more

app.py

@@ -28,6 +28,7 @@ print(f"low memory: {LOW_MEMORY}")
 
 
 model = "stabilityai/stable-diffusion-xl-base-1.0"
+# model = "stabilityai/sdxl-turbo"
 # vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=dtype)
 scheduler = DDIMScheduler.from_pretrained(model, subfolder="scheduler")
 controlnet = ControlNetModel.from_pretrained(
@@ -132,12 +133,11 @@ with gr.Blocks(css=css) as demo:
     gr.Markdown(
         """
 # Enhance This  
-### DemoFusion SDXL
+### HiDiffusion SDXL
 
-[DemoFusion](https://ruoyidu.github.io/demofusion/demofusion.html) enables higher-resolution image generation.  
+[HiDiffusion](https://github.com/megvii-research/HiDiffusion) enables higher-resolution image generation.  
 You can upload an initial image and prompt to generate an enhanced version. 
-[Duplicate Space](https://huggingface.co/spaces/radames/Enhance-This-DemoFusion-SDXL?duplicate=true) to avoid the queue.  
-GPU Time Comparison: T4: ~276s - A10G: ~113.6s A100: ~43.5s RTX 4090: ~48.1s
+[Duplicate Space](https://huggingface.co/spaces/radames/Enhance-This-HiDiffusion-SDXL?duplicate=true) to avoid the queue.  
 
 <small>
 <b>Notes</b> The author advises against the term "super resolution" because it's more like image-to-image generation than enhancement, but it's still a lot of fun!
@@ -179,7 +179,7 @@ GPU Time Comparison: T4: ~276s - A10G: ~113.6s A100: ~43.5s RTX 4090: ~48.1s
                     value=2,
                     step=1,
                     label="Magnification Scale",
-                    # interactive=False,
+                    interactive=False,
                 )
                 controlnet_conditioning_scale = gr.Slider(
                     minimum=0,
@@ -212,7 +212,8 @@ GPU Time Comparison: T4: ~276s - A10G: ~113.6s A100: ~43.5s RTX 4090: ~48.1s
 
             btn = gr.Button()
         with gr.Column(scale=2):
-            image_slider = ImageSlider(position=0.5)
+            with gr.Group():
+                image_slider = ImageSlider(position=0.5)
     inputs = [
         image_input,
         prompt,
@@ -226,7 +227,9 @@ GPU Time Comparison: T4: ~276s - A10G: ~113.6s A100: ~43.5s RTX 4090: ~48.1s
         controlnet_end,
     ]
     outputs = [image_slider]
-    btn.click(predict, inputs=inputs, outputs=outputs, concurrency_limit=1)
+    btn.click(lambda x: None, inputs=None, outputs=image_slider).then(
+        predict, inputs=inputs, outputs=outputs, concurrency_limit=1
+    )
     gr.Examples(
         fn=predict,
         examples=[
@@ -297,7 +300,7 @@ GPU Time Comparison: T4: ~276s - A10G: ~113.6s A100: ~43.5s RTX 4090: ~48.1s
                 5532144938416372000,
                 0.101,
                 25.206,
-                4.64,
+                4,
                 0.8,
                 0.0,
                 1.0,