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Updated README

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rossiyareich
2024-07-25 17:40:19 +07:00
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@@ -37,11 +37,14 @@ sampling > custom_sampling > samplers > Runge-Kutta Sampler
### Best defaults
- These methods support normal as well as high CFG scales, but the results may depend on the specific model.
- If you don't know the right step count or CFG scale:
1. Try the `adaptive_pid` controller with the base CFG and increment it until the results get worse.
2. Tune the CFG to the desired output.
3. Try the `fixed_scheduled` controller with AYS scheduler at 28 steps using the same CFG.
4. Tune the scheduler step count from 28 steps.
- For SDXL, the best results I got were from CFG scales between 7-35
- Try the `adaptive_pid` controller with the base CFG and increment it until the results get worse.
- Tune the CFG to the desired output.
- Try the `fixed_scheduled` controller:
- Use the Align Your Steps scheduler.
- Set the scheduler's step count to be the same as the number of steps taken by the `adaptive_pid` controller.
- Leave the CFG scale unchanged.
- Tune the scheduler step count.
- For SDXL, the best results I got were from CFG scales between 7-35.
```
Fixed step size
method: fe_ralston3
@@ -87,7 +90,7 @@ cfg: 7-35
> method: Determines the solver method used.
#### `a`, `f`, and `s` classes
- `a` = adaptive, `f` = fixed, `s` = scipy, `e` = explicit
- Use `a`-class methods with either
- Use `a`-class methods with either:
- `adaptive_pid` for automatically determined step sizes/count.
- `fixed_scheduled` for scheduler determined step sizes/count.
- Use `f`-class methods with `fixed_scheduled`.
@@ -98,7 +101,7 @@ cfg: 7-35
- Try `fe_ralston3`, `ae_bosh3`, and `fe_ssprk3` with the `fixed_scheduled` step size controller.
#### Quality ranking
Tested on `RTX3090`, `SDXL`, `896x1152`, `CFG=30`, `batch size 1`, `fixed_scheduled`, `AYS 28 steps`
Tested on `RTX3090`, `SDXL`, `896x1152`, `CFG=30`, `batch size 1`, `fixed_scheduled`, `Align Your Steps 28 steps`
| Rank | Name | Method | Order | NFEs | Time |
| ----------- | ----------- | ----------- | ----------- | ----------- | ----------- |
| 1 | `fe_ralston3` | Ralston | 3 | 3 | 23.08s |
@@ -175,34 +178,34 @@ The Runge-Kutta methods are a family of methods used for solving approximate sol
Runge-Kutta methods generally have less discretization error than standard diffusion sampling methods, allowing for the use of high CFG scales (within practical limits) to create high-quality results without artifacts.
## Conditions for new solvers and methods
- No implicit solvers
- No implicit solvers:
- Implicit solvers require root-finding.
- This means a backward pass through the model has to be ran to compute the Jacobian.
- The Jacobian is then used during LBFGS optimization.
- This is also **very** costly in terms of compute and memory usage.
- No non-RK methods
- No non-RK methods:
- This would cut off linear multistep methods like Adams-Bashforth or Adams predictor-corrector.
- From my testing, RK methods perform better, even for explicit RK vs. predictor-corrector linear multistep.
- No duplicate methods
- No duplicate methods:
- If two methods have different coefficients, they aren't duplicates.
- SciPy methods have different coefficients and implementations.
## Changelog
#### 24/07/24
- Added solver settings for `adaptive_scipy`
- Progress bar improvements
- Adaptive solvers now show the number of steps taken
- Accurate $\sigma$ info is now displayed
- Bugfixes and small refactors
- Added solver settings for `adaptive_scipy`.
- Progress bar improvements:
- Adaptive solvers now show the number of steps taken.
- Accurate $\sigma$ info is now displayed.
- Bugfixes and small refactors.
#### 23/07/24
- Installation from ComfyUI-Manager
- Bugfixes and small refactors
- Installation from ComfyUI-Manager.
- Bugfixes and small refactors.
#### 22/07/24
- Added wrappers for explicit solvers from `scipy.integrate`
- Added wrappers for explicit solvers from `scipy.integrate`:
- `se_RK23`
- `se_RK45`
- `se_DOP853`
- Some notes about the wrapped scipy solvers
- Some notes about the wrapped scipy solvers:
- To use the new solvers, select `adaptive_scipy` as the step size controller.
- No fixed step size controllers are available for wrapped scipy methods.
- These methods do not support parallel IVP solve, meaning the batch elements are processed sequentially.