import gradio as gr
import spaces
import torch
from diffusers import AutoencoderKL, TCDScheduler
from diffusers.models.model_loading_utils import load_state_dict
from gradio_imageslider import ImageSlider
from huggingface_hub import hf_hub_download
from controlnet_union import ControlNetModel_Union
from pipeline_fill_sd_xl import StableDiffusionXLFillPipeline
from PIL import Image, ImageDraw
import numpy as np
MODELS = {
"RealVisXL V5.0 Lightning": "SG161222/RealVisXL_V5.0_Lightning",
}
config_file = hf_hub_download(
"xinsir/controlnet-union-sdxl-1.0",
filename="config_promax.json",
)
config = ControlNetModel_Union.load_config(config_file)
controlnet_model = ControlNetModel_Union.from_config(config)
model_file = hf_hub_download(
"xinsir/controlnet-union-sdxl-1.0",
filename="diffusion_pytorch_model_promax.safetensors",
)
state_dict = load_state_dict(model_file)
model, _, _, _, _ = ControlNetModel_Union._load_pretrained_model(
controlnet_model, state_dict, model_file, "xinsir/controlnet-union-sdxl-1.0"
)
model.to(device="cuda", dtype=torch.float16)
vae = AutoencoderKL.from_pretrained(
"madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16
).to("cuda")
pipe = StableDiffusionXLFillPipeline.from_pretrained(
"SG161222/RealVisXL_V5.0_Lightning",
torch_dtype=torch.float16,
vae=vae,
controlnet=model,
variant="fp16",
).to("cuda")
pipe.scheduler = TCDScheduler.from_config(pipe.scheduler.config)
@spaces.GPU
def infer(image, model_selection, width, height, overlap_width, num_inference_steps, prompt_input=None):
source = image
target_size = (width, height)
target_ratio = (width, height) # Calculate aspect ratio from width and height
overlap = overlap_width
# Upscale if source is smaller than target in both dimensions
if source.width < target_size[0] and source.height < target_size[1]:
scale_factor = min(target_size[0] / source.width, target_size[1] / source.height)
new_width = int(source.width * scale_factor)
new_height = int(source.height * scale_factor)
source = source.resize((new_width, new_height), Image.LANCZOS)
if source.width > target_size[0] or source.height > target_size[1]:
scale_factor = min(target_size[0] / source.width, target_size[1] / source.height)
new_width = int(source.width * scale_factor)
new_height = int(source.height * scale_factor)
source = source.resize((new_width, new_height), Image.LANCZOS)
margin_x = (target_size[0] - source.width) // 2
margin_y = (target_size[1] - source.height) // 2
background = Image.new('RGB', target_size, (255, 255, 255))
background.paste(source, (margin_x, margin_y))
mask = Image.new('L', target_size, 255)
mask_draw = ImageDraw.Draw(mask)
mask_draw.rectangle([
(margin_x + overlap, margin_y + overlap),
(margin_x + source.width - overlap, margin_y + source.height - overlap)
], fill=0)
cnet_image = background.copy()
cnet_image.paste(0, (0, 0), mask)
final_prompt = "high quality"
if prompt_input.strip() != "":
final_prompt += ", " + prompt_input
(
prompt_embeds,
negative_prompt_embeds,
pooled_prompt_embeds,
negative_pooled_prompt_embeds,
) = pipe.encode_prompt(final_prompt, "cuda", True)
for image in pipe(
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
pooled_prompt_embeds=pooled_prompt_embeds,
negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
image=cnet_image,
num_inference_steps=num_inference_steps
):
yield cnet_image, image
image = image.convert("RGBA")
cnet_image.paste(image, (0, 0), mask)
yield background, cnet_image
def preload_presets(target_ratio):
if target_ratio == "9:16":
changed_width = 720
changed_height = 1024
return changed_width, changed_height, gr.update(open=False)
elif target_ratio == "16:9":
changed_width = 1024
changed_height = 720
return changed_width, changed_height, gr.update(open=False)
elif target_ratio == "Custom":
return 720, 1024, gr.update(open=True)
def clear_result():
return gr.update(value=None)
css = """
.gradio-container {
width: 1800px !important;
}
"""
title = """<h1 align="center">Diffusers Image Outpaint</h1>
<div align="center">Drop an image you would like to extend, pick your expected ratio and hit Generate.</div>
"""
with gr.Blocks(css=css) as demo:
with gr.Column():
gr.HTML(title)
with gr.Row():
with gr.Column():
input_image = gr.Image(
type="pil",
label="Input Image",
sources=["upload"],
)
target_ratio = gr.Radio(
label = "Expected Ratio",
choices = ["9:16", "16:9", "Custom"],
value = "9:16"
)
with gr.Row():
with gr.Column(scale=2):
prompt_input = gr.Textbox(label="Prompt (Optional)")
with gr.Column(scale=1):
run_button = gr.Button("Generate")
with gr.Accordion(label="Advanced settings", open=False) as settings_panel:
with gr.Column():
with gr.Row():
width_slider = gr.Slider(
label="Width",
minimum=720,
maximum=1440,
step=8,
value=720, # Set a default value
)
height_slider = gr.Slider(
label="Height",
minimum=720,
maximum=1440,
step=8,
value=1024, # Set a default value
)
with gr.Row():
model_selection = gr.Dropdown(
choices=list(MODELS.keys()),
value="RealVisXL V5.0 Lightning",
label="Model",
)
num_inference_steps = gr.Slider(label="Steps", minimum=4, maximum=12, step=1, value=8 )
overlap_width = gr.Slider(
label="Mask overlap width",
minimum=1,
maximum=50,
value=42,
step=1
)
gr.Examples(
examples=[
["./examples/example_1.webp", "RealVisXL V5.0 Lightning", 1280, 720],
["./examples/example_2.jpg", "RealVisXL V5.0 Lightning", 720, 1280],
["./examples/example_3.jpg", "RealVisXL V5.0 Lightning", 1024, 1024],
],
inputs=[input_image, model_selection, width_slider, height_slider],
)
with gr.Column():
result = ImageSlider(
interactive=False,
label="Generated Image",
)
target_ratio.change(
fn = preload_presets,
inputs = [target_ratio],
outputs = [width_slider, height_slider, settings_panel],
queue = False
)
run_button.click(
fn=clear_result,
inputs=None,
outputs=result,
).then(
fn=infer,
inputs=[input_image, model_selection, width_slider, height_slider, overlap_width, num_inference_steps, prompt_input],
outputs=result,
)
prompt_input.submit(
fn=clear_result,
inputs=None,
outputs=result,
).then(
fn=infer,
inputs=[input_image, model_selection, width_slider, height_slider, overlap_width, num_inference_steps, prompt_input],
outputs=result,
)
demo.launch(share=False)