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import os
import gradio as gr
import spaces
import torch
from diffusers import AutoPipelineForImage2Image, StableDiffusionInstructPix2PixPipeline
from loguru import logger
from PIL import Image
SUPPORTED_MODELS = [
"stabilityai/sdxl-turbo",
"stabilityai/stable-diffusion-3-medium-diffusers",
"stabilityai/stable-diffusion-xl-refiner-1.0",
"timbrooks/instruct-pix2pix",
]
DEFAULT_MODEL = "stabilityai/stable-diffusion-xl-refiner-1.0"
MAX_IMAGE_SIZE = 1024
model = os.environ.get("MODEL_ID", DEFAULT_MODEL)
gpu_duration = int(os.environ.get("GPU_DURATION", 60))
def load_pipeline(model):
pipeline_type = (
StableDiffusionInstructPix2PixPipeline
if model == "timbrooks/instruct-pix2pix"
else AutoPipelineForImage2Image
)
return pipeline_type.from_pretrained(
model, torch_dtype=torch.float16, use_safetensors=True, variant="fp16"
)
logger.debug(f"Loading pipeline: {dict(model=model)}")
pipe = load_pipeline(DEFAULT_MODEL).to("cuda")
@logger.catch(reraise=True)
@spaces.GPU(duration=gpu_duration)
def infer(
prompt: str,
init_image: Image.Image,
negative_prompt: str,
width: int,
height: int,
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=init_image)}"
)
# Downscale the image
init_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,
negative_prompt=negative_prompt,
width=width,
height=height,
**additional_args,
).images
return images[0]
css = """
#col-container {
margin: 0 auto;
max-width: 640px;
}
"""
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
gr.Markdown("# Image-to-Image")
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():
init_image = gr.Image(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():
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():
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,
init_image,
negative_prompt,
width,
height,
strength,
num_inference_steps,
guidance_scale,
],
outputs=[result],
)
if __name__ == "__main__":
demo.launch()
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