Update app.py

app.py

@@ -7,7 +7,6 @@ from diffusers import  DiffusionPipeline, FlowMatchEulerDiscreteScheduler, Autoe
 from transformers import CLIPTextModel, CLIPTokenizer,T5EncoderModel, T5TokenizerFast
 from live_preview_helpers import calculate_shift, retrieve_timesteps, flux_pipe_call_that_returns_an_iterable_of_images
 
-
 dtype = torch.bfloat16
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
@@ -21,20 +20,6 @@ MAX_IMAGE_SIZE = 2048
 
 pipe.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_that_returns_an_iterable_of_images.__get__(pipe)
 
-article_text = """
-<div style="text-align: center;">
-    <p>Enjoying the tool? Buy me a coffee and get exclusive prompt guides!</p>
-    <p><i>Instantly unlock helpful tips for creating better prompts!</i></p>
-    <div style="display: flex; justify-content: center;">
-        <a href="https://piczify.lemonsqueezy.com/buy/0f5206fa-68e8-42f6-9ca8-4f80c587c83e">
-            <img src="https://www.buymeacoffee.com/assets/img/custom_images/yellow_img.png" 
-                 alt="Buy Me a Coffee" 
-                 style="height: 40px; width: auto; box-shadow: 0 4px 8px rgba(0, 0, 0, 0.2); border-radius: 10px;">
-        </a>
-    </div>
-</div>
-"""
-
 @spaces.GPU()
 def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, guidance_scale=3.5, num_inference_steps=28, lora_id=None, lora_scale=0.95, progress=gr.Progress(track_tqdm=True)):
     if randomize_seed:
@@ -81,128 +66,117 @@ def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, guidan
         # Unload LoRA weights if they were loaded
         if lora_id:
             pipe.unload_lora_weights()
-
-# def query(lora_id, prompt, steps=28, cfg_scale=3.5, randomize_seed=True, seed=-1, width=1024, height=1024):
-#     if prompt == "" or prompt == None:
-#         return None
-
-#     if lora_id.strip() == "" or lora_id == None:
-#         lora_id = "black-forest-labs/FLUX.1-dev" 
-
-#     key = random.randint(0, 999)
-
-#     API_URL = "https://api-inference.huggingface.co/models/"+ lora_id.strip()
-    
-#     API_TOKEN = random.choice([os.getenv("HF_READ_TOKEN")])
-#     headers = {"Authorization": f"Bearer {API_TOKEN}"}
-    
-#     # prompt = GoogleTranslator(source='ru', target='en').translate(prompt)
-#     # print(f'\033[1mGeneration {key} translation:\033[0m {prompt}')
-
-#     prompt = f"{prompt} | ultra detail, ultra elaboration, ultra quality, perfect."
-#     # print(f'\033[1mGeneration {key}:\033[0m {prompt}')
-
-#     # If seed is -1, generate a random seed and use it
-#     if randomize_seed:
-#         seed = random.randint(1, 4294967296)
-    
-#     payload = {
-#         "inputs": prompt,
-#         "steps": steps,
-#         "cfg_scale": cfg_scale,
-#         "seed": seed,
-#         "parameters": {
-#             "width": width,  # Pass the width to the API
-#             "height": height  # Pass the height to the API
-#         }
-#     }
-
-#     response = requests.post(API_URL, headers=headers, json=payload, timeout=timeout)
-#     if response.status_code != 200:
-#         print(f"Error: Failed to get image. Response status: {response.status_code}")
-#         print(f"Response content: {response.text}")
-#         if response.status_code == 503:
-#             raise gr.Error(f"{response.status_code} : The model is being loaded")
-#         raise gr.Error(f"{response.status_code}")
     
-#     try:
-#         image_bytes = response.content
-#         image = Image.open(io.BytesIO(image_bytes))
-#         print(f'\033[1mGeneration {key} completed!\033[0m ({prompt})')
-#         return image, seed, seed
-#     except Exception as e:
-#         print(f"Error when trying to open the image: {e}")
-#         return None
-
-
 examples = [
     "a tiny astronaut hatching from an egg on the moon",
     "a cat holding a sign that says hello world",
     "an anime illustration of a wiener schnitzel",
 ]
 
-css = """
+css="""
 #col-container {
     margin: 0 auto;
-    max-width: 960px;
-}
-.generate-btn {
-    background: linear-gradient(90deg, #4B79A1 0%, #283E51 100%) !important;
-    border: none !important;
-    color: white !important;
-}
-.generate-btn:hover {
-    transform: translateY(-2px);
-    box-shadow: 0 5px 15px rgba(0,0,0,0.2);
+    max-width: 520px;
 }
 """
 
-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 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):
-                        with gr.Row():
-                            lora_scale = gr.Slider(
-                                label="LoRA Scale",
-                                minimum=0,
-                                maximum=2,
-                                step=0.01,
-                                value=0.95,
-                            )
-                            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)
         
-        gr.Markdown(article_text)
-        with gr.Column():
-            gr.Examples(
-                examples = examples,
-                inputs = [text_prompt],
+        result = gr.Image(label="Result", show_label=False)
+        
+        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,
+                )
         
-        # 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()
+        gr.Examples(
+            examples = examples,
+            fn = infer,
+            inputs = [prompt],
+            outputs = [result, seed],
+            cache_examples="lazy"
+        )
+
+    gr.on(
+        triggers=[run_button.click, prompt.submit],
+        fn = infer,
+        inputs = [prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps,lora_id,lora_scale],
+        outputs = [result, seed]
+    )
+
+demo.launch()