| | import gradio as gr |
| | import numpy as np |
| | import torch |
| | from diffusers import ControlNetModel, StableDiffusionControlNetInpaintPipeline |
| | 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, |
| | ) |
| |
|
| |
|
| | class StableDiffusionControlNetInpaintGenerator(ControlnetPipeline): |
| | def __init__(self): |
| | super().__init__() |
| |
|
| | 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 = ( |
| | StableDiffusionControlNetInpaintPipeline.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.to("cuda") |
| | self.pipe.enable_xformers_memory_efficient_attention() |
| |
|
| | return self.pipe |
| |
|
| | def load_image(self, image): |
| | image = np.array(image) |
| | image = Image.fromarray(image) |
| | return image |
| |
|
| | 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, |
| | prompt: str, |
| | negative_prompt: str, |
| | num_images_per_prompt: int, |
| | height: int, |
| | width: int, |
| | strength: int, |
| | guess_mode: bool, |
| | guidance_scale: int, |
| | num_inference_step: int, |
| | controlnet_conditioning_scale: int, |
| | scheduler: str, |
| | seed_generator: int, |
| | preprocces_type: str, |
| | ): |
| | normal_image = image_path["image"].convert("RGB").resize((512, 512)) |
| | mask_image = image_path["mask"].convert("RGB").resize((512, 512)) |
| |
|
| | normal_image = self.load_image(image=normal_image) |
| | mask_image = self.load_image(image=mask_image) |
| |
|
| | control_image = self.controlnet_preprocces( |
| | read_image=normal_image, preprocces_type=preprocces_type |
| | ) |
| | pipe = self.load_model( |
| | stable_model_path=stable_model_path, |
| | controlnet_model_path=controlnet_model_path, |
| | scheduler=scheduler, |
| | ) |
| |
|
| | 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=normal_image, |
| | height=height, |
| | width=width, |
| | mask_image=mask_image, |
| | strength=strength, |
| | guess_mode=guess_mode, |
| | control_image=control_image, |
| | negative_prompt=negative_prompt, |
| | num_images_per_prompt=num_images_per_prompt, |
| | num_inference_steps=num_inference_step, |
| | guidance_scale=guidance_scale, |
| | controlnet_conditioning_scale=float(controlnet_conditioning_scale), |
| | generator=generator, |
| | ).images |
| |
|
| | return output |
| |
|
| | def app(): |
| | with gr.Blocks(): |
| | with gr.Row(): |
| | with gr.Column(): |
| | controlnet_inpaint_image_path = gr.Image( |
| | source="upload", |
| | tool="sketch", |
| | elem_id="image_upload", |
| | type="pil", |
| | label="Upload", |
| | ).style(height=260) |
| |
|
| | controlnet_inpaint_prompt = gr.Textbox( |
| | lines=1, placeholder="Prompt", show_label=False |
| | ) |
| | controlnet_inpaint_negative_prompt = gr.Textbox( |
| | lines=1, placeholder="Negative Prompt", show_label=False |
| | ) |
| |
|
| | with gr.Row(): |
| | with gr.Column(): |
| | controlnet_inpaint_stable_model_path = gr.Dropdown( |
| | choices=stable_model_list, |
| | value=stable_model_list[0], |
| | label="Stable Model Path", |
| | ) |
| | controlnet_inpaint_preprocces_type = gr.Dropdown( |
| | choices=list(PREPROCCES_DICT.keys()), |
| | value=list(PREPROCCES_DICT.keys())[0], |
| | label="Preprocess Type", |
| | ) |
| | controlnet_inpaint_conditioning_scale = gr.Slider( |
| | minimum=0.0, |
| | maximum=1.0, |
| | step=0.1, |
| | value=1.0, |
| | label="ControlNet Conditioning Scale", |
| | ) |
| | controlnet_inpaint_guidance_scale = gr.Slider( |
| | minimum=0.1, |
| | maximum=15, |
| | step=0.1, |
| | value=7.5, |
| | label="Guidance Scale", |
| | ) |
| | controlnet_inpaint_height = gr.Slider( |
| | minimum=128, |
| | maximum=1280, |
| | step=32, |
| | value=512, |
| | label="Height", |
| | ) |
| | controlnet_inpaint_width = gr.Slider( |
| | minimum=128, |
| | maximum=1280, |
| | step=32, |
| | value=512, |
| | label="Width", |
| | ) |
| | controlnet_inpaint_guess_mode = gr.Checkbox( |
| | label="Guess Mode" |
| | ) |
| |
|
| | with gr.Column(): |
| | controlnet_inpaint_model_path = gr.Dropdown( |
| | choices=controlnet_model_list, |
| | value=controlnet_model_list[0], |
| | label="ControlNet Model Path", |
| | ) |
| | controlnet_inpaint_scheduler = gr.Dropdown( |
| | choices=list(SCHEDULER_MAPPING.keys()), |
| | value=list(SCHEDULER_MAPPING.keys())[0], |
| | label="Scheduler", |
| | ) |
| | controlnet_inpaint_strength = gr.Slider( |
| | minimum=0.1, |
| | maximum=15, |
| | step=0.1, |
| | value=7.5, |
| | label="Strength", |
| | ) |
| | controlnet_inpaint_num_inference_step = gr.Slider( |
| | minimum=1, |
| | maximum=150, |
| | step=1, |
| | value=30, |
| | label="Num Inference Step", |
| | ) |
| | controlnet_inpaint_num_images_per_prompt = ( |
| | gr.Slider( |
| | minimum=1, |
| | maximum=4, |
| | step=1, |
| | value=1, |
| | label="Number Of Images", |
| | ) |
| | ) |
| | controlnet_inpaint_seed_generator = gr.Slider( |
| | minimum=0, |
| | maximum=1000000, |
| | step=1, |
| | value=0, |
| | label="Seed(0 for random)", |
| | ) |
| |
|
| | |
| | controlnet_inpaint_predict_button = gr.Button( |
| | value="Generate Image" |
| | ) |
| |
|
| | with gr.Column(): |
| | |
| | controlnet_inpaint_output_image = gr.Gallery( |
| | label="Generated images", |
| | show_label=False, |
| | elem_id="gallery", |
| | ).style(grid=(1, 2)) |
| |
|
| | controlnet_inpaint_predict_button.click( |
| | fn=StableDiffusionControlNetInpaintGenerator().generate_image, |
| | inputs=[ |
| | controlnet_inpaint_image_path, |
| | controlnet_inpaint_stable_model_path, |
| | controlnet_inpaint_model_path, |
| | controlnet_inpaint_prompt, |
| | controlnet_inpaint_negative_prompt, |
| | controlnet_inpaint_num_images_per_prompt, |
| | controlnet_inpaint_height, |
| | controlnet_inpaint_width, |
| | controlnet_inpaint_strength, |
| | controlnet_inpaint_guess_mode, |
| | controlnet_inpaint_guidance_scale, |
| | controlnet_inpaint_num_inference_step, |
| | controlnet_inpaint_conditioning_scale, |
| | controlnet_inpaint_scheduler, |
| | controlnet_inpaint_seed_generator, |
| | controlnet_inpaint_preprocces_type, |
| | ], |
| | outputs=[controlnet_inpaint_output_image], |
| | ) |
| |
|
