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import huggingface_hub
import gradio as gr
from stable_diffusion_reference_only.pipelines.pipeline_stable_diffusion_reference_only import (
StableDiffusionReferenceOnlyPipeline,
)
from anime_segmentation import get_model as get_anime_segmentation_model
from anime_segmentation import character_segment as anime_character_segment
from diffusers.schedulers import UniPCMultistepScheduler
from PIL import Image
import cv2
import numpy as np
import os
import torch
print(f"Is CUDA available: {torch.cuda.is_available()}")
if torch.cuda.is_available():
device = "cuda"
else:
device = "cpu"
automatic_coloring_pipeline = StableDiffusionReferenceOnlyPipeline.from_pretrained(
"AisingioroHao0/stable-diffusion-reference-only-automatic-coloring-0.1.2"
).to(device)
automatic_coloring_pipeline.scheduler = UniPCMultistepScheduler.from_config(
automatic_coloring_pipeline.scheduler.config
)
segment_model = get_anime_segmentation_model(
model_path=huggingface_hub.hf_hub_download("skytnt/anime-seg", "isnetis.ckpt")
).to(device)
def character_segment(img):
if img is None:
return None
img = anime_character_segment(segment_model, img)
img = cv2.cvtColor(img, cv2.COLOR_RGBA2RGB)
return img
def color_inversion(img):
if img is None:
return None
return 255 - img
def get_line_art(img):
if img is None:
return None
img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
img = cv2.adaptiveThreshold(
img,
255,
cv2.ADAPTIVE_THRESH_MEAN_C,
cv2.THRESH_BINARY,
blockSize=5,
C=7,
)
img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
return img
def inference(prompt, blueprint, num_inference_steps):
if prompt is None or blueprint is None:
return None
return np.array(
automatic_coloring_pipeline(
prompt=Image.fromarray(prompt),
blueprint=Image.fromarray(blueprint),
num_inference_steps=num_inference_steps,
).images[0]
)
def automatic_coloring(prompt, blueprint, num_inference_steps):
if prompt is None or blueprint is None:
return None
blueprint = color_inversion(blueprint)
return inference(prompt, blueprint, num_inference_steps)
def style_transfer(prompt, blueprint, num_inference_steps):
if prompt is None or blueprint is None:
return None
prompt = character_segment(prompt)
blueprint = character_segment(blueprint)
blueprint = get_line_art(blueprint)
blueprint = color_inversion(blueprint)
return inference(prompt, blueprint, num_inference_steps)
def resize(img, new_height, new_width):
img = Image.fromarray(img).resize((int(new_width), int(new_height)), Image.BILINEAR)
return np.array(img)
with gr.Blocks() as demo:
gr.Markdown(
"""
# Stable Diffusion Reference Only Automatic Coloring 0.1.2\n\n
demo for [https://github.com/aihao2000/stable-diffusion-reference-only](https://github.com/aihao2000/stable-diffusion-reference-only)
"""
)
with gr.Row():
with gr.Column():
prompt_input_compoent = gr.Image(label="prompt")
with gr.Row():
prompt_new_height = gr.Number(512, label="height", minimum=1)
prompt_new_width = gr.Number(512, label="width", minimum=1)
prompt_resize_button = gr.Button("prompt resize")
prompt_resize_button.click(
resize,
inputs=[
prompt_input_compoent,
prompt_new_height,
prompt_new_width,
],
outputs=prompt_input_compoent,
)
prompt_character_segment_button = gr.Button(
"character segment",
)
prompt_character_segment_button.click(
character_segment,
inputs=prompt_input_compoent,
outputs=prompt_input_compoent,
)
with gr.Column():
blueprint_input_compoent = gr.Image(label="blueprint")
with gr.Row():
blueprint_new_height = gr.Number(512, label="height", minimum=1)
blueprint_new_width = gr.Number(512, label="width", minimum=1)
blueprint_resize_button = gr.Button("blueprint resize")
blueprint_resize_button.click(
resize,
inputs=[
blueprint_input_compoent,
blueprint_new_height,
blueprint_new_width,
],
outputs=blueprint_input_compoent,
)
blueprint_character_segment_button = gr.Button("character segment")
blueprint_character_segment_button.click(
character_segment,
inputs=blueprint_input_compoent,
outputs=blueprint_input_compoent,
)
get_line_art_button = gr.Button(
"get line art",
)
get_line_art_button.click(
get_line_art,
inputs=blueprint_input_compoent,
outputs=blueprint_input_compoent,
)
color_inversion_button = gr.Button(
"color inversion",
)
color_inversion_button.click(
color_inversion,
inputs=blueprint_input_compoent,
outputs=blueprint_input_compoent,
)
with gr.Column():
result_output_component = gr.Image(label="result")
num_inference_steps_input_component = gr.Number(
20, label="num inference steps", minimum=1, maximum=1000, step=1
)
inference_button = gr.Button("inference")
inference_button.click(
inference,
inputs=[
prompt_input_compoent,
blueprint_input_compoent,
num_inference_steps_input_component,
],
outputs=result_output_component,
)
automatic_coloring_button = gr.Button("automatic coloring")
automatic_coloring_button.click(
automatic_coloring,
inputs=[
prompt_input_compoent,
blueprint_input_compoent,
num_inference_steps_input_component,
],
outputs=result_output_component,
)
style_transfer_button = gr.Button("style transfer")
style_transfer_button.click(
style_transfer,
inputs=[
prompt_input_compoent,
blueprint_input_compoent,
num_inference_steps_input_component,
],
outputs=result_output_component,
)
with gr.Row():
gr.Examples(
examples=[
[
os.path.join(
os.path.dirname(__file__), "README.assets", "3x9_prompt.png"
),
os.path.join(
os.path.dirname(__file__), "README.assets", "3x9_blueprint.png"
),
],
],
inputs=[prompt_input_compoent, blueprint_input_compoent],
outputs=result_output_component,
fn=lambda x, y: None,
cache_examples=True,
)
if __name__ == "__main__":
demo.queue(max_size=5).launch()
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