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* post release 0.36.0 * Apply style fixes --------- Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
1449 lines
59 KiB
Python
1449 lines
59 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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import argparse
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import copy
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import functools
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import logging
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import math
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import os
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import random
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import shutil
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from contextlib import nullcontext
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from pathlib import Path
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import accelerate
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import numpy as np
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import torch
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import torch.nn.functional as F
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import torch.utils.checkpoint
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import transformers
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from accelerate import Accelerator
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from accelerate.logging import get_logger
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from accelerate.utils import DistributedType, ProjectConfiguration, set_seed
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from datasets import load_dataset
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from huggingface_hub import create_repo, upload_folder
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from packaging import version
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from PIL import Image
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from torchvision import transforms
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from tqdm.auto import tqdm
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from transformers import (
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AutoTokenizer,
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CLIPTextModel,
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T5EncoderModel,
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)
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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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FluxTransformer2DModel,
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)
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from diffusers.models.controlnets.controlnet_flux import FluxControlNetModel
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from diffusers.optimization import get_scheduler
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from diffusers.pipelines.flux.pipeline_flux_controlnet import FluxControlNetPipeline
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from diffusers.training_utils import compute_density_for_timestep_sampling, free_memory
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from diffusers.utils import check_min_version, is_wandb_available, make_image_grid
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from diffusers.utils.hub_utils import load_or_create_model_card, populate_model_card
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from diffusers.utils.import_utils import is_torch_npu_available, 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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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("0.37.0.dev0")
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logger = get_logger(__name__)
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if is_torch_npu_available():
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torch.npu.config.allow_internal_format = False
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def log_validation(
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vae, flux_transformer, flux_controlnet, args, accelerator, weight_dtype, step, is_final_validation=False
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):
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logger.info("Running validation... ")
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if not is_final_validation:
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flux_controlnet = accelerator.unwrap_model(flux_controlnet)
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pipeline = FluxControlNetPipeline.from_pretrained(
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args.pretrained_model_name_or_path,
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controlnet=flux_controlnet,
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transformer=flux_transformer,
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torch_dtype=torch.bfloat16,
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)
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else:
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flux_controlnet = FluxControlNetModel.from_pretrained(
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args.output_dir, torch_dtype=torch.bfloat16, variant=args.save_weight_dtype
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)
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pipeline = FluxControlNetPipeline.from_pretrained(
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args.pretrained_model_name_or_path,
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controlnet=flux_controlnet,
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transformer=flux_transformer,
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torch_dtype=torch.bfloat16,
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)
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pipeline.to(accelerator.device)
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pipeline.set_progress_bar_config(disable=True)
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if args.enable_xformers_memory_efficient_attention:
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pipeline.enable_xformers_memory_efficient_attention()
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if args.seed is None:
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generator = None
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else:
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generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)
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if len(args.validation_image) == len(args.validation_prompt):
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validation_images = args.validation_image
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validation_prompts = args.validation_prompt
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elif len(args.validation_image) == 1:
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validation_images = args.validation_image * len(args.validation_prompt)
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validation_prompts = args.validation_prompt
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elif len(args.validation_prompt) == 1:
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validation_images = args.validation_image
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validation_prompts = args.validation_prompt * len(args.validation_image)
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else:
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raise ValueError(
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"number of `args.validation_image` and `args.validation_prompt` should be checked in `parse_args`"
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)
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image_logs = []
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if is_final_validation or torch.backends.mps.is_available():
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autocast_ctx = nullcontext()
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else:
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autocast_ctx = torch.autocast(accelerator.device.type)
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for validation_prompt, validation_image in zip(validation_prompts, validation_images):
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from diffusers.utils import load_image
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validation_image = load_image(validation_image)
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# maybe need to inference on 1024 to get a good image
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validation_image = validation_image.resize((args.resolution, args.resolution))
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images = []
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# pre calculate prompt embeds, pooled prompt embeds, text ids because t5 does not support autocast
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prompt_embeds, pooled_prompt_embeds, text_ids = pipeline.encode_prompt(
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validation_prompt, prompt_2=validation_prompt
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)
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for _ in range(args.num_validation_images):
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with autocast_ctx:
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# need to fix in pipeline_flux_controlnet
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image = pipeline(
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prompt_embeds=prompt_embeds,
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pooled_prompt_embeds=pooled_prompt_embeds,
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control_image=validation_image,
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num_inference_steps=28,
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controlnet_conditioning_scale=1,
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guidance_scale=3.5,
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generator=generator,
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).images[0]
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image = image.resize((args.resolution, args.resolution))
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images.append(image)
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image_logs.append(
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{"validation_image": validation_image, "images": images, "validation_prompt": validation_prompt}
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)
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tracker_key = "test" if is_final_validation else "validation"
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for tracker in accelerator.trackers:
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if tracker.name == "tensorboard":
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for log in image_logs:
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images = log["images"]
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validation_prompt = log["validation_prompt"]
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validation_image = log["validation_image"]
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formatted_images = [np.asarray(validation_image)]
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for image in images:
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formatted_images.append(np.asarray(image))
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formatted_images = np.stack(formatted_images)
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tracker.writer.add_images(validation_prompt, formatted_images, step, dataformats="NHWC")
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elif tracker.name == "wandb":
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formatted_images = []
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for log in image_logs:
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images = log["images"]
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validation_prompt = log["validation_prompt"]
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validation_image = log["validation_image"]
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formatted_images.append(wandb.Image(validation_image, caption="Controlnet conditioning"))
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for image in images:
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image = wandb.Image(image, caption=validation_prompt)
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formatted_images.append(image)
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tracker.log({tracker_key: formatted_images})
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else:
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logger.warning(f"image logging not implemented for {tracker.name}")
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del pipeline
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free_memory()
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return image_logs
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def save_model_card(repo_id: str, image_logs=None, base_model=str, repo_folder=None):
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img_str = ""
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if image_logs is not None:
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img_str = "You can find some example images below.\n\n"
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for i, log in enumerate(image_logs):
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images = log["images"]
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validation_prompt = log["validation_prompt"]
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validation_image = log["validation_image"]
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validation_image.save(os.path.join(repo_folder, "image_control.png"))
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img_str += f"prompt: {validation_prompt}\n"
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images = [validation_image] + images
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make_image_grid(images, 1, len(images)).save(os.path.join(repo_folder, f"images_{i}.png"))
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img_str += f"\n"
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model_description = f"""
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# controlnet-{repo_id}
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These are controlnet weights trained on {base_model} with new type of conditioning.
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{img_str}
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## 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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model_description=model_description,
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inference=True,
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)
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tags = [
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"flux",
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"flux-diffusers",
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"text-to-image",
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"diffusers",
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"controlnet",
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"diffusers-training",
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]
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model_card = populate_model_card(model_card, tags=tags)
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model_card.save(os.path.join(repo_folder, "README.md"))
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def parse_args(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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"--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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"--pretrained_vae_model_name_or_path",
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type=str,
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default=None,
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help="Path to an improved VAE to stabilize training. For more details check out: https://github.com/huggingface/diffusers/pull/4038.",
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)
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parser.add_argument(
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"--controlnet_model_name_or_path",
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type=str,
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default=None,
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help="Path to pretrained controlnet model or model identifier from huggingface.co/models."
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" If not specified controlnet weights are initialized from unet.",
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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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"--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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"--tokenizer_name",
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type=str,
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default=None,
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help="Pretrained tokenizer name or path if not the same as model_name",
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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(
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"--cache_dir",
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type=str,
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default=None,
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help="The directory where the downloaded models and datasets will be stored.",
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)
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parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
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parser.add_argument(
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"--resolution",
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type=int,
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default=512,
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help=(
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"The resolution for input images, all the images in the train/validation dataset will be resized to this"
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" resolution"
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),
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)
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parser.add_argument(
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"--crops_coords_top_left_h",
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type=int,
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default=0,
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help=("Coordinate for (the height) to be included in the crop coordinate embeddings needed by SDXL UNet."),
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)
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parser.add_argument(
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"--crops_coords_top_left_w",
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type=int,
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default=0,
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help=("Coordinate for (the height) to be included in the crop coordinate embeddings needed by SDXL UNet."),
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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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"--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
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)
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parser.add_argument(
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"--use_adafactor",
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action="store_true",
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help=(
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"Adafactor is a stochastic optimization method based on Adam that reduces memory usage while retaining"
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"the empirical benefits of adaptivity. This is achieved through maintaining a factored representation "
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"of the squared gradient accumulator across training steps."
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),
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)
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parser.add_argument(
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"--dataloader_num_workers",
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type=int,
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default=0,
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help=(
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"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process."
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),
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)
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parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
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parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
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parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
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parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
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parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
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parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
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parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
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parser.add_argument(
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"--hub_model_id",
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type=str,
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default=None,
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help="The name of the repository to keep in sync with the local `output_dir`.",
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)
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parser.add_argument(
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"--logging_dir",
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type=str,
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default="logs",
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help=(
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"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
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" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
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),
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)
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|
parser.add_argument(
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"--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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|
"--enable_npu_flash_attention", action="store_true", help="Whether or not to use npu flash attention."
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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:"
|
|
" https://pytorch.org/docs/stable/generated/torch.optim.Optimizer.zero_grad.html"
|
|
),
|
|
)
|
|
parser.add_argument(
|
|
"--dataset_name",
|
|
type=str,
|
|
default=None,
|
|
help=(
|
|
"The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,"
|
|
" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,"
|
|
" or to a folder containing files that 🤗 Datasets can understand."
|
|
),
|
|
)
|
|
parser.add_argument(
|
|
"--dataset_config_name",
|
|
type=str,
|
|
default=None,
|
|
help="The config of the Dataset, leave as None if there's only one config.",
|
|
)
|
|
parser.add_argument(
|
|
"--image_column", type=str, default="image", help="The column of the dataset containing the target image."
|
|
)
|
|
parser.add_argument(
|
|
"--conditioning_image_column",
|
|
type=str,
|
|
default="conditioning_image",
|
|
help="The column of the dataset containing the controlnet conditioning image.",
|
|
)
|
|
parser.add_argument(
|
|
"--caption_column",
|
|
type=str,
|
|
default="text",
|
|
help="The column of the dataset containing a caption or a list of captions.",
|
|
)
|
|
parser.add_argument(
|
|
"--max_train_samples",
|
|
type=int,
|
|
default=None,
|
|
help=(
|
|
"For debugging purposes or quicker training, truncate the number of training examples to this "
|
|
"value if set."
|
|
),
|
|
)
|
|
parser.add_argument(
|
|
"--proportion_empty_prompts",
|
|
type=float,
|
|
default=0,
|
|
help="Proportion of image prompts to be replaced with empty strings. Defaults to 0 (no prompt replacement).",
|
|
)
|
|
parser.add_argument(
|
|
"--validation_prompt",
|
|
type=str,
|
|
default=None,
|
|
nargs="+",
|
|
help=(
|
|
"A set of prompts evaluated every `--validation_steps` and logged to `--report_to`."
|
|
" Provide either a matching number of `--validation_image`s, a single `--validation_image`"
|
|
" to be used with all prompts, or a single prompt that will be used with all `--validation_image`s."
|
|
),
|
|
)
|
|
parser.add_argument(
|
|
"--validation_image",
|
|
type=str,
|
|
default=None,
|
|
nargs="+",
|
|
help=(
|
|
"A set of paths to the controlnet conditioning image be evaluated every `--validation_steps`"
|
|
" and logged to `--report_to`. Provide either a matching number of `--validation_prompt`s, a"
|
|
" a single `--validation_prompt` to be used with all `--validation_image`s, or a single"
|
|
" `--validation_image` that will be used with all `--validation_prompt`s."
|
|
),
|
|
)
|
|
parser.add_argument(
|
|
"--num_double_layers",
|
|
type=int,
|
|
default=4,
|
|
help="Number of double layers in the controlnet (default: 4).",
|
|
)
|
|
parser.add_argument(
|
|
"--num_single_layers",
|
|
type=int,
|
|
default=4,
|
|
help="Number of single layers in the controlnet (default: 4).",
|
|
)
|
|
parser.add_argument(
|
|
"--num_validation_images",
|
|
type=int,
|
|
default=2,
|
|
help="Number of images to be generated for each `--validation_image`, `--validation_prompt` pair",
|
|
)
|
|
parser.add_argument(
|
|
"--validation_steps",
|
|
type=int,
|
|
default=100,
|
|
help=(
|
|
"Run validation every X steps. Validation consists of running the prompt"
|
|
" `args.validation_prompt` multiple times: `args.num_validation_images`"
|
|
" and logging the images."
|
|
),
|
|
)
|
|
parser.add_argument(
|
|
"--tracker_project_name",
|
|
type=str,
|
|
default="flux_train_controlnet",
|
|
help=(
|
|
"The `project_name` argument passed to Accelerator.init_trackers for"
|
|
" more information see https://huggingface.co/docs/accelerate/v0.17.0/en/package_reference/accelerator#accelerate.Accelerator"
|
|
),
|
|
)
|
|
parser.add_argument(
|
|
"--jsonl_for_train",
|
|
type=str,
|
|
default=None,
|
|
help="Path to the jsonl file containing the training data.",
|
|
)
|
|
|
|
parser.add_argument(
|
|
"--guidance_scale",
|
|
type=float,
|
|
default=3.5,
|
|
help="the guidance scale used for transformer.",
|
|
)
|
|
|
|
parser.add_argument(
|
|
"--save_weight_dtype",
|
|
type=str,
|
|
default="fp32",
|
|
choices=[
|
|
"fp16",
|
|
"bf16",
|
|
"fp32",
|
|
],
|
|
help=("Preserve precision type according to selected weight"),
|
|
)
|
|
|
|
parser.add_argument(
|
|
"--weighting_scheme",
|
|
type=str,
|
|
default="logit_normal",
|
|
choices=["sigma_sqrt", "logit_normal", "mode", "cosmap", "none"],
|
|
help=('We default to the "none" weighting scheme for uniform sampling and uniform loss'),
|
|
)
|
|
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(
|
|
"--enable_model_cpu_offload",
|
|
action="store_true",
|
|
help="Enable model cpu offload and save memory.",
|
|
)
|
|
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.",
|
|
)
|
|
|
|
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.jsonl_for_train is None:
|
|
raise ValueError("Specify either `--dataset_name` or `--jsonl_for_train`")
|
|
|
|
if args.dataset_name is not None and args.jsonl_for_train is not None:
|
|
raise ValueError("Specify only one of `--dataset_name` or `--jsonl_for_train`")
|
|
|
|
if args.proportion_empty_prompts < 0 or args.proportion_empty_prompts > 1:
|
|
raise ValueError("`--proportion_empty_prompts` must be in the range [0, 1].")
|
|
|
|
if args.validation_prompt is not None and args.validation_image is None:
|
|
raise ValueError("`--validation_image` must be set if `--validation_prompt` is set")
|
|
|
|
if args.validation_prompt is None and args.validation_image is not None:
|
|
raise ValueError("`--validation_prompt` must be set if `--validation_image` is set")
|
|
|
|
if (
|
|
args.validation_image is not None
|
|
and args.validation_prompt is not None
|
|
and len(args.validation_image) != 1
|
|
and len(args.validation_prompt) != 1
|
|
and len(args.validation_image) != len(args.validation_prompt)
|
|
):
|
|
raise ValueError(
|
|
"Must provide either 1 `--validation_image`, 1 `--validation_prompt`,"
|
|
" or the same number of `--validation_prompt`s and `--validation_image`s"
|
|
)
|
|
|
|
if args.resolution % 8 != 0:
|
|
raise ValueError(
|
|
"`--resolution` must be divisible by 8 for consistently sized encoded images between the VAE and the controlnet encoder."
|
|
)
|
|
|
|
return args
|
|
|
|
|
|
def get_train_dataset(args, accelerator):
|
|
dataset = None
|
|
if args.dataset_name is not None:
|
|
# Downloading and loading a dataset from the hub.
|
|
dataset = load_dataset(
|
|
args.dataset_name,
|
|
args.dataset_config_name,
|
|
cache_dir=args.cache_dir,
|
|
)
|
|
if args.jsonl_for_train is not None:
|
|
# load from json
|
|
dataset = load_dataset("json", data_files=args.jsonl_for_train, cache_dir=args.cache_dir)
|
|
dataset = dataset.flatten_indices()
|
|
# Preprocessing the datasets.
|
|
# We need to tokenize inputs and targets.
|
|
column_names = dataset["train"].column_names
|
|
|
|
# 6. Get the column names for input/target.
|
|
if args.image_column is None:
|
|
image_column = column_names[0]
|
|
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)}"
|
|
)
|
|
|
|
if args.caption_column is None:
|
|
caption_column = column_names[1]
|
|
logger.info(f"caption column defaulting to {caption_column}")
|
|
else:
|
|
caption_column = args.caption_column
|
|
if 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)}"
|
|
)
|
|
|
|
if args.conditioning_image_column is None:
|
|
conditioning_image_column = column_names[2]
|
|
logger.info(f"conditioning image column defaulting to {conditioning_image_column}")
|
|
else:
|
|
conditioning_image_column = args.conditioning_image_column
|
|
if conditioning_image_column not in column_names:
|
|
raise ValueError(
|
|
f"`--conditioning_image_column` value '{args.conditioning_image_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}"
|
|
)
|
|
|
|
with accelerator.main_process_first():
|
|
train_dataset = dataset["train"].shuffle(seed=args.seed)
|
|
if args.max_train_samples is not None:
|
|
train_dataset = train_dataset.select(range(args.max_train_samples))
|
|
return train_dataset
|
|
|
|
|
|
def prepare_train_dataset(dataset, accelerator):
|
|
interpolation = getattr(transforms.InterpolationMode, args.image_interpolation_mode.upper(), None)
|
|
if interpolation is None:
|
|
raise ValueError(f"Unsupported interpolation mode {interpolation=}.")
|
|
|
|
image_transforms = transforms.Compose(
|
|
[
|
|
transforms.Resize(args.resolution, interpolation=interpolation),
|
|
transforms.CenterCrop(args.resolution),
|
|
transforms.ToTensor(),
|
|
transforms.Normalize([0.5], [0.5]),
|
|
]
|
|
)
|
|
|
|
conditioning_image_transforms = transforms.Compose(
|
|
[
|
|
transforms.Resize(args.resolution, interpolation=interpolation),
|
|
transforms.CenterCrop(args.resolution),
|
|
transforms.ToTensor(),
|
|
transforms.Normalize([0.5], [0.5]),
|
|
]
|
|
)
|
|
|
|
def preprocess_train(examples):
|
|
images = [
|
|
(image.convert("RGB") if not isinstance(image, str) else Image.open(image).convert("RGB"))
|
|
for image in examples[args.image_column]
|
|
]
|
|
images = [image_transforms(image) for image in images]
|
|
|
|
conditioning_images = [
|
|
(image.convert("RGB") if not isinstance(image, str) else Image.open(image).convert("RGB"))
|
|
for image in examples[args.conditioning_image_column]
|
|
]
|
|
conditioning_images = [conditioning_image_transforms(image) for image in conditioning_images]
|
|
examples["pixel_values"] = images
|
|
examples["conditioning_pixel_values"] = conditioning_images
|
|
|
|
return examples
|
|
|
|
with accelerator.main_process_first():
|
|
dataset = dataset.with_transform(preprocess_train)
|
|
|
|
return dataset
|
|
|
|
|
|
def collate_fn(examples):
|
|
pixel_values = torch.stack([example["pixel_values"] for example in examples])
|
|
pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
|
|
|
|
conditioning_pixel_values = torch.stack([example["conditioning_pixel_values"] for example in examples])
|
|
conditioning_pixel_values = conditioning_pixel_values.to(memory_format=torch.contiguous_format).float()
|
|
|
|
prompt_ids = torch.stack([torch.tensor(example["prompt_embeds"]) for example in examples])
|
|
|
|
pooled_prompt_embeds = torch.stack([torch.tensor(example["pooled_prompt_embeds"]) for example in examples])
|
|
text_ids = torch.stack([torch.tensor(example["text_ids"]) for example in examples])
|
|
|
|
return {
|
|
"pixel_values": pixel_values,
|
|
"conditioning_pixel_values": conditioning_pixel_values,
|
|
"prompt_ids": prompt_ids,
|
|
"unet_added_conditions": {"pooled_prompt_embeds": pooled_prompt_embeds, "time_ids": 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."
|
|
)
|
|
|
|
logging_out_dir = Path(args.output_dir, args.logging_dir)
|
|
|
|
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."
|
|
)
|
|
|
|
accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=str(logging_out_dir))
|
|
|
|
accelerator = Accelerator(
|
|
gradient_accumulation_steps=args.gradient_accumulation_steps,
|
|
mixed_precision=args.mixed_precision,
|
|
log_with=args.report_to,
|
|
project_config=accelerator_project_config,
|
|
)
|
|
|
|
# Disable AMP for MPS. A technique for accelerating machine learning computations on iOS and macOS devices.
|
|
if torch.backends.mps.is_available():
|
|
print("MPS is enabled. Disabling AMP.")
|
|
accelerator.native_amp = False
|
|
|
|
# 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",
|
|
# DEBUG, INFO, WARNING, ERROR, CRITICAL
|
|
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)
|
|
|
|
# Handle the repository creation
|
|
if accelerator.is_main_process:
|
|
if args.output_dir is not None:
|
|
os.makedirs(args.output_dir, exist_ok=True)
|
|
|
|
if args.push_to_hub:
|
|
repo_id = create_repo(
|
|
repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token
|
|
).repo_id
|
|
|
|
# Load the tokenizers
|
|
# load clip tokenizer
|
|
tokenizer_one = AutoTokenizer.from_pretrained(
|
|
args.pretrained_model_name_or_path,
|
|
subfolder="tokenizer",
|
|
revision=args.revision,
|
|
)
|
|
# load t5 tokenizer
|
|
tokenizer_two = AutoTokenizer.from_pretrained(
|
|
args.pretrained_model_name_or_path,
|
|
subfolder="tokenizer_2",
|
|
revision=args.revision,
|
|
)
|
|
# load clip text encoder
|
|
text_encoder_one = CLIPTextModel.from_pretrained(
|
|
args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision, variant=args.variant
|
|
)
|
|
# load t5 text encoder
|
|
text_encoder_two = T5EncoderModel.from_pretrained(
|
|
args.pretrained_model_name_or_path, subfolder="text_encoder_2", revision=args.revision, variant=args.variant
|
|
)
|
|
|
|
vae = AutoencoderKL.from_pretrained(
|
|
args.pretrained_model_name_or_path,
|
|
subfolder="vae",
|
|
revision=args.revision,
|
|
variant=args.variant,
|
|
)
|
|
flux_transformer = FluxTransformer2DModel.from_pretrained(
|
|
args.pretrained_model_name_or_path,
|
|
subfolder="transformer",
|
|
revision=args.revision,
|
|
variant=args.variant,
|
|
)
|
|
if args.controlnet_model_name_or_path:
|
|
logger.info("Loading existing controlnet weights")
|
|
flux_controlnet = FluxControlNetModel.from_pretrained(args.controlnet_model_name_or_path)
|
|
else:
|
|
logger.info("Initializing controlnet weights from transformer")
|
|
# we can define the num_layers, num_single_layers,
|
|
flux_controlnet = FluxControlNetModel.from_transformer(
|
|
flux_transformer,
|
|
attention_head_dim=flux_transformer.config["attention_head_dim"],
|
|
num_attention_heads=flux_transformer.config["num_attention_heads"],
|
|
num_layers=args.num_double_layers,
|
|
num_single_layers=args.num_single_layers,
|
|
)
|
|
logger.info("all models loaded successfully")
|
|
|
|
noise_scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(
|
|
args.pretrained_model_name_or_path,
|
|
subfolder="scheduler",
|
|
)
|
|
noise_scheduler_copy = copy.deepcopy(noise_scheduler)
|
|
vae.requires_grad_(False)
|
|
flux_transformer.requires_grad_(False)
|
|
text_encoder_one.requires_grad_(False)
|
|
text_encoder_two.requires_grad_(False)
|
|
flux_controlnet.train()
|
|
|
|
# use some pipeline function
|
|
flux_controlnet_pipeline = FluxControlNetPipeline(
|
|
scheduler=noise_scheduler,
|
|
vae=vae,
|
|
text_encoder=text_encoder_one,
|
|
tokenizer=tokenizer_one,
|
|
text_encoder_2=text_encoder_two,
|
|
tokenizer_2=tokenizer_two,
|
|
transformer=flux_transformer,
|
|
controlnet=flux_controlnet,
|
|
)
|
|
if args.enable_model_cpu_offload:
|
|
flux_controlnet_pipeline.enable_model_cpu_offload()
|
|
else:
|
|
flux_controlnet_pipeline.to(accelerator.device)
|
|
|
|
def unwrap_model(model):
|
|
model = accelerator.unwrap_model(model)
|
|
model = model._orig_mod if is_compiled_module(model) else model
|
|
return model
|
|
|
|
# `accelerate` 0.16.0 will have better support for customized saving
|
|
if version.parse(accelerate.__version__) >= version.parse("0.16.0"):
|
|
# create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format
|
|
def save_model_hook(models, weights, output_dir):
|
|
if accelerator.is_main_process:
|
|
i = len(weights) - 1
|
|
|
|
while len(weights) > 0:
|
|
weights.pop()
|
|
model = models[i]
|
|
|
|
sub_dir = "flux_controlnet"
|
|
model.save_pretrained(os.path.join(output_dir, sub_dir))
|
|
|
|
i -= 1
|
|
|
|
def load_model_hook(models, input_dir):
|
|
while len(models) > 0:
|
|
# pop models so that they are not loaded again
|
|
model = models.pop()
|
|
|
|
# load diffusers style into model
|
|
load_model = FluxControlNetModel.from_pretrained(input_dir, subfolder="flux_controlnet")
|
|
model.register_to_config(**load_model.config)
|
|
|
|
model.load_state_dict(load_model.state_dict())
|
|
del load_model
|
|
|
|
accelerator.register_save_state_pre_hook(save_model_hook)
|
|
accelerator.register_load_state_pre_hook(load_model_hook)
|
|
|
|
if args.enable_npu_flash_attention:
|
|
if is_torch_npu_available():
|
|
logger.info("npu flash attention enabled.")
|
|
flux_transformer.enable_npu_flash_attention()
|
|
else:
|
|
raise ValueError("npu flash attention requires torch_npu extensions and is supported only on npu devices.")
|
|
|
|
if args.enable_xformers_memory_efficient_attention:
|
|
if is_xformers_available():
|
|
import xformers
|
|
|
|
xformers_version = version.parse(xformers.__version__)
|
|
if xformers_version == version.parse("0.0.16"):
|
|
logger.warning(
|
|
"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."
|
|
)
|
|
flux_transformer.enable_xformers_memory_efficient_attention()
|
|
flux_controlnet.enable_xformers_memory_efficient_attention()
|
|
else:
|
|
raise ValueError("xformers is not available. Make sure it is installed correctly")
|
|
|
|
if args.gradient_checkpointing:
|
|
flux_transformer.enable_gradient_checkpointing()
|
|
flux_controlnet.enable_gradient_checkpointing()
|
|
|
|
# Check that all trainable models are in full precision
|
|
low_precision_error_string = (
|
|
" Please make sure to always have all model weights in full float32 precision when starting training - even if"
|
|
" doing mixed precision training, copy of the weights should still be float32."
|
|
)
|
|
|
|
if unwrap_model(flux_controlnet).dtype != torch.float32:
|
|
raise ValueError(
|
|
f"Controlnet loaded as datatype {unwrap_model(flux_controlnet).dtype}. {low_precision_error_string}"
|
|
)
|
|
|
|
# Enable TF32 for faster training on Ampere GPUs,
|
|
# cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices
|
|
if args.allow_tf32:
|
|
torch.backends.cuda.matmul.allow_tf32 = True
|
|
|
|
if args.scale_lr:
|
|
args.learning_rate = (
|
|
args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
|
|
)
|
|
|
|
# Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs
|
|
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 creation
|
|
params_to_optimize = flux_controlnet.parameters()
|
|
# use adafactor optimizer to save gpu memory
|
|
if args.use_adafactor:
|
|
from transformers import Adafactor
|
|
|
|
optimizer = Adafactor(
|
|
params_to_optimize,
|
|
lr=args.learning_rate,
|
|
scale_parameter=False,
|
|
relative_step=False,
|
|
# warmup_init=True,
|
|
weight_decay=args.adam_weight_decay,
|
|
)
|
|
else:
|
|
optimizer = optimizer_class(
|
|
params_to_optimize,
|
|
lr=args.learning_rate,
|
|
betas=(args.adam_beta1, args.adam_beta2),
|
|
weight_decay=args.adam_weight_decay,
|
|
eps=args.adam_epsilon,
|
|
)
|
|
|
|
# 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
|
|
|
|
vae.to(accelerator.device, dtype=weight_dtype)
|
|
flux_transformer.to(accelerator.device, dtype=weight_dtype)
|
|
|
|
def compute_embeddings(batch, proportion_empty_prompts, flux_controlnet_pipeline, weight_dtype, is_train=True):
|
|
prompt_batch = batch[args.caption_column]
|
|
captions = []
|
|
for caption in prompt_batch:
|
|
if random.random() < proportion_empty_prompts:
|
|
captions.append("")
|
|
elif isinstance(caption, str):
|
|
captions.append(caption)
|
|
elif isinstance(caption, (list, np.ndarray)):
|
|
# take a random caption if there are multiple
|
|
captions.append(random.choice(caption) if is_train else caption[0])
|
|
prompt_batch = captions
|
|
prompt_embeds, pooled_prompt_embeds, text_ids = flux_controlnet_pipeline.encode_prompt(
|
|
prompt_batch, prompt_2=prompt_batch
|
|
)
|
|
prompt_embeds = prompt_embeds.to(dtype=weight_dtype)
|
|
pooled_prompt_embeds = pooled_prompt_embeds.to(dtype=weight_dtype)
|
|
text_ids = text_ids.to(dtype=weight_dtype)
|
|
|
|
# text_ids [512,3] to [bs,512,3]
|
|
text_ids = text_ids.unsqueeze(0).expand(prompt_embeds.shape[0], -1, -1)
|
|
return {"prompt_embeds": prompt_embeds, "pooled_prompt_embeds": pooled_prompt_embeds, "text_ids": text_ids}
|
|
|
|
train_dataset = get_train_dataset(args, accelerator)
|
|
compute_embeddings_fn = functools.partial(
|
|
compute_embeddings,
|
|
flux_controlnet_pipeline=flux_controlnet_pipeline,
|
|
proportion_empty_prompts=args.proportion_empty_prompts,
|
|
weight_dtype=weight_dtype,
|
|
)
|
|
with accelerator.main_process_first():
|
|
from datasets.fingerprint import Hasher
|
|
|
|
# fingerprint used by the cache for the other processes to load the result
|
|
# details: https://github.com/huggingface/diffusers/pull/4038#discussion_r1266078401
|
|
new_fingerprint = Hasher.hash(args)
|
|
train_dataset = train_dataset.map(
|
|
compute_embeddings_fn, batched=True, new_fingerprint=new_fingerprint, batch_size=50
|
|
)
|
|
|
|
text_encoder_one.to("cpu")
|
|
text_encoder_two.to("cpu")
|
|
free_memory()
|
|
|
|
# Then get the training dataset ready to be passed to the dataloader.
|
|
train_dataset = prepare_train_dataset(train_dataset, accelerator)
|
|
|
|
train_dataloader = torch.utils.data.DataLoader(
|
|
train_dataset,
|
|
shuffle=True,
|
|
collate_fn=collate_fn,
|
|
batch_size=args.train_batch_size,
|
|
num_workers=args.dataloader_num_workers,
|
|
)
|
|
|
|
# Scheduler and math around the number of training steps.
|
|
# Check the PR https://github.com/huggingface/diffusers/pull/8312 for detailed explanation.
|
|
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 * num_update_steps_per_epoch * accelerator.num_processes
|
|
)
|
|
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=args.lr_warmup_steps * accelerator.num_processes,
|
|
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`.
|
|
flux_controlnet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
|
|
flux_controlnet, 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 * accelerator.num_processes:
|
|
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_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,
|
|
)
|
|
|
|
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
|
|
|
|
image_logs = None
|
|
for epoch in range(first_epoch, args.num_train_epochs):
|
|
for step, batch in enumerate(train_dataloader):
|
|
with accelerator.accumulate(flux_controlnet):
|
|
# Convert images to latent space
|
|
# vae encode
|
|
pixel_values = batch["pixel_values"].to(dtype=weight_dtype)
|
|
pixel_latents_tmp = vae.encode(pixel_values).latent_dist.sample()
|
|
pixel_latents_tmp = (pixel_latents_tmp - vae.config.shift_factor) * vae.config.scaling_factor
|
|
pixel_latents = FluxControlNetPipeline._pack_latents(
|
|
pixel_latents_tmp,
|
|
pixel_values.shape[0],
|
|
pixel_latents_tmp.shape[1],
|
|
pixel_latents_tmp.shape[2],
|
|
pixel_latents_tmp.shape[3],
|
|
)
|
|
|
|
control_values = batch["conditioning_pixel_values"].to(dtype=weight_dtype)
|
|
control_latents = vae.encode(control_values).latent_dist.sample()
|
|
control_latents = (control_latents - vae.config.shift_factor) * vae.config.scaling_factor
|
|
control_image = FluxControlNetPipeline._pack_latents(
|
|
control_latents,
|
|
control_values.shape[0],
|
|
control_latents.shape[1],
|
|
control_latents.shape[2],
|
|
control_latents.shape[3],
|
|
)
|
|
|
|
latent_image_ids = FluxControlNetPipeline._prepare_latent_image_ids(
|
|
batch_size=pixel_latents_tmp.shape[0],
|
|
height=pixel_latents_tmp.shape[2] // 2,
|
|
width=pixel_latents_tmp.shape[3] // 2,
|
|
device=pixel_values.device,
|
|
dtype=pixel_values.dtype,
|
|
)
|
|
|
|
bsz = pixel_latents.shape[0]
|
|
noise = torch.randn_like(pixel_latents).to(accelerator.device).to(dtype=weight_dtype)
|
|
# 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=pixel_latents.device)
|
|
|
|
# Add noise according to flow matching.
|
|
sigmas = get_sigmas(timesteps, n_dim=pixel_latents.ndim, dtype=pixel_latents.dtype)
|
|
noisy_model_input = (1.0 - sigmas) * pixel_latents + sigmas * noise
|
|
|
|
# handle guidance
|
|
if flux_transformer.config.guidance_embeds:
|
|
guidance_vec = torch.full(
|
|
(noisy_model_input.shape[0],),
|
|
args.guidance_scale,
|
|
device=noisy_model_input.device,
|
|
dtype=weight_dtype,
|
|
)
|
|
else:
|
|
guidance_vec = None
|
|
|
|
controlnet_block_samples, controlnet_single_block_samples = flux_controlnet(
|
|
hidden_states=noisy_model_input,
|
|
controlnet_cond=control_image,
|
|
timestep=timesteps / 1000,
|
|
guidance=guidance_vec,
|
|
pooled_projections=batch["unet_added_conditions"]["pooled_prompt_embeds"].to(dtype=weight_dtype),
|
|
encoder_hidden_states=batch["prompt_ids"].to(dtype=weight_dtype),
|
|
txt_ids=batch["unet_added_conditions"]["time_ids"][0].to(dtype=weight_dtype),
|
|
img_ids=latent_image_ids,
|
|
return_dict=False,
|
|
)
|
|
|
|
noise_pred = flux_transformer(
|
|
hidden_states=noisy_model_input,
|
|
timestep=timesteps / 1000,
|
|
guidance=guidance_vec,
|
|
pooled_projections=batch["unet_added_conditions"]["pooled_prompt_embeds"].to(dtype=weight_dtype),
|
|
encoder_hidden_states=batch["prompt_ids"].to(dtype=weight_dtype),
|
|
controlnet_block_samples=[sample.to(dtype=weight_dtype) for sample in controlnet_block_samples]
|
|
if controlnet_block_samples is not None
|
|
else None,
|
|
controlnet_single_block_samples=[
|
|
sample.to(dtype=weight_dtype) for sample in controlnet_single_block_samples
|
|
]
|
|
if controlnet_single_block_samples is not None
|
|
else None,
|
|
txt_ids=batch["unet_added_conditions"]["time_ids"][0].to(dtype=weight_dtype),
|
|
img_ids=latent_image_ids,
|
|
return_dict=False,
|
|
)[0]
|
|
|
|
loss = F.mse_loss(noise_pred.float(), (noise - pixel_latents).float(), reduction="mean")
|
|
accelerator.backward(loss)
|
|
# Check if the gradient of each model parameter contains NaN
|
|
for name, param in flux_controlnet.named_parameters():
|
|
if param.grad is not None and torch.isnan(param.grad).any():
|
|
logger.error(f"Gradient for {name} contains NaN!")
|
|
|
|
if accelerator.sync_gradients:
|
|
params_to_clip = flux_controlnet.parameters()
|
|
accelerator.clip_grad_norm_(params_to_clip, 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
|
|
|
|
# DeepSpeed requires saving weights on every device; saving weights only on the main process would cause issues.
|
|
if accelerator.distributed_type == DistributedType.DEEPSPEED or accelerator.is_main_process:
|
|
if global_step % args.checkpointing_steps == 0:
|
|
# _before_ saving state, check if this save would set us over the `checkpoints_total_limit`
|
|
if args.checkpoints_total_limit is not None:
|
|
checkpoints = os.listdir(args.output_dir)
|
|
checkpoints = [d for d in checkpoints if d.startswith("checkpoint")]
|
|
checkpoints = sorted(checkpoints, key=lambda x: int(x.split("-")[1]))
|
|
|
|
# before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints
|
|
if len(checkpoints) >= args.checkpoints_total_limit:
|
|
num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1
|
|
removing_checkpoints = checkpoints[0:num_to_remove]
|
|
|
|
logger.info(
|
|
f"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints"
|
|
)
|
|
logger.info(f"removing checkpoints: {', '.join(removing_checkpoints)}")
|
|
|
|
for removing_checkpoint in removing_checkpoints:
|
|
removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)
|
|
shutil.rmtree(removing_checkpoint)
|
|
|
|
save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
|
|
accelerator.save_state(save_path)
|
|
logger.info(f"Saved state to {save_path}")
|
|
|
|
if args.validation_prompt is not None and global_step % args.validation_steps == 0:
|
|
image_logs = log_validation(
|
|
vae=vae,
|
|
flux_transformer=flux_transformer,
|
|
flux_controlnet=flux_controlnet,
|
|
args=args,
|
|
accelerator=accelerator,
|
|
weight_dtype=weight_dtype,
|
|
step=global_step,
|
|
)
|
|
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:
|
|
flux_controlnet = unwrap_model(flux_controlnet)
|
|
save_weight_dtype = torch.float32
|
|
if args.save_weight_dtype == "fp16":
|
|
save_weight_dtype = torch.float16
|
|
elif args.save_weight_dtype == "bf16":
|
|
save_weight_dtype = torch.bfloat16
|
|
flux_controlnet.to(save_weight_dtype)
|
|
if args.save_weight_dtype != "fp32":
|
|
flux_controlnet.save_pretrained(args.output_dir, variant=args.save_weight_dtype)
|
|
else:
|
|
flux_controlnet.save_pretrained(args.output_dir)
|
|
# Run a final round of validation.
|
|
# Setting `vae`, `unet`, and `controlnet` to None to load automatically from `args.output_dir`.
|
|
image_logs = None
|
|
if args.validation_prompt is not None:
|
|
image_logs = log_validation(
|
|
vae=vae,
|
|
flux_transformer=flux_transformer,
|
|
flux_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)
|