.

options/Banner_Model/Image2Image_2.py

@@ -1,27 +1,34 @@
-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")
+# @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
 
-    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=image,prompt=prompt,additional_prompt="best quality, extremely detailed",negative_prompt="longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",num_images=1,image_resolution=size,preprocess_resolution=size,num_steps=num_inference_steps,guidance_scale=guidance_scale,seed=0,preprocessor_name="Lineart")
+    print(res)
+    return res