Hzzone
cce0e5af64
* add training code of gligen * fix code quality tests. --------- Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
716 lines
29 KiB
Python
716 lines
29 KiB
Python
# from accelerate.utils import write_basic_config
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#
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# write_basic_config()
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import argparse
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import logging
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import math
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import os
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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 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 packaging import version
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from tqdm.auto import tqdm
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import diffusers
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from diffusers import (
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AutoencoderKL,
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DDPMScheduler,
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EulerDiscreteScheduler,
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StableDiffusionGLIGENPipeline,
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UNet2DConditionModel,
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)
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from diffusers.optimization import get_scheduler
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from diffusers.utils import is_wandb_available, make_image_grid
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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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if is_wandb_available():
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pass
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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("0.28.0.dev0")
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logger = get_logger(__name__)
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@torch.no_grad()
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def log_validation(vae, text_encoder, tokenizer, unet, noise_scheduler, args, accelerator, step, weight_dtype):
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if accelerator.is_main_process:
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print("generate test images...")
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unet = accelerator.unwrap_model(unet)
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vae.to(accelerator.device, dtype=torch.float32)
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pipeline = StableDiffusionGLIGENPipeline(
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vae,
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text_encoder,
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tokenizer,
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unet,
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EulerDiscreteScheduler.from_config(noise_scheduler.config),
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safety_checker=None,
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feature_extractor=None,
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)
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pipeline = pipeline.to(accelerator.device)
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pipeline.set_progress_bar_config(disable=not accelerator.is_main_process)
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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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prompt = "A realistic image of landscape scene depicting a green car parking on the left of a blue truck, with a red air balloon and a bird in the sky"
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boxes = [
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[0.041015625, 0.548828125, 0.453125, 0.859375],
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[0.525390625, 0.552734375, 0.93359375, 0.865234375],
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[0.12890625, 0.015625, 0.412109375, 0.279296875],
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[0.578125, 0.08203125, 0.857421875, 0.27734375],
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]
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gligen_phrases = ["a green car", "a blue truck", "a red air balloon", "a bird"]
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images = pipeline(
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prompt=prompt,
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gligen_phrases=gligen_phrases,
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gligen_boxes=boxes,
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gligen_scheduled_sampling_beta=1.0,
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output_type="pil",
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num_inference_steps=50,
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negative_prompt="artifacts, blurry, smooth texture, bad quality, distortions, unrealistic, distorted image, bad proportions, duplicate",
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num_images_per_prompt=4,
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generator=generator,
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).images
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os.makedirs(os.path.join(args.output_dir, "images"), exist_ok=True)
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make_image_grid(images, 1, 4).save(
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os.path.join(args.output_dir, "images", f"generated-images-{step:06d}-{accelerator.process_index:02d}.png")
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)
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vae.to(accelerator.device, dtype=weight_dtype)
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def parse_args(input_args=None):
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parser = argparse.ArgumentParser(description="Simple example of a ControlNet training script.")
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parser.add_argument(
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"--data_path",
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type=str,
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default="coco_train2017.pth",
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help="Path to training dataset.",
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)
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parser.add_argument(
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"--image_path",
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type=str,
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default="coco_train2017.pth",
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help="Path to training images.",
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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="controlnet-model",
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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("--seed", type=int, default=0, 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=4, 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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"--checkpointing_steps",
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type=int,
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default=500,
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help=(
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"Save a checkpoint of the training state every X updates. Checkpoints can be used for resuming training via `--resume_from_checkpoint`. "
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"In the case that the checkpoint is better than the final trained model, the checkpoint can also be used for inference."
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"Using a checkpoint for inference requires separate loading of the original pipeline and the individual checkpointed model components."
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"See https://huggingface.co/docs/diffusers/main/en/training/dreambooth#performing-inference-using-a-saved-checkpoint for step by step"
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"instructions."
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),
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)
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parser.add_argument(
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"--checkpoints_total_limit",
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type=int,
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default=None,
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help=("Max number of checkpoints to store."),
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)
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parser.add_argument(
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"--resume_from_checkpoint",
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type=str,
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default=None,
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help=(
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"Whether training should be resumed from a previous checkpoint. Use a path saved by"
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' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
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),
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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=5e-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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"--lr_num_cycles",
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type=int,
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default=1,
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help="Number of hard resets of the lr in cosine_with_restarts scheduler.",
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)
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parser.add_argument("--lr_power", type=float, default=1.0, help="Power factor of the polynomial scheduler.")
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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(
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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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"--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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"--report_to",
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type=str,
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default="tensorboard",
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help=(
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'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
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' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
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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=(
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"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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)
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parser.add_argument(
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"--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers."
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)
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parser.add_argument(
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"--set_grads_to_none",
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action="store_true",
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help=(
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"Save more memory by using setting grads to None instead of zero. Be aware, that this changes certain"
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" behaviors, so disable this argument if it causes any problems. More info:"
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" https://pytorch.org/docs/stable/generated/torch.optim.Optimizer.zero_grad.html"
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),
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)
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parser.add_argument(
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"--tracker_project_name",
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type=str,
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default="train_controlnet",
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help=(
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"The `project_name` argument passed to Accelerator.init_trackers for"
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" more information see https://huggingface.co/docs/accelerate/v0.17.0/en/package_reference/accelerator#accelerate.Accelerator"
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),
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)
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args = parser.parse_args()
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return args
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def main(args):
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logging_dir = Path(args.output_dir, args.logging_dir)
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accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)
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accelerator = Accelerator(
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gradient_accumulation_steps=args.gradient_accumulation_steps,
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mixed_precision=args.mixed_precision,
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log_with=args.report_to,
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project_config=accelerator_project_config,
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)
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# Disable AMP for MPS.
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if torch.backends.mps.is_available():
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accelerator.native_amp = False
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# Make one log on every process with the configuration for debugging.
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logging.basicConfig(
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format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
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datefmt="%m/%d/%Y %H:%M:%S",
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level=logging.INFO,
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)
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logger.info(accelerator.state, main_process_only=False)
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if accelerator.is_local_main_process:
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transformers.utils.logging.set_verbosity_warning()
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diffusers.utils.logging.set_verbosity_info()
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else:
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transformers.utils.logging.set_verbosity_error()
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diffusers.utils.logging.set_verbosity_error()
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# If passed along, set the training seed now.
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if args.seed is not None:
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set_seed(args.seed)
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# Handle the repository creation
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if accelerator.is_main_process:
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if args.output_dir is not None:
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os.makedirs(args.output_dir, exist_ok=True)
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# import correct text encoder class
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# text_encoder_cls = import_model_class_from_model_name_or_path(args.pretrained_model_name_or_path, args.revision)
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# Load scheduler and models
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from transformers import CLIPTextModel, CLIPTokenizer
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pretrained_model_name_or_path = "masterful/gligen-1-4-generation-text-box"
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tokenizer = CLIPTokenizer.from_pretrained(pretrained_model_name_or_path, subfolder="tokenizer")
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noise_scheduler = DDPMScheduler.from_pretrained(pretrained_model_name_or_path, subfolder="scheduler")
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text_encoder = CLIPTextModel.from_pretrained(pretrained_model_name_or_path, subfolder="text_encoder")
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vae = AutoencoderKL.from_pretrained(pretrained_model_name_or_path, subfolder="vae")
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unet = UNet2DConditionModel.from_pretrained(pretrained_model_name_or_path, subfolder="unet")
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# Taken from [Sayak Paul's Diffusers PR #6511](https://github.com/huggingface/diffusers/pull/6511/files)
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def unwrap_model(model):
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model = accelerator.unwrap_model(model)
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model = model._orig_mod if is_compiled_module(model) else model
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return model
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# `accelerate` 0.16.0 will have better support for customized saving
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if version.parse(accelerate.__version__) >= version.parse("0.16.0"):
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# create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format
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def save_model_hook(models, weights, output_dir):
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if accelerator.is_main_process:
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i = len(weights) - 1
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while len(weights) > 0:
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weights.pop()
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model = models[i]
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sub_dir = "unet"
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model.save_pretrained(os.path.join(output_dir, sub_dir))
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i -= 1
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def load_model_hook(models, input_dir):
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while len(models) > 0:
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# pop models so that they are not loaded again
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model = models.pop()
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# load diffusers style into model
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load_model = unet.from_pretrained(input_dir, subfolder="unet")
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model.register_to_config(**load_model.config)
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model.load_state_dict(load_model.state_dict())
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del load_model
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accelerator.register_save_state_pre_hook(save_model_hook)
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accelerator.register_load_state_pre_hook(load_model_hook)
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vae.requires_grad_(False)
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unet.requires_grad_(False)
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text_encoder.requires_grad_(False)
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if args.enable_xformers_memory_efficient_attention:
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if is_xformers_available():
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import xformers
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xformers_version = version.parse(xformers.__version__)
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if xformers_version == version.parse("0.0.16"):
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logger.warning(
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"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."
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)
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unet.enable_xformers_memory_efficient_attention()
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# controlnet.enable_xformers_memory_efficient_attention()
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else:
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raise ValueError("xformers is not available. Make sure it is installed correctly")
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# if args.gradient_checkpointing:
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# controlnet.enable_gradient_checkpointing()
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# Check that all trainable models are in full precision
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low_precision_error_string = (
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" Please make sure to always have all model weights in full float32 precision when starting training - even if"
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" doing mixed precision training, copy of the weights should still be float32."
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)
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if unwrap_model(unet).dtype != torch.float32:
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raise ValueError(f"Controlnet loaded as datatype {unwrap_model(unet).dtype}. {low_precision_error_string}")
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# Enable TF32 for faster training on Ampere GPUs,
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# cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices
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if args.allow_tf32:
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torch.backends.cuda.matmul.allow_tf32 = True
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if args.scale_lr:
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args.learning_rate = (
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args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
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)
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optimizer_class = torch.optim.AdamW
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# Optimizer creation
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for n, m in unet.named_modules():
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if ("fuser" in n) or ("position_net" in n):
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import torch.nn as nn
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if isinstance(m, (nn.Linear, nn.LayerNorm)):
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m.reset_parameters()
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params_to_optimize = []
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for n, p in unet.named_parameters():
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if ("fuser" in n) or ("position_net" in n):
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p.requires_grad = True
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params_to_optimize.append(p)
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optimizer = optimizer_class(
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params_to_optimize,
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lr=args.learning_rate,
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betas=(args.adam_beta1, args.adam_beta2),
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weight_decay=args.adam_weight_decay,
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eps=args.adam_epsilon,
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)
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from dataset import COCODataset
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train_dataset = COCODataset(
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data_path=args.data_path,
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image_path=args.image_path,
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tokenizer=tokenizer,
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image_size=args.resolution,
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max_boxes_per_data=30,
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)
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print("num samples: ", len(train_dataset))
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train_dataloader = torch.utils.data.DataLoader(
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train_dataset,
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shuffle=True,
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# collate_fn=collate_fn,
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batch_size=args.train_batch_size,
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num_workers=args.dataloader_num_workers,
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)
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# Scheduler and math around the number of training steps.
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overrode_max_train_steps = False
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num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
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if args.max_train_steps is None:
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args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
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overrode_max_train_steps = True
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lr_scheduler = get_scheduler(
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args.lr_scheduler,
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optimizer=optimizer,
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num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,
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num_training_steps=args.max_train_steps * accelerator.num_processes,
|
|
num_cycles=args.lr_num_cycles,
|
|
power=args.lr_power,
|
|
)
|
|
|
|
# Prepare everything with our `accelerator`.
|
|
unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
|
|
unet, optimizer, train_dataloader, lr_scheduler
|
|
)
|
|
|
|
# For mixed precision training we cast the text_encoder and vae weights to half-precision
|
|
# as these models 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
|
|
|
|
# Move vae, unet and text_encoder to device and cast to weight_dtype
|
|
vae.to(accelerator.device, dtype=weight_dtype)
|
|
# unet.to(accelerator.device, dtype=weight_dtype)
|
|
unet.to(accelerator.device, dtype=torch.float32)
|
|
text_encoder.to(accelerator.device, dtype=weight_dtype)
|
|
|
|
# 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:
|
|
tracker_config = dict(vars(args))
|
|
|
|
# tensorboard cannot handle list types for config
|
|
# tracker_config.pop("validation_prompt")
|
|
# tracker_config.pop("validation_image")
|
|
|
|
accelerator.init_trackers(args.tracker_project_name, config=tracker_config)
|
|
|
|
# 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 batches each epoch = {len(train_dataloader)}")
|
|
# 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,
|
|
)
|
|
|
|
log_validation(
|
|
vae,
|
|
text_encoder,
|
|
tokenizer,
|
|
unet,
|
|
noise_scheduler,
|
|
args,
|
|
accelerator,
|
|
global_step,
|
|
weight_dtype,
|
|
)
|
|
|
|
# image_logs = None
|
|
for epoch in range(first_epoch, args.num_train_epochs):
|
|
for step, batch in enumerate(train_dataloader):
|
|
with accelerator.accumulate(unet):
|
|
# Convert images to latent space
|
|
latents = vae.encode(batch["pixel_values"].to(dtype=weight_dtype)).latent_dist.sample()
|
|
latents = latents * vae.config.scaling_factor
|
|
|
|
# Sample noise that we'll add to the latents
|
|
noise = torch.randn_like(latents)
|
|
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)
|
|
|
|
with torch.no_grad():
|
|
# Get the text embedding for conditioning
|
|
encoder_hidden_states = text_encoder(
|
|
batch["caption"]["input_ids"].squeeze(1),
|
|
# batch['caption']['attention_mask'].squeeze(1),
|
|
return_dict=False,
|
|
)[0]
|
|
|
|
cross_attention_kwargs = {}
|
|
cross_attention_kwargs["gligen"] = {
|
|
"boxes": batch["boxes"],
|
|
"positive_embeddings": batch["text_embeddings_before_projection"],
|
|
"masks": batch["masks"],
|
|
}
|
|
# Predict the noise residual
|
|
model_pred = unet(
|
|
noisy_latents,
|
|
timesteps,
|
|
encoder_hidden_states=encoder_hidden_states,
|
|
cross_attention_kwargs=cross_attention_kwargs,
|
|
return_dict=False,
|
|
)[0]
|
|
|
|
# Get the target for loss depending on the 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}")
|
|
loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
|
|
|
|
accelerator.backward(loss)
|
|
if accelerator.sync_gradients:
|
|
accelerator.clip_grad_norm_(params_to_optimize, args.max_grad_norm)
|
|
optimizer.step()
|
|
lr_scheduler.step()
|
|
optimizer.zero_grad(set_to_none=args.set_grads_to_none)
|
|
|
|
# Checks if the accelerator has performed an optimization step behind the scenes
|
|
if accelerator.sync_gradients:
|
|
progress_bar.update(1)
|
|
global_step += 1
|
|
|
|
if global_step % args.checkpointing_steps == 0:
|
|
if accelerator.is_main_process:
|
|
# _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:06d}")
|
|
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,
|
|
text_encoder,
|
|
tokenizer,
|
|
unet,
|
|
noise_scheduler,
|
|
args,
|
|
accelerator,
|
|
global_step,
|
|
weight_dtype,
|
|
)
|
|
logs = {"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
|
|
|
|
# Create the pipeline using using the trained modules and save it.
|
|
accelerator.wait_for_everyone()
|
|
if accelerator.is_main_process:
|
|
unet = unwrap_model(unet)
|
|
unet.save_pretrained(args.output_dir)
|
|
#
|
|
# # Run a final round of validation.
|
|
# image_logs = None
|
|
# if args.validation_prompt is not None:
|
|
# image_logs = log_validation(
|
|
# vae=vae,
|
|
# text_encoder=text_encoder,
|
|
# tokenizer=tokenizer,
|
|
# unet=unet,
|
|
# controlnet=None,
|
|
# args=args,
|
|
# accelerator=accelerator,
|
|
# weight_dtype=weight_dtype,
|
|
# step=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,
|
|
# 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__":
|
|
args = parse_args()
|
|
main(args)
|