Update app.py

app.py

@@ -9,26 +9,35 @@ from diffusers import EulerAncestralDiscreteScheduler
 import torch
 from compel import Compel, ReturnedEmbeddingsType
 import gc
+import os
 
 # Check if CUDA is available
 print(f"CUDA available: {torch.cuda.is_available()}")
 if torch.cuda.is_available():
     print(f"CUDA device: {torch.cuda.get_device_name(0)}")
 
-# Initialize the pipeline with CPU first to avoid CUDA errors during model loading
+# Initialize the pipeline ONCE at startup
+print("Loading pipeline...")
 pipe = StableDiffusionXLPipeline.from_pretrained(
     "dhead/wai-nsfw-illustrious-sdxl-v140-sdxl",
     torch_dtype=torch.float16,
     variant="fp16",
     use_safetensors=True,
-    device_map=None,  # Don't auto-assign devices
-    low_cpu_mem_usage=True  # Optimize CPU memory usage
+    low_cpu_mem_usage=True
 )
 
 pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
 
-# Initialize Compel for long prompt processing (will be moved to GPU when needed)
-compel = None  # Will be initialized in the GPU function
+# Enable memory efficient attention if available
+if hasattr(pipe, "enable_model_cpu_offload"):
+    pipe.enable_model_cpu_offload()
+elif hasattr(pipe, "enable_attention_slicing"):
+    pipe.enable_attention_slicing()
+
+print("Pipeline loaded successfully!")
+
+# Initialize Compel for long prompt processing
+compel = None
 
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 1216
@@ -40,45 +49,50 @@ def initialize_compel():
     """Initialize Compel with the current pipeline's components"""
     global compel
     if compel is None:
-        compel = Compel(
-            tokenizer=[pipe.tokenizer, pipe.tokenizer_2],
-            text_encoder=[pipe.text_encoder, pipe.text_encoder_2],
-            returned_embeddings_type=ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NON_NORMALIZED,
-            requires_pooled=[False, True],
-            truncate_long_prompts=False
-        )
+        try:
+            compel = Compel(
+                tokenizer=[pipe.tokenizer, pipe.tokenizer_2],
+                text_encoder=[pipe.text_encoder, pipe.text_encoder_2],
+                returned_embeddings_type=ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NON_NORMALIZED,
+                requires_pooled=[False, True],
+                truncate_long_prompts=False
+            )
+        except Exception as e:
+            print(f"Failed to initialize Compel: {e}")
+            compel = None
     return compel
 
 def process_long_prompt(prompt, negative_prompt=""):
     """Simple long prompt processing using Compel"""
     try:
         comp = initialize_compel()
+        if comp is None:
+            return None, None
         conditioning, pooled = comp([prompt, negative_prompt])
         return conditioning, pooled
     except Exception as e:
         print(f"Long prompt processing failed: {e}, falling back to standard processing")
         return None, None
 
-@spaces.GPU(duration=60)  # Add duration parameter for longer generation times
+@spaces.GPU(duration=90)  # Increased duration for stability
 def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
-    # Move pipeline to GPU inside the GPU-decorated function
-    global pipe
-    pipe = pipe.to("cuda")
-    
-    # Ensure all components are on GPU with correct dtype
-    pipe.text_encoder = pipe.text_encoder.to(dtype=torch.float16)
-    pipe.text_encoder_2 = pipe.text_encoder_2.to(dtype=torch.float16)
-    pipe.vae = pipe.vae.to(dtype=torch.float16)
-    pipe.unet = pipe.unet.to(dtype=torch.float16)
-    
-    use_long_prompt = len(prompt.split()) > 60 or len(prompt) > 300
+    try:
+        # Move pipeline to GPU inside the GPU-decorated function
+        pipe.to("cuda")
         
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
+        # Ensure all components are on GPU with correct dtype
+        pipe.text_encoder = pipe.text_encoder.to(dtype=torch.float16)
+        pipe.text_encoder_2 = pipe.text_encoder_2.to(dtype=torch.float16)
+        pipe.vae = pipe.vae.to(dtype=torch.float16)
+        pipe.unet = pipe.unet.to(dtype=torch.float16)
+        
+        use_long_prompt = len(prompt.split()) > 60 or len(prompt) > 300
+            
+        if randomize_seed:
+            seed = random.randint(0, MAX_SEED)
 
-    generator = torch.Generator(device="cuda").manual_seed(seed)
-    
-    try:
+        generator = torch.Generator(device="cuda").manual_seed(seed)
+        
         # Try long prompt processing first if prompt is long
         if use_long_prompt:
             print("Using long prompt processing...")
@@ -96,6 +110,11 @@ def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance
                     height=height,
                     generator=generator
                 ).images[0]
+                
+                # Clear GPU cache
+                torch.cuda.empty_cache()
+                gc.collect()
+                
                 return output_image
         
         # Fall back to standard processing
@@ -109,13 +128,195 @@ def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance
             generator=generator
         ).images[0]
         
+        # Clear GPU cache
+        torch.cuda.empty_cache()
+        gc.collect()
+        
         return output_image
+        
     except RuntimeError as e:
-        print(f"Error during generation: {e}")
+        print(f"Runtime error during generation: {e}")
+        torch.cuda.empty_cache()
+        gc.collect()
         # Return a blank image with error message
         error_img = Image.new('RGB', (width, height), color=(50, 50, 50))
         return error_img
     except Exception as e:
         print(f"Unexpected error: {e}")
+        torch.cuda.empty_cache()
+        gc.collect()
         error_img = Image.new('RGB', (width, height), color=(100, 0, 0))
-        return error_img
+        return error_img
+
+css = """
+/* Main container styling */
+#col-container {
+    margin: 0 auto;
+    max-width: 1024px;
+}
+/* Gradient background for the entire app */
+.gradio-container {
+    background: linear-gradient(135deg, #667eea 0%, #764ba2 25%, #f093fb 50%, #f5576c 75%, #ffc947 100%);
+    min-height: 100vh;
+}
+/* Main block styling with semi-transparent background */
+.contain {
+    background: rgba(255, 255, 255, 0.95);
+    border-radius: 20px;
+    padding: 20px;
+    box-shadow: 0 8px 32px 0 rgba(31, 38, 135, 0.37);
+    backdrop-filter: blur(4px);
+    border: 1px solid rgba(255, 255, 255, 0.18);
+}
+/* Input field styling */
+.gr-text-input {
+    background: rgba(255, 255, 255, 0.9) !important;
+    border: 2px solid rgba(102, 126, 234, 0.3) !important;
+    border-radius: 10px !important;
+}
+/* Button styling */
+.gr-button {
+    background: linear-gradient(135deg, #667eea 0%, #764ba2 100%) !important;
+    border: none !important;
+    color: white !important;
+    font-weight: bold !important;
+    transition: all 0.3s ease !important;
+}
+.gr-button:hover {
+    transform: translateY(-2px);
+    box-shadow: 0 5px 15px rgba(102, 126, 234, 0.4);
+}
+/* Accordion styling */
+.gr-accordion {
+    background: rgba(255, 255, 255, 0.8) !important;
+    border-radius: 10px !important;
+    margin-top: 10px !important;
+}
+/* Result image container */
+.gr-image {
+    border-radius: 15px !important;
+    box-shadow: 0 4px 15px rgba(0, 0, 0, 0.1) !important;
+}
+/* Slider styling */
+.gr-slider {
+    background: rgba(255, 255, 255, 0.8) !important;
+}
+/* Additional styling for headers */
+h1, h2, h3 {
+    color: #333 !important;
+    text-align: center;
+}
+/* Markdown text styling */
+.gr-markdown {
+    text-align: center;
+    margin-bottom: 20px;
+}
+"""
+
+print("Building Gradio interface...")
+
+# Build the Gradio interface
+with gr.Blocks(css=css) as demo:
+    with gr.Column(elem_id="col-container"):
+        gr.Markdown(
+            """
+            # 🎨 Stable Diffusion XL Image Generator
+            ### Create stunning AI-generated images with advanced controls
+            """
+        )
+
+        # Badge section
+        gr.HTML(
+            """
+            <div style="display: flex; justify-content: center; align-items: center; gap: 20px; margin: 20px 0;">
+                <a href="https://huggingface.co/spaces/Heartsync/Wan-2.2-ADULT" target="_blank">
+                    <img src="https://img.shields.io/static/v1?label=T2I%20%26%20TI2V&message=Wan-2.2-ADULT&color=%230000ff&labelColor=%23800080&logo=huggingface&logoColor=white&style=for-the-badge" alt="badge">
+                </a>
+                <a href="https://huggingface.co/spaces/Heartsync/PornHUB" target="_blank">
+                    <img src="https://img.shields.io/static/v1?label=T2I%20&message=PornHUB&color=%230000ff&labelColor=%23800080&logo=huggingface&logoColor=white&style=for-the-badge" alt="badge">
+                </a>
+                <a href="https://huggingface.co/spaces/Heartsync/Hentai-Adult" target="_blank">
+                    <img src="https://img.shields.io/static/v1?label=T2I%20&message=Hentai-Adult&color=%230000ff&labelColor=%23800080&logo=huggingface&logoColor=white&style=for-the-badge" alt="badge">
+                </a>
+            </div>
+            """
+        )
+        
+        with gr.Row():
+            prompt = gr.Text(
+                label="Prompt",
+                show_label=False,
+                max_lines=1,
+                placeholder="Enter your prompt (long prompts are automatically supported)",
+                container=False,
+                value=DEFAULT_PROMPT
+            )
+
+            run_button = gr.Button("Run", scale=0)
+
+        result = gr.Image(format="png", label="Result", show_label=False)
+        
+        with gr.Accordion("Advanced Settings", open=False):
+            negative_prompt = gr.Text(
+                label="Negative prompt",
+                max_lines=1,
+                placeholder="Enter a negative prompt",
+                value="monochrome, (low quality, worst quality:1.2), very displeasing, 3d, watermark, signature, ugly, poorly drawn,"
+            )
+
+            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=MAX_IMAGE_SIZE,
+                )
+
+            with gr.Row():
+                guidance_scale = gr.Slider(
+                    label="Guidance scale",
+                    minimum=0.0,
+                    maximum=20.0,
+                    step=0.1,
+                    value=7,
+                )
+
+                num_inference_steps = gr.Slider(
+                    label="Number of inference steps",
+                    minimum=1,
+                    maximum=28,
+                    step=1,
+                    value=28,
+                )
+
+    # Connect the run button to the inference function
+    run_button.click(
+        fn=infer,
+        inputs=[prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
+        outputs=[result]
+    )
+
+print("Starting Gradio app...")
+
+# Launch the app - CRITICAL: This must be at the module level for Spaces
+demo.queue(max_size=20)
+demo.launch()