param, param in zip(self.collected_params, parameters):
|
80 |
+
param.data.copy_(c_param.data)
|
ldm/modules/encoders/__init__.py
ADDED
File without changes
|
ldm/modules/encoders/__pycache__/__init__.cpython-38.pyc
ADDED
Binary file (206 Bytes). View file
|
|
ldm/modules/encoders/__pycache__/adapter.cpython-38.pyc
ADDED
Binary file (11.6 kB). View file
|
|
ldm/modules/encoders/__pycache__/modules.cpython-38.pyc
ADDED
Binary file (14.9 kB). View file
|
|