File size: 8,383 Bytes
3a25a0a |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 |
# Run this script to convert the Stable Cascade model weights to a diffusers pipeline.
import argparse
from contextlib import nullcontext
import torch
from safetensors.torch import load_file
from transformers import (
AutoTokenizer,
CLIPConfig,
CLIPImageProcessor,
CLIPTextModelWithProjection,
CLIPVisionModelWithProjection,
)
from diffusers import (
DDPMWuerstchenScheduler,
StableCascadeCombinedPipeline,
StableCascadeDecoderPipeline,
StableCascadePriorPipeline,
)
from diffusers.loaders.single_file_utils import convert_stable_cascade_unet_single_file_to_diffusers
from diffusers.models import StableCascadeUNet
from diffusers.models.modeling_utils import load_model_dict_into_meta
from diffusers.pipelines.wuerstchen import PaellaVQModel
from diffusers.utils import is_accelerate_available
if is_accelerate_available():
from accelerate import init_empty_weights
parser = argparse.ArgumentParser(description="Convert Stable Cascade model weights to a diffusers pipeline")
parser.add_argument("--model_path", type=str, help="Location of Stable Cascade weights")
parser.add_argument("--stage_c_name", type=str, default="stage_c.safetensors", help="Name of stage c checkpoint file")
parser.add_argument("--stage_b_name", type=str, default="stage_b.safetensors", help="Name of stage b checkpoint file")
parser.add_argument("--skip_stage_c", action="store_true", help="Skip converting stage c")
parser.add_argument("--skip_stage_b", action="store_true", help="Skip converting stage b")
parser.add_argument("--use_safetensors", action="store_true", help="Use SafeTensors for conversion")
parser.add_argument(
"--prior_output_path", default="stable-cascade-prior", type=str, help="Hub organization to save the pipelines to"
)
parser.add_argument(
"--decoder_output_path",
type=str,
default="stable-cascade-decoder",
help="Hub organization to save the pipelines to",
)
parser.add_argument(
"--combined_output_path",
type=str,
default="stable-cascade-combined",
help="Hub organization to save the pipelines to",
)
parser.add_argument("--save_combined", action="store_true")
parser.add_argument("--push_to_hub", action="store_true", help="Push to hub")
parser.add_argument("--variant", type=str, help="Set to bf16 to save bfloat16 weights")
args = parser.parse_args()
if args.skip_stage_b and args.skip_stage_c:
raise ValueError("At least one stage should be converted")
if (args.skip_stage_b or args.skip_stage_c) and args.save_combined:
raise ValueError("Cannot skip stages when creating a combined pipeline")
model_path = args.model_path
device = "cpu"
if args.variant == "bf16":
dtype = torch.bfloat16
else:
dtype = torch.float32
# set paths to model weights
prior_checkpoint_path = f"{model_path}/{args.stage_c_name}"
decoder_checkpoint_path = f"{model_path}/{args.stage_b_name}"
# Clip Text encoder and tokenizer
config = CLIPConfig.from_pretrained("laion/CLIP-ViT-bigG-14-laion2B-39B-b160k")
config.text_config.projection_dim = config.projection_dim
text_encoder = CLIPTextModelWithProjection.from_pretrained(
"laion/CLIP-ViT-bigG-14-laion2B-39B-b160k", config=config.text_config
)
tokenizer = AutoTokenizer.from_pretrained("laion/CLIP-ViT-bigG-14-laion2B-39B-b160k")
# image processor
feature_extractor = CLIPImageProcessor()
image_encoder = CLIPVisionModelWithProjection.from_pretrained("openai/clip-vit-large-patch14")
# scheduler for prior and decoder
scheduler = DDPMWuerstchenScheduler()
ctx = init_empty_weights if is_accelerate_available() else nullcontext
if not args.skip_stage_c:
# Prior
if args.use_safetensors:
prior_orig_state_dict = load_file(prior_checkpoint_path, device=device)
else:
prior_orig_state_dict = torch.load(prior_checkpoint_path, map_location=device)
prior_state_dict = convert_stable_cascade_unet_single_file_to_diffusers(prior_orig_state_dict)
with ctx():
prior_model = StableCascadeUNet(
in_channels=16,
out_channels=16,
timestep_ratio_embedding_dim=64,
patch_size=1,
conditioning_dim=2048,
block_out_channels=[2048, 2048],
num_attention_heads=[32, 32],
down_num_layers_per_block=[8, 24],
up_num_layers_per_block=[24, 8],
down_blocks_repeat_mappers=[1, 1],
up_blocks_repeat_mappers=[1, 1],
block_types_per_layer=[
["SDCascadeResBlock", "SDCascadeTimestepBlock", "SDCascadeAttnBlock"],
["SDCascadeResBlock", "SDCascadeTimestepBlock", "SDCascadeAttnBlock"],
],
clip_text_in_channels=1280,
clip_text_pooled_in_channels=1280,
clip_image_in_channels=768,
clip_seq=4,
kernel_size=3,
dropout=[0.1, 0.1],
self_attn=True,
timestep_conditioning_type=["sca", "crp"],
switch_level=[False],
)
if is_accelerate_available():
load_model_dict_into_meta(prior_model, prior_state_dict)
else:
prior_model.load_state_dict(prior_state_dict)
# Prior pipeline
prior_pipeline = StableCascadePriorPipeline(
prior=prior_model,
tokenizer=tokenizer,
text_encoder=text_encoder,
image_encoder=image_encoder,
scheduler=scheduler,
feature_extractor=feature_extractor,
)
prior_pipeline.to(dtype).save_pretrained(
args.prior_output_path, push_to_hub=args.push_to_hub, variant=args.variant
)
if not args.skip_stage_b:
# Decoder
if args.use_safetensors:
decoder_orig_state_dict = load_file(decoder_checkpoint_path, device=device)
else:
decoder_orig_state_dict = torch.load(decoder_checkpoint_path, map_location=device)
decoder_state_dict = convert_stable_cascade_unet_single_file_to_diffusers(decoder_orig_state_dict)
with ctx():
decoder = StableCascadeUNet(
in_channels=4,
out_channels=4,
timestep_ratio_embedding_dim=64,
patch_size=2,
conditioning_dim=1280,
block_out_channels=[320, 640, 1280, 1280],
down_num_layers_per_block=[2, 6, 28, 6],
up_num_layers_per_block=[6, 28, 6, 2],
down_blocks_repeat_mappers=[1, 1, 1, 1],
up_blocks_repeat_mappers=[3, 3, 2, 2],
num_attention_heads=[0, 0, 20, 20],
block_types_per_layer=[
["SDCascadeResBlock", "SDCascadeTimestepBlock"],
["SDCascadeResBlock", "SDCascadeTimestepBlock"],
["SDCascadeResBlock", "SDCascadeTimestepBlock", "SDCascadeAttnBlock"],
["SDCascadeResBlock", "SDCascadeTimestepBlock", "SDCascadeAttnBlock"],
],
clip_text_pooled_in_channels=1280,
clip_seq=4,
effnet_in_channels=16,
pixel_mapper_in_channels=3,
kernel_size=3,
dropout=[0, 0, 0.1, 0.1],
self_attn=True,
timestep_conditioning_type=["sca"],
)
if is_accelerate_available():
load_model_dict_into_meta(decoder, decoder_state_dict)
else:
decoder.load_state_dict(decoder_state_dict)
# VQGAN from Wuerstchen-V2
vqmodel = PaellaVQModel.from_pretrained("warp-ai/wuerstchen", subfolder="vqgan")
# Decoder pipeline
decoder_pipeline = StableCascadeDecoderPipeline(
decoder=decoder, text_encoder=text_encoder, tokenizer=tokenizer, vqgan=vqmodel, scheduler=scheduler
)
decoder_pipeline.to(dtype).save_pretrained(
args.decoder_output_path, push_to_hub=args.push_to_hub, variant=args.variant
)
if args.save_combined:
# Stable Cascade combined pipeline
stable_cascade_pipeline = StableCascadeCombinedPipeline(
# Decoder
text_encoder=text_encoder,
tokenizer=tokenizer,
decoder=decoder,
scheduler=scheduler,
vqgan=vqmodel,
# Prior
prior_text_encoder=text_encoder,
prior_tokenizer=tokenizer,
prior_prior=prior_model,
prior_scheduler=scheduler,
prior_image_encoder=image_encoder,
prior_feature_extractor=feature_extractor,
)
stable_cascade_pipeline.to(dtype).save_pretrained(
args.combined_output_path, push_to_hub=args.push_to_hub, variant=args.variant
)
|