Update app.py

Remove interactive scribble

app.py

@@ -37,8 +37,6 @@ def process(input_image, prompt, input_control, a_prompt, n_prompt, num_samples,
     # TODO: Add other control tasks
     if input_control == "Scribble":
         return process_scribble(input_image, prompt, a_prompt, n_prompt, num_samples, image_resolution, ddim_steps, scale, seed, eta)
-    elif input_control == "Interactive Scribble":
-        return process_scribble_interactive(input_image, prompt, a_prompt, n_prompt, num_samples, image_resolution, ddim_steps, scale, seed, eta)
     return process_canny(input_image, prompt, a_prompt, n_prompt, num_samples, image_resolution, ddim_steps, scale, seed, eta, low_threshold, high_threshold)
     
 def process_canny(input_image, prompt, a_prompt, n_prompt, num_samples, image_resolution, ddim_steps, scale, seed, eta, low_threshold, high_threshold):
@@ -98,40 +96,6 @@ def process_scribble(input_image, prompt, a_prompt, n_prompt, num_samples, image
 
         results = [x_samples[i] for i in range(num_samples)]
     return [255 - detected_map] + results
-
-def process_scribble_interactive(input_image, prompt, a_prompt, n_prompt, num_samples, image_resolution, ddim_steps, scale, seed, eta):
-    with torch.no_grad():
-        img = resize_image(HWC3(input_image['mask'][:, :, 0]), image_resolution)
-        H, W, C = img.shape
-
-        detected_map = np.zeros_like(img, dtype=np.uint8)
-        detected_map[np.min(img, axis=2) > 127] = 255
-
-        control = torch.from_numpy(detected_map.copy()).float().cuda() / 255.0
-        control = torch.stack([control for _ in range(num_samples)], dim=0)
-        control = einops.rearrange(control, 'b h w c -> b c h w').clone()
-
-        if seed == -1:
-            seed = random.randint(0, 65535)
-        seed_everything(seed)
-
-
-        cond = {"c_concat": [control], "c_crossattn": [scribble_model.get_learned_conditioning([prompt + ', ' + a_prompt] * num_samples)]}
-        un_cond = {"c_concat": [control], "c_crossattn": [scribble_model.get_learned_conditioning([n_prompt] * num_samples)]}
-        shape = (4, H // 8, W // 8)
-
-
-        samples, intermediates = ddim_sampler_scribble.sample(ddim_steps, num_samples,
-                                                     shape, cond, verbose=False, eta=eta,
-                                                     unconditional_guidance_scale=scale,
-                                                     unconditional_conditioning=un_cond)
-  
-        x_samples = scribble_model.decode_first_stage(samples)
-        x_samples = (einops.rearrange(x_samples, 'b c h w -> b h w c') * 127.5 + 127.5).cpu().numpy().clip(0, 255).astype(np.uint8)
-
-        results = [x_samples[i] for i in range(num_samples)]
-    return [255 - detected_map] + results
-
     
 def create_canvas(w, h):
     new_control_options = ["Interactive Scribble"]
@@ -141,8 +105,7 @@ def create_canvas(w, h):
 block = gr.Blocks().queue()
 control_task_list = [
     "Canny Edge Map",
-    "Scribble",
-    "Interactive Scribble"
+    "Scribble"
 ]
 with block:
     gr.Markdown("## Adding Conditional Control to Text-to-Image Diffusion Models")
@@ -153,18 +116,7 @@ with block:
               ''')
     with gr.Row():
         with gr.Column():
-            with gr.Tab("Upload"):
-                input_image = gr.Image(source='upload', type="numpy")
-                
-            with gr.Tab("Interactive Scribble"):
-                canvas_width = gr.Slider(label="Canvas Width", minimum=256, maximum=1024, value=512, step=1)
-                canvas_height = gr.Slider(label="Canvas Height", minimum=256, maximum=1024, value=512, step=1)
-                create_button = gr.Button(label="Start", value='Open drawing canvas!')
-                input_image = gr.Image(source='upload', type='numpy', tool='sketch')
-                gr.Markdown(value='Do not forget to change your brush width to make it thinner. (Gradio do not allow developers to set brush width so you need to do it manually.) '
-                                  'Just click on the small pencil icon in the upper right corner of the above block.')
-                create_button.click(fn=create_canvas, inputs=[canvas_width, canvas_height], outputs=[input_image])
-
+            input_image = gr.Image(source='upload', type="numpy")
             input_control = gr.Dropdown(control_task_list, value="Scribble", label="Control Task")
             prompt = gr.Textbox(label="Prompt")
             run_button = gr.Button(label="Run")