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import os
import gradio as gr
import spaces
import torch
from diffusers import AutoPipelineForInpainting
from loguru import logger
from PIL import Image, ImageChops
SUPPORTED_MODELS = [
"stabilityai/sdxl-turbo",
"stabilityai/stable-diffusion-3-medium-diffusers",
"stabilityai/stable-diffusion-xl-base-1.0",
"stable-diffusion-v1-5/stable-diffusion-v1-5",
"timbrooks/instruct-pix2pix",
]
DEFAULT_MODEL = "stabilityai/stable-diffusion-xl-base-1.0"
model = os.environ.get("MODEL_ID", DEFAULT_MODEL)
gpu_duration = int(os.environ.get("GPU_DURATION", 60))
def load_pipeline(model):
return AutoPipelineForInpainting.from_pretrained(
model, torch_dtype=torch.float16, use_safetensors=True, variant="fp16"
)
logger.debug(f"Loading pipeline: {dict(model=model)}")
pipe = load_pipeline(model).to("cuda" if torch.cuda.is_available() else "mps")
@logger.catch(reraise=True)
@spaces.GPU(duration=gpu_duration)
def infer(
prompt: str,
image_editor: dict,
negative_prompt: str,
strength: float,
num_inference_steps: int,
guidance_scale: float,
progress=gr.Progress(track_tqdm=True),
):
logger.info(
f"Starting image generation: {dict(model=model, prompt=prompt, image_editor=image_editor)}"
)
init_image: Image.Image = image_editor["background"].convert("RGB")
# Downscale the image
init_image.thumbnail((1024, 1024))
mask_layer = image_editor["layers"][0]
mask_image = Image.new("RGBA", mask_layer.size, "white")
mask_image = Image.alpha_composite(mask_image, mask_layer).convert("RGB")
mask_image = ImageChops.invert(mask_image)
mask_image.thumbnail((1024, 1024))
additional_args = {
k: v
for k, v in dict(
strength=strength,
num_inference_steps=num_inference_steps,
guidance_scale=guidance_scale,
).items()
if v
}
logger.debug(f"Generating image: {dict(prompt=prompt, **additional_args)}")
images = pipe(
prompt=prompt,
image=init_image,
mask_image=mask_image,
negative_prompt=negative_prompt,
**additional_args,
).images
return images[0]
css = """
@media (max-width: 1280px) {
#images-container {
flex-direction: column;
}
}
"""
with gr.Blocks(css=css) as demo:
with gr.Column():
gr.Markdown("# Inpainting")
gr.Markdown(f"## Model: `{model}`")
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, variant="primary")
with gr.Row(elem_id="images-container"):
image_editor = gr.ImageMask(label="Initial image", type="pil")
result = gr.Image(label="Result")
with gr.Accordion("Advanced Settings", open=False):
negative_prompt = gr.Text(
label="Negative prompt",
max_lines=1,
placeholder="Enter a negative prompt",
)
with gr.Row():
strength = gr.Slider(
label="Strength",
minimum=0.0,
maximum=1.0,
step=0.01,
value=0.0,
)
num_inference_steps = gr.Slider(
label="Number of inference steps",
minimum=0,
maximum=100,
step=1,
value=0,
)
guidance_scale = gr.Slider(
label="Guidance scale",
minimum=0.0,
maximum=100.0,
step=0.1,
value=0.0,
)
gr.on(
triggers=[run_button.click, prompt.submit],
fn=infer,
inputs=[
prompt,
image_editor,
negative_prompt,
strength,
num_inference_steps,
guidance_scale,
],
outputs=[result],
)
if __name__ == "__main__":
demo.launch()
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