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JoyAI-Image Edit Plus — Diffusers Inference Script
Multi-image instruction-guided editing: provide reference images and a text
instruction to generate a new image that combines elements from the references.
"""
import argparse
import torch
from PIL import Image
from diffusers import JoyImageEditPlusPipeline
def parse_args():
parser = argparse.ArgumentParser(description="JoyAI-Image Edit Plus inference")
parser.add_argument(
"--model_path",
type=str,
default="jdopensource/JoyAI-Image-Edit-Plus-Diffusers",
help="Path or HuggingFace repo ID of the model",
)
parser.add_argument(
"--images",
type=str,
nargs="+",
required=True,
help="Paths to reference images (1-6 images)",
)
parser.add_argument(
"--prompt",
type=str,
required=True,
help="Text instruction for editing",
)
parser.add_argument(
"--negative_prompt",
type=str,
default="low quality, blurry, deformed",
help="Negative prompt",
)
parser.add_argument("--num_inference_steps", type=int, default=30)
parser.add_argument("--guidance_scale", type=float, default=4.0)
parser.add_argument("--seed", type=int, default=42)
parser.add_argument("--output", type=str, default="output.png")
return parser.parse_args()
def main():
args = parse_args()
pipe = JoyImageEditPlusPipeline.from_pretrained(
args.model_path,
torch_dtype=torch.bfloat16,
).to("cuda")
images = [Image.open(p).convert("RGB") for p in args.images]
target_h, target_w = pipe._get_bucket_size(images[-1])
result = pipe(
images=images,
prompt=args.prompt,
negative_prompt=args.negative_prompt,
height=target_h,
width=target_w,
num_inference_steps=args.num_inference_steps,
guidance_scale=args.guidance_scale,
generator=torch.Generator(device="cuda").manual_seed(args.seed),
)
result.images[0].save(args.output)
print(f"Saved to {args.output}")
if __name__ == "__main__":
main()
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