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1243 lines
51 KiB
Python
1243 lines
51 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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import argparse
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import copy
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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 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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import diffusers
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from diffusers import (
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AutoencoderKL,
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CogView4ControlPipeline,
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CogView4Transformer2DModel,
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FlowMatchEulerDiscreteScheduler,
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)
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from diffusers.optimization import get_scheduler
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from diffusers.training_utils import (
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compute_density_for_timestep_sampling,
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compute_loss_weighting_for_sd3,
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free_memory,
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)
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from diffusers.utils import check_min_version, is_wandb_available, load_image, 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.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("0.35.0.dev0")
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logger = get_logger(__name__)
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NORM_LAYER_PREFIXES = ["norm_q", "norm_k", "norm_added_q", "norm_added_k"]
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def encode_images(pixels: torch.Tensor, vae: torch.nn.Module, weight_dtype):
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pixel_latents = vae.encode(pixels.to(vae.dtype)).latent_dist.sample()
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pixel_latents = (pixel_latents - vae.config.shift_factor) * vae.config.scaling_factor
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return pixel_latents.to(weight_dtype)
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def log_validation(cogview4_transformer, args, accelerator, weight_dtype, step, is_final_validation=False):
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logger.info("Running validation... ")
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if not is_final_validation:
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cogview4_transformer = accelerator.unwrap_model(cogview4_transformer)
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pipeline = CogView4ControlPipeline.from_pretrained(
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args.pretrained_model_name_or_path,
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transformer=cogview4_transformer,
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torch_dtype=weight_dtype,
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)
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else:
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transformer = CogView4Transformer2DModel.from_pretrained(args.output_dir, torch_dtype=weight_dtype)
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pipeline = CogView4ControlPipeline.from_pretrained(
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args.pretrained_model_name_or_path,
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transformer=transformer,
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torch_dtype=weight_dtype,
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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.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, weight_dtype)
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for validation_prompt, validation_image in zip(validation_prompts, validation_images):
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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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for _ in range(args.num_validation_images):
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with autocast_ctx:
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image = pipeline(
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prompt=validation_prompt,
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control_image=validation_image,
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num_inference_steps=50,
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guidance_scale=args.guidance_scale,
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max_sequence_length=args.max_sequence_length,
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generator=generator,
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height=args.resolution,
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width=args.resolution,
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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 = []
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formatted_images.append(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="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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# cogview4-control-{repo_id}
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These are Control 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/THUDM/CogView4-6b/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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"cogview4",
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"cogview4-diffusers",
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"text-to-image",
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"diffusers",
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"control",
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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 CogView4 Control 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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"--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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"--output_dir",
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type=str,
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default="cogview4-control",
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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=1024,
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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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"--max_sequence_length", type=int, default=128, help="The maximum sequence length for the prompt."
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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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"--proportion_empty_prompts",
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type=float,
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default=0,
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help="Proportion of image prompts to be replaced with empty strings. Defaults to 0 (no prompt replacement).",
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)
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parser.add_argument(
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"--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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"--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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"--dataset_name",
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type=str,
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default=None,
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help=(
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"The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,"
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" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,"
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" or to a folder containing files that 🤗 Datasets can understand."
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),
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)
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parser.add_argument(
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"--dataset_config_name",
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type=str,
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default=None,
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help="The config of the Dataset, leave as None if there's only one config.",
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)
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parser.add_argument(
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"--image_column", type=str, default="image", help="The column of the dataset containing the target image."
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)
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parser.add_argument(
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"--conditioning_image_column",
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type=str,
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default="conditioning_image",
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help="The column of the dataset containing the control conditioning image.",
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)
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parser.add_argument(
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"--caption_column",
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type=str,
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default="text",
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help="The column of the dataset containing a caption or a list of captions.",
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)
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parser.add_argument("--log_dataset_samples", action="store_true", help="Whether to log somple dataset samples.")
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parser.add_argument(
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"--max_train_samples",
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type=int,
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default=None,
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help=(
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"For debugging purposes or quicker training, truncate the number of training examples to this "
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"value if set."
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),
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)
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parser.add_argument(
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"--validation_prompt",
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type=str,
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default=None,
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nargs="+",
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help=(
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"A set of prompts evaluated every `--validation_steps` and logged to `--report_to`."
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" Provide either a matching number of `--validation_image`s, a single `--validation_image`"
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" to be used with all prompts, or a single prompt that will be used with all `--validation_image`s."
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),
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)
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parser.add_argument(
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"--validation_image",
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type=str,
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default=None,
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nargs="+",
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help=(
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"A set of paths to the control conditioning image be evaluated every `--validation_steps`"
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" and logged to `--report_to`. Provide either a matching number of `--validation_prompt`s, a"
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" a single `--validation_prompt` to be used with all `--validation_image`s, or a single"
|
|
" `--validation_image` that will be used with all `--validation_prompt`s."
|
|
),
|
|
)
|
|
parser.add_argument(
|
|
"--num_validation_images",
|
|
type=int,
|
|
default=1,
|
|
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="cogview4_train_control",
|
|
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(
|
|
"--only_target_transformer_blocks",
|
|
action="store_true",
|
|
help="If we should only target the transformer blocks to train along with the input layer (`x_embedder`).",
|
|
)
|
|
parser.add_argument(
|
|
"--guidance_scale",
|
|
type=float,
|
|
default=3.5,
|
|
help="the guidance scale used for transformer.",
|
|
)
|
|
|
|
parser.add_argument(
|
|
"--upcast_before_saving",
|
|
action="store_true",
|
|
help=(
|
|
"Whether to upcast the trained transformer layers to float32 before saving (at the end of training). "
|
|
"Defaults to precision dtype used for training to save memory"
|
|
),
|
|
)
|
|
|
|
parser.add_argument(
|
|
"--weighting_scheme",
|
|
type=str,
|
|
default="none",
|
|
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(
|
|
"--offload",
|
|
action="store_true",
|
|
help="Whether to offload the VAE and the text encoders to CPU when they are not used.",
|
|
)
|
|
|
|
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 cogview4 transformer."
|
|
)
|
|
|
|
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):
|
|
image_transforms = transforms.Compose(
|
|
[
|
|
transforms.Resize((args.resolution, args.resolution), interpolation=transforms.InterpolationMode.BILINEAR),
|
|
transforms.ToTensor(),
|
|
transforms.Lambda(lambda x: x * 2 - 1),
|
|
]
|
|
)
|
|
|
|
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 = [image_transforms(image) for image in conditioning_images]
|
|
examples["pixel_values"] = images
|
|
examples["conditioning_pixel_values"] = conditioning_images
|
|
|
|
is_caption_list = isinstance(examples[args.caption_column][0], list)
|
|
if is_caption_list:
|
|
examples["captions"] = [max(example, key=len) for example in examples[args.caption_column]]
|
|
else:
|
|
examples["captions"] = list(examples[args.caption_column])
|
|
|
|
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()
|
|
captions = [example["captions"] for example in examples]
|
|
return {"pixel_values": pixel_values, "conditioning_pixel_values": conditioning_pixel_values, "captions": captions}
|
|
|
|
|
|
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():
|
|
logger.info("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 models. We will load the text encoders later in a pipeline to compute
|
|
# embeddings.
|
|
vae = AutoencoderKL.from_pretrained(
|
|
args.pretrained_model_name_or_path,
|
|
subfolder="vae",
|
|
revision=args.revision,
|
|
variant=args.variant,
|
|
)
|
|
cogview4_transformer = CogView4Transformer2DModel.from_pretrained(
|
|
args.pretrained_model_name_or_path,
|
|
subfolder="transformer",
|
|
revision=args.revision,
|
|
variant=args.variant,
|
|
)
|
|
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)
|
|
if not args.only_target_transformer_blocks:
|
|
cogview4_transformer.requires_grad_(True)
|
|
vae.requires_grad_(False)
|
|
|
|
# cast down and move to the CPU
|
|
weight_dtype = torch.float32
|
|
if accelerator.mixed_precision == "fp16":
|
|
weight_dtype = torch.float16
|
|
elif accelerator.mixed_precision == "bf16":
|
|
weight_dtype = torch.bfloat16
|
|
|
|
# let's not move the VAE to the GPU yet.
|
|
vae.to(dtype=torch.float32) # keep the VAE in float32.
|
|
|
|
# enable image inputs
|
|
with torch.no_grad():
|
|
patch_size = cogview4_transformer.config.patch_size
|
|
initial_input_channels = cogview4_transformer.config.in_channels * patch_size**2
|
|
new_linear = torch.nn.Linear(
|
|
cogview4_transformer.patch_embed.proj.in_features * 2,
|
|
cogview4_transformer.patch_embed.proj.out_features,
|
|
bias=cogview4_transformer.patch_embed.proj.bias is not None,
|
|
dtype=cogview4_transformer.dtype,
|
|
device=cogview4_transformer.device,
|
|
)
|
|
new_linear.weight.zero_()
|
|
new_linear.weight[:, :initial_input_channels].copy_(cogview4_transformer.patch_embed.proj.weight)
|
|
if cogview4_transformer.patch_embed.proj.bias is not None:
|
|
new_linear.bias.copy_(cogview4_transformer.patch_embed.proj.bias)
|
|
cogview4_transformer.patch_embed.proj = new_linear
|
|
|
|
assert torch.all(cogview4_transformer.patch_embed.proj.weight[:, initial_input_channels:].data == 0)
|
|
cogview4_transformer.register_to_config(
|
|
in_channels=cogview4_transformer.config.in_channels * 2, out_channels=cogview4_transformer.config.in_channels
|
|
)
|
|
|
|
if args.only_target_transformer_blocks:
|
|
cogview4_transformer.patch_embed.proj.requires_grad_(True)
|
|
for name, module in cogview4_transformer.named_modules():
|
|
if "transformer_blocks" in name:
|
|
module.requires_grad_(True)
|
|
else:
|
|
module.requirs_grad_(False)
|
|
|
|
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"):
|
|
|
|
def save_model_hook(models, weights, output_dir):
|
|
if accelerator.is_main_process:
|
|
for model in models:
|
|
if isinstance(unwrap_model(model), type(unwrap_model(cogview4_transformer))):
|
|
model = unwrap_model(model)
|
|
model.save_pretrained(os.path.join(output_dir, "transformer"))
|
|
else:
|
|
raise ValueError(f"unexpected save model: {model.__class__}")
|
|
|
|
# make sure to pop weight so that corresponding model is not saved again
|
|
if weights:
|
|
weights.pop()
|
|
|
|
def load_model_hook(models, input_dir):
|
|
transformer_ = None
|
|
|
|
if not accelerator.distributed_type == DistributedType.DEEPSPEED:
|
|
while len(models) > 0:
|
|
model = models.pop()
|
|
|
|
if isinstance(unwrap_model(model), type(unwrap_model(cogview4_transformer))):
|
|
transformer_ = model # noqa: F841
|
|
else:
|
|
raise ValueError(f"unexpected save model: {unwrap_model(model).__class__}")
|
|
|
|
else:
|
|
transformer_ = CogView4Transformer2DModel.from_pretrained(input_dir, subfolder="transformer") # noqa: F841
|
|
|
|
accelerator.register_save_state_pre_hook(save_model_hook)
|
|
accelerator.register_load_state_pre_hook(load_model_hook)
|
|
|
|
if args.gradient_checkpointing:
|
|
cogview4_transformer.enable_gradient_checkpointing()
|
|
|
|
# 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
|
|
|
|
# Optimization parameters
|
|
optimizer = optimizer_class(
|
|
cogview4_transformer.parameters(),
|
|
lr=args.learning_rate,
|
|
betas=(args.adam_beta1, args.adam_beta2),
|
|
weight_decay=args.adam_weight_decay,
|
|
eps=args.adam_epsilon,
|
|
)
|
|
|
|
# Prepare dataset and dataloader.
|
|
train_dataset = get_train_dataset(args, accelerator)
|
|
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`.
|
|
cogview4_transformer, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
|
|
cogview4_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 * 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
|
|
|
|
# Create a pipeline for text encoding. We will move this pipeline to GPU/CPU as needed.
|
|
text_encoding_pipeline = CogView4ControlPipeline.from_pretrained(
|
|
args.pretrained_model_name_or_path, transformer=None, vae=None, torch_dtype=weight_dtype
|
|
)
|
|
tokenizer = text_encoding_pipeline.tokenizer
|
|
|
|
# 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:
|
|
logger.info(f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run.")
|
|
args.resume_from_checkpoint = None
|
|
initial_global_step = 0
|
|
else:
|
|
logger.info(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
|
|
|
|
if accelerator.is_main_process and args.report_to == "wandb" and args.log_dataset_samples:
|
|
logger.info("Logging some dataset samples.")
|
|
formatted_images = []
|
|
formatted_control_images = []
|
|
all_prompts = []
|
|
for i, batch in enumerate(train_dataloader):
|
|
images = (batch["pixel_values"] + 1) / 2
|
|
control_images = (batch["conditioning_pixel_values"] + 1) / 2
|
|
prompts = batch["captions"]
|
|
|
|
if len(formatted_images) > 10:
|
|
break
|
|
|
|
for img, control_img, prompt in zip(images, control_images, prompts):
|
|
formatted_images.append(img)
|
|
formatted_control_images.append(control_img)
|
|
all_prompts.append(prompt)
|
|
|
|
logged_artifacts = []
|
|
for img, control_img, prompt in zip(formatted_images, formatted_control_images, all_prompts):
|
|
logged_artifacts.append(wandb.Image(control_img, caption="Conditioning"))
|
|
logged_artifacts.append(wandb.Image(img, caption=prompt))
|
|
|
|
wandb_tracker = [tracker for tracker in accelerator.trackers if tracker.name == "wandb"]
|
|
wandb_tracker[0].log({"dataset_samples": logged_artifacts})
|
|
|
|
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,
|
|
)
|
|
|
|
for epoch in range(first_epoch, args.num_train_epochs):
|
|
cogview4_transformer.train()
|
|
for step, batch in enumerate(train_dataloader):
|
|
with accelerator.accumulate(cogview4_transformer):
|
|
# Convert images to latent space
|
|
# vae encode
|
|
prompts = batch["captions"]
|
|
attention_mask = tokenizer(
|
|
prompts,
|
|
padding="longest", # not use max length
|
|
max_length=args.max_sequence_length,
|
|
truncation=True,
|
|
add_special_tokens=True,
|
|
return_tensors="pt",
|
|
).attention_mask.float()
|
|
|
|
pixel_latents = encode_images(batch["pixel_values"], vae.to(accelerator.device), weight_dtype)
|
|
control_latents = encode_images(
|
|
batch["conditioning_pixel_values"], vae.to(accelerator.device), weight_dtype
|
|
)
|
|
if args.offload:
|
|
vae.cpu()
|
|
|
|
# Sample a random timestep for each image
|
|
# for weighting schemes where we sample timesteps non-uniformly
|
|
bsz = pixel_latents.shape[0]
|
|
noise = torch.randn_like(pixel_latents, device=accelerator.device, dtype=weight_dtype)
|
|
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,
|
|
)
|
|
|
|
# Add noise according for cogview4
|
|
indices = (u * noise_scheduler_copy.config.num_train_timesteps).long()
|
|
timesteps = noise_scheduler_copy.timesteps[indices].to(device=pixel_latents.device)
|
|
sigmas = noise_scheduler_copy.sigmas[indices].to(device=pixel_latents.device)
|
|
captions = batch["captions"]
|
|
image_seq_lens = torch.tensor(
|
|
pixel_latents.shape[2] * pixel_latents.shape[3] // patch_size**2,
|
|
dtype=pixel_latents.dtype,
|
|
device=pixel_latents.device,
|
|
) # H * W / VAE patch_size
|
|
mu = torch.sqrt(image_seq_lens / 256)
|
|
mu = mu * 0.75 + 0.25
|
|
scale_factors = mu / (mu + (1 / sigmas - 1) ** 1.0).to(
|
|
dtype=pixel_latents.dtype, device=pixel_latents.device
|
|
)
|
|
scale_factors = scale_factors.view(len(batch["captions"]), 1, 1, 1)
|
|
noisy_model_input = (1.0 - scale_factors) * pixel_latents + scale_factors * noise
|
|
concatenated_noisy_model_input = torch.cat([noisy_model_input, control_latents], dim=1)
|
|
text_encoding_pipeline = text_encoding_pipeline.to("cuda")
|
|
|
|
with torch.no_grad():
|
|
(
|
|
prompt_embeds,
|
|
pooled_prompt_embeds,
|
|
) = text_encoding_pipeline.encode_prompt(captions, "")
|
|
original_size = (args.resolution, args.resolution)
|
|
original_size = torch.tensor([original_size], dtype=prompt_embeds.dtype, device=prompt_embeds.device)
|
|
|
|
target_size = (args.resolution, args.resolution)
|
|
target_size = torch.tensor([target_size], dtype=prompt_embeds.dtype, device=prompt_embeds.device)
|
|
|
|
target_size = target_size.repeat(len(batch["captions"]), 1)
|
|
original_size = original_size.repeat(len(batch["captions"]), 1)
|
|
crops_coords_top_left = torch.tensor([(0, 0)], dtype=prompt_embeds.dtype, device=prompt_embeds.device)
|
|
crops_coords_top_left = crops_coords_top_left.repeat(len(batch["captions"]), 1)
|
|
|
|
# this could be optimized by not having to do any text encoding and just
|
|
# doing zeros on specified shapes for `prompt_embeds` and `pooled_prompt_embeds`
|
|
if args.proportion_empty_prompts and random.random() < args.proportion_empty_prompts:
|
|
# Here, we directly pass 16 pad tokens from pooled_prompt_embeds to prompt_embeds.
|
|
prompt_embeds = pooled_prompt_embeds
|
|
if args.offload:
|
|
text_encoding_pipeline = text_encoding_pipeline.to("cpu")
|
|
# Predict.
|
|
noise_pred_cond = cogview4_transformer(
|
|
hidden_states=concatenated_noisy_model_input,
|
|
encoder_hidden_states=prompt_embeds,
|
|
timestep=timesteps,
|
|
original_size=original_size,
|
|
target_size=target_size,
|
|
crop_coords=crops_coords_top_left,
|
|
return_dict=False,
|
|
attention_mask=attention_mask,
|
|
)[0]
|
|
# 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 - pixel_latents
|
|
|
|
weighting = weighting.view(len(batch["captions"]), 1, 1, 1)
|
|
loss = torch.mean(
|
|
(weighting.float() * (noise_pred_cond.float() - target.float()) ** 2).reshape(target.shape[0], -1),
|
|
1,
|
|
)
|
|
loss = loss.mean()
|
|
accelerator.backward(loss)
|
|
|
|
if accelerator.sync_gradients:
|
|
params_to_clip = cogview4_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
|
|
|
|
# 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(
|
|
cogview4_transformer=cogview4_transformer,
|
|
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:
|
|
cogview4_transformer = unwrap_model(cogview4_transformer)
|
|
if args.upcast_before_saving:
|
|
cogview4_transformer.to(torch.float32)
|
|
cogview4_transformer.save_pretrained(args.output_dir)
|
|
|
|
del cogview4_transformer
|
|
del text_encoding_pipeline
|
|
del vae
|
|
free_memory()
|
|
|
|
# Run a final round of validation.
|
|
image_logs = None
|
|
if args.validation_prompt is not None:
|
|
image_logs = log_validation(
|
|
cogview4_transformer=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_*", "checkpoint-*"],
|
|
)
|
|
|
|
accelerator.end_training()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
args = parse_args()
|
|
main(args)
|