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108 lines
3.7 KiB
Python
108 lines
3.7 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 pandas as pd
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import torch
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from datasets import load_dataset
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from huggingface_hub.utils import insecure_hashlib
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from tqdm.auto import tqdm
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from transformers import T5EncoderModel
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from diffusers import FluxPipeline
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MAX_SEQ_LENGTH = 77
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OUTPUT_PATH = "embeddings.parquet"
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def generate_image_hash(image):
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return insecure_hashlib.sha256(image.tobytes()).hexdigest()
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def load_flux_dev_pipeline():
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id = "black-forest-labs/FLUX.1-dev"
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text_encoder = T5EncoderModel.from_pretrained(id, subfolder="text_encoder_2", load_in_8bit=True, device_map="auto")
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pipeline = FluxPipeline.from_pretrained(
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id, text_encoder_2=text_encoder, transformer=None, vae=None, device_map="balanced"
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)
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return pipeline
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@torch.no_grad()
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def compute_embeddings(pipeline, prompts, max_sequence_length):
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all_prompt_embeds = []
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all_pooled_prompt_embeds = []
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all_text_ids = []
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for prompt in tqdm(prompts, desc="Encoding prompts."):
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(
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prompt_embeds,
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pooled_prompt_embeds,
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text_ids,
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) = pipeline.encode_prompt(prompt=prompt, prompt_2=None, max_sequence_length=max_sequence_length)
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all_prompt_embeds.append(prompt_embeds)
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all_pooled_prompt_embeds.append(pooled_prompt_embeds)
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all_text_ids.append(text_ids)
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max_memory = torch.cuda.max_memory_allocated() / 1024 / 1024 / 1024
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print(f"Max memory allocated: {max_memory:.3f} GB")
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return all_prompt_embeds, all_pooled_prompt_embeds, all_text_ids
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def run(args):
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dataset = load_dataset("Norod78/Yarn-art-style", split="train")
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image_prompts = {generate_image_hash(sample["image"]): sample["text"] for sample in dataset}
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all_prompts = list(image_prompts.values())
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print(f"{len(all_prompts)=}")
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pipeline = load_flux_dev_pipeline()
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all_prompt_embeds, all_pooled_prompt_embeds, all_text_ids = compute_embeddings(
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pipeline, all_prompts, args.max_sequence_length
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)
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data = []
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for i, (image_hash, _) in enumerate(image_prompts.items()):
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data.append((image_hash, all_prompt_embeds[i], all_pooled_prompt_embeds[i], all_text_ids[i]))
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print(f"{len(data)=}")
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# Create a DataFrame
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embedding_cols = ["prompt_embeds", "pooled_prompt_embeds", "text_ids"]
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df = pd.DataFrame(data, columns=["image_hash"] + embedding_cols)
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print(f"{len(df)=}")
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# Convert embedding lists to arrays (for proper storage in parquet)
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for col in embedding_cols:
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df[col] = df[col].apply(lambda x: x.cpu().numpy().flatten().tolist())
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# Save the dataframe to a parquet file
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df.to_parquet(args.output_path)
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print(f"Data successfully serialized to {args.output_path}")
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--max_sequence_length",
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type=int,
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default=MAX_SEQ_LENGTH,
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help="Maximum sequence length to use for computing the embeddings. The more the higher computational costs.",
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)
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parser.add_argument("--output_path", type=str, default=OUTPUT_PATH, help="Path to serialize the parquet file.")
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args = parser.parse_args()
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run(args)
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