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Running
on
Zero
import gradio as gr | |
import numpy as np | |
import PIL.Image | |
import spaces | |
import torch | |
from diffusers import DiffusionPipeline | |
dtype = torch.bfloat16 | |
device = "cuda" if torch.cuda.is_available() else "cpu" | |
pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=dtype).to(device) | |
MAX_SEED = np.iinfo(np.int32).max | |
MAX_IMAGE_SIZE = 2048 | |
def get_seed(randomize_seed: bool, seed: int) -> int: | |
"""Determine and return the random seed to use for model generation. | |
- MAX_SEED is the maximum value for a 32-bit integer (np.iinfo(np.int32).max). | |
- This function is typically used to ensure reproducibility or to introduce randomness in model generation. | |
Args: | |
randomize_seed (bool): If True, a random seed (an integer in [0, MAX_SEED)) is generated using NumPy's default random number generator. If False, the provided seed argument is returned as-is. | |
seed (int): The seed value to use if randomize_seed is False. | |
Returns: | |
int: The selected seed value. If randomize_seed is True, a randomly generated integer; otherwise, the value of the seed argument. | |
""" | |
rng = np.random.default_rng() | |
return int(rng.integers(0, MAX_SEED)) if randomize_seed else seed | |
def infer( | |
prompt: str, | |
seed: int, | |
width: int = 1024, | |
height: int = 1024, | |
num_inference_steps: int = 4, | |
progress: gr.Progress = gr.Progress(track_tqdm=True), # noqa: ARG001, B008 | |
) -> PIL.Image.Image: | |
"""Generate an image from a text prompt using the FLUX.1 [schnell] model. | |
- Prompts must be in English. Other languages are not currently supported. | |
- Prompts are limited to a maximum of 77 tokens, due to the CLIP tokenizer constraint. | |
Args: | |
prompt: A text prompt in English used to guide the image generation. Limited to 77 tokens. | |
seed: The seed used for deterministic random number generation. | |
width: Width of the generated image in pixels. Defaults to 1024. | |
height: Height of the generated image in pixels. Defaults to 1024. | |
num_inference_steps: Number of inference steps to perform. A higher value may improve image quality. Defaults to 4. | |
progress: (Internal) Used to display progress in the UI; should not be modified by the user. | |
Returns: | |
A PIL.Image.Image object representing the generated image. | |
""" | |
generator = torch.Generator().manual_seed(seed) | |
return pipe( | |
prompt=prompt, | |
width=width, | |
height=height, | |
num_inference_steps=num_inference_steps, | |
generator=generator, | |
guidance_scale=0.0, | |
).images[0] | |
def run_example(prompt: str) -> tuple[PIL.Image.Image, int]: | |
return infer(prompt, seed=42) | |
examples = [ | |
"a tiny astronaut hatching from an egg on the moon", | |
"a cat holding a sign that says hello world", | |
"an anime illustration of a wiener schnitzel", | |
] | |
css = """ | |
#col-container { | |
margin: 0 auto; | |
max-width: 520px; | |
} | |
""" | |
with gr.Blocks(css=css) as demo: | |
with gr.Column(elem_id="col-container"): | |
gr.Markdown("""# FLUX.1 [schnell] | |
12B param rectified flow transformer distilled from [FLUX.1 [pro]](https://blackforestlabs.ai/) for 4 step generation | |
[[blog](https://blackforestlabs.ai/announcing-black-forest-labs/)] [[model](https://huggingface.co/black-forest-labs/FLUX.1-schnell)] | |
""") | |
with gr.Row(): | |
prompt = gr.Text( | |
label="Prompt", | |
show_label=False, | |
max_lines=1, | |
placeholder="Enter your prompt", | |
submit_btn=True, | |
) | |
result = gr.Image(label="Result", show_label=False) | |
with gr.Accordion("Advanced Settings", open=False): | |
seed = gr.Slider( | |
label="Seed", | |
minimum=0, | |
maximum=MAX_SEED, | |
step=1, | |
value=0, | |
) | |
randomize_seed = gr.Checkbox(label="Randomize seed", value=True) | |
with gr.Row(): | |
width = gr.Slider( | |
label="Width", | |
minimum=256, | |
maximum=MAX_IMAGE_SIZE, | |
step=32, | |
value=1024, | |
) | |
height = gr.Slider( | |
label="Height", | |
minimum=256, | |
maximum=MAX_IMAGE_SIZE, | |
step=32, | |
value=1024, | |
) | |
with gr.Row(): | |
num_inference_steps = gr.Slider( | |
label="Number of inference steps", | |
minimum=1, | |
maximum=50, | |
step=1, | |
value=4, | |
) | |
gr.Examples( | |
examples=examples, | |
fn=run_example, | |
inputs=prompt, | |
outputs=result, | |
) | |
prompt.submit( | |
fn=get_seed, | |
inputs=[randomize_seed, seed], | |
outputs=seed, | |
).then( | |
fn=infer, | |
inputs=[prompt, seed, width, height, num_inference_steps], | |
outputs=result, | |
) | |
if __name__ == "__main__": | |
demo.launch(mcp_server=True) | |