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import os
import requests
import sys
import copy
from tqdm import tqdm
import torch
from transformers import AutoTokenizer, CLIPTextModel
from diffusers import AutoencoderKL, UNet2DConditionModel
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, pretrained_name=None, pretrained_path=None, ckpt_folder="checkpoints", lora_rank_unet=8, lora_rank_vae=4):
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")
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.decoder.ignore_skip = False
unet = UNet2DConditionModel.from_pretrained("stabilityai/sd-turbo", subfolder="unet")
if pretrained_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"])
vae_lora_config = LoraConfig(r=sd["rank_vae"], init_lora_weights="gaussian", target_modules=sd["vae_lora_target_modules"])
vae.add_adapter(vae_lora_config, adapter_name="vae_skip")
_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.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)
elif pretrained_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
convin_pretrained = copy.deepcopy(unet.conv_in)
unet.conv_in = TwinConv(convin_pretrained, unet.conv_in)
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"])
vae_lora_config = LoraConfig(r=sd["rank_vae"], init_lora_weights="gaussian", target_modules=sd["vae_lora_target_modules"])
vae.add_adapter(vae_lora_config, adapter_name="vae_skip")
_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.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)
elif pretrained_path is not None:
sd = torch.load(pretrained_path, map_location="cpu")
unet_lora_config = LoraConfig(r=sd["rank_unet"], init_lora_weights="gaussian", target_modules=sd["unet_lora_target_modules"])
vae_lora_config = LoraConfig(r=sd["rank_vae"], init_lora_weights="gaussian", target_modules=sd["vae_lora_target_modules"])
vae.add_adapter(vae_lora_config, adapter_name="vae_skip")
_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.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)
elif pretrained_name is None and pretrained_path is None:
print("Initializing model with random weights")
torch.nn.init.constant_(vae.decoder.skip_conv_1.weight, 1e-5)
torch.nn.init.constant_(vae.decoder.skip_conv_2.weight, 1e-5)
torch.nn.init.constant_(vae.decoder.skip_conv_3.weight, 1e-5)
torch.nn.init.constant_(vae.decoder.skip_conv_4.weight, 1e-5)
target_modules_vae = ["conv1", "conv2", "conv_in", "conv_shortcut", "conv", "conv_out",
"skip_conv_1", "skip_conv_2", "skip_conv_3", "skip_conv_4",
"to_k", "to_q", "to_v", "to_out.0",
]
vae_lora_config = LoraConfig(r=lora_rank_vae, init_lora_weights="gaussian",
target_modules=target_modules_vae)
vae.add_adapter(vae_lora_config, adapter_name="vae_skip")
target_modules_unet = [
"to_k", "to_q", "to_v", "to_out.0", "conv", "conv1", "conv2", "conv_shortcut", "conv_out",
"proj_in", "proj_out", "ff.net.2", "ff.net.0.proj"
]
unet_lora_config = LoraConfig(r=lora_rank_unet, init_lora_weights="gaussian",
target_modules=target_modules_unet
)
unet.add_adapter(unet_lora_config)
self.lora_rank_unet = lora_rank_unet
self.lora_rank_vae = lora_rank_vae
self.target_modules_vae = target_modules_vae
self.target_modules_unet = target_modules_unet
# unet.enable_xformers_memory_efficient_attention()
unet.to("cuda")
vae.to("cuda")
self.unet, self.vae = unet, vae
self.vae.decoder.gamma = 1
self.timesteps = torch.tensor([999], device="cuda").long()
self.text_encoder.requires_grad_(False)
def set_eval(self):
self.unet.eval()
self.vae.eval()
self.unet.requires_grad_(False)
self.vae.requires_grad_(False)
def set_train(self):
self.unet.train()
self.vae.train()
for n, _p in self.unet.named_parameters():
if "lora" in n:
_p.requires_grad = True
self.unet.conv_in.requires_grad_(True)
for n, _p in self.vae.named_parameters():
if "lora" in n:
_p.requires_grad = True
self.vae.decoder.skip_conv_1.requires_grad_(True)
self.vae.decoder.skip_conv_2.requires_grad_(True)
self.vae.decoder.skip_conv_3.requires_grad_(True)
self.vae.decoder.skip_conv_4.requires_grad_(True)
def forward(self, c_t, prompt=None, prompt_tokens=None, deterministic=True, r=1.0, noise_map=None):
# either the prompt or the prompt_tokens should be provided
assert (prompt is None) != (prompt_tokens is None), "Either prompt or prompt_tokens should be provided"
if prompt is not 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]
else:
caption_enc = self.text_encoder(prompt_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
def save_model(self, outf):
sd = {}
sd["unet_lora_target_modules"] = self.target_modules_unet
sd["vae_lora_target_modules"] = self.target_modules_vae
sd["rank_unet"] = self.lora_rank_unet
sd["rank_vae"] = self.lora_rank_vae
sd["state_dict_unet"] = {k: v for k, v in self.unet.state_dict().items() if "lora" in k or "conv_in" in k}
sd["state_dict_vae"] = {k: v for k, v in self.vae.state_dict().items() if "lora" in k or "skip" in k}
torch.save(sd, outf)
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