Update app.py

app.py

@@ -7,7 +7,6 @@ import numpy as np
 import torch
 from diffusers import FluxControlNetModel
 from diffusers.pipelines import FluxControlNetPipeline
-from gradio_imageslider import ImageSlider
 from PIL import Image
 from huggingface_hub import snapshot_download
 
@@ -18,6 +17,7 @@ css = """
 }
 """
 
+# Check for GPU availability
 if torch.cuda.is_available():
     power_device = "GPU"
     device = "cuda"
@@ -25,11 +25,11 @@ else:
     power_device = "CPU"
     device = "cpu"
 
+# Load HuggingFace model
 huggingface_token = os.getenv("HUGGINFACE_TOKEN")
-
 model_path = snapshot_download(
-    repo_id="black-forest-labs/FLUX.1-dev", 
-    repo_type="model", 
+    repo_id="black-forest-labs/FLUX.1-dev",
+    repo_type="model",
     ignore_patterns=["*.md", "*..gitattributes"],
     local_dir="FLUX.1-dev",
     token=huggingface_token,
@@ -47,7 +47,7 @@ pipe.to(device)
 MAX_SEED = 1000000
 MAX_PIXEL_BUDGET = 1024 * 1024
 
-def process_input(input_image, upscale_factor, **kwargs):
+def process_input(input_image, upscale_factor):
     w, h = input_image.size
     w_original, h_original = w, h
     aspect_ratio = w / h
@@ -57,9 +57,6 @@ def process_input(input_image, upscale_factor, **kwargs):
         warnings.warn(
             f"Requested output image is too large ({w * upscale_factor}x{h * upscale_factor}). Resizing to ({int(aspect_ratio * MAX_PIXEL_BUDGET ** 0.5 // upscale_factor), int(MAX_PIXEL_BUDGET ** 0.5 // aspect_ratio // upscale_factor)}) pixels."
         )
-        gr.Info(
-            f"Requested output image is too large ({w * upscale_factor}x{h * upscale_factor}). Resizing input to ({int(aspect_ratio * MAX_PIXEL_BUDGET ** 0.5 // upscale_factor), int(MAX_PIXEL_BUDGET ** 0.5 // aspect_ratio // upscale_factor)}) pixels budget."
-        )
         input_image = input_image.resize(
             (
                 int(aspect_ratio * MAX_PIXEL_BUDGET**0.5 // upscale_factor),
@@ -68,35 +65,31 @@ def process_input(input_image, upscale_factor, **kwargs):
         )
         was_resized = True
 
+    # Resize to multiple of 8
     w, h = input_image.size
     w = w - w % 8
     h = h - h % 8
-
     return input_image.resize((w, h)), w_original, h_original, was_resized
 
-@spaces.GPU
 def infer(
-    seed,
-    randomize_seed,
-    input_image,
-    num_inference_steps,
-    upscale_factor,
-    controlnet_conditioning_scale,
-    progress=gr.Progress(track_tqdm=True),
+    seed, randomize_seed, input_image_path, num_inference_steps, upscale_factor, controlnet_conditioning_scale
 ):
+    # Load image
+    input_image = Image.open(input_image_path)
+    
+    # Handle random seed if specified
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
+    
     true_input_image = input_image
-    input_image, w_original, h_original, was_resized = process_input(
-        input_image, upscale_factor
-    )
-
+    input_image, w_original, h_original, was_resized = process_input(input_image, upscale_factor)
+    
+    # Rescale with upscale factor
     w, h = input_image.size
     control_image = input_image.resize((w * upscale_factor, h * upscale_factor))
-
     generator = torch.Generator().manual_seed(seed)
 
-    gr.Info("Upscaling image...")
+    # Upscale
     image = pipe(
         prompt="",
         control_image=control_image,
@@ -108,103 +101,37 @@ def infer(
         generator=generator,
     ).images[0]
 
+    # Resize output if initially resized
     if was_resized:
-        gr.Info(
-            f"Resizing output image to targeted {w_original * upscale_factor}x{h_original * upscale_factor} size."
-        )
-
-    image = image.resize((w_original * upscale_factor, h_original * upscale_factor))
+        image = image.resize((w_original * upscale_factor, h_original * upscale_factor))
     image.save("output.jpg")
+    
+    return true_input_image, image, seed
 
-    return [true_input_image, image, seed]
-
+# Gradio setup without ImageSlider
 with gr.Blocks(css=css) as demo:
     gr.Markdown(
         f"""
     # ⚡ Flux.1-dev Upscaler ControlNet ⚡
-    This is an interactive demo of [Flux.1-dev Upscaler ControlNet](https://huggingface.co/jasperai/Flux.1-dev-Controlnet-Upscaler) taking as input a low resolution image to generate a high resolution image.
-    Currently running on {power_device}.
-
-    *Note*: Even though the model can handle higher resolution images, due to GPU memory constraints, this demo was limited to a generated output not exceeding a pixel budget of 1024x1024. If the requested size exceeds that limit, the input will be first resized keeping the aspect ratio such that the output of the controlNet model does not exceed the allocated pixel budget. The output is then resized to the targeted shape using a simple resizing. This may explain some artifacts for high resolution input. To adress this, run the demo locally or consider implementing a tiling strategy. Happy upscaling! 🚀
+    This is an interactive demo of [Flux.1-dev Upscaler ControlNet](https://huggingface.co/jasperai/Flux.1-dev-Controlnet-Upscaler).
     """
     )
 
-    with gr.Row():
-        run_button = gr.Button(value="Run")
-
-    with gr.Row():
-        with gr.Column(scale=4):
-            input_im = gr.Image(label="Input Image", type="pil")
-        with gr.Column(scale=1):
-            num_inference_steps = gr.Slider(
-                label="Number of Inference Steps",
-                minimum=8,
-                maximum=50,
-                step=1,
-                value=28,
-            )
-            upscale_factor = gr.Slider(
-                label="Upscale Factor",
-                minimum=1,
-                maximum=4,
-                step=1,
-                value=4,
-            )
-            controlnet_conditioning_scale = gr.Slider(
-                label="Controlnet Conditioning Scale",
-                minimum=0.1,
-                maximum=1.5,
-                step=0.1,
-                value=0.6,
-            )
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=42,
-            )
-
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-
-    with gr.Row():
-        result = ImageSlider(label="Input / Output", type="pil", interactive=True)
-
-    examples = gr.Examples(
-        examples=[
-            [42, False, "examples/image_2.jpg", 28, 4, 0.6],
-            [42, False, "examples/image_4.jpg", 28, 4, 0.6],
-        ],
-        inputs=[
-            seed,
-            randomize_seed,
-            input_im,
-            num_inference_steps,
-            upscale_factor,
-            controlnet_conditioning_scale,
-        ],
-        fn=infer,
-        outputs=result,
-        cache_examples="lazy",
-    )
-
-    gr.Markdown("**Disclaimer:**")
-    gr.Markdown(
-        "This demo is only for research purpose. Jasper cannot be held responsible for the generation of NSFW (Not Safe For Work) content through the use of this demo. Users are solely responsible for any content they create, and it is their obligation to ensure that it adheres to appropriate and ethical standards. Jasper provides the tools, but the responsibility for their use lies with the individual user."
-    )
-    gr.on(
-        [run_button.click],
-        fn=infer,
-        inputs=[
-            seed,
-            randomize_seed,
-            input_im,
-            num_inference_steps,
-            upscale_factor,
-            controlnet_conditioning_scale,
-        ],
-        outputs=result,
-        show_api=True,
+    run_button = gr.Button(value="Run")
+    input_im = gr.Image(label="Input Image", type="filepath")
+    num_inference_steps = gr.Slider(label="Number of Inference Steps", minimum=8, maximum=50, step=1, value=28)
+    upscale_factor = gr.Slider(label="Upscale Factor", minimum=1, maximum=4, step=1, value=4)
+    controlnet_conditioning_scale = gr.Slider(label="Controlnet Conditioning Scale", minimum=0.1, maximum=1.5, step=0.1, value=0.6)
+    seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=42)
+    randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+    
+    input_image_display = gr.Image(label="Input Image Display")
+    output_image_display = gr.Image(label="Upscaled Image Display")
+
+    run_button.click(
+        infer,
+        inputs=[seed, randomize_seed, input_im, num_inference_steps, upscale_factor, controlnet_conditioning_scale],
+        outputs=[input_image_display, output_image_display, gr.Textbox(label="Used Seed")]
     )
 
-demo.queue().launch(share=True, show_api=True, show_error=True)
+demo.queue().launch(share=False, show_api=True, show_error=True)