Spaces:
Runtime error
Runtime error
| from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler | |
| from inpaint_zoom.utils.zoom_out_utils import preprocess_image, preprocess_mask_image, write_video, dummy | |
| from PIL import Image | |
| import gradio as gr | |
| import torch | |
| import os | |
| os.environ["CUDA_VISIBLE_DEVICES"]="0" | |
| stable_paint_model_list = [ | |
| "stabilityai/stable-diffusion-2-inpainting", | |
| "runwayml/stable-diffusion-inpainting" | |
| ] | |
| stable_paint_prompt_list = [ | |
| "children running in the forest , sunny, bright, by studio ghibli painting, superior quality, masterpiece, traditional Japanese colors, by Grzegorz Rutkowski, concept art", | |
| "A beautiful landscape of a mountain range with a lake in the foreground", | |
| ] | |
| stable_paint_negative_prompt_list = [ | |
| "lurry, bad art, blurred, text, watermark", | |
| ] | |
| def stable_diffusion_zoom_out( | |
| model_id, | |
| original_prompt, | |
| negative_prompt, | |
| guidance_scale, | |
| num_inference_steps, | |
| step_size, | |
| num_frames, | |
| ): | |
| pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) | |
| pipe.set_use_memory_efficient_attention_xformers(True) | |
| pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config) | |
| pipe = pipe.to("cuda") | |
| pipe.safety_checker = dummy | |
| new_image = Image.new(mode="RGBA", size=(512,512)) | |
| current_image, mask_image = preprocess_mask_image(new_image) | |
| current_image = pipe( | |
| prompt=[original_prompt], | |
| negative_prompt=[negative_prompt], | |
| image=current_image, | |
| mask_image=mask_image, | |
| num_inference_steps=num_inference_steps, | |
| guidance_scale=guidance_scale | |
| ).images[0] | |
| all_frames = [] | |
| all_frames.append(current_image) | |
| for i in range(num_frames): | |
| prev_image = preprocess_image(current_image, step_size, 512) | |
| current_image = prev_image | |
| current_image, mask_image = preprocess_mask_image(current_image) | |
| current_image = pipe(prompt=[original_prompt], negative_prompt=[negative_prompt], image=current_image, mask_image=mask_image, num_inference_steps=num_inference_steps).images[0] | |
| current_image.paste(prev_image, mask=prev_image) | |
| all_frames.append(current_image) | |
| save_path = "output.mp4" | |
| write_video(save_path, all_frames, fps=30) | |
| return save_path | |
| def stable_diffusion_zoom_out_app(): | |
| with gr.Blocks(): | |
| with gr.Row(): | |
| with gr.Column(): | |
| text2image_out_model_path = gr.Dropdown( | |
| choices=stable_paint_model_list, | |
| value=stable_paint_model_list[0], | |
| label='Text-Image Model Id' | |
| ) | |
| text2image_out_prompt = gr.Textbox( | |
| lines=2, | |
| value=stable_paint_prompt_list[0], | |
| label='Prompt' | |
| ) | |
| text2image_out_negative_prompt = gr.Textbox( | |
| lines=1, | |
| value=stable_paint_negative_prompt_list[0], | |
| label='Negative Prompt' | |
| ) | |
| with gr.Row(): | |
| with gr.Column(): | |
| text2image_out_guidance_scale = gr.Slider( | |
| minimum=0.1, | |
| maximum=15, | |
| step=0.1, | |
| value=7.5, | |
| label='Guidance Scale' | |
| ) | |
| text2image_out_num_inference_step = gr.Slider( | |
| minimum=1, | |
| maximum=100, | |
| step=1, | |
| value=50, | |
| label='Num Inference Step' | |
| ) | |
| with gr.Row(): | |
| with gr.Column(): | |
| text2image_out_step_size = gr.Slider( | |
| minimum=1, | |
| maximum=100, | |
| step=1, | |
| value=10, | |
| label='Step Size' | |
| ) | |
| text2image_out_num_frames = gr.Slider( | |
| minimum=1, | |
| maximum=100, | |
| step=1, | |
| value=10, | |
| label='Frames' | |
| ) | |
| text2image_out_predict = gr.Button(value='Generator') | |
| with gr.Column(): | |
| output_image = gr.Video(label="Output Video") | |
| text2image_out_predict.click( | |
| fn=stable_diffusion_zoom_out, | |
| inputs=[ | |
| text2image_out_model_path, | |
| text2image_out_prompt, | |
| text2image_out_negative_prompt, | |
| text2image_out_guidance_scale, | |
| text2image_out_num_inference_step, | |
| text2image_out_step_size, | |
| text2image_out_num_frames, | |
| ], | |
| outputs=output_image | |
| ) | |