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from diffusers import StableDiffusion3Pipeline
import torch
from PIL import Image
import os
import json
import argparse
parser = argparse.ArgumentParser(description="Diffusion Pipeline with Arguments")
parser.add_argument(
"--json_filename",
type=str,
required=True,
help="Path to the JSON file containing text data",
)
parser.add_argument(
"--cuda", type=int, required=True, help="CUDA device to use for processing"
)
args = parser.parse_args()
json_filename = args.json_filename
cuda_device = f"cuda:{args.cuda}"
print(json_filename, cuda_device)
image_dir = "/mnt/petrelfs/zhuchenglin/LLaVA/playground/data"
with open(json_filename, "r") as f:
json_data = json.load(f)
pipe = StableDiffusion3Pipeline.from_pretrained("stabilityai/stable-diffusion-3-medium-diffusers", torch_dtype=torch.float16)
pipe.to(cuda_device)
for text in json_data:
prompt = ""
for caption in text['conversations']:
if caption['from'] == 'gpt':
prompt += caption['value']
# for caption in text['conversations']:
# prompt += caption['value']
image = pipe(
prompt=prompt,
prompt_3=prompt,
negative_prompt="",
num_inference_steps=60,
height=1024,
width=1024,
guidance_scale=10.0,
max_sequence_length=512,
).images[0]
subdir = text["image"].split("/")[0]
if not os.path.exists(os.path.join(image_dir, subdir)):
os.makedirs(os.path.join(image_dir, subdir))
image_path = os.path.join(image_dir, text["image"])
image.save(image_path)
print("所有图像已成功生成并保存。") |