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import gradio as gr
import spaces
import torch
from diffusers import AutoencoderKL, TCDScheduler, DPMSolverMultistepScheduler
from diffusers.models.model_loading_utils import load_state_dict
from gradio_imageslider import ImageSlider
from huggingface_hub import hf_hub_download
from PIL import ImageDraw, ImageFont, Image
from controlnet_union import ControlNetModel_Union
from pipeline_fill_sd_xl import StableDiffusionXLFillPipeline
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,algorithm_type="dpmsolver++",use_karras_sigmas=True)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
def add_watermark(image, text="ProFaker", font_path="BRLNSDB.TTF", font_size=25):
# Load the Berlin Sans Demi font with the specified size
font = ImageFont.truetype(font_path, font_size)
# Position the watermark in the bottom right corner, adjusting for text size
text_bbox = font.getbbox(text)
text_width, text_height = text_bbox[2], text_bbox[3]
watermark_position = (image.width - text_width - 100, image.height - text_height - 150)
# Draw the watermark text with a translucent white color
draw = ImageDraw.Draw(image)
draw.text(watermark_position, text, font=font, fill=(255, 255, 255, 150)) # RGBA for transparency
return image
@spaces.GPU
def fill_image(prompt, negative_prompt, image, model_selection, paste_back, guidance_scale, num_steps):
(
prompt_embeds,
negative_prompt_embeds,
pooled_prompt_embeds,
negative_pooled_prompt_embeds,
) = pipe.encode_prompt(prompt, "cuda", True,negative_prompt=negative_prompt)
source = image["background"]
mask = image["layers"][0]
alpha_channel = mask.split()[3]
binary_mask = alpha_channel.point(lambda p: p > 0 and 255)
cnet_image = source.copy()
cnet_image.paste(0, (0, 0), binary_mask)
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,
guidance_scale = guidance_scale,
num_inference_steps = num_steps,
):
yield image, cnet_image
print(f"{model_selection=}")
print(f"{paste_back=}")
if paste_back:
image = image.convert("RGBA")
cnet_image.paste(image, (0, 0), binary_mask)
else:
cnet_image = image
cnet_image = add_watermark(cnet_image)
yield source, cnet_image
def clear_result():
return gr.update(value=None)
title = """<h1 align="center">ProFaker</h1>"""
with gr.Blocks() as demo:
gr.HTML(title)
with gr.Row():
with gr.Column():
prompt = gr.Textbox(
label="Prompt",
info="Describe what to inpaint the mask with",
lines=3,
)
with gr.Accordion("Advanced Options", open=False):
negative_prompt = gr.Textbox(
label="Negative Prompt",
info="Describe what you dont want in the mask",
lines=3,
)
guidance_scale = gr.Slider(
minimum=1,
maximum=10,
value=1.5,
step=0.1,
label="Guidance Scale"
)
num_steps = gr.Slider(
minimum=5,
maximum=100,
value=10,
step=1,
label="Steps"
)
input_image = gr.ImageMask(
type="pil", label="Input Image",crop_size=(1200,1200), layers=False
)
with gr.Column():
model_selection = gr.Dropdown(
choices=list(MODELS.keys()),
value="RealVisXL V5.0 Lightning",
label="Model",
)
with gr.Row():
with gr.Column():
run_button = gr.Button("Generate")
with gr.Column():
paste_back = gr.Checkbox(True, label="Paste back original")
result = ImageSlider(
interactive=False,
label="Generated Image",
type="pil"
)
use_as_input_button = gr.Button("Use as Input Image", visible=False)
def use_output_as_input(output_image):
return gr.update(value=output_image[1])
use_as_input_button.click(
fn=use_output_as_input, inputs=[result], outputs=[input_image]
)
run_button.click(
fn=clear_result,
inputs=None,
outputs=result,
).then(
fn=lambda: gr.update(visible=False),
inputs=None,
outputs=use_as_input_button,
).then(
fn=fill_image,
inputs=[prompt, negative_prompt, input_image, model_selection, paste_back, guidance_scale, num_steps],
outputs=result,
).then(
fn=lambda: gr.update(visible=True),
inputs=None,
outputs=use_as_input_button,
)
prompt.submit(
fn=clear_result,
inputs=None,
outputs=result,
).then(
fn=lambda: gr.update(visible=False),
inputs=None,
outputs=use_as_input_button,
).then(
fn=fill_image,
inputs=[prompt, negative_prompt, input_image, model_selection, paste_back, guidance_scale, num_steps],
outputs=result,
).then(
fn=lambda: gr.update(visible=True),
inputs=None,
outputs=use_as_input_button,
)
demo.queue(max_size=12).launch(share=False)
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