fix button label kwarg

app_openpose.py

@@ -18,7 +18,7 @@ def create_demo(process):
             with gr.Column():
                 image = gr.Image()
                 prompt = gr.Textbox(label="Prompt")
-                run_button = gr.Button(label="Run")
+                run_button = gr.Button("Run")
                 with gr.Accordion("Advanced options", open=False):
                     preprocessor_name = gr.Radio(
                         label="Preprocessor", choices=["Openpose", "None"], type="value", value="Openpose"

model.py

@@ -47,11 +47,11 @@ class Model:
             unet.set_adapter(task_name)
             return self.pipe
         unet: UNet2DConditionModelEx = UNet2DConditionModelEx.from_pretrained(
-            base_model_id, subfolder="unet", torch_dtype=torch.float16
+            base_model_id, subfolder="unet", torch_dtype=torch.float16 if self.device.type == "cuda" else torch.float32
         )
         unet.add_extra_conditions(["Placeholder"])
         pipe: StableDiffusionControlLoraV3Pipeline = StableDiffusionControlLoraV3Pipeline.from_pretrained(
-            base_model_id, safety_checker=None, unet=unet, torch_dtype=torch.float16
+            base_model_id, safety_checker=None, unet=unet, torch_dtype=torch.float16 if self.device.type == "cuda" else torch.float32
         )
         for _task_name, subfolder in CONTROL_LORA_V3_MODEL_IDS.items():
             pipe.load_lora_weights("HighCWu/control-lora-v3", adapter_name=_task_name, subfolder=subfolder)
@@ -92,7 +92,6 @@ class Model:
             prompt = f"{prompt}, {additional_prompt}"
         return prompt
 
-    # @torch.autocast("cuda")
     def run_pipe(
         self,
         prompt: str,
@@ -103,16 +102,20 @@ class Model:
         guidance_scale: float,
         seed: int,
     ) -> list[PIL.Image.Image]:
-        generator = torch.Generator().manual_seed(seed)
-        return self.pipe(
-            prompt=prompt,
-            negative_prompt=negative_prompt,
-            guidance_scale=guidance_scale,
-            num_images_per_prompt=num_images,
-            num_inference_steps=num_steps,
-            generator=generator,
-            image=control_image,
-        ).images
+        def run():
+            generator = torch.Generator().manual_seed(seed)
+            return self.pipe(
+                prompt=prompt,
+                negative_prompt=negative_prompt,
+                guidance_scale=guidance_scale,
+                num_images_per_prompt=num_images,
+                num_inference_steps=num_steps,
+                generator=generator,
+                image=control_image,
+            ).images
+        if self.device.type == "cuda":
+            run = torch.autocast("cuda")(run)
+        return run()
 
     @torch.inference_mode()
     def process_canny(