FilesExpand file tree

main

/

convert_stable_cascade.py

Copy path

Blame

More file actions

Blame

More file actions

Latest commit

 

History

History

History

218 lines (195 loc) · 8.19 KB

main

/

convert_stable_cascade.py

Copy path

Top

File metadata and controls

• Code
• Blame

218 lines (195 loc) · 8.19 KB

Raw

Copy raw file

Download raw file

Open symbols panel

Edit and raw actions

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

# 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

)