113 lines
3.7 KiB
Markdown
113 lines
3.7 KiB
Markdown
Latent blending enables the creation of super-smooth video transitions between prompts. Powered by [stable diffusion 2.1](https://stability.ai/blog/stablediffusion2-1-release7-dec-2022), 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.
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# Quickstart
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```python
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fp_ckpt = 'path_to_SD2.ckpt'
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fp_config = 'path_to_config.yaml'
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sdh = StableDiffusionHolder(fp_ckpt, fp_config, 'cuda')
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lb = LatentBlending(sdh)
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lb.load_branching_profile(quality='medium', depth_strength=0.4)
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lb.set_prompt1('photo of my first prompt1')
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lb.set_prompt2('photo of my second prompt')
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imgs_transition = lb.run_transition()
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```
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## Gradio UI
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To run the UI on your local machine, run `gradio_ui.py`
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## Example 1: Simple transition
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To run a simple transition between two prompts, run `example1_standard.py`
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## Example 2: Inpainting transition
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To run a transition between two prompts where you want some part of the image to remain static, run `example2_inpaint.py`
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## Example 3: Multi transition
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To run multiple transition between K prompts, resulting in a stitched video, run `example3_multitrans.py`
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# Customization
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## Most relevant parameters
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### Change the height/width
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```python
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lb.set_height(512)
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lb.set_width(1024)
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```
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### Change guidance scale
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```python
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lb.set_guidance_scale(5.0)
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```
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### depth_strength / list_injection_strength
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The strength dictates how early the blending process starts. The closer its value is to zero, the more inventive the results will be; whereas, a value closer to one indicates a more simple alpha blending.
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## Set up the branching structure
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There are three ways to change the branching structure.
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### Presets
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```python
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quality = 'medium' #choose from lowest, low, medium, high, ultra
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depth_strength = 0.5 # see above (Most relevant parameters)
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lb.load_branching_profile(quality, depth_strength)
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```
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### Autosetup tree setup
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```python
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num_inference_steps = 30 # the number of diffusion steps
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list_nmb_branches = [2, 4, 8, 20]
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list_injection_strength = [0.0, 0.3, 0.5, 0.9]
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lb.autosetup_branching(num_inference_steps, list_nmb_branches, list_injection_strength)
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```
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### Fully manual
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```python
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depth_strength = 0.5 # see above (Most relevant parameters)
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num_inference_steps = 30 # the number of diffusion steps
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nmb_branches_final = 20 # how many diffusion images will be generated for the transition
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lb.setup_branching(depth_strength, num_inference_steps, nmb_branches_final)
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```
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# Installation
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#### Packages
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```commandline
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pip install -r requirements.txt
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```
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#### Download Models from Huggingface
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[Download the Stable Diffusion v2-1_768 Model](https://huggingface.co/stabilityai/stable-diffusion-2-1)
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[Download the Stable Diffusion Inpainting Model](https://huggingface.co/stabilityai/stable-diffusion-2-inpainting)
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[Download the Stable Diffusion x4 Upscaler](https://huggingface.co/stabilityai/stable-diffusion-x4-upscaler)
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#### (Optional but recommended) Install [Xformers](https://github.com/facebookresearch/xformers)
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With xformers, stable diffusion will run faster with smaller memory inprint. Necessary for higher resolutions / upscaling model.
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```commandline
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conda install xformers -c xformers/label/dev
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```
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Alternatively, you can build it from source:
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```commandline
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# (Optional) Makes the build much faster
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pip install ninja
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# Set TORCH_CUDA_ARCH_LIST if running and building on different GPU types
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pip install -v -U git+https://github.com/facebookresearch/xformers.git@main#egg=xformers
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# (this can take dozens of minutes)
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```
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# How does it work
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what makes a transition a good transition?
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* absence of movement
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* every frame looks like a credible photo
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