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import gradio as gr
import torch
from controlnet_aux import HEDdetector
from diffusers import ControlNetModel, StableDiffusionControlNetPipeline
from PIL import Image
from diffusion_webui.utils.model_list import (
controlnet_hed_model_list,
stable_model_list,
)
from diffusion_webui.utils.scheduler_list import (
SCHEDULER_LIST,
get_scheduler_list,
)
class StableDiffusionControlNetHEDGenerator:
def __init__(self):
self.pipe = None
def load_model(self, stable_model_path, controlnet_model_path, scheduler):
if self.pipe is None:
controlnet = ControlNetModel.from_pretrained(
controlnet_model_path, torch_dtype=torch.float16
)
self.pipe = StableDiffusionControlNetPipeline.from_pretrained(
pretrained_model_name_or_path=stable_model_path,
controlnet=controlnet,
safety_checker=None,
torch_dtype=torch.float16,
)
self.pipe = get_scheduler_list(pipe=self.pipe, scheduler=scheduler)
self.pipe.to("cuda")
self.pipe.enable_xformers_memory_efficient_attention()
return self.pipe
def controlnet_hed(self, image_path: str):
hed = HEDdetector.from_pretrained("lllyasviel/ControlNet")
image = Image.open(image_path)
image = hed(image)
return image
def generate_image(
self,
image_path: str,
stable_model_path: str,
controlnet_hed_model_path: str,
prompt: str,
negative_prompt: str,
num_images_per_prompt: int,
guidance_scale: int,
num_inference_step: int,
sheduler: str,
seed_generator: int,
):
image = self.controlnet_hed(image_path=image_path)
pipe = self.load_model(
stable_model_path=stable_model_path,
controlnet_model_path=controlnet_hed_model_path,
scheduler=sheduler,
)
if seed_generator == 0:
random_seed = torch.randint(0, 1000000, (1,))
generator = torch.manual_seed(random_seed)
else:
generator = torch.manual_seed(seed_generator)
output = pipe(
prompt=prompt,
image=image,
negative_prompt=negative_prompt,
num_images_per_prompt=num_images_per_prompt,
num_inference_steps=num_inference_step,
guidance_scale=guidance_scale,
generator=generator,
).images
return output
def app():
with gr.Blocks():
with gr.Row():
with gr.Column():
controlnet_hed_image_file = gr.Image(
type="filepath", label="Image"
)
controlnet_hed_prompt = gr.Textbox(
lines=1,
show_label=False,
placeholder="Prompt",
)
controlnet_hed_negative_prompt = gr.Textbox(
lines=1,
show_label=False,
placeholder="Negative Prompt",
)
with gr.Row():
with gr.Column():
controlnet_hed_stable_model_id = gr.Dropdown(
choices=stable_model_list,
value=stable_model_list[0],
label="Stable Model Id",
)
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_num_images_per_prompt = gr.Slider(
minimum=1,
maximum=10,
step=1,
value=1,
label="Number Of Images",
)
with gr.Row():
with gr.Column():
controlnet_hed_model_id = gr.Dropdown(
choices=controlnet_hed_model_list,
value=controlnet_hed_model_list[0],
label="ControlNet Model Id",
)
controlnet_hed_scheduler = gr.Dropdown(
choices=SCHEDULER_LIST,
value=SCHEDULER_LIST[0],
label="Scheduler",
)
controlnet_hed_seed_generator = gr.Number(
minimum=0,
maximum=1000000,
step=1,
value=0,
label="Seed Generator",
)
controlnet_hed_predict = gr.Button(value="Generator")
with gr.Column():
output_image = gr.Gallery(
label="Generated images",
show_label=False,
elem_id="gallery",
).style(grid=(1, 2))
controlnet_hed_predict.click(
fn=StableDiffusionControlNetHEDGenerator().generate_image,
inputs=[
controlnet_hed_image_file,
controlnet_hed_stable_model_id,
controlnet_hed_model_id,
controlnet_hed_prompt,
controlnet_hed_negative_prompt,
controlnet_hed_num_images_per_prompt,
controlnet_hed_guidance_scale,
controlnet_hed_num_inference_step,
controlnet_hed_scheduler,
controlnet_hed_seed_generator,
],
outputs=[output_image],
)
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