add tile controlnet

app.py

@@ -14,12 +14,9 @@ from diffusers.models import SD3ControlNetModel, SD3MultiControlNetModel
 from diffusers.utils import load_image
 
 # load pipeline
-controlnet = SD3ControlNetModel.from_pretrained("InstantX/SD3-Controlnet-Canny")
-pipe = StableDiffusion3ControlNetPipeline.from_pretrained(
-    "stabilityai/stable-diffusion-3-medium-diffusers",
-    controlnet=controlnet
-)
-pipe.to("cuda", torch.float16)
+controlnet_canny = SD3ControlNetModel.from_pretrained("InstantX/SD3-Controlnet-Canny")
+controlne_tile = SD3ControlNetModel.from_pretrained("InstantX/SD3-Controlnet-Tile")
+
 
 def resize_image(input_path, output_path, target_height):
     # Open the input image
@@ -41,34 +38,57 @@ def resize_image(input_path, output_path, target_height):
     return output_path, new_width, target_height
 
 @spaces.GPU(duration=90)
-def infer(image_in, prompt, inference_steps, guidance_scale, control_weight, progress=gr.Progress(track_tqdm=True)):
+def infer_canny(image_in, prompt, control_type, inference_steps, guidance_scale, control_weight, progress=gr.Progress(track_tqdm=True)):
     
     n_prompt = 'NSFW, nude, naked, porn, ugly'
+
+    if control_type == "canny":
+        pipe = StableDiffusion3ControlNetPipeline.from_pretrained(
+            "stabilityai/stable-diffusion-3-medium-diffusers",
+            controlnet=controlnet_canny
+        )
+
+        # Canny preprocessing
+        image_to_canny = load_image(image_in)
+        image_to_canny = np.array(image_to_canny)
+        image_to_canny = cv2.Canny(image_to_canny, 100, 200)
+        image_to_canny = image_to_canny[:, :, None]
+        image_to_canny = np.concatenate([image_to_canny, image_to_canny, image_to_canny], axis=2)
+        image_to_canny = Image.fromarray(image_to_canny)
+
+        control_image = image_to_canny
     
-    # Canny preprocessing
-    image_to_canny = load_image(image_in)
-    image_to_canny = np.array(image_to_canny)
-    image_to_canny = cv2.Canny(image_to_canny, 100, 200)
-    image_to_canny = image_to_canny[:, :, None]
-    image_to_canny = np.concatenate([image_to_canny, image_to_canny, image_to_canny], axis=2)
-    image_to_canny = Image.fromarray(image_to_canny)
-    
+    elif control_type == "tile":
+        pipe = StableDiffusion3ControlNetPipeline.from_pretrained(
+            "stabilityai/stable-diffusion-3-medium-diffusers",
+            controlnet=controlnet_tile
+        )
+
+        control_image = load_image(image_in)
+
+   
+    pipe.to("cuda", torch.float16)
     
     # infer
     image = pipe(
         prompt=prompt,
         negative_prompt=n_prompt,
-        control_image=image_to_canny, 
+        control_image=control_image, 
         controlnet_conditioning_scale=control_weight,
         num_inference_steps=inference_steps,
         guidance_scale=guidance_scale,
     ).images[0]
 
-    image_redim, w, h = resize_image(image_in, "resized_input.jpg", 1024)
-    
-    image = image.resize((w, h), Image.LANCZOS)
+    if control_type == "canny":
 
-    return image, image_to_canny
+        image_redim, w, h = resize_image(image_in, "resized_input.jpg", 1024)
+        image = image.resize((w, h), Image.LANCZOS)
+    
+        return image, gr.update(value=image_to_canny, visible=True)
+    
+    elif control_type == "tile":
+        return image, gr.update(value=None, visible=False)
+   
 
 css="""
 #col-container{
@@ -82,28 +102,40 @@ with gr.Blocks(css=css) as demo:
         # SD3 ControlNet
 
         Experiment with Stable Diffusion 3 ControlNet models proposed and maintained by the InstantX team.<br />
-        Model Card: [InstantX/SD3-Controlnet-Canny](https://huggingface.co/InstantX/SD3-Controlnet-Canny)
-
         """)
-        with gr.Row():
-            with gr.Column():
-                image_in = gr.Image(label="Image reference", sources=["upload"], type="filepath")
-                prompt = gr.Textbox(label="Prompt")
-                with gr.Accordion("Advanced settings", open=False):
-                    with gr.Column():
-                        with gr.Row():
-                            inference_steps = gr.Slider(label="Inference steps", minimum=1, maximum=50, step=1, value=25)
-                            guidance_scale = gr.Slider(label="Guidance scale", minimum=1.0, maximum=10.0, step=0.1, value=7.0)
-                        control_weight = gr.Slider(label="Control Weight", minimum=0.0, maximum=1.0, step=0.01, value=0.7)
+        
+        with gr.Column():
+            gr.Mardown("""
+            Model Card: [InstantX/SD3-Controlnet-Canny](https://huggingface.co/InstantX/SD3-Controlnet-Canny)
+            """)
+            with gr.Row():
+                with gr.Column():
+                    image_in = gr.Image(label="Image reference", sources=["upload"], type="filepath")
+                    prompt = gr.Textbox(label="Prompt")
+                    control_type = gr.Radio(
+                        label="Control type",
+                        choices = [
+                            "canny",
+                            "tile"
+                        ],
+                        value="canny"
+                    )
+                    with gr.Accordion("Advanced settings", open=False):
+                        with gr.Column():
+                            with gr.Row():
+                                inference_steps = gr.Slider(label="Inference steps", minimum=1, maximum=50, step=1, value=25)
+                                guidance_scale = gr.Slider(label="Guidance scale", minimum=1.0, maximum=10.0, step=0.1, value=7.0)
+                            control_weight = gr.Slider(label="Control Weight", minimum=0.0, maximum=1.0, step=0.01, value=0.7)
                     
-                submit_btn = gr.Button("Submit")
-            with gr.Column():
-                result = gr.Image(label="Result")
-                canny_used = gr.Image(label="Preprocessed Canny")
-    
-    submit_btn.click(
-        fn = infer,
-        inputs = [image_in, prompt, inference_steps, guidance_scale, control_weight],
+                    submit_canny_btn = gr.Button("Submit")
+                with gr.Column():
+                    result = gr.Image(label="Result")
+                    canny_used = gr.Image(label="Preprocessed Canny", visible=False)
+
+
+    submit_canny_btn.click(
+        fn = infer_canny,
+        inputs = [image_in, prompt, control_type, inference_steps, guidance_scale, control_weight],
         outputs = [result, canny_used],
         show_api=False
     )