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124 lines
4.1 KiB
Python
124 lines
4.1 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 glob
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import hashlib
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import pandas as pd
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import torch
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from transformers import T5EncoderModel
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from diffusers import StableDiffusion3Pipeline
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PROMPT = "a photo of sks dog"
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MAX_SEQ_LENGTH = 77
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LOCAL_DATA_DIR = "dog"
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OUTPUT_PATH = "sample_embeddings.parquet"
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def bytes_to_giga_bytes(bytes):
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return bytes / 1024 / 1024 / 1024
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def generate_image_hash(image_path):
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with open(image_path, "rb") as f:
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img_data = f.read()
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return hashlib.sha256(img_data).hexdigest()
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def load_sd3_pipeline():
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id = "stabilityai/stable-diffusion-3-medium-diffusers"
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text_encoder = T5EncoderModel.from_pretrained(id, subfolder="text_encoder_3", load_in_8bit=True, device_map="auto")
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pipeline = StableDiffusion3Pipeline.from_pretrained(
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id, text_encoder_3=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, prompt, max_sequence_length):
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(
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prompt_embeds,
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negative_prompt_embeds,
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pooled_prompt_embeds,
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negative_pooled_prompt_embeds,
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) = pipeline.encode_prompt(prompt=prompt, prompt_2=None, prompt_3=None, max_sequence_length=max_sequence_length)
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print(
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f"{prompt_embeds.shape=}, {negative_prompt_embeds.shape=}, {pooled_prompt_embeds.shape=}, {negative_pooled_prompt_embeds.shape}"
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)
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max_memory = bytes_to_giga_bytes(torch.cuda.max_memory_allocated())
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print(f"Max memory allocated: {max_memory:.3f} GB")
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return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds
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def run(args):
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pipeline = load_sd3_pipeline()
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prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds = compute_embeddings(
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pipeline, args.prompt, args.max_sequence_length
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)
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# Assumes that the images within `args.local_image_dir` have a JPEG extension. Change
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# as needed.
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image_paths = glob.glob(f"{args.local_data_dir}/*.jpeg")
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data = []
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for image_path in image_paths:
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img_hash = generate_image_hash(image_path)
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data.append(
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(img_hash, prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds)
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)
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# Create a DataFrame
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embedding_cols = [
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"prompt_embeds",
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"negative_prompt_embeds",
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"pooled_prompt_embeds",
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"negative_pooled_prompt_embeds",
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]
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df = pd.DataFrame(
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data,
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columns=["image_hash"] + embedding_cols,
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)
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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("--prompt", type=str, default=PROMPT, help="The instance prompt.")
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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(
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"--local_data_dir", type=str, default=LOCAL_DATA_DIR, help="Path to the directory containing instance images."
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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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