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import gradio as gr
import torch
from controlnet_aux import HEDdetector
from diffusers import (
ControlNetModel,
StableDiffusionControlNetPipeline,
UniPCMultistepScheduler,
)
from PIL import Image
stable_model_list = [
"runwayml/stable-diffusion-v1-5",
"stabilityai/stable-diffusion-2-1",
]
controlnet_hed_model_list = [
"lllyasviel/sd-controlnet-hed",
"thibaud/controlnet-sd21-hed-diffusers",
]
stable_prompt_list = ["a photo of a man.", "a photo of a girl."]
stable_negative_prompt_list = ["bad, ugly", "deformed"]
data_list = [
"data/test.png",
]
def controlnet_hed(image_path: str, controlnet_hed_model_path: str):
hed = HEDdetector.from_pretrained("lllyasviel/ControlNet")
image = Image.open(image_path)
image = hed(image)
controlnet = ControlNetModel.from_pretrained(
controlnet_hed_model_path, torch_dtype=torch.float16
)
return controlnet, image
def stable_diffusion_controlnet_hed(
image_path: str,
stable_model_path: str,
controlnet_hed_model_path: str,
prompt: str,
negative_prompt: str,
guidance_scale: int,
num_inference_step: int,
):
controlnet, image = controlnet_hed(
image_path=image_path,
controlnet_hed_model_path=controlnet_hed_model_path,
)
pipe = StableDiffusionControlNetPipeline.from_pretrained(
pretrained_model_name_or_path=stable_model_path,
controlnet=controlnet,
safety_checker=None,
torch_dtype=torch.float16,
)
pipe.to("cuda")
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
pipe.enable_xformers_memory_efficient_attention()
output = pipe(
prompt=prompt,
image=image,
negative_prompt=negative_prompt,
num_inference_steps=num_inference_step,
guidance_scale=guidance_scale,
).images
return output[0]
def stable_diffusion_controlnet_hed_app():
with gr.Blocks():
with gr.Row():
with gr.Column():
controlnet_hed_image_file = gr.Image(
type="filepath", label="Image"
)
controlnet_hed_stable_model_id = gr.Dropdown(
choices=stable_model_list,
value=stable_model_list[0],
label="Stable Model Id",
)
controlnet_hed_model_id = gr.Dropdown(
choices=controlnet_hed_model_list,
value=controlnet_hed_model_list[1],
label="ControlNet Model Id",
)
controlnet_hed_prompt = gr.Textbox(
lines=1, value=stable_prompt_list[0], label="Prompt"
)
controlnet_hed_negative_prompt = gr.Textbox(
lines=1,
value=stable_negative_prompt_list[0],
label="Negative Prompt",
)
with gr.Accordion("Advanced Options", open=False):
controlnet_hed_guidance_scale = gr.Slider(
minimum=0.1,
maximum=15,
step=0.1,
value=7.5,
label="Guidance Scale",
)
controlnet_hed_num_inference_step = gr.Slider(
minimum=1,
maximum=100,
step=1,
value=50,
label="Num Inference Step",
)
controlnet_hed_predict = gr.Button(value="Generator")
with gr.Column():
output_image = gr.Image(label="Output")
gr.Examples(
fn=stable_diffusion_controlnet_hed,
examples=[
[
data_list[0],
stable_model_list[0],
controlnet_hed_model_list[0],
stable_prompt_list[0],
stable_negative_prompt_list[0],
7.5,
50,
]
],
inputs=[
controlnet_hed_image_file,
controlnet_hed_stable_model_id,
controlnet_hed_model_id,
controlnet_hed_prompt,
controlnet_hed_negative_prompt,
controlnet_hed_guidance_scale,
controlnet_hed_num_inference_step,
],
outputs=[output_image],
cache_examples=False,
label="ControlNet HED Example",
)
controlnet_hed_predict.click(
fn=stable_diffusion_controlnet_hed,
inputs=[
controlnet_hed_image_file,
controlnet_hed_stable_model_id,
controlnet_hed_model_id,
controlnet_hed_prompt,
controlnet_hed_negative_prompt,
controlnet_hed_guidance_scale,
controlnet_hed_num_inference_step,
],
outputs=[output_image],
)
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