| 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], |
| ) |
|
|
