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import torch
from utils import import_model_class_from_model_name_or_path
from transformers import AutoTokenizer
from diffusers import (
AutoencoderKL,
DDPMScheduler,
StableDiffusionXLPipeline,
UNet2DConditionModel,
)
from accelerate import Accelerator
from tqdm.auto import tqdm
from utils import sdxl_prepare_input_decom, save_images
import torch.nn.functional as F
import itertools
from peft import LoraConfig
from controller import GroupedCAController, register_attention_disentangled_control, DummyController
from utils import image2latent, latent2image
import matplotlib.pyplot as plt
from utils_mask import check_mask_overlap_torch
device = torch.device('cuda:0') if torch.cuda.is_available() else torch.device('cpu')
max_length = 40
class DEditSDXLPipeline:
def __init__(
self,
mask_list,
mask_label_list,
mask_list_2 = None,
mask_label_list_2 = None,
resolution = 1024,
num_tokens = 1
):
super().__init__()
model_id = "stabilityai/stable-diffusion-xl-base-1.0"
self.model_id = model_id
self.tokenizer = AutoTokenizer.from_pretrained(model_id, subfolder="tokenizer", use_fast=False)
self.tokenizer_2 = AutoTokenizer.from_pretrained(model_id, subfolder="tokenizer_2", use_fast=False)
text_encoder_cls_one = import_model_class_from_model_name_or_path(model_id, subfolder = "text_encoder")
text_encoder_cls_two = import_model_class_from_model_name_or_path(model_id, subfolder="text_encoder_2")
self.text_encoder = text_encoder_cls_one.from_pretrained(model_id, subfolder="text_encoder" ).to(device)
self.text_encoder_2 = text_encoder_cls_two.from_pretrained(model_id, subfolder="text_encoder_2").to(device)
self.unet = UNet2DConditionModel.from_pretrained(model_id, subfolder="unet" )
self.unet.ca_dim = 2048
self.vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix")
self.scheduler = DDPMScheduler.from_pretrained(model_id , subfolder="scheduler")
self.mixed_precision = "fp16"
self.resolution = resolution
self.num_tokens = num_tokens
self.mask_list = mask_list
self.mask_label_list = mask_label_list
notation_token_list = [phrase.split(" ")[-1] for phrase in mask_label_list]
placeholder_token_list = ["#"+word+"{}".format(widx) for widx, word in enumerate(notation_token_list)]
self.set_string_list, placeholder_token_ids = self.add_tokens(placeholder_token_list)
self.min_added_id = min(placeholder_token_ids)
self.max_added_id = max(placeholder_token_ids)
if mask_list_2 is not None:
self.mask_list_2 = mask_list_2
self.mask_label_list_2 = mask_label_list_2
notation_token_list_2 = [phrase.split(" ")[-1] for phrase in mask_label_list_2]
placeholder_token_list_2 = ["$"+word+"{}".format(widx) for widx, word in enumerate(notation_token_list_2)]
self.set_string_list_2, placeholder_token_ids_2 = self.add_tokens(placeholder_token_list_2)
self.max_added_id = max(placeholder_token_ids_2)
def add_tokens_text_encoder_random_init(self, placeholder_token, num_tokens=1):
# Add the placeholder token in tokenizer
placeholder_tokens = [placeholder_token]
# add dummy tokens for multi-vector
additional_tokens = []
for i in range(1, num_tokens):
additional_tokens.append(f"{placeholder_token}_{i}")
placeholder_tokens += additional_tokens
num_added_tokens = self.tokenizer.add_tokens(placeholder_tokens) # 49408
num_added_tokens = self.tokenizer_2.add_tokens(placeholder_tokens) # 49408
if num_added_tokens != num_tokens:
raise ValueError(
f"The tokenizer already contains the token {placeholder_token}. Please pass a different"
" `placeholder_token` that is not already in the tokenizer."
)
placeholder_token_ids = self.tokenizer.convert_tokens_to_ids(placeholder_tokens)
placeholder_token_ids_2 = self.tokenizer_2.convert_tokens_to_ids(placeholder_tokens)
assert placeholder_token_ids == placeholder_token_ids_2, "Two text encoders are expected to have same vocabs"
self.text_encoder.resize_token_embeddings(len(self.tokenizer))
token_embeds = self.text_encoder.get_input_embeddings().weight.data
std, mean = torch.std_mean(token_embeds)
with torch.no_grad():
for token_id in placeholder_token_ids:
token_embeds[token_id] = torch.randn_like(token_embeds[token_id])*std + mean
self.text_encoder_2.resize_token_embeddings(len(self.tokenizer))
token_embeds = self.text_encoder_2.get_input_embeddings().weight.data
std, mean = torch.std_mean(token_embeds)
with torch.no_grad():
for token_id in placeholder_token_ids:
token_embeds[token_id] = torch.randn_like(token_embeds[token_id])*std + mean
set_string = " ".join(self.tokenizer.convert_ids_to_tokens(placeholder_token_ids))
return set_string, placeholder_token_ids
def add_tokens(self, placeholder_token_list):
set_string_list = []
placeholder_token_ids_list = []
for str_idx in range(len(placeholder_token_list)):
placeholder_token = placeholder_token_list[str_idx]
set_string, placeholder_token_ids = self.add_tokens_text_encoder_random_init(placeholder_token, num_tokens=self.num_tokens)
set_string_list.append(set_string)
placeholder_token_ids_list.append(placeholder_token_ids)
placeholder_token_ids = list(itertools.chain(*placeholder_token_ids_list))
return set_string_list, placeholder_token_ids
def train_emb(
self,
image_gt,
set_string_list,
gradient_accumulation_steps = 5,
embedding_learning_rate = 1e-4,
max_emb_train_steps = 100,
train_batch_size = 1,
train_full_lora = False
):
decom_controller = GroupedCAController(mask_list = self.mask_list)
register_attention_disentangled_control(self.unet, decom_controller)
accelerator = Accelerator(mixed_precision=self.mixed_precision, gradient_accumulation_steps=gradient_accumulation_steps)
self.vae.requires_grad_(False)
self.unet.requires_grad_(False)
self.text_encoder.requires_grad_(True)
self.text_encoder_2.requires_grad_(True)
self.text_encoder.text_model.encoder.requires_grad_(False)
self.text_encoder.text_model.final_layer_norm.requires_grad_(False)
self.text_encoder.text_model.embeddings.position_embedding.requires_grad_(False)
self.text_encoder_2.text_model.encoder.requires_grad_(False)
self.text_encoder_2.text_model.final_layer_norm.requires_grad_(False)
self.text_encoder_2.text_model.embeddings.position_embedding.requires_grad_(False)
weight_dtype = torch.float32
if accelerator.mixed_precision == "fp16":
weight_dtype = torch.float16
elif accelerator.mixed_precision == "bf16":
weight_dtype = torch.bfloat16
self.unet.to(device, dtype=weight_dtype)
self.vae.to(device, dtype=weight_dtype)
trainable_embmat_list_1 = [param for param in self.text_encoder.get_input_embeddings().parameters()]
trainable_embmat_list_2 = [param for param in self.text_encoder_2.get_input_embeddings().parameters()]
optimizer = torch.optim.AdamW(trainable_embmat_list_1 + trainable_embmat_list_2, lr=embedding_learning_rate)
self.text_encoder, self.text_encoder_2, optimizer = accelerator.prepare(self.text_encoder, self.text_encoder_2, optimizer)
orig_embeds_params_1 = accelerator.unwrap_model(self.text_encoder) .get_input_embeddings().weight.data.clone()
orig_embeds_params_2 = accelerator.unwrap_model(self.text_encoder_2).get_input_embeddings().weight.data.clone()
self.text_encoder.train()
self.text_encoder_2.train()
effective_emb_train_steps = max_emb_train_steps//gradient_accumulation_steps
if accelerator.is_main_process:
accelerator.init_trackers("DEdit EmbSteps", config={
"embedding_learning_rate": embedding_learning_rate,
"text_embedding_optimization_steps": effective_emb_train_steps,
})
global_step = 0
noise_scheduler = DDPMScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0" , subfolder="scheduler")
progress_bar = tqdm(range(0, effective_emb_train_steps), initial = global_step, desc="EmbSteps")
latents0 = image2latent(image_gt, vae = self.vae, dtype=weight_dtype)
latents0 = latents0.repeat(train_batch_size, 1, 1, 1)
for _ in range(max_emb_train_steps):
with accelerator.accumulate(self.text_encoder, self.text_encoder_2):
latents = latents0.clone().detach()
noise = torch.randn_like(latents)
bsz = latents.shape[0]
timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)
timesteps = timesteps.long()
noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
encoder_hidden_states_list, add_text_embeds, add_time_ids = sdxl_prepare_input_decom(
set_string_list,
self.tokenizer,
self.tokenizer_2,
self.text_encoder,
self.text_encoder_2,
length = max_length,
bsz = train_batch_size,
weight_dtype = weight_dtype
)
model_pred = self.unet(
noisy_latents,
timesteps,
encoder_hidden_states = encoder_hidden_states_list,
cross_attention_kwargs = None,
added_cond_kwargs={"text_embeds": add_text_embeds, "time_ids": add_time_ids},
return_dict=False
)[0]
loss = F.mse_loss(model_pred.float(), noise.float(), reduction="mean")
accelerator.backward(loss)
optimizer.step()
optimizer.zero_grad()
index_no_updates = torch.ones((len(self.tokenizer),), dtype=torch.bool)
index_no_updates[self.min_added_id : self.max_added_id + 1] = False
with torch.no_grad():
accelerator.unwrap_model(self.text_encoder).get_input_embeddings().weight[
index_no_updates] = orig_embeds_params_1[index_no_updates]
index_no_updates = torch.ones((len(self.tokenizer_2),), dtype=torch.bool)
index_no_updates[self.min_added_id : self.max_added_id + 1] = False
with torch.no_grad():
accelerator.unwrap_model(self.text_encoder_2).get_input_embeddings().weight[
index_no_updates] = orig_embeds_params_2[index_no_updates]
logs = {"loss": loss.detach().item(), "lr": embedding_learning_rate}
progress_bar.set_postfix(**logs)
accelerator.log(logs, step=global_step)
if accelerator.sync_gradients:
progress_bar.update(1)
global_step += 1
if global_step >= max_emb_train_steps:
break
accelerator.wait_for_everyone()
accelerator.end_training()
self.text_encoder = accelerator.unwrap_model(self.text_encoder).to(dtype = weight_dtype)
self.text_encoder_2 = accelerator.unwrap_model(self.text_encoder_2).to(dtype = weight_dtype)
def train_model(
self,
image_gt,
set_string_list,
gradient_accumulation_steps = 5,
max_diffusion_train_steps = 100,
diffusion_model_learning_rate = 1e-5,
train_batch_size = 1,
train_full_lora = False,
lora_rank = 4,
lora_alpha = 4
):
self.unet = UNet2DConditionModel.from_pretrained(self.model_id, subfolder="unet").to(device)
self.unet.ca_dim = 2048
decom_controller = GroupedCAController(mask_list = self.mask_list)
register_attention_disentangled_control(self.unet, decom_controller)
mixed_precision = "fp16"
accelerator = Accelerator(gradient_accumulation_steps = gradient_accumulation_steps, mixed_precision = mixed_precision)
weight_dtype = torch.float32
if accelerator.mixed_precision == "fp16":
weight_dtype = torch.float16
elif accelerator.mixed_precision == "bf16":
weight_dtype = torch.bfloat16
self.vae.requires_grad_(False)
self.vae.to(device, dtype=weight_dtype)
self.unet.requires_grad_(False)
self.unet.train()
self.text_encoder.requires_grad_(False)
self.text_encoder_2.requires_grad_(False)
if not train_full_lora:
trainable_params_list = []
for _, module in self.unet.named_modules():
module_name = type(module).__name__
if module_name == "Attention":
if module.to_k.in_features == 2048: # this is cross attention:
module.to_k.weight.requires_grad = True
trainable_params_list.append(module.to_k.weight)
if module.to_k.bias is not None:
module.to_k.bias.requires_grad = True
trainable_params_list.append(module.to_k.bias)
module.to_v.weight.requires_grad = True
trainable_params_list.append(module.to_v.weight)
if module.to_v.bias is not None:
module.to_v.bias.requires_grad = True
trainable_params_list.append(module.to_v.bias)
module.to_q.weight.requires_grad = True
trainable_params_list.append(module.to_q.weight)
if module.to_q.bias is not None:
module.to_q.bias.requires_grad = True
trainable_params_list.append(module.to_q.bias)
else:
unet_lora_config = LoraConfig(
r=lora_rank,
lora_alpha=lora_alpha,
init_lora_weights="gaussian",
target_modules=["to_k", "to_q", "to_v", "to_out.0"],
)
self.unet.add_adapter(unet_lora_config)
print("training full parameters using lora!")
trainable_params_list = list(filter(lambda p: p.requires_grad, self.unet.parameters()))
self.text_encoder.to(device, dtype=weight_dtype)
self.text_encoder_2.to(device, dtype=weight_dtype)
optimizer = torch.optim.AdamW(trainable_params_list, lr=diffusion_model_learning_rate)
self.unet, optimizer = accelerator.prepare(self.unet, optimizer)
psum2 = sum(p.numel() for p in trainable_params_list)
effective_diffusion_train_steps = max_diffusion_train_steps // gradient_accumulation_steps
if accelerator.is_main_process:
accelerator.init_trackers("textual_inversion", config={
"diffusion_model_learning_rate": diffusion_model_learning_rate,
"diffusion_model_optimization_steps": effective_diffusion_train_steps,
})
global_step = 0
progress_bar = tqdm( range(0, effective_diffusion_train_steps),initial=global_step, desc="ModelSteps")
noise_scheduler = DDPMScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0" , subfolder="scheduler")
latents0 = image2latent(image_gt, vae = self.vae, dtype=weight_dtype)
latents0 = latents0.repeat(train_batch_size, 1, 1, 1)
with torch.no_grad():
encoder_hidden_states_list, add_text_embeds, add_time_ids = sdxl_prepare_input_decom(
set_string_list,
self.tokenizer,
self.tokenizer_2,
self.text_encoder,
self.text_encoder_2,
length = max_length,
bsz = train_batch_size,
weight_dtype = weight_dtype
)
for _ in range(max_diffusion_train_steps):
with accelerator.accumulate(self.unet):
latents = latents0.clone().detach()
noise = torch.randn_like(latents)
bsz = latents.shape[0]
timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)
timesteps = timesteps.long()
noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
model_pred = self.unet(
noisy_latents,
timesteps,
encoder_hidden_states=encoder_hidden_states_list,
cross_attention_kwargs=None, return_dict=False,
added_cond_kwargs={"text_embeds": add_text_embeds, "time_ids": add_time_ids}
)[0]
loss = F.mse_loss(model_pred.float(), noise.float(), reduction="mean")
accelerator.backward(loss)
optimizer.step()
optimizer.zero_grad()
logs = {"loss": loss.detach().item(), "lr": diffusion_model_learning_rate}
progress_bar.set_postfix(**logs)
accelerator.log(logs, step=global_step)
if accelerator.sync_gradients:
progress_bar.update(1)
global_step += 1
if global_step >=max_diffusion_train_steps:
break
accelerator.wait_for_everyone()
accelerator.end_training()
self.unet = accelerator.unwrap_model(self.unet).to(dtype = weight_dtype)
def train_emb_2imgs(
self,
image_gt_1,
image_gt_2,
set_string_list_1,
set_string_list_2,
gradient_accumulation_steps = 5,
embedding_learning_rate = 1e-4,
max_emb_train_steps = 100,
train_batch_size = 1,
train_full_lora = False
):
decom_controller_1 = GroupedCAController(mask_list = self.mask_list)
decom_controller_2 = GroupedCAController(mask_list = self.mask_list_2)
accelerator = Accelerator(mixed_precision=self.mixed_precision, gradient_accumulation_steps=gradient_accumulation_steps)
self.vae.requires_grad_(False)
self.unet.requires_grad_(False)
self.text_encoder.requires_grad_(True)
self.text_encoder_2.requires_grad_(True)
self.text_encoder.text_model.encoder.requires_grad_(False)
self.text_encoder.text_model.final_layer_norm.requires_grad_(False)
self.text_encoder.text_model.embeddings.position_embedding.requires_grad_(False)
self.text_encoder_2.text_model.encoder.requires_grad_(False)
self.text_encoder_2.text_model.final_layer_norm.requires_grad_(False)
self.text_encoder_2.text_model.embeddings.position_embedding.requires_grad_(False)
weight_dtype = torch.float32
if accelerator.mixed_precision == "fp16":
weight_dtype = torch.float16
elif accelerator.mixed_precision == "bf16":
weight_dtype = torch.bfloat16
self.unet.to(device, dtype=weight_dtype)
self.vae.to(device, dtype=weight_dtype)
trainable_embmat_list_1 = [param for param in self.text_encoder.get_input_embeddings().parameters()]
trainable_embmat_list_2 = [param for param in self.text_encoder_2.get_input_embeddings().parameters()]
optimizer = torch.optim.AdamW(trainable_embmat_list_1 + trainable_embmat_list_2, lr=embedding_learning_rate)
self.text_encoder, self.text_encoder_2, optimizer= accelerator.prepare(self.text_encoder, self.text_encoder_2, optimizer) ###
orig_embeds_params_1 = accelerator.unwrap_model(self.text_encoder) .get_input_embeddings().weight.data.clone()
orig_embeds_params_2 = accelerator.unwrap_model(self.text_encoder_2).get_input_embeddings().weight.data.clone()
self.text_encoder.train()
self.text_encoder_2.train()
effective_emb_train_steps = max_emb_train_steps//gradient_accumulation_steps
if accelerator.is_main_process:
accelerator.init_trackers("EmbFt", config={
"embedding_learning_rate": embedding_learning_rate,
"text_embedding_optimization_steps": effective_emb_train_steps,
})
global_step = 0
noise_scheduler = DDPMScheduler.from_pretrained(self.model_id , subfolder="scheduler")
progress_bar = tqdm(range(0, effective_emb_train_steps),initial=global_step,desc="EmbSteps")
latents0_1 = image2latent(image_gt_1, vae = self.vae, dtype=weight_dtype)
latents0_1 = latents0_1.repeat(train_batch_size,1,1,1)
latents0_2 = image2latent(image_gt_2, vae = self.vae, dtype=weight_dtype)
latents0_2 = latents0_2.repeat(train_batch_size,1,1,1)
for step in range(max_emb_train_steps):
with accelerator.accumulate(self.text_encoder, self.text_encoder_2):
latents_1 = latents0_1.clone().detach()
noise_1 = torch.randn_like(latents_1)
latents_2 = latents0_2.clone().detach()
noise_2 = torch.randn_like(latents_2)
bsz = latents_1.shape[0]
timesteps_1 = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents_1.device)
timesteps_1 = timesteps_1.long()
noisy_latents_1 = noise_scheduler.add_noise(latents_1, noise_1, timesteps_1)
timesteps_2 = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents_2.device)
timesteps_2 = timesteps_2.long()
noisy_latents_2 = noise_scheduler.add_noise(latents_2, noise_2, timesteps_2)
register_attention_disentangled_control(self.unet, decom_controller_1)
encoder_hidden_states_list_1, add_text_embeds_1, add_time_ids_1 = sdxl_prepare_input_decom(
set_string_list_1,
self.tokenizer,
self.tokenizer_2,
self.text_encoder,
self.text_encoder_2,
length = max_length,
bsz = train_batch_size,
weight_dtype = weight_dtype
)
model_pred_1 = self.unet(
noisy_latents_1,
timesteps_1,
encoder_hidden_states=encoder_hidden_states_list_1,
cross_attention_kwargs=None,
added_cond_kwargs={"text_embeds": add_text_embeds_1, "time_ids": add_time_ids_1},
return_dict=False
)[0]
register_attention_disentangled_control(self.unet, decom_controller_2)
# import pdb; pdb.set_trace()
encoder_hidden_states_list_2, add_text_embeds_2, add_time_ids_2 = sdxl_prepare_input_decom(
set_string_list_2,
self.tokenizer,
self.tokenizer_2,
self.text_encoder,
self.text_encoder_2,
length = max_length,
bsz = train_batch_size,
weight_dtype = weight_dtype
)
model_pred_2 = self.unet(
noisy_latents_2,
timesteps_2,
encoder_hidden_states = encoder_hidden_states_list_2,
cross_attention_kwargs=None,
added_cond_kwargs={"text_embeds": add_text_embeds_2, "time_ids": add_time_ids_2},
return_dict=False
)[0]
loss_1 = F.mse_loss(model_pred_1.float(), noise_1.float(), reduction="mean") /2
loss_2 = F.mse_loss(model_pred_2.float(), noise_2.float(), reduction="mean") /2
loss = loss_1 + loss_2
accelerator.backward(loss)
optimizer.step()
optimizer.zero_grad()
index_no_updates = torch.ones((len(self.tokenizer),), dtype=torch.bool)
index_no_updates[self.min_added_id : self.max_added_id + 1] = False
with torch.no_grad():
accelerator.unwrap_model(self.text_encoder).get_input_embeddings().weight[
index_no_updates] = orig_embeds_params_1[index_no_updates]
index_no_updates = torch.ones((len(self.tokenizer_2),), dtype=torch.bool)
index_no_updates[self.min_added_id : self.max_added_id + 1] = False
with torch.no_grad():
accelerator.unwrap_model(self.text_encoder_2).get_input_embeddings().weight[
index_no_updates] = orig_embeds_params_2[index_no_updates]
logs = {"loss": loss.detach().item(), "lr": embedding_learning_rate}
progress_bar.set_postfix(**logs)
accelerator.log(logs, step=global_step)
if accelerator.sync_gradients:
progress_bar.update(1)
global_step += 1
if global_step >= max_emb_train_steps:
break
accelerator.wait_for_everyone()
accelerator.end_training()
self.text_encoder = accelerator.unwrap_model(self.text_encoder) .to(dtype = weight_dtype)
self.text_encoder_2 = accelerator.unwrap_model(self.text_encoder_2).to(dtype = weight_dtype)
def train_model_2imgs(
self,
image_gt_1,
image_gt_2,
set_string_list_1,
set_string_list_2,
gradient_accumulation_steps = 5,
max_diffusion_train_steps = 100,
diffusion_model_learning_rate = 1e-5,
train_batch_size = 1,
train_full_lora = False,
lora_rank = 4,
lora_alpha = 4
):
self.unet = UNet2DConditionModel.from_pretrained(self.model_id, subfolder="unet").to(device)
self.unet.ca_dim = 2048
decom_controller_1 = GroupedCAController(mask_list = self.mask_list)
decom_controller_2 = GroupedCAController(mask_list = self.mask_list_2)
mixed_precision = "fp16"
accelerator = Accelerator(gradient_accumulation_steps=gradient_accumulation_steps,mixed_precision=mixed_precision)
weight_dtype = torch.float32
if accelerator.mixed_precision == "fp16":
weight_dtype = torch.float16
elif accelerator.mixed_precision == "bf16":
weight_dtype = torch.bfloat16
self.vae.requires_grad_(False)
self.vae.to(device, dtype=weight_dtype)
self.unet.requires_grad_(False)
self.unet.train()
self.text_encoder.requires_grad_(False)
self.text_encoder_2.requires_grad_(False)
if not train_full_lora:
trainable_params_list = []
for name, module in self.unet.named_modules():
module_name = type(module).__name__
if module_name == "Attention":
if module.to_k.in_features == 2048: # this is cross attention:
module.to_k.weight.requires_grad = True
trainable_params_list.append(module.to_k.weight)
if module.to_k.bias is not None:
module.to_k.bias.requires_grad = True
trainable_params_list.append(module.to_k.bias)
module.to_v.weight.requires_grad = True
trainable_params_list.append(module.to_v.weight)
if module.to_v.bias is not None:
module.to_v.bias.requires_grad = True
trainable_params_list.append(module.to_v.bias)
module.to_q.weight.requires_grad = True
trainable_params_list.append(module.to_q.weight)
if module.to_q.bias is not None:
module.to_q.bias.requires_grad = True
trainable_params_list.append(module.to_q.bias)
else:
unet_lora_config = LoraConfig(
r = lora_rank,
lora_alpha = lora_alpha,
init_lora_weights="gaussian",
target_modules=["to_k", "to_q", "to_v", "to_out.0"],
)
self.unet.add_adapter(unet_lora_config)
print("training full parameters using lora!")
trainable_params_list = list(filter(lambda p: p.requires_grad, self.unet.parameters()))
self.text_encoder.to(device, dtype=weight_dtype)
self.text_encoder_2.to(device, dtype=weight_dtype)
optimizer = torch.optim.AdamW(trainable_params_list, lr=diffusion_model_learning_rate)
self.unet, optimizer = accelerator.prepare(self.unet, optimizer)
psum2 = sum(p.numel() for p in trainable_params_list)
effective_diffusion_train_steps = max_diffusion_train_steps // gradient_accumulation_steps
if accelerator.is_main_process:
accelerator.init_trackers("ModelFt", config={
"diffusion_model_learning_rate": diffusion_model_learning_rate,
"diffusion_model_optimization_steps": effective_diffusion_train_steps,
})
global_step = 0
progress_bar = tqdm(range(0, effective_diffusion_train_steps),initial=global_step, desc="ModelSteps")
noise_scheduler = DDPMScheduler.from_pretrained(self.model_id, subfolder="scheduler")
latents0_1 = image2latent(image_gt_1, vae = self.vae, dtype=weight_dtype)
latents0_1 = latents0_1.repeat(train_batch_size, 1, 1, 1)
latents0_2 = image2latent(image_gt_2, vae = self.vae, dtype=weight_dtype)
latents0_2 = latents0_2.repeat(train_batch_size,1, 1, 1)
with torch.no_grad():
encoder_hidden_states_list_1, add_text_embeds_1, add_time_ids_1 = sdxl_prepare_input_decom(
set_string_list_1,
self.tokenizer,
self.tokenizer_2,
self.text_encoder,
self.text_encoder_2,
length = max_length,
bsz = train_batch_size,
weight_dtype = weight_dtype
)
encoder_hidden_states_list_2, add_text_embeds_2, add_time_ids_2 = sdxl_prepare_input_decom(
set_string_list_2,
self.tokenizer,
self.tokenizer_2,
self.text_encoder,
self.text_encoder_2,
length = max_length,
bsz = train_batch_size,
weight_dtype = weight_dtype
)
for _ in range(max_diffusion_train_steps):
with accelerator.accumulate(self.unet):
latents_1 = latents0_1.clone().detach()
noise_1 = torch.randn_like(latents_1)
bsz = latents_1.shape[0]
timesteps_1 = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents_1.device)
timesteps_1 = timesteps_1.long()
noisy_latents_1 = noise_scheduler.add_noise(latents_1, noise_1, timesteps_1)
latents_2 = latents0_2.clone().detach()
noise_2 = torch.randn_like(latents_2)
bsz = latents_2.shape[0]
timesteps_2 = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents_2.device)
timesteps_2 = timesteps_2.long()
noisy_latents_2 = noise_scheduler.add_noise(latents_2, noise_2, timesteps_2)
register_attention_disentangled_control(self.unet, decom_controller_1)
model_pred_1 = self.unet(
noisy_latents_1,
timesteps_1,
encoder_hidden_states = encoder_hidden_states_list_1,
cross_attention_kwargs = None,
return_dict = False,
added_cond_kwargs = {"text_embeds": add_text_embeds_1, "time_ids": add_time_ids_1}
)[0]
register_attention_disentangled_control(self.unet, decom_controller_2)
model_pred_2 = self.unet(
noisy_latents_2,
timesteps_2,
encoder_hidden_states = encoder_hidden_states_list_2,
cross_attention_kwargs = None,
return_dict=False,
added_cond_kwargs={"text_embeds": add_text_embeds_2, "time_ids": add_time_ids_2}
)[0]
loss_1 = F.mse_loss(model_pred_1.float(), noise_1.float(), reduction="mean")
loss_2 = F.mse_loss(model_pred_2.float(), noise_2.float(), reduction="mean")
loss = loss_1 + loss_2
accelerator.backward(loss)
optimizer.step()
optimizer.zero_grad()
logs = {"loss": loss.detach().item(), "lr": diffusion_model_learning_rate}
progress_bar.set_postfix(**logs)
accelerator.log(logs, step=global_step)
if accelerator.sync_gradients:
progress_bar.update(1)
global_step += 1
if global_step >=max_diffusion_train_steps:
break
accelerator.wait_for_everyone()
accelerator.end_training()
self.unet = accelerator.unwrap_model(self.unet).to(dtype = weight_dtype)
@torch.no_grad()
def backward_zT_to_z0_euler_decom(
self,
zT,
cond_emb_list,
cond_add_text_embeds,
add_time_ids,
uncond_emb=None,
guidance_scale = 1,
num_sampling_steps = 20,
cond_controller = None,
uncond_controller = None,
mask_hard = None,
mask_soft = None,
orig_image = None,
return_intermediate = False,
strength = 1
):
latent_cur = zT
if uncond_emb is None:
uncond_emb = torch.zeros(zT.shape[0], 77, 2048).to(dtype = zT.dtype, device = zT.device)
uncond_add_text_embeds = torch.zeros(1, 1280).to(dtype = zT.dtype, device = zT.device)
if mask_soft is not None:
init_latents_orig = image2latent(orig_image, self.vae, dtype=self.vae.dtype)
length = init_latents_orig.shape[-1]
noise = torch.randn_like(init_latents_orig)
mask_soft = torch.nn.functional.interpolate(mask_soft.float().unsqueeze(0).unsqueeze(0), (length, length)).to(self.vae.dtype) ###
if mask_hard is not None:
init_latents_orig = image2latent(orig_image, self.vae, dtype=self.vae.dtype)
length = init_latents_orig.shape[-1]
noise = torch.randn_like(init_latents_orig)
mask_hard = torch.nn.functional.interpolate(mask_hard.float().unsqueeze(0).unsqueeze(0), (length, length)).to(self.vae.dtype) ###
intermediate_list = [latent_cur.detach()]
for i in tqdm(range(num_sampling_steps)):
t = self.scheduler.timesteps[i]
latent_input = self.scheduler.scale_model_input(latent_cur, t)
register_attention_disentangled_control(self.unet, uncond_controller)
noise_pred_uncond = self.unet(latent_input, t,
encoder_hidden_states=uncond_emb,
added_cond_kwargs={"text_embeds": uncond_add_text_embeds, "time_ids": add_time_ids},
return_dict=False,)[0]
register_attention_disentangled_control(self.unet, cond_controller)
noise_pred_cond = self.unet(latent_input, t,
encoder_hidden_states=cond_emb_list,
added_cond_kwargs={"text_embeds": cond_add_text_embeds, "time_ids": add_time_ids},
return_dict=False,)[0]
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_cond - noise_pred_uncond)
latent_cur = self.scheduler.step(noise_pred, t, latent_cur, generator = None, return_dict=False)[0]
if return_intermediate is True:
intermediate_list.append(latent_cur)
if mask_hard is not None and mask_soft is not None and i <= strength *num_sampling_steps:
init_latents_proper = self.scheduler.add_noise(init_latents_orig, noise, torch.tensor([t]))
mask = mask_soft.to(latent_cur.device, latent_cur.dtype) + mask_hard.to(latent_cur.device, latent_cur.dtype)
latent_cur = (init_latents_proper * mask) + (latent_cur * (1 - mask))
elif mask_hard is not None and mask_soft is not None and i > strength *num_sampling_steps:
init_latents_proper = self.scheduler.add_noise(init_latents_orig, noise, torch.tensor([t]))
mask = mask_hard.to(latent_cur.device, latent_cur.dtype)
latent_cur = (init_latents_proper * mask) + (latent_cur * (1 - mask))
elif mask_hard is None and mask_soft is not None and i <= strength *num_sampling_steps:
init_latents_proper = self.scheduler.add_noise(init_latents_orig, noise, torch.tensor([t]))
mask = mask_soft.to(latent_cur.device, latent_cur.dtype)
latent_cur = (init_latents_proper * mask) + (latent_cur * (1 - mask))
elif mask_hard is None and mask_soft is not None and i > strength *num_sampling_steps:
pass
elif mask_hard is not None and mask_soft is None:
init_latents_proper = self.scheduler.add_noise(init_latents_orig, noise, torch.tensor([t]))
mask = mask_hard.to(latent_cur.dtype)
latent_cur = (init_latents_proper * mask) + (latent_cur * (1 - mask))
else: # hard and soft are both none
pass
if return_intermediate is True:
return latent_cur, intermediate_list
else:
return latent_cur
@torch.no_grad()
def sampling(
self,
set_string_list,
cond_controller = None,
uncond_controller = None,
guidance_scale = 7,
num_sampling_steps = 20,
mask_hard = None,
mask_soft = None,
orig_image = None,
strength = 1.,
num_imgs = 1,
normal_token_id_list = [],
seed = 1
):
weight_dtype = torch.float16
self.scheduler.set_timesteps(num_sampling_steps)
self.unet.to(device, dtype=weight_dtype)
self.vae.to(device, dtype=weight_dtype)
self.text_encoder.to(device, dtype=weight_dtype)
self.text_encoder_2.to(device, dtype=weight_dtype)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
zT = torch.randn(num_imgs, 4, self.resolution//vae_scale_factor,self.resolution//vae_scale_factor).to(device,dtype=weight_dtype)
zT = zT * self.scheduler.init_noise_sigma
cond_emb_list, cond_add_text_embeds, add_time_ids = sdxl_prepare_input_decom(
set_string_list,
self.tokenizer,
self.tokenizer_2,
self.text_encoder,
self.text_encoder_2,
length = max_length,
bsz = num_imgs,
weight_dtype = weight_dtype,
normal_token_id_list = normal_token_id_list
)
z0 = self.backward_zT_to_z0_euler_decom(zT, cond_emb_list, cond_add_text_embeds, add_time_ids,
guidance_scale = guidance_scale, num_sampling_steps = num_sampling_steps,
cond_controller = cond_controller, uncond_controller = uncond_controller,
mask_hard = mask_hard, mask_soft = mask_soft, orig_image =orig_image, strength = strength
)
x0 = latent2image(z0, vae = self.vae)
return x0
@torch.no_grad()
def inference_with_mask(
self,
save_path,
guidance_scale = 3,
num_sampling_steps = 50,
strength = 1,
mask_soft = None,
mask_hard= None,
orig_image=None,
mask_list = None,
num_imgs = 1,
seed = 1,
set_string_list = None
):
if mask_list is not None:
mask_list = [m.to(device) for m in mask_list]
else:
mask_list = self.mask_list
if set_string_list is not None:
self.set_string_list = set_string_list
if mask_hard is not None and mask_soft is not None:
check_mask_overlap_torch(mask_hard, mask_soft)
null_controller = DummyController()
decom_controller = GroupedCAController(mask_list = mask_list)
x0 = self.sampling(
self.set_string_list,
guidance_scale = guidance_scale,
num_sampling_steps = num_sampling_steps,
strength = strength,
cond_controller = decom_controller,
uncond_controller = null_controller,
mask_soft = mask_soft,
mask_hard = mask_hard,
orig_image = orig_image,
num_imgs = num_imgs,
seed = seed
)
save_images(x0, save_path)
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