Spaces:
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Waifu-Inpaint-XL Gradio App
----------------------------
Free-GPU-friendly inpainting UI for ShinoharaHare/Waifu-Inpaint-XL.
Works as-is on: HF Spaces (ZeroGPU), Kaggle Notebooks, Google Colab.
Setup:
pip install -r requirements.txt
huggingface-cli login # needed once, model is gated
Run:
python app.py
"""
import spaces # MUST be imported before torch/anything CUDA-related, ZeroGPU requirement
import os
import torch
import gradio as gr
from diffusers import StableDiffusionXLInpaintPipeline
from PIL import Image
MODEL_ID = "ShinoharaHare/Waifu-Inpaint-XL"
DTYPE = torch.float16
# Load once at startup. Moving to 'cuda' here is fine under ZeroGPU -- the actual
# GPU device is only allocated when a @spaces.GPU-decorated function is called.
pipe = StableDiffusionXLInpaintPipeline.from_pretrained(
MODEL_ID,
torch_dtype=DTYPE,
use_safetensors=True,
)
pipe.to("cuda")
pipe.enable_vae_slicing()
pipe.enable_attention_slicing()
@spaces.GPU(duration=60) # seconds of GPU time requested per call; raise if you increase steps/variations
def run_inpaint(
editor_value, # gr.ImageEditor output: {"background":..., "layers":[...], "composite":...}
prompt,
negative_prompt,
steps,
guidance,
num_variations,
seed,
):
if editor_value is None or editor_value.get("background") is None:
raise gr.Error("Upload an image first.")
base_image = editor_value["background"].convert("RGB")
# Build mask from the drawn layer (painted area = white = inpaint region)
if not editor_value.get("layers"):
raise gr.Error("Paint over the area you want to inpaint (use the brush tool).")
mask_layer = editor_value["layers"][0]
mask = mask_layer.split()[-1].convert("L") # alpha channel -> grayscale mask
results = []
base_seed = int(seed) if seed >= 0 else torch.seed()
for i in range(int(num_variations)):
gen = torch.Generator(device="cuda").manual_seed(base_seed + i)
out = pipe(
prompt=prompt,
negative_prompt=negative_prompt or None,
image=base_image,
mask_image=mask,
num_inference_steps=int(steps),
guidance_scale=float(guidance),
height=base_image.height,
width=base_image.width,
generator=gen,
).images[0]
results.append(out)
return results
with gr.Blocks(title="Waifu-Inpaint-XL") as demo:
gr.Markdown("## Waifu-Inpaint-XL — paint a mask, describe the change, generate")
with gr.Row():
with gr.Column():
editor = gr.ImageEditor(
label="Upload image, then paint the mask (brush tool)",
type="pil",
brush=gr.Brush(colors=["#ffffff"], default_size=25),
)
prompt = gr.Textbox(label="Prompt", placeholder="orange striped sweater, red sparkle eyes")
negative_prompt = gr.Textbox(label="Negative prompt (optional)", value="blurry, low quality, extra limbs")
with gr.Row():
steps = gr.Slider(10, 50, value=28, step=1, label="Steps")
guidance = gr.Slider(1, 12, value=5.0, step=0.5, label="Guidance scale")
with gr.Row():
num_variations = gr.Slider(1, 6, value=1, step=1, label="Variations to generate")
seed = gr.Number(value=-1, label="Seed (-1 = random)")
run_btn = gr.Button("Generate", variant="primary")
with gr.Column():
gallery = gr.Gallery(label="Results", columns=3, height=500)
run_btn.click(
fn=run_inpaint,
inputs=[editor, prompt, negative_prompt, steps, guidance, num_variations, seed],
outputs=gallery,
)
if __name__ == "__main__":
# Spaces already serves a public URL -- do NOT pass share=True here (errors on Spaces).
demo.launch()
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