nodes/Clybius-ComfyUI-Extra-Samplers
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Update README.md to reflect previous changes.

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* LCM Custom Noise (Supports different types of noise other than generic gaussian) * LCM Custom Noise (Supports different types of noise other than generic gaussian)
* DPMPP 3M SDE with Dynamic ETA (Anneals down towards a minimum eta via a cosine curve) * DPMPP 3M SDE with Dynamic ETA (Anneals down towards a minimum eta via a cosine curve)
* Supreme (Many extra functionalities and step methods available) * Supreme (Many extra functionalities and step methods available)
* SENS (SDE-Endowed Nimble Sampler. Based off of DPM-Solver++(2M) SDE and DPM-Solver++(3M) SDE. R-SDE for reversible SDE, T-SDE for tertiary SDE.)
* IPNDM VAPP (IPNDM_V with ancestral sampling and (somewhat) CFGPP)
* STRIKE (Stochastic, Temporal, Reversible, Improvised K-Diffusion Experiment. Based off of Euler A with an ancestral and SDE twist.)
### Currently included extra K-Sampling nodes: ### Currently included extra K-Sampling nodes:
* SamplerCustomNoise (Supports custom noises other than gaussian noise for init noise) * SamplerCustomNoise (Supports custom noises other than gaussian noise for init noise)
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* ScaledCFGGuider: Samples the two conditionings, then adds it using a method similar to "Add Trained Difference" from merging models. * ScaledCFGGuider: Samples the two conditionings, then adds it using a method similar to "Add Trained Difference" from merging models.
* ImageAssistedCFGGuider: Samples the conditioning, then adds in the latent image using vector projection onto the CFG. Image latent ought to be of the same size as the diffusion latent. * ImageAssistedCFGGuider: Samples the conditioning, then adds in the latent image using vector projection onto the CFG. Image latent ought to be of the same size as the diffusion latent.
### Currently included extra schedulers:
* SimpleExponentialScheduler: Using Simple scheduler as a base, apply an exponential decay. (Works with ZSNR)
* KLOptimalScheduler: KL Optimal/'Gaussian' scheduler, may be good at low step counts.
* SimpleKLOptimalScheduler: KL Optimal/'Gaussian' scheduler, but using Simple scheduler as a base to work off of. (Works with ZSNR)
### Currently included extra noise types:
* Immiscible Noise: Aligns the noise with the given image latent. For ancestral sampling, we utilize `cond_denoised` as the reference image latent.
#### Supreme Sampler features: #### Supreme Sampler features:
* step_method: You have the ability to choose your own step method with this sampler! Optionally, there's a dynamic step method, which chooses the appropriate order based on the calculated error between steps, allowing you to obtain higher quality when it matters in the sampling process. Defaults to **(Euler)**. * step_method: You have the ability to choose your own step method with this sampler! Optionally, there's a dynamic step method, which chooses the appropriate order based on the calculated error between steps, allowing you to obtain higher quality when it matters in the sampling process. Defaults to **(Euler)**.
* centralization: Subtracts mean from the denoised latent. This can lead to perceptually sharper results, though may change the perceivable brightness of the image. Conservatively defaults to **(0.05)**. * centralization: Subtracts mean from the denoised latent. This can lead to perceptually sharper results, though may change the perceivable brightness of the image. Conservatively defaults to **(0.05)**.
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* Trapezoidal: A [method to solve ODEs](https://en.wikipedia.org/wiki/Trapezoidal_rule_(differential_equations)) derived from the [Trapezoidal Rule](https://en.wikipedia.org/wiki/Trapezoidal_rule) for computing integrals. * Trapezoidal: A [method to solve ODEs](https://en.wikipedia.org/wiki/Trapezoidal_rule_(differential_equations)) derived from the [Trapezoidal Rule](https://en.wikipedia.org/wiki/Trapezoidal_rule) for computing integrals.
* Dynamic: Chooses a sampler based on error. Error is the same methodization as in adaptive_rk. The choices between samplers are as follows, in order from high error to low error: RKF45, RK4, Bogacki_Shampine, Trapezoidal, Euler. May change in the future. * Dynamic: Chooses a sampler based on error. Error is the same methodization as in adaptive_rk. The choices between samplers are as follows, in order from high error to low error: RKF45, RK4, Bogacki_Shampine, Trapezoidal, Euler. May change in the future.
Utilizing custom sampling within ComfyUI is encouraged for these samplers! Utilizing custom sampling within ComfyUI is encouraged for these samplers!