| 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], |
| ) |
|
|
