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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() as demo: |
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with gr.Column(): |
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with gr.Row(): |
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with gr.Column(): |
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num_images = gr.Slider(label="Number of Images", minimum=1, maximum=8, step=1, value=4, interactive=True) |
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height = gr.Number(label="Image Height", value=1024, interactive=True) |
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width = gr.Number(label="Image Width", value=1024, interactive=True) |
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prompt = gr.Text(label="Prompt", value="a photo of a cat", interactive=True) |
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seed = gr.Number(label="Seed", value=3413, interactive=True) |
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btn = gr.Button(value="run") |
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with gr.Column(): |
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output = gr.Gallery(height=1024) |
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@spaces.GPU |
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def process_image(num_images, height, width, 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]*num_images, |
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generator=torch.Generator().manual_seed(int(seed)), |
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num_inference_steps=8, |
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guidance_scale=3.5, |
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height=int(height), |
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width=int(width) |
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).images |
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reactive_controls = [num_images, height, width, prompt, seed] |
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btn.click(process_image, inputs=reactive_controls, outputs=[output]) |
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if __name__ == "__main__": |
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demo.launch() |
