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import gradio as gr
import torch
from controlnet_aux import MLSDdetector
from diffusers import (
ControlNetModel,
StableDiffusionControlNetPipeline,
UniPCMultistepScheduler,
)
from PIL import Image
stable_model_list = [
"runwayml/stable-diffusion-v1-5",
]
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_mlsd(image_path: str):
mlsd = MLSDdetector.from_pretrained("lllyasviel/ControlNet")
image = Image.open(image_path)
image = mlsd(image)
controlnet = ControlNetModel.from_pretrained(
"lllyasviel/sd-controlnet-mlsd",
torch_dtype=torch.float16,
)
return controlnet, image
def stable_diffusion_controlnet_mlsd(
image_path: str,
model_path: str,
prompt: str,
negative_prompt: str,
guidance_scale: int,
num_inference_step: int,
):
controlnet, image = controlnet_mlsd(image_path=image_path)
pipe = StableDiffusionControlNetPipeline.from_pretrained(
pretrained_model_name_or_path=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_mlsd_app():
with gr.Blocks():
with gr.Row():
with gr.Column():
controlnet_mlsd_image_file = gr.Image(
type="filepath", label="Image"
)
controlnet_mlsd_model_id = gr.Dropdown(
choices=stable_model_list,
value=stable_model_list[0],
label="Stable Model Id",
)
controlnet_mlsd_prompt = gr.Textbox(
lines=1, value=stable_prompt_list[0], label="Prompt"
)
controlnet_mlsd_negative_prompt = gr.Textbox(
lines=1,
value=stable_negative_prompt_list[0],
label="Negative Prompt",
)
with gr.Accordion("Advanced Options", open=False):
controlnet_mlsd_guidance_scale = gr.Slider(
minimum=0.1,
maximum=15,
step=0.1,
value=7.5,
label="Guidance Scale",
)
controlnet_mlsd_num_inference_step = gr.Slider(
minimum=1,
maximum=100,
step=1,
value=50,
label="Num Inference Step",
)
controlnet_mlsd_predict = gr.Button(value="Generator")
with gr.Column():
output_image = gr.Image(label="Output")
gr.Examples(
fn=stable_diffusion_controlnet_mlsd,
examples=[
[
data_list[0],
stable_model_list[0],
stable_prompt_list[0],
stable_negative_prompt_list[0],
7.5,
50,
]
],
inputs=[
controlnet_mlsd_image_file,
controlnet_mlsd_model_id,
controlnet_mlsd_prompt,
controlnet_mlsd_negative_prompt,
controlnet_mlsd_guidance_scale,
controlnet_mlsd_num_inference_step,
],
outputs=[output_image],
label="ControlNet-MLSD Example",
cache_examples=False,
)
controlnet_mlsd_predict.click(
fn=stable_diffusion_controlnet_mlsd,
inputs=[
controlnet_mlsd_image_file,
controlnet_mlsd_model_id,
controlnet_mlsd_prompt,
controlnet_mlsd_negative_prompt,
controlnet_mlsd_guidance_scale,
controlnet_mlsd_num_inference_step,
],
outputs=output_image,
)
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