de

app.py

@@ -89,24 +89,23 @@ def resize_image_old(image):
 
 
 @spaces.GPU
-def generate_(prompt, negative_prompt, pose_image, input_image, controlnet_conditioning_scale):
-    generator = torch.Generator()
-    generator.manual_seed(random.randint(0, 2147483647))
+def generate_(prompt, negative_prompt, pose_image, input_image, num_steps, controlnet_conditioning_scale, seed):
+    generator = torch.Generator("cuda").manual_seed(seed)    
     images = pipe(
-    prompt, negative_prompt=negative_prompt, image=pose_image, num_inference_steps=20, controlnet_conditioning_scale=float(controlnet_conditioning_scale),
+    prompt, negative_prompt=negative_prompt, image=pose_image, num_inference_steps=num_steps, controlnet_conditioning_scale=float(controlnet_conditioning_scale),
     generator=generator, height=input_image.size[1], width=input_image.size[0],
     ).images 
     return images
 
 @spaces.GPU
-def process(input_image, prompt, negative_prompt, controlnet_conditioning_scale):
+def process(input_image, prompt, negative_prompt, num_steps, controlnet_conditioning_scale, seed):
     
     # resize input_image to 1024x1024
     input_image = resize_image(input_image)
     
     pose_image = openpose(input_image, include_body=True, include_hand=True, include_face=True)
   
-    images = generate_(prompt, negative_prompt, pose_image, input_image, controlnet_conditioning_scale)
+    images = generate_(prompt, negative_prompt, pose_image, input_image, num_steps, controlnet_conditioning_scale, seed)
 
     return [pose_image,images[0]]
 
@@ -247,12 +246,21 @@ block = gr.Blocks().queue()
 
 with block:
     gr.Markdown("## BRIA 2.3 ControlNet Pose")
+    gr.HTML('''
+      <p style="margin-bottom: 10px; font-size: 94%">
+        This is a demo for ControlNet Pose that using
+        <a href="https://huggingface.co/briaai/BRIA-2.3" target="_blank">BRIA 2.3 text-to-image model</a> as backbone. 
+        Trained on licensed data, BRIA 2.3 provide full legal liability coverage for copyright and privacy infringement.
+      </p>
+    ''')
     with gr.Row():
         with gr.Column():
             input_image = gr.Image(sources=None, type="pil") # None for upload, ctrl+v and webcam
             prompt = gr.Textbox(label="Prompt")
             negative_prompt = gr.Textbox(label="Negative prompt", value="Logo,Watermark,Text,Ugly,Morbid,Extra fingers,Poorly drawn hands,Mutation,Blurry,Extra limbs,Gross proportions,Missing arms,Mutated hands,Long neck,Duplicate,Mutilated,Mutilated hands,Poorly drawn face,Deformed,Bad anatomy,Cloned face,Malformed limbs,Missing legs,Too many fingers")
+            num_steps = gr.Slider(label="Number of steps", minimum=25, maximum=100, value=50, step=1)
             controlnet_conditioning_scale = gr.Slider(label="ControlNet conditioning scale", minimum=0.1, maximum=2.0, value=1.0, step=0.05)
+            seed = gr.Slider(label="Seed", minimum=0, maximum=2147483647, step=1, randomize=True,)
             run_button = gr.Button(value="Run")
                         
         with gr.Column():
@@ -260,7 +268,8 @@ with block:
                 pose_image_output = gr.Image(label="Pose Image", type="pil", interactive=False)
                 generated_image_output = gr.Image(label="Generated Image", type="pil", interactive=False)
 
-    run_button.click(fn=process, inputs=[input_image, prompt, negative_prompt, controlnet_conditioning_scale], outputs=[pose_image_output, generated_image_output])
+    ips = [input_image, prompt, negative_prompt, num_steps, controlnet_conditioning_scale, seed]
+    run_button.click(fn=process, inputs=ips, outputs=[pose_image_output, generated_image_output])
 
 
 block.launch(debug = True)