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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)
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()
def I2I_2(image, prompt,size,num_inference_steps):
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, num_inference_steps=num_inference_steps, generator=generator, image=image).images[0]
return image |