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import os
import sys
import copy
import torch
import torch.nn as nn
from transformers import AutoTokenizer, CLIPTextModel
from diffusers import AutoencoderKL, UNet2DConditionModel
from peft import LoraConfig
from peft.utils import get_peft_model_state_dict
p = "src/"
sys.path.append(p)
from model import make_1step_sched, my_vae_encoder_fwd, my_vae_decoder_fwd, download_url
class VAE_encode(nn.Module):
def __init__(self, vae, vae_b2a=None):
super(VAE_encode, self).__init__()
self.vae = vae
self.vae_b2a = vae_b2a
def forward(self, x, direction):
assert direction in ["a2b", "b2a"]
if direction == "a2b":
_vae = self.vae
else:
_vae = self.vae_b2a
return _vae.encode(x).latent_dist.sample() * _vae.config.scaling_factor
class VAE_decode(nn.Module):
def __init__(self, vae, vae_b2a=None):
super(VAE_decode, self).__init__()
self.vae = vae
self.vae_b2a = vae_b2a
def forward(self, x, direction):
assert direction in ["a2b", "b2a"]
if direction == "a2b":
_vae = self.vae
else:
_vae = self.vae_b2a
assert _vae.encoder.current_down_blocks is not None
_vae.decoder.incoming_skip_acts = _vae.encoder.current_down_blocks
x_decoded = (_vae.decode(x / _vae.config.scaling_factor).sample).clamp(-1, 1)
return x_decoded
def initialize_unet(rank, return_lora_module_names=False):
unet = UNet2DConditionModel.from_pretrained("stabilityai/sd-turbo", subfolder="unet")
unet.requires_grad_(False)
unet.train()
l_target_modules_encoder, l_target_modules_decoder, l_modules_others = [], [], []
l_grep = ["to_k", "to_q", "to_v", "to_out.0", "conv", "conv1", "conv2", "conv_in", "conv_shortcut", "conv_out", "proj_out", "proj_in", "ff.net.2", "ff.net.0.proj"]
for n, p in unet.named_parameters():
if "bias" in n or "norm" in n: continue
for pattern in l_grep:
if pattern in n and ("down_blocks" in n or "conv_in" in n):
l_target_modules_encoder.append(n.replace(".weight",""))
break
elif pattern in n and "up_blocks" in n:
l_target_modules_decoder.append(n.replace(".weight",""))
break
elif pattern in n:
l_modules_others.append(n.replace(".weight",""))
break
lora_conf_encoder = LoraConfig(r=rank, init_lora_weights="gaussian",target_modules=l_target_modules_encoder, lora_alpha=rank)
lora_conf_decoder = LoraConfig(r=rank, init_lora_weights="gaussian",target_modules=l_target_modules_decoder, lora_alpha=rank)
lora_conf_others = LoraConfig(r=rank, init_lora_weights="gaussian",target_modules=l_modules_others, lora_alpha=rank)
unet.add_adapter(lora_conf_encoder, adapter_name="default_encoder")
unet.add_adapter(lora_conf_decoder, adapter_name="default_decoder")
unet.add_adapter(lora_conf_others, adapter_name="default_others")
unet.set_adapters(["default_encoder", "default_decoder", "default_others"])
if return_lora_module_names:
return unet, l_target_modules_encoder, l_target_modules_decoder, l_modules_others
else:
return unet
def initialize_vae(rank=4, return_lora_module_names=False):
vae = AutoencoderKL.from_pretrained("stabilityai/sd-turbo", subfolder="vae")
vae.requires_grad_(False)
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__)
vae.requires_grad_(True)
vae.train()
# 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().requires_grad_(True)
vae.decoder.skip_conv_2 = torch.nn.Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1), bias=False).cuda().requires_grad_(True)
vae.decoder.skip_conv_3 = torch.nn.Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False).cuda().requires_grad_(True)
vae.decoder.skip_conv_4 = torch.nn.Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1), bias=False).cuda().requires_grad_(True)
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)
vae.decoder.ignore_skip = False
vae.decoder.gamma = 1
l_vae_target_modules = ["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=rank, init_lora_weights="gaussian", target_modules=l_vae_target_modules)
vae.add_adapter(vae_lora_config, adapter_name="vae_skip")
if return_lora_module_names:
return vae, l_vae_target_modules
else:
return vae
class CycleGAN_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")
unet = UNet2DConditionModel.from_pretrained("stabilityai/sd-turbo", subfolder="unet")
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
self.unet, self.vae = unet, vae
if pretrained_name == "day_to_night":
url = "https://www.cs.cmu.edu/~img2img-turbo/models/day2night.pkl"
self.load_ckpt_from_url(url, ckpt_folder)
self.timesteps = torch.tensor([999], device="cuda").long()
self.caption = "driving in the night"
self.direction = "a2b"
elif pretrained_name == "night_to_day":
url = "https://www.cs.cmu.edu/~img2img-turbo/models/night2day.pkl"
self.load_ckpt_from_url(url, ckpt_folder)
self.timesteps = torch.tensor([999], device="cuda").long()
self.caption = "driving in the day"
self.direction = "b2a"
elif pretrained_name == "clear_to_rainy":
url = "https://www.cs.cmu.edu/~img2img-turbo/models/clear2rainy.pkl"
self.load_ckpt_from_url(url, ckpt_folder)
self.timesteps = torch.tensor([999], device="cuda").long()
self.caption = "driving in heavy rain"
self.direction = "a2b"
elif pretrained_name == "rainy_to_clear":
url = "https://www.cs.cmu.edu/~img2img-turbo/models/rainy2clear.pkl"
self.load_ckpt_from_url(url, ckpt_folder)
self.timesteps = torch.tensor([999], device="cuda").long()
self.caption = "driving in the day"
self.direction = "b2a"
elif pretrained_path is not None:
sd = torch.load(pretrained_path)
self.load_ckpt_from_state_dict(sd)
self.timesteps = torch.tensor([999], device="cuda").long()
self.caption = None
self.direction = None
self.vae_enc.cuda()
self.vae_dec.cuda()
self.unet.cuda()
def load_ckpt_from_state_dict(self, sd):
lora_conf_encoder = LoraConfig(r=sd["rank_unet"], init_lora_weights="gaussian", target_modules=sd["l_target_modules_encoder"], lora_alpha=sd["rank_unet"])
lora_conf_decoder = LoraConfig(r=sd["rank_unet"], init_lora_weights="gaussian", target_modules=sd["l_target_modules_decoder"], lora_alpha=sd["rank_unet"])
lora_conf_others = LoraConfig(r=sd["rank_unet"], init_lora_weights="gaussian", target_modules=sd["l_modules_others"], lora_alpha=sd["rank_unet"])
self.unet.add_adapter(lora_conf_encoder, adapter_name="default_encoder")
self.unet.add_adapter(lora_conf_decoder, adapter_name="default_decoder")
self.unet.add_adapter(lora_conf_others, adapter_name="default_others")
for n, p in self.unet.named_parameters():
name_sd = n.replace(".default_encoder.weight", ".weight")
if "lora" in n and "default_encoder" in n:
p.data.copy_(sd["sd_encoder"][name_sd])
for n, p in self.unet.named_parameters():
name_sd = n.replace(".default_decoder.weight", ".weight")
if "lora" in n and "default_decoder" in n:
p.data.copy_(sd["sd_decoder"][name_sd])
for n, p in self.unet.named_parameters():
name_sd = n.replace(".default_others.weight", ".weight")
if "lora" in n and "default_others" in n:
p.data.copy_(sd["sd_other"][name_sd])
self.unet.set_adapter(["default_encoder", "default_decoder", "default_others"])
vae_lora_config = LoraConfig(r=sd["rank_vae"], init_lora_weights="gaussian", target_modules=sd["vae_lora_target_modules"])
self.vae.add_adapter(vae_lora_config, adapter_name="vae_skip")
self.vae.decoder.gamma = 1
self.vae_b2a = copy.deepcopy(self.vae)
self.vae_enc = VAE_encode(self.vae, vae_b2a=self.vae_b2a)
self.vae_enc.load_state_dict(sd["sd_vae_enc"])
self.vae_dec = VAE_decode(self.vae, vae_b2a=self.vae_b2a)
self.vae_dec.load_state_dict(sd["sd_vae_dec"])
def load_ckpt_from_url(self, url, ckpt_folder):
os.makedirs(ckpt_folder, exist_ok=True)
outf = os.path.join(ckpt_folder, os.path.basename(url))
download_url(url, outf)
sd = torch.load(outf)
self.load_ckpt_from_state_dict(sd)
@staticmethod
def forward_with_networks(x, direction, vae_enc, unet, vae_dec, sched, timesteps, text_emb):
B = x.shape[0]
assert direction in ["a2b", "b2a"]
x_enc = vae_enc(x, direction=direction).to(x.dtype)
model_pred = unet(x_enc, timesteps, encoder_hidden_states=text_emb,).sample
x_out = torch.stack([sched.step(model_pred[i], timesteps[i], x_enc[i], return_dict=True).prev_sample for i in range(B)])
x_out_decoded = vae_dec(x_out, direction=direction)
return x_out_decoded
@staticmethod
def get_traininable_params(unet, vae_a2b, vae_b2a):
# add all unet parameters
params_gen = list(unet.conv_in.parameters())
unet.conv_in.requires_grad_(True)
unet.set_adapters(["default_encoder", "default_decoder", "default_others"])
for n,p in unet.named_parameters():
if "lora" in n and "default" in n:
assert p.requires_grad
params_gen.append(p)
# add all vae_a2b parameters
for n,p in vae_a2b.named_parameters():
if "lora" in n and "vae_skip" in n:
assert p.requires_grad
params_gen.append(p)
params_gen = params_gen + list(vae_a2b.decoder.skip_conv_1.parameters())
params_gen = params_gen + list(vae_a2b.decoder.skip_conv_2.parameters())
params_gen = params_gen + list(vae_a2b.decoder.skip_conv_3.parameters())
params_gen = params_gen + list(vae_a2b.decoder.skip_conv_4.parameters())
# add all vae_b2a parameters
for n,p in vae_b2a.named_parameters():
if "lora" in n and "vae_skip" in n:
assert p.requires_grad
params_gen.append(p)
params_gen = params_gen + list(vae_b2a.decoder.skip_conv_1.parameters())
params_gen = params_gen + list(vae_b2a.decoder.skip_conv_2.parameters())
params_gen = params_gen + list(vae_b2a.decoder.skip_conv_3.parameters())
params_gen = params_gen + list(vae_b2a.decoder.skip_conv_4.parameters())
return params_gen
def forward(self, x_t, direction=None, caption=None, caption_emb=None):
if direction is None:
assert self.direction is not None
direction = self.direction
if caption is None and caption_emb is None:
assert self.caption is not None
caption = self.caption
if caption_emb is not None:
caption_enc = caption_emb
else:
caption_tokens = self.tokenizer(caption, max_length=self.tokenizer.model_max_length,
padding="max_length", truncation=True, return_tensors="pt").input_ids.to(x_t.device)
caption_enc = self.text_encoder(caption_tokens)[0].detach().clone()
return self.forward_with_networks(x_t, direction, self.vae_enc, self.unet, self.vae_dec, self.sched, self.timesteps, caption_enc)
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