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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
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)
def can_expand(source_width, source_height, target_width, target_height, alignment):
"""Checks if the image can be expanded based on the alignment."""
if alignment in ("Left", "Right") and source_width >= target_width:
return False
if alignment in ("Top", "Bottom") and source_height >= target_height:
return False
return True
def prepare_image_and_mask(image, width, height, overlap_percentage, resize_option, custom_resize_percentage, alignment, overlap_left, overlap_right, overlap_top, overlap_bottom):
target_size = (width, height)
# Calculate the scaling factor to fit the image within the target size
scale_factor = min(target_size[0] / image.width, target_size[1] / image.height)
new_width = int(image.width * scale_factor)
new_height = int(image.height * scale_factor)
# Resize the source image to fit within target size
source = image.resize((new_width, new_height), Image.LANCZOS)
# Apply resize option using percentages
if resize_option == "Full":
resize_percentage = 100
elif resize_option == "50%":
resize_percentage = 50
elif resize_option == "33%":
resize_percentage = 33
elif resize_option == "25%":
resize_percentage = 25
else: # Custom
resize_percentage = custom_resize_percentage
# Calculate new dimensions based on percentage
resize_factor = resize_percentage / 100
new_width = int(source.width * resize_factor)
new_height = int(source.height * resize_factor)
# Ensure minimum size of 64 pixels
new_width = max(new_width, 64)
new_height = max(new_height, 64)
# Resize the image
source = source.resize((new_width, new_height), Image.LANCZOS)
# Calculate the overlap in pixels based on the percentage
overlap_x = int(new_width * (overlap_percentage / 100))
overlap_y = int(new_height * (overlap_percentage / 100))
# Ensure minimum overlap of 1 pixel
overlap_x = max(overlap_x, 1)
overlap_y = max(overlap_y, 1)
# Calculate margins based on alignment
if alignment == "Middle":
margin_x = (target_size[0] - new_width) // 2
margin_y = (target_size[1] - new_height) // 2
elif alignment == "Left":
margin_x = 0
margin_y = (target_size[1] - new_height) // 2
elif alignment == "Right":
margin_x = target_size[0] - new_width
margin_y = (target_size[1] - new_height) // 2
elif alignment == "Top":
margin_x = (target_size[0] - new_width) // 2
margin_y = 0
elif alignment == "Bottom":
margin_x = (target_size[0] - new_width) // 2
margin_y = target_size[1] - new_height
# Adjust margins to eliminate gaps
margin_x = max(0, min(margin_x, target_size[0] - new_width))
margin_y = max(0, min(margin_y, target_size[1] - new_height))
# Create a new background image and paste the resized source image
background = Image.new('RGB', target_size, (255, 255, 255))
background.paste(source, (margin_x, margin_y))
# Create the mask
mask = Image.new('L', target_size, 255)
mask_draw = ImageDraw.Draw(mask)
# Calculate overlap areas
white_gaps_patch = 2
left_overlap = margin_x + overlap_x if overlap_left else margin_x + white_gaps_patch
right_overlap = margin_x + new_width - overlap_x if overlap_right else margin_x + new_width - white_gaps_patch
top_overlap = margin_y + overlap_y if overlap_top else margin_y + white_gaps_patch
bottom_overlap = margin_y + new_height - overlap_y if overlap_bottom else margin_y + new_height - white_gaps_patch
if alignment == "Left":
left_overlap = margin_x + overlap_x if overlap_left else margin_x
elif alignment == "Right":
right_overlap = margin_x + new_width - overlap_x if overlap_right else margin_x + new_width
elif alignment == "Top":
top_overlap = margin_y + overlap_y if overlap_top else margin_y
elif alignment == "Bottom":
bottom_overlap = margin_y + new_height - overlap_y if overlap_bottom else margin_y + new_height
# Draw the mask
mask_draw.rectangle([
(left_overlap, top_overlap),
(right_overlap, bottom_overlap)
], fill=0)
return background, mask
def preview_image_and_mask(image, width, height, overlap_percentage, resize_option, custom_resize_percentage, alignment, overlap_left, overlap_right, overlap_top, overlap_bottom):
background, mask = prepare_image_and_mask(image, width, height, overlap_percentage, resize_option, custom_resize_percentage, alignment, overlap_left, overlap_right, overlap_top, overlap_bottom)
# Create a preview image showing the mask
preview = background.copy().convert('RGBA')
# Create a semi-transparent red overlay
red_overlay = Image.new('RGBA', background.size, (255, 0, 0, 64)) # Reduced alpha to 64 (25% opacity)
# Convert black pixels in the mask to semi-transparent red
red_mask = Image.new('RGBA', background.size, (0, 0, 0, 0))
red_mask.paste(red_overlay, (0, 0), mask)
# Overlay the red mask on the background
preview = Image.alpha_composite(preview, red_mask)
return preview
@spaces.GPU(duration=24)
def infer(image, width, height, overlap_percentage, num_inference_steps, resize_option, custom_resize_percentage, prompt_input, alignment, overlap_left, overlap_right, overlap_top, overlap_bottom):
background, mask = prepare_image_and_mask(image, width, height, overlap_percentage, resize_option, custom_resize_percentage, alignment, overlap_left, overlap_right, overlap_top, overlap_bottom)
if not can_expand(background.width, background.height, width, height, alignment):
alignment = "Middle"
cnet_image = background.copy()
cnet_image.paste(0, (0, 0), mask)
final_prompt = f"{prompt_input} , high quality, 4k"
(
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 clear_result():
"""Clears the result ImageSlider."""
return gr.update(value=None)
def preload_presets(target_ratio, ui_width, ui_height):
"""Updates the width and height sliders based on the selected aspect ratio."""
if target_ratio == "9:16":
changed_width = 720
changed_height = 1280
return changed_width, changed_height, gr.update()
elif target_ratio == "16:9":
changed_width = 1280
changed_height = 720
return changed_width, changed_height, gr.update()
elif target_ratio == "1:1":
changed_width = 1024
changed_height = 1024
return changed_width, changed_height, gr.update()
elif target_ratio == "Custom":
return ui_width, ui_height, gr.update(open=True)
def select_the_right_preset(user_width, user_height):
if user_width == 720 and user_height == 1280:
return "9:16"
elif user_width == 1280 and user_height == 720:
return "16:9"
elif user_width == 1024 and user_height == 1024:
return "1:1"
else:
return "Custom"
def toggle_custom_resize_slider(resize_option):
return gr.update(visible=(resize_option == "Custom"))
def update_history(new_image, history):
"""Updates the history gallery with the new image."""
if history is None:
history = []
history.insert(0, new_image)
return history
css = """
.gradio-container {
width: 1200px !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>
<div style="display: flex; justify-content: center; align-items: center; text-align: center;">
<p style="display: flex;gap: 6px;">
<a href="https://huggingface.co/spaces/fffiloni/diffusers-image-outpout?duplicate=true">
<img src="https://huggingface.co/datasets/huggingface/badges/resolve/main/duplicate-this-space-md.svg" alt="Duplicate this Space">
</a> to skip the queue and enjoy faster inference on the GPU of your choice
</p>
</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"
)
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.Row():
target_ratio = gr.Radio(
label="Expected Ratio",
choices=["9:16", "16:9", "1:1", "Custom"],
value="9:16",
scale=2
)
alignment_dropdown = gr.Dropdown(
choices=["Middle", "Left", "Right", "Top", "Bottom"],
value="Middle",
label="Alignment"
)
with gr.Accordion(label="Advanced settings", open=False) as settings_panel:
with gr.Column():
with gr.Row():
width_slider = gr.Slider(
label="Target Width",
minimum=720,
maximum=1536,
step=8,
value=720, # Set a default value
)
height_slider = gr.Slider(
label="Target Height",
minimum=720,
maximum=1536,
step=8,
value=1280, # Set a default value
)
num_inference_steps = gr.Slider(label="Steps", minimum=4, maximum=12, step=1, value=8)
with gr.Group():
overlap_percentage = gr.Slider(
label="Mask overlap (%)",
minimum=1,
maximum=50,
value=10,
step=1
)
with gr.Row():
overlap_top = gr.Checkbox(label="Overlap Top", value=True)
overlap_right = gr.Checkbox(label="Overlap Right", value=True)
with gr.Row():
overlap_left = gr.Checkbox(label="Overlap Left", value=True)
overlap_bottom = gr.Checkbox(label="Overlap Bottom", value=True)
with gr.Row():
resize_option = gr.Radio(
label="Resize input image",
choices=["Full", "50%", "33%", "25%", "Custom"],
value="Full"
)
custom_resize_percentage = gr.Slider(
label="Custom resize (%)",
minimum=1,
maximum=100,
step=1,
value=50,
visible=False
)
with gr.Column():
preview_button = gr.Button("Preview alignment and mask")
gr.Examples(
examples=[
["./examples/example_1.webp", 1280, 720, "Middle"],
["./examples/example_2.jpg", 1440, 810, "Left"],
["./examples/example_3.jpg", 1024, 1024, "Top"],
["./examples/example_3.jpg", 1024, 1024, "Bottom"],
],
inputs=[input_image, width_slider, height_slider, alignment_dropdown],
)
with gr.Column():
result = ImageSlider(
interactive=False,
label="Generated Image",
)
use_as_input_button = gr.Button("Use as Input Image", visible=False)
history_gallery = gr.Gallery(label="History", columns=6, object_fit="contain", interactive=False)
preview_image = gr.Image(label="Preview")
def use_output_as_input(output_image):
"""Sets the generated output as the new input image."""
return gr.update(value=output_image[1])
use_as_input_button.click(
fn=use_output_as_input,
inputs=[result],
outputs=[input_image]
)
target_ratio.change(
fn=preload_presets,
inputs=[target_ratio, width_slider, height_slider],
outputs=[width_slider, height_slider, settings_panel],
queue=False
)
width_slider.change(
fn=select_the_right_preset,
inputs=[width_slider, height_slider],
outputs=[target_ratio],
queue=False
)
height_slider.change(
fn=select_the_right_preset,
inputs=[width_slider, height_slider],
outputs=[target_ratio],
queue=False
)
resize_option.change(
fn=toggle_custom_resize_slider,
inputs=[resize_option],
outputs=[custom_resize_percentage],
queue=False
)
run_button.click( # Clear the result
fn=clear_result,
inputs=None,
outputs=result,
).then( # Generate the new image
fn=infer,
inputs=[input_image, width_slider, height_slider, overlap_percentage, num_inference_steps,
resize_option, custom_resize_percentage, prompt_input, alignment_dropdown,
overlap_left, overlap_right, overlap_top, overlap_bottom],
outputs=result,
).then( # Update the history gallery
fn=lambda x, history: update_history(x[1], history),
inputs=[result, history_gallery],
outputs=history_gallery,
).then( # Show the "Use as Input Image" button
fn=lambda: gr.update(visible=True),
inputs=None,
outputs=use_as_input_button,
)
prompt_input.submit( # Clear the result
fn=clear_result,
inputs=None,
outputs=result,
).then( # Generate the new image
fn=infer,
inputs=[input_image, width_slider, height_slider, overlap_percentage, num_inference_steps,
resize_option, custom_resize_percentage, prompt_input, alignment_dropdown,
overlap_left, overlap_right, overlap_top, overlap_bottom],
outputs=result,
).then( # Update the history gallery
fn=lambda x, history: update_history(x[1], history),
inputs=[result, history_gallery],
outputs=history_gallery,
).then( # Show the "Use as Input Image" button
fn=lambda: gr.update(visible=True),
inputs=None,
outputs=use_as_input_button,
)
preview_button.click(
fn=preview_image_and_mask,
inputs=[input_image, width_slider, height_slider, overlap_percentage, resize_option, custom_resize_percentage, alignment_dropdown,
overlap_left, overlap_right, overlap_top, overlap_bottom],
outputs=preview_image,
queue=False
)
demo.queue(max_size=12).launch(share=False) |