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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 spaces
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import torch
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from diffusers import DiffusionPipeline
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dtype = torch.bfloat16
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device = "cuda" if torch.cuda.is_available() else "cpu"
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pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=dtype).to(device)
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MAX_SEED = np.iinfo(np.int32).max
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MAX_IMAGE_SIZE = 2048
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@spaces.GPU()
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def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, num_inference_steps=4, progress=gr.Progress(track_tqdm=True)):
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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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width = width,
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height = height,
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num_inference_steps = num_inference_steps,
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generator = generator,
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guidance_scale=0.0
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).images[0]
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return image, seed
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examples = [
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"a tiny astronaut hatching from an egg on the moon",
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"a cat holding a sign that says hello world",
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"an anime illustration of a wiener schnitzel",
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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: 520px;
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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 [schnell]
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12B param rectified flow transformer distilled from [FLUX.1 [pro]](https://blackforestlabs.ai/) for 4 step generation
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[[blog](https://blackforestlabs.ai/announcing-black-forest-labs/)] [[model](https://huggingface.co/black-forest-labs/FLUX.1-schnell)]
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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)
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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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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=4,
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)
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gr.Examples(
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examples = examples,
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fn = infer,
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inputs = [prompt],
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outputs = [result, seed],
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cache_examples="lazy"
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)
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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 = [prompt, seed, randomize_seed, width, height, num_inference_steps],
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outputs = [result, seed]
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)
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demo.launch() |
