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# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Originally by jiwooya1000, put together together by sayakpaul.
Documentation: https://huggingface.co/docs/diffusers/main/en/training/distributed_inference
Run:
accelerate launch distributed_image_generation.py --batch_size 8
# Enable memory optimizations for large models like SD3
accelerate launch distributed_image_generation.py --batch_size 8 --low_mem
"""
import os
import time
import fire
import torch
from datasets import load_dataset
from diffusers import DiffusionPipeline
from tqdm import tqdm
from accelerate import PartialState
from accelerate.utils import gather_object
START_TIME = time.strftime("%Y%m%d_%H%M%S")
DTYPE_MAP = {"fp32": torch.float32, "fp16": torch.float16, "bf16": torch.bfloat16}
def get_batches(items, batch_size):
num_batches = (len(items) + batch_size - 1) // batch_size
batches = []
for i in range(num_batches):
start_index = i * batch_size
end_index = min((i + 1) * batch_size, len(items))
batch = items[start_index:end_index]
batches.append(batch)
return batches
def main(
ckpt_id: str = "PixArt-alpha/PixArt-Sigma-XL-2-1024-MS",
save_dir: str = "./evaluation/examples",
seed: int = 1,
batch_size: int = 4,
num_inference_steps: int = 20,
guidance_scale: float = 4.5,
dtype: str = "fp16",
low_mem: bool = False,
):
pipeline = DiffusionPipeline.from_pretrained(ckpt_id, torch_dtype=DTYPE_MAP[dtype])
save_dir = save_dir + f"_{START_TIME}"
parti_prompts = load_dataset("nateraw/parti-prompts", split="train")
data_loader = get_batches(items=parti_prompts["Prompt"], batch_size=batch_size)
distributed_state = PartialState()
if low_mem:
pipeline.enable_model_cpu_offload(gpu_id=distributed_state.device.index)
else:
pipeline = pipeline.to(distributed_state.device)
if distributed_state.is_main_process:
if not os.path.exists(save_dir):
os.makedirs(save_dir)
print(f"Directory '{save_dir}' created successfully.")
else:
print(f"Directory '{save_dir}' already exists.")
count = 0
for _, prompts_raw in tqdm(enumerate(data_loader), total=len(data_loader)):
input_prompts = []
with distributed_state.split_between_processes(prompts_raw) as prompts:
generator = torch.manual_seed(seed)
images = pipeline(
prompts, num_inference_steps=num_inference_steps, guidance_scale=guidance_scale, generator=generator
).images
input_prompts.extend(prompts)
distributed_state.wait_for_everyone()
images = gather_object(images)
input_prompts = gather_object(input_prompts)
if distributed_state.is_main_process:
for image, prompt in zip(images, input_prompts):
count += 1
temp_dir = os.path.join(save_dir, f"example_{count}")
os.makedirs(temp_dir)
prompt = "_".join(prompt.split())
image.save(f"image_{prompt}.png")
if distributed_state.is_main_process:
print(f">>> Image Generation Finished. Saved in {save_dir}")
if __name__ == "__main__":
fire.Fire(main)
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