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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], | |
) | |