Stepping into 2024, I can’t help but get excited about the latest breakthrough in AI technology. Sure, we’ve all seen those mind-blowing AI-generated images popping up everywhere – you know, the ones from MidJourney and Stable Diffusion that flood our social media feeds. But here’s what’s got me absolutely buzzing: AI video generation.
I recently stumbled upon this game-changing tool called Pyramid Flow, and I’ve got to tell you, it’s pretty wild. You literally type in what you want to see, hit enter, and boom – you’ve got a video. Even better? You can take your favorite photos and watch them come alive with movement. Trust me, the first time I saw it happen, I had to pick my jaw up off the floor.
Here’s what really sold me on Pyramid Flow: you don’t need to be some coding wizard to use it. They’ve built this super clean browser interface that makes the whole process feel as simple as posting on social media. Of course, you’ll need to do some initial setup (more on that in a bit), but once you’re in, it’s surprisingly straightforward.
In this post, I’m going to walk you through my hands-on experience with Pyramid Flow. Whether you’re a content creator looking to level up your game or just someone who loves playing with cutting-edge tech, you’re in for a treat.
What’s Pyramid Flow? (And Why I’m Excited About It)
Remember when turning your ideas into videos meant spending hours learning complex editing software? Those days are over. Pyramid Flow is this incredible open-source project that takes either your written descriptions or static images and transforms them into smooth, flowing videos. And the best part? Since it’s built entirely on open-source technology (they call it Flow Matching), you can use it completely free.
I’ve spent countless hours testing it out, and I’ve got to say, the results are impressive. We’re talking professional-quality videos here – up to 10 seconds long in crisp 768p resolution running at a smooth 24 frames per second. Whether you’re typing out a scene description or uploading a photo you want to animate, the whole process happens right in your web browser.
The developers have done something really clever with the memory optimization too. Even on my modest setup, it runs surprisingly well. If you want to check it out yourself, you can find everything over at
Here’s what really got me excited about Pyramid Flow:
- Create videos from text descriptions (perfect for bringing your ideas to life)
- Transform still images into moving scenes
- Super clean browser interface (no coding needed!)
- Handles memory like a champ
Before We Dive In: What You’ll Need
Before we start creating AI videos, let’s make sure you’ve got everything set up properly. I learned this the hard way – there’s nothing more frustrating than getting halfway through a tutorial only to realize you’re missing something essential!
Here’s your tech checklist (don’t worry if some of these sound unfamiliar – I’ll guide you through each part):
- A Windows PC with an NVIDIA graphics card
- Python version 3.8.10 installed (stick with this specific version – trust me on this one)
- Up-to-date NVIDIA drivers that support CUDA
- Visual Studio Code (or VSCode as most people call it)
Think of this setup like building a workshop – we need the right tools before we can start creating. VSCode will be our workbench, Python our toolset, and that NVIDIA graphics card? That’s our power tool that’ll do the heavy lifting.
Getting Your Digital Workshop Ready
I’ll be showing you how to set everything up using Windows and VSCode. I picked VSCode because it makes managing all these moving parts so much easier – it’s like having a well-organized tool drawer where everything’s right where you need it.
Setting Up Python and Getting the Project Files
Managing Your Python Version
The first crucial step is getting the right version of Python installed. Pyramid Flow works best with Python 3.8.10 specifically, so we need to make sure that’s what we’re using. I personally use pyenv for managing different Python versions – it’s like having a Swiss Army knife for Python installations.
Let’s start by checking what version we currently have:
python --versionWhen I ran this command, I found I was using Python 3.10.11. Since we need 3.8.10, I had to switch versions. If you’re not using pyenv yet, you could also download Python 3.8.10 directly from python.org – whatever works best for your setup.
Downloading the Project
Once we’ve got Python sorted, it’s time to get the actual project files. We’ll use Git to clone the project repository:
git clone https://github.com/jy0205/Pyramid-Flow
cd Pyramid-Flow💡 Pro Tip: If you’re not familiar with Git, you can also download the project directly from GitHub by clicking the green “Code” button and selecting “Download ZIP”. Just remember to extract it somewhere convenient on your computer.
Setting Up Your Python Environment
Let’s get Python 3.8.10 running on your system. During my setup process, I found there are a couple of ways to do this, and I’ll share both so you can choose what works best for you.
Checking Your Current Python Version
First, let’s see what version you’re currently running:
python --versionIf you’re already running Python 3.8.10, you’re all set! If not, you’ve got two options:
Option 1: Fresh Python Installation
If you’re new to Python or prefer keeping things simple, installing Python 3.8.10 directly is your best bet. Head over to Python’s official website and download version 3.8.10. Remember to uninstall any existing Python versions first to avoid confusion.
Option 2: Using pyenv (My Preferred Method)
If you’re planning to work on different Python projects that might need different versions, I’d recommend using pyenv. I actually ran into so many version conflicts before discovering this tool that I wrote a complete guide on how to set up pyenv on Windows. If you decide to go this route, you can set your Python version with:
pyenv local 3.8.10Here’s something crucial I discovered the hard way: if you’re using VSCode like I am, you’ll need to completely close and reopen it for the version change to take effect. After reopening VSCode, always double-check your version:
python --versionCreating a Clean Workspace with Virtual Environment
Before we install any project-specific tools, we need to set up what’s called a “virtual environment.” If you’ve never used one before, think of it like creating a fresh, clean workspace specifically for this project. Why is this important? Well, I learned from experience that mixing Python packages from different projects can lead to some really frustrating conflicts.
Setting up a virtual environment is actually pretty straightforward. We’ll use two simple commands:
python -m venv venv
venv\Scripts\activateAfter running these commands, you should see (venv) appear at the beginning of your command prompt. That’s your signal that you’re now working in the isolated environment.
Note: If you run into any issues with the activation command on Windows, it might be related to PowerShell execution policy settings. In that case, just try running PowerShell as administrator and typing
Set-ExecutionPolicy RemoteSignedbefore trying the activation command again.
Installing Required Packages
Now that our virtual environment is set up, we need to install all the tools this project needs to run. First though, let’s make sure our package installer (pip) is up to date:
python -m pip install --upgrade pipWhen it comes to installing the required packages, I tested two different approaches:
Option 1: Using the Requirements File
This is the recommended way, as it ensures you’re getting the exact package versions that were tested with the project:
pip install -r requirements.txtOption 2: Manual Package Installation
If you prefer to see exactly what’s being installed, you can add each package individually:
pip install gradio torch Pillow diffusers huggingface_hubI personally tested both methods, and they both worked fine in my setup. I’d recommend starting with Option 1 (the requirements file) since it’s been properly tested. If you run into any issues, you can always fall back to installing packages one by one.
Pro Tip: While the requirements file method is installing, you might see a lot of text scrolling by – that’s completely normal! It’s just showing you the progress as each package and its dependencies are being downloaded and installed.
Launching the Application
Now comes the exciting part – launching the application! After all that setup, the command itself is surprisingly simple:
python app.py
diffusion_transformer_768p/config.json: 100%|█████████████████| 465/465 [00:00<00:00, 226kB/s]
README.md: 100%|█████████████████████████████████████████| 9.38k/9.38k [00:00<00:00, 4.45MB/s]
diffusion_transformer_image/config.json: 100%|████████████████| 465/465 [00:00<00:00, 233kB/s]
text_encoder_2/config.json: 100%|█████████████████████████████| 782/782 [00:00<00:00, 391kB/s]
text_encoder/config.json: 100%|███████████████████████████████| 613/613 [00:00<00:00, 204kB/s]
(…)t_encoder_2/model.safetensors.index.json: 100%|███████| 19.9k/19.9k [00:00<00:00, 6.63MB/s]
tokenizer/merges.txt: 100%|████████████████████████████████| 525k/525k [00:00<00:00, 1.21MB/s]
tokenizer/special_tokens_map.json: 100%|██████████████████████| 588/588 [00:00<00:00, 235kB/s]
tokenizer/tokenizer_config.json: 100%|████████████████████████| 705/705 [00:00<00:00, 276kB/s]
tokenizer/vocab.json: 100%|██████████████████████████████| 1.06M/1.06M [00:00<00:00, 1.61MB/s]
tokenizer_2/special_tokens_map.json: 100%|███████████████| 2.54k/2.54k [00:00<00:00, 1.26MB/s]
spiece.model: 100%|████████████████████████████████████████| 792k/792k [00:00<00:00, 2.17MB/s]
tokenizer_2/tokenizer.json: 100%|████████████████████████| 2.42M/2.42M [00:01<00:00, 1.68MB/s]
tokenizer_2/tokenizer_config.json: 100%|█████████████████| 20.8k/20.8k [00:00<00:00, 5.93MB/s]
model.safetensors: 100%|███████████████████████████████████| 246M/246M [00:49<00:00, 5.00MB/s]
diffusion_pytorch_model.bin: 100%|███████████████████████| 1.34G/1.34G [02:03<00:00, 10.9MB/s]
Fetching 24 files: 17%|██████▌ | 4/24 [02:03<12:53, 38.69s/it]
diffusion_pytorch_model.safetensors: 18%|██▋ | 1.38G/7.89G [02:02<12:30, 8.66MB/s]
diffusion_pytorch_model.safetensors: 40%|██████ | 3.16G/7.89G [04:16<04:17, 18.4MB/s]
diffusion_pytorch_model.safetensors: 32%|████▊ | 2.53G/7.89G [04:16<05:16, 16.9MB/s]
diffusion_pytorch_model.safetensors: 32%|████▊ | 2.55G/7.89G [04:15<15:01, 5.92MB/s]
model-00001-of-00002.safetensors: 29%|█████▏ | 1.43G/4.99G [02:01<03:59, 14.9MB/s]
model-00001-of-00002.safetensors: 64%|███████████▌ | 3.22G/4.99G [04:15<02:12, 13.4MB/s]
model-00002-of-00002.safetensors: 27%|████▉ | 1.24G/4.53G [01:59<06:22, 8.62MB/s]
model-00002-of-00002.safetensors: 59%|██████████▋ | 2.69G/4.53G [04:14<03:21, 9.12MB/s]
When you run this command for the first time, the application will start downloading several model files. Don’t be surprised – these are large AI models that power the video generation, so this initial download might take a while depending on your internet speed.
What to expect: The first launch will download several gigabytes of model files. This only happens once, but you might want to grab a coffee while you wait!
You might see this warning message pop up:
[WARNING] CUDA is not available. Proceeding without GPU.
Don’t worry if you see this – we’ll tackle GPU setup in the next section.
Setting Up Your GPU
Remember that CUDA warning we saw earlier? Let’s fix that now. This part is crucial because your GPU will make everything run much faster.
First, we need to check what CUDA version you have installed:
nvcc -VIn my setup, I found I was running CUDA 12.4. Once you know your CUDA version, you’ll need to install the matching PyTorch version. Here’s the command I used for CUDA 12.4:
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu124Important: Make sure you run this command while your virtual environment is activated (you should see that
(venv)prefix in your command prompt).
I spent quite a bit of time figuring out the right combination of CUDA and PyTorch versions. The good news? Once you get this working, everything else should run smoothly. Let’s move on to actually using Pyramid Flow…
Common Setup Issues: Learn from My Mistakes
While the steps above might seem straightforward now, I’ll be honest – my actual setup journey had its fair share of “wait, what?” moments. Let me share some of the puzzling situations I encountered, so you don’t have to learn these lessons the hard way.
The Mysterious Python Version Issue
This one really had me scratching my head. Remember when we set up Python 3.8.10? Well, check out what happened when I tried to verify my Python version after setting it with pyenv:
python --version
Python 3.10.11Confused? So was I! Even though I had run pyenv local 3.8.10, Python was still showing version 3.10.11.
The solution turned out to be surprisingly simple, but it’s something most tutorials don’t mention: VSCode needs to be completely closed and reopened before it recognizes the Python version change. Not just the terminal, not just the project – the entire VSCode application needs a restart.
Time Saver: If you’re using pyenv and VSCode together, always restart VSCode after changing Python versions. And if you want to dive deeper into managing Python versions with pyenv, check out my detailed guide on setting up pyenv on Windows.
Digging Deeper: The Hidden Version File
After restarting VSCode, my curiosity got the better of me. Something had to be controlling this Python version switch, right? So I decided to take a closer look at what was actually in my project folder:
dir
2024/11/22 16:52 <DIR> .
2024/11/22 16:51 <DIR> ..
2024/11/22 16:51 1,446 .gitignore
2024/11/22 16:52 8 .python-version
2024/11/22 16:51 <DIR> annotation
2024/11/22 16:51 15,269 app.py
2024/11/22 16:51 5,619 app_multigpu.py
2024/11/22 16:51 <DIR> assets
2024/11/22 16:51 8,105 causal_video_vae_demo.ipynb
2024/11/22 16:51 <DIR> dataset
2024/11/22 16:51 <DIR> diffusion_schedulers
2024/11/22 16:51 <DIR> docs
2024/11/22 16:51 3,391 image_generation_demo.ipynb
2024/11/22 16:51 4,909 inference_multigpu.py
2024/11/22 16:51 1,086 LICENSE
2024/11/22 16:51 <DIR> pyramid_dit
2024/11/22 16:51 16,508 README.md
2024/11/22 16:51 406 requirements.txt
2024/11/22 16:51 <DIR> scripts
2024/11/22 16:51 <DIR> tools
2024/11/22 16:51 <DIR> train
2024/11/22 16:51 <DIR> trainer_misc
2024/11/22 16:51 14,387 utils.py
2024/11/22 16:51 7,052 video_generation_demo.ipynb
2024/11/22 16:51 <DIR> video_vae
That’s when I discovered something interesting: a hidden file called .python-version. It’s easy to miss, but this little file is actually what tells pyenv which Python version to use. When I opened it, I found it contained just one line: “3.8.10”.
You can find this file in two ways:
- Through VSCode’s explorer (it shows hidden files by default)
- Using Windows File Explorer (you’ll need to enable “Show hidden files” in View options)
After this discovery, I ran the version check one more time:
python --version
Python 3.8.10Success at last! The Python version was finally set correctly.
Why This Matters: Understanding where pyenv stores its version information can be super helpful if you ever need to troubleshoot version issues in future projects.
The Package Installation Adventure
Just when I thought everything was set up perfectly, I tried to run the application:
python app.pyAnd hit another wall:
Traceback (most recent call last):
File "app.py", line 3, in
import gradio as gr
ModuleNotFoundError: No module named 'gradio'
Ah, the classic “module not found” error! So I tried installing the packages:
pip install gradio torch Pillow diffusers huggingface_hubBut Python had other plans:
WARNING: You are using pip version 21.1.1; however, version 24.3.1 is available.
Alright, let’s do this the right way. First, update pip itself:
python -m pip install --upgrade pipAnd then install everything properly using the requirements file:
pip install -r requirements.txtLearning Moment: This is actually a great example of why virtual environments are so useful. All these package issues stay contained within this project and don’t affect anything else on your system.
Final Steps: Getting Your GPU Ready
After tackling all those initial challenges, we’re down to the last crucial piece: GPU setup. This part is especially important because AI video generation needs some serious computing power.
When you first run the application, you might see this warning:
[WARNING] CUDA is not available. Proceeding without GPU.
This is your computer telling you that it can’t access your GPU yet. Don’t worry – we can fix this!
First, let’s check your CUDA version:
nvcc -VIn my case, I got this output:
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2024 NVIDIA Corporation
Built on Thu_Mar_28_02:30:10_Pacific_Daylight_Time_2024
Cuda compilation tools, release 12.4, V12.4.131
Build cuda_12.4.r12.4/compiler.34097967_0
Now comes the important part – installing PyTorch with the correct CUDA version:
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu124And with that, we’ve conquered the final boss of our setup journey! Now your GPU is ready to handle the intensive task of AI video generation. Trust me – after going through all these steps, seeing your first AI-generated video will make it all worth it.
Coming Up Next: Now that we’ve got everything set up properly, let’s dive into actually creating some videos!
Let’s Create Some AI Videos!
Finally – we’ve made it through all the setup, and now we’re ready for the exciting part! Let’s fire up the application:
python app.pyFirst Launch Notice: When you run this for the first time, the application will start downloading several large AI model files. These are essential for video generation, but they’re quite big (several gigabytes in total). Perfect time to grab a coffee or catch up on some reading!
Once all the models are downloaded, your default web browser will automatically open, displaying the Gradio interface. Think of Gradio as your creative dashboard – it’s where all the magic happens.
This web interface might seem simple at first glance, but don’t let that fool you – it’s packing some serious AI power under the hood. Each button and slider has been carefully designed to give you precise control over your video generation.
Pro Tip: Keep this browser tab open while you experiment. If you accidentally close it, you can always access it again at http://localhost:7860 while the app is running.
Understanding the Interface & Creating Videos
Meet Your Creative Dashboard
The interface is split into two powerful modes, each with its own unique capabilities:
✨ Text-to-Video Mode
Transform your written descriptions into moving scenes with these controls:
- Prompt: Your creative canvas – describe what you want to see
- Duration: Choose video length (384p: up to 16 frames, 768p: up to 31 frames)
- Guidance Scale: Fine-tune how closely the AI follows your description
- Video Guidance Scale: Control the amount of motion
- Resolution: Pick between 384p or 768p quality
🎨 Image-to-Video Mode
Breathe life into static images:
- Input Image: Upload your starting picture
- Prompt: Guide how you want the image to animate
- Additional Settings: Similar to text-to-video controls
Real-World Examples & Insights
Text-to-Video Success Story
I started with this imaginative prompt:
A movie trailer featuring the adventures of the 30 year old space man wearing a red wool knitted motorcycle helmet, blue sky, salt desert, cinematic style, shot on 35mm film, vivid colors
Settings that worked well:
- Resolution: 384p
- Duration: 16 frames
- Guidance Scale: 7.0
- Video Guidance Scale: 5.0
The result? A cinematic scene featuring an astronaut with a distinctive red knitted helmet, traversing a desert landscape – exactly what I had envisioned!
Bringing Still Images to Life
For my image-to-video experiment, I used the Great Wall sample image with this prompt:
FPV flying over the Great Wall
Optimal settings:
- Resolution: 384p
- Duration: 16
- Video Guidance Scale: 4.0
The transformation was remarkable – creating a smooth, drone-like flight over the Great Wall.
Understanding Memory Usage
Curious about performance, I checked my GPU resources:
import torch
print(f"Total Memory: {torch.cuda.get_device_properties(0).total_memory / 1024**2:.0f}MB")
print(f"Allocated: {torch.cuda.memory_allocated() / 1024**2:.0f}MB")
print(f"Cached: {torch.cuda.memory_reserved() / 1024**2:.0f}MB")Results:
Total Memory: 8188MB
Allocated: 0MB
Cached: 0MB
Pro Tips from My Testing
- Start with 384p resolution
- Easier on your GPU
- Faster generation times
- Perfect for testing ideas
- Be specific with prompts
- Vague descriptions = vague results
- Include details about style, lighting, and movement
- Think cinematically
- Memory Management
- With 8GB GPU, watch for “CUDA out of memory” errors
- Refresh browser if needed
- Consider closing other GPU-intensive applications
Memory Tip: Think of your GPU memory like a canvas – higher resolutions need more space. Start small, then scale up once you know what works!
Tips, Tricks, and Troubleshooting Guide
Managing GPU Memory Like a Pro
Let me share some hard-learned lessons from working with my 8GB GPU setup. While it might not be top-of-the-line hardware, I’ve discovered how to make the most of it.
Common Challenges
- 768p resolution proved to be a bit too ambitious
- Memory errors after generating multiple 384p videos
- Those pesky “CUDA out of memory” errors popping up
Smart Workarounds That Actually Work
After lots of trial and error, here’s what I found most effective:
Memory Management Strategies
- Resolution Control
- Start with 384p for all initial tests
- Save 768p attempts for your final, perfected prompts
- Resource Optimization
- Reduce frame count when memory gets tight
- Regular browser refreshes help clear things up
- When in doubt, restart the application
- Workflow Tips
- Test concepts at low resolution first
- Scale up only after you’re happy with the results
- Take short breaks during memory issues
Pro Tip: Think of your GPU memory like a performance budget – spend it wisely on the features that matter most for your specific video.
Looking Ahead: The Future of AI Video Generation
While Pyramid Flow works best with high-end GPUs (16GB+ would be ideal), don’t let that discourage you. I’ve created some amazing videos with my modest 8GB setup – it’s all about working smart within your system’s capabilities.
The field of AI video generation is moving at lightning speed. What seems challenging today might be effortless tomorrow. For now, focus on:
- Mastering prompt engineering
- Understanding your hardware limits
- Being creative within those constraints
Remember: Every major technology started somewhere. Today’s “minimum requirements” are tomorrow’s entry-level specs. The key is to start experimenting now, no matter your hardware setup.
Final Thought: The most impressive videos don’t always come from the most powerful hardware – they come from creative minds working cleverly within their limits.
