Mirrored_Repos/InvokeAI
1
0
Fork 0
mirror of https://github.com/invoke-ai/InvokeAI synced 2024-08-30 20:32:17 +00:00
Code Issues Packages Projects Releases Wiki Activity

fix SAMPLER_CONVERGENCE and add emoji

Browse Source
This commit is contained in:
mauwii 2022-10-11 00:35:48 +02:00
parent e2e86d2d11
commit 7c8ffe2feb
No known key found for this signature in database
GPG Key ID: D923DB04ADB3F5AB
1 changed files with 6 additions and 2 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

8
docs/help/SAMPLER_CONVERGENCE.md
View File

@ -1,8 +1,8 @@
--- ---
title: SAMPLER CONVERGENCE title: Sampler Convergence
--- ---
## *Sampler Convergence* # :material-palette-advanced: *Sampler Convergence*
As features keep increasing, making the right choices for your needs can become increasingly difficult. What sampler to use? And for how many steps? Do you change the CFG value? Do you use prompt weighting? Do you allow variations? As features keep increasing, making the right choices for your needs can become increasingly difficult. What sampler to use? And for how many steps? Do you change the CFG value? Do you use prompt weighting? Do you allow variations?
@ -21,6 +21,8 @@ Looking for a short version? Here's a TL;DR in 3 tables.
| `K_HEUN` and `K_DPM_2` converge in less steps (but are slower). | | `K_HEUN` and `K_DPM_2` converge in less steps (but are slower). |
| `K_DPM_2_A` and `K_EULER_A` incorporate a lot of creativity/variability. | | `K_DPM_2_A` and `K_EULER_A` incorporate a lot of creativity/variability. |
<div align="center" markdown>
| Sampler | (3 sample avg) it/s (M1 Max 64GB, 512x512) | | Sampler | (3 sample avg) it/s (M1 Max 64GB, 512x512) |
|---|---| |---|---|
| `DDIM` | 1.89 | | `DDIM` | 1.89 |
@ -32,6 +34,8 @@ Looking for a short version? Here's a TL;DR in 3 tables.
| `K_DPM_2_A` | 0.95 (slower) | | `K_DPM_2_A` | 0.95 (slower) |
| `K_EULER_A` | 1.86 | | `K_EULER_A` | 1.86 |
</div>
| Suggestions | | Suggestions |
|:---| |:---|
| For most use cases, `K_LMS`, `K_HEUN` and `K_DPM_2` are the best choices (the latter 2 run 0.5x as quick, but tend to converge 2x as quick as `K_LMS`). At very low steps (≤ `-s8`), `K_HEUN` and `K_DPM_2` are not recommended. Use `K_LMS` instead.| | For most use cases, `K_LMS`, `K_HEUN` and `K_DPM_2` are the best choices (the latter 2 run 0.5x as quick, but tend to converge 2x as quick as `K_LMS`). At very low steps (≤ `-s8`), `K_HEUN` and `K_DPM_2` are not recommended. Use `K_LMS` instead.|