File size: 7,277 Bytes
43b7e92 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 |
import gc
import tempfile
import unittest
import torch
from diffusers import ControlNetModel, StableDiffusionControlNetPipeline
from diffusers.utils import load_image
from diffusers.utils.testing_utils import (
enable_full_determinism,
numpy_cosine_similarity_distance,
require_torch_gpu,
slow,
torch_device,
)
from .single_file_testing_utils import (
SDSingleFileTesterMixin,
download_diffusers_config,
download_original_config,
download_single_file_checkpoint,
)
enable_full_determinism()
@slow
@require_torch_gpu
class StableDiffusionControlNetPipelineSingleFileSlowTests(unittest.TestCase, SDSingleFileTesterMixin):
pipeline_class = StableDiffusionControlNetPipeline
ckpt_path = "https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/v1-5-pruned-emaonly.safetensors"
original_config = (
"https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml"
)
repo_id = "runwayml/stable-diffusion-v1-5"
def setUp(self):
super().setUp()
gc.collect()
torch.cuda.empty_cache()
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def get_inputs(self, device, generator_device="cpu", dtype=torch.float32, seed=0):
generator = torch.Generator(device=generator_device).manual_seed(seed)
init_image = load_image(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_img2img/sketch-mountains-input.png"
)
control_image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"
).resize((512, 512))
prompt = "bird"
inputs = {
"prompt": prompt,
"image": init_image,
"control_image": control_image,
"generator": generator,
"num_inference_steps": 3,
"strength": 0.75,
"guidance_scale": 7.5,
"output_type": "np",
}
return inputs
def test_single_file_format_inference_is_same_as_pretrained(self):
controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_canny")
pipe = self.pipeline_class.from_pretrained(self.repo_id, controlnet=controlnet)
pipe.unet.set_default_attn_processor()
pipe.enable_model_cpu_offload()
pipe_sf = self.pipeline_class.from_single_file(
self.ckpt_path,
controlnet=controlnet,
)
pipe_sf.unet.set_default_attn_processor()
pipe_sf.enable_model_cpu_offload()
inputs = self.get_inputs(torch_device)
output = pipe(**inputs).images[0]
inputs = self.get_inputs(torch_device)
output_sf = pipe_sf(**inputs).images[0]
max_diff = numpy_cosine_similarity_distance(output_sf.flatten(), output.flatten())
assert max_diff < 1e-3
def test_single_file_components(self):
controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_canny")
pipe = self.pipeline_class.from_pretrained(
self.repo_id, variant="fp16", safety_checker=None, controlnet=controlnet
)
pipe_single_file = self.pipeline_class.from_single_file(
self.ckpt_path,
safety_checker=None,
controlnet=controlnet,
)
super()._compare_component_configs(pipe, pipe_single_file)
def test_single_file_components_local_files_only(self):
controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_canny")
pipe = self.pipeline_class.from_pretrained(self.repo_id, controlnet=controlnet)
with tempfile.TemporaryDirectory() as tmpdir:
ckpt_filename = self.ckpt_path.split("/")[-1]
local_ckpt_path = download_single_file_checkpoint(self.repo_id, ckpt_filename, tmpdir)
pipe_single_file = self.pipeline_class.from_single_file(
local_ckpt_path, controlnet=controlnet, safety_checker=None, local_files_only=True
)
super()._compare_component_configs(pipe, pipe_single_file)
def test_single_file_components_with_original_config(self):
controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_canny", variant="fp16")
pipe = self.pipeline_class.from_pretrained(self.repo_id, controlnet=controlnet)
pipe_single_file = self.pipeline_class.from_single_file(
self.ckpt_path, controlnet=controlnet, safety_checker=None, original_config=self.original_config
)
super()._compare_component_configs(pipe, pipe_single_file)
def test_single_file_components_with_original_config_local_files_only(self):
controlnet = ControlNetModel.from_pretrained(
"lllyasviel/control_v11p_sd15_canny", torch_dtype=torch.float16, variant="fp16"
)
pipe = self.pipeline_class.from_pretrained(
self.repo_id,
controlnet=controlnet,
)
with tempfile.TemporaryDirectory() as tmpdir:
ckpt_filename = self.ckpt_path.split("/")[-1]
local_ckpt_path = download_single_file_checkpoint(self.repo_id, ckpt_filename, tmpdir)
local_original_config = download_original_config(self.original_config, tmpdir)
pipe_single_file = self.pipeline_class.from_single_file(
local_ckpt_path,
original_config=local_original_config,
controlnet=controlnet,
safety_checker=None,
local_files_only=True,
)
super()._compare_component_configs(pipe, pipe_single_file)
def test_single_file_components_with_diffusers_config(self):
controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_canny", variant="fp16")
pipe = self.pipeline_class.from_pretrained(self.repo_id, controlnet=controlnet)
pipe_single_file = self.pipeline_class.from_single_file(
self.ckpt_path, controlnet=controlnet, safety_checker=None, original_config=self.original_config
)
super()._compare_component_configs(pipe, pipe_single_file)
def test_single_file_components_with_diffusers_config_local_files_only(self):
controlnet = ControlNetModel.from_pretrained(
"lllyasviel/control_v11p_sd15_canny", torch_dtype=torch.float16, variant="fp16"
)
pipe = self.pipeline_class.from_pretrained(
self.repo_id,
controlnet=controlnet,
)
with tempfile.TemporaryDirectory() as tmpdir:
ckpt_filename = self.ckpt_path.split("/")[-1]
local_ckpt_path = download_single_file_checkpoint(self.repo_id, ckpt_filename, tmpdir)
local_diffusers_config = download_diffusers_config(self.repo_id, tmpdir)
pipe_single_file = self.pipeline_class.from_single_file(
local_ckpt_path,
config=local_diffusers_config,
safety_checker=None,
controlnet=controlnet,
local_files_only=True,
)
super()._compare_component_configs(pipe, pipe_single_file)
|