import argparse import itertools import math import os import random from pathlib import Path from typing import Iterable import numpy as np import PIL import torch import torch.nn.functional as F import torch.utils.checkpoint from accelerate import Accelerator from accelerate.utils import ProjectConfiguration, set_seed from huggingface_hub import create_repo, upload_folder from neural_compressor.utils import logger from packaging import version from PIL import Image from torch.utils.data import Dataset from torchvision import transforms from tqdm.auto import tqdm from transformers import CLIPTextModel, CLIPTokenizer from diffusers import AutoencoderKL, DDPMScheduler, StableDiffusionPipeline, UNet2DConditionModel from diffusers.optimization import get_scheduler from diffusers.utils import make_image_grid if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"): PIL_INTERPOLATION = { "linear": PIL.Image.Resampling.BILINEAR, "bilinear": PIL.Image.Resampling.BILINEAR, "bicubic": PIL.Image.Resampling.BICUBIC, "lanczos": PIL.Image.Resampling.LANCZOS, "nearest": PIL.Image.Resampling.NEAREST, } else: PIL_INTERPOLATION = { "linear": PIL.Image.LINEAR, "bilinear": PIL.Image.BILINEAR, "bicubic": PIL.Image.BICUBIC, "lanczos": PIL.Image.LANCZOS, "nearest": PIL.Image.NEAREST, } # ------------------------------------------------------------------------------ def save_progress(text_encoder, placeholder_token_id, accelerator, args, save_path): logger.info("Saving embeddings") learned_embeds = accelerator.unwrap_model(text_encoder).get_input_embeddings().weight[placeholder_token_id] learned_embeds_dict = {args.placeholder_token: learned_embeds.detach().cpu()} torch.save(learned_embeds_dict, save_path) def parse_args(): parser = argparse.ArgumentParser(description="Example of distillation for quantization on Textual Inversion.") parser.add_argument( "--save_steps", type=int, default=500, help="Save learned_embeds.bin every X updates steps.", ) parser.add_argument( "--pretrained_model_name_or_path", type=str, default=None, required=True, help="Path to pretrained model or model identifier from huggingface.co/models.", ) parser.add_argument( "--revision", type=str, default=None, required=False, help="Revision of pretrained model identifier from huggingface.co/models.", ) parser.add_argument( "--tokenizer_name", type=str, default=None, help="Pretrained tokenizer name or path if not the same as model_name", ) parser.add_argument( "--train_data_dir", type=str, default=None, required=True, help="A folder containing the training data." ) parser.add_argument( "--placeholder_token", type=str, default=None, required=True, help="A token to use as a placeholder for the concept.", ) parser.add_argument( "--initializer_token", type=str, default=None, required=True, help="A token to use as initializer word." ) parser.add_argument("--learnable_property", type=str, default="object", help="Choose between 'object' and 'style'") parser.add_argument("--repeats", type=int, default=100, help="How many times to repeat the training data.") parser.add_argument( "--output_dir", type=str, default="text-inversion-model", help="The output directory where the model predictions and checkpoints will be written.", ) parser.add_argument( "--cache_dir", type=str, default=None, help="The directory where the downloaded models and datasets will be stored.", ) parser.add_argument("--seed", type=int, default=42, help="A seed for reproducible training.") parser.add_argument( "--resolution", type=int, default=512, help=( "The resolution for input images, all the images in the train/validation dataset will be resized to this" " resolution" ), ) parser.add_argument( "--center_crop", action="store_true", help="Whether to center crop images before resizing to resolution" ) parser.add_argument( "--train_batch_size", type=int, default=16, help="Batch size (per device) for the training dataloader." ) parser.add_argument("--num_train_epochs", type=int, default=100) parser.add_argument( "--max_train_steps", type=int, default=5000, help="Total number of training steps to perform. If provided, overrides num_train_epochs.", ) parser.add_argument( "--gradient_accumulation_steps", type=int, default=1, help="Number of updates steps to accumulate before performing a backward/update pass.", ) parser.add_argument( "--learning_rate", type=float, default=1e-4, help="Initial learning rate (after the potential warmup period) to use.", ) parser.add_argument( "--scale_lr", action="store_true", default=False, help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.", ) parser.add_argument( "--lr_scheduler", type=str, default="constant", help=( 'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",' ' "constant", "constant_with_warmup"]' ), ) parser.add_argument( "--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler." ) parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.") parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.") parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.") parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer") parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.") parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.") parser.add_argument( "--hub_model_id", type=str, default=None, help="The name of the repository to keep in sync with the local `output_dir`.", ) parser.add_argument( "--logging_dir", type=str, default="logs", help=( "[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to" " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***." ), ) parser.add_argument( "--mixed_precision", type=str, default="no", choices=["no", "fp16", "bf16"], help=( "Whether to use mixed precision. Choose" "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10." "and an Nvidia Ampere GPU." ), ) parser.add_argument("--use_ema", action="store_true", help="Whether to use EMA model.") parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.") parser.add_argument("--do_quantization", action="store_true", help="Whether or not to do quantization.") parser.add_argument("--do_distillation", action="store_true", help="Whether or not to do distillation.") parser.add_argument( "--verify_loading", action="store_true", help="Whether or not to verify the loading of the quantized model." ) parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank") args = parser.parse_args() env_local_rank = int(os.environ.get("LOCAL_RANK", -1)) if env_local_rank != -1 and env_local_rank != args.local_rank: args.local_rank = env_local_rank if args.train_data_dir is None: raise ValueError("You must specify a train data directory.") return args imagenet_templates_small = [ "a photo of a {}", "a rendering of a {}", "a cropped photo of the {}", "the photo of a {}", "a photo of a clean {}", "a photo of a dirty {}", "a dark photo of the {}", "a photo of my {}", "a photo of the cool {}", "a close-up photo of a {}", "a bright photo of the {}", "a cropped photo of a {}", "a photo of the {}", "a good photo of the {}", "a photo of one {}", "a close-up photo of the {}", "a rendition of the {}", "a photo of the clean {}", "a rendition of a {}", "a photo of a nice {}", "a good photo of a {}", "a photo of the nice {}", "a photo of the small {}", "a photo of the weird {}", "a photo of the large {}", "a photo of a cool {}", "a photo of a small {}", ] imagenet_style_templates_small = [ "a painting in the style of {}", "a rendering in the style of {}", "a cropped painting in the style of {}", "the painting in the style of {}", "a clean painting in the style of {}", "a dirty painting in the style of {}", "a dark painting in the style of {}", "a picture in the style of {}", "a cool painting in the style of {}", "a close-up painting in the style of {}", "a bright painting in the style of {}", "a cropped painting in the style of {}", "a good painting in the style of {}", "a close-up painting in the style of {}", "a rendition in the style of {}", "a nice painting in the style of {}", "a small painting in the style of {}", "a weird painting in the style of {}", "a large painting in the style of {}", ] # Adapted from torch-ema https://github.com/fadel/pytorch_ema/blob/master/torch_ema/ema.py#L14 class EMAModel: """ Exponential Moving Average of models weights """ def __init__(self, parameters: Iterable[torch.nn.Parameter], decay=0.9999): parameters = list(parameters) self.shadow_params = [p.clone().detach() for p in parameters] self.decay = decay self.optimization_step = 0 def get_decay(self, optimization_step): """ Compute the decay factor for the exponential moving average. """ value = (1 + optimization_step) / (10 + optimization_step) return 1 - min(self.decay, value) @torch.no_grad() def step(self, parameters): parameters = list(parameters) self.optimization_step += 1 self.decay = self.get_decay(self.optimization_step) for s_param, param in zip(self.shadow_params, parameters): if param.requires_grad: tmp = self.decay * (s_param - param) s_param.sub_(tmp) else: s_param.copy_(param) torch.cuda.empty_cache() def copy_to(self, parameters: Iterable[torch.nn.Parameter]) -> None: """ Copy current averaged parameters into given collection of parameters. Args: parameters: Iterable of `torch.nn.Parameter`; the parameters to be updated with the stored moving averages. If `None`, the parameters with which this `ExponentialMovingAverage` was initialized will be used. """ parameters = list(parameters) for s_param, param in zip(self.shadow_params, parameters): param.data.copy_(s_param.data) def to(self, device=None, dtype=None) -> None: r"""Move internal buffers of the ExponentialMovingAverage to `device`. Args: device: like `device` argument to `torch.Tensor.to` """ # .to() on the tensors handles None correctly self.shadow_params = [ p.to(device=device, dtype=dtype) if p.is_floating_point() else p.to(device=device) for p in self.shadow_params ] class TextualInversionDataset(Dataset): def __init__( self, data_root, tokenizer, learnable_property="object", # [object, style] size=512, repeats=100, interpolation="bicubic", flip_p=0.5, set="train", placeholder_token="*", center_crop=False, ): self.data_root = data_root self.tokenizer = tokenizer self.learnable_property = learnable_property self.size = size self.placeholder_token = placeholder_token self.center_crop = center_crop self.flip_p = flip_p self.image_paths = [os.path.join(self.data_root, file_path) for file_path in os.listdir(self.data_root)] self.num_images = len(self.image_paths) self._length = self.num_images if set == "train": self._length = self.num_images * repeats self.interpolation = { "linear": PIL_INTERPOLATION["linear"], "bilinear": PIL_INTERPOLATION["bilinear"], "bicubic": PIL_INTERPOLATION["bicubic"], "lanczos": PIL_INTERPOLATION["lanczos"], }[interpolation] self.templates = imagenet_style_templates_small if learnable_property == "style" else imagenet_templates_small self.flip_transform = transforms.RandomHorizontalFlip(p=self.flip_p) def __len__(self): return self._length def __getitem__(self, i): example = {} image = Image.open(self.image_paths[i % self.num_images]) if not image.mode == "RGB": image = image.convert("RGB") placeholder_string = self.placeholder_token text = random.choice(self.templates).format(placeholder_string) example["input_ids"] = self.tokenizer( text, padding="max_length", truncation=True, max_length=self.tokenizer.model_max_length, return_tensors="pt", ).input_ids[0] # default to score-sde preprocessing img = np.array(image).astype(np.uint8) if self.center_crop: crop = min(img.shape[0], img.shape[1]) ( h, w, ) = ( img.shape[0], img.shape[1], ) img = img[(h - crop) // 2 : (h + crop) // 2, (w - crop) // 2 : (w + crop) // 2] image = Image.fromarray(img) image = image.resize((self.size, self.size), resample=self.interpolation) image = self.flip_transform(image) image = np.array(image).astype(np.uint8) image = (image / 127.5 - 1.0).astype(np.float32) example["pixel_values"] = torch.from_numpy(image).permute(2, 0, 1) return example def freeze_params(params): for param in params: param.requires_grad = False def generate_images(pipeline, prompt="", guidance_scale=7.5, num_inference_steps=50, num_images_per_prompt=1, seed=42): generator = torch.Generator(pipeline.device).manual_seed(seed) images = pipeline( prompt, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps, generator=generator, num_images_per_prompt=num_images_per_prompt, ).images _rows = int(math.sqrt(num_images_per_prompt)) grid = make_image_grid(images, rows=_rows, cols=num_images_per_prompt // _rows) return grid def main(): args = parse_args() logging_dir = os.path.join(args.output_dir, args.logging_dir) accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir) accelerator = Accelerator( gradient_accumulation_steps=args.gradient_accumulation_steps, mixed_precision=args.mixed_precision, log_with="tensorboard", project_config=accelerator_project_config, ) # If passed along, set the training seed now. if args.seed is not None: set_seed(args.seed) # Handle the repository creation if accelerator.is_main_process: if args.output_dir is not None: os.makedirs(args.output_dir, exist_ok=True) if args.push_to_hub: repo_id = create_repo( repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token ).repo_id # Load the tokenizer and add the placeholder token as a additional special token if args.tokenizer_name: tokenizer = CLIPTokenizer.from_pretrained(args.tokenizer_name) elif args.pretrained_model_name_or_path: tokenizer = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="tokenizer") # Load models and create wrapper for stable diffusion noise_scheduler = DDPMScheduler.from_config(args.pretrained_model_name_or_path, subfolder="scheduler") text_encoder = CLIPTextModel.from_pretrained( args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision, ) vae = AutoencoderKL.from_pretrained( args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision, ) unet = UNet2DConditionModel.from_pretrained( args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision, ) train_unet = False # Freeze vae and unet freeze_params(vae.parameters()) if not args.do_quantization and not args.do_distillation: # Add the placeholder token in tokenizer num_added_tokens = tokenizer.add_tokens(args.placeholder_token) if num_added_tokens == 0: raise ValueError( f"The tokenizer already contains the token {args.placeholder_token}. Please pass a different" " `placeholder_token` that is not already in the tokenizer." ) # Convert the initializer_token, placeholder_token to ids token_ids = tokenizer.encode(args.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(args.placeholder_token) # Resize the token embeddings as we are adding new special tokens to the tokenizer text_encoder.resize_token_embeddings(len(tokenizer)) # Initialise the newly added placeholder token with the embeddings of the initializer token token_embeds = text_encoder.get_input_embeddings().weight.data token_embeds[placeholder_token_id] = token_embeds[initializer_token_id] freeze_params(unet.parameters()) # Freeze all parameters except for the token embeddings in text encoder 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(), ) freeze_params(params_to_freeze) else: train_unet = True freeze_params(text_encoder.parameters()) if args.scale_lr: args.learning_rate = ( args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes ) # Initialize the optimizer optimizer = torch.optim.AdamW( # only optimize the unet or embeddings of text_encoder unet.parameters() if train_unet else text_encoder.get_input_embeddings().parameters(), lr=args.learning_rate, betas=(args.adam_beta1, args.adam_beta2), weight_decay=args.adam_weight_decay, eps=args.adam_epsilon, ) train_dataset = TextualInversionDataset( data_root=args.train_data_dir, tokenizer=tokenizer, size=args.resolution, placeholder_token=args.placeholder_token, repeats=args.repeats, learnable_property=args.learnable_property, center_crop=args.center_crop, set="train", ) train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=args.train_batch_size, shuffle=True) # Scheduler and math around the number of training steps. overrode_max_train_steps = False num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) if args.max_train_steps is None: args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch overrode_max_train_steps = True lr_scheduler = get_scheduler( args.lr_scheduler, optimizer=optimizer, num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes, num_training_steps=args.max_train_steps * accelerator.num_processes, ) if not train_unet: text_encoder = accelerator.prepare(text_encoder) unet.to(accelerator.device) unet.eval() else: unet = accelerator.prepare(unet) text_encoder.to(accelerator.device) text_encoder.eval() optimizer, train_dataloader, lr_scheduler = accelerator.prepare(optimizer, train_dataloader, lr_scheduler) # Move vae to device vae.to(accelerator.device) # Keep vae in eval model as we don't train these vae.eval() compression_manager = None def train_func(model): if train_unet: unet_ = model text_encoder_ = text_encoder else: unet_ = unet text_encoder_ = model # We need to recalculate our total training steps as the size of the training dataloader may have changed. num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) if overrode_max_train_steps: args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch # Afterwards we recalculate our number of training epochs args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) # We need to initialize the trackers we use, and also store our configuration. # The trackers initializes automatically on the main process. if accelerator.is_main_process: accelerator.init_trackers("textual_inversion", config=vars(args)) # Train! total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps logger.info("***** Running training *****") logger.info(f" Num examples = {len(train_dataset)}") logger.info(f" Num Epochs = {args.num_train_epochs}") logger.info(f" Instantaneous batch size per device = {args.train_batch_size}") logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}") logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}") logger.info(f" Total optimization steps = {args.max_train_steps}") # Only show the progress bar once on each machine. progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process) progress_bar.set_description("Steps") global_step = 0 if train_unet and args.use_ema: ema_unet = EMAModel(unet_.parameters()) for epoch in range(args.num_train_epochs): model.train() train_loss = 0.0 for step, batch in enumerate(train_dataloader): with accelerator.accumulate(model): # Convert images to latent space latents = vae.encode(batch["pixel_values"]).latent_dist.sample().detach() latents = latents * 0.18215 # Sample noise that we'll add to the latents noise = torch.randn(latents.shape).to(latents.device) bsz = latents.shape[0] # Sample a random timestep for each image timesteps = torch.randint( 0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device ).long() # Add noise to the latents according to the noise magnitude at each timestep # (this is the forward diffusion process) noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps) # Get the text embedding for conditioning encoder_hidden_states = text_encoder_(batch["input_ids"])[0] # Predict the noise residual model_pred = unet_(noisy_latents, timesteps, encoder_hidden_states).sample loss = F.mse_loss(model_pred, noise, reduction="none").mean([1, 2, 3]).mean() if train_unet and compression_manager: unet_inputs = { "sample": noisy_latents, "timestep": timesteps, "encoder_hidden_states": encoder_hidden_states, } loss = compression_manager.callbacks.on_after_compute_loss(unet_inputs, model_pred, loss) # Gather the losses across all processes for logging (if we use distributed training). avg_loss = accelerator.gather(loss.repeat(args.train_batch_size)).mean() train_loss += avg_loss.item() / args.gradient_accumulation_steps # Backpropagate accelerator.backward(loss) if train_unet: if accelerator.sync_gradients: accelerator.clip_grad_norm_(unet_.parameters(), args.max_grad_norm) else: # Zero out the gradients for all token embeddings except the newly added # embeddings for the concept, as we only want to optimize the concept embeddings if accelerator.num_processes > 1: grads = text_encoder_.module.get_input_embeddings().weight.grad else: grads = text_encoder_.get_input_embeddings().weight.grad # Get the index for tokens that we want to zero the grads for index_grads_to_zero = torch.arange(len(tokenizer)) != placeholder_token_id grads.data[index_grads_to_zero, :] = grads.data[index_grads_to_zero, :].fill_(0) optimizer.step() lr_scheduler.step() optimizer.zero_grad() # Checks if the accelerator has performed an optimization step behind the scenes if accelerator.sync_gradients: if train_unet and args.use_ema: ema_unet.step(unet_.parameters()) progress_bar.update(1) global_step += 1 accelerator.log({"train_loss": train_loss}, step=global_step) train_loss = 0.0 if not train_unet and global_step % args.save_steps == 0: save_path = os.path.join(args.output_dir, f"learned_embeds-steps-{global_step}.bin") save_progress(text_encoder_, placeholder_token_id, accelerator, args, save_path) logs = {"step_loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]} progress_bar.set_postfix(**logs) accelerator.log(logs, step=global_step) if global_step >= args.max_train_steps: break accelerator.wait_for_everyone() if train_unet and args.use_ema: ema_unet.copy_to(unet_.parameters()) if not train_unet: return text_encoder_ if not train_unet: text_encoder = train_func(text_encoder) else: import copy model = copy.deepcopy(unet) confs = [] if args.do_quantization: from neural_compressor import QuantizationAwareTrainingConfig q_conf = QuantizationAwareTrainingConfig() confs.append(q_conf) if args.do_distillation: teacher_model = copy.deepcopy(model) def attention_fetcher(x): return x.sample layer_mappings = [ [ [ "conv_in", ] ], [ [ "time_embedding", ] ], [["down_blocks.0.attentions.0", attention_fetcher]], [["down_blocks.0.attentions.1", attention_fetcher]], [ [ "down_blocks.0.resnets.0", ] ], [ [ "down_blocks.0.resnets.1", ] ], [ [ "down_blocks.0.downsamplers.0", ] ], [["down_blocks.1.attentions.0", attention_fetcher]], [["down_blocks.1.attentions.1", attention_fetcher]], [ [ "down_blocks.1.resnets.0", ] ], [ [ "down_blocks.1.resnets.1", ] ], [ [ "down_blocks.1.downsamplers.0", ] ], [["down_blocks.2.attentions.0", attention_fetcher]], [["down_blocks.2.attentions.1", attention_fetcher]], [ [ "down_blocks.2.resnets.0", ] ], [ [ "down_blocks.2.resnets.1", ] ], [ [ "down_blocks.2.downsamplers.0", ] ], [ [ "down_blocks.3.resnets.0", ] ], [ [ "down_blocks.3.resnets.1", ] ], [ [ "up_blocks.0.resnets.0", ] ], [ [ "up_blocks.0.resnets.1", ] ], [ [ "up_blocks.0.resnets.2", ] ], [ [ "up_blocks.0.upsamplers.0", ] ], [["up_blocks.1.attentions.0", attention_fetcher]], [["up_blocks.1.attentions.1", attention_fetcher]], [["up_blocks.1.attentions.2", attention_fetcher]], [ [ "up_blocks.1.resnets.0", ] ], [ [ "up_blocks.1.resnets.1", ] ], [ [ "up_blocks.1.resnets.2", ] ], [ [ "up_blocks.1.upsamplers.0", ] ], [["up_blocks.2.attentions.0", attention_fetcher]], [["up_blocks.2.attentions.1", attention_fetcher]], [["up_blocks.2.attentions.2", attention_fetcher]], [ [ "up_blocks.2.resnets.0", ] ], [ [ "up_blocks.2.resnets.1", ] ], [ [ "up_blocks.2.resnets.2", ] ], [ [ "up_blocks.2.upsamplers.0", ] ], [["up_blocks.3.attentions.0", attention_fetcher]], [["up_blocks.3.attentions.1", attention_fetcher]], [["up_blocks.3.attentions.2", attention_fetcher]], [ [ "up_blocks.3.resnets.0", ] ], [ [ "up_blocks.3.resnets.1", ] ], [ [ "up_blocks.3.resnets.2", ] ], [["mid_block.attentions.0", attention_fetcher]], [ [ "mid_block.resnets.0", ] ], [ [ "mid_block.resnets.1", ] ], [ [ "conv_out", ] ], ] layer_names = [layer_mapping[0][0] for layer_mapping in layer_mappings] if not set(layer_names).issubset([n[0] for n in model.named_modules()]): raise ValueError( "Provided model is not compatible with the default layer_mappings, " 'please use the model fine-tuned from "CompVis/stable-diffusion-v1-4", ' "or modify the layer_mappings variable to fit your model." f"\nDefault layer_mappings are as such:\n{layer_mappings}" ) from neural_compressor.config import DistillationConfig, IntermediateLayersKnowledgeDistillationLossConfig distillation_criterion = IntermediateLayersKnowledgeDistillationLossConfig( layer_mappings=layer_mappings, loss_types=["MSE"] * len(layer_mappings), loss_weights=[1.0 / len(layer_mappings)] * len(layer_mappings), add_origin_loss=True, ) d_conf = DistillationConfig(teacher_model=teacher_model, criterion=distillation_criterion) confs.append(d_conf) from neural_compressor.training import prepare_compression compression_manager = prepare_compression(model, confs) compression_manager.callbacks.on_train_begin() model = compression_manager.model train_func(model) compression_manager.callbacks.on_train_end() # Save the resulting model and its corresponding configuration in the given directory model.save(args.output_dir) logger.info(f"Optimized model saved to: {args.output_dir}.") # change to framework model for further use model = model.model # Create the pipeline using using the trained modules and save it. templates = imagenet_style_templates_small if args.learnable_property == "style" else imagenet_templates_small prompt = templates[0].format(args.placeholder_token) if accelerator.is_main_process: pipeline = StableDiffusionPipeline.from_pretrained( args.pretrained_model_name_or_path, text_encoder=accelerator.unwrap_model(text_encoder), vae=vae, unet=accelerator.unwrap_model(unet), tokenizer=tokenizer, ) pipeline.save_pretrained(args.output_dir) pipeline = pipeline.to(unet.device) baseline_model_images = generate_images(pipeline, prompt=prompt, seed=args.seed) baseline_model_images.save( os.path.join(args.output_dir, "{}_baseline_model.png".format("_".join(prompt.split()))) ) if not train_unet: # Also save the newly trained embeddings save_path = os.path.join(args.output_dir, "learned_embeds.bin") save_progress(text_encoder, placeholder_token_id, accelerator, args, save_path) else: setattr(pipeline, "unet", accelerator.unwrap_model(model)) if args.do_quantization: pipeline = pipeline.to(torch.device("cpu")) optimized_model_images = generate_images(pipeline, prompt=prompt, seed=args.seed) optimized_model_images.save( os.path.join(args.output_dir, "{}_optimized_model.png".format("_".join(prompt.split()))) ) if args.push_to_hub: upload_folder( repo_id=repo_id, folder_path=args.output_dir, commit_message="End of training", ignore_patterns=["step_*", "epoch_*"], ) accelerator.end_training() if args.do_quantization and args.verify_loading: # Load the model obtained after Intel Neural Compressor quantization from neural_compressor.utils.pytorch import load loaded_model = load(args.output_dir, model=unet) loaded_model.eval() setattr(pipeline, "unet", loaded_model) if args.do_quantization: pipeline = pipeline.to(torch.device("cpu")) loaded_model_images = generate_images(pipeline, prompt=prompt, seed=args.seed) if loaded_model_images != optimized_model_images: logger.info("The quantized model was not successfully loaded.") else: logger.info("The quantized model was successfully loaded.") if __name__ == "__main__": main()