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import gradio as gr |
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import numpy as np |
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import random |
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import os |
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import spaces |
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from diffusers import AutoPipelineForText2Image |
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import torch |
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from huggingface_hub import login |
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login(os.environ.get("HF_TOKEN")) |
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device = "cuda" if torch.cuda.is_available() else "cpu" |
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if torch.cuda.is_available(): |
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torch_dtype = torch.float16 |
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else: |
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torch_dtype = torch.float32 |
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pipe = AutoPipelineForText2Image.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16) |
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pipe.load_lora_weights('enhanceaiteam/Flux-uncensored', weight_name='lora.safetensors') |
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pipe = pipe.to(device) |
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MAX_SEED = np.iinfo(np.int32).max |
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MAX_IMAGE_SIZE = 1024 |
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@spaces.GPU |
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def infer( |
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prompt, |
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seed, |
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randomize_seed, |
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width, |
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height, |
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guidance_scale, |
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num_inference_steps, |
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progress=gr.Progress(track_tqdm=True), |
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): |
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if randomize_seed: |
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seed = random.randint(0, MAX_SEED) |
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generator = torch.Generator().manual_seed(seed) |
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image = pipe( |
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prompt=prompt, |
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guidance_scale=guidance_scale, |
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num_inference_steps=num_inference_steps, |
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width=width, |
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height=height, |
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generator=generator, |
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).images[0] |
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return image, seed |
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examples = [ |
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"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k", |
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"An astronaut riding a green horse", |
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"A delicious ceviche cheesecake slice", |
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] |
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css = """ |
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#col-container { |
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margin: 0 auto; |
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max-width: 640px; |
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} |
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""" |
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with gr.Blocks(css=css) as demo: |
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with gr.Column(elem_id="col-container"): |
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gr.Markdown(f"""# [FLUX.1](https://blackforestlabs.ai/) |
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Generate any type of image. |
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""") |
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with gr.Row(): |
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prompt = gr.Text( |
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label="Prompt", |
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show_label=False, |
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max_lines=1, |
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placeholder="Enter your prompt", |
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container=False, |
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) |
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run_button = gr.Button("Run", scale=0, variant="primary") |
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result = gr.Image(label="Result", show_label=False) |
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with gr.Accordion("Advanced Settings", open=False): |
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seed = gr.Slider( |
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label="Seed", |
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minimum=0, |
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maximum=MAX_SEED, |
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step=1, |
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value=0, |
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) |
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randomize_seed = gr.Checkbox(label="Randomize seed", value=True) |
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with gr.Row(): |
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width = gr.Slider( |
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label="Width", |
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minimum=256, |
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maximum=MAX_IMAGE_SIZE, |
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step=32, |
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value=1024, |
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) |
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height = gr.Slider( |
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label="Height", |
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minimum=256, |
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maximum=MAX_IMAGE_SIZE, |
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step=32, |
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value=1024, |
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) |
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with gr.Row(): |
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guidance_scale = gr.Slider( |
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label="Guidance scale", |
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minimum=0.0, |
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maximum=10.0, |
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step=0.1, |
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value=3.5, |
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) |
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num_inference_steps = gr.Slider( |
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label="Number of inference steps", |
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minimum=1, |
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maximum=50, |
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step=1, |
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value=28, |
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) |
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gr.Examples(examples=examples, inputs=[prompt]) |
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gr.on( |
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triggers=[run_button.click, prompt.submit], |
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fn=infer, |
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inputs=[ |
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prompt, |
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seed, |
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randomize_seed, |
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width, |
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height, |
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guidance_scale, |
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num_inference_steps, |
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], |
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outputs=[result, seed], |
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) |
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if __name__ == "__main__": |
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demo.launch() |
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