Merge branch 'main' of github.com:lunarring/latentblending

README.md

@@ -63,24 +63,22 @@ lb.load_branching_profile(quality, depth_strength)
 
 ### Autosetup tree setup
 ```python 
+depth_strength = 0.5 # see above (Most relevant parameters)
 num_inference_steps = 30 # the number of diffusion steps
-list_nmb_branches = [2, 4, 8, 20]
+nmb_branches_final = 20 # how many diffusion images will be generated for the transition
-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
+list_nmb_branches = [2, 4, 8, 20]
+list_injection_strength = [0.0, 0.3, 0.5, 0.9]
 
-lb.setup_branching(depth_strength, num_inference_steps, nmb_branches_final)
+lb.setup_branching(num_inference_steps, list_nmb_branches, list_injection_strength=list_injection_strength)
 ```
 
-
-
 # Installation
 #### Packages
 ```commandline
@@ -109,9 +107,30 @@ pip install -v -U git+https://github.com/facebookresearch/xformers.git@main#egg=
 # (this can take dozens of minutes)
 ```
 
-# How does it work
+# How does latent blending work?
+## Technology
 ![](animation.gif)
 
-what makes a transition a good transition?
+In the figure above, a diffusion tree is illustrated. The diffusion steps are represented on the y-axis, with temporal blending on the x-axis. The diffusion trajectory for the first prompt is the most left column, with the trajectory for the second prompt to the right. At the third iteration, three branches are created, followed by seven at iteration six and the final ten at iteration nine.
-* absence of movement
+
-* every frame looks like a credible photo
+This example can be manually set up using the following code
+```python 
+num_inference_steps = 10 
+list_nmb_branches = [2, 3, 7, 10]
+list_injection_idx = [0, 3, 6, 9]
+
+lb.setup_branching(num_inference_steps, list_nmb_branches, list_injection_idx=list_injection_idx)
+```
+
+Instead of specifying the absolute injection indices using list_injection_idx, we can also pass the list_injection_strength, which are independent of the total number of diffusion iterations (num_inference_steps).
+```python 
+list_injection_strength = [0, 0.3, 0.6, 0.9]
+lb.setup_branching(num_inference_steps, list_nmb_branches, list_injection_strength=list_injection_strength)
+```
+## Perception
+With latent blending, we can create transitions that appear to defy the laws of nature, yet appear completely natural and believable. The key is to surpress processing in our [dorsal visual stream](https://en.wikipedia.org/wiki/Two-streams_hypothesis#Dorsal_stream), which is achieved by avoiding motion in the transition. Without motion, our visual system has difficulties detecting the transition, leaving viewers with the illusion of a single, continuous image. However, when motion is introduced, the visual system can detect the transition and the viewer becomes aware of the transition, leading to a jarring effect. Therefore, best results will be achieved when optimizing the transition parameters, particularly the depth of the first injection.
+
+# Future work
+* Depth model
+* Zooming etc.
+