mirror of
https://github.com/huggingface/diffusers.git
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0fd7ee79ea
* NPU attention refactor for FLUX transformer * Apply style fixes --------- Co-authored-by: J石页 <jiangshuo9@h-partners.com> Co-authored-by: Aryan <aryan@huggingface.co> Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
1871 lines
74 KiB
Python
1871 lines
74 KiB
Python
#!/usr/bin/env python
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# coding=utf-8
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# Copyright 2025 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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# /// script
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# dependencies = [
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# "diffusers @ git+https://github.com/huggingface/diffusers.git",
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# "torch>=2.0.0",
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# "accelerate>=0.31.0",
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# "transformers>=4.41.2",
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# "ftfy",
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# "tensorboard",
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# "Jinja2",
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# "peft>=0.11.1",
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# "sentencepiece",
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# ]
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# ///
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import argparse
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import copy
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import gc
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import itertools
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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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import warnings
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from contextlib import nullcontext
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from pathlib import Path
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import numpy as np
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import torch
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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 DistributedDataParallelKwargs, ProjectConfiguration, set_seed
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from huggingface_hub import create_repo, upload_folder
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from huggingface_hub.utils import insecure_hashlib
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from PIL import Image
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from PIL.ImageOps import exif_transpose
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from torch.utils.data import Dataset
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from torchvision import transforms
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from torchvision.transforms.functional import crop
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from tqdm.auto import tqdm
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from transformers import CLIPTextModelWithProjection, CLIPTokenizer, PretrainedConfig, T5EncoderModel, T5TokenizerFast
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import diffusers
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from diffusers import (
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AutoencoderKL,
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FlowMatchEulerDiscreteScheduler,
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FluxPipeline,
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FluxTransformer2DModel,
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)
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from diffusers.optimization import get_scheduler
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from diffusers.training_utils import compute_density_for_timestep_sampling, compute_loss_weighting_for_sd3
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from diffusers.utils import (
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check_min_version,
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is_wandb_available,
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)
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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_torch_npu_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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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("0.36.0.dev0")
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logger = get_logger(__name__)
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if is_torch_npu_available():
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import torch_npu
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torch.npu.config.allow_internal_format = False
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torch.npu.set_compile_mode(jit_compile=False)
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def save_model_card(
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repo_id: str,
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images=None,
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base_model: str = None,
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train_text_encoder=False,
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instance_prompt=None,
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validation_prompt=None,
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repo_folder=None,
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):
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widget_dict = []
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if images is not None:
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for i, image in enumerate(images):
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image.save(os.path.join(repo_folder, f"image_{i}.png"))
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widget_dict.append(
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{"text": validation_prompt if validation_prompt else " ", "output": {"url": f"image_{i}.png"}}
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)
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model_description = f"""
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# Flux [dev] DreamBooth - {repo_id}
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<Gallery />
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## Model description
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These are {repo_id} DreamBooth weights for {base_model}.
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The weights were trained using [DreamBooth](https://dreambooth.github.io/) with the [Flux diffusers trainer](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/README_flux.md).
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Was the text encoder fine-tuned? {train_text_encoder}.
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## Trigger words
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You should use `{instance_prompt}` to trigger the image generation.
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## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
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```py
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from diffusers import AutoPipelineForText2Image
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import torch
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pipeline = AutoPipelineForText2Image.from_pretrained('{repo_id}', torch_dtype=torch.bfloat16).to('cuda')
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image = pipeline('{validation_prompt if validation_prompt else instance_prompt}').images[0]
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```
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## License
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Please adhere to the licensing terms as described [here](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md).
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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="other",
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base_model=base_model,
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prompt=instance_prompt,
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model_description=model_description,
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widget=widget_dict,
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)
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tags = [
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"text-to-image",
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"diffusers-training",
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"diffusers",
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"flux",
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"flux-diffusers",
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"template:sd-lora",
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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 load_text_encoders(class_one, class_two):
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text_encoder_one = class_one.from_pretrained(
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args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision, variant=args.variant
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)
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text_encoder_two = class_two.from_pretrained(
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args.pretrained_model_name_or_path, subfolder="text_encoder_2", revision=args.revision, variant=args.variant
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)
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return text_encoder_one, text_encoder_two
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def log_validation(
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pipeline,
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args,
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accelerator,
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pipeline_args,
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epoch,
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torch_dtype,
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is_final_validation=False,
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):
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logger.info(
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f"Running validation... \n Generating {args.num_validation_images} images with prompt:"
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f" {args.validation_prompt}."
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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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# run inference
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generator = torch.Generator(device=accelerator.device).manual_seed(args.seed) if args.seed is not None else None
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# autocast_ctx = torch.autocast(accelerator.device.type) if not is_final_validation else nullcontext()
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autocast_ctx = nullcontext()
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with autocast_ctx:
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images = [pipeline(**pipeline_args, generator=generator).images[0] for _ in range(args.num_validation_images)]
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for tracker in accelerator.trackers:
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phase_name = "test" if is_final_validation else "validation"
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if tracker.name == "tensorboard":
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np_images = np.stack([np.asarray(img) for img in images])
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tracker.writer.add_images(phase_name, np_images, epoch, dataformats="NHWC")
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if tracker.name == "wandb":
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tracker.log(
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{
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phase_name: [
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wandb.Image(image, caption=f"{i}: {args.validation_prompt}") for i, image in enumerate(images)
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]
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}
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)
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del pipeline
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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elif is_torch_npu_available():
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torch_npu.npu.empty_cache()
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return images
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def import_model_class_from_model_name_or_path(
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pretrained_model_name_or_path: str, revision: str, subfolder: str = "text_encoder"
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):
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text_encoder_config = PretrainedConfig.from_pretrained(
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pretrained_model_name_or_path, subfolder=subfolder, revision=revision
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)
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model_class = text_encoder_config.architectures[0]
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if model_class == "CLIPTextModel":
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from transformers import CLIPTextModel
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return CLIPTextModel
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elif model_class == "T5EncoderModel":
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from transformers import T5EncoderModel
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return T5EncoderModel
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else:
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raise ValueError(f"{model_class} is not supported.")
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def parse_args(input_args=None):
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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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"--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) containing the training data of instance images (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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"--instance_data_dir",
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type=str,
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default=None,
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help=("A folder containing the training data. "),
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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(
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"--image_column",
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type=str,
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default="image",
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help="The column of the dataset containing the target image. By "
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"default, the standard Image Dataset maps out 'file_name' "
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"to '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=None,
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help="The column of the dataset containing the instance prompt for each image",
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)
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parser.add_argument("--repeats", type=int, default=1, help="How many times to repeat the training data.")
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parser.add_argument(
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"--class_data_dir",
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type=str,
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default=None,
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required=False,
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help="A folder containing the training data of class images.",
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)
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parser.add_argument(
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"--instance_prompt",
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type=str,
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default=None,
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required=True,
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help="The prompt with identifier specifying the instance, e.g. 'photo of a TOK dog', 'in the style of TOK'",
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)
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parser.add_argument(
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"--class_prompt",
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type=str,
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default=None,
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help="The prompt to specify images in the same class as provided instance images.",
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)
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parser.add_argument(
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"--max_sequence_length",
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type=int,
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default=77,
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help="Maximum sequence length to use with with the T5 text encoder",
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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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default=None,
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help="A prompt that is used during validation to verify that the model is learning.",
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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_epochs",
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type=int,
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default=50,
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help=(
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"Run dreambooth validation every X epochs. Dreambooth validation 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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"--with_prior_preservation",
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default=False,
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action="store_true",
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help="Flag to add prior preservation loss.",
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)
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parser.add_argument("--prior_loss_weight", type=float, default=1.0, help="The weight of prior preservation loss.")
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parser.add_argument(
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"--num_class_images",
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type=int,
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default=100,
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help=(
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"Minimal class images for prior preservation loss. If there are not enough images already present in"
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" class_data_dir, additional images will be sampled with class_prompt."
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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="flux-dreambooth",
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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=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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"--center_crop",
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default=False,
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action="store_true",
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help=(
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"Whether to center crop the input images to the resolution. If not set, the images will be randomly"
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" cropped. The images will be resized to the resolution first before cropping."
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),
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)
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parser.add_argument(
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"--random_flip",
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action="store_true",
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help="whether to randomly flip images horizontally",
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)
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parser.add_argument(
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"--train_text_encoder",
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action="store_true",
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help="Whether to train the text encoder. If set, the text encoder should be float32 precision.",
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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(
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"--sample_batch_size", type=int, default=4, help="Batch size (per device) for sampling images."
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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. These checkpoints can be used both as final"
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" checkpoints in case they are better than the last checkpoint, and are also suitable for resuming"
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" training using `--resume_from_checkpoint`."
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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=1e-4,
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help="Initial learning rate (after the potential warmup period) to use.",
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)
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|
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|
parser.add_argument(
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"--guidance_scale",
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type=float,
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default=3.5,
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|
help="the FLUX.1 dev variant is a guidance distilled model",
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)
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|
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|
parser.add_argument(
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"--text_encoder_lr",
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type=float,
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default=5e-6,
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help="Text encoder learning rate 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(
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"--weighting_scheme",
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|
type=str,
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|
default="none",
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|
choices=["sigma_sqrt", "logit_normal", "mode", "cosmap", "none"],
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|
help=('We default to the "none" weighting scheme for uniform sampling and uniform loss'),
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)
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|
parser.add_argument(
|
|
"--logit_mean", type=float, default=0.0, help="mean to use when using the `'logit_normal'` weighting scheme."
|
|
)
|
|
parser.add_argument(
|
|
"--logit_std", type=float, default=1.0, help="std to use when using the `'logit_normal'` weighting scheme."
|
|
)
|
|
parser.add_argument(
|
|
"--mode_scale",
|
|
type=float,
|
|
default=1.29,
|
|
help="Scale of mode weighting scheme. Only effective when using the `'mode'` as the `weighting_scheme`.",
|
|
)
|
|
parser.add_argument(
|
|
"--optimizer",
|
|
type=str,
|
|
default="AdamW",
|
|
help=('The optimizer type to use. Choose between ["AdamW", "prodigy"]'),
|
|
)
|
|
|
|
parser.add_argument(
|
|
"--use_8bit_adam",
|
|
action="store_true",
|
|
help="Whether or not to use 8-bit Adam from bitsandbytes. Ignored if optimizer is not set to AdamW",
|
|
)
|
|
|
|
parser.add_argument(
|
|
"--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam and Prodigy optimizers."
|
|
)
|
|
parser.add_argument(
|
|
"--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam and Prodigy optimizers."
|
|
)
|
|
parser.add_argument(
|
|
"--prodigy_beta3",
|
|
type=float,
|
|
default=None,
|
|
help="coefficients for computing the Prodigy stepsize using running averages. If set to None, "
|
|
"uses the value of square root of beta2. Ignored if optimizer is adamW",
|
|
)
|
|
parser.add_argument("--prodigy_decouple", type=bool, default=True, help="Use AdamW style decoupled weight decay")
|
|
parser.add_argument("--adam_weight_decay", type=float, default=1e-04, help="Weight decay to use for unet params")
|
|
parser.add_argument(
|
|
"--adam_weight_decay_text_encoder", type=float, default=1e-03, help="Weight decay to use for text_encoder"
|
|
)
|
|
|
|
parser.add_argument(
|
|
"--adam_epsilon",
|
|
type=float,
|
|
default=1e-08,
|
|
help="Epsilon value for the Adam optimizer and Prodigy optimizers.",
|
|
)
|
|
|
|
parser.add_argument(
|
|
"--prodigy_use_bias_correction",
|
|
type=bool,
|
|
default=True,
|
|
help="Turn on Adam's bias correction. True by default. Ignored if optimizer is adamW",
|
|
)
|
|
parser.add_argument(
|
|
"--prodigy_safeguard_warmup",
|
|
type=bool,
|
|
default=True,
|
|
help="Remove lr from the denominator of D estimate to avoid issues during warm-up stage. True by default. "
|
|
"Ignored if optimizer is adamW",
|
|
)
|
|
parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
|
|
parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
|
|
parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
|
|
parser.add_argument(
|
|
"--hub_model_id",
|
|
type=str,
|
|
default=None,
|
|
help="The name of the repository to keep in sync with the local `output_dir`.",
|
|
)
|
|
parser.add_argument(
|
|
"--logging_dir",
|
|
type=str,
|
|
default="logs",
|
|
help=(
|
|
"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
|
|
" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
|
|
),
|
|
)
|
|
parser.add_argument(
|
|
"--allow_tf32",
|
|
action="store_true",
|
|
help=(
|
|
"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
|
|
" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
|
|
),
|
|
)
|
|
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(
|
|
"--mixed_precision",
|
|
type=str,
|
|
default=None,
|
|
choices=["no", "fp16", "bf16"],
|
|
help=(
|
|
"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
|
|
" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the"
|
|
" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
|
|
),
|
|
)
|
|
parser.add_argument(
|
|
"--prior_generation_precision",
|
|
type=str,
|
|
default=None,
|
|
choices=["no", "fp32", "fp16", "bf16"],
|
|
help=(
|
|
"Choose prior generation precision between fp32, fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
|
|
" 1.10.and an Nvidia Ampere GPU. Default to fp16 if a GPU is available else fp32."
|
|
),
|
|
)
|
|
parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
|
|
parser.add_argument(
|
|
"--image_interpolation_mode",
|
|
type=str,
|
|
default="lanczos",
|
|
choices=[
|
|
f.lower() for f in dir(transforms.InterpolationMode) if not f.startswith("__") and not f.endswith("__")
|
|
],
|
|
help="The image interpolation method to use for resizing images.",
|
|
)
|
|
parser.add_argument("--enable_npu_flash_attention", action="store_true", help="Enabla Flash Attention for NPU")
|
|
|
|
if input_args is not None:
|
|
args = parser.parse_args(input_args)
|
|
else:
|
|
args = parser.parse_args()
|
|
|
|
if args.dataset_name is None and args.instance_data_dir is None:
|
|
raise ValueError("Specify either `--dataset_name` or `--instance_data_dir`")
|
|
|
|
if args.dataset_name is not None and args.instance_data_dir is not None:
|
|
raise ValueError("Specify only one of `--dataset_name` or `--instance_data_dir`")
|
|
|
|
env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
|
|
if env_local_rank != -1 and env_local_rank != args.local_rank:
|
|
args.local_rank = env_local_rank
|
|
|
|
if args.with_prior_preservation:
|
|
if args.class_data_dir is None:
|
|
raise ValueError("You must specify a data directory for class images.")
|
|
if args.class_prompt is None:
|
|
raise ValueError("You must specify prompt for class images.")
|
|
else:
|
|
# logger is not available yet
|
|
if args.class_data_dir is not None:
|
|
warnings.warn("You need not use --class_data_dir without --with_prior_preservation.")
|
|
if args.class_prompt is not None:
|
|
warnings.warn("You need not use --class_prompt without --with_prior_preservation.")
|
|
|
|
return args
|
|
|
|
|
|
class DreamBoothDataset(Dataset):
|
|
"""
|
|
A dataset to prepare the instance and class images with the prompts for fine-tuning the model.
|
|
It pre-processes the images.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
instance_data_root,
|
|
instance_prompt,
|
|
class_prompt,
|
|
class_data_root=None,
|
|
class_num=None,
|
|
size=1024,
|
|
repeats=1,
|
|
center_crop=False,
|
|
):
|
|
self.size = size
|
|
self.center_crop = center_crop
|
|
|
|
self.instance_prompt = instance_prompt
|
|
self.custom_instance_prompts = None
|
|
self.class_prompt = class_prompt
|
|
|
|
# if --dataset_name is provided or a metadata jsonl file is provided in the local --instance_data directory,
|
|
# we load the training data using load_dataset
|
|
if args.dataset_name is not None:
|
|
try:
|
|
from datasets import load_dataset
|
|
except ImportError:
|
|
raise ImportError(
|
|
"You are trying to load your data using the datasets library. If you wish to train using custom "
|
|
"captions please install the datasets library: `pip install datasets`. If you wish to load a "
|
|
"local folder containing images only, specify --instance_data_dir instead."
|
|
)
|
|
# Downloading and loading a dataset from the hub.
|
|
# See more about loading custom images at
|
|
# https://huggingface.co/docs/datasets/v2.0.0/en/dataset_script
|
|
dataset = load_dataset(
|
|
args.dataset_name,
|
|
args.dataset_config_name,
|
|
cache_dir=args.cache_dir,
|
|
)
|
|
# Preprocessing the datasets.
|
|
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]
|
|
logger.info(f"image column defaulting to {image_column}")
|
|
else:
|
|
image_column = args.image_column
|
|
if image_column not in column_names:
|
|
raise ValueError(
|
|
f"`--image_column` value '{args.image_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}"
|
|
)
|
|
instance_images = dataset["train"][image_column]
|
|
|
|
if args.caption_column is None:
|
|
logger.info(
|
|
"No caption column provided, defaulting to instance_prompt for all images. If your dataset "
|
|
"contains captions/prompts for the images, make sure to specify the "
|
|
"column as --caption_column"
|
|
)
|
|
self.custom_instance_prompts = None
|
|
else:
|
|
if args.caption_column not in column_names:
|
|
raise ValueError(
|
|
f"`--caption_column` value '{args.caption_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}"
|
|
)
|
|
custom_instance_prompts = dataset["train"][args.caption_column]
|
|
# create final list of captions according to --repeats
|
|
self.custom_instance_prompts = []
|
|
for caption in custom_instance_prompts:
|
|
self.custom_instance_prompts.extend(itertools.repeat(caption, repeats))
|
|
else:
|
|
self.instance_data_root = Path(instance_data_root)
|
|
if not self.instance_data_root.exists():
|
|
raise ValueError("Instance images root doesn't exists.")
|
|
|
|
instance_images = [Image.open(path) for path in list(Path(instance_data_root).iterdir())]
|
|
self.custom_instance_prompts = None
|
|
|
|
self.instance_images = []
|
|
for img in instance_images:
|
|
self.instance_images.extend(itertools.repeat(img, repeats))
|
|
|
|
self.pixel_values = []
|
|
interpolation = getattr(transforms.InterpolationMode, args.image_interpolation_mode.upper(), None)
|
|
if interpolation is None:
|
|
raise ValueError(f"Unsupported interpolation mode {interpolation=}.")
|
|
train_resize = transforms.Resize(size, interpolation=interpolation)
|
|
train_crop = transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size)
|
|
train_flip = transforms.RandomHorizontalFlip(p=1.0)
|
|
train_transforms = transforms.Compose(
|
|
[
|
|
transforms.ToTensor(),
|
|
transforms.Normalize([0.5], [0.5]),
|
|
]
|
|
)
|
|
for image in self.instance_images:
|
|
image = exif_transpose(image)
|
|
if not image.mode == "RGB":
|
|
image = image.convert("RGB")
|
|
image = train_resize(image)
|
|
if args.random_flip and random.random() < 0.5:
|
|
# flip
|
|
image = train_flip(image)
|
|
if args.center_crop:
|
|
y1 = max(0, int(round((image.height - args.resolution) / 2.0)))
|
|
x1 = max(0, int(round((image.width - args.resolution) / 2.0)))
|
|
image = train_crop(image)
|
|
else:
|
|
y1, x1, h, w = train_crop.get_params(image, (args.resolution, args.resolution))
|
|
image = crop(image, y1, x1, h, w)
|
|
image = train_transforms(image)
|
|
self.pixel_values.append(image)
|
|
|
|
self.num_instance_images = len(self.instance_images)
|
|
self._length = self.num_instance_images
|
|
|
|
if class_data_root is not None:
|
|
self.class_data_root = Path(class_data_root)
|
|
self.class_data_root.mkdir(parents=True, exist_ok=True)
|
|
self.class_images_path = list(self.class_data_root.iterdir())
|
|
if class_num is not None:
|
|
self.num_class_images = min(len(self.class_images_path), class_num)
|
|
else:
|
|
self.num_class_images = len(self.class_images_path)
|
|
self._length = max(self.num_class_images, self.num_instance_images)
|
|
else:
|
|
self.class_data_root = None
|
|
|
|
self.image_transforms = transforms.Compose(
|
|
[
|
|
transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR),
|
|
transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size),
|
|
transforms.ToTensor(),
|
|
transforms.Normalize([0.5], [0.5]),
|
|
]
|
|
)
|
|
|
|
def __len__(self):
|
|
return self._length
|
|
|
|
def __getitem__(self, index):
|
|
example = {}
|
|
instance_image = self.pixel_values[index % self.num_instance_images]
|
|
example["instance_images"] = instance_image
|
|
|
|
if self.custom_instance_prompts:
|
|
caption = self.custom_instance_prompts[index % self.num_instance_images]
|
|
if caption:
|
|
example["instance_prompt"] = caption
|
|
else:
|
|
example["instance_prompt"] = self.instance_prompt
|
|
|
|
else: # custom prompts were provided, but length does not match size of image dataset
|
|
example["instance_prompt"] = self.instance_prompt
|
|
|
|
if self.class_data_root:
|
|
class_image = Image.open(self.class_images_path[index % self.num_class_images])
|
|
class_image = exif_transpose(class_image)
|
|
|
|
if not class_image.mode == "RGB":
|
|
class_image = class_image.convert("RGB")
|
|
example["class_images"] = self.image_transforms(class_image)
|
|
example["class_prompt"] = self.class_prompt
|
|
|
|
return example
|
|
|
|
|
|
def collate_fn(examples, with_prior_preservation=False):
|
|
pixel_values = [example["instance_images"] for example in examples]
|
|
prompts = [example["instance_prompt"] for example in examples]
|
|
|
|
# Concat class and instance examples for prior preservation.
|
|
# We do this to avoid doing two forward passes.
|
|
if with_prior_preservation:
|
|
pixel_values += [example["class_images"] for example in examples]
|
|
prompts += [example["class_prompt"] for example in examples]
|
|
|
|
pixel_values = torch.stack(pixel_values)
|
|
pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
|
|
|
|
batch = {"pixel_values": pixel_values, "prompts": prompts}
|
|
return batch
|
|
|
|
|
|
class PromptDataset(Dataset):
|
|
"A simple dataset to prepare the prompts to generate class images on multiple GPUs."
|
|
|
|
def __init__(self, prompt, num_samples):
|
|
self.prompt = prompt
|
|
self.num_samples = num_samples
|
|
|
|
def __len__(self):
|
|
return self.num_samples
|
|
|
|
def __getitem__(self, index):
|
|
example = {}
|
|
example["prompt"] = self.prompt
|
|
example["index"] = index
|
|
return example
|
|
|
|
|
|
def tokenize_prompt(tokenizer, prompt, max_sequence_length):
|
|
text_inputs = tokenizer(
|
|
prompt,
|
|
padding="max_length",
|
|
max_length=max_sequence_length,
|
|
truncation=True,
|
|
return_length=False,
|
|
return_overflowing_tokens=False,
|
|
return_tensors="pt",
|
|
)
|
|
text_input_ids = text_inputs.input_ids
|
|
return text_input_ids
|
|
|
|
|
|
def _encode_prompt_with_t5(
|
|
text_encoder,
|
|
tokenizer,
|
|
max_sequence_length=512,
|
|
prompt=None,
|
|
num_images_per_prompt=1,
|
|
device=None,
|
|
text_input_ids=None,
|
|
):
|
|
prompt = [prompt] if isinstance(prompt, str) else prompt
|
|
batch_size = len(prompt)
|
|
|
|
if tokenizer is not None:
|
|
text_inputs = tokenizer(
|
|
prompt,
|
|
padding="max_length",
|
|
max_length=max_sequence_length,
|
|
truncation=True,
|
|
return_length=False,
|
|
return_overflowing_tokens=False,
|
|
return_tensors="pt",
|
|
)
|
|
text_input_ids = text_inputs.input_ids
|
|
else:
|
|
if text_input_ids is None:
|
|
raise ValueError("text_input_ids must be provided when the tokenizer is not specified")
|
|
|
|
prompt_embeds = text_encoder(text_input_ids.to(device))[0]
|
|
|
|
if hasattr(text_encoder, "module"):
|
|
dtype = text_encoder.module.dtype
|
|
else:
|
|
dtype = text_encoder.dtype
|
|
prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
|
|
|
|
_, seq_len, _ = prompt_embeds.shape
|
|
|
|
# duplicate text embeddings and attention mask for each generation per prompt, using mps friendly method
|
|
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
|
|
prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
|
|
|
|
return prompt_embeds
|
|
|
|
|
|
def _encode_prompt_with_clip(
|
|
text_encoder,
|
|
tokenizer,
|
|
prompt: str,
|
|
device=None,
|
|
text_input_ids=None,
|
|
num_images_per_prompt: int = 1,
|
|
):
|
|
prompt = [prompt] if isinstance(prompt, str) else prompt
|
|
batch_size = len(prompt)
|
|
|
|
if tokenizer is not None:
|
|
text_inputs = tokenizer(
|
|
prompt,
|
|
padding="max_length",
|
|
max_length=77,
|
|
truncation=True,
|
|
return_overflowing_tokens=False,
|
|
return_length=False,
|
|
return_tensors="pt",
|
|
)
|
|
|
|
text_input_ids = text_inputs.input_ids
|
|
else:
|
|
if text_input_ids is None:
|
|
raise ValueError("text_input_ids must be provided when the tokenizer is not specified")
|
|
|
|
prompt_embeds = text_encoder(text_input_ids.to(device), output_hidden_states=False)
|
|
|
|
if hasattr(text_encoder, "module"):
|
|
dtype = text_encoder.module.dtype
|
|
else:
|
|
dtype = text_encoder.dtype
|
|
# Use pooled output of CLIPTextModel
|
|
prompt_embeds = prompt_embeds.pooler_output
|
|
prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
|
|
|
|
# duplicate text embeddings for each generation per prompt, using mps friendly method
|
|
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
|
|
prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, -1)
|
|
|
|
return prompt_embeds
|
|
|
|
|
|
def encode_prompt(
|
|
text_encoders,
|
|
tokenizers,
|
|
prompt: str,
|
|
max_sequence_length,
|
|
device=None,
|
|
num_images_per_prompt: int = 1,
|
|
text_input_ids_list=None,
|
|
):
|
|
prompt = [prompt] if isinstance(prompt, str) else prompt
|
|
batch_size = len(prompt)
|
|
|
|
if hasattr(text_encoders[0], "module"):
|
|
dtype = text_encoders[0].module.dtype
|
|
else:
|
|
dtype = text_encoders[0].dtype
|
|
|
|
device = device if device is not None else text_encoders[1].device
|
|
pooled_prompt_embeds = _encode_prompt_with_clip(
|
|
text_encoder=text_encoders[0],
|
|
tokenizer=tokenizers[0],
|
|
prompt=prompt,
|
|
device=device,
|
|
num_images_per_prompt=num_images_per_prompt,
|
|
text_input_ids=text_input_ids_list[0] if text_input_ids_list else None,
|
|
)
|
|
|
|
prompt_embeds = _encode_prompt_with_t5(
|
|
text_encoder=text_encoders[1],
|
|
tokenizer=tokenizers[1],
|
|
max_sequence_length=max_sequence_length,
|
|
prompt=prompt,
|
|
num_images_per_prompt=num_images_per_prompt,
|
|
device=device,
|
|
text_input_ids=text_input_ids_list[1] if text_input_ids_list else None,
|
|
)
|
|
|
|
text_ids = torch.zeros(batch_size, prompt_embeds.shape[1], 3).to(device=device, dtype=dtype)
|
|
text_ids = text_ids.repeat(num_images_per_prompt, 1, 1)
|
|
|
|
return prompt_embeds, pooled_prompt_embeds, text_ids
|
|
|
|
|
|
def main(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."
|
|
)
|
|
|
|
if torch.backends.mps.is_available() and args.mixed_precision == "bf16":
|
|
# due to pytorch#99272, MPS does not yet support bfloat16.
|
|
raise ValueError(
|
|
"Mixed precision training with bfloat16 is not supported on MPS. Please use fp16 (recommended) or fp32 instead."
|
|
)
|
|
|
|
logging_dir = Path(args.output_dir, args.logging_dir)
|
|
|
|
accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)
|
|
kwargs = DistributedDataParallelKwargs(find_unused_parameters=True)
|
|
accelerator = Accelerator(
|
|
gradient_accumulation_steps=args.gradient_accumulation_steps,
|
|
mixed_precision=args.mixed_precision,
|
|
log_with=args.report_to,
|
|
project_config=accelerator_project_config,
|
|
kwargs_handlers=[kwargs],
|
|
)
|
|
|
|
# Disable AMP for MPS.
|
|
if torch.backends.mps.is_available():
|
|
accelerator.native_amp = False
|
|
|
|
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:
|
|
transformers.utils.logging.set_verbosity_warning()
|
|
diffusers.utils.logging.set_verbosity_info()
|
|
else:
|
|
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)
|
|
|
|
# Generate class images if prior preservation is enabled.
|
|
if args.with_prior_preservation:
|
|
class_images_dir = Path(args.class_data_dir)
|
|
if not class_images_dir.exists():
|
|
class_images_dir.mkdir(parents=True)
|
|
cur_class_images = len(list(class_images_dir.iterdir()))
|
|
|
|
if cur_class_images < args.num_class_images:
|
|
has_supported_fp16_accelerator = (
|
|
torch.cuda.is_available() or torch.backends.mps.is_available() or is_torch_npu_available()
|
|
)
|
|
torch_dtype = torch.float16 if has_supported_fp16_accelerator else torch.float32
|
|
if args.prior_generation_precision == "fp32":
|
|
torch_dtype = torch.float32
|
|
elif args.prior_generation_precision == "fp16":
|
|
torch_dtype = torch.float16
|
|
elif args.prior_generation_precision == "bf16":
|
|
torch_dtype = torch.bfloat16
|
|
pipeline = FluxPipeline.from_pretrained(
|
|
args.pretrained_model_name_or_path,
|
|
torch_dtype=torch_dtype,
|
|
revision=args.revision,
|
|
variant=args.variant,
|
|
)
|
|
pipeline.set_progress_bar_config(disable=True)
|
|
|
|
num_new_images = args.num_class_images - cur_class_images
|
|
logger.info(f"Number of class images to sample: {num_new_images}.")
|
|
|
|
sample_dataset = PromptDataset(args.class_prompt, num_new_images)
|
|
sample_dataloader = torch.utils.data.DataLoader(sample_dataset, batch_size=args.sample_batch_size)
|
|
|
|
sample_dataloader = accelerator.prepare(sample_dataloader)
|
|
pipeline.to(accelerator.device)
|
|
|
|
for example in tqdm(
|
|
sample_dataloader, desc="Generating class images", disable=not accelerator.is_local_main_process
|
|
):
|
|
images = pipeline(example["prompt"]).images
|
|
|
|
for i, image in enumerate(images):
|
|
hash_image = insecure_hashlib.sha1(image.tobytes()).hexdigest()
|
|
image_filename = class_images_dir / f"{example['index'][i] + cur_class_images}-{hash_image}.jpg"
|
|
image.save(image_filename)
|
|
|
|
del pipeline
|
|
if torch.cuda.is_available():
|
|
torch.cuda.empty_cache()
|
|
elif is_torch_npu_available():
|
|
torch_npu.npu.empty_cache()
|
|
|
|
# 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,
|
|
).repo_id
|
|
|
|
# Load the tokenizers
|
|
tokenizer_one = CLIPTokenizer.from_pretrained(
|
|
args.pretrained_model_name_or_path,
|
|
subfolder="tokenizer",
|
|
revision=args.revision,
|
|
)
|
|
tokenizer_two = T5TokenizerFast.from_pretrained(
|
|
args.pretrained_model_name_or_path,
|
|
subfolder="tokenizer_2",
|
|
revision=args.revision,
|
|
)
|
|
|
|
# import correct text encoder classes
|
|
text_encoder_cls_one = import_model_class_from_model_name_or_path(
|
|
args.pretrained_model_name_or_path, args.revision
|
|
)
|
|
text_encoder_cls_two = import_model_class_from_model_name_or_path(
|
|
args.pretrained_model_name_or_path, args.revision, subfolder="text_encoder_2"
|
|
)
|
|
|
|
# Load scheduler and models
|
|
noise_scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(
|
|
args.pretrained_model_name_or_path, subfolder="scheduler"
|
|
)
|
|
noise_scheduler_copy = copy.deepcopy(noise_scheduler)
|
|
text_encoder_one, text_encoder_two = load_text_encoders(text_encoder_cls_one, text_encoder_cls_two)
|
|
vae = AutoencoderKL.from_pretrained(
|
|
args.pretrained_model_name_or_path,
|
|
subfolder="vae",
|
|
revision=args.revision,
|
|
variant=args.variant,
|
|
)
|
|
transformer = FluxTransformer2DModel.from_pretrained(
|
|
args.pretrained_model_name_or_path, subfolder="transformer", revision=args.revision, variant=args.variant
|
|
)
|
|
|
|
transformer.requires_grad_(True)
|
|
vae.requires_grad_(False)
|
|
if args.train_text_encoder:
|
|
text_encoder_one.requires_grad_(True)
|
|
text_encoder_two.requires_grad_(False)
|
|
else:
|
|
text_encoder_one.requires_grad_(False)
|
|
text_encoder_two.requires_grad_(False)
|
|
|
|
if args.enable_npu_flash_attention:
|
|
if is_torch_npu_available():
|
|
logger.info("npu flash attention enabled.")
|
|
transformer.set_attention_backend("_native_npu")
|
|
else:
|
|
raise ValueError("npu flash attention requires torch_npu extensions and is supported only on npu device ")
|
|
|
|
# For mixed precision training we cast all non-trainable weights (vae, text_encoder and transformer) 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
|
|
|
|
if torch.backends.mps.is_available() and weight_dtype == torch.bfloat16:
|
|
# due to pytorch#99272, MPS does not yet support bfloat16.
|
|
raise ValueError(
|
|
"Mixed precision training with bfloat16 is not supported on MPS. Please use fp16 (recommended) or fp32 instead."
|
|
)
|
|
|
|
vae.to(accelerator.device, dtype=weight_dtype)
|
|
if not args.train_text_encoder:
|
|
text_encoder_one.to(accelerator.device, dtype=weight_dtype)
|
|
text_encoder_two.to(accelerator.device, dtype=weight_dtype)
|
|
|
|
if args.gradient_checkpointing:
|
|
transformer.enable_gradient_checkpointing()
|
|
if args.train_text_encoder:
|
|
text_encoder_one.gradient_checkpointing_enable()
|
|
|
|
def unwrap_model(model):
|
|
model = accelerator.unwrap_model(model)
|
|
model = model._orig_mod if is_compiled_module(model) else model
|
|
return model
|
|
|
|
# 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 i, model in enumerate(models):
|
|
if isinstance(unwrap_model(model), FluxTransformer2DModel):
|
|
unwrap_model(model).save_pretrained(os.path.join(output_dir, "transformer"))
|
|
elif isinstance(unwrap_model(model), (CLIPTextModelWithProjection, T5EncoderModel)):
|
|
if isinstance(unwrap_model(model), CLIPTextModelWithProjection):
|
|
unwrap_model(model).save_pretrained(os.path.join(output_dir, "text_encoder"))
|
|
else:
|
|
unwrap_model(model).save_pretrained(os.path.join(output_dir, "text_encoder_2"))
|
|
else:
|
|
raise ValueError(f"Wrong model supplied: {type(model)=}.")
|
|
|
|
# make sure to pop weight so that corresponding model is not saved again
|
|
weights.pop()
|
|
|
|
def load_model_hook(models, input_dir):
|
|
for _ in range(len(models)):
|
|
# pop models so that they are not loaded again
|
|
model = models.pop()
|
|
|
|
# load diffusers style into model
|
|
if isinstance(unwrap_model(model), FluxTransformer2DModel):
|
|
load_model = FluxTransformer2DModel.from_pretrained(input_dir, subfolder="transformer")
|
|
model.register_to_config(**load_model.config)
|
|
|
|
model.load_state_dict(load_model.state_dict())
|
|
elif isinstance(unwrap_model(model), (CLIPTextModelWithProjection, T5EncoderModel)):
|
|
try:
|
|
load_model = CLIPTextModelWithProjection.from_pretrained(input_dir, subfolder="text_encoder")
|
|
model(**load_model.config)
|
|
model.load_state_dict(load_model.state_dict())
|
|
except Exception:
|
|
try:
|
|
load_model = T5EncoderModel.from_pretrained(input_dir, subfolder="text_encoder_2")
|
|
model(**load_model.config)
|
|
model.load_state_dict(load_model.state_dict())
|
|
except Exception:
|
|
raise ValueError(f"Couldn't load the model of type: ({type(model)}).")
|
|
else:
|
|
raise ValueError(f"Unsupported model found: {type(model)=}")
|
|
|
|
del load_model
|
|
|
|
accelerator.register_save_state_pre_hook(save_model_hook)
|
|
accelerator.register_load_state_pre_hook(load_model_hook)
|
|
|
|
# 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 and torch.cuda.is_available():
|
|
torch.backends.cuda.matmul.allow_tf32 = True
|
|
|
|
if args.scale_lr:
|
|
args.learning_rate = (
|
|
args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
|
|
)
|
|
|
|
# Optimization parameters
|
|
transformer_parameters_with_lr = {"params": transformer.parameters(), "lr": args.learning_rate}
|
|
if args.train_text_encoder:
|
|
# different learning rate for text encoder and unet
|
|
text_parameters_one_with_lr = {
|
|
"params": text_encoder_one.parameters(),
|
|
"weight_decay": args.adam_weight_decay_text_encoder,
|
|
"lr": args.text_encoder_lr if args.text_encoder_lr else args.learning_rate,
|
|
}
|
|
params_to_optimize = [transformer_parameters_with_lr, text_parameters_one_with_lr]
|
|
else:
|
|
params_to_optimize = [transformer_parameters_with_lr]
|
|
|
|
# Optimizer creation
|
|
if not (args.optimizer.lower() == "prodigy" or args.optimizer.lower() == "adamw"):
|
|
logger.warning(
|
|
f"Unsupported choice of optimizer: {args.optimizer}.Supported optimizers include [adamW, prodigy]."
|
|
"Defaulting to adamW"
|
|
)
|
|
args.optimizer = "adamw"
|
|
|
|
if args.use_8bit_adam and not args.optimizer.lower() == "adamw":
|
|
logger.warning(
|
|
f"use_8bit_adam is ignored when optimizer is not set to 'AdamW'. Optimizer was "
|
|
f"set to {args.optimizer.lower()}"
|
|
)
|
|
|
|
if args.optimizer.lower() == "adamw":
|
|
if args.use_8bit_adam:
|
|
try:
|
|
import bitsandbytes as bnb
|
|
except ImportError:
|
|
raise ImportError(
|
|
"To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`."
|
|
)
|
|
|
|
optimizer_class = bnb.optim.AdamW8bit
|
|
else:
|
|
optimizer_class = torch.optim.AdamW
|
|
|
|
optimizer = optimizer_class(
|
|
params_to_optimize,
|
|
betas=(args.adam_beta1, args.adam_beta2),
|
|
weight_decay=args.adam_weight_decay,
|
|
eps=args.adam_epsilon,
|
|
)
|
|
|
|
if args.optimizer.lower() == "prodigy":
|
|
try:
|
|
import prodigyopt
|
|
except ImportError:
|
|
raise ImportError("To use Prodigy, please install the prodigyopt library: `pip install prodigyopt`")
|
|
|
|
optimizer_class = prodigyopt.Prodigy
|
|
|
|
if args.learning_rate <= 0.1:
|
|
logger.warning(
|
|
"Learning rate is too low. When using prodigy, it's generally better to set learning rate around 1.0"
|
|
)
|
|
if args.train_text_encoder and args.text_encoder_lr:
|
|
logger.warning(
|
|
f"Learning rates were provided both for the transformer and the text encoder- e.g. text_encoder_lr:"
|
|
f" {args.text_encoder_lr} and learning_rate: {args.learning_rate}. "
|
|
f"When using prodigy only learning_rate is used as the initial learning rate."
|
|
)
|
|
# changes the learning rate of text_encoder_parameters_one and text_encoder_parameters_two to be
|
|
# --learning_rate
|
|
params_to_optimize[1]["lr"] = args.learning_rate
|
|
|
|
optimizer = optimizer_class(
|
|
params_to_optimize,
|
|
betas=(args.adam_beta1, args.adam_beta2),
|
|
beta3=args.prodigy_beta3,
|
|
weight_decay=args.adam_weight_decay,
|
|
eps=args.adam_epsilon,
|
|
decouple=args.prodigy_decouple,
|
|
use_bias_correction=args.prodigy_use_bias_correction,
|
|
safeguard_warmup=args.prodigy_safeguard_warmup,
|
|
)
|
|
|
|
# Dataset and DataLoaders creation:
|
|
train_dataset = DreamBoothDataset(
|
|
instance_data_root=args.instance_data_dir,
|
|
instance_prompt=args.instance_prompt,
|
|
class_prompt=args.class_prompt,
|
|
class_data_root=args.class_data_dir if args.with_prior_preservation else None,
|
|
class_num=args.num_class_images,
|
|
size=args.resolution,
|
|
repeats=args.repeats,
|
|
center_crop=args.center_crop,
|
|
)
|
|
|
|
train_dataloader = torch.utils.data.DataLoader(
|
|
train_dataset,
|
|
batch_size=args.train_batch_size,
|
|
shuffle=True,
|
|
collate_fn=lambda examples: collate_fn(examples, args.with_prior_preservation),
|
|
num_workers=args.dataloader_num_workers,
|
|
)
|
|
|
|
if not args.train_text_encoder:
|
|
tokenizers = [tokenizer_one, tokenizer_two]
|
|
text_encoders = [text_encoder_one, text_encoder_two]
|
|
|
|
def compute_text_embeddings(prompt, text_encoders, tokenizers):
|
|
with torch.no_grad():
|
|
prompt_embeds, pooled_prompt_embeds, text_ids = encode_prompt(
|
|
text_encoders, tokenizers, prompt, args.max_sequence_length
|
|
)
|
|
prompt_embeds = prompt_embeds.to(accelerator.device)
|
|
pooled_prompt_embeds = pooled_prompt_embeds.to(accelerator.device)
|
|
text_ids = text_ids.to(accelerator.device)
|
|
return prompt_embeds, pooled_prompt_embeds, text_ids
|
|
|
|
# If no type of tuning is done on the text_encoder and custom instance prompts are NOT
|
|
# provided (i.e. the --instance_prompt is used for all images), we encode the instance prompt once to avoid
|
|
# the redundant encoding.
|
|
if not args.train_text_encoder and not train_dataset.custom_instance_prompts:
|
|
instance_prompt_hidden_states, instance_pooled_prompt_embeds, instance_text_ids = compute_text_embeddings(
|
|
args.instance_prompt, text_encoders, tokenizers
|
|
)
|
|
|
|
# Handle class prompt for prior-preservation.
|
|
if args.with_prior_preservation:
|
|
if not args.train_text_encoder:
|
|
class_prompt_hidden_states, class_pooled_prompt_embeds, class_text_ids = compute_text_embeddings(
|
|
args.class_prompt, text_encoders, tokenizers
|
|
)
|
|
|
|
# Clear the memory here
|
|
if not args.train_text_encoder and not train_dataset.custom_instance_prompts:
|
|
del tokenizers, text_encoders
|
|
# Explicitly delete the objects as well, otherwise only the lists are deleted and the original references remain, preventing garbage collection
|
|
del text_encoder_one, text_encoder_two
|
|
gc.collect()
|
|
if torch.cuda.is_available():
|
|
torch.cuda.empty_cache()
|
|
elif is_torch_npu_available():
|
|
torch_npu.npu.empty_cache()
|
|
|
|
# If custom instance prompts are NOT provided (i.e. the instance prompt is used for all images),
|
|
# pack the statically computed variables appropriately here. This is so that we don't
|
|
# have to pass them to the dataloader.
|
|
|
|
if not train_dataset.custom_instance_prompts:
|
|
if not args.train_text_encoder:
|
|
prompt_embeds = instance_prompt_hidden_states
|
|
pooled_prompt_embeds = instance_pooled_prompt_embeds
|
|
text_ids = instance_text_ids
|
|
if args.with_prior_preservation:
|
|
prompt_embeds = torch.cat([prompt_embeds, class_prompt_hidden_states], dim=0)
|
|
pooled_prompt_embeds = torch.cat([pooled_prompt_embeds, class_pooled_prompt_embeds], dim=0)
|
|
text_ids = torch.cat([text_ids, class_text_ids], dim=0)
|
|
# if we're optimizing the text encoder (both if instance prompt is used for all images or custom prompts) we need to tokenize and encode the
|
|
# batch prompts on all training steps
|
|
else:
|
|
tokens_one = tokenize_prompt(tokenizer_one, args.instance_prompt, max_sequence_length=77)
|
|
tokens_two = tokenize_prompt(tokenizer_two, args.instance_prompt, max_sequence_length=512)
|
|
if args.with_prior_preservation:
|
|
class_tokens_one = tokenize_prompt(tokenizer_one, args.class_prompt, max_sequence_length=77)
|
|
class_tokens_two = tokenize_prompt(tokenizer_two, args.class_prompt, max_sequence_length=512)
|
|
tokens_one = torch.cat([tokens_one, class_tokens_one], dim=0)
|
|
tokens_two = torch.cat([tokens_two, class_tokens_two], dim=0)
|
|
|
|
# Scheduler and math around the number of training steps.
|
|
# Check the PR https://github.com/huggingface/diffusers/pull/8312 for detailed explanation.
|
|
num_warmup_steps_for_scheduler = args.lr_warmup_steps * accelerator.num_processes
|
|
if args.max_train_steps is None:
|
|
len_train_dataloader_after_sharding = math.ceil(len(train_dataloader) / accelerator.num_processes)
|
|
num_update_steps_per_epoch = math.ceil(len_train_dataloader_after_sharding / args.gradient_accumulation_steps)
|
|
num_training_steps_for_scheduler = (
|
|
args.num_train_epochs * accelerator.num_processes * num_update_steps_per_epoch
|
|
)
|
|
else:
|
|
num_training_steps_for_scheduler = args.max_train_steps * accelerator.num_processes
|
|
|
|
lr_scheduler = get_scheduler(
|
|
args.lr_scheduler,
|
|
optimizer=optimizer,
|
|
num_warmup_steps=num_warmup_steps_for_scheduler,
|
|
num_training_steps=num_training_steps_for_scheduler,
|
|
num_cycles=args.lr_num_cycles,
|
|
power=args.lr_power,
|
|
)
|
|
|
|
# Prepare everything with our `accelerator`.
|
|
if args.train_text_encoder:
|
|
(
|
|
transformer,
|
|
text_encoder_one,
|
|
optimizer,
|
|
train_dataloader,
|
|
lr_scheduler,
|
|
) = accelerator.prepare(
|
|
transformer,
|
|
text_encoder_one,
|
|
optimizer,
|
|
train_dataloader,
|
|
lr_scheduler,
|
|
)
|
|
else:
|
|
transformer, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
|
|
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 args.max_train_steps is None:
|
|
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
|
|
if num_training_steps_for_scheduler != args.max_train_steps:
|
|
logger.warning(
|
|
f"The length of the 'train_dataloader' after 'accelerator.prepare' ({len(train_dataloader)}) does not match "
|
|
f"the expected length ({len_train_dataloader_after_sharding}) when the learning rate scheduler was created. "
|
|
f"This inconsistency may result in the learning rate scheduler not functioning properly."
|
|
)
|
|
# 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_name = "dreambooth-flux-dev-lora"
|
|
accelerator.init_trackers(tracker_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 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 mos 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,
|
|
)
|
|
|
|
def get_sigmas(timesteps, n_dim=4, dtype=torch.float32):
|
|
sigmas = noise_scheduler_copy.sigmas.to(device=accelerator.device, dtype=dtype)
|
|
schedule_timesteps = noise_scheduler_copy.timesteps.to(accelerator.device)
|
|
timesteps = timesteps.to(accelerator.device)
|
|
step_indices = [(schedule_timesteps == t).nonzero().item() for t in timesteps]
|
|
|
|
sigma = sigmas[step_indices].flatten()
|
|
while len(sigma.shape) < n_dim:
|
|
sigma = sigma.unsqueeze(-1)
|
|
return sigma
|
|
|
|
for epoch in range(first_epoch, args.num_train_epochs):
|
|
transformer.train()
|
|
if args.train_text_encoder:
|
|
text_encoder_one.train()
|
|
|
|
for step, batch in enumerate(train_dataloader):
|
|
models_to_accumulate = [transformer]
|
|
if args.train_text_encoder:
|
|
models_to_accumulate.extend([text_encoder_one])
|
|
with accelerator.accumulate(models_to_accumulate):
|
|
pixel_values = batch["pixel_values"].to(dtype=vae.dtype)
|
|
prompts = batch["prompts"]
|
|
|
|
# encode batch prompts when custom prompts are provided for each image -
|
|
if train_dataset.custom_instance_prompts:
|
|
if not args.train_text_encoder:
|
|
prompt_embeds, pooled_prompt_embeds, text_ids = compute_text_embeddings(
|
|
prompts, text_encoders, tokenizers
|
|
)
|
|
else:
|
|
tokens_one = tokenize_prompt(tokenizer_one, prompts, max_sequence_length=77)
|
|
tokens_two = tokenize_prompt(
|
|
tokenizer_two, prompts, max_sequence_length=args.max_sequence_length
|
|
)
|
|
prompt_embeds, pooled_prompt_embeds, text_ids = encode_prompt(
|
|
text_encoders=[text_encoder_one, text_encoder_two],
|
|
tokenizers=[None, None],
|
|
text_input_ids_list=[tokens_one, tokens_two],
|
|
max_sequence_length=args.max_sequence_length,
|
|
prompt=prompts,
|
|
)
|
|
else:
|
|
if args.train_text_encoder:
|
|
prompt_embeds, pooled_prompt_embeds, text_ids = encode_prompt(
|
|
text_encoders=[text_encoder_one, text_encoder_two],
|
|
tokenizers=[None, None],
|
|
text_input_ids_list=[tokens_one, tokens_two],
|
|
max_sequence_length=args.max_sequence_length,
|
|
prompt=args.instance_prompt,
|
|
)
|
|
|
|
# Convert images to latent space
|
|
model_input = vae.encode(pixel_values).latent_dist.sample()
|
|
model_input = (model_input - vae.config.shift_factor) * vae.config.scaling_factor
|
|
model_input = model_input.to(dtype=weight_dtype)
|
|
|
|
vae_scale_factor = 2 ** (len(vae.config.block_out_channels) - 1)
|
|
|
|
latent_image_ids = FluxPipeline._prepare_latent_image_ids(
|
|
model_input.shape[0],
|
|
model_input.shape[2] // 2,
|
|
model_input.shape[3] // 2,
|
|
accelerator.device,
|
|
weight_dtype,
|
|
)
|
|
|
|
# Sample noise that we'll add to the latents
|
|
noise = torch.randn_like(model_input)
|
|
bsz = model_input.shape[0]
|
|
|
|
# Sample a random timestep for each image
|
|
# for weighting schemes where we sample timesteps non-uniformly
|
|
u = compute_density_for_timestep_sampling(
|
|
weighting_scheme=args.weighting_scheme,
|
|
batch_size=bsz,
|
|
logit_mean=args.logit_mean,
|
|
logit_std=args.logit_std,
|
|
mode_scale=args.mode_scale,
|
|
)
|
|
indices = (u * noise_scheduler_copy.config.num_train_timesteps).long()
|
|
timesteps = noise_scheduler_copy.timesteps[indices].to(device=model_input.device)
|
|
|
|
# Add noise according to flow matching.
|
|
# zt = (1 - texp) * x + texp * z1
|
|
sigmas = get_sigmas(timesteps, n_dim=model_input.ndim, dtype=model_input.dtype)
|
|
noisy_model_input = (1.0 - sigmas) * model_input + sigmas * noise
|
|
|
|
packed_noisy_model_input = FluxPipeline._pack_latents(
|
|
noisy_model_input,
|
|
batch_size=model_input.shape[0],
|
|
num_channels_latents=model_input.shape[1],
|
|
height=model_input.shape[2],
|
|
width=model_input.shape[3],
|
|
)
|
|
|
|
# handle guidance
|
|
if unwrap_model(transformer).config.guidance_embeds:
|
|
guidance = torch.tensor([args.guidance_scale], device=accelerator.device)
|
|
guidance = guidance.expand(model_input.shape[0])
|
|
else:
|
|
guidance = None
|
|
|
|
# Predict the noise residual
|
|
model_pred = transformer(
|
|
hidden_states=packed_noisy_model_input,
|
|
# YiYi notes: divide it by 1000 for now because we scale it by 1000 in the transformer model (we should not keep it but I want to keep the inputs same for the model for testing)
|
|
timestep=timesteps / 1000,
|
|
guidance=guidance,
|
|
pooled_projections=pooled_prompt_embeds,
|
|
encoder_hidden_states=prompt_embeds,
|
|
txt_ids=text_ids,
|
|
img_ids=latent_image_ids,
|
|
return_dict=False,
|
|
)[0]
|
|
# upscaling height & width as discussed in https://github.com/huggingface/diffusers/pull/9257#discussion_r1731108042
|
|
model_pred = FluxPipeline._unpack_latents(
|
|
model_pred,
|
|
height=model_input.shape[2] * vae_scale_factor,
|
|
width=model_input.shape[3] * vae_scale_factor,
|
|
vae_scale_factor=vae_scale_factor,
|
|
)
|
|
|
|
# these weighting schemes use a uniform timestep sampling
|
|
# and instead post-weight the loss
|
|
weighting = compute_loss_weighting_for_sd3(weighting_scheme=args.weighting_scheme, sigmas=sigmas)
|
|
|
|
# flow matching loss
|
|
target = noise - model_input
|
|
|
|
if args.with_prior_preservation:
|
|
# Chunk the noise and model_pred into two parts and compute the loss on each part separately.
|
|
model_pred, model_pred_prior = torch.chunk(model_pred, 2, dim=0)
|
|
target, target_prior = torch.chunk(target, 2, dim=0)
|
|
|
|
# Compute prior loss
|
|
prior_loss = torch.mean(
|
|
(weighting.float() * (model_pred_prior.float() - target_prior.float()) ** 2).reshape(
|
|
target_prior.shape[0], -1
|
|
),
|
|
1,
|
|
)
|
|
prior_loss = prior_loss.mean()
|
|
|
|
# Compute regular loss.
|
|
loss = torch.mean(
|
|
(weighting.float() * (model_pred.float() - target.float()) ** 2).reshape(target.shape[0], -1),
|
|
1,
|
|
)
|
|
loss = loss.mean()
|
|
|
|
if args.with_prior_preservation:
|
|
# Add the prior loss to the instance loss.
|
|
loss = loss + args.prior_loss_weight * prior_loss
|
|
|
|
accelerator.backward(loss)
|
|
if accelerator.sync_gradients:
|
|
params_to_clip = (
|
|
itertools.chain(transformer.parameters(), text_encoder_one.parameters())
|
|
if args.train_text_encoder
|
|
else transformer.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
|
|
|
|
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}")
|
|
|
|
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
|
|
|
|
if accelerator.is_main_process:
|
|
if args.validation_prompt is not None and epoch % args.validation_epochs == 0:
|
|
# create pipeline
|
|
if not args.train_text_encoder:
|
|
text_encoder_one, text_encoder_two = load_text_encoders(text_encoder_cls_one, text_encoder_cls_two)
|
|
text_encoder_one.to(weight_dtype)
|
|
text_encoder_two.to(weight_dtype)
|
|
else: # even when training the text encoder we're only training text encoder one
|
|
text_encoder_two = text_encoder_cls_two.from_pretrained(
|
|
args.pretrained_model_name_or_path,
|
|
subfolder="text_encoder_2",
|
|
revision=args.revision,
|
|
variant=args.variant,
|
|
)
|
|
pipeline = FluxPipeline.from_pretrained(
|
|
args.pretrained_model_name_or_path,
|
|
vae=vae,
|
|
text_encoder=unwrap_model(text_encoder_one, keep_fp32_wrapper=False),
|
|
text_encoder_2=unwrap_model(text_encoder_two, keep_fp32_wrapper=False),
|
|
transformer=unwrap_model(transformer, keep_fp32_wrapper=False),
|
|
revision=args.revision,
|
|
variant=args.variant,
|
|
torch_dtype=weight_dtype,
|
|
)
|
|
pipeline_args = {"prompt": args.validation_prompt}
|
|
images = log_validation(
|
|
pipeline=pipeline,
|
|
args=args,
|
|
accelerator=accelerator,
|
|
pipeline_args=pipeline_args,
|
|
epoch=epoch,
|
|
torch_dtype=weight_dtype,
|
|
)
|
|
if not args.train_text_encoder:
|
|
del text_encoder_one, text_encoder_two
|
|
if torch.cuda.is_available():
|
|
torch.cuda.empty_cache()
|
|
elif is_torch_npu_available():
|
|
torch_npu.npu.empty_cache()
|
|
gc.collect()
|
|
|
|
images = None
|
|
del pipeline
|
|
|
|
# Save the lora layers
|
|
accelerator.wait_for_everyone()
|
|
if accelerator.is_main_process:
|
|
transformer = unwrap_model(transformer)
|
|
|
|
if args.train_text_encoder:
|
|
text_encoder_one = unwrap_model(text_encoder_one)
|
|
pipeline = FluxPipeline.from_pretrained(
|
|
args.pretrained_model_name_or_path,
|
|
transformer=transformer,
|
|
text_encoder=text_encoder_one,
|
|
)
|
|
else:
|
|
pipeline = FluxPipeline.from_pretrained(args.pretrained_model_name_or_path, transformer=transformer)
|
|
|
|
# save the pipeline
|
|
pipeline.save_pretrained(args.output_dir)
|
|
|
|
# Final inference
|
|
# Load previous pipeline
|
|
pipeline = FluxPipeline.from_pretrained(
|
|
args.output_dir,
|
|
revision=args.revision,
|
|
variant=args.variant,
|
|
torch_dtype=weight_dtype,
|
|
)
|
|
|
|
# run inference
|
|
images = []
|
|
if args.validation_prompt and args.num_validation_images > 0:
|
|
pipeline_args = {"prompt": args.validation_prompt}
|
|
images = log_validation(
|
|
pipeline=pipeline,
|
|
args=args,
|
|
accelerator=accelerator,
|
|
pipeline_args=pipeline_args,
|
|
epoch=epoch,
|
|
is_final_validation=True,
|
|
torch_dtype=weight_dtype,
|
|
)
|
|
|
|
if args.push_to_hub:
|
|
save_model_card(
|
|
repo_id,
|
|
images=images,
|
|
base_model=args.pretrained_model_name_or_path,
|
|
train_text_encoder=args.train_text_encoder,
|
|
instance_prompt=args.instance_prompt,
|
|
validation_prompt=args.validation_prompt,
|
|
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_*"],
|
|
)
|
|
|
|
images = None
|
|
del pipeline
|
|
|
|
accelerator.end_training()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
args = parse_args()
|
|
main(args)
|