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Update README.md

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Johannes Stelzer 2024-01-26 11:52:04 +00:00 committed by GitHub
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@ -51,19 +51,19 @@ To run multiple transition between K prompts, resulting in a stitched video, see
### Change the height/width ### Change the height/width
```python ```python
size_output = (1024, 768) size_output = (1024, 768)
lb.set_dimensions(size_output) be.set_dimensions(size_output)
``` ```
### Change the number of diffusion steps (set_num_inference_steps) ### Change the number of diffusion steps (set_num_inference_steps)
```python ```python
lb.set_num_inference_steps(50) be.set_num_inference_steps(50)
``` ```
For SDXL this is set as default=30, for SDXL Turbo a value of 4 is taken. For SDXL this is set as default=30, for SDXL Turbo a value of 4 is taken.
### Change the guidance scale ### Change the guidance scale
```python ```python
lb.set_guidance_scale(3.0) be.set_guidance_scale(3.0)
``` ```
For SDXL this is set as default=4.0, for SDXL Turbo a value of 0 is taken. For SDXL this is set as default=4.0, for SDXL Turbo a value of 0 is taken.
@ -71,7 +71,7 @@ For SDXL this is set as default=4.0, for SDXL Turbo a value of 0 is taken.
```python ```python
depth_strength = 0.5 depth_strength = 0.5
nmb_max_branches = 15 nmb_max_branches = 15
lb.set_branching(depth_strength=depth_strength, t_compute_max_allowed=None, nmb_max_branches=None) be.set_branching(depth_strength=depth_strength, t_compute_max_allowed=None, nmb_max_branches=None)
``` ```
* depth_strength: The strength of the diffusion iterations determines when the blending process will begin. A value close to zero results in more creative and intricate outcomes, while a value closer to one indicates a simpler alpha blending. However, low values may also bring about the introduction of additional objects and motion. * depth_strength: The strength of the diffusion iterations determines when the blending process will begin. A value close to zero results in more creative and intricate outcomes, while a value closer to one indicates a simpler alpha blending. However, low values may also bring about the introduction of additional objects and motion.
* t_compute_max_allowed: maximum time allowed for computation. Higher values give better results but take longer. Either provide t_compute_max_allowed or nmb_max_branches. Does not work for SDXL Turbo. * t_compute_max_allowed: maximum time allowed for computation. Higher values give better results but take longer. Either provide t_compute_max_allowed or nmb_max_branches. Does not work for SDXL Turbo.
@ -82,7 +82,7 @@ You can find the [most relevant parameters here.](parameters.md)
### Change guidance scale ### Change guidance scale
```python ```python
lb.set_guidance_scale(5.0) be.set_guidance_scale(5.0)
``` ```
### Crossfeeding to the last image. ### Crossfeeding to the last image.
@ -92,7 +92,7 @@ Cross-feeding latents is a key feature of latent blending. Here, you can set how
crossfeed_power = 0.5 # 50% of the latents in the last branch are copied from branch1 crossfeed_power = 0.5 # 50% of the latents in the last branch are copied from branch1
crossfeed_range = 0.7 # The crossfeed is active until 70% of num_iteration, then switched off crossfeed_range = 0.7 # The crossfeed is active until 70% of num_iteration, then switched off
crossfeed_decay = 0.2 # The power of the crossfeed decreases over diffusion iterations, here it would be 0.5*0.2=0.1 in the end of the range. crossfeed_decay = 0.2 # The power of the crossfeed decreases over diffusion iterations, here it would be 0.5*0.2=0.1 in the end of the range.
lb.set_branch1_crossfeed(crossfeed_power, crossfeed_range, crossfeed_decay) be.set_branch1_crossfeed(crossfeed_power, crossfeed_range, crossfeed_decay)
``` ```
### Crossfeeding to all transition images ### Crossfeeding to all transition images
@ -102,7 +102,7 @@ Here, you can set how much the parent branches influence the mixed one. In the a
crossfeed_power = 0.5 # 50% of the latents in the last branch are copied from the parents crossfeed_power = 0.5 # 50% of the latents in the last branch are copied from the parents
crossfeed_range = 0.7 # The crossfeed is active until 70% of num_iteration, then switched off crossfeed_range = 0.7 # The crossfeed is active until 70% of num_iteration, then switched off
crossfeed_decay = 0.2 # The power of the crossfeed decreases over diffusion iterations, here it would be 0.5*0.2=0.1 in the end of the range. crossfeed_decay = 0.2 # The power of the crossfeed decreases over diffusion iterations, here it would be 0.5*0.2=0.1 in the end of the range.
lb.set_parental_crossfeed(crossfeed_power, crossfeed_range, crossfeed_decay) be.set_parental_crossfeed(crossfeed_power, crossfeed_range, crossfeed_decay)
``` ```
@ -114,9 +114,9 @@ In the figure above, a diffusion tree is illustrated. The diffusion steps are re
The concrete parameters for the transition above would be: The concrete parameters for the transition above would be:
``` ```
lb.set_branch1_crossfeed(crossfeed_power=0.8, crossfeed_range=0.6, crossfeed_decay=0.4) be.set_branch1_crossfeed(crossfeed_power=0.8, crossfeed_range=0.6, crossfeed_decay=0.4)
lb.set_parental_crossfeed(crossfeed_power=0.8, crossfeed_range=0.8, crossfeed_decay=0.2) be.set_parental_crossfeed(crossfeed_power=0.8, crossfeed_range=0.8, crossfeed_decay=0.2)
imgs_transition = lb.run_transition(num_inference_steps=10, depth_strength=0.2, nmb_max_branches=7) imgs_transition = be.run_transition(num_inference_steps=10, depth_strength=0.2, nmb_max_branches=7)
``` ```
## Perceptual aspects ## Perceptual aspects