Example - morphing between "blueberry spaghetti" and "strawberry spaghetti"
berry_good_spaghetti.2.mp4
The in-browser Colab demo allows you to generate videos by interpolating the latent space of Stable Diffusion.
You can either dream up different versions of the same prompt, or morph between different text prompts (with seeds set for each for reproducibility).
The app is built with Gradio, which allows you to interact with the model in a web app. Here's how I suggest you use it:
-
Use the "Images" tab to generate images you like.
- Find two images you want to morph between
- These images should use the same settings (guidance scale, scheduler, height, width)
- Keep track of the seeds/settings you used so you can reproduce them
-
Generate videos using the "Videos" tab
- Using the images you found from the step above, provide the prompts/seeds you recorded
- Set the
num_walk_steps
- for testing you can use a small number like 3 or 5, but to get great results you'll want to use something larger (60-200 steps). - You can set the
output_dir
to the directory you wish to save to
Install the package
pip install stable_diffusion_videos
Authenticate with Hugging Face
huggingface-cli login
from stable_diffusion_videos import walk
walk(
prompts=['a cat', 'a dog'],
seeds=[42, 1337],
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_steps=5, # Change to 60-200 for better results...3-5 for testing
num_inference_steps=50,
scheduler='klms', # One of: "klms", "default", "ddim"
disable_tqdm=False, # Set to True to disable tqdm progress bar
make_video=True, # If false, just save images
use_lerp_for_text=True, # Use lerp for text embeddings instead of slerp
do_loop=False, # Change to True if you want last prompt to loop back to first prompt
)
from stable_diffusion_videos import interface
interface.launch()
This work built off of a script shared by @karpathy. The script was modified to this gist, which was then updated/modified to this repo.
You can file any issues/feature requests here
Enjoy 🤗