Update app.py

app.py

@@ -44,7 +44,7 @@ async def gen(prompt, basemodel, width, height, scales, steps, seed, upscale_fac
     model = enable_lora(lora_model, basemodel) if process_lora else basemodel
     image, seed = await generate_image(prompt, model, "", width, height, scales, steps, seed)
     if image is None:
-        return [None, None, None]
+        return [None, None]
     
     image_path = "temp_image.jpg"
     image.save(image_path, format="JPEG")
@@ -54,17 +54,17 @@ async def gen(prompt, basemodel, width, height, scales, steps, seed, upscale_fac
         if upscale_image_path is not None:
             upscale_image = Image.open(upscale_image_path)
             upscale_image.save("upscale_image.jpg", format="JPEG")
-            return [image_path, "upscale_image.jpg", seed]
+            return [image_path, "upscale_image.jpg"]
         else:
             print("Error: The scaled image path is None")
-            return [image_path, image_path, seed]
+            return [image_path, image_path]
     else:
-        return [image_path, image_path, seed]
+        return [image_path, image_path]
 
 css = """
 #col-container{ margin: 0 auto; max-width: 1024px;}
 """
-# Update the interface
+
 with gr.Blocks(css=css) as demo:
     with gr.Column(elem_id="col-container"):
         with gr.Row():
@@ -72,24 +72,20 @@ with gr.Blocks(css=css) as demo:
                 output_res = ImageSlider(label="Flux / Upscaled")
             with gr.Column(scale=2):
                 prompt = gr.Textbox(label="Image Description")
-                basemodel_choice = gr.Dropdown(label="Model", choices=["black-forest-labs/FLUX.1-schnell", "Shakker-Labs/FLUX.1-dev-LoRA-add-details"], value="black-forest-labs/FLUX.1-schnell")
-                lora_model_choice = gr.Dropdown(label="LoRA", choices=["XLabs-AI/flux-RealismLora"], value="XLabs-AI/flux-RealismLora")
+                basemodel_choice = gr.Dropdown(label="Model", choices=["black-forest-labs/FLUX.1-schnell", "black-forest-labs/FLUX.1-DEV", "enhanceaiteam/Flux-uncensored", "Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro", "Shakker-Labs/FLUX.1-dev-LoRA-add-details", "city96/FLUX.1-dev-gguf"], value="black-forest-labs/FLUX.1-schnell")
+                lora_model_choice = gr.Dropdown(label="LoRA", choices=["Shakker-Labs/FLUX.1-dev-LoRA-add-details", "XLabs-AI/flux-RealismLora", "enhanceaiteam/Flux-uncensored"], value="XLabs-AI/flux-RealismLora")
                 process_lora = gr.Checkbox(label="LoRA Process")
                 process_upscale = gr.Checkbox(label="Scale Process")
                 upscale_factor = gr.Radio(label="Scaling Factor", choices=[2, 4, 8], value=2)
                 
                 with gr.Accordion(label="Advanced Options", open=False):
                     width = gr.Slider(label="Width", minimum=512, maximum=1280, step=8, value=1280)
-                    height = gr.Slider(label="Height", minimum=512, maximum=1280, step=8, value=1280)
-                    scales = gr.Slider(label="Scale", minimum=1, maximum=20, step=1, value=4)
-                    steps = gr.Slider(label="Steps", minimum=1, maximum=100, step=1, value=4)
+                    height = gr.Slider(label="Height", minimum=512, maximum=1280, step=8, value=768)
+                    scales = gr.Slider(label="Scale", minimum=1, maximum=20, step=1, value=8)
+                    steps = gr.Slider(label="Steps", minimum=1, maximum=100, step=1, value=8)
                     seed = gr.Number(label="Seed", value=-1)
+                    print(seed)
     
                 btn = gr.Button("Generate")
-                seed_output = gr.Textbox(label="Seed Value")  # Added Textbox for Seed Output
-                btn.click(
-                    fn=gen,
-                    inputs=[prompt, basemodel_choice, width, height, scales, steps, seed, upscale_factor, process_upscale, lora_model_choice, process_lora],
-                    outputs=[output_res, seed_output],  # Updated outputs to include seed
-                )
-    demo.launch()
+                btn.click(fn=gen, inputs=[prompt, basemodel_choice, width, height, scales, steps, seed, upscale_factor, process_upscale, lora_model_choice, process_lora], outputs=output_res,)
+    demo.launch()