
## Overview
The latest announcement from Google and DeepMind, Gemini 1.0, is making waves in the AI community. In this video, we delve into the key features and capabilities of Gemini, comparing it to GPT-4 and exploring its potential impact on AI applications.

## Summary
The video explores the groundbreaking Gemini 1.0, a multimodal AI from Google and DeepMind. Released on December 6, 2023, Gemini boasts three models: Ultra, Pro, and Nano, each designed for specific tasks. Unlike its predecessors, Gemini is built from the ground up to seamlessly understand and combine various data types, including text, code, audio, image, and video.

## Detailed Introduction
- **0:00 - 2:06 (Intro & Google's Announcement)**
  - Mr. Eflow introduces Gemini, highlighting its three models. Gemini Ultra, Pro, and Nano cater to diverse tasks, with benchmarking tests showing Ultra outperforming GPT-4 in various aspects.
  - [YouTube Link: Google's Announcement](https://blog.google/technology/ai/google-gemini-ai/)

- **2:06 - 13:18 (Performance Benchmarks)**
  - Benchmarking results reveal Gemini's impressive performance, surpassing GPT-4 in most categories. Gemini Ultra shines in tasks ranging from math problems to audio recognition, presenting a significant leap in AI capabilities.
  - [YouTube Link: Gemini Multimodal AI](https://www.youtube.com/watch?v=UIZAiXYceBI)

- **13:18 - 23:39 (Gemini Applications & Future Tools)**
  - Gemini's applications are showcased, from understanding visuals to generating code. Mr. Eflow emphasizes Gemini's potential in educational scenarios, demonstrating its ability to explain nuanced concepts and even turn images into code.
  - [YouTube Link: Gemini in Bard NOW](https://bard.google.com/u/2/chat)

- **23:39 - End (Gemini Availability & Future Outlook)**
  - Gemini's availability is discussed, with plans for Gemini Pro in Bard, and Gemini Ultra coming soon. The video concludes with insights into Gemini's impact on AI tools and the anticipation of further advancements.
  - [YouTube Link: Free Meta Smart Glasses Giveaway](https://tally.so/r/nrBVlp)

## What is Gemini 1.0?

Gemini 1.0 stands as the latest AI model introduced by Google and DeepMind. It is a multimodal AI, capable of understanding and generating text, code, audio, image, and video. Google positions it as a significant advancement beyond models like GPT-4, with three variants: Gemini Ultra, Gemini Pro, and Gemini Nano.

## How Does Gemini Differ from Previous Models?

Unlike previous models, Gemini is built from the ground up to be multimodal, seamlessly integrating and comprehending various types of information. It outperforms GPT-4 in several benchmarks, especially excelling in tasks involving multimodal inputs.

## Applications in Coding

Gemini demonstrates impressive coding abilities, particularly in languages like Python, Java, C++, and Go. It substantially improves coding tasks over previous models, making it a valuable tool for developers and programmers.

## Image Generation Capability

While Gemini Ultra has the capability to generate images, this feature is not available in the initial rollout of Gemini 1.0. Google plans to add image generation functionality in the future updates.

## Availability for Public Use

Gemini Pro is currently accessible in Google products, including Bard. The API for Gemini Pro will be made available to developers starting December 13, 2023. Gemini Ultra, the most advanced variant, is expected to be released next year.

## Adding FAQs

### Q1: What is Gemini 1.0?
Gemini 1.0 is the latest AI model introduced by Google and DeepMind. It is a multimodal AI, capable of understanding and generating text, code, audio, image, and video. Google positions it as a significant advancement beyond models like GPT-4, with three variants: Gemini Ultra, Gemini Pro, and Gemini Nano.

### Q2: How does Gemini differ from previous AI models like GPT-4?
Unlike previous models, Gemini is built from the ground up to be multimodal, meaning it seamlessly integrates and comprehends various types of information. It outperforms GPT-4 in several benchmarks, especially excelling in tasks involving multimodal inputs.

### Q3: What are the applications of Gemini in coding?
Gemini demonstrates impressive coding abilities, particularly in languages like Python, Java, C++, and Go. It substantially improves coding tasks over previous models, making it a valuable tool for developers and programmers.

### Q4: Can Gemini generate images?
While Gemini Ultra has the capability to generate images, this feature is not available in the initial rollout of Gemini 1.0. Google plans to add image generation functionality in the future updates.

### Q5: Is Gemini available for public use?
Gemini Pro is currently accessible in Google products, including Bard. The API for Gemini Pro will be made available to developers starting December 13, 2023. Gemini Ultra, the most advanced variant, is expected to be released next year.

Google Gemini is Here And Its Better Than GPT-4


## Introduction
AI videos have become one of the hottest trends in technology, with applications ranging from deep fakes to animated videos and text-to-video generation. In this guide, we'll explore the process of creating AI videos using tools like AnimateDiff, Stable Diffusion, and ComfyUI. Whether you prefer the easy way with services like Runway ml.com or the more hands-on approach of running your own stable diffusion instance, we've got you covered.

## 0:00 - 1:05 - Intro
The video kicks off with a brief introduction, highlighting the popularity of AI videos and teasing the different techniques covered.

## 1:05 - 2:08 - Using AnimateDiff, ComfyUI, Stable Diffusion
Learn the basics of using AnimateDiff, ComfyUI, and Stable Diffusion. Discover how these tools work together to animate images and generate AI videos.

[Watch here](https://www.youtube.com/watch?v=dFJxwl-azEA&t=65s)

## 2:08 - 3:28 - Applying Anime-Style with Runway
Explore the process of applying an anime-style to videos using Runway ml.com. Understand the workflow and how to leverage pre-trained models for unique stylizations.

[Watch here](https://www.youtube.com/watch?v=dFJxwl-azEA&t=128s)

## 3:28 - 5:05 - Deepfakes and RealTime SDXL Turbo
Delve into the world of deepfakes and real-time image generation using SDXL Turbo. Understand the nuances of selecting appropriate models for different styles.

[Watch here](https://www.youtube.com/watch?v=dFJxwl-azEA&t=208s)

## Frequently Asked Questions (FAQs)

### Q1: Can I use stable diffusion on a Mac?
Yes, stable diffusion can be used on a Mac by utilizing a hosted version of stable diffusion. The tutorial in the video demonstrates the process on a Mac.

### Q2: What is the difference between AnimateDiff and Runway ml.com?
AnimateDiff is a framework for animating images, while Runway ml.com is a service that provides a hosted version of stable diffusion. Runway offers a user-friendly interface for generating AI videos.

### Q3: Are there alternative tools for deepfake video creation?
Yes, aside from stable diffusion and Runway ml.com, tools like "wav to lip" and "replicate tocom" are mentioned in the video for lip syncing and voice cloning, respectively.

### Q4: How can I experiment with different AI art styles?
You can explore different AI art styles by using pre-trained models from websites like civitai.com. The video demonstrates the process of integrating different styles into the workflow.

### Q5: Is there a real-time AI video generation tool?
Yes, the video introduces "sdxl turbo," which is a real-time text-to-image generation tool. It allows for quick and dynamic AI video creation.

## Conclusion
Whether you choose the DIY route with stable diffusion or the convenience of Runway ml.com, creating AI videos has never been more accessible. Experiment with different styles and tools to unleash your creativity in the world of AI art.

[Check out the video](https://www.youtube.com/watch?v=dFJxwl-azEA)

How to Make AI VIDEOS (with Stable Diffusion, Runway)


#### Links from the Video
- [Download Workflow](https://drive.google.com/file/d/17UQXmDRvPLLI7c6g76LlZF4BjMuoRHQa/view?usp=sharing)
- [SVD Model Download](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid/tree/main)
- [Enigmatic_e Video](https://youtu.be/imyQuIiuRnA)

#### Join and Support
- [Discord Group](https://discord.gg/XKAk7GUzAW)

Stable Video Diffusion has officially launched, and this article provides a comprehensive summary of the installation guide video. The tutorial walks through the process of setting up Stable Video Diffusion on your local machine, offering insights into its features and usage.

## Overview of the Video

The video covers various aspects of Stable Video Diffusion, from downloading necessary files to exploring the workflow and making crucial adjustments. Let's break down the content into key time intervals:

### 00:00:00 - Introduction
The video begins with an introduction to Stable Video Diffusion, highlighting its significance in AI image and video rendering.

**[Watch the Introduction](#00:00:00)**

### 01:58:00 - Model Download
Learn how to download the Stable Video Diffusion models from the provided link. This segment provides essential information on acquiring the necessary components.

**[Watch the Model Download](#01:58:00)**

### 03:02:00 - ComfyUI Nodes Installation
Detailed instructions on installing and setting up ComfyUI Nodes for Stable Video Diffusion. This step is crucial for a seamless workflow.

**[Watch the ComfyUI Nodes Installation](#03:02:00)**

### 04:20:00 - SVD Workflow Explanation
An in-depth explanation of the Stable Video Diffusion workflow and its components. Understand how the system processes images and creates stunning video outputs.

**[Watch the SVD Workflow Explanation](#04:20:00)**

### 06:23:00 - Stable Video Diffusion Thoughts
Gain insights into the capabilities of Stable Video Diffusion and the creator's future plans for this innovative tool.

**[Watch the Stable Video Diffusion Thoughts](#06:23:00)**

## Frequently Asked Questions (FAQs)

### Q: How do I install ComfyUI Manager?
A: Follow the steps outlined in the video to install ComfyUI Manager for Stable Video Diffusion.

### Q: What resolution should my image be for Stable Video Diffusion?
A: The recommended resolution is 576 by 1240 pixels.

### Q: Can I use complex images with Stable Video Diffusion?
A: It's advisable to use simpler images with less complex actions for better results.

### Q: Where can I find additional support for Stable Video Diffusion?
A: Join the provided support groups on [Facebook](https://www.facebook.com/groups/theairevolution) and [Discord](https://discord.gg/XKAk7GUzAW) for assistance.

Summary of "Stable Video Diffusion - Local Install Guide" Video


## Introduction

Impact Frames presents an in-depth exploration of Stable Video Diffusion (SVD) within the ComfyUI framework. The video discusses the latest advancements, accessibility improvements, and practical applications of the SVD model. Let's break down the key highlights at different intervals.

## Video Links
[New Workflow](https://comfyworkflows.com/latest?workflow_id=7a0db355-30c5-4986-9a2e-eb556336b0d7)

[Another Workflow](https://comfyworkflows.com/latest?workflow_id=3f6fe372-efa6-4d8f-b771-cf68b0bbee5f)

## Models
Explore the SVD model and related repositories

[Hugging Face - Stable Video Diffusion Img2vid](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid/tree/main)

[ComfyUI-SVD Repository by KJ](https://github.com/kijai/ComfyUI-SVD/tree/main)

[Another Repository](https://github.com/thecooltechguy/ComfyUI-Stable-Video-Diffusion)

00:00:00 - 03:40:00

**Overview:**
Impact Frames introduces the Stable Video Diffusion model and highlights its reduced VRAM requirements, making it more accessible. Viewers gain insights into the social media links, character support on Ko-fi, and related content on Civitai.

[Watch the beginning here](https://youtu.be/teIKjugRFlo?t=0s)

03:40:00 - 13:20:00

**SVD Workflow and Resources:**
Impact Frames delves into the various video links, workflows, and repositories associated with SVD. The discussion includes new and deprecated workflows, model repositories on Hugging Face, and GitHub links for ComfyUI-SVD.

[Explore SVD workflows and resources](https://youtu.be/teIKjugRFlo?t=220s)

13:20:00 - 21:00:00

**Demonstration of New SVD Model:**
The video proceeds to showcase the capabilities of the new SVD model within ComfyUI. Impact Frames emphasizes the model's accessibility, requiring only 20 gigabytes of VRAM compared to the previous 40 gigabytes.

[See the demonstration here](https://youtu.be/teIKjugRFlo?t=800s)

21:00:00 - 32:00:00

**Installation and Workflow Details:**
Impact Frames provides a step-by-step guide on installing the SVD model and accessing specific workflows. The discussion covers different repositories and highlights the variety of options available for users.

[Learn how to install and use the SVD model](https://youtu.be/teIKjugRFlo?t=1260s)

32:00:00 - 43:00:00

**Practical Demonstrations:**
The video includes practical demonstrations of SVD in action, showcasing its application in image-to-video conversion, integration with a Metro game, and animation based on song-driven contextual information.

[Watch practical demonstrations](https://youtu.be/teIKjugRFlo?t=1920s)

43:00:00 - 51:00:00

**Rendering Considerations:**
Impact Frames discusses important considerations for rendering, including frame selection, VRAM management, and choosing the right SVD model variant based on specific needs.

[Understand rendering considerations](https://youtu.be/teIKjugRFlo?t=2580s)

51:00:00 - 54:00:00

**Conclusion and Call to Action:**
The video concludes with a call to action, encouraging viewers to explore provided resources, subscribe to the channel, and stay tuned for more exciting content.

[Watch the conclusion](https://youtu.be/teIKjugRFlo?t=3060s)

---

## Frequently Asked Questions (FAQs):

### Q1: What are the system requirements for using the SVD model in ComfyUI?

**Answer:** The new SVD model is designed to be more accessible, requiring only 20 gigabytes of VRAM. This makes it suitable for a broader range of users compared to the previous version that needed 40 gigabytes.

### Q2: Where can I find the workflows and repositories mentioned in the video?

**Answer:** You can find the workflows on ComfyUI's website, and the model repositories are available on Hugging Face and GitHub. Check the video for specific links and details.

### Q3: How do I install the SVD model, and what are the considerations for rendering?

**Answer:** The video provides a step-by-step installation guide. Rendering considerations include selecting the right number of frames, managing VRAM usage, and choosing the appropriate SVD model variant based on your needs.

### Q4: Are there specific examples of SVD applications demonstrated in the video?

**Answer:** Yes, the video showcases practical demonstrations, including image-to-video conversion, integration with a Metro game, and animation driven by contextual information from a song.


How to run Stable Video Diffusion in ComfyUI ？img2vid


Stable Video Diffusion produces much better videos than I anticipated, with significant subject variation and distortion-free visuals. Among AI video generation tools, its performance stands out.

Currently, its drawback is the resolution, only 1024x576. The video below doesn't use the HD feature, allowing you to experience the video quality.

## Stable Video Diffusion Video Generation Speed

Currently, it's recommended to use Colab for deploying Stable Video Diffusion. With a Colab membership, using the A100 GPU, it takes 53 seconds to generate a 4-second video, while using the T4 GPU takes 7 minutes.

![Stable Video Diffusion Video Generation Speed](/assets/blog/how-to-deploy-stable-video-diffusion-in-colab/640-0.png)

## First, Open the Colab Notebook Link

[Colab Notebook Link](https://colab.research.google.com/github/mkshing/notebooks/blob/main/stable_video_diffusion_img2vid.ipynb)

## Next, Click the Play Icon to Run Different Cells

- **Cell 1: Setup**

![cell 1 setup](/assets/blog/how-to-deploy-stable-video-diffusion-in-colab/640-1.png)

    - Ignore any errors after running; it won't affect the generation. A green checkmark next to the play icon indicates completion.

![cell 1 setup](/assets/blog/how-to-deploy-stable-video-diffusion-in-colab/640-2.png)

- **Cell 2: Colab Hack for SVD**

![cell 2 Colab Hack for SVD](/assets/blog/how-to-deploy-stable-video-diffusion-in-colab/640-3.png)

- **Cell 3: Download Weights**

![cell 3 Download weights](/assets/blog/how-to-deploy-stable-video-diffusion-in-colab/640-4.png)

- **Cell 4: Load Model**

![cell 4 Load Model](/assets/blog/how-to-deploy-stable-video-diffusion-in-colab/640-5.png)

- **Cell 5: Sampling Function**

![Cell 5: Sampling Function](/assets/blog/how-to-deploy-stable-video-diffusion-in-colab/640-6.png)

- **Cell 6: Do the Run!**

![Cell 6: Do the Run](/assets/blog/how-to-deploy-stable-video-diffusion-in-colab/640-7.png)

This is the final cell. After successful execution, you'll see a link. Clicking the link opens a webpage where you can happily upload images for generation. Remember, it supports only PNG format; convert other formats to PNG. The generated resolution is 1024x576, and images not in this aspect ratio will be automatically compressed.

## Successfully Generated Video

![Successfully Generated Video](/assets/blog/how-to-deploy-stable-video-diffusion-in-colab/640-8.png)

The interface after video generation looks like the image. Remember to download and save the video. Advanced options generally don't need adjustment, as setting them too high may overload the GPU memory.

![Advanced options ](/assets/blog/how-to-deploy-stable-video-diffusion-in-colab/640-9.png)


How to Deploy Stable Video Diffusion in Colab?


For those into AI drawing, Stable Diffusion is a familiar term.

![Stable Diffusion image generator](/assets/blog/for-those-into-ai-drawing-stable-diffusion-is-a-familiar-term/img1.jpeg)

Since Stability AI released Stable Diffusion (SD), it has gained a lot of popularity.
Expanding its expertise in the image domain, Stability AI recently introduced Stable Video Diffusion. Now, you can create a video with just one image!

## Installation Guide for Stable Video Diffusion
Stability AI has open-sourced two models:
- **SVD Model:** Generates videos at a frame rate of 14 with a resolution of 576x1024.
- **SVD-XT Model:** An upgraded version with finetuning, maintaining the resolution but capable of a frame rate of 25.

**Reminder:** To run Stable Video Diffusion on your local computer, you need a GPU with at least 20GB of memory.

### Step 1: Download the Codebase
Click [this link](https://github.com/camenduru/stable-video-diffusion-colab) to download the codebase.
![download the codebase](/assets/blog/for-those-into-ai-drawing-stable-diffusion-is-a-familiar-term/img2.PNG)

### Step 2: Set Up Python Environment
Python version needs to be 3.10 or higher due to third-party dependencies.
We recommend creating a new environment using conda. 
![creating a new environment using conda](/assets/blog/for-those-into-ai-drawing-stable-diffusion-is-a-familiar-term/img3.PNG)

Activate the environment and install the necessary dependencies. 
![Activate the environment](/assets/blog/for-those-into-ai-drawing-stable-diffusion-is-a-familiar-term/img4.PNG)

Finally, install sgm by running it in the project's root directory. 
If you want to train the model, you'll also need to install sdata, but I won't cover that here due to my computer's configuration limitations.
![install sgm](/assets/blog/for-those-into-ai-drawing-stable-diffusion-is-a-familiar-term/img5.PNG)

### Step 3: Model Download Links
- **SVD Model Download Link:** [SVD Model](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid)
- **SVD-XT Model Download Link:** [SVD-XT Model](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt)

After downloading the models, place them in the checkpoints directory.

### Step 4: Run the Algorithm
You can use Streamlit to run the algorithm.
![Run the Algorithm](/assets/blog/for-those-into-ai-drawing-stable-diffusion-is-a-familiar-term/img6.PNG)


How to install Stable Video Difussion?


Stability AI, a renowned company in AI graphics, has finally ventured into AI-generated videos. This week, the AI community is abuzz with discussions about the Stable Video Diffusion model, which is based on Stable Diffusion. Many are expressing their excitement, saying, "We've been waiting for this."

![the ai community](/assets/blog/stability-ai-enters-the-ai-video-generation-arena-with-stable-video-diffusion/the-ai-community.png)

## What is Stable Video Diffusion?

Stable Video Diffusion, an extension of Stability AI's original Stable Diffusion image generation model, has entered the scene as one of the few open-source or commercially available video generation models. While not yet universally accessible, Stability AI has opened a waitlist for users interested in trying out Stable Video Diffusion. You can submit your waitlist application [here](https://stability.ai/contact).

According to reports, Stable Video Diffusion seamlessly adapts to various downstream tasks, including synthesizing multiple views from a single image through fine-tuning on multi-view datasets. Stability AI plans to build and expand various models around this foundational one, similar to the ecosystem developed around stable diffusion.

It is released in two forms, capable of generating videos at customizable frame rates ranging from 3 to 30 frames per second, resulting in 14 and 25 frames videos.

External evaluations confirm that these models by Stability AI surpass leading closed-source models in user preference studies. However, Stability AI emphasizes that, at this stage, Stable Video Diffusion is not suitable for real-world or direct commercial applications. Subsequent improvements will be made based on user insights and feedback regarding safety and quality.

![the ai community](/assets/blog/stability-ai-enters-the-ai-video-generation-arena-with-stable-video-diffusion/svd-vs-competition.png)

For a deep dive into the research paper, you can visit [here](https://stability.ai/research/stable-video-diffusion-scaling-latent-video-diffusion-models-to-large-datasets).

Stable Video Diffusion adds to Stability AI's diverse family of open-source models, showcasing their commitment to enhancing AI across various modalities, including images, language, audio, 3D, and code.

## The Mechanics of this AI Tool

Stable Video Diffusion, as a high-resolution video latent diffusion model, has achieved state-of-the-art levels in text-to-video or image-to-video synthesis. Recently, the latent diffusion model, originally trained for 2D image synthesis, evolved into a video generation model by introducing a temporal layer and fine-tuning on a small high-quality video dataset. However, training methods in the literature vary significantly, and there is yet to be a consensus on a unified strategy for organizing video data.

In the research paper for Stable Video Diffusion, Stability AI identifies and evaluates three distinct stages for successfully training video latent diffusion models: text-to-image pretraining, video pretraining, and high-quality video fine-tuning. They demonstrate the importance of a carefully curated pretraining dataset for generating high-quality videos and introduce a systematic planning process for training a robust foundational model, including captioning and filtering strategies.

The paper also explores the impact of fine-tuning the base model on high-quality data, leading to a text-to-video model comparable to closed-source video generators. This model provides powerful motion representations for downstream tasks, such as image-to-video generation and adaptability to camera motion-specific LoRA modules. Additionally, the model offers robust multi-view 3D priors, serving as the foundation for multi-view diffusion models. It generates multiple views of an object in a feed-forward manner, requiring minimal computational resources and outperforming image-based methods.

![the ai community](/assets/blog/stability-ai-enters-the-ai-video-generation-arena-with-stable-video-diffusion/samples.jpg)

To sum up, successful training of this model involves three stages:

**Stage 1: Image Pretraining**
The paper views image pretraining as the first stage in the training pipeline. It establishes the initial model based on Stable Diffusion 2.1, providing the video model with powerful visual representations. Results indicate a preference for the quality and prompt tracking of the image pretraining model.

![the ai community](/assets/blog/stability-ai-enters-the-ai-video-generation-arena-with-stable-video-diffusion/figure-3.jpg)

**Stage 2: Video Pretraining Dataset**
The authors rely on human preferences as signals to create a suitable pretraining dataset, called LVD (Large Video Dataset), consisting of 580M annotated video clips. The paper emphasizes the importance of a well-prepared pretraining dataset and introduces annotation strategies using dense optical flow and optical character recognition to eliminate clips with excessive text.

![the ai community](/assets/blog/stability-ai-enters-the-ai-video-generation-arena-with-stable-video-diffusion/figure-2.jpg)

**Stage 3: High-Quality Fine-Tuning**
To analyze the impact of video pretraining on the final stage, the paper fine-tunes three models that differ only in their initialization. Results indicate the effectiveness of high-quality fine-tuning.

![the ai community](/assets/blog/stability-ai-enters-the-ai-video-generation-arena-with-stable-video-diffusion/table1.jpg)

It looks like a promising start. When can we expect AI to directly generate a full-length movie?


Stable Video Diffusion:  a Free AI Video Generator

Stable Video Diffusion produces much better videos than I anticipated, with significant subject variation and distortion-free visuals. Among AI video generation tools, its performance stands out.
Currently, its drawback is the resolution, only 1024x576. The video below doesn't use the HD feature, allowing you to experience the video quality.
<h2>Stable Video Diffusion Video Generation Speed</h2>
Currently, it's recommended to use Colab for deploying Stable Video Diffusion. With a Colab membership, using the A100 GPU, it takes 53 seconds to generate a 4-second video, while using the T4 GPU takes 7 minutes.
<img src="/assets/blog/how-to-deploy-stable-video-diffusion-in-colab/640-0.png" alt="Stable Video Diffusion Video Generation Speed">
<h2>First, Open the Colab Notebook Link</h2>
<a href="https://colab.research.google.com/github/mkshing/notebooks/blob/main/stable_video_diffusion_img2vid.ipynb">Colab Notebook Link</a>
<h2>Next, Click the Play Icon to Run Different Cells</h2>
<ul>
<li>Cell 1: Setup</li>
</ul>
<img src="/assets/blog/how-to-deploy-stable-video-diffusion-in-colab/640-1.png" alt="cell 1 setup">
<pre><code>- Ignore any errors after running; it won't affect the generation. A green checkmark next to the play icon indicates completion.
</code></pre>
<img src="/assets/blog/how-to-deploy-stable-video-diffusion-in-colab/640-2.png" alt="cell 1 setup">
<ul>
<li>Cell 2: Colab Hack for SVD</li>
</ul>
<img src="/assets/blog/how-to-deploy-stable-video-diffusion-in-colab/640-3.png" alt="cell 2 Colab Hack for SVD">
<ul>
<li>Cell 3: Download Weights</li>
</ul>
<img src="/assets/blog/how-to-deploy-stable-video-diffusion-in-colab/640-4.png" alt="cell 3 Download weights">
<ul>
<li>Cell 4: Load Model</li>
</ul>
<img src="/assets/blog/how-to-deploy-stable-video-diffusion-in-colab/640-5.png" alt="cell 4 Load Model">
<ul>
<li>Cell 5: Sampling Function</li>
</ul>
<img src="/assets/blog/how-to-deploy-stable-video-diffusion-in-colab/640-6.png" alt="Cell 5: Sampling Function">
<ul>
<li>Cell 6: Do the Run!</li>
</ul>
<img src="/assets/blog/how-to-deploy-stable-video-diffusion-in-colab/640-7.png" alt="Cell 6: Do the Run">
This is the final cell. After successful execution, you'll see a link. Clicking the link opens a webpage where you can happily upload images for generation. Remember, it supports only PNG format; convert other formats to PNG. The generated resolution is 1024x576, and images not in this aspect ratio will be automatically compressed.
<h2>Successfully Generated Video</h2>
<img src="/assets/blog/how-to-deploy-stable-video-diffusion-in-colab/640-8.png" alt="Successfully Generated Video">
The interface after video generation looks like the image. Remember to download and save the video. Advanced options generally don't need adjustment, as setting them too high may overload the GPU memory.
<img src="/assets/blog/how-to-deploy-stable-video-diffusion-in-colab/640-9.png" alt="Advanced options ">