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import spaces |
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import argparse |
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import os |
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import time |
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from os import path |
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from safetensors.torch import load_file |
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from huggingface_hub import hf_hub_download |
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cache_path = path.join(path.dirname(path.abspath(__file__)), "models") |
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os.environ["TRANSFORMERS_CACHE"] = cache_path |
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os.environ["HF_HUB_CACHE"] = cache_path |
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os.environ["HF_HOME"] = cache_path |
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import gradio as gr |
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import torch |
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from diffusers import FluxPipeline |
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torch.backends.cuda.matmul.allow_tf32 = True |
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class timer: |
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def __init__(self, method_name="timed process"): |
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self.method = method_name |
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def __enter__(self): |
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self.start = time.time() |
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print(f"{self.method} starts") |
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def __exit__(self, exc_type, exc_val, exc_tb): |
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end = time.time() |
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print(f"{self.method} took {str(round(end - self.start, 2))}s") |
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if not path.exists(cache_path): |
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os.makedirs(cache_path, exist_ok=True) |
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pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16) |
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pipe.load_lora_weights(hf_hub_download("ByteDance/Hyper-SD", "Hyper-FLUX.1-dev-8steps-lora.safetensors")) |
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pipe.fuse_lora(lora_scale=0.125) |
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pipe.to(device="cuda", dtype=torch.bfloat16) |
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with gr.Blocks(theme=gr.themes.Soft()) as demo: |
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gr.Markdown( |
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""" |
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<div style="text-align: center; max-width: 650px; margin: 0 auto;"> |
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<h1 style="font-size: 2.5rem; font-weight: 700; margin-bottom: 1rem; display: contents;">Hyper-FLUX-8steps-LoRA</h1> |
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<p style="font-size: 1rem; margin-bottom: 1.5rem;">AutoML team from ByteDance</p> |
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</div> |
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""" |
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) |
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with gr.Row(): |
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with gr.Column(scale=3): |
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with gr.Group(): |
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prompt = gr.Textbox( |
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label="Your Image Description", |
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placeholder="E.g., A serene landscape with mountains and a lake at sunset", |
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lines=3 |
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) |
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with gr.Accordion("Advanced Settings", open=False): |
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with gr.Group(): |
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with gr.Row(): |
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height = gr.Slider(label="Height", minimum=256, maximum=1152, step=64, value=1024) |
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width = gr.Slider(label="Width", minimum=256, maximum=1152, step=64, value=1024) |
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with gr.Row(): |
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steps = gr.Slider(label="Inference Steps", minimum=6, maximum=25, step=1, value=8) |
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scales = gr.Slider(label="Guidance Scale", minimum=0.0, maximum=5.0, step=0.1, value=3.5) |
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seed = gr.Number(label="Seed (for reproducibility)", value=3413, precision=0) |
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generate_btn = gr.Button("Generate Image", variant="primary", scale=1) |
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with gr.Column(scale=4): |
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output = gr.Image(label="Your Generated Image") |
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gr.Markdown( |
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""" |
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<div style="max-width: 650px; margin: 2rem auto; padding: 1rem; border-radius: 10px; background-color: #f0f0f0;"> |
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<h2 style="font-size: 1.5rem; margin-bottom: 1rem;">How to Use</h2> |
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<ol style="padding-left: 1.5rem;"> |
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<li>Enter a detailed description of the image you want to create.</li> |
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<li>Adjust advanced settings if desired (tap to expand).</li> |
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<li>Tap "Generate Image" and wait for your creation!</li> |
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</ol> |
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<p style="margin-top: 1rem; font-style: italic;">Tip: Be specific in your description for best results!</p> |
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</div> |
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""" |
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) |
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@spaces.GPU |
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def process_image(height, width, steps, scales, prompt, seed): |
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global pipe |
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with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16), timer("inference"): |
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return pipe( |
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prompt=[prompt], |
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generator=torch.Generator().manual_seed(int(seed)), |
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num_inference_steps=int(steps), |
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guidance_scale=float(scales), |
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height=int(height), |
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width=int(width), |
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max_sequence_length=256 |
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).images[0] |
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generate_btn.click( |
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process_image, |
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inputs=[height, width, steps, scales, prompt, seed], |
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outputs=output |
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) |
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if __name__ == "__main__": |
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demo.launch() |
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