.

options/Banner_Model/Image2Image_2.py

@@ -1,34 +1,27 @@
-# import spaces
-# import torch
-# from controlnet_aux import LineartDetector
-# from diffusers import ControlNetModel,UniPCMultistepScheduler,StableDiffusionControlNetPipeline
-# from PIL import Image
+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)
+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
+@spaces.GPU(duration=100)
 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
+    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