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import gradio as gr
import numpy as np
import random
import spaces
import torch
from diffusers import DiffusionPipeline
# Define constants
dtype = torch.bfloat16
device = "cuda" if torch.cuda.is_available() else "cpu"
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 2048
# Load the diffusion pipeline
pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=dtype).to(device)
@spaces.GPU()
def infer(prompt, init_image=None, seed=42, randomize_seed=False, width=1024, height=1024, num_inference_steps=4, progress=gr.Progress(track_tqdm=True)):
if randomize_seed:
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)
if init_image is not None:
# Process img2img
init_image = init_image.convert("RGB")
init_image = pipe.preprocess(init_image).unsqueeze(0).to(device, dtype)
image = pipe(
prompt=prompt,
init_image=init_image,
width=width,
height=height,
num_inference_steps=num_inference_steps,
generator=generator,
guidance_scale=0.0
).images[0]
else:
# Process text2img
image = pipe(
prompt=prompt,
width=width,
height=height,
num_inference_steps=num_inference_steps,
generator=generator,
guidance_scale=0.0
).images[0]
return image, seed
# Define example prompts
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 styling for the Japanese-inspired interface
css = """
body {
background-color: #fff;
font-family: 'Noto Sans JP', sans-serif;
color: #333;
}
#col-container {
margin: 0 auto;
max-width: 520px;
border: 2px solid #000;
padding: 20px;
background-color: #f7f7f7;
border-radius: 10px;
}
.gr-button {
background-color: #e60012;
color: #fff;
border: 2px solid #000;
}
.gr-button:hover {
background-color: #c20010;
}
.gr-slider, .gr-checkbox, .gr-textbox {
border: 2px solid #000;
}
.gr-accordion {
border: 2px solid #000;
background-color: #fff;
}
.gr-image {
border: 2px solid #000;
}
"""
# Create the Gradio interface
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.Textbox(
label="Prompt",
show_label=False,
max_lines=1,
placeholder="Enter your prompt",
container=False,
)
run_button = gr.Button("Run", scale=0)
with gr.Row():
init_image = gr.Image(label="Initial Image (optional)", type="pil")
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=42,
)
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=infer,
inputs=[prompt],
outputs=[result, seed],
cache_examples="lazy"
)
gr.on(
triggers=[run_button.click, prompt.submit],
fn=infer,
inputs=[prompt, init_image, seed, randomize_seed, width, height, num_inference_steps],
outputs=[result, seed]
)
demo.launch()
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