Update app.py

app.py

@@ -16,55 +16,20 @@ from cldm.ddim_hacked import DDIMSampler
 import dlib
 from PIL import Image, ImageDraw
 
-if torch.cuda.is_available():
-    device = torch.device("cuda")
-else:
-    device = torch.device("cpu")
-
 model = create_model('./models/cldm_v15.yaml').cpu()
-model.load_state_dict(load_state_dict(
-    './models/control_sd15_landmarks.pth', location='cpu'))
-model = model.to(device)
+model.load_state_dict(load_state_dict('./models/control_sd15_landmarks.pth', location='cuda'))
+model = model.cuda()
 ddim_sampler = DDIMSampler(model)
 
 detector = dlib.get_frontal_face_detector()
 predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")
 
-
-canvas_html = "<face-canvas id='canvas-root' data-mode='points' style='display:flex;max-width: 500px;margin: 0 auto;'></face-canvas>"
-load_js = """
-async () => {
-const url = "https://huggingface.co/datasets/radames/gradio-components/raw/main/face-canvas.js"
-fetch(url)
-  .then(res => res.text())
-  .then(text => {
-    const script = document.createElement('script');
-    script.type = "module"
-    script.src = URL.createObjectURL(new Blob([text], { type: 'application/javascript' }));
-    document.head.appendChild(script);
-  });
-}
-"""
-get_js_image = """
-async (input_image, prompt, a_prompt, n_prompt, num_samples, image_resolution, ddim_steps, guess_mode, landmark_direct_mode, strength, scale, seed, eta, image_file_live_opt) => {
-  const canvasEl = document.getElementById("canvas-root");
-  const imageData = canvasEl? canvasEl._data : null;
-  if(image_file_live_opt === 'webcam'){
-    input_image = imageData['image']
-    landmark_direct_mode = true
-  }
-  return [input_image, prompt, a_prompt, n_prompt, num_samples, image_resolution, ddim_steps, guess_mode, landmark_direct_mode, strength, scale, seed, eta, image_file_live_opt]
-}
-"""
-
-
 def draw_landmarks(image, landmarks, color="white", radius=2.5):
     draw = ImageDraw.Draw(image)
     for dot in landmarks:
         x, y = dot
         draw.ellipse((x-radius, y-radius, x+radius, y+radius), fill=color)
 
-
 def get_68landmarks_img(img):
     gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
     faces = detector(gray)
@@ -80,14 +45,7 @@ def get_68landmarks_img(img):
     con_img = np.array(con_img)
     return con_img
 
-
-def process(input_image, prompt, a_prompt, n_prompt, num_samples, image_resolution, ddim_steps, guess_mode, landmark_direct_mode, strength, scale, seed, eta, image_file_live_opt="file"):
-    input_image = input_image.convert('RGB')
-    input_image = np.array(input_image)
-    input_image = np.flip(input_image, axis=2)
-    print('input_image.shape', input_image.shape)
-    # Limit the number of samples to 2 for Spaces only
-    num_samples = min(num_samples, 2)
+def process(input_image, prompt, a_prompt, n_prompt, num_samples, image_resolution, ddim_steps, guess_mode, landmark_direct_mode, strength, scale, seed, eta):
     with torch.no_grad():
         img = resize_image(HWC3(input_image), image_resolution)
         H, W, C = img.shape
@@ -98,29 +56,25 @@ def process(input_image, prompt, a_prompt, n_prompt, num_samples, image_resoluti
             detected_map = get_68landmarks_img(img)
         detected_map = HWC3(detected_map)
 
-        control = torch.from_numpy(
-            detected_map.copy()).float().to(device) / 255.0
+        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, 2**32 - 1)
+            seed = random.randint(0, 65535)
         seed_everything(seed)
 
         if config.save_memory:
             model.low_vram_shift(is_diffusing=False)
 
-        cond = {"c_concat": [control], "c_crossattn": [
-            model.get_learned_conditioning([prompt + ', ' + a_prompt] * num_samples)]}
-        un_cond = {"c_concat": None if guess_mode else [control], "c_crossattn": [
-            model.get_learned_conditioning([n_prompt] * num_samples)]}
+        cond = {"c_concat": [control], "c_crossattn": [model.get_learned_conditioning([prompt + ', ' + a_prompt] * num_samples)]}
+        un_cond = {"c_concat": None if guess_mode else [control], "c_crossattn": [model.get_learned_conditioning([n_prompt] * num_samples)]}
         shape = (4, H // 8, W // 8)
 
         if config.save_memory:
             model.low_vram_shift(is_diffusing=True)
 
-        model.control_scales = [strength * (0.825 ** float(12 - i)) for i in range(13)] if guess_mode else (
-            [strength] * 13)  # Magic number. IDK why. Perhaps because 0.825**12<0.01 but 0.826**12>0.01
+        model.control_scales = [strength * (0.825 ** float(12 - i)) for i in range(13)] if guess_mode else ([strength] * 13)  # Magic number. IDK why. Perhaps because 0.825**12<0.01 but 0.826**12>0.01
         samples, intermediates = ddim_sampler.sample(ddim_steps, num_samples,
                                                      shape, cond, verbose=False, eta=eta,
                                                      unconditional_guidance_scale=scale,
@@ -130,82 +84,38 @@ def process(input_image, prompt, a_prompt, n_prompt, num_samples, image_resoluti
             model.low_vram_shift(is_diffusing=False)
 
         x_samples = 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)
+        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 toggle(choice):
-    if choice == "file":
-        return gr.update(visible=True, value=None), gr.update(visible=False, value=None)
-    elif choice == "webcam":
-        return gr.update(visible=False, value=None), gr.update(visible=True, value=canvas_html)
-
-
 block = gr.Blocks().queue()
 with block:
-    live_conditioning = gr.JSON(value={}, visible=False)
     with gr.Row():
         gr.Markdown("## Control Stable Diffusion with Face Landmarks")
     with gr.Row():
         with gr.Column():
-            image_file_live_opt = gr.Radio(["file", "webcam"], value="file",
-                                           label="How would you like to upload your image?")
-            input_image = gr.Image(source="upload", visible=True, type="pil")
-            canvas = gr.HTML(None, elem_id="canvas_html", visible=False)
-
-            image_file_live_opt.change(fn=toggle,
-                                       inputs=[image_file_live_opt],
-                                       outputs=[input_image, canvas],
-                                       queue=False)
-
+            input_image = gr.Image(source='upload', type="numpy")
             prompt = gr.Textbox(label="Prompt")
             run_button = gr.Button(label="Run")
             with gr.Accordion("Advanced options", open=False):
-                num_samples = gr.Slider(
-                    label="Images", minimum=1, maximum=2, value=1, step=1)
-                image_resolution = gr.Slider(
-                    label="Image Resolution", minimum=256, maximum=768, value=512, step=64)
-                strength = gr.Slider(
-                    label="Control Strength", minimum=0.0, maximum=2.0, value=1.0, step=0.01)
+                num_samples = gr.Slider(label="Images", minimum=1, maximum=12, value=1, step=1)
+                image_resolution = gr.Slider(label="Image Resolution", minimum=256, maximum=768, value=512, step=64)
+                strength = gr.Slider(label="Control Strength", minimum=0.0, maximum=2.0, value=1.0, step=0.01)
                 guess_mode = gr.Checkbox(label='Guess Mode', value=False)
-                landmark_direct_mode = gr.Checkbox(
-                    label='Input Landmark Directly', value=False)
-                ddim_steps = gr.Slider(
-                    label="Steps", minimum=1, maximum=100, value=20, step=1)
-                scale = gr.Slider(label="Guidance Scale",
-                                  minimum=0.1, maximum=30.0, value=9.0, step=0.1)
-                seed = gr.Slider(label="Seed", minimum=-1,
-                                 maximum=2147483647, step=1, randomize=True)
+                landmark_direct_mode = gr.Checkbox(label='Input Landmark Directly', value=False)
+                ddim_steps = gr.Slider(label="Steps", minimum=1, maximum=100, value=20, step=1)
+                scale = gr.Slider(label="Guidance Scale", minimum=0.1, maximum=30.0, value=9.0, step=0.1)
+                seed = gr.Slider(label="Seed", minimum=-1, maximum=2147483647, step=1, randomize=True)
                 eta = gr.Number(label="eta (DDIM)", value=0.0)
-                a_prompt = gr.Textbox(
-                    label="Added Prompt", value='best quality, extremely detailed')
+                a_prompt = gr.Textbox(label="Added Prompt", value='best quality, extremely detailed')
                 n_prompt = gr.Textbox(label="Negative Prompt",
                                       value='cartoon, disfigured, bad art, deformed, poorly drawn, extra limbs, weird colors, blurry, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality')
         with gr.Column():
-            result_gallery = gr.Gallery(
-                label='Output', show_label=False, elem_id="gallery").style(grid=2, height='auto')
-    ips = [input_image, prompt, a_prompt, n_prompt, num_samples, image_resolution,
-           ddim_steps, guess_mode, landmark_direct_mode, strength, scale, seed, eta]
-
-    gr.Examples(fn=process, examples=[
-        ["examples/image0.jpg", "a silly clown face", "best quality, extremely detailed",
-            "cartoon, disfigured, bad art, deformed, poorly drawn, extra limbs, weird colors, blurry, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality", 1, 512, 20, False, False, 1.0, 9.0, -1, 0.0],
-        ["examples/image1.png", "a photo of a woman wearing glasses", "best quality, extremely detailed",
-            "cartoon, disfigured, bad art, deformed, poorly drawn, extra limbs, weird colors, blurry, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality", 1, 512, 20, False, False, 1.0, 9.0, -1, 0.0],
-        ["examples/image2.png", "a silly portrait of man with head tilted and a beautiful hair on the side", "best quality, extremely detailed",
-            "cartoon, disfigured, bad art, deformed, poorly drawn, extra limbs, weird colors, blurry, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality", 1, 512, 20, False, False, 1.0, 9.0, -1, 0.0],
-        ["examples/image3.png", "portrait handsome men", "best quality, extremely detailed",
-            "cartoon, disfigured, bad art, deformed, poorly drawn, extra limbs, weird colors, blurry, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality", 1, 512, 20, False, False, 1.0, 9.0, -1, 0.0],
-        ["examples/image4.jpg", "a beautiful  woman looking at the sky", "best quality, extremely detailed",
-            "cartoon, disfigured, bad art, deformed, poorly drawn, extra limbs, weird colors, blurry, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality", 1, 512, 20, False, False, 1.0, 9.0, -1, 0.0],
-    ], inputs=ips, outputs=[result_gallery], cache_examples=True)
-    run_button.click(fn=process, inputs=ips + [image_file_live_opt],
-                     outputs=[result_gallery], _js=get_js_image)
-    block.load(None, None, None, _js=load_js)
-
-
-block.launch()
+            result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery").style(grid=2, height='auto')
+    ips = [input_image, prompt, a_prompt, n_prompt, num_samples, image_resolution, ddim_steps, guess_mode, landmark_direct_mode, strength, scale, seed, eta]
+    run_button.click(fn=process, inputs=ips, outputs=[result_gallery])
+
+
+block.launch(server_name='0.0.0.0')