Duplicate from raylander/mountdrive

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README.md

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+---
+title: Mountdrive
+emoji: 🌍
+colorFrom: blue
+colorTo: blue
+sdk: gradio
+sdk_version: 3.9
+app_file: app.py
+pinned: true
+duplicated_from: raylander/mountdrive
+---

app.py

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+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)