Update app.py

app.py

@@ -7,6 +7,7 @@ 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"
 
@@ -20,6 +21,20 @@ 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:
@@ -66,117 +81,129 @@ 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: 520px;
+    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);
 }
 """
 
-with gr.Blocks(css=css) as demo:
-    
+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"):
-        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():
-            
-            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)
+            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)
         
-        with gr.Accordion("Advanced Settings", open=False):
-            
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=0,
+        gr.Markdown(article_text)
+        with gr.Column():
+            gr.Examples(
+                examples = examples,
+                inputs = [text_prompt],
             )
-            
-            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=[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()
+        # 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()
+