-
Notifications
You must be signed in to change notification settings - Fork 4
/
utils.py
190 lines (138 loc) · 6.64 KB
/
utils.py
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
import torch
import torchvision
import os
import shutil
import gc
import tqdm
import matplotlib.pyplot as plt
import torchvision.transforms as transforms
from transformers import CLIPTextModel
from lora_w2w import LoRAw2w
from diffusers import AutoencoderKL, DDPMScheduler, DiffusionPipeline, UNet2DConditionModel, LMSDiscreteScheduler
from safetensors.torch import save_file
from transformers import AutoTokenizer, PretrainedConfig
from PIL import Image
import warnings
warnings.filterwarnings("ignore")
from diffusers import (
AutoencoderKL,
DDPMScheduler,
DiffusionPipeline,
DPMSolverMultistepScheduler,
UNet2DConditionModel,
PNDMScheduler,
StableDiffusionPipeline
)
######## Basic utilities
### load base models
def load_models(device):
pretrained_model_name_or_path = "stablediffusionapi/realistic-vision-v51"
revision = None
rank = 1
weight_dtype = torch.bfloat16
# Load scheduler, tokenizer and models.
pipe = StableDiffusionPipeline.from_pretrained("stablediffusionapi/realistic-vision-v51",
torch_dtype=torch.float16,safety_checker = None,
requires_safety_checker = False).to(device)
noise_scheduler = pipe.scheduler
del pipe
tokenizer = AutoTokenizer.from_pretrained(
pretrained_model_name_or_path, subfolder="tokenizer", revision=revision
)
text_encoder = CLIPTextModel.from_pretrained(
pretrained_model_name_or_path, subfolder="text_encoder", revision=revision
)
vae = AutoencoderKL.from_pretrained(pretrained_model_name_or_path, subfolder="vae", revision=revision)
unet = UNet2DConditionModel.from_pretrained(
pretrained_model_name_or_path, subfolder="unet", revision=revision
)
unet.requires_grad_(False)
unet.to(device, dtype=weight_dtype)
vae.requires_grad_(False)
text_encoder.requires_grad_(False)
vae.requires_grad_(False)
vae.to(device, dtype=weight_dtype)
text_encoder.to(device, dtype=weight_dtype)
print("")
return unet, vae, text_encoder, tokenizer, noise_scheduler
### basic inference to generate images conditioned on text prompts
@torch.no_grad
def inference(network, unet, vae, text_encoder, tokenizer, prompt, negative_prompt, guidance_scale, noise_scheduler, ddim_steps, seed, generator, device):
generator = generator.manual_seed(seed)
latents = torch.randn(
(1, unet.in_channels, 512 // 8, 512 // 8),
generator = generator,
device = device
).bfloat16()
text_input = tokenizer(prompt, padding="max_length", max_length=tokenizer.model_max_length, truncation=True, return_tensors="pt")
text_embeddings = text_encoder(text_input.input_ids.to(device))[0]
max_length = text_input.input_ids.shape[-1]
uncond_input = tokenizer(
[negative_prompt], padding="max_length", max_length=max_length, return_tensors="pt"
)
uncond_embeddings = text_encoder(uncond_input.input_ids.to(device))[0]
text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
noise_scheduler.set_timesteps(ddim_steps)
latents = latents * noise_scheduler.init_noise_sigma
for i,t in enumerate(tqdm.tqdm(noise_scheduler.timesteps)):
latent_model_input = torch.cat([latents] * 2)
latent_model_input = noise_scheduler.scale_model_input(latent_model_input, timestep=t)
with network:
noise_pred = unet(latent_model_input, t, encoder_hidden_states=text_embeddings, timestep_cond= None).sample
#guidance
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
latents = noise_scheduler.step(noise_pred, t, latents).prev_sample
latents = 1 / 0.18215 * latents
image = vae.decode(latents).sample
image = (image / 2 + 0.5).clamp(0, 1)
return image
### save model in w2w space (principal component representation)
def save_model_w2w(network, path):
proj = network.proj.clone().detach().float()
if not os.path.exists(path):
os.makedirs(path)
torch.save(proj, path+"/"+"w2wmodel.pt")
### save model in format compatible with Diffusers
def save_model_for_diffusers(network,std, mean, v, weight_dimensions, path):
proj = network.proj.clone().detach()
unproj = torch.matmul(proj,v[:, :].T)*std+mean
final_weights0 = {}
counter = 0
for key in weight_dimensions.keys():
final_weights0[key] = unproj[0, counter:counter+weight_dimensions[key][0][0]].unflatten(0, weight_dimensions[key][1])
counter += weight_dimensions[key][0][0]
#renaming keys to be compatible with Diffusers
for key in list(final_weights0.keys()):
final_weights0[key.replace( "lora_unet_", "base_model.model.").replace("A", "down").replace("B", "up").replace( "weight", "identity1.weight").replace("_lora", ".lora").replace("lora_down", "lora_A").replace("lora_up", "lora_B")] = final_weights0.pop(key)
final_weights0_keys = sorted(final_weights0.keys())
final_weights = {}
for i,key in enumerate(final_weights0_keys):
final_weights[key] = final_weights0[key]
if not os.path.exists(path):
os.makedirs(path+"/unet")
else:
os.mkdir(path+"/unet")
#add config for PeftConfig
shutil.copyfile("../files/adapter_config.json", path+"/unet/adapter_config.json")
save_file(final_weights, path+"/unet/adapter_model.safetensors")
def unflatten(flattened_weights, weight_dimensions, path):
final_weights0 = {}
counter = 0
for key in weight_dimensions.keys():
final_weights0[key] = flattened_weights[0, counter:counter+weight_dimensions[key][0][0]].unflatten(0, weight_dimensions[key][1])
counter += weight_dimensions[key][0][0]
#renaming keys to be compatible with Diffusers
for key in list(final_weights0.keys()):
final_weights0[key.replace( "lora_unet_", "base_model.model.").replace("A", "down").replace("B", "up").replace( "weight", "identity1.weight").replace("_lora", ".lora").replace("lora_down", "lora_A").replace("lora_up", "lora_B")] = final_weights0.pop(key)
final_weights0_keys = sorted(final_weights0.keys())
final_weights = {}
for i,key in enumerate(final_weights0_keys):
final_weights[key] = final_weights0[key]
if not os.path.exists(path):
os.makedirs(path+"/unet")
else:
os.mkdir(path+"/unet")
#add config for PeftConfig
shutil.copyfile("../files/adapter_config.json", path+"/unet/adapter_config.json")
save_file(final_weights, path+"/unet/adapter_model.safetensors")