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Create 🔥 videos with Stable Diffusion by exploring the latent space and morphing between text prompts
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Today
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Momentum
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growth rate 0.00%/day
# stable-diffusion-videos
Try it yourself in Colab: [](https://colab.research.google.com/github/nateraw/stable-diffusion-videos/blob/main/stable_diffusion_videos.ipynb)
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**Example** - morphing between "blueberry spaghetti" and "strawberry spaghetti"
https://user-images.githubusercontent.com/32437151/188721341-6f28abf9-699b-46b0-a72e-fa2a624ba0bb.mp4
## Installation
```bash
pip install stable_diffusion_videos
```
## Usage
Check out the [examples](./examples) folder for example scripts 👀
### Making Videos
Note: For Apple M1 architecture, use ```torch.float32``` instead, as ```torch.float16``` is not available on MPS.
```python
from stable_diffusion_videos import StableDiffusionWalkPipeline
import torch
pipeline = StableDiffusionWalkPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4",
torch_dtype=torch.float16,
).to("cuda")
video_path = pipeline.walk(
prompts=['a cat', 'a dog'],
seeds=[42, 1337],
num_interpolation_steps=3,
height=512, # use multiples of 64 if > 512. Multiples of 8 if < 512.
width=512, # use multiples of 64 if > 512. Multiples of 8 if < 512.
output_dir='dreams', # Where images/videos will be saved
name='animals_test', # Subdirectory of output_dir where images/videos will be saved
guidance_scale=8.5, # Higher adheres to prompt more, lower lets model take the wheel
num_inference_steps=50, # Number of diffusion steps per image generated. 50 is good default
)
```
### Making Music Videos
*New!* Music can be added to the video by providing a path to an audio file. The audio will inform the rate of interpolation so the videos move to the beat 🎶
```python
from stable_diffusion_videos import StableDiffusionWalkPipeline
import torch
pipeline = StableDiffusionWalkPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4",
torch_dtype=torch.float16,
).to("cuda")
# Seconds in the song.
audio_offsets = [146, 148] # [Start, end]
fps = 30 # Use lower values for testing (5 or 10), higher values for better quality (30 or 60)
# Convert seconds to frames
num_interpolation_steps = [(b-a) * fps for a, b in zip(audio_offsets, audio_offsets[1:])]
video_path = pipeline.walk(
prompts=['a cat', 'a dog'],
seeds=[42, 1337],
num_interpolation_steps=num_interpolation_steps,
audio_filepath='audio.mp3',
audio_start_sec=audio_offsets[0],
fps=fps,
height=512, # use multiples of 64 if > 512. Multiples of 8 if < 512.
width=512, # use multiples of 64 if > 512. Multiples of 8 if < 512.
output_dir='dreams', # Where images/videos will be saved
guidance_scale=7.5, # Higher adheres to prompt more, lower lets model take the wheel
num_inference_steps=50, # Number of diffusion steps per image generated. 50 is good default
)
```
### Using the UI
```python
from stable_diffusion_videos import StableDiffusionWalkPipeline, Interface
import torch
pipeline = StableDiffusionWalkPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4",
torch_dtype=torch.float16,
).to("cuda")
interface = Interface(pipeline)
interface.launch()
```
## Credits
This work built off of [a script](https://gist.github.com/karpathy/00103b0037c5aaea32fe1da1af553355
) shared by [@karpathy](https://github.com/karpathy). The script was modified to [this gist](https://gist.github.com/nateraw/c989468b74c616ebbc6474aa8cdd9e53), which was then updated/modified to this repo.
## Contributing
You can file any issues/feature requests [here](https://github.com/nateraw/stable-diffusion-videos/issues)
Enjoy 🤗
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:d9ddd202132151c8, topic:stable-diffusion, name:stable diffusion, desc:stable diffusion