options/Banner_Model/Image2Image_2.py

# import spaces
# import torch
# from controlnet_aux import LineartDetector
# from diffusers import ControlNetModel,UniPCMultistepScheduler,StableDiffusionControlNetPipeline
# from PIL import Image

# device= "cuda" if torch.cuda.is_available() else "cpu"
# print("Using device for I2I_2:", device)

# @spaces.GPU(duration=100)
# def I2I_2(image, prompt,size,num_inference_steps,guidance_scale):
#     processor = LineartDetector.from_pretrained("lllyasviel/Annotators")

#     checkpoint = "ControlNet-1-1-preview/control_v11p_sd15_lineart"
#     controlnet = ControlNetModel.from_pretrained(checkpoint, torch_dtype=torch.float16).to(device)
#     pipe = StableDiffusionControlNetPipeline.from_pretrained(
#         "radames/stable-diffusion-v1-5-img2img", controlnet=controlnet, torch_dtype=torch.float16
#     ).to(device)
#     pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
#     pipe.enable_model_cpu_offload()
#     if not isinstance(image, Image.Image):
#         image = Image.fromarray(image)
#     image.resize((size,size))
#     image=processor(image)
#     generator = torch.Generator(device=device).manual_seed(0)
#     image = pipe(prompt+"best quality, extremely detailed", num_inference_steps=num_inference_steps, generator=generator, image=image,negative_prompt="longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",guidance_scale=guidance_scale).images[0]
#     return image

from gradio_client import Client
def I2I_2(image, prompt,size,num_inference_steps,guidance_scale):
    client = Client("https://hysts-controlnet-v1-1.hf.space/")
    res=client.predict([image,prompt,"best quality, extremely detailed","longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",1,size,size,num_inference_steps,guidance_scale,0,"Lineart","/lineart"])
    print(res)
    return res