Spaces:
Running
Running
| import os | |
| os.system(f"pip install gradio > /dev/null 2>&1") | |
| os.system(f"pip install -qq transformers scipy ftfy accelerate > /dev/null 2>&1") | |
| os.system(f"pip install -qq --upgrade diffusers[torch] > /dev/null 2>&1") | |
| os.system(f"git clone https://github.com/v8hid/infinite-zoom-stable-diffusion.git") | |
| os.system(f"pip install imageio") | |
| os.system(f"pip install diffusers") | |
| import sys | |
| sys.path.extend(['infinite-zoom-stable-diffusion/']) | |
| from helpers import * | |
| from diffusers import StableDiffusionInpaintPipeline, EulerAncestralDiscreteScheduler | |
| from PIL import Image | |
| import gradio as gr | |
| import numpy as np | |
| import torch | |
| import os | |
| import time | |
| os.environ["CUDA_VISIBLE_DEVICES"] = "0" | |
| inpaint_model_list = [ | |
| "stabilityai/stable-diffusion-2-inpainting", | |
| "runwayml/stable-diffusion-inpainting", | |
| "parlance/dreamlike-diffusion-1.0-inpainting", | |
| "ghunkins/stable-diffusion-liberty-inpainting", | |
| "ImNoOne/f222-inpainting-diffusers" | |
| ] | |
| default_prompt = "A psychedelic jungle with trees that have glowing, fractal-like patterns, Simon stalenhag poster 1920s style, street level view, hyper futuristic, 8k resolution, hyper realistic" | |
| default_negative_prompt = "frames, borderline, text, charachter, duplicate, error, out of frame, watermark, low quality, ugly, deformed, blur" | |
| def zoom( | |
| model_id, | |
| prompts_array, | |
| negative_prompt, | |
| num_outpainting_steps, | |
| guidance_scale, | |
| num_inference_steps, | |
| custom_init_image | |
| ): | |
| prompts = {} | |
| for x in prompts_array: | |
| try: | |
| key = int(x[0]) | |
| value = str(x[1]) | |
| prompts[key] = value | |
| except ValueError: | |
| pass | |
| pipe = StableDiffusionInpaintPipeline.from_pretrained( | |
| model_id, | |
| torch_dtype=torch.float16, | |
| ) | |
| pipe.scheduler = EulerAncestralDiscreteScheduler.from_config( | |
| pipe.scheduler.config) | |
| pipe = pipe.to("cuda") | |
| pipe.safety_checker = None | |
| pipe.enable_attention_slicing() | |
| g_cuda = torch.Generator(device='cuda') | |
| height = 512 | |
| width = height | |
| current_image = Image.new(mode="RGBA", size=(height, width)) | |
| mask_image = np.array(current_image)[:, :, 3] | |
| mask_image = Image.fromarray(255-mask_image).convert("RGB") | |
| current_image = current_image.convert("RGB") | |
| if (custom_init_image): | |
| current_image = custom_init_image.resize( | |
| (width, height), resample=Image.LANCZOS) | |
| else: | |
| init_images = pipe(prompt=prompts[min(k for k in prompts.keys() if k >= 0)], | |
| negative_prompt=negative_prompt, | |
| image=current_image, | |
| guidance_scale=guidance_scale, | |
| height=height, | |
| width=width, | |
| mask_image=mask_image, | |
| num_inference_steps=num_inference_steps)[0] | |
| current_image = init_images[0] | |
| mask_width = 128 | |
| num_interpol_frames = 30 | |
| all_frames = [] | |
| all_frames.append(current_image) | |
| for i in range(num_outpainting_steps): | |
| print('Outpaint step: ' + str(i+1) + | |
| ' / ' + str(num_outpainting_steps)) | |
| prev_image_fix = current_image | |
| prev_image = shrink_and_paste_on_blank(current_image, mask_width) | |
| current_image = prev_image | |
| # create mask (black image with white mask_width width edges) | |
| mask_image = np.array(current_image)[:, :, 3] | |
| mask_image = Image.fromarray(255-mask_image).convert("RGB") | |
| # inpainting step | |
| current_image = current_image.convert("RGB") | |
| images = pipe(prompt=prompts[max(k for k in prompts.keys() if k <= i)], | |
| negative_prompt=negative_prompt, | |
| image=current_image, | |
| guidance_scale=guidance_scale, | |
| height=height, | |
| width=width, | |
| # generator = g_cuda.manual_seed(seed), | |
| mask_image=mask_image, | |
| num_inference_steps=num_inference_steps)[0] | |
| current_image = images[0] | |
| current_image.paste(prev_image, mask=prev_image) | |
| # interpolation steps bewteen 2 inpainted images (=sequential zoom and crop) | |
| for j in range(num_interpol_frames - 1): | |
| interpol_image = current_image | |
| interpol_width = round( | |
| (1 - (1-2*mask_width/height)**(1-(j+1)/num_interpol_frames))*height/2 | |
| ) | |
| interpol_image = interpol_image.crop((interpol_width, | |
| interpol_width, | |
| width - interpol_width, | |
| height - interpol_width)) | |
| interpol_image = interpol_image.resize((height, width)) | |
| # paste the higher resolution previous image in the middle to avoid drop in quality caused by zooming | |
| interpol_width2 = round( | |
| (1 - (height-2*mask_width) / (height-2*interpol_width)) / 2*height | |
| ) | |
| prev_image_fix_crop = shrink_and_paste_on_blank( | |
| prev_image_fix, interpol_width2) | |
| interpol_image.paste(prev_image_fix_crop, mask=prev_image_fix_crop) | |
| all_frames.append(interpol_image) | |
| all_frames.append(current_image) | |
| interpol_image.show() | |
| video_file_name = "infinite_zoom_" + str(time.time()) | |
| fps = 30 | |
| save_path = video_file_name + ".mp4" | |
| start_frame_dupe_amount = 15 | |
| last_frame_dupe_amount = 15 | |
| write_video(save_path, all_frames, fps, False, | |
| start_frame_dupe_amount, last_frame_dupe_amount) | |
| return save_path | |
| def zoom_app(): | |
| with gr.Blocks(): | |
| with gr.Row(): | |
| with gr.Column(): | |
| outpaint_prompts = gr.Dataframe( | |
| type="array", | |
| headers=["outpaint steps", "prompt"], | |
| datatype=["number", "str"], | |
| row_count=1, | |
| col_count=(2, "fixed"), | |
| value=[[0, default_prompt]], | |
| wrap=True | |
| ) | |
| outpaint_negative_prompt = gr.Textbox( | |
| lines=1, | |
| value=default_negative_prompt, | |
| label='Negative Prompt' | |
| ) | |
| outpaint_steps = gr.Slider( | |
| minimum=5, | |
| maximum=25, | |
| step=1, | |
| value=12, | |
| label='Total Outpaint Steps' | |
| ) | |
| with gr.Accordion("Advanced Options", open=False): | |
| model_id = gr.Dropdown( | |
| choices=inpaint_model_list, | |
| value=inpaint_model_list[0], | |
| label='Pre-trained Model ID' | |
| ) | |
| guidance_scale = gr.Slider( | |
| minimum=0.1, | |
| maximum=15, | |
| step=0.1, | |
| value=7, | |
| label='Guidance Scale' | |
| ) | |
| sampling_step = gr.Slider( | |
| minimum=1, | |
| maximum=100, | |
| step=1, | |
| value=50, | |
| label='Sampling Steps for each outpaint' | |
| ) | |
| init_image = gr.Image(type="pil",label="custom initial image") | |
| generate_btn = gr.Button(value='Generate video') | |
| with gr.Column(): | |
| output_image = gr.Video(label='Output', format="mp4").style( | |
| width=512, height=512) | |
| generate_btn.click( | |
| fn=zoom, | |
| inputs=[ | |
| model_id, | |
| outpaint_prompts, | |
| outpaint_negative_prompt, | |
| outpaint_steps, | |
| guidance_scale, | |
| sampling_step, | |
| init_image | |
| ], | |
| outputs=output_image, | |
| ) | |
| import gradio as gr | |
| app = gr.Blocks() | |
| with app: | |
| gr.HTML( | |
| """ | |
| <h2 style='text-align: center'> | |
| <a href="https://github.com/v8hid/infinite-zoom-stable-diffusion/" style="display:inline-block;"> | |
| <img src="https://img.shields.io/static/v1?label=github&message=repository&color=blue&style=for-the-badge&logo=github&logoColor=white" alt="build status"> | |
| </a> | |
| <br> | |
| Text to Video - Infinite zoom effect | |
| </h2> | |
| """ | |
| ) | |
| zoom_app() | |
| app.launch(debug=True,enable_queue=True) | |