polu commited on
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  1. app.py +109 -0
app.py ADDED
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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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+
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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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+
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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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+
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+ torch.backends.cuda.matmul.allow_tf32 = True
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+
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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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+
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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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+
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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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+
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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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+
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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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+
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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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+
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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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+
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+ seed = gr.Number(label="Seed (for reproducibility)", value=3413, precision=0)
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+
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+ generate_btn = gr.Button("Generate Image", variant="primary", scale=1)
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+
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+ with gr.Column(scale=4):
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+ output = gr.Image(label="Your Generated Image")
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+
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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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+
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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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+
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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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+
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+ if __name__ == "__main__":
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+ demo.launch()