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@ -32,19 +32,49 @@ To run multiple transition between K prompts, resulting in a stitched video, run
## Most relevant parameters ## Most relevant parameters
set_height ### Change the height/width
```python
lb.set_height(512)
lb.set_width(1024)
```
### Change guidance scale
```python
lb.set_guidance_scale(5.0)
```
### depth_strength / list_injection_strength
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.
## Set up the branching structure ## Set up the branching structure
There are three ways to change the branching structure. There are three ways to change the branching structure.
### Use a preset ### Presets
```python ```python
quality = 'medium' #choose from lowest, low, medium, high, ultra quality = 'medium' #choose from lowest, low, medium, high, ultra
depth_strength = 0.5 # see above (Most relevant parameters) depth_strength = 0.5 # see above (Most relevant parameters)
lb.load_branching_profile(quality, depth_strength) lb.load_branching_profile(quality, depth_strength)
``` ```
### Autosetup tree setup
```python
num_inference_steps = 30 # the number of diffusion steps
list_nmb_branches = [2, 4, 8, 20]
list_injection_strength = [0.0, 0.3, 0.5, 0.9]
lb.autosetup_branching(num_inference_steps, list_nmb_branches, list_injection_strength)
```
### Fully manual
```python
depth_strength = 0.5 # see above (Most relevant parameters)
num_inference_steps = 30 # the number of diffusion steps
nmb_branches_final = 20 # how many diffusion images will be generated for the transition
lb.setup_branching(depth_strength, num_inference_steps, nmb_branches_final)
```
# Installation # Installation
#### Packages #### Packages