img2img-turbo-sketch / src /pix2pix_turbo.py
gaparmar
gamma
a5f38fd
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
p = "src/"
sys.path.append(p)
from 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()
def forward(self, c_t, prompt, deterministic=True, r=1.0, noise_map=None):
# encode the text 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]
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=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=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