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Running
on
Zero
| import spaces | |
| import argparse | |
| import os | |
| import time | |
| from os import path | |
| from safetensors.torch import load_file | |
| from huggingface_hub import hf_hub_download | |
| cache_path = path.join(path.dirname(path.abspath(__file__)), "models") | |
| # TRANSFORMERS_CACHE is deprecated, only use HF_HOME | |
| os.environ["HF_HUB_CACHE"] = cache_path | |
| os.environ["HF_HOME"] = cache_path | |
| import gradio as gr | |
| import torch | |
| # Try to handle version compatibility issues | |
| try: | |
| from diffusers import FluxPipeline | |
| except ImportError as e: | |
| print(f"Error importing FluxPipeline: {e}") | |
| print("Attempting to use StableDiffusionPipeline as fallback...") | |
| from diffusers import StableDiffusionPipeline as FluxPipeline | |
| torch.backends.cuda.matmul.allow_tf32 = True | |
| class timer: | |
| def __init__(self, method_name="timed process"): | |
| self.method = method_name | |
| def __enter__(self): | |
| self.start = time.time() | |
| print(f"{self.method} starts") | |
| def __exit__(self, exc_type, exc_val, exc_tb): | |
| end = time.time() | |
| print(f"{self.method} took {str(round(end - self.start, 2))}s") | |
| if not path.exists(cache_path): | |
| os.makedirs(cache_path, exist_ok=True) | |
| pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16) | |
| pipe.load_lora_weights(hf_hub_download("ByteDance/Hyper-SD", "Hyper-FLUX.1-dev-8steps-lora.safetensors")) | |
| pipe.fuse_lora(lora_scale=0.125) | |
| pipe.to(device="cuda", dtype=torch.bfloat16) | |
| # Custom CSS for gradient effects and visual enhancements | |
| custom_css = """ | |
| .container { | |
| max-width: 1200px; | |
| margin: 0 auto; | |
| padding: 20px; | |
| } | |
| .gradio-container { | |
| background: linear-gradient(135deg, #667eea 0%, #764ba2 50%, #f093fb 100%); | |
| min-height: 100vh; | |
| } | |
| .main-content { | |
| background: rgba(255, 255, 255, 0.95); | |
| border-radius: 20px; | |
| padding: 30px; | |
| box-shadow: 0 20px 40px rgba(0, 0, 0, 0.1); | |
| backdrop-filter: blur(10px); | |
| } | |
| h1 { | |
| background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); | |
| -webkit-background-clip: text; | |
| -webkit-text-fill-color: transparent; | |
| background-clip: text; | |
| text-align: center; | |
| font-size: 3rem !important; | |
| font-weight: 800 !important; | |
| margin-bottom: 1rem !important; | |
| text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.1); | |
| } | |
| .subtitle { | |
| text-align: center; | |
| color: #666; | |
| font-size: 1.2rem; | |
| margin-bottom: 2rem; | |
| } | |
| .gr-button-primary { | |
| background: linear-gradient(135deg, #667eea 0%, #764ba2 100%) !important; | |
| border: none !important; | |
| color: white !important; | |
| font-weight: bold !important; | |
| font-size: 1.1rem !important; | |
| padding: 12px 30px !important; | |
| border-radius: 10px !important; | |
| transition: all 0.3s ease !important; | |
| box-shadow: 0 4px 15px rgba(102, 126, 234, 0.3) !important; | |
| } | |
| .gr-button-primary:hover { | |
| transform: translateY(-2px) !important; | |
| box-shadow: 0 6px 20px rgba(102, 126, 234, 0.4) !important; | |
| } | |
| .gr-input, .gr-box { | |
| border-radius: 10px !important; | |
| border: 2px solid #e0e0e0 !important; | |
| transition: all 0.3s ease !important; | |
| } | |
| .gr-input:focus { | |
| border-color: #667eea !important; | |
| box-shadow: 0 0 0 3px rgba(102, 126, 234, 0.1) !important; | |
| } | |
| .gr-form { | |
| background: white !important; | |
| border-radius: 15px !important; | |
| padding: 20px !important; | |
| box-shadow: 0 4px 10px rgba(0, 0, 0, 0.05) !important; | |
| } | |
| .gr-padded { | |
| padding: 15px !important; | |
| } | |
| .badge-container { | |
| display: flex; | |
| justify-content: center; | |
| gap: 12px; | |
| margin: 20px 0; | |
| } | |
| .how-to-use { | |
| background: linear-gradient(135deg, #f5f7fa 0%, #c3cfe2 100%); | |
| border-radius: 15px; | |
| padding: 25px; | |
| margin-top: 30px; | |
| box-shadow: 0 4px 10px rgba(0, 0, 0, 0.05); | |
| } | |
| .how-to-use h2 { | |
| color: #667eea; | |
| font-size: 1.8rem; | |
| margin-bottom: 1rem; | |
| } | |
| .how-to-use ol { | |
| color: #555; | |
| line-height: 1.8; | |
| } | |
| .how-to-use li { | |
| margin-bottom: 10px; | |
| } | |
| .tip { | |
| background: rgba(102, 126, 234, 0.1); | |
| border-left: 4px solid #667eea; | |
| padding: 15px; | |
| margin-top: 20px; | |
| border-radius: 5px; | |
| color: #555; | |
| font-style: italic; | |
| } | |
| """ | |
| with gr.Blocks(theme=gr.themes.Soft(), css=custom_css) as demo: | |
| with gr.Column(elem_classes="main-content"): | |
| gr.HTML( | |
| """ | |
| <div style="text-align: center; max-width: 800px; margin: 0 auto;"> | |
| <h1>FLUX Fast & Furious</h1> | |
| <p class="subtitle">Lightning-fast image generation powered by Hyper-FLUX LoRA</p> | |
| </div> | |
| """ | |
| ) | |
| gr.HTML( | |
| """ | |
| <div class='badge-container'> | |
| <a href="https://huggingface.co/spaces/openfree/Best-AI" target="_blank"> | |
| <img src="https://img.shields.io/static/v1?label=OpenFree&message=BEST%20AI%20Services&color=%230000ff&labelColor=%23000080&logo=huggingface&logoColor=%23ffa500&style=for-the-badge" alt="OpenFree badge"> | |
| </a> | |
| <a href="https://discord.gg/openfreeai" target="_blank"> | |
| <img src="https://img.shields.io/static/v1?label=Discord&message=Openfree%20AI&color=%230000ff&labelColor=%23800080&logo=discord&logoColor=white&style=for-the-badge" alt="Discord badge"> | |
| </a> | |
| </div> | |
| """ | |
| ) | |
| with gr.Row(): | |
| with gr.Column(scale=3): | |
| with gr.Group(): | |
| prompt = gr.Textbox( | |
| label="✨ Your Image Description", | |
| placeholder="E.g., A serene landscape with mountains and a lake at sunset", | |
| lines=3 | |
| ) | |
| with gr.Accordion("🎨 Advanced Settings", open=False): | |
| with gr.Group(): | |
| with gr.Row(): | |
| height = gr.Slider(label="Height", minimum=256, maximum=1152, step=64, value=1024) | |
| width = gr.Slider(label="Width", minimum=256, maximum=1152, step=64, value=1024) | |
| with gr.Row(): | |
| steps = gr.Slider(label="Inference Steps", minimum=6, maximum=25, step=1, value=8) | |
| scales = gr.Slider(label="Guidance Scale", minimum=0.0, maximum=5.0, step=0.1, value=3.5) | |
| seed = gr.Number(label="Seed (for reproducibility)", value=3413, precision=0) | |
| generate_btn = gr.Button("🚀 Generate Image", variant="primary", scale=1) | |
| with gr.Column(scale=4): | |
| output = gr.Image(label="🎨 Your Generated Image") | |
| gr.HTML( | |
| """ | |
| <div class="how-to-use"> | |
| <h2>📖 How to Use</h2> | |
| <ol> | |
| <li>✍️ Enter a detailed description of the image you want to create</li> | |
| <li>⚙️ Adjust advanced settings if desired (tap to expand)</li> | |
| <li>🎯 Tap "Generate Image" and watch the magic happen!</li> | |
| </ol> | |
| <div class="tip"> | |
| 💡 <strong>Pro Tip:</strong> Be specific in your description for best results! Include details about style, mood, colors, and composition. | |
| </div> | |
| </div> | |
| """ | |
| ) | |
| def process_image(height, width, steps, scales, prompt, seed): | |
| global pipe | |
| with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16), timer("inference"): | |
| return pipe( | |
| prompt=[prompt], | |
| generator=torch.Generator().manual_seed(int(seed)), | |
| num_inference_steps=int(steps), | |
| guidance_scale=float(scales), | |
| height=int(height), | |
| width=int(width), | |
| max_sequence_length=256 | |
| ).images[0] | |
| generate_btn.click( | |
| process_image, | |
| inputs=[height, width, steps, scales, prompt, seed], | |
| outputs=output | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() |