| import gradio as gr | |
| import torch | |
| from diffusers import DiffusionPipeline | |
| from diffusion_webui.utils.model_list import stable_inpiant_model_list | |
| class StableDiffusionInpaintGenerator: | |
| def __init__(self): | |
| self.pipe = None | |
| def load_model(self, stable_model_path): | |
| if self.pipe is None or self.pipe.model_name != stable_model_path: | |
| self.pipe = DiffusionPipeline.from_pretrained( | |
| stable_model_path, revision="fp16", torch_dtype=torch.float16 | |
| ) | |
| self.pipe.to("cuda") | |
| self.pipe.enable_xformers_memory_efficient_attention() | |
| self.pipe.model_name = stable_model_path | |
| return self.pipe | |
| def generate_image( | |
| self, | |
| pil_image: str, | |
| stable_model_path: str, | |
| prompt: str, | |
| negative_prompt: str, | |
| num_images_per_prompt: int, | |
| guidance_scale: int, | |
| num_inference_step: int, | |
| seed_generator=0, | |
| ): | |
| image = pil_image["image"].convert("RGB").resize((512, 512)) | |
| mask_image = pil_image["mask"].convert("RGB").resize((512, 512)) | |
| pipe = self.load_model(stable_model_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, | |
| mask_image=mask_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(): | |
| stable_diffusion_inpaint_image_file = gr.Image( | |
| source="upload", | |
| tool="sketch", | |
| elem_id="image_upload", | |
| type="pil", | |
| label="Upload", | |
| ).style(height=260) | |
| stable_diffusion_inpaint_prompt = gr.Textbox( | |
| lines=1, | |
| placeholder="Prompt", | |
| show_label=False, | |
| ) | |
| stable_diffusion_inpaint_negative_prompt = gr.Textbox( | |
| lines=1, | |
| placeholder="Negative Prompt", | |
| show_label=False, | |
| ) | |
| stable_diffusion_inpaint_model_id = gr.Dropdown( | |
| choices=stable_inpiant_model_list, | |
| value=stable_inpiant_model_list[0], | |
| label="Inpaint Model Id", | |
| ) | |
| with gr.Row(): | |
| with gr.Column(): | |
| stable_diffusion_inpaint_guidance_scale = gr.Slider( | |
| minimum=0.1, | |
| maximum=15, | |
| step=0.1, | |
| value=7.5, | |
| label="Guidance Scale", | |
| ) | |
| stable_diffusion_inpaint_num_inference_step = ( | |
| gr.Slider( | |
| minimum=1, | |
| maximum=100, | |
| step=1, | |
| value=50, | |
| label="Num Inference Step", | |
| ) | |
| ) | |
| with gr.Row(): | |
| with gr.Column(): | |
| stable_diffusion_inpiant_num_images_per_prompt = gr.Slider( | |
| minimum=1, | |
| maximum=4, | |
| step=1, | |
| value=1, | |
| label="Number Of Images", | |
| ) | |
| stable_diffusion_inpaint_seed_generator = ( | |
| gr.Slider( | |
| minimum=0, | |
| maximum=1000000, | |
| step=1, | |
| value=0, | |
| label="Seed(0 for random)", | |
| ) | |
| ) | |
| stable_diffusion_inpaint_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)) | |
| stable_diffusion_inpaint_predict.click( | |
| fn=StableDiffusionInpaintGenerator().generate_image, | |
| inputs=[ | |
| stable_diffusion_inpaint_image_file, | |
| stable_diffusion_inpaint_model_id, | |
| stable_diffusion_inpaint_prompt, | |
| stable_diffusion_inpaint_negative_prompt, | |
| stable_diffusion_inpiant_num_images_per_prompt, | |
| stable_diffusion_inpaint_guidance_scale, | |
| stable_diffusion_inpaint_num_inference_step, | |
| stable_diffusion_inpaint_seed_generator, | |
| ], | |
| outputs=[output_image], | |
| ) | |