From 646a3c757ecf9fd29a3cd3706c91ed9111066bbd Mon Sep 17 00:00:00 2001 From: Johannes Stelzer Date: Fri, 26 Jan 2024 11:52:04 +0000 Subject: [PATCH] Update README.md --- README.md | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/README.md b/README.md index a1c1d42..a6559c7 100644 --- a/README.md +++ b/README.md @@ -51,19 +51,19 @@ To run multiple transition between K prompts, resulting in a stitched video, see ### Change the height/width ```python size_output = (1024, 768) -lb.set_dimensions(size_output) +be.set_dimensions(size_output) ``` ### Change the number of diffusion steps (set_num_inference_steps) ```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. ### Change the guidance scale ```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. @@ -71,7 +71,7 @@ For SDXL this is set as default=4.0, for SDXL Turbo a value of 0 is taken. ```python depth_strength = 0.5 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. * 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 ```python -lb.set_guidance_scale(5.0) +be.set_guidance_scale(5.0) ``` ### 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_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. -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 @@ -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_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. -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: ``` -lb.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) -imgs_transition = lb.run_transition(num_inference_steps=10, depth_strength=0.2, nmb_max_branches=7) +be.set_branch1_crossfeed(crossfeed_power=0.8, crossfeed_range=0.6, crossfeed_decay=0.4) +be.set_parental_crossfeed(crossfeed_power=0.8, crossfeed_range=0.8, crossfeed_decay=0.2) +imgs_transition = be.run_transition(num_inference_steps=10, depth_strength=0.2, nmb_max_branches=7) ``` ## Perceptual aspects