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import gradio as gr
import spaces
from diffusers import AutoPipelineForImage2Image
from loguru import logger
from PIL import Image
models = [
"stabilityai/stable-diffusion-xl-refiner-1.0",
"stabilityai/sdxl-turbo",
"timbrooks/instruct-pix2pix",
]
@spaces.GPU
@logger.catch(reraise=True)
def generate(
model: str,
prompt: str,
init_image: Image.Image,
strength: float,
progress=gr.Progress(),
):
logger.info(
f"Starting image generation: {dict(model=model, prompt=prompt, image=init_image, strength=strength)}"
)
pipe = AutoPipelineForImage2Image.from_pretrained(model).to("cuda")
# Downscale the image
init_image.thumbnail((1024, 1024))
def progress_callback(pipe, step_index, timestep, callback_kwargs):
logger.debug(
f"Callback: {dict(num_timesteps=pipe.num_timesteps, step_index=step_index, timestep=timestep)}"
)
progress((step_index + 1, pipe.num_timesteps))
return callback_kwargs
images = pipe(
prompt=prompt,
image=init_image,
strength=strength,
callback_on_step_end=progress_callback,
).images
return images[0]
demo = gr.Interface(
fn=generate,
inputs=[
gr.Dropdown(
label="Model", choices=models, value=models[0], allow_custom_value=True
),
gr.Text(label="Prompt"),
gr.Image(label="Init image", type="pil"),
gr.Slider(label="Strength", minimum=0, maximum=1, value=0.3),
],
outputs=[gr.Image(label="Output")],
)
demo.launch()
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