Update app.py

app.py

@@ -1,139 +1,104 @@
 import gradio as gr
-import numpy as np
-import random
-import spaces
 import torch
-from diffusers import  DiffusionPipeline, FlowMatchEulerDiscreteScheduler, AutoencoderTiny, AutoencoderKL
-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"
-
-taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype).to(device)
-good_vae = AutoencoderKL.from_pretrained("black-forest-labs/FLUX.1-dev", subfolder="vae", torch_dtype=dtype).to(device)
-pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=dtype, vae=taef1).to(device)
-torch.cuda.empty_cache()
-
-MAX_SEED = np.iinfo(np.int32).max
-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)
-
-@spaces.GPU(duration=75)
-def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, guidance_scale=3.5, num_inference_steps=28, progress=gr.Progress(track_tqdm=True)):
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-    generator = torch.Generator().manual_seed(seed)
-    
-    for img in pipe.flux_pipe_call_that_returns_an_iterable_of_images(
-            prompt=prompt,
-            guidance_scale=guidance_scale,
-            num_inference_steps=num_inference_steps,
-            width=width,
-            height=height,
-            generator=generator,
-            output_type="pil",
-            good_vae=good_vae,
-        ):
-            yield img, seed
-    
-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="""
-#col-container {
-    margin: 0 auto;
-    max-width: 520px;
-}
-"""
-
-with gr.Blocks(css=css) as demo:
-    
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(f"""# FLUX.1 [dev]
-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)]
+from diffusers import StableDiffusionXLPipeline
+from PIL import Image as PILImage
+import concurrent.futures
+
+# Model cache to avoid reloading the model multiple times
+model_cache = {}
+
+def load_model(model_name):
+    # Check if the model is already cached to avoid reloading every time
+    if model_name in model_cache:
+        return model_cache[model_name]
+
+    print(f"Loading model: {model_name}")
+    try:
+        # Select device (CPU only for ZeroGPU plan)
+        device = "cpu"  # Set to CPU, as you don't have GPU access
+
+        # Load the model with float32 (since float16 is not supported on CPU)
+        model = StableDiffusionXLPipeline.from_pretrained(model_name, torch_dtype=torch.float32)
+        model.to(device)
+
+        # Cache the model for future use
+        model_cache[model_name] = model
+        print("Model loaded successfully.")
+        return model
+    except Exception as e:
+        print(f"Error loading model: {e}")
+        return None
+
+# Function to generate the image with a timeout
+def generate_image_with_timeout(prompt):
+    timeout = 180  # Timeout after 180 seconds
+
+    try:
+        # Use ThreadPoolExecutor to handle the timeout
+        with concurrent.futures.ThreadPoolExecutor() as executor:
+            future = executor.submit(generate_image, prompt)
+            return future.result(timeout=timeout)  # Will raise TimeoutError if the process exceeds timeout
+
+    except concurrent.futures.TimeoutError:
+        return "Error: The image generation timed out. Please try again."
+
+# Function to generate the image
+def generate_image(prompt):
+    model_name = "SG161222/RealVisXL_V5.0_Lightning"
+    model = load_model(model_name)
+
+    if model is None:
+        return "Error loading the model."
+
+    try:
+        # Generate the image from the prompt
+        with torch.no_grad():
+            output = model(prompt)
+            image = output.images[0]  # Assuming the first image is the one we need
+            image = PILImage.fromarray(image)  # Convert to PIL image format for Gradio
+        return image
+    except Exception as e:
+        print(f"Error generating image: {e}")
+        return "Error generating the image."
+
+# Define the Gradio interface using gr.Blocks
+def create_gradio_interface():
+    with gr.Blocks(theme=gr.themes.Soft()) as demo:
+        gr.Markdown("""
+            <h1 style="
+                text-align: center;
+                color: white;
+                font-weight: bold;
+                text-transform: uppercase;
+                text-decoration: underline;
+                margin-top: 30px;
+                font-family: 'Arial', sans-serif;
+                background: linear-gradient(45deg, #ff6b6b, #f06595);
+                padding: 10px 20px;
+                border-radius: 15px;
+                box-shadow: 0px 4px 8px rgba(0, 0, 0, 0.3);
+            ">
+                SNAPSCRIBE
+            </h1>
         """)
-        
+
         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.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=32,
-                    value=1024,
-                )
-                
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    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,
-                )
-        
-        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],
-        outputs = [result, seed]
-    )
-
-demo.launch()
+            with gr.Column(scale=3, min_width=300):  # Changed scale to integer
+                prompt_input = gr.Textbox(label="Enter your prompt here", placeholder="e.g., A futuristic city skyline")
+                submit_button = gr.Button("Generate Image")
+
+            with gr.Column(scale=7, min_width=600):  # Changed scale to integer
+                output_image = gr.Image(label="Generated Image", height=640)
+
+        submit_button.click(fn=generate_image_with_timeout, inputs=[prompt_input], outputs=output_image)
+
+        gr.Markdown("""
+            <div style="position: relative; left: 0; bottom: 0; width: 100%; background-color: #0B0F19; color: white; text-align: center; padding: 10px 0;">
+                <p>Developed with ❤ by Aklavya (Bucky)</p>
+            </div>
+        """)
+
+    demo.launch()  # Removed `share=True`
+
+# Launch the Gradio interface
+create_gradio_interface()