Latent blending enables the creation of super-smooth video transitions between prompts. Powered by stable diffusion 2.1, this method involves specific mixing of intermediate latent representations to create a seamless transition – with users having the option to choose full customization or preset options.

Quickstart

fp_ckpt = 'path_to_SD2.ckpt'
fp_config = 'path_to_config.yaml'

sdh = StableDiffusionHolder(fp_ckpt, fp_config, 'cuda')
lb = LatentBlending(sdh)

lb.load_branching_profile(quality='medium', depth_strength=0.4)
lb.set_prompt1('photo of my first prompt1')
lb.set_prompt2('photo of my second prompt')

imgs_transition = lb.run_transition()

Gradio UI

To run the UI on your local machine, run gradio_ui.py

Example 1: Simple transition

To run a simple transition between two prompts, run example1_standard.py

Example 2: Inpainting transition

To run a transition between two prompts where you want some part of the image to remain static, run example2_inpaint.py

Example 3: concatenated transition

To run multiple transition between K prompts, resulting in a stitched video, run example3_multitrans.py

Relevant parameters

Installation

Packages

pip install -r requirements.txt

Download Models from Huggingface

Download the Stable Diffusion v2-1_768 Model

Download the Stable Diffusion 2.0 Inpainting Model (optional)

(Optional) Install Xformers

With xformers, stable diffusion 2 will run a bit faster. The recommended way of installation is via the supplied binaries (Linux).

conda install xformers -c xformers/label/dev

Alternatively, you can build it from source:

# (Optional) Makes the build much faster
pip install ninja
# Set TORCH_CUDA_ARCH_LIST if running and building on different GPU types
pip install -v -U git+https://github.com/facebookresearch/xformers.git@main#egg=xformers
# (this can take dozens of minutes)

How does it work

what makes a transition a good transition?

• absence of movement
• every frame looks like a credible photo