Update app.py

app.py

@@ -9,6 +9,8 @@ from pytorch_lightning import seed_everything
 from util import resize_image, HWC3, apply_canny
 from ldm.models.diffusion.ddim import DDIMSampler
 
+from annotator.openpose import apply_openpose
+
 from cldm.model import create_model, load_state_dict
 
 from huggingface_hub import hf_hub_url, cached_download
@@ -54,7 +56,36 @@ def process_canny(input_image, prompt, a_prompt, n_prompt, num_samples, image_re
         results = [x_samples[i] for i in range(num_samples)]
     return [255 - detected_map] + results
 
+def process_pose(input_image, prompt, a_prompt, n_prompt, num_samples, image_resolution, detect_resolution, ddim_steps, scale, seed, eta):
+    with torch.no_grad():
+        input_image = HWC3(input_image)
+        detected_map, _ = apply_openpose(resize_image(input_image, detect_resolution))
+        detected_map = HWC3(detected_map)
+        img = resize_image(input_image, image_resolution)
+        H, W, C = img.shape
+
+        detected_map = cv2.resize(detected_map, (W, H), interpolation=cv2.INTER_NEAREST)
+
+        control = torch.from_numpy(detected_map.copy()).float() / 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()
+
+        seed_everything(seed)
+
+        cond = {"c_concat": [control], "c_crossattn": [model.get_learned_conditioning([prompt + ', ' + a_prompt] * num_samples)]}
+        un_cond = {"c_concat": [control], "c_crossattn": [model.get_learned_conditioning([n_prompt] * num_samples)]}
+        shape = (4, H // 8, W // 8)
+
+        samples, intermediates = ddim_sampler.sample(ddim_steps, num_samples,
+                                                     shape, cond, verbose=False, eta=eta,
+                                                     unconditional_guidance_scale=scale,
+                                                     unconditional_conditioning=un_cond)
+        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)
 
+        results = [x_samples[i] for i in range(num_samples)]
+    return [detected_map] + results
+    
 block = gr.Blocks().queue()
 control_task_list = [
     "Canny Edge Map",
@@ -65,7 +96,7 @@ with block:
     with gr.Row():
         with gr.Column():
             input_image = gr.Image(source='upload', type="numpy")
-            input_control = gr.Dropdown(control_list, value="Canny Edge Map", label="Control Task"),
+            input_control = gr.Dropdown(control_task_list, value="Canny Edge Map", label="Control Task"),
             prompt = gr.Textbox(label="Prompt")
             run_button = gr.Button(label="Run")
             with gr.Accordion("Advanced options", open=False):