Spaces:
Running
on
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Running
on
Zero
File size: 3,435 Bytes
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import torch
import spaces
import gradio as gr
from diffusers import FluxInpaintPipeline
import random
import numpy as np
MAX_SEED = np.iinfo(np.int32).max
model = "black-forest-labs/FLUX.1-dev"
if torch.cuda.is_available():
transformer = FluxTransformer2DModel.from_single_file(
"https://huggingface.co/black-forest-labs/FLUX.1-Fill-dev/blob/main/flux1-fill-dev.safetensors",
torch_dtype=torch.bfloat16
)
pipe = FluxPipeline.from_pretrained(
model,
transformer=transformer,
torch_dtype=torch.bfloat16)
pipe.scheduler = FlowMatchEulerDiscreteScheduler.from_config(
pipe.scheduler.config, use_beta_sigmas=True
)
pipe.to("cuda")
@spaces.GPU()
def inpaintGen(
imgMask,
inpaint_prompt: str,
strength: float,
guidance: float,
num_steps: int,
seed: int,
randomize_seed: bool,
progress=gr.Progress(track_tqdm=True)):
source_img = imgMask["background"]
mask_img = imgMask["layers"][0]
if not source_path:
raise gr.Error("Please upload an image.")
if not mask_path:
raise gr.Error("Please draw a mask on the image.")
width, height = source_img.size
if randomize_seed:
seed = random.randint(0, MAX_SEED)
generator = torch.Generator(device=DEVICE).manual_seed(seed)
result = pipe(
prompt=inpaint_prompt,
image=source_img,
seed=seed,
mask_image=mask_img,
width=width,
height=height,
strength=strength,
num_inference_steps=num_steps,
generator=generator,
guidance_scale=guidance
).images[0]
return result
with gr.Blocks(theme="ocean", title="Flux.1 dev inpaint", css=CSS) as demo:
gr.HTML("<h1><center>Flux.1 dev Inpaint</center></h1>")
gr.HTML("""
<p>
<center>
A partial redraw of the image based on your prompt words and occluded parts.
</center>
</p>
""")
with gr.Row():
with gr.Column():
imgMask = gr.ImageMask(type="pil", label="Image", layers=False, height=800)
inpaint_prompt = gr.Textbox(label='Prompts ✏️', placeholder="A hat...")
with gr.Row():
Inpaint_sendBtn = gr.Button(value="Submit", variant='primary')
Inpaint_clearBtn = gr.ClearButton([imgMask, inpaint_prompt], value="Clear")
image_out = gr.Image(type="pil", label="Output", height=960)
with gr.Accordion("Advanced ⚙️", open=False):
strength = gr.Slider(label="Strength", minimum=0, maximum=1, value=1, step=0.1)
guidance = gr.Slider(label="Guidance scale", minimum=1, maximum=20, value=7.5, step=0.1)
num_steps = gr.Slider(label="Steps", minimum=1, maximum=20, value=20, step=1)
seed = gr.Number(label="Seed", value=42, precision=0)
randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
gr.on(
triggers = [
inpaint_prompt.submit,
Inpaint_sendBtn.click,
],
fn = inpaintGen,
inputs = [
imgMask,
inpaint_prompt,
strength,
guidance,
num_steps,
seed,
randomize_seed
],
outputs = [image_out, seed]
)
if __name__ == "__main__":
demo.queue(api_open=False).launch(show_api=False, share=False) |