From 388b2b2a2534cc76b78b3ee99a579de5937bfe49 Mon Sep 17 00:00:00 2001 From: SimoRyu Date: Sun, 25 Dec 2022 08:11:06 +0000 Subject: [PATCH 01/10] feat : save utils on lora --- lora_diffusion/lora.py | 57 ++++++++++++++++++++++++++++++++++-------- 1 file changed, 47 insertions(+), 10 deletions(-) diff --git a/lora_diffusion/lora.py b/lora_diffusion/lora.py index 7419086..492b051 100644 --- a/lora_diffusion/lora.py +++ b/lora_diffusion/lora.py @@ -124,6 +124,7 @@ def inject_trainable_lora( _tmp.linear.bias = bias # switch the module + _tmp.to(_child_module.weight.device).to(_child_module.weight.dtype) _module._modules[name] = _tmp require_grad_params.append(_module._modules[name].lora_up.parameters()) @@ -592,18 +593,54 @@ def patch_pipe( @torch.no_grad() def inspect_lora(model, target_replace_module=DEFAULT_TARGET_REPLACE): + fnorm = {} - fnorm = {k: [] for k in target_replace_module} - - for _module in model.modules(): - if _module.__class__.__name__ in target_replace_module: - for name, _child_module in _module.named_modules(): - if _child_module.__class__.__name__ == "LoraInjectedLinear": - ups = _module._modules[name].lora_up.weight - downs = _module._modules[name].lora_down.weight + for _module, name, _child_module in _find_modules( + model, target_replace_module, search_class=[LoraInjectedLinear] + ): + ups = _module._modules[name].lora_up.weight + downs = _module._modules[name].lora_down.weight - wght: torch.Tensor = downs @ ups - fnorm[name].append(wght.flatten().pow(2).mean().item()) + wght: torch.Tensor = ups @ downs + fnormval = wght.flatten().pow(2).mean().item() + if name in fnorm: + fnorm[name].append(fnormval) + else: + fnorm[name] = [fnormval] for k, v in fnorm.items(): print(f"F norm on Current LoRA of {k} : {v}") + + +def save_all( + unet, + text_encoder, + placeholder_token_id, + placeholder_token, + save_path, + save_lora=True, + target_replace_module_text=TEXT_ENCODER_DEFAULT_TARGET_REPLACE, + target_replace_module_unet=DEFAULT_TARGET_REPLACE, +): + + # save ti + ti_path = _ti_lora_path(save_path) + learned_embeds = text_encoder.get_input_embeddings().weight[placeholder_token_id] + print("Current Learned Embeddings: ", learned_embeds[:4]) + + learned_embeds_dict = {placeholder_token: learned_embeds.detach().cpu()} + torch.save(learned_embeds_dict, ti_path) + print("Ti saved to ", ti_path) + + # save text encoder + if save_lora: + save_lora_weight( + text_encoder, + _text_lora_path(save_path), + target_replace_module=target_replace_module_text, + ) + print("Text Encoder saved to ", _text_lora_path(save_path)) + save_lora_weight( + unet, save_path, target_replace_module_unet=target_replace_module_unet + ) + print("Unet saved to ", save_path) From 4408792c9bd3f69331e89f9f118f00979126bd68 Mon Sep 17 00:00:00 2001 From: SimoRyu Date: Sun, 25 Dec 2022 08:11:29 +0000 Subject: [PATCH 02/10] fix : stochastic attribute --- lora_diffusion/dataset.py | 11 ++++++++--- 1 file changed, 8 insertions(+), 3 deletions(-) diff --git a/lora_diffusion/dataset.py b/lora_diffusion/dataset.py index 1d15d38..a7bbe1a 100644 --- a/lora_diffusion/dataset.py +++ b/lora_diffusion/dataset.py @@ -188,7 +188,6 @@ class PivotalTuningDatasetCapation(Dataset): def __init__( self, instance_data_root, - learnable_property, placeholder_token, stochastic_attribute, tokenizer, @@ -210,7 +209,9 @@ def __init__( self.num_instance_images = len(self.instance_images_path) self.placeholder_token = placeholder_token - self.stochastic_attribute = stochastic_attribute.split(",") + self.stochastic_attribute_split = ( + None if stochastic_attribute is None else stochastic_attribute.split(",") + ) self._length = self.num_instance_images @@ -253,7 +254,11 @@ def __getitem__(self, index): instance_image = instance_image.convert("RGB") example["instance_images"] = self.image_transforms(instance_image) - text = self.instance_images_path[index % self.num_instance_images].stem + text = self.instance_images_path[index % self.num_instance_images].stem + ( + ", ".join(_randomset(self.stochastic_attribute_split)) + if self.stochastic_attribute_split + else "" + ) example["instance_prompt_ids"] = self.tokenizer( text, From 4543171639b906f121d178664e2ec2ddb6c922aa Mon Sep 17 00:00:00 2001 From: SimoRyu Date: Sun, 25 Dec 2022 08:11:46 +0000 Subject: [PATCH 03/10] feat : cleaner training code --- pti.py | 448 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 448 insertions(+) create mode 100644 pti.py diff --git a/pti.py b/pti.py new file mode 100644 index 0000000..ab286be --- /dev/null +++ b/pti.py @@ -0,0 +1,448 @@ +# Bootstrapped from: +# https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/train_dreambooth.py + +import argparse +import hashlib +import inspect +import itertools +import math +import os +import random +import re +from pathlib import Path +from typing import Optional + +import torch +import torch.nn.functional as F +import torch.optim as optim +import torch.utils.checkpoint +from diffusers import ( + AutoencoderKL, + DDPMScheduler, + StableDiffusionPipeline, + UNet2DConditionModel, +) +from diffusers.optimization import get_scheduler +from huggingface_hub import HfFolder, Repository, whoami +from PIL import Image +from torch.utils.data import Dataset +from torchvision import transforms +from tqdm.auto import tqdm +from transformers import CLIPTextModel, CLIPTokenizer + +import fire + +from lora_diffusion import ( + PivotalTuningDatasetCapation, + extract_lora_ups_down, + inject_trainable_lora, + inspect_lora, + save_lora_weight, + save_all, +) + + +def get_models( + pretrained_model_name_or_path, + pretrained_vae_name_or_path, + revision, + placeholder_token, + initializer_token, + device="cuda:0", +): + + tokenizer = CLIPTokenizer.from_pretrained( + pretrained_model_name_or_path, + subfolder="tokenizer", + revision=revision, + ) + # Add the placeholder token in tokenizer + num_added_tokens = tokenizer.add_tokens(placeholder_token) + if num_added_tokens == 0: + raise ValueError( + f"The tokenizer already contains the token {placeholder_token}. Please pass a different" + " `placeholder_token` that is not already in the tokenizer." + ) + + token_ids = tokenizer.encode(initializer_token, add_special_tokens=False) + # Check if initializer_token is a single token or a sequence of tokens + if len(token_ids) > 1: + raise ValueError("The initializer token must be a single token.") + + initializer_token_id = token_ids[0] + placeholder_token_id = tokenizer.convert_tokens_to_ids(placeholder_token) + + # Load models and create wrapper for stable diffusion + text_encoder = CLIPTextModel.from_pretrained( + pretrained_model_name_or_path, + subfolder="text_encoder", + revision=revision, + ) + + text_encoder.resize_token_embeddings(len(tokenizer)) + token_embeds = text_encoder.get_input_embeddings().weight.data + token_embeds[placeholder_token_id] = token_embeds[initializer_token_id] + + vae = AutoencoderKL.from_pretrained( + pretrained_vae_name_or_path or pretrained_model_name_or_path, + subfolder=None if pretrained_vae_name_or_path else "vae", + revision=None if pretrained_vae_name_or_path else revision, + ) + unet = UNet2DConditionModel.from_pretrained( + pretrained_model_name_or_path, + subfolder="unet", + revision=revision, + ) + + return ( + text_encoder.to(device), + vae.to(device), + unet.to(device), + tokenizer, + placeholder_token_id, + ) + + +def text2img_dataloader(train_dataset, train_batch_size, tokenizer, vae, text_encoder): + def collate_fn(examples): + input_ids = [example["instance_prompt_ids"] for example in examples] + pixel_values = [example["instance_images"] for example in examples] + + # Concat class and instance examples for prior preservation. + # We do this to avoid doing two forward passes. + if examples[0].get("class_prompt_ids", None) is not None: + input_ids += [example["class_prompt_ids"] for example in examples] + pixel_values += [example["class_images"] for example in examples] + + pixel_values = torch.stack(pixel_values) + pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float() + + input_ids = tokenizer.pad( + {"input_ids": input_ids}, + padding="max_length", + max_length=tokenizer.model_max_length, + return_tensors="pt", + ).input_ids + + batch = { + "input_ids": input_ids, + "pixel_values": pixel_values, + } + return batch + + train_dataloader = torch.utils.data.DataLoader( + train_dataset, + batch_size=train_batch_size, + shuffle=True, + collate_fn=collate_fn, + num_workers=2, + ) + + return train_dataloader + + +def loss_step(batch, unet, vae, text_encoder, scheduler, weight_dtype): + latents = vae.encode( + batch["pixel_values"].to(weight_dtype).to(unet.device) + ).latent_dist.sample() + latents = latents * 0.18215 + + noise = torch.randn_like(latents) + bsz = latents.shape[0] + + timesteps = torch.randint( + 0, + scheduler.config.num_train_timesteps, + (bsz,), + device=latents.device, + ) + timesteps = timesteps.long() + + noisy_latents = scheduler.add_noise(latents, noise, timesteps) + + encoder_hidden_states = text_encoder(batch["input_ids"].to(text_encoder.device))[0] + + model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample + + if scheduler.config.prediction_type == "epsilon": + target = noise + elif scheduler.config.prediction_type == "v_prediction": + target = scheduler.get_velocity(latents, noise, timesteps) + else: + raise ValueError(f"Unknown prediction type {scheduler.config.prediction_type}") + + loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean") + return loss + + +@torch.autocast("cuda") +def perform_inversion( + unet, + vae, + text_encoder, + dataloader, + num_steps, + scheduler, + index_no_updates, + optimizer, + save_steps, + placeholder_token_id, + placeholder_token, + save_path, +): + + progress_bar = tqdm(range(num_steps)) + progress_bar.set_description("Steps") + global_step = 0 + + # Original Emb for TI + orig_embeds_params = text_encoder.get_input_embeddings().weight.data.clone() + + weight_dtype = torch.float16 + + for epoch in range(math.ceil(num_steps / len(dataloader))): + unet.eval() + text_encoder.train() + for batch in dataloader: + + loss = loss_step(batch, unet, vae, text_encoder, scheduler, weight_dtype) + loss.backward() + optimizer.step() + progress_bar.update(1) + optimizer.zero_grad() + + with torch.no_grad(): + text_encoder.get_input_embeddings().weight[ + index_no_updates + ] = orig_embeds_params[index_no_updates] + + global_step += 1 + + if global_step % save_steps == 0: + save_all( + unet=unet, + text_encoder=text_encoder, + placeholder_token_id=placeholder_token_id, + placeholder_token=placeholder_token, + save_path=os.path.join(save_path, f"step_inv_{global_step}.pt"), + save_lora=False, + ) + + +@torch.autocast("cuda") +def perform_tuning( + unet, + vae, + text_encoder, + dataloader, + num_steps, + scheduler, + optimizer, + save_steps: int, + placeholder_token_id, + placeholder_token, + save_path, +): + + progress_bar = tqdm(range(num_steps)) + progress_bar.set_description("Steps") + global_step = 0 + + weight_dtype = torch.float16 + + for epoch in range(math.ceil(num_steps / len(dataloader))): + for batch in dataloader: + + loss = loss_step(batch, unet, vae, text_encoder, scheduler, weight_dtype) + loss.backward() + optimizer.step() + progress_bar.update(1) + optimizer.zero_grad() + global_step += 1 + + if global_step % save_steps == 0: + save_all( + unet, + text_encoder, + placeholder_token_id=placeholder_token_id, + placeholder_token=placeholder_token, + save_path=os.path.join(save_path, f"step_{global_step}.pt"), + ) + + +def main( + instance_data_dir: str, + pretrained_model_name_or_path: str, + output_dir: str, + train_text_encoder: bool = False, + pretrained_vae_name_or_path: str = None, + revision: Optional[str] = None, + class_data_dir: Optional[str] = None, + stochastic_attribute: Optional[str] = None, + perform_inverse: bool = True, + learnable_property: str = "object", + placeholder_token: str = "", + initializer_token: str = "dog", + class_prompt: Optional[str] = None, + with_prior_preservation: bool = False, + prior_loss_weight: float = 1.0, + num_class_images: int = 100, + seed: int = 42, + resolution: int = 512, + center_crop: bool = False, + color_jitter: bool = True, + train_batch_size: int = 1, + sample_batch_size: int = 1, + max_train_steps_tuning: int = 10000, + max_train_steps_ti: int = 2000, + save_steps: int = 500, + gradient_accumulation_steps: bool = False, + gradient_checkpointing: bool = False, + mixed_precision="fp16", + lora_rank: int = 4, + lora_unet_target_modules=["CrossAttention", "Attention", "GELU"], + lora_clip_target_modules=["CLIPAttention"], + learning_rate_unet: float = 1e-5, + learning_rate_text: float = 1e-5, + learning_rate_ti: float = 5e-4, + scale_lr: bool = False, + lr_scheduler: str = "constant", + lr_warmup_steps: int = 100, + use_8bit_adam: bool = False, + device="cuda:1", +): + torch.manual_seed(seed) + + if output_dir is not None: + os.makedirs(output_dir, exist_ok=True) + + # get the models + text_encoder, vae, unet, tokenizer, placeholder_token_id = get_models( + pretrained_model_name_or_path, + pretrained_vae_name_or_path, + revision, + placeholder_token, + initializer_token, + device=device, + ) + + noise_scheduler = DDPMScheduler.from_config( + pretrained_model_name_or_path, subfolder="scheduler" + ) + + if gradient_checkpointing: + text_encoder.gradient_checkpointing_enable() + unet.gradient_checkpointing_enable() + + if scale_lr: + unet_lr = learning_rate_unet * gradient_accumulation_steps * train_batch_size + text_encoder_lr = ( + learning_rate_text * gradient_accumulation_steps * train_batch_size + ) + ti_lr = learning_rate_ti * gradient_accumulation_steps * train_batch_size + else: + unet_lr = learning_rate_unet + text_encoder_lr = learning_rate_text + ti_lr = learning_rate_ti + + train_dataset = PivotalTuningDatasetCapation( + instance_data_root=instance_data_dir, + placeholder_token=placeholder_token, + stochastic_attribute=stochastic_attribute, + class_data_root=class_data_dir if with_prior_preservation else None, + class_prompt=class_prompt, + tokenizer=tokenizer, + size=resolution, + center_crop=center_crop, + color_jitter=color_jitter, + ) + + train_dataloader = text2img_dataloader( + train_dataset, train_batch_size, tokenizer, vae, text_encoder + ) + + index_no_updates = torch.arange(len(tokenizer)) != placeholder_token_id + + unet.requires_grad_(False) + vae.requires_grad_(False) + + params_to_freeze = itertools.chain( + text_encoder.text_model.encoder.parameters(), + text_encoder.text_model.final_layer_norm.parameters(), + text_encoder.text_model.embeddings.position_embedding.parameters(), + ) + for param in params_to_freeze: + param.requires_grad = False + + # STEP 1 : Perform Inversion + if perform_inverse: + ti_optimizer = optim.AdamW( + text_encoder.get_input_embeddings().parameters(), lr=ti_lr + ) + + perform_inversion( + unet, + vae, + text_encoder, + train_dataloader, + max_train_steps_ti, + scheduler=noise_scheduler, + index_no_updates=index_no_updates, + optimizer=ti_optimizer, + save_steps=save_steps, + placeholder_token=placeholder_token, + placeholder_token_id=placeholder_token_id, + save_path=output_dir, + ) + + del ti_optimizer + + # Next perform Tuning with LoRA: + unet_lora_params, _ = inject_trainable_lora( + unet, r=lora_rank, target_replace_module=lora_unet_target_modules + ) + + print("Before training:") + inspect_lora(unet, target_replace_module=lora_unet_target_modules) + + params_to_optimize = [ + {"params": itertools.chain(*unet_lora_params), "lr": unet_lr}, + ] + + if train_text_encoder: + text_encoder_lora_params, _ = inject_trainable_lora( + text_encoder, + target_replace_module=lora_clip_target_modules, + r=lora_rank, + ) + params_to_optimize += [ + { + "params": itertools.chain(*text_encoder_lora_params), + "lr": text_encoder_lr, + } + ] + inspect_lora(text_encoder, target_replace_module=lora_clip_target_modules) + + lora_optimizers = optim.AdamW(params_to_optimize, weight_decay=0.001) + + unet.train() + if train_text_encoder: + text_encoder.train() + + perform_tuning( + unet, + vae, + text_encoder, + train_dataloader, + max_train_steps_tuning, + scheduler=noise_scheduler, + optimizer=lora_optimizers, + save_steps=save_steps, + placeholder_token=placeholder_token, + placeholder_token_id=placeholder_token_id, + save_path=output_dir, + ) + + +if __name__ == "__main__": + fire.Fire(main) From 3b879268598819670e1a7c64d28cb5934795ae26 Mon Sep 17 00:00:00 2001 From: SimoRyu Date: Sun, 25 Dec 2022 09:11:21 +0000 Subject: [PATCH 04/10] fix : bit of bugs on inspect and trainer --- lora_diffusion/lora.py | 31 +++++++++++++++---------------- pti.py | 36 ++++++++++++++++++++++++++++++------ 2 files changed, 45 insertions(+), 22 deletions(-) diff --git a/lora_diffusion/lora.py b/lora_diffusion/lora.py index 492b051..6e12808 100644 --- a/lora_diffusion/lora.py +++ b/lora_diffusion/lora.py @@ -592,24 +592,23 @@ def patch_pipe( @torch.no_grad() -def inspect_lora(model, target_replace_module=DEFAULT_TARGET_REPLACE): - fnorm = {} +def inspect_lora(model): + moved = {} - for _module, name, _child_module in _find_modules( - model, target_replace_module, search_class=[LoraInjectedLinear] - ): - ups = _module._modules[name].lora_up.weight - downs = _module._modules[name].lora_down.weight + for name, _module in model.named_modules(): + if _module.__class__.__name__ == "LoraInjectedLinear": + ups = _module.lora_up.weight.data + downs = _module.lora_down.weight.data + + wght: torch.Tensor = ups @ downs - wght: torch.Tensor = ups @ downs - fnormval = wght.flatten().pow(2).mean().item() - if name in fnorm: - fnorm[name].append(fnormval) - else: - fnorm[name] = [fnormval] + dist = wght.flatten().abs().mean().item() + if name in moved: + moved[name].append(dist) + else: + moved[name] = [dist] - for k, v in fnorm.items(): - print(f"F norm on Current LoRA of {k} : {v}") + return moved def save_all( @@ -641,6 +640,6 @@ def save_all( ) print("Text Encoder saved to ", _text_lora_path(save_path)) save_lora_weight( - unet, save_path, target_replace_module_unet=target_replace_module_unet + unet, save_path, target_replace_module=target_replace_module_unet ) print("Unet saved to ", save_path) diff --git a/pti.py b/pti.py index ab286be..ac874e8 100644 --- a/pti.py +++ b/pti.py @@ -176,7 +176,7 @@ def loss_step(batch, unet, vae, text_encoder, scheduler, weight_dtype): @torch.autocast("cuda") -def perform_inversion( +def train_inversion( unet, vae, text_encoder, @@ -228,6 +228,9 @@ def perform_inversion( save_lora=False, ) + if global_step >= num_steps: + return + @torch.autocast("cuda") def perform_tuning( @@ -268,6 +271,25 @@ def perform_tuning( placeholder_token=placeholder_token, save_path=os.path.join(save_path, f"step_{global_step}.pt"), ) + moved = ( + torch.tensor(list(itertools.chain(*inspect_lora(unet).values()))) + .mean() + .item() + ) + + print("LORA Unet Moved", moved) + moved = ( + torch.tensor( + list(itertools.chain(*inspect_lora(text_encoder).values())) + ) + .mean() + .item() + ) + + print("LORA CLIP Moved", moved) + + if global_step >= num_steps: + return def main( @@ -279,7 +301,7 @@ def main( revision: Optional[str] = None, class_data_dir: Optional[str] = None, stochastic_attribute: Optional[str] = None, - perform_inverse: bool = True, + perform_inversion: bool = True, learnable_property: str = "object", placeholder_token: str = "", initializer_token: str = "dog", @@ -375,12 +397,12 @@ def main( param.requires_grad = False # STEP 1 : Perform Inversion - if perform_inverse: + if perform_inversion: ti_optimizer = optim.AdamW( text_encoder.get_input_embeddings().parameters(), lr=ti_lr ) - perform_inversion( + train_inversion( unet, vae, text_encoder, @@ -403,12 +425,14 @@ def main( ) print("Before training:") - inspect_lora(unet, target_replace_module=lora_unet_target_modules) + inspect_lora(unet) params_to_optimize = [ {"params": itertools.chain(*unet_lora_params), "lr": unet_lr}, ] + text_encoder.requires_grad_(False) + if train_text_encoder: text_encoder_lora_params, _ = inject_trainable_lora( text_encoder, @@ -421,7 +445,7 @@ def main( "lr": text_encoder_lr, } ] - inspect_lora(text_encoder, target_replace_module=lora_clip_target_modules) + inspect_lora(text_encoder) lora_optimizers = optim.AdamW(params_to_optimize, weight_decay=0.001) From 0821ac15a0d015876c9a93c826fad92349970b0b Mon Sep 17 00:00:00 2001 From: SimoRyu Date: Sun, 25 Dec 2022 09:29:06 +0000 Subject: [PATCH 05/10] fix : moved pti training to cli --- pti.py => lora_diffusion/cli_lora_pti.py | 16 ++++++++++------ requirements.txt | 3 ++- setup.py | 3 ++- 3 files changed, 14 insertions(+), 8 deletions(-) rename pti.py => lora_diffusion/cli_lora_pti.py (97%) diff --git a/pti.py b/lora_diffusion/cli_lora_pti.py similarity index 97% rename from pti.py rename to lora_diffusion/cli_lora_pti.py index ac874e8..3759dbf 100644 --- a/pti.py +++ b/lora_diffusion/cli_lora_pti.py @@ -292,7 +292,7 @@ def perform_tuning( return -def main( +def train( instance_data_dir: str, pretrained_model_name_or_path: str, output_dir: str, @@ -302,7 +302,7 @@ def main( class_data_dir: Optional[str] = None, stochastic_attribute: Optional[str] = None, perform_inversion: bool = True, - learnable_property: str = "object", + learnable_property: str = "object", # not used placeholder_token: str = "", initializer_token: str = "dog", class_prompt: Optional[str] = None, @@ -330,6 +330,8 @@ def main( scale_lr: bool = False, lr_scheduler: str = "constant", lr_warmup_steps: int = 100, + weight_decay_ti: float = 0.01, + weight_decay_lora: float = 0.01, use_8bit_adam: bool = False, device="cuda:1", ): @@ -399,7 +401,9 @@ def main( # STEP 1 : Perform Inversion if perform_inversion: ti_optimizer = optim.AdamW( - text_encoder.get_input_embeddings().parameters(), lr=ti_lr + text_encoder.get_input_embeddings().parameters(), + lr=ti_lr, + weight_decay=weight_decay_ti, ) train_inversion( @@ -447,7 +451,7 @@ def main( ] inspect_lora(text_encoder) - lora_optimizers = optim.AdamW(params_to_optimize, weight_decay=0.001) + lora_optimizers = optim.AdamW(params_to_optimize, weight_decay=weight_decay_lora) unet.train() if train_text_encoder: @@ -468,5 +472,5 @@ def main( ) -if __name__ == "__main__": - fire.Fire(main) +def main(): + fire.Fire(train) diff --git a/requirements.txt b/requirements.txt index e0d883e..4855484 100644 --- a/requirements.txt +++ b/requirements.txt @@ -2,4 +2,5 @@ diffusers>=0.9.0 transformers scipy ftfy -fire \ No newline at end of file +fire +wandb \ No newline at end of file diff --git a/setup.py b/setup.py index ffe16c0..be6859b 100644 --- a/setup.py +++ b/setup.py @@ -6,13 +6,14 @@ setup( name="lora_diffusion", py_modules=["lora_diffusion"], - version="0.0.6", + version="0.0.7", description="Low Rank Adaptation for Diffusion Models. Works with Stable Diffusion out-of-the-box.", author="Simo Ryu", packages=find_packages(), entry_points={ "console_scripts": [ "lora_add = lora_diffusion.cli_lora_add:main", + "lora_pti = lora_diffusion.cli_lora_pti:main", ], }, install_requires=[ From c71ad0efadd51a2cad0f94f75501d8e2bfbae224 Mon Sep 17 00:00:00 2001 From: SimoRyu Date: Sun, 25 Dec 2022 09:46:50 +0000 Subject: [PATCH 06/10] feat : patch now accepts target arg --- lora_diffusion/lora.py | 11 +++++++++-- 1 file changed, 9 insertions(+), 2 deletions(-) diff --git a/lora_diffusion/lora.py b/lora_diffusion/lora.py index 6e12808..dfa7bba 100644 --- a/lora_diffusion/lora.py +++ b/lora_diffusion/lora.py @@ -560,6 +560,8 @@ def patch_pipe( patch_text=False, patch_ti=False, idempotent_token=True, + unet_target_replace_module=DEFAULT_TARGET_REPLACE, + text_target_replace_module=TEXT_ENCODER_DEFAULT_TARGET_REPLACE, ): assert ( len(token) > 0 @@ -570,14 +572,19 @@ def patch_pipe( if patch_unet: print("LoRA : Patching Unet") - monkeypatch_or_replace_lora(pipe.unet, torch.load(unet_path), r=r) + monkeypatch_or_replace_lora( + pipe.unet, + torch.load(unet_path), + r=r, + target_replace_module=unet_target_replace_module, + ) if patch_text: print("LoRA : Patching text encoder") monkeypatch_or_replace_lora( pipe.text_encoder, torch.load(text_path), - target_replace_module=TEXT_ENCODER_DEFAULT_TARGET_REPLACE, + target_replace_module=text_target_replace_module, r=r, ) if patch_ti: From c8a073f85d7891d75cb566f6dc979714d7a05507 Mon Sep 17 00:00:00 2001 From: SimoRyu Date: Sun, 25 Dec 2022 09:47:15 +0000 Subject: [PATCH 07/10] fix : gelu in target --- lora_diffusion/cli_lora_pti.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/lora_diffusion/cli_lora_pti.py b/lora_diffusion/cli_lora_pti.py index 3759dbf..3298d82 100644 --- a/lora_diffusion/cli_lora_pti.py +++ b/lora_diffusion/cli_lora_pti.py @@ -322,7 +322,7 @@ def train( gradient_checkpointing: bool = False, mixed_precision="fp16", lora_rank: int = 4, - lora_unet_target_modules=["CrossAttention", "Attention", "GELU"], + lora_unet_target_modules=["CrossAttention", "Attention", "GEGLU"], lora_clip_target_modules=["CLIPAttention"], learning_rate_unet: float = 1e-5, learning_rate_text: float = 1e-5, @@ -333,7 +333,7 @@ def train( weight_decay_ti: float = 0.01, weight_decay_lora: float = 0.01, use_8bit_adam: bool = False, - device="cuda:1", + device="cuda:0", ): torch.manual_seed(seed) From 7769ea06b7148e17cd9546d8ecf7f908fb3c5001 Mon Sep 17 00:00:00 2001 From: SimoRyu Date: Sun, 25 Dec 2022 13:11:16 +0000 Subject: [PATCH 08/10] fix : gradient being way too large : autocast was the problem --- lora_diffusion/cli_lora_pti.py | 20 +++++++---- lora_diffusion/dataset.py | 66 +++++++++++++++++++++------------- lora_diffusion/lora.py | 36 +++++++++++++++---- 3 files changed, 84 insertions(+), 38 deletions(-) diff --git a/lora_diffusion/cli_lora_pti.py b/lora_diffusion/cli_lora_pti.py index 3298d82..83e2148 100644 --- a/lora_diffusion/cli_lora_pti.py +++ b/lora_diffusion/cli_lora_pti.py @@ -141,9 +141,10 @@ def collate_fn(examples): return train_dataloader +@torch.autocast("cuda") def loss_step(batch, unet, vae, text_encoder, scheduler, weight_dtype): latents = vae.encode( - batch["pixel_values"].to(weight_dtype).to(unet.device) + batch["pixel_values"].to(dtype=weight_dtype).to(unet.device) ).latent_dist.sample() latents = latents * 0.18215 @@ -175,7 +176,6 @@ def loss_step(batch, unet, vae, text_encoder, scheduler, weight_dtype): return loss -@torch.autocast("cuda") def train_inversion( unet, vae, @@ -232,7 +232,6 @@ def train_inversion( return -@torch.autocast("cuda") def perform_tuning( unet, vae, @@ -253,14 +252,21 @@ def perform_tuning( weight_dtype = torch.float16 + unet.train() + text_encoder.train() + for epoch in range(math.ceil(num_steps / len(dataloader))): for batch in dataloader: + optimizer.zero_grad() loss = loss_step(batch, unet, vae, text_encoder, scheduler, weight_dtype) loss.backward() + torch.nn.utils.clip_grad_norm_( + itertools.chain(unet.parameters(), text_encoder.parameters()), 1.0 + ) optimizer.step() progress_bar.update(1) - optimizer.zero_grad() + global_step += 1 if global_step % save_steps == 0: @@ -318,12 +324,12 @@ def train( max_train_steps_tuning: int = 10000, max_train_steps_ti: int = 2000, save_steps: int = 500, - gradient_accumulation_steps: bool = False, + gradient_accumulation_steps: int = 1, gradient_checkpointing: bool = False, mixed_precision="fp16", lora_rank: int = 4, - lora_unet_target_modules=["CrossAttention", "Attention", "GEGLU"], - lora_clip_target_modules=["CLIPAttention"], + lora_unet_target_modules={"CrossAttention", "Attention", "GEGLU"}, + lora_clip_target_modules={"CLIPAttention"}, learning_rate_unet: float = 1e-5, learning_rate_text: float = 1e-5, learning_rate_ti: float = 5e-4, diff --git a/lora_diffusion/dataset.py b/lora_diffusion/dataset.py index a7bbe1a..fa7e6ab 100644 --- a/lora_diffusion/dataset.py +++ b/lora_diffusion/dataset.py @@ -185,6 +185,11 @@ def __getitem__(self, index): class PivotalTuningDatasetCapation(Dataset): + """ + A dataset to prepare the instance and class images with the prompts for fine-tuning the model. + It pre-processes the images and the tokenizes prompts. + """ + def __init__( self, instance_data_root, @@ -194,12 +199,15 @@ def __init__( class_data_root=None, class_prompt=None, size=512, + h_flip=True, center_crop=False, color_jitter=False, + resize=True, ): self.size = size self.center_crop = center_crop self.tokenizer = tokenizer + self.resize = resize self.instance_data_root = Path(instance_data_root) if not self.instance_data_root.exists(): @@ -209,9 +217,6 @@ def __init__( self.num_instance_images = len(self.instance_images_path) self.placeholder_token = placeholder_token - self.stochastic_attribute_split = ( - None if stochastic_attribute is None else stochastic_attribute.split(",") - ) self._length = self.num_instance_images @@ -225,22 +230,38 @@ def __init__( else: self.class_data_root = None - self.image_transforms = transforms.Compose( - [ - transforms.Resize( - size, interpolation=transforms.InterpolationMode.BILINEAR - ), - transforms.CenterCrop(size) - if center_crop - else transforms.RandomCrop(size), - transforms.ColorJitter(0.2, 0.1) - if color_jitter - else transforms.Lambda(lambda x: x), - transforms.RandomHorizontalFlip(), - transforms.ToTensor(), - transforms.Normalize([0.5], [0.5]), - ] - ) + if resize: + self.image_transforms = transforms.Compose( + [ + transforms.Resize( + size, interpolation=transforms.InterpolationMode.BILINEAR + ), + transforms.ColorJitter(0.2, 0.1) + if color_jitter + else transforms.Lambda(lambda x: x), + transforms.RandomHorizontalFlip() + if h_flip + else transforms.Lambda(lambda x: x), + transforms.ToTensor(), + transforms.Normalize([0.5], [0.5]), + ] + ) + else: + self.image_transforms = transforms.Compose( + [ + transforms.CenterCrop(size) + if center_crop + else transforms.Lambda(lambda x: x), + transforms.ColorJitter(0.2, 0.1) + if color_jitter + else transforms.Lambda(lambda x: x), + transforms.RandomHorizontalFlip() + if h_flip + else transforms.Lambda(lambda x: x), + transforms.ToTensor(), + transforms.Normalize([0.5], [0.5]), + ] + ) def __len__(self): return self._length @@ -254,12 +275,9 @@ def __getitem__(self, index): instance_image = instance_image.convert("RGB") example["instance_images"] = self.image_transforms(instance_image) - text = self.instance_images_path[index % self.num_instance_images].stem + ( - ", ".join(_randomset(self.stochastic_attribute_split)) - if self.stochastic_attribute_split - else "" - ) + text = self.instance_images_path[index % self.num_instance_images].stem + # print(text) example["instance_prompt_ids"] = self.tokenizer( text, padding="do_not_pad", diff --git a/lora_diffusion/lora.py b/lora_diffusion/lora.py index dfa7bba..7672b48 100644 --- a/lora_diffusion/lora.py +++ b/lora_diffusion/lora.py @@ -53,7 +53,7 @@ def _find_children( yield parent, name, module -def _find_modules( +def _find_modules_v2( model, ancestor_class: Set[str] = DEFAULT_TARGET_REPLACE, search_class: List[Type[nn.Module]] = [nn.Linear], @@ -92,6 +92,26 @@ def _find_modules( yield parent, name, module +def _find_modules_old( + model, + ancestor_class: Set[str] = DEFAULT_TARGET_REPLACE, + search_class: List[Type[nn.Module]] = [nn.Linear], + exclude_children_of: Optional[List[Type[nn.Module]]] = [LoraInjectedLinear], +): + ret = [] + for _module in model.modules(): + if _module.__class__.__name__ in ancestor_class: + + for name, _child_module in _module.named_modules(): + if _child_module.__class__ in search_class: + ret.append((_module, name, _child_module)) + print(ret) + return ret + + +_find_modules = _find_modules_v2 + + def inject_trainable_lora( model: nn.Module, target_replace_module: Set[str] = DEFAULT_TARGET_REPLACE, @@ -604,8 +624,8 @@ def inspect_lora(model): for name, _module in model.named_modules(): if _module.__class__.__name__ == "LoraInjectedLinear": - ups = _module.lora_up.weight.data - downs = _module.lora_down.weight.data + ups = _module.lora_up.weight.data.clone() + downs = _module.lora_down.weight.data.clone() wght: torch.Tensor = ups @ downs @@ -640,13 +660,15 @@ def save_all( # save text encoder if save_lora: + + save_lora_weight( + unet, save_path, target_replace_module=target_replace_module_unet + ) + print("Unet saved to ", save_path) + save_lora_weight( text_encoder, _text_lora_path(save_path), target_replace_module=target_replace_module_text, ) print("Text Encoder saved to ", _text_lora_path(save_path)) - save_lora_weight( - unet, save_path, target_replace_module=target_replace_module_unet - ) - print("Unet saved to ", save_path) From af756e17fddef6c3a7b3a412bd29f0c6b9e26bec Mon Sep 17 00:00:00 2001 From: SimoRyu Date: Sun, 25 Dec 2022 13:11:23 +0000 Subject: [PATCH 09/10] fix : hflip --- train_lora_pt_caption.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/train_lora_pt_caption.py b/train_lora_pt_caption.py index e896c42..0673a94 100644 --- a/train_lora_pt_caption.py +++ b/train_lora_pt_caption.py @@ -76,6 +76,7 @@ def __init__( class_data_root=None, class_prompt=None, size=512, + h_flip=True, center_crop=False, color_jitter=False, resize=False, @@ -508,7 +509,7 @@ def parse_args(input_args=None): type=bool, default=True, required=False, - help="Should images be resized to --resolution before training?" + help="Should images be resized to --resolution before training?", ) if input_args is not None: From 19306954d68dd380017fcb99c97e84f9e300a37f Mon Sep 17 00:00:00 2001 From: SimoRyu Date: Sun, 25 Dec 2022 14:03:48 +0000 Subject: [PATCH 10/10] fix : example running well! --- lora_illust.pt | Bin 3397249 -> 0 bytes lora_kiriko.pt | Bin 3397249 -> 0 bytes lora_kiriko.text_encoder.pt | Bin 3070033 -> 0 bytes lora_kiriko2.pt | Bin 0 -> 5073089 bytes lora_kiriko2.text_encoder.pt | Bin 0 -> 1208069 bytes lora_kiriko2.ti.pt | Bin 0 -> 3819 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