Update app.py

app.py

@@ -63,7 +63,7 @@ def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, guidan
             good_vae=good_vae,  # Assuming good_vae is defined elsewhere
             joint_attention_kwargs=joint_attention_kwargs,  # Fixed parameter name
         ):
-            yield img, seed, seed
+            yield img, seed
     finally:
         # Unload LoRA weights if they were loaded
         if lora_id:
@@ -93,57 +93,159 @@ css = """
 }
 """
 
-with gr.Blocks(css=css) as app:
-    gr.HTML("<center><h1>FLUX.1-Dev with LoRA support</h1></center>")
+# with gr.Blocks(css=css) as app:
+#     gr.HTML("<center><h1>FLUX.1-Dev with LoRA support</h1></center>")
+#     with gr.Column(elem_id="col-container"):
+#         with gr.Row():
+#             with gr.Column():
+#                 with gr.Row():
+#                     text_prompt = gr.Textbox(label="Prompt", placeholder="Enter a prompt here", lines=3, elem_id="prompt-text-input")
+#                 with gr.Row():
+#                     custom_lora = gr.Textbox(label="Custom LoRA", info="LoRA Hugging Face path (optional)", placeholder="multimodalart/vintage-ads-flux")
+#                 with gr.Row():
+#                     with gr.Accordion("Advanced Settings", open=False):
+#                         lora_scale = gr.Slider(
+#                             label="LoRA Scale",
+#                             minimum=0,
+#                             maximum=2,
+#                             step=0.01,
+#                             value=0.95,
+#                         )
+#                         with gr.Row():
+#                             width = gr.Slider(label="Width", value=1024, minimum=64, maximum=1216, step=8)
+#                             height = gr.Slider(label="Height", value=1024, minimum=64, maximum=1216, step=8)
+#                         seed = gr.Slider(label="Seed", value=-1, minimum=-1, maximum=4294967296, step=1)
+#                         randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+#                         with gr.Row():
+#                             steps = gr.Slider(label="Inference steps steps", value=28, minimum=1, maximum=100, step=1)
+#                             cfg = gr.Slider(label="Guidance Scale", value=3.5, minimum=1, maximum=20, step=0.5)
+#                         # method = gr.Radio(label="Sampling method", value="DPM++ 2M Karras", choices=["DPM++ 2M Karras", "DPM++ SDE Karras", "Euler", "Euler a", "Heun", "DDIM"])
+
+#                 with gr.Row():
+#                     # text_button = gr.Button("Run", variant='primary', elem_id="gen-button")
+#                     text_button = gr.Button("✨ Generate Image", variant='primary', elem_classes=["generate-btn"])
+#             with gr.Column():
+#                 with gr.Row():
+#                     image_output = gr.Image(type="pil", label="Image Output", elem_id="gallery")
+#                 with gr.Row():
+#                     seed_output = gr.Textbox(label="Seed Used", show_copy_button = True)
+        
+#         # gr.Markdown(article_text)
+#         with gr.Column():
+#             gr.Examples(
+#                 examples = examples,
+#                 inputs = [text_prompt],
+#             )
+#     gr.on(
+#         triggers=[text_button.click, text_prompt.submit],
+#         fn = infer,
+#         inputs=[text_prompt, seed, randomize_seed, width, height, cfg, steps, custom_lora, lora_scale], 
+#         outputs=[image_output,seed_output, seed]
+#     )
+        
+#         # text_button.click(query, inputs=[custom_lora, text_prompt, steps, cfg, randomize_seed, seed, width, height], outputs=[image_output,seed_output, seed])
+#         # text_button.click(infer, inputs=[text_prompt, seed, randomize_seed, width, height, cfg, steps, custom_lora, lora_scale], outputs=[image_output,seed_output, seed])
+
+# app.launch(share=True)
+
+
+with gr.Blocks(css=css) as demo:
+    
     with gr.Column(elem_id="col-container"):
+        gr.Markdown(f"""# FLUX.1 [dev] LoRA
+12B param rectified flow transformer guidance-distilled from [FLUX.1 [pro]](https://blackforestlabs.ai/)  
+[[non-commercial license](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md)] [[blog](https://blackforestlabs.ai/announcing-black-forest-labs/)] [[model](https://huggingface.co/black-forest-labs/FLUX.1-dev)]
+        """)
+        
         with gr.Row():
-            with gr.Column():
-                with gr.Row():
-                    text_prompt = gr.Textbox(label="Prompt", placeholder="Enter a prompt here", lines=3, elem_id="prompt-text-input")
-                with gr.Row():
-                    custom_lora = gr.Textbox(label="Custom LoRA", info="LoRA Hugging Face path (optional)", placeholder="multimodalart/vintage-ads-flux")
-                with gr.Row():
-                    with gr.Accordion("Advanced Settings", open=False):
-                        lora_scale = gr.Slider(
-                            label="LoRA Scale",
-                            minimum=0,
-                            maximum=2,
-                            step=0.01,
-                            value=0.95,
-                        )
-                        with gr.Row():
-                            width = gr.Slider(label="Width", value=1024, minimum=64, maximum=1216, step=8)
-                            height = gr.Slider(label="Height", value=1024, minimum=64, maximum=1216, step=8)
-                        seed = gr.Slider(label="Seed", value=-1, minimum=-1, maximum=4294967296, step=1)
-                        randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-                        with gr.Row():
-                            steps = gr.Slider(label="Inference steps steps", value=28, minimum=1, maximum=100, step=1)
-                            cfg = gr.Slider(label="Guidance Scale", value=3.5, minimum=1, maximum=20, step=0.5)
-                        # method = gr.Radio(label="Sampling method", value="DPM++ 2M Karras", choices=["DPM++ 2M Karras", "DPM++ SDE Karras", "Euler", "Euler a", "Heun", "DDIM"])
-
-                with gr.Row():
-                    # text_button = gr.Button("Run", variant='primary', elem_id="gen-button")
-                    text_button = gr.Button("✨ Generate Image", variant='primary', elem_classes=["generate-btn"])
-            with gr.Column():
-                with gr.Row():
-                    image_output = gr.Image(type="pil", label="Image Output", elem_id="gallery")
-                with gr.Row():
-                    seed_output = gr.Textbox(label="Seed Used", show_copy_button = True)
+            
+            prompt = gr.Text(
+                label="Prompt",
+                show_label=False,
+                max_lines=1,
+                placeholder="Enter your prompt",
+                container=False,
+            )
+            
+            run_button = gr.Button("Run", scale=0)
+        
+        result = gr.Image(label="Result", show_label=False)
         
-        # gr.Markdown(article_text)
-        with gr.Column():
-            gr.Examples(
-                examples = examples,
-                inputs = [text_prompt],
+        with gr.Accordion("Advanced Settings", open=False):
+            
+            seed = gr.Slider(
+                label="Seed",
+                minimum=0,
+                maximum=MAX_SEED,
+                step=1,
+                value=0,
             )
+            
+            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+            
+            with gr.Row():
+                
+                width = gr.Slider(
+                    label="Width",
+                    minimum=256,
+                    maximum=MAX_IMAGE_SIZE,
+                    step=8,
+                    value=1024,
+                )
+                
+                height = gr.Slider(
+                    label="Height",
+                    minimum=256,
+                    maximum=MAX_IMAGE_SIZE,
+                    step=8,
+                    value=1024,
+                )
+            
+            with gr.Row():
+
+                guidance_scale = gr.Slider(
+                    label="Guidance Scale",
+                    minimum=1,
+                    maximum=15,
+                    step=0.1,
+                    value=3.5,
+                )
+  
+                num_inference_steps = gr.Slider(
+                    label="Number of inference steps",
+                    minimum=1,
+                    maximum=50,
+                    step=1,
+                    value=28,
+                )
+
+            with gr.Row():
+                lora_id = gr.Textbox(
+                    label="LoRA Model ID (HuggingFace path)",
+                    placeholder="username/lora-model",
+                    max_lines=1
+                )
+                lora_scale = gr.Slider(
+                    label="LoRA Scale",
+                    minimum=0,
+                    maximum=2,
+                    step=0.01,
+                    value=0.95,
+                )
+        
+        gr.Examples(
+            examples = examples,
+            fn = infer,
+            inputs = [prompt],
+            outputs = [result, seed],
+            cache_examples="lazy"
+        )
+
     gr.on(
-        triggers=[text_button.click, text_prompt.submit],
+        triggers=[run_button.click, prompt.submit],
         fn = infer,
-        inputs=[text_prompt, seed, randomize_seed, width, height, cfg, steps, custom_lora, lora_scale], 
-        outputs=[image_output,seed_output, seed]
+        inputs = [prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps,lora_id,lora_scale],
+        outputs = [result, seed]
     )
-        
-        # text_button.click(query, inputs=[custom_lora, text_prompt, steps, cfg, randomize_seed, seed, width, height], outputs=[image_output,seed_output, seed])
-        # text_button.click(infer, inputs=[text_prompt, seed, randomize_seed, width, height, cfg, steps, custom_lora, lora_scale], outputs=[image_output,seed_output, seed])
 
-app.launch(share=True)
+demo.launch()