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import gradio as gr
import numpy as np
import random
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-dev", torch_dtype=torch.bfloat16).to(device)
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 2048
@spaces.GPU(duration=190)
def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, guidance_scale=5.0, num_inference_steps=28, progress=gr.Progress(track_tqdm=True)):
if randomize_seed:
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)
image = pipe(
prompt=prompt,
width=width,
height=height,
num_inference_steps=num_inference_steps,
generator=generator,
guidance_scale=guidance_scale
).images[0]
return image, seed
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(f"""# FLUX.1 [dev]
12B param rectified flow transformer guidance-distilled from [FLUX.1 [pro]](https://blackforestlabs.ai/)
[[non-commercial license](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md)] [[blog](https://blackforestlabs.ai/announcing-black-forest-labs/)] [[model](https://huggingface.co/black-forest-labs/FLUX.1-dev)]
""")
with gr.Row():
prompt = gr.Text(
label="Prompt",
show_label=False,
max_lines=1,
placeholder="Enter your prompt",
container=False,
)
run_button = gr.Button("Run", scale=0)
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():
guidance_scale = gr.Slider(
label="Guidance Scale",
minimum=1,
maximum=15,
step=0.1,
value=3.5,
)
num_inference_steps = gr.Slider(
label="Number of inference steps",
minimum=1,
maximum=50,
step=1,
value=28,
)
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, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
outputs=[result, seed]
)
# Adding image input options at the bottom
gr.Markdown("## Upload or select an additional image")
with gr.Row():
uploaded_image = gr.Image(label="Upload Image", type="pil")
image_url = gr.Textbox(label="Image URL", placeholder="Enter image URL")
use_generated_image = gr.Button("Use Generated Image")
additional_image_output = gr.Image(label="Selected Image", show_label=False)
def select_image(uploaded_image, image_url, use_generated=False):
if use_generated:
return result.value
elif uploaded_image is not None:
return uploaded_image
elif image_url:
try:
img = gr.Image.load(image_url)
return img
except Exception as e:
return f"Failed to load image from URL: {e}"
return None
use_generated_image.click(fn=lambda: select_image(None, None, True), inputs=[], outputs=additional_image_output)
uploaded_image.change(fn=select_image, inputs=[uploaded_image, image_url, gr.State(False)], outputs=additional_image_output)
image_url.submit(fn=select_image, inputs=[uploaded_image, image_url, gr.State(False)], outputs=additional_image_output)
demo.launch()
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