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from diffusion_webui.controlnet.controlnet_canny import stable_diffusion_controlnet_canny_app, stable_diffusion_controlnet_canny
from diffusion_webui.controlnet.controlnet_depth import stable_diffusion_controlnet_depth_app, stable_diffusion_controlnet_depth
from diffusion_webui.controlnet.controlnet_hed import stable_diffusion_controlnet_hed_app, stable_diffusion_controlnet_hed
from diffusion_webui.controlnet.controlnet_mlsd import stable_diffusion_controlnet_mlsd_app, stable_diffusion_controlnet_mlsd
from diffusion_webui.controlnet.controlnet_pose import stable_diffusion_controlnet_pose_app, stable_diffusion_controlnet_pose
from diffusion_webui.controlnet.controlnet_scribble import stable_diffusion_controlnet_scribble_app, stable_diffusion_controlnet_scribble
from diffusion_webui.controlnet.controlnet_seg import stable_diffusion_controlnet_seg_app, stable_diffusion_controlnet_seg
from diffusion_webui.stable_diffusion.text2img_app import stable_diffusion_text2img_app, stable_diffusion_text2img
from diffusion_webui.stable_diffusion.img2img_app import stable_diffusion_img2img_app, stable_diffusion_img2img
from diffusion_webui.stable_diffusion.inpaint_app import stable_diffusion_inpaint_app, stable_diffusion_inpaint
from diffusion_webui.stable_diffusion.keras_txt2img import keras_stable_diffusion, keras_stable_diffusion_app
import gradio as gr
app = gr.Blocks()
with app:
gr.HTML(
"""
<h1 style='text-align: center'>
Stable Diffusion + ControlNet WebUI
</h1>
"""
)
gr.Markdown(
"""
<h4 style='text-align: center'>
Follow me for more!
<a href='https://twitter.com/kadirnar_ai' target='_blank'>Twitter</a> | <a href='https://github.com/kadirnar' target='_blank'>Github</a> | <a href='https://www.linkedin.com/in/kadir-nar/' target='_blank'>Linkedin</a>
</h4>
"""
)
with gr.Row():
with gr.Column():
text2image_app = stable_diffusion_text2img_app()
img2img_app = stable_diffusion_img2img_app()
inpaint_app = stable_diffusion_inpaint_app()
with gr.Tab('ControlNet'):
controlnet_canny_app = stable_diffusion_controlnet_canny_app()
controlnet_hed_app = stable_diffusion_controlnet_hed_app()
controlnet_mlsd_app = stable_diffusion_controlnet_mlsd_app()
controlnet_depth_app = stable_diffusion_controlnet_depth_app()
controlnet_pose_app = stable_diffusion_controlnet_pose_app()
controlnet_scribble_app = stable_diffusion_controlnet_scribble_app()
controlnet_seg_app = stable_diffusion_controlnet_seg_app()
keras_diffusion_app = keras_stable_diffusion_app()
with gr.Tab('Output'):
with gr.Column():
output_image = gr.Image(label='Image')
text2image_app['predict'].click(
fn = stable_diffusion_text2img,
inputs = [
text2image_app['model_path'],
text2image_app['prompt'],
text2image_app['negative_prompt'],
text2image_app['guidance_scale'],
text2image_app['num_inference_step'],
text2image_app['height'],
text2image_app['width'],
],
outputs = [output_image],
)
img2img_app['predict'].click(
fn = stable_diffusion_img2img,
inputs = [
img2img_app['image_path'],
img2img_app['model_path'],
img2img_app['prompt'],
img2img_app['negative_prompt'],
img2img_app['guidance_scale'],
img2img_app['num_inference_step'],
],
outputs = [output_image],
)
inpaint_app['predict'].click(
fn = stable_diffusion_inpaint,
inputs = [
inpaint_app['image_path'],
inpaint_app['model_path'],
inpaint_app['prompt'],
inpaint_app['negative_prompt'],
inpaint_app['guidance_scale'],
inpaint_app['num_inference_step'],
],
outputs = [output_image],
)
controlnet_canny_app['predict'].click(
fn = stable_diffusion_controlnet_canny,
inputs = [
controlnet_canny_app['image_path'],
controlnet_canny_app['model_path'],
controlnet_canny_app['prompt'],
controlnet_canny_app['negative_prompt'],
controlnet_canny_app['guidance_scale'],
controlnet_canny_app['num_inference_step'],
],
outputs = [output_image],
)
controlnet_hed_app['predict'].click(
fn = stable_diffusion_controlnet_hed,
inputs = [
controlnet_hed_app['image_path'],
controlnet_hed_app['model_path'],
controlnet_hed_app['prompt'],
controlnet_hed_app['negative_prompt'],
controlnet_hed_app['guidance_scale'],
controlnet_hed_app['num_inference_step'],
],
outputs = [output_image],
)
controlnet_mlsd_app['predict'].click(
fn = stable_diffusion_controlnet_mlsd,
inputs = [
controlnet_mlsd_app['image_path'],
controlnet_mlsd_app['model_path'],
controlnet_mlsd_app['prompt'],
controlnet_mlsd_app['negative_prompt'],
controlnet_mlsd_app['guidance_scale'],
controlnet_mlsd_app['num_inference_step'],
],
outputs = [output_image],
)
controlnet_depth_app['predict'].click(
fn = stable_diffusion_controlnet_seg,
inputs = [
controlnet_depth_app['image_path'],
controlnet_depth_app['model_path'],
controlnet_depth_app['prompt'],
controlnet_depth_app['negative_prompt'],
controlnet_depth_app['guidance_scale'],
controlnet_depth_app['num_inference_step'],
],
outputs = [output_image],
)
controlnet_pose_app['predict'].click(
fn = stable_diffusion_controlnet_depth,
inputs = [
controlnet_pose_app['image_path'],
controlnet_pose_app['model_path'],
controlnet_pose_app['prompt'],
controlnet_pose_app['negative_prompt'],
controlnet_pose_app['guidance_scale'],
controlnet_pose_app['num_inference_step'],
],
outputs = [output_image],
)
controlnet_scribble_app['predict'].click(
fn = stable_diffusion_controlnet_scribble,
inputs = [
controlnet_scribble_app['image_path'],
controlnet_scribble_app['model_path'],
controlnet_scribble_app['prompt'],
controlnet_scribble_app['negative_prompt'],
controlnet_scribble_app['guidance_scale'],
controlnet_scribble_app['num_inference_step'],
],
outputs = [output_image],
)
controlnet_seg_app['predict'].click(
fn = stable_diffusion_controlnet_pose,
inputs = [
controlnet_seg_app['image_path'],
controlnet_seg_app['model_path'],
controlnet_seg_app['prompt'],
controlnet_seg_app['negative_prompt'],
controlnet_seg_app['guidance_scale'],
controlnet_seg_app['num_inference_step'],
],
outputs = [output_image],
)
keras_diffusion_app['predict'].click(
fn = keras_stable_diffusion,
inputs = [
keras_diffusion_app['model_path'],
keras_diffusion_app['prompt'],
keras_diffusion_app['negative_prompt'],
keras_diffusion_app['guidance_scale'],
keras_diffusion_app['num_inference_step'],
keras_diffusion_app['height'],
keras_diffusion_app['width'],
],
outputs = [output_image],
)
app.launch(debug=True) |