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| import gradio as gr | |
| import torch | |
| from diffusers import ControlNetModel, StableDiffusionControlNetPipeline | |
| from diffusers.utils import load_image | |
| from diffusion_webui.utils.model_list import ( | |
| controlnet_lineart_model_list, | |
| stable_model_list, | |
| ) | |
| from diffusion_webui.utils.scheduler_list import ( | |
| SCHEDULER_LIST, | |
| get_scheduler_list, | |
| ) | |
| class StableDiffusionControlNetPix2PixGenerator: | |
| 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_pix2pix( | |
| self, | |
| image_path: str, | |
| ): | |
| control_image = load_image(image_path).convert("RGB") | |
| return control_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, | |
| guidance_scale: int, | |
| num_inference_step: int, | |
| scheduler: str, | |
| seed_generator: int, | |
| ): | |
| pipe = self.load_model( | |
| stable_model_path=stable_model_path, | |
| controlnet_model_path=controlnet_model_path, | |
| scheduler=scheduler, | |
| ) | |
| image = self.controlnet_pix2pix(image_path) | |
| 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_canny_image_file = gr.Image( | |
| type="filepath", label="Image" | |
| ) | |
| controlnet_canny_prompt = gr.Textbox( | |
| lines=1, | |
| placeholder="Prompt", | |
| show_label=False, | |
| ) | |
| controlnet_canny_negative_prompt = gr.Textbox( | |
| lines=1, | |
| placeholder="Negative Prompt", | |
| show_label=False, | |
| ) | |
| with gr.Row(): | |
| with gr.Column(): | |
| controlnet_canny_stable_model_id = gr.Dropdown( | |
| choices=stable_model_list, | |
| value=stable_model_list[0], | |
| label="Stable Model Id", | |
| ) | |
| controlnet_canny_guidance_scale = gr.Slider( | |
| minimum=0.1, | |
| maximum=15, | |
| step=0.1, | |
| value=7.5, | |
| label="Guidance Scale", | |
| ) | |
| controlnet_canny_num_inference_step = gr.Slider( | |
| minimum=1, | |
| maximum=100, | |
| step=1, | |
| value=50, | |
| label="Num Inference Step", | |
| ) | |
| controlnet_canny_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_canny_model_id = gr.Dropdown( | |
| choices=controlnet_lineart_model_list, | |
| value=controlnet_lineart_model_list[0], | |
| label="ControlNet Model Id", | |
| ) | |
| controlnet_canny_scheduler = gr.Dropdown( | |
| choices=SCHEDULER_LIST, | |
| value=SCHEDULER_LIST[0], | |
| label="Scheduler", | |
| ) | |
| controlnet_canny_seed_generator = gr.Number( | |
| value=0, | |
| label="Seed Generator", | |
| ) | |
| controlnet_canny_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_canny_predict.click( | |
| fn=StableDiffusionControlNetPix2PixGenerator().generate_image, | |
| inputs=[ | |
| controlnet_canny_image_file, | |
| controlnet_canny_stable_model_id, | |
| controlnet_canny_model_id, | |
| controlnet_canny_prompt, | |
| controlnet_canny_negative_prompt, | |
| controlnet_canny_num_images_per_prompt, | |
| controlnet_canny_guidance_scale, | |
| controlnet_canny_num_inference_step, | |
| controlnet_canny_scheduler, | |
| controlnet_canny_seed_generator, | |
| ], | |
| outputs=[output_image], | |
| ) | |