Álvaro Somoza
edcbe8038b
* login * more logins * uploads * missed login * another missed login * downloads * examples and more logins * fix * setup * Apply style fixes * fix * Apply style fixes
1175 lines
48 KiB
Python
1175 lines
48 KiB
Python
# coding=utf-8
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# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Fine-tuning script for Stable Diffusion for text2image with HuggingFace diffusers."""
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import argparse
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import gc
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import logging
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import math
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import os
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import random
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import shutil
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from pathlib import Path
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import accelerate
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import datasets
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import numpy as np
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import torch
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import torch.nn.functional as F
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import torch.utils.checkpoint
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import transformers
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from accelerate import Accelerator
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from accelerate.logging import get_logger
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from accelerate.utils import ProjectConfiguration, set_seed
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from datasets import load_dataset
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from huggingface_hub import create_repo, upload_folder
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from packaging import version
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from PIL import Image
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from pipeline_pixart_alpha_controlnet import PixArtAlphaControlnetPipeline
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from torchvision import transforms
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from tqdm.auto import tqdm
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from transformers import T5EncoderModel, T5Tokenizer
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import diffusers
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from diffusers import AutoencoderKL, DDPMScheduler
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from diffusers.models import PixArtTransformer2DModel
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from diffusers.optimization import get_scheduler
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from diffusers.training_utils import compute_snr
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from diffusers.utils import check_min_version, is_wandb_available, make_image_grid
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from diffusers.utils.hub_utils import load_or_create_model_card, populate_model_card
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from diffusers.utils.import_utils import is_xformers_available
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from diffusers.utils.torch_utils import is_compiled_module
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from examples.research_projects.pixart.controlnet_pixart_alpha import (
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PixArtControlNetAdapterModel,
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PixArtControlNetTransformerModel,
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)
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if is_wandb_available():
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import wandb
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# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
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check_min_version("0.29.2")
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logger = get_logger(__name__, log_level="INFO")
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def log_validation(
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vae,
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transformer,
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controlnet,
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tokenizer,
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scheduler,
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text_encoder,
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args,
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accelerator,
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weight_dtype,
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step,
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is_final_validation=False,
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):
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if weight_dtype == torch.float16 or weight_dtype == torch.bfloat16:
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raise ValueError(
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"Validation is not supported with mixed precision training, disable validation and use the validation script, that will generate images from the saved checkpoints."
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)
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if not is_final_validation:
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logger.info(f"Running validation step {step} ... ")
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controlnet = accelerator.unwrap_model(controlnet)
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pipeline = PixArtAlphaControlnetPipeline.from_pretrained(
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args.pretrained_model_name_or_path,
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vae=vae,
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transformer=transformer,
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scheduler=scheduler,
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text_encoder=text_encoder,
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tokenizer=tokenizer,
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controlnet=controlnet,
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revision=args.revision,
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variant=args.variant,
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torch_dtype=weight_dtype,
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)
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else:
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logger.info("Running validation - final ... ")
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controlnet = PixArtControlNetAdapterModel.from_pretrained(args.output_dir, torch_dtype=weight_dtype)
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pipeline = PixArtAlphaControlnetPipeline.from_pretrained(
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args.pretrained_model_name_or_path,
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controlnet=controlnet,
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revision=args.revision,
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variant=args.variant,
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torch_dtype=weight_dtype,
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)
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pipeline = pipeline.to(accelerator.device)
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pipeline.set_progress_bar_config(disable=True)
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if args.enable_xformers_memory_efficient_attention:
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pipeline.enable_xformers_memory_efficient_attention()
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if args.seed is None:
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generator = None
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else:
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generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)
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if len(args.validation_image) == len(args.validation_prompt):
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validation_images = args.validation_image
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validation_prompts = args.validation_prompt
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elif len(args.validation_image) == 1:
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validation_images = args.validation_image * len(args.validation_prompt)
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validation_prompts = args.validation_prompt
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elif len(args.validation_prompt) == 1:
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validation_images = args.validation_image
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validation_prompts = args.validation_prompt * len(args.validation_image)
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else:
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raise ValueError(
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"number of `args.validation_image` and `args.validation_prompt` should be checked in `parse_args`"
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)
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image_logs = []
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for validation_prompt, validation_image in zip(validation_prompts, validation_images):
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validation_image = Image.open(validation_image).convert("RGB")
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validation_image = validation_image.resize((args.resolution, args.resolution))
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images = []
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for _ in range(args.num_validation_images):
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image = pipeline(
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prompt=validation_prompt, image=validation_image, num_inference_steps=20, generator=generator
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).images[0]
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images.append(image)
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image_logs.append(
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{"validation_image": validation_image, "images": images, "validation_prompt": validation_prompt}
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)
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tracker_key = "test" if is_final_validation else "validation"
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for tracker in accelerator.trackers:
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if tracker.name == "tensorboard":
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for log in image_logs:
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images = log["images"]
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validation_prompt = log["validation_prompt"]
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validation_image = log["validation_image"]
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formatted_images = [np.asarray(validation_image)]
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for image in images:
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formatted_images.append(np.asarray(image))
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formatted_images = np.stack(formatted_images)
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tracker.writer.add_images(validation_prompt, formatted_images, step, dataformats="NHWC")
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elif tracker.name == "wandb":
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formatted_images = []
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for log in image_logs:
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images = log["images"]
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validation_prompt = log["validation_prompt"]
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validation_image = log["validation_image"]
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formatted_images.append(wandb.Image(validation_image, caption="Controlnet conditioning"))
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for image in images:
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image = wandb.Image(image, caption=validation_prompt)
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formatted_images.append(image)
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tracker.log({tracker_key: formatted_images})
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else:
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logger.warning(f"image logging not implemented for {tracker.name}")
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del pipeline
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gc.collect()
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torch.cuda.empty_cache()
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logger.info("Validation done!!")
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return image_logs
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def save_model_card(repo_id: str, image_logs=None, base_model=str, dataset_name=str, repo_folder=None):
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img_str = ""
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if image_logs is not None:
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img_str = "You can find some example images below.\n\n"
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for i, log in enumerate(image_logs):
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images = log["images"]
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validation_prompt = log["validation_prompt"]
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validation_image = log["validation_image"]
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validation_image.save(os.path.join(repo_folder, "image_control.png"))
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img_str += f"prompt: {validation_prompt}\n"
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images = [validation_image] + images
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make_image_grid(images, 1, len(images)).save(os.path.join(repo_folder, f"images_{i}.png"))
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img_str += f"\n"
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model_description = f"""
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# controlnet-{repo_id}
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These are controlnet weights trained on {base_model} with new type of conditioning.
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{img_str}
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"""
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model_card = load_or_create_model_card(
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repo_id_or_path=repo_id,
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from_training=True,
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license="openrail++",
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base_model=base_model,
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model_description=model_description,
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inference=True,
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)
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tags = [
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"pixart-alpha",
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"pixart-alpha-diffusers",
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"text-to-image",
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"diffusers",
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"controlnet",
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"diffusers-training",
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]
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model_card = populate_model_card(model_card, tags=tags)
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model_card.save(os.path.join(repo_folder, "README.md"))
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def parse_args():
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parser = argparse.ArgumentParser(description="Simple example of a training script.")
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parser.add_argument(
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"--pretrained_model_name_or_path",
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type=str,
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default=None,
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required=True,
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help="Path to pretrained model or model identifier from huggingface.co/models.",
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)
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parser.add_argument(
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"--revision",
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type=str,
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default=None,
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required=False,
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help="Revision of pretrained model identifier from huggingface.co/models.",
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)
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parser.add_argument(
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"--variant",
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type=str,
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default=None,
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help="Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16",
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)
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parser.add_argument(
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"--controlnet_model_name_or_path",
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type=str,
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default=None,
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help="Path to pretrained controlnet model or model identifier from huggingface.co/models."
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" If not specified controlnet weights are initialized from the transformer.",
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)
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parser.add_argument(
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"--dataset_name",
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type=str,
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default=None,
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help=(
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"The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,"
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" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,"
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" or to a folder containing files that 🤗 Datasets can understand."
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),
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)
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parser.add_argument(
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"--dataset_config_name",
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type=str,
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default=None,
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help="The config of the Dataset, leave as None if there's only one config.",
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)
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parser.add_argument(
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"--train_data_dir",
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type=str,
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default=None,
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help=(
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"A folder containing the training data. Folder contents must follow the structure described in"
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" https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file"
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" must exist to provide the captions for the images. Ignored if `dataset_name` is specified."
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),
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)
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parser.add_argument(
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"--image_column", type=str, default="image", help="The column of the dataset containing an image."
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)
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parser.add_argument(
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"--conditioning_image_column",
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type=str,
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default="conditioning_image",
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help="The column of the dataset containing the controlnet conditioning image.",
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)
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parser.add_argument(
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"--caption_column",
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type=str,
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default="text",
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help="The column of the dataset containing a caption or a list of captions.",
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)
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parser.add_argument(
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"--validation_prompt",
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type=str,
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nargs="+",
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default=None,
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help="One or more prompts to be evaluated every `--validation_steps`."
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" Provide either a matching number of `--validation_image`s, a single `--validation_image`"
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" to be used with all prompts, or a single prompt that will be used with all `--validation_image`s.",
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)
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parser.add_argument(
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"--validation_image",
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type=str,
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default=None,
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nargs="+",
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help=(
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"A set of paths to the controlnet conditioning image be evaluated every `--validation_steps`"
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" and logged to `--report_to`. Provide either a matching number of `--validation_prompt`s, a"
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" a single `--validation_prompt` to be used with all `--validation_image`s, or a single"
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" `--validation_image` that will be used with all `--validation_prompt`s."
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),
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)
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parser.add_argument(
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"--num_validation_images",
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type=int,
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default=4,
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help="Number of images that should be generated during validation with `validation_prompt`.",
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)
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parser.add_argument(
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"--validation_steps",
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type=int,
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default=100,
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help=(
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"Run fine-tuning validation every X epochs. The validation process consists of running the prompt"
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" `args.validation_prompt` multiple times: `args.num_validation_images`."
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),
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)
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parser.add_argument(
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"--max_train_samples",
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type=int,
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default=None,
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help=(
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"For debugging purposes or quicker training, truncate the number of training examples to this "
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"value if set."
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),
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)
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parser.add_argument(
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"--output_dir",
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type=str,
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default="pixart-controlnet",
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help="The output directory where the model predictions and checkpoints will be written.",
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)
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parser.add_argument(
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"--cache_dir",
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type=str,
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default=None,
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help="The directory where the downloaded models and datasets will be stored.",
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)
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parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
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parser.add_argument(
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"--resolution",
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type=int,
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default=512,
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help=(
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"The resolution for input images, all the images in the train/validation dataset will be resized to this"
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" resolution"
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),
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)
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parser.add_argument(
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"--train_batch_size", type=int, default=16, help="Batch size (per device) for the training dataloader."
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)
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parser.add_argument("--num_train_epochs", type=int, default=1)
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parser.add_argument(
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"--max_train_steps",
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type=int,
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default=None,
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help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
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)
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parser.add_argument(
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"--gradient_accumulation_steps",
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type=int,
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default=1,
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help="Number of updates steps to accumulate before performing a backward/update pass.",
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)
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parser.add_argument(
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"--gradient_checkpointing",
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action="store_true",
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help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
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)
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parser.add_argument(
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"--learning_rate",
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type=float,
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default=1e-6,
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help="Initial learning rate (after the potential warmup period) to use.",
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)
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parser.add_argument(
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"--scale_lr",
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action="store_true",
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default=False,
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help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
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)
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parser.add_argument(
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"--lr_scheduler",
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type=str,
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default="constant",
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help=(
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'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
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' "constant", "constant_with_warmup"]'
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),
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)
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parser.add_argument(
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"--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
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)
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parser.add_argument(
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"--snr_gamma",
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type=float,
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default=None,
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help="SNR weighting gamma to be used if rebalancing the loss. Recommended value is 5.0. "
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"More details here: https://huggingface.co/papers/2303.09556.",
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)
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parser.add_argument(
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"--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
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)
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parser.add_argument(
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"--allow_tf32",
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action="store_true",
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help=(
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"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
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" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
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),
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)
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parser.add_argument(
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"--dataloader_num_workers",
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type=int,
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default=0,
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help=(
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"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process."
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),
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)
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parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
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parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
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parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
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parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
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parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
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|
parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
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|
parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
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# ----Diffusion Training Arguments----
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|
parser.add_argument(
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"--proportion_empty_prompts",
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type=float,
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default=0,
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|
help="Proportion of image prompts to be replaced with empty strings. Defaults to 0 (no prompt replacement).",
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)
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parser.add_argument(
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"--prediction_type",
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type=str,
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default=None,
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|
help="The prediction_type that shall be used for training. Choose between 'epsilon' or 'v_prediction' or leave `None`. If left to `None` the default prediction type of the scheduler: `noise_scheduler.config.prediciton_type` is chosen.",
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)
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parser.add_argument(
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"--hub_model_id",
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type=str,
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default=None,
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help="The name of the repository to keep in sync with the local `output_dir`.",
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)
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|
parser.add_argument(
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"--logging_dir",
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type=str,
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default="logs",
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|
help=(
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"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
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" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
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),
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)
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|
parser.add_argument(
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"--mixed_precision",
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type=str,
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|
default=None,
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|
choices=["no", "fp16", "bf16"],
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|
help=(
|
|
"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
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|
" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the"
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" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
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),
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)
|
|
parser.add_argument(
|
|
"--report_to",
|
|
type=str,
|
|
default="tensorboard",
|
|
help=(
|
|
'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
|
|
' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
|
|
),
|
|
)
|
|
parser.add_argument(
|
|
"--checkpointing_steps",
|
|
type=int,
|
|
default=500,
|
|
help=(
|
|
"Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming"
|
|
" training using `--resume_from_checkpoint`."
|
|
),
|
|
)
|
|
parser.add_argument(
|
|
"--checkpoints_total_limit",
|
|
type=int,
|
|
default=None,
|
|
help=("Max number of checkpoints to store."),
|
|
)
|
|
parser.add_argument(
|
|
"--resume_from_checkpoint",
|
|
type=str,
|
|
default=None,
|
|
help=(
|
|
"Whether training should be resumed from a previous checkpoint. Use a path saved by"
|
|
' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
|
|
),
|
|
)
|
|
parser.add_argument(
|
|
"--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers."
|
|
)
|
|
parser.add_argument("--noise_offset", type=float, default=0, help="The scale of noise offset.")
|
|
|
|
parser.add_argument(
|
|
"--tracker_project_name",
|
|
type=str,
|
|
default="pixart_controlnet",
|
|
help=(
|
|
"The `project_name` argument passed to Accelerator.init_trackers for"
|
|
" more information see https://huggingface.co/docs/accelerate/v0.17.0/en/package_reference/accelerator#accelerate.Accelerator"
|
|
),
|
|
)
|
|
|
|
args = parser.parse_args()
|
|
|
|
# Sanity checks
|
|
if args.dataset_name is None and args.train_data_dir is None:
|
|
raise ValueError("Need either a dataset name or a training folder.")
|
|
|
|
if args.proportion_empty_prompts < 0 or args.proportion_empty_prompts > 1:
|
|
raise ValueError("`--proportion_empty_prompts` must be in the range [0, 1].")
|
|
|
|
return args
|
|
|
|
|
|
def main():
|
|
args = parse_args()
|
|
|
|
if args.report_to == "wandb" and args.hub_token is not None:
|
|
raise ValueError(
|
|
"You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
|
|
" Please use `hf auth login` to authenticate with the Hub."
|
|
)
|
|
|
|
logging_dir = Path(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=args.report_to,
|
|
project_config=accelerator_project_config,
|
|
)
|
|
if args.report_to == "wandb":
|
|
if not is_wandb_available():
|
|
raise ImportError("Make sure to install wandb if you want to use it for logging during training.")
|
|
|
|
# Make one log on every process with the configuration for debugging.
|
|
logging.basicConfig(
|
|
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
|
|
datefmt="%m/%d/%Y %H:%M:%S",
|
|
level=logging.INFO,
|
|
)
|
|
logger.info(accelerator.state, main_process_only=False)
|
|
if accelerator.is_local_main_process:
|
|
datasets.utils.logging.set_verbosity_warning()
|
|
transformers.utils.logging.set_verbosity_warning()
|
|
diffusers.utils.logging.set_verbosity_info()
|
|
else:
|
|
datasets.utils.logging.set_verbosity_error()
|
|
transformers.utils.logging.set_verbosity_error()
|
|
diffusers.utils.logging.set_verbosity_error()
|
|
|
|
# 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
|
|
|
|
# See Section 3.1. of the paper.
|
|
max_length = 120
|
|
|
|
# For mixed precision training we cast all non-trainable weights (vae, text_encoder) to half-precision
|
|
# as these weights are only used for inference, keeping weights in full precision is not required.
|
|
weight_dtype = torch.float32
|
|
if accelerator.mixed_precision == "fp16":
|
|
weight_dtype = torch.float16
|
|
elif accelerator.mixed_precision == "bf16":
|
|
weight_dtype = torch.bfloat16
|
|
|
|
# Load scheduler, tokenizer and models.
|
|
noise_scheduler = DDPMScheduler.from_pretrained(
|
|
args.pretrained_model_name_or_path, subfolder="scheduler", torch_dtype=weight_dtype
|
|
)
|
|
tokenizer = T5Tokenizer.from_pretrained(
|
|
args.pretrained_model_name_or_path, subfolder="tokenizer", revision=args.revision, torch_dtype=weight_dtype
|
|
)
|
|
|
|
text_encoder = T5EncoderModel.from_pretrained(
|
|
args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision, torch_dtype=weight_dtype
|
|
)
|
|
text_encoder.requires_grad_(False)
|
|
text_encoder.to(accelerator.device)
|
|
|
|
vae = AutoencoderKL.from_pretrained(
|
|
args.pretrained_model_name_or_path,
|
|
subfolder="vae",
|
|
revision=args.revision,
|
|
variant=args.variant,
|
|
torch_dtype=weight_dtype,
|
|
)
|
|
vae.requires_grad_(False)
|
|
vae.to(accelerator.device)
|
|
|
|
transformer = PixArtTransformer2DModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="transformer")
|
|
transformer.to(accelerator.device)
|
|
transformer.requires_grad_(False)
|
|
|
|
if args.controlnet_model_name_or_path:
|
|
logger.info("Loading existing controlnet weights")
|
|
controlnet = PixArtControlNetAdapterModel.from_pretrained(args.controlnet_model_name_or_path)
|
|
else:
|
|
logger.info("Initializing controlnet weights from transformer.")
|
|
controlnet = PixArtControlNetAdapterModel.from_transformer(transformer)
|
|
|
|
transformer.to(dtype=weight_dtype)
|
|
|
|
controlnet.to(accelerator.device)
|
|
controlnet.train()
|
|
|
|
def unwrap_model(model, keep_fp32_wrapper=True):
|
|
model = accelerator.unwrap_model(model, keep_fp32_wrapper=keep_fp32_wrapper)
|
|
model = model._orig_mod if is_compiled_module(model) else model
|
|
return model
|
|
|
|
# 10. Handle saving and loading of checkpoints
|
|
# `accelerate` 0.16.0 will have better support for customized saving
|
|
if version.parse(accelerate.__version__) >= version.parse("0.16.0"):
|
|
# create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format
|
|
def save_model_hook(models, weights, output_dir):
|
|
if accelerator.is_main_process:
|
|
for _, model in enumerate(models):
|
|
if isinstance(model, PixArtControlNetTransformerModel):
|
|
print(f"Saving model {model.__class__.__name__} to {output_dir}")
|
|
model.controlnet.save_pretrained(os.path.join(output_dir, "controlnet"))
|
|
|
|
# make sure to pop weight so that corresponding model is not saved again
|
|
weights.pop()
|
|
|
|
def load_model_hook(models, input_dir):
|
|
# rc todo: test and load the controlenet adapter and transformer
|
|
raise ValueError("load model hook not tested")
|
|
|
|
for i in range(len(models)):
|
|
# pop models so that they are not loaded again
|
|
model = models.pop()
|
|
|
|
if isinstance(model, PixArtControlNetTransformerModel):
|
|
load_model = PixArtControlNetAdapterModel.from_pretrained(input_dir, subfolder="controlnet")
|
|
model.register_to_config(**load_model.config)
|
|
|
|
model.load_state_dict(load_model.state_dict())
|
|
del load_model
|
|
|
|
accelerator.register_save_state_pre_hook(save_model_hook)
|
|
accelerator.register_load_state_pre_hook(load_model_hook)
|
|
|
|
if args.enable_xformers_memory_efficient_attention:
|
|
if is_xformers_available():
|
|
import xformers
|
|
|
|
xformers_version = version.parse(xformers.__version__)
|
|
if xformers_version == version.parse("0.0.16"):
|
|
logger.warn(
|
|
"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details."
|
|
)
|
|
transformer.enable_xformers_memory_efficient_attention()
|
|
controlnet.enable_xformers_memory_efficient_attention()
|
|
else:
|
|
raise ValueError("xformers is not available. Make sure it is installed correctly")
|
|
|
|
if unwrap_model(controlnet).dtype != torch.float32:
|
|
raise ValueError(
|
|
f"Transformer loaded as datatype {unwrap_model(controlnet).dtype}. The trainable parameters should be in torch.float32."
|
|
)
|
|
|
|
# Enable TF32 for faster training on Ampere GPUs,
|
|
# cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices
|
|
if args.allow_tf32:
|
|
torch.backends.cuda.matmul.allow_tf32 = True
|
|
|
|
if args.gradient_checkpointing:
|
|
transformer.enable_gradient_checkpointing()
|
|
controlnet.enable_gradient_checkpointing()
|
|
|
|
if args.scale_lr:
|
|
args.learning_rate = (
|
|
args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
|
|
)
|
|
|
|
# Initialize the optimizer
|
|
if args.use_8bit_adam:
|
|
try:
|
|
import bitsandbytes as bnb
|
|
except ImportError:
|
|
raise ImportError(
|
|
"Please install bitsandbytes to use 8-bit Adam. You can do so by running `pip install bitsandbytes`"
|
|
)
|
|
|
|
optimizer_cls = bnb.optim.AdamW8bit
|
|
else:
|
|
optimizer_cls = torch.optim.AdamW
|
|
|
|
params_to_optimize = controlnet.parameters()
|
|
optimizer = optimizer_cls(
|
|
params_to_optimize,
|
|
lr=args.learning_rate,
|
|
betas=(args.adam_beta1, args.adam_beta2),
|
|
weight_decay=args.adam_weight_decay,
|
|
eps=args.adam_epsilon,
|
|
)
|
|
|
|
# Get the datasets: you can either provide your own training and evaluation files (see below)
|
|
# or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub).
|
|
|
|
# In distributed training, the load_dataset function guarantees that only one local process can concurrently
|
|
# download the dataset.
|
|
if args.dataset_name is not None:
|
|
# Downloading and loading a dataset from the hub.
|
|
dataset = load_dataset(
|
|
args.dataset_name,
|
|
args.dataset_config_name,
|
|
cache_dir=args.cache_dir,
|
|
data_dir=args.train_data_dir,
|
|
)
|
|
else:
|
|
data_files = {}
|
|
if args.train_data_dir is not None:
|
|
data_files["train"] = os.path.join(args.train_data_dir, "**")
|
|
dataset = load_dataset(
|
|
"imagefolder",
|
|
data_files=data_files,
|
|
cache_dir=args.cache_dir,
|
|
)
|
|
# See more about loading custom images at
|
|
# https://huggingface.co/docs/datasets/v2.4.0/en/image_load#imagefolder
|
|
|
|
# Preprocessing the datasets.
|
|
# We need to tokenize inputs and targets.
|
|
column_names = dataset["train"].column_names
|
|
|
|
# 6. Get the column names for input/target.
|
|
if args.image_column is None:
|
|
image_column = column_names[0]
|
|
else:
|
|
image_column = args.image_column
|
|
if image_column not in column_names:
|
|
raise ValueError(
|
|
f"--image_column' value '{args.image_column}' needs to be one of: {', '.join(column_names)}"
|
|
)
|
|
if args.caption_column is None:
|
|
caption_column = column_names[1]
|
|
else:
|
|
caption_column = args.caption_column
|
|
if caption_column not in column_names:
|
|
raise ValueError(
|
|
f"--caption_column' value '{args.caption_column}' needs to be one of: {', '.join(column_names)}"
|
|
)
|
|
|
|
if args.conditioning_image_column is None:
|
|
conditioning_image_column = column_names[2]
|
|
logger.info(f"conditioning image column defaulting to {conditioning_image_column}")
|
|
else:
|
|
conditioning_image_column = args.conditioning_image_column
|
|
if conditioning_image_column not in column_names:
|
|
raise ValueError(
|
|
f"`--conditioning_image_column` value '{args.conditioning_image_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}"
|
|
)
|
|
|
|
# Preprocessing the datasets.
|
|
# We need to tokenize input captions and transform the images.
|
|
def tokenize_captions(examples, is_train=True, proportion_empty_prompts=0.0, max_length=120):
|
|
captions = []
|
|
for caption in examples[caption_column]:
|
|
if random.random() < proportion_empty_prompts:
|
|
captions.append("")
|
|
elif isinstance(caption, str):
|
|
captions.append(caption)
|
|
elif isinstance(caption, (list, np.ndarray)):
|
|
# take a random caption if there are multiple
|
|
captions.append(random.choice(caption) if is_train else caption[0])
|
|
else:
|
|
raise ValueError(
|
|
f"Caption column `{caption_column}` should contain either strings or lists of strings."
|
|
)
|
|
inputs = tokenizer(captions, max_length=max_length, padding="max_length", truncation=True, return_tensors="pt")
|
|
return inputs.input_ids, inputs.attention_mask
|
|
|
|
# Preprocessing the datasets.
|
|
train_transforms = transforms.Compose(
|
|
[
|
|
transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR),
|
|
transforms.CenterCrop(args.resolution),
|
|
transforms.ToTensor(),
|
|
transforms.Normalize([0.5], [0.5]),
|
|
]
|
|
)
|
|
|
|
conditioning_image_transforms = transforms.Compose(
|
|
[
|
|
transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR),
|
|
transforms.CenterCrop(args.resolution),
|
|
transforms.ToTensor(),
|
|
]
|
|
)
|
|
|
|
def preprocess_train(examples):
|
|
images = [image.convert("RGB") for image in examples[image_column]]
|
|
examples["pixel_values"] = [train_transforms(image) for image in images]
|
|
|
|
conditioning_images = [image.convert("RGB") for image in examples[args.conditioning_image_column]]
|
|
examples["conditioning_pixel_values"] = [conditioning_image_transforms(image) for image in conditioning_images]
|
|
|
|
examples["input_ids"], examples["prompt_attention_mask"] = tokenize_captions(
|
|
examples, proportion_empty_prompts=args.proportion_empty_prompts, max_length=max_length
|
|
)
|
|
|
|
return examples
|
|
|
|
with accelerator.main_process_first():
|
|
if args.max_train_samples is not None:
|
|
dataset["train"] = dataset["train"].shuffle(seed=args.seed).select(range(args.max_train_samples))
|
|
# Set the training transforms
|
|
train_dataset = dataset["train"].with_transform(preprocess_train)
|
|
|
|
def collate_fn(examples):
|
|
pixel_values = torch.stack([example["pixel_values"] for example in examples])
|
|
pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
|
|
|
|
conditioning_pixel_values = torch.stack([example["conditioning_pixel_values"] for example in examples])
|
|
conditioning_pixel_values = conditioning_pixel_values.to(memory_format=torch.contiguous_format).float()
|
|
|
|
input_ids = torch.stack([example["input_ids"] for example in examples])
|
|
prompt_attention_mask = torch.stack([example["prompt_attention_mask"] for example in examples])
|
|
|
|
return {
|
|
"pixel_values": pixel_values,
|
|
"conditioning_pixel_values": conditioning_pixel_values,
|
|
"input_ids": input_ids,
|
|
"prompt_attention_mask": prompt_attention_mask,
|
|
}
|
|
|
|
# DataLoaders creation:
|
|
train_dataloader = torch.utils.data.DataLoader(
|
|
train_dataset,
|
|
shuffle=True,
|
|
collate_fn=collate_fn,
|
|
batch_size=args.train_batch_size,
|
|
num_workers=args.dataloader_num_workers,
|
|
)
|
|
|
|
# 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,
|
|
)
|
|
|
|
# Prepare everything with our `accelerator`.
|
|
controlnet_transformer = PixArtControlNetTransformerModel(transformer, controlnet, training=True)
|
|
controlnet_transformer, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
|
|
controlnet_transformer, optimizer, train_dataloader, lr_scheduler
|
|
)
|
|
|
|
# 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(args.tracker_project_name, 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}")
|
|
global_step = 0
|
|
first_epoch = 0
|
|
|
|
# Potentially load in the weights and states from a previous save
|
|
if args.resume_from_checkpoint:
|
|
if args.resume_from_checkpoint != "latest":
|
|
path = os.path.basename(args.resume_from_checkpoint)
|
|
else:
|
|
# Get the most recent checkpoint
|
|
dirs = os.listdir(args.output_dir)
|
|
dirs = [d for d in dirs if d.startswith("checkpoint")]
|
|
dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
|
|
path = dirs[-1] if len(dirs) > 0 else None
|
|
|
|
if path is None:
|
|
accelerator.print(
|
|
f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run."
|
|
)
|
|
args.resume_from_checkpoint = None
|
|
initial_global_step = 0
|
|
else:
|
|
accelerator.print(f"Resuming from checkpoint {path}")
|
|
accelerator.load_state(os.path.join(args.output_dir, path))
|
|
global_step = int(path.split("-")[1])
|
|
|
|
initial_global_step = global_step
|
|
first_epoch = global_step // num_update_steps_per_epoch
|
|
else:
|
|
initial_global_step = 0
|
|
|
|
progress_bar = tqdm(
|
|
range(0, args.max_train_steps),
|
|
initial=initial_global_step,
|
|
desc="Steps",
|
|
# Only show the progress bar once on each machine.
|
|
disable=not accelerator.is_local_main_process,
|
|
)
|
|
|
|
latent_channels = transformer.config.in_channels
|
|
for epoch in range(first_epoch, args.num_train_epochs):
|
|
controlnet_transformer.controlnet.train()
|
|
train_loss = 0.0
|
|
for step, batch in enumerate(train_dataloader):
|
|
with accelerator.accumulate(controlnet):
|
|
# Convert images to latent space
|
|
latents = vae.encode(batch["pixel_values"].to(dtype=weight_dtype)).latent_dist.sample()
|
|
latents = latents * vae.config.scaling_factor
|
|
|
|
# Convert control images to latent space
|
|
controlnet_image_latents = vae.encode(
|
|
batch["conditioning_pixel_values"].to(dtype=weight_dtype)
|
|
).latent_dist.sample()
|
|
controlnet_image_latents = controlnet_image_latents * vae.config.scaling_factor
|
|
|
|
# Sample noise that we'll add to the latents
|
|
noise = torch.randn_like(latents)
|
|
if args.noise_offset:
|
|
# https://www.crosslabs.org//blog/diffusion-with-offset-noise
|
|
noise += args.noise_offset * torch.randn(
|
|
(latents.shape[0], latents.shape[1], 1, 1), device=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)
|
|
timesteps = timesteps.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
|
|
prompt_embeds = text_encoder(batch["input_ids"], attention_mask=batch["prompt_attention_mask"])[0]
|
|
prompt_attention_mask = batch["prompt_attention_mask"]
|
|
# Get the target for loss depending on the prediction type
|
|
if args.prediction_type is not None:
|
|
# set prediction_type of scheduler if defined
|
|
noise_scheduler.register_to_config(prediction_type=args.prediction_type)
|
|
|
|
if noise_scheduler.config.prediction_type == "epsilon":
|
|
target = noise
|
|
elif noise_scheduler.config.prediction_type == "v_prediction":
|
|
target = noise_scheduler.get_velocity(latents, noise, timesteps)
|
|
else:
|
|
raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
|
|
|
|
# Prepare micro-conditions.
|
|
added_cond_kwargs = {"resolution": None, "aspect_ratio": None}
|
|
if getattr(transformer, "module", transformer).config.sample_size == 128:
|
|
resolution = torch.tensor([args.resolution, args.resolution]).repeat(bsz, 1)
|
|
aspect_ratio = torch.tensor([float(args.resolution / args.resolution)]).repeat(bsz, 1)
|
|
resolution = resolution.to(dtype=weight_dtype, device=latents.device)
|
|
aspect_ratio = aspect_ratio.to(dtype=weight_dtype, device=latents.device)
|
|
added_cond_kwargs = {"resolution": resolution, "aspect_ratio": aspect_ratio}
|
|
|
|
# Predict the noise residual and compute loss
|
|
model_pred = controlnet_transformer(
|
|
noisy_latents,
|
|
encoder_hidden_states=prompt_embeds,
|
|
encoder_attention_mask=prompt_attention_mask,
|
|
timestep=timesteps,
|
|
controlnet_cond=controlnet_image_latents,
|
|
added_cond_kwargs=added_cond_kwargs,
|
|
return_dict=False,
|
|
)[0]
|
|
|
|
if transformer.config.out_channels // 2 == latent_channels:
|
|
model_pred = model_pred.chunk(2, dim=1)[0]
|
|
else:
|
|
model_pred = model_pred
|
|
|
|
if args.snr_gamma is None:
|
|
loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
|
|
else:
|
|
# Compute loss-weights as per Section 3.4 of https://huggingface.co/papers/2303.09556.
|
|
# Since we predict the noise instead of x_0, the original formulation is slightly changed.
|
|
# This is discussed in Section 4.2 of the same paper.
|
|
snr = compute_snr(noise_scheduler, timesteps)
|
|
if noise_scheduler.config.prediction_type == "v_prediction":
|
|
# Velocity objective requires that we add one to SNR values before we divide by them.
|
|
snr = snr + 1
|
|
mse_loss_weights = (
|
|
torch.stack([snr, args.snr_gamma * torch.ones_like(timesteps)], dim=1).min(dim=1)[0] / snr
|
|
)
|
|
|
|
loss = F.mse_loss(model_pred.float(), target.float(), reduction="none")
|
|
loss = loss.mean(dim=list(range(1, len(loss.shape)))) * mse_loss_weights
|
|
loss = loss.mean()
|
|
|
|
# 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 accelerator.sync_gradients:
|
|
params_to_clip = controlnet_transformer.controlnet.parameters()
|
|
accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
|
|
optimizer.step()
|
|
lr_scheduler.step()
|
|
optimizer.zero_grad()
|
|
|
|
# Checks if the accelerator has performed an optimization step behind the scenes
|
|
if accelerator.sync_gradients:
|
|
progress_bar.update(1)
|
|
global_step += 1
|
|
accelerator.log({"train_loss": train_loss}, step=global_step)
|
|
train_loss = 0.0
|
|
|
|
if accelerator.is_main_process:
|
|
if global_step % args.checkpointing_steps == 0:
|
|
# _before_ saving state, check if this save would set us over the `checkpoints_total_limit`
|
|
if args.checkpoints_total_limit is not None:
|
|
checkpoints = os.listdir(args.output_dir)
|
|
checkpoints = [d for d in checkpoints if d.startswith("checkpoint")]
|
|
checkpoints = sorted(checkpoints, key=lambda x: int(x.split("-")[1]))
|
|
|
|
# before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints
|
|
if len(checkpoints) >= args.checkpoints_total_limit:
|
|
num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1
|
|
removing_checkpoints = checkpoints[0:num_to_remove]
|
|
|
|
logger.info(
|
|
f"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints"
|
|
)
|
|
logger.info(f"removing checkpoints: {', '.join(removing_checkpoints)}")
|
|
|
|
for removing_checkpoint in removing_checkpoints:
|
|
removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)
|
|
shutil.rmtree(removing_checkpoint)
|
|
|
|
save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
|
|
accelerator.save_state(save_path)
|
|
|
|
logger.info(f"Saved state to {save_path}")
|
|
|
|
if args.validation_prompt is not None and global_step % args.validation_steps == 0:
|
|
log_validation(
|
|
vae,
|
|
transformer,
|
|
controlnet_transformer.controlnet,
|
|
tokenizer,
|
|
noise_scheduler,
|
|
text_encoder,
|
|
args,
|
|
accelerator,
|
|
weight_dtype,
|
|
global_step,
|
|
is_final_validation=False,
|
|
)
|
|
|
|
logs = {"step_loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
|
|
progress_bar.set_postfix(**logs)
|
|
|
|
if global_step >= args.max_train_steps:
|
|
break
|
|
|
|
# Save the lora layers
|
|
accelerator.wait_for_everyone()
|
|
if accelerator.is_main_process:
|
|
controlnet = unwrap_model(controlnet_transformer.controlnet, keep_fp32_wrapper=False)
|
|
controlnet.save_pretrained(os.path.join(args.output_dir, "controlnet"))
|
|
|
|
image_logs = None
|
|
if args.validation_prompt is not None:
|
|
image_logs = log_validation(
|
|
vae,
|
|
transformer,
|
|
controlnet,
|
|
tokenizer,
|
|
noise_scheduler,
|
|
text_encoder,
|
|
args,
|
|
accelerator,
|
|
weight_dtype,
|
|
global_step,
|
|
is_final_validation=True,
|
|
)
|
|
|
|
if args.push_to_hub:
|
|
save_model_card(
|
|
repo_id,
|
|
image_logs=image_logs,
|
|
base_model=args.pretrained_model_name_or_path,
|
|
dataset_name=args.dataset_name,
|
|
repo_folder=args.output_dir,
|
|
)
|
|
upload_folder(
|
|
repo_id=repo_id,
|
|
folder_path=args.output_dir,
|
|
commit_message="End of training",
|
|
ignore_patterns=["step_*", "epoch_*"],
|
|
)
|
|
|
|
accelerator.end_training()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|