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<!--- | ||
Copyright 2022 The HuggingFace 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. | ||
--> | ||
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# Stable Diffusion Examples | ||
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This directory contains a script that showcases how to run distributed inferenc of text-to-image generation using Stable Diffusion on Habana Gaudi. | ||
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Stable Diffusion was proposed in [Stable Diffusion Announcement](https://stability.ai/blog/stable-diffusion-announcement) by Patrick Esser and Robin Rombach and the Stability AI team. | ||
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## Distributed inference with multiple HPUs | ||
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Here is how to generate two images with two prompts on two HPUs : | ||
```python | ||
python ../gaudi_spawn.py \ | ||
--world_size 2 run_distributed.py \ | ||
--model_name_or_path runwayml/stable-diffusion-v1-5 \ | ||
--prompts "a cat" "a dog" \ | ||
--use_habana \ | ||
--use_hpu_graphs \ | ||
--gaudi_config Habana/stable-diffusion \ | ||
--bf16 | ||
``` | ||
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# coding=utf-8 | ||
# Copyright 2022 The HuggingFace 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. | ||
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""" | ||
Adapted from: https://huggingface.co/docs/diffusers/en/training/distributed_inference | ||
- Use the GaudiStableDiffusionPipeline | ||
""" | ||
import torch | ||
import logging | ||
import argparse | ||
from accelerate import PartialState | ||
from optimum.habana.diffusers import GaudiStableDiffusionPipeline | ||
from optimum.habana.utils import set_seed | ||
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logger = logging.getLogger(__name__) | ||
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def main(): | ||
parser = argparse.ArgumentParser() | ||
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parser.add_argument( | ||
"--model_name_or_path", | ||
default="runwayml/stable-diffusion-v1-5", | ||
type=str, | ||
help="Path to pre-trained model", | ||
) | ||
# Pipeline arguments | ||
parser.add_argument( | ||
"--prompts", | ||
type=str, | ||
nargs="*", | ||
default=["a dog", "a cat"], | ||
help="The prompt or prompts to guide the image generation.", | ||
) | ||
parser.add_argument( | ||
"--num_images_per_prompt", type=int, default=1, help="The number of images to generate per prompt." | ||
) | ||
parser.add_argument("--seed", type=int, default=None, help="Random seed for initialization.") | ||
parser.add_argument("--bf16", action="store_true", help="Whether to perform generation in bf16 precision.") | ||
parser.add_argument( | ||
"--gaudi_config", | ||
type=str, | ||
default="Habana/stable-diffusion", | ||
help=( | ||
"Name or path of the Gaudi configuration. In particular, it enables to specify how to apply Habana Mixed" | ||
" Precision." | ||
), | ||
) | ||
# HPU-specific arguments | ||
parser.add_argument("--use_habana", action="store_true", help="Use HPU.") | ||
parser.add_argument( | ||
"--use_hpu_graphs", action="store_true", help="Use HPU graphs on HPU. This should lead to faster generations." | ||
) | ||
args = parser.parse_args() | ||
# Set seed before running the model | ||
if args.seed: | ||
logger.info("Set the random seed {}!".format(args.seed)) | ||
set_seed(args.seed) | ||
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kwargs = { | ||
"use_habana": args.use_habana, | ||
"use_hpu_graphs": args.use_hpu_graphs, | ||
"gaudi_config": args.gaudi_config, | ||
"torch_dtype": torch.bfloat16 if args.bf16 else None | ||
} | ||
print(f"kwargs={kwargs}") | ||
pipeline = GaudiStableDiffusionPipeline.from_pretrained( | ||
args.model_name_or_path, use_safetensors=True, **kwargs | ||
) | ||
distributed_state = PartialState() | ||
kwargs= { | ||
"num_images_per_prompt": args.num_images_per_prompt | ||
} | ||
with distributed_state.split_between_processes(args.prompts) as prompt: | ||
outputs = pipeline(prompt, **kwargs) | ||
for i, image in enumerate(outputs.images): | ||
image.save(f"result_{distributed_state.process_index}_{i}.png") | ||
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if __name__ == "__main__": | ||
main() |
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