|
import gradio as gr |
|
import torch |
|
import cv2 |
|
from diffusers import ControlNetModel, StableDiffusionControlNetPipeline |
|
from PIL import Image |
|
|
|
from diffusion_webui.diffusion_models.base_controlnet_pipeline import ( |
|
ControlnetPipeline, |
|
) |
|
from diffusion_webui.utils.model_list import ( |
|
controlnet_model_list, |
|
stable_model_list, |
|
) |
|
from diffusion_webui.utils.preprocces_utils import PREPROCCES_DICT |
|
from diffusion_webui.utils.scheduler_list import ( |
|
SCHEDULER_MAPPING, |
|
get_scheduler, |
|
) |
|
|
|
|
|
stable_model_list = [ |
|
"runwayml/stable-diffusion-v1-5", |
|
"dreamlike-art/dreamlike-diffusion-1.0", |
|
"kadirnar/maturemalemix_v0", |
|
"kadirnar/DreamShaper_v6" |
|
] |
|
|
|
stable_inpiant_model_list = [ |
|
"stabilityai/stable-diffusion-2-inpainting", |
|
"runwayml/stable-diffusion-inpainting", |
|
"saik0s/realistic_vision_inpainting", |
|
] |
|
|
|
controlnet_model_list = [ |
|
"lllyasviel/control_v11p_sd15_canny", |
|
"lllyasviel/control_v11f1p_sd15_depth", |
|
"lllyasviel/control_v11p_sd15_openpose", |
|
"lllyasviel/control_v11p_sd15_scribble", |
|
"lllyasviel/control_v11p_sd15_mlsd", |
|
"lllyasviel/control_v11e_sd15_shuffle", |
|
"lllyasviel/control_v11e_sd15_ip2p", |
|
"lllyasviel/control_v11p_sd15_lineart", |
|
"lllyasviel/control_v11p_sd15s2_lineart_anime", |
|
"lllyasviel/control_v11p_sd15_softedge", |
|
] |
|
|
|
class StableDiffusionControlNetGenerator(ControlnetPipeline): |
|
def __init__(self): |
|
self.pipe = None |
|
|
|
def load_model(self, stable_model_path, controlnet_model_path, scheduler): |
|
if self.pipe is None or self.pipe.model_name != stable_model_path or self.pipe.scheduler_name != scheduler: |
|
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.model_name = stable_model_path |
|
self.pipe.scheduler_name = scheduler |
|
|
|
self.pipe = get_scheduler(pipe=self.pipe, scheduler=scheduler) |
|
self.pipe.scheduler_name = scheduler |
|
self.pipe.to("cuda") |
|
self.pipe.enable_xformers_memory_efficient_attention() |
|
|
|
return self.pipe |
|
|
|
|
|
def controlnet_preprocces( |
|
self, |
|
read_image: str, |
|
preprocces_type: str, |
|
): |
|
processed_image = PREPROCCES_DICT[preprocces_type](read_image) |
|
return processed_image |
|
|
|
def generate_image( |
|
self, |
|
image_path: str, |
|
stable_model_path: str, |
|
controlnet_model_path: str, |
|
height: int, |
|
width: int, |
|
guess_mode: bool, |
|
controlnet_conditioning_scale: int, |
|
prompt: str, |
|
negative_prompt: str, |
|
num_images_per_prompt: int, |
|
guidance_scale: int, |
|
num_inference_step: int, |
|
scheduler: str, |
|
seed_generator: int, |
|
preprocces_type: str, |
|
): |
|
pipe = self.load_model( |
|
stable_model_path=stable_model_path, |
|
controlnet_model_path=controlnet_model_path, |
|
scheduler=scheduler, |
|
) |
|
if preprocces_type== "ScribbleXDOG": |
|
read_image = cv2.imread(image_path) |
|
controlnet_image = self.controlnet_preprocces(read_image=read_image, preprocces_type=preprocces_type)[0] |
|
controlnet_image = Image.fromarray(controlnet_image) |
|
|
|
elif preprocces_type== "None": |
|
controlnet_image = self.controlnet_preprocces(read_image=image_path, preprocces_type=preprocces_type) |
|
else: |
|
read_image = Image.open(image_path) |
|
controlnet_image = self.controlnet_preprocces(read_image=read_image, preprocces_type=preprocces_type) |
|
|
|
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, |
|
height=height, |
|
width=width, |
|
controlnet_conditioning_scale=float(controlnet_conditioning_scale), |
|
guess_mode=guess_mode, |
|
image=controlnet_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_image_path = gr.Image( |
|
type="filepath", label="Image" |
|
).style(height=260) |
|
controlnet_prompt = gr.Textbox( |
|
lines=1, placeholder="Prompt", show_label=False |
|
) |
|
controlnet_negative_prompt = gr.Textbox( |
|
lines=1, placeholder="Negative Prompt", show_label=False |
|
) |
|
|
|
with gr.Row(): |
|
with gr.Column(): |
|
controlnet_stable_model_path = gr.Dropdown( |
|
choices=stable_model_list, |
|
value=stable_model_list[0], |
|
label="Stable Model Path", |
|
) |
|
controlnet_preprocces_type = gr.Dropdown( |
|
choices=list(PREPROCCES_DICT.keys()), |
|
value=list(PREPROCCES_DICT.keys())[0], |
|
label="Preprocess Type", |
|
) |
|
controlnet_conditioning_scale = gr.Slider( |
|
minimum=0.0, |
|
maximum=1.0, |
|
step=0.1, |
|
value=1.0, |
|
label="ControlNet Conditioning Scale", |
|
) |
|
controlnet_guidance_scale = gr.Slider( |
|
minimum=0.1, |
|
maximum=15, |
|
step=0.1, |
|
value=7.5, |
|
label="Guidance Scale", |
|
) |
|
controlnet_height = gr.Slider( |
|
minimum=128, |
|
maximum=1280, |
|
step=32, |
|
value=512, |
|
label="Height", |
|
) |
|
controlnet_width = gr.Slider( |
|
minimum=128, |
|
maximum=1280, |
|
step=32, |
|
value=512, |
|
label="Width", |
|
) |
|
|
|
with gr.Row(): |
|
with gr.Column(): |
|
controlnet_model_path = gr.Dropdown( |
|
choices=controlnet_model_list, |
|
value=controlnet_model_list[0], |
|
label="ControlNet Model Path", |
|
) |
|
controlnet_scheduler = gr.Dropdown( |
|
choices=list(SCHEDULER_MAPPING.keys()), |
|
value=list(SCHEDULER_MAPPING.keys())[0], |
|
label="Scheduler", |
|
) |
|
controlnet_num_inference_step = gr.Slider( |
|
minimum=1, |
|
maximum=150, |
|
step=1, |
|
value=30, |
|
label="Num Inference Step", |
|
) |
|
|
|
controlnet_num_images_per_prompt = gr.Slider( |
|
minimum=1, |
|
maximum=4, |
|
step=1, |
|
value=1, |
|
label="Number Of Images", |
|
) |
|
controlnet_seed_generator = gr.Slider( |
|
minimum=0, |
|
maximum=1000000, |
|
step=1, |
|
value=0, |
|
label="Seed(0 for random)", |
|
) |
|
controlnet_guess_mode = gr.Checkbox( |
|
label="Guess Mode" |
|
) |
|
|
|
|
|
predict_button = gr.Button(value="Generate Image") |
|
|
|
with gr.Column(): |
|
|
|
output_image = gr.Gallery( |
|
label="Generated images", |
|
show_label=False, |
|
elem_id="gallery", |
|
).style(grid=(1, 2)) |
|
|
|
predict_button.click( |
|
fn=StableDiffusionControlNetGenerator().generate_image, |
|
inputs=[ |
|
controlnet_image_path, |
|
controlnet_stable_model_path, |
|
controlnet_model_path, |
|
controlnet_height, |
|
controlnet_width, |
|
controlnet_guess_mode, |
|
controlnet_conditioning_scale, |
|
controlnet_prompt, |
|
controlnet_negative_prompt, |
|
controlnet_num_images_per_prompt, |
|
controlnet_guidance_scale, |
|
controlnet_num_inference_step, |
|
controlnet_scheduler, |
|
controlnet_seed_generator, |
|
controlnet_preprocces_type, |
|
], |
|
outputs=[output_image], |
|
) |
|
|