File size: 4,206 Bytes
ca32b0e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 | #!/usr/bin/env python
# coding=utf-8
# Copyright 2025 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.
import argparse
import glob
import hashlib
import pandas as pd
import torch
from transformers import T5EncoderModel
from diffusers import StableDiffusion3Pipeline
PROMPT = "a photo of sks dog"
MAX_SEQ_LENGTH = 77
LOCAL_DATA_DIR = "dog"
OUTPUT_PATH = "sample_embeddings.parquet"
def bytes_to_giga_bytes(bytes):
return bytes / 1024 / 1024 / 1024
def generate_image_hash(image_path):
with open(image_path, "rb") as f:
img_data = f.read()
return hashlib.sha256(img_data).hexdigest()
def load_sd3_pipeline():
id = "stabilityai/stable-diffusion-3-medium-diffusers"
text_encoder = T5EncoderModel.from_pretrained(id, subfolder="text_encoder_3", load_in_8bit=True, device_map="auto")
pipeline = StableDiffusion3Pipeline.from_pretrained(
id, text_encoder_3=text_encoder, transformer=None, vae=None, device_map="balanced"
)
return pipeline
@torch.no_grad()
def compute_embeddings(pipeline, prompt, max_sequence_length):
(
prompt_embeds,
negative_prompt_embeds,
pooled_prompt_embeds,
negative_pooled_prompt_embeds,
) = pipeline.encode_prompt(prompt=prompt, prompt_2=None, prompt_3=None, max_sequence_length=max_sequence_length)
print(
f"{prompt_embeds.shape=}, {negative_prompt_embeds.shape=}, {pooled_prompt_embeds.shape=}, {negative_pooled_prompt_embeds.shape}"
)
max_memory = bytes_to_giga_bytes(torch.cuda.max_memory_allocated())
print(f"Max memory allocated: {max_memory:.3f} GB")
return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds
def run(args):
pipeline = load_sd3_pipeline()
prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds = compute_embeddings(
pipeline, args.prompt, args.max_sequence_length
)
# Assumes that the images within `args.local_image_dir` have a JPEG extension. Change
# as needed.
image_paths = glob.glob(f"{args.local_data_dir}/*.jpeg")
data = []
for image_path in image_paths:
img_hash = generate_image_hash(image_path)
data.append(
(img_hash, prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds)
)
# Create a DataFrame
embedding_cols = [
"prompt_embeds",
"negative_prompt_embeds",
"pooled_prompt_embeds",
"negative_pooled_prompt_embeds",
]
df = pd.DataFrame(
data,
columns=["image_hash"] + embedding_cols,
)
# Convert embedding lists to arrays (for proper storage in parquet)
for col in embedding_cols:
df[col] = df[col].apply(lambda x: x.cpu().numpy().flatten().tolist())
# Save the dataframe to a parquet file
df.to_parquet(args.output_path)
print(f"Data successfully serialized to {args.output_path}")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--prompt", type=str, default=PROMPT, help="The instance prompt.")
parser.add_argument(
"--max_sequence_length",
type=int,
default=MAX_SEQ_LENGTH,
help="Maximum sequence length to use for computing the embeddings. The more the higher computational costs.",
)
parser.add_argument(
"--local_data_dir", type=str, default=LOCAL_DATA_DIR, help="Path to the directory containing instance images."
)
parser.add_argument("--output_path", type=str, default=OUTPUT_PATH, help="Path to serialize the parquet file.")
args = parser.parse_args()
run(args)
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