| import gradio as gr | |
| import torch | |
| from diffusers import StableDiffusionImg2ImgPipeline | |
| from PIL import Image | |
| from diffusion_webui.utils.model_list import stable_model_list | |
| from diffusion_webui.utils.scheduler_list import ( | |
| SCHEDULER_MAPPING, | |
| get_scheduler, | |
| ) | |
| class StableDiffusionImage2ImageGenerator: | |
| def __init__(self): | |
| self.pipe = None | |
| def load_model(self, stable_model_path, scheduler): | |
| if self.pipe is None or self.pipe.model_name != stable_model_path or self.pipe.scheduler_name != scheduler: | |
| self.pipe = StableDiffusionImg2ImgPipeline.from_pretrained( | |
| stable_model_path, 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 generate_image( | |
| self, | |
| image_path: str, | |
| stable_model_path: str, | |
| prompt: str, | |
| negative_prompt: str, | |
| num_images_per_prompt: int, | |
| scheduler: str, | |
| guidance_scale: int, | |
| num_inference_step: int, | |
| seed_generator=0, | |
| ): | |
| pipe = self.load_model( | |
| stable_model_path=stable_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) | |
| image = Image.open(image_path) | |
| images = pipe( | |
| 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 images | |
| def app(): | |
| with gr.Blocks(): | |
| with gr.Row(): | |
| with gr.Column(): | |
| image2image_image_file = gr.Image( | |
| type="filepath", label="Image" | |
| ).style(height=260) | |
| image2image_prompt = gr.Textbox( | |
| lines=1, | |
| placeholder="Prompt", | |
| show_label=False, | |
| ) | |
| image2image_negative_prompt = gr.Textbox( | |
| lines=1, | |
| placeholder="Negative Prompt", | |
| show_label=False, | |
| ) | |
| with gr.Row(): | |
| with gr.Column(): | |
| image2image_model_path = gr.Dropdown( | |
| choices=stable_model_list, | |
| value=stable_model_list[0], | |
| label="Stable Model Id", | |
| ) | |
| image2image_guidance_scale = gr.Slider( | |
| minimum=0.1, | |
| maximum=15, | |
| step=0.1, | |
| value=7.5, | |
| label="Guidance Scale", | |
| ) | |
| image2image_num_inference_step = gr.Slider( | |
| minimum=1, | |
| maximum=100, | |
| step=1, | |
| value=50, | |
| label="Num Inference Step", | |
| ) | |
| with gr.Row(): | |
| with gr.Column(): | |
| image2image_scheduler = gr.Dropdown( | |
| choices=list(SCHEDULER_MAPPING.keys()), | |
| value=list(SCHEDULER_MAPPING.keys())[0], | |
| label="Scheduler", | |
| ) | |
| image2image_num_images_per_prompt = gr.Slider( | |
| minimum=1, | |
| maximum=4, | |
| step=1, | |
| value=1, | |
| label="Number Of Images", | |
| ) | |
| image2image_seed_generator = gr.Slider( | |
| minimum=0, | |
| maximum=1000000, | |
| step=1, | |
| value=0, | |
| label="Seed(0 for random)", | |
| ) | |
| image2image_predict_button = gr.Button(value="Generator") | |
| with gr.Column(): | |
| output_image = gr.Gallery( | |
| label="Generated images", | |
| show_label=False, | |
| elem_id="gallery", | |
| ).style(grid=(1, 2)) | |
| image2image_predict_button.click( | |
| fn=StableDiffusionImage2ImageGenerator().generate_image, | |
| inputs=[ | |
| image2image_image_file, | |
| image2image_model_path, | |
| image2image_prompt, | |
| image2image_negative_prompt, | |
| image2image_num_images_per_prompt, | |
| image2image_scheduler, | |
| image2image_guidance_scale, | |
| image2image_num_inference_step, | |
| image2image_seed_generator, | |
| ], | |
| outputs=[output_image], | |
| ) | |