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import gradio as gr
import numpy as np
import spaces
import torch
from diffusers import FluxPriorReduxPipeline, FluxPipeline
from diffusers.utils import load_image
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 2048
pipe_prior_redux = FluxPriorReduxPipeline.from_pretrained(
"black-forest-labs/FLUX.1-Redux-dev",
revision="refs/pr/8",
torch_dtype=torch.bfloat16
).to("cuda")
pipe = FluxPipeline.from_pretrained(
"black-forest-labs/FLUX.1-dev" ,
text_encoder=None,
text_encoder_2=None,
torch_dtype=torch.bfloat16
).to("cuda")
@spaces.GPU
def infer(control_image, seed=42, randomize_seed=False, width=1024, height=1024, guidance_scale=3.5, num_inference_steps=28, progress=gr.Progress(track_tqdm=True)):
if randomize_seed:
seed = random.randint(0, MAX_SEED)
pipe_prior_output = pipe_prior_redux(control_image)
images = pipe(
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
generator=torch.Generator("cpu").manual_seed(seed),
**pipe_prior_output,
).images[0]
return images, seed
css="""
#col-container {
margin: 0 auto;
max-width: 600px;
}
"""
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
gr.Markdown(f"""# FLUX.1 Redux [dev]
An adapter for FLUX [dev] to create image variations
[[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)]
""")
input_image = gr.Image(label="Image to create variations", type="pil")
run_button = gr.Button("Run")
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.on(
triggers=[run_button.click],
fn = infer,
inputs = [input_image, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
outputs = [result, seed]
)
demo.launch() |