File size: 8,911 Bytes
266dbe0 d8457bc 266dbe0 |
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 |
# https://github.com/GaParmar/img2img-turbo/blob/main/src/pix2pix_turbo.py
import os
import requests
import sys
import pdb
import copy
from tqdm import tqdm
import torch
from transformers import AutoTokenizer, PretrainedConfig, CLIPTextModel
from diffusers import AutoencoderKL, UNet2DConditionModel, DDPMScheduler
from diffusers.utils.peft_utils import set_weights_and_activate_adapters
from peft import LoraConfig
from pipelines.pix2pix.model import (
make_1step_sched,
my_vae_encoder_fwd,
my_vae_decoder_fwd,
)
class TwinConv(torch.nn.Module):
def __init__(self, convin_pretrained, convin_curr):
super(TwinConv, self).__init__()
self.conv_in_pretrained = copy.deepcopy(convin_pretrained)
self.conv_in_curr = copy.deepcopy(convin_curr)
self.r = None
def forward(self, x):
x1 = self.conv_in_pretrained(x).detach()
x2 = self.conv_in_curr(x)
return x1 * (1 - self.r) + x2 * (self.r)
class Pix2Pix_Turbo(torch.nn.Module):
def __init__(self, name, ckpt_folder="checkpoints"):
super().__init__()
self.tokenizer = AutoTokenizer.from_pretrained(
"stabilityai/sd-turbo", subfolder="tokenizer"
)
self.text_encoder = CLIPTextModel.from_pretrained(
"stabilityai/sd-turbo", subfolder="text_encoder"
).cuda()
self.sched = make_1step_sched()
vae = AutoencoderKL.from_pretrained("stabilityai/sd-turbo", subfolder="vae")
unet = UNet2DConditionModel.from_pretrained(
"stabilityai/sd-turbo", subfolder="unet"
)
if name == "edge_to_image":
url = "https://www.cs.cmu.edu/~img2img-turbo/models/edge_to_image_loras.pkl"
os.makedirs(ckpt_folder, exist_ok=True)
outf = os.path.join(ckpt_folder, "edge_to_image_loras.pkl")
if not os.path.exists(outf):
print(f"Downloading checkpoint to {outf}")
response = requests.get(url, stream=True)
total_size_in_bytes = int(response.headers.get("content-length", 0))
block_size = 1024 # 1 Kibibyte
progress_bar = tqdm(
total=total_size_in_bytes, unit="iB", unit_scale=True
)
with open(outf, "wb") as file:
for data in response.iter_content(block_size):
progress_bar.update(len(data))
file.write(data)
progress_bar.close()
if total_size_in_bytes != 0 and progress_bar.n != total_size_in_bytes:
print("ERROR, something went wrong")
print(f"Downloaded successfully to {outf}")
p_ckpt = outf
sd = torch.load(p_ckpt, map_location="cpu")
unet_lora_config = LoraConfig(
r=sd["rank_unet"],
init_lora_weights="gaussian",
target_modules=sd["unet_lora_target_modules"],
)
if name == "sketch_to_image_stochastic":
# download from url
url = "https://www.cs.cmu.edu/~img2img-turbo/models/sketch_to_image_stochastic_lora.pkl"
os.makedirs(ckpt_folder, exist_ok=True)
outf = os.path.join(ckpt_folder, "sketch_to_image_stochastic_lora.pkl")
if not os.path.exists(outf):
print(f"Downloading checkpoint to {outf}")
response = requests.get(url, stream=True)
total_size_in_bytes = int(response.headers.get("content-length", 0))
block_size = 1024 # 1 Kibibyte
progress_bar = tqdm(
total=total_size_in_bytes, unit="iB", unit_scale=True
)
with open(outf, "wb") as file:
for data in response.iter_content(block_size):
progress_bar.update(len(data))
file.write(data)
progress_bar.close()
if total_size_in_bytes != 0 and progress_bar.n != total_size_in_bytes:
print("ERROR, something went wrong")
print(f"Downloaded successfully to {outf}")
p_ckpt = outf
sd = torch.load(p_ckpt, map_location="cpu")
unet_lora_config = LoraConfig(
r=sd["rank_unet"],
init_lora_weights="gaussian",
target_modules=sd["unet_lora_target_modules"],
)
convin_pretrained = copy.deepcopy(unet.conv_in)
unet.conv_in = TwinConv(convin_pretrained, unet.conv_in)
vae.encoder.forward = my_vae_encoder_fwd.__get__(
vae.encoder, vae.encoder.__class__
)
vae.decoder.forward = my_vae_decoder_fwd.__get__(
vae.decoder, vae.decoder.__class__
)
# add the skip connection convs
vae.decoder.skip_conv_1 = torch.nn.Conv2d(
512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
).cuda()
vae.decoder.skip_conv_2 = torch.nn.Conv2d(
256, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
).cuda()
vae.decoder.skip_conv_3 = torch.nn.Conv2d(
128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
).cuda()
vae.decoder.skip_conv_4 = torch.nn.Conv2d(
128, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
).cuda()
vae_lora_config = LoraConfig(
r=sd["rank_vae"],
init_lora_weights="gaussian",
target_modules=sd["vae_lora_target_modules"],
)
vae.decoder.ignore_skip = False
vae.add_adapter(vae_lora_config, adapter_name="vae_skip")
unet.add_adapter(unet_lora_config)
_sd_unet = unet.state_dict()
for k in sd["state_dict_unet"]:
_sd_unet[k] = sd["state_dict_unet"][k]
unet.load_state_dict(_sd_unet)
unet.enable_xformers_memory_efficient_attention()
_sd_vae = vae.state_dict()
for k in sd["state_dict_vae"]:
_sd_vae[k] = sd["state_dict_vae"][k]
vae.load_state_dict(_sd_vae)
unet.to("cuda")
vae.to("cuda")
unet.eval()
vae.eval()
self.unet, self.vae = unet, vae
self.vae.decoder.gamma = 1
self.timesteps = torch.tensor([999], device="cuda").long()
self.last_prompt = ""
self.caption_enc = None
self.device = "cuda"
@torch.no_grad()
def forward(self, c_t, prompt, deterministic=True, r=1.0, noise_map=1.0):
# encode the text prompt
if prompt != self.last_prompt:
caption_tokens = self.tokenizer(
prompt,
max_length=self.tokenizer.model_max_length,
padding="max_length",
truncation=True,
return_tensors="pt",
).input_ids.cuda()
caption_enc = self.text_encoder(caption_tokens)[0]
self.caption_enc = caption_enc
self.last_prompt = prompt
if deterministic:
encoded_control = (
self.vae.encode(c_t).latent_dist.sample()
* self.vae.config.scaling_factor
)
model_pred = self.unet(
encoded_control,
self.timesteps,
encoder_hidden_states=self.caption_enc,
).sample
x_denoised = self.sched.step(
model_pred, self.timesteps, encoded_control, return_dict=True
).prev_sample
self.vae.decoder.incoming_skip_acts = self.vae.encoder.current_down_blocks
output_image = (
self.vae.decode(x_denoised / self.vae.config.scaling_factor).sample
).clamp(-1, 1)
else:
# scale the lora weights based on the r value
self.unet.set_adapters(["default"], weights=[r])
set_weights_and_activate_adapters(self.vae, ["vae_skip"], [r])
encoded_control = (
self.vae.encode(c_t).latent_dist.sample()
* self.vae.config.scaling_factor
)
# combine the input and noise
unet_input = encoded_control * r + noise_map * (1 - r)
self.unet.conv_in.r = r
unet_output = self.unet(
unet_input,
self.timesteps,
encoder_hidden_states=self.caption_enc,
).sample
self.unet.conv_in.r = None
x_denoised = self.sched.step(
unet_output, self.timesteps, unet_input, return_dict=True
).prev_sample
self.vae.decoder.incoming_skip_acts = self.vae.encoder.current_down_blocks
self.vae.decoder.gamma = r
output_image = (
self.vae.decode(x_denoised / self.vae.config.scaling_factor).sample
).clamp(-1, 1)
return output_image
|