# ComfyUI-Prediction Fully customizable Classifier Free Guidance for [ComfyUI](https://github.com/comfyanonymous/ComfyUI). ![Avoid and Erase Workflow](examples/avoid_and_erase.png) Copyright 2024 by @RedHotTensors and released by [Project RedRocket](https://huggingface.co/RedRocket). # Installation Clone this repo into ``ComfyUI/custom_nodes`` or use [ComfyUI-Manager](https://github.com/ltdrdata/ComfyUI-Manager). (Optional) If you want beautiful teal PREDICTION edges like the example apply [patches/colorPalette.js.patch](https://raw.githubusercontent.com/redhottensors/ComfyUI-Prediction/main/patches/colorPalette.js.patch) to ``ComfyUI/web/extensions/core/colorPalette.js``. # Usage All custom nodes are provided under Add Node > sampling > prediction. An example workflow is in ``examples/avoid_and_erase.json``. Follow these steps for fully custom prediction: 1. You will need to use the sampling > prediction > Sample Predictions node as your sampler. 2. The *sampler* input comes from sampling > custom_sampling > samplers. Generally you'll use **KSamplerSelect**. 3. The *sigmas* input comes from sampling > custom_sampling > schedulers. If you don't know what sigmas you are using, try **BasicScheduler**. (NOTE: These nodes are **not** in the "sigmas" menu.) 4. You'll need one or more prompts. Chain conditioning > CLIP Text Encode (Prompt) to sampling > prediction > Conditioned Prediction to get started. 5. After your prediction chain, connect the result to the *noise_prediction* input of your **Sample Predictions** node. Some utility nodes for manipulating sigams are provided under Add Node > sampling > custom_sampling > sigmas. # Predictors ## Primitive Nodes All other predictions can be implemented in terms of these nodes. However, it may get a little messy. ### Conditioned Prediction Evaluates your chosen model with a prompt (conditioning). You need to pick a unique conditioning name like "positive", "negative", or "empty". The names are arbitrary and you can choose any name, but the names may eventually interact with ControlNet if/when it's implemented. ### Combine Predictions Operates on two predictions. Supports add (+), subtract (-), multiply (*), divide (/), [vector projection](https://en.wikipedia.org/wiki/Vector_projection) (proj), [vector rejection](https://en.wikipedia.org/wiki/Vector_projection) (oproj), min, and max. ``prediction_A prediction_B`` ### Scale Prediction Linearly scales a prediction. ``prediction * scale`` ### Switch Predictions Switches from one prediction to another one based on the timestep sigma. Use sampling > custom_sampling > sigmas > Select Sigmas to create a sub-ranges of timestep sigmas. ``prediction_B when current_sigma in sigmas_B otherwise prediction_A`` ### Scaled Guidance Prediction Combines a baseline prediction with a scaled guidance prediction using optional standard deviation rescaling, similar to CFG. Without ``stddev_rescale``: ``baseline + guidance * scale``
With ``stddev_rescale``: [See §3.4 of this paper.](https://arxiv.org/pdf/2305.08891.pdf) As usual, start out around 0.7 and tune from there. ### Characteristic Guidance Prediction Combines an unconditioned (or negative) prediction with a desired, conditioned (or positive) prediction using a [characteristic correction](https://arxiv.org/pdf/2312.07586.pdf). ``cond``: Desired conditioned or positive prediction. This prediction should not be independent of the unconditioned prediction.
``uncond``: Unconditioned or negative prediction.
``fallback``: Optional prediction to use in the case of non-convergence. Defaults to vanilla CFG if not connected.
``guidance_scale``: Scale of the independent conditioned prediction, like vanilla CFG.
``history``: Number of prior states to retain for Anderson acceleration. Generally improves convergence speed but may introduce instabilities. Setting to 1 disables Anderson acceleration, which will require a larger max_steps and smaller log_step_size to converge.
``log_step_size``: log₁₀ learning rate. Higher values improve convergence speed but will introduce instabilities.
``log_tolerance``: log₁₀ convergence tolerance. Higher values improve convergence speed but will introduce artifacts.
``keep_tolerance``: A multiplier greater than 1 relaxes the tolerance requirement on the final step, returning a mostly-converged result instead of using the fallback.
``reuse_scale``: A multiplier greater than 0 retains a portion of the prior correction term between samples. May improve convergence speed and consistency, but may also introduce instabilities. Use with caution.
``max_steps``: Maximum number of optimizer steps before giving up and using the fallback prediction.
``precondition_gradients``: Precondition gradients during Anderson acceleration. This is strongly recommended, but may decrease result quality in specific cases where the gradients are reliably well-conditioned. This node is extremely expensive to evaluate. It requires four full model evaluations plus two full evaluations for each optimizer step required. It's recommended that you use the **Switch Predictions** node to skip CHG on the first timestep as convergence is unlikely and to disable it towards the end of sampling as it has marginal effect. ## Prebuilt Nodes ### Switch Early/Middle/Late Predictions Convienence node similar to a three-way **Switch Predictions** node, where the number of ``early_steps`` and ``late_steps`` can be specified directly. The whole applicable ``sigmas`` schedule should be provided to the node, and the schedule must be unambiguous when split. (Restart schedules can often be ambiguous.) ### Interpolate Predictions Linearly interpolates two predictions. ``prediction_A * (1.0 - scale_B) + prediction_B * scale_B`` ### CFG Prediction Vanilla Classifier Free Guidance (CFG) with a positive prompt and a negative/empty prompt. Does not support CFG rescale. ``(positive - negative) * cfg_scale + negative`` ### Perp-Neg Prediction Implements https://arxiv.org/abs/2304.04968. ``pos_ind = positive - empty; neg_ind = negative - empty``
``(pos_ind - (neg_ind oproj pos_ind) * neg_scale) * cfg_scale + empty`` ### Avoid and Erase Prediction (v2) Implements a modification of the Perp-Neg algorithm that avoids moving in the direction of the negative guidance. The result is a guidance vector that should be recombined with the empty prediction using the **Scaled Guidance Prediction** node or addition in preparation for **Characteristic Guidance Prediction**. ``pos_ind = positive - empty; neg_ind = negative - empty``
``pos_ind oproj neg_ind - (neg_ind oproj pos_ind) * erase_scale`` # Sigma Utilities These utility nodes are provided for manipulating sigmas for use with the **Switch Predictions** node. These nodes are under sampling > custom_sampling > sigmas. One important thing to keep in mind is that ComfyUI SIGMAS lists contain the ending output sigma, even though the model is not evaulated at this sigma. So, a 30-step full denoising schedule actually contains 31 sigmas (indexed 0 to 30), with the final sigma being ``0.0``. ### Select Sigmas This node is an alternative to the **Split Sigmas** built-in node. It allows you to specify the specific ``sigmas`` to ``select`` by 0-based index as a comma seperated list. Ranges are supported with ``[start]:[end]`` syntax. The ending bound is exclusive and negative indices are supported, which index from the end of the list. Both start and/or end may be ommitted. As a convienience, instead of specifing a list, you may specify ``mod `` to select every N'th sigma. You can use this to easily alternate between prediction strageies. If ``chained`` is disabled, the final sigma in the input list will be dropped. If you're chaning **Select Sigmas** nodes, you should enable ``chained`` in almost all cases. If a specified index is out of range, it is ignored. Examples, assuming a 10-timestep schedule with sigmas ``10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0``: ``` select: 0, 1, 2 chained: false => 10, 9, 8 select: 0, 1, 3:6, -2 chained: false => 10, 9, 7, 6, 5, 2 select: mod 2 chained: false => 9, 7, 5, 3, 1 select: mod 3 chained: false => 8, 5, 2 select: 100, 0, -100 chained: false => 10 select: 5: chained: false => 5, 4, 3, 2, 1 select: :5 chained: false => 10, 9, 8, 7, 6 select: : chained: false => 10, 9, 8, 7, 6, 5, 4, 3, 2, 1 select: -1 chained: false => 1 select: -1 chained: true => 0 select: -1:-4 chained: true => 0, 1, 2 ``` ### Split At Sigma Similar to the built in **Split Sigmas** node, this node splits a list of sigmas based on the *value* of the sigma instead of the index. This node takess a monotonically decreasing list of ``sigmas``, and splits at the earliest sigma which is less than or equal to the specified ``sigma``. This node is primarily useful in setting up refiner models without having to reverse engineer the timestep schedule for differing numbers of steps. ### Log Sigmas Prints the provided list of ``sigmas`` to the text console, with an optional ``message`` prefix to differentiate nodes. This is useful for debugging workflows. If the sigmas list and message don't change, ComfyUI will not evaluate the node and so nothing will be printed. # Limitations ControlNet is not supported at this time. Regional prompting may work but is totally untested. Any other advanced features affecting conditioning are not likely to work. # License The license is the same as ComfyUI, GPL 3.0.

Name	Type	Default	Description
prediction_A	PREDICTION		—
prediction_B	PREDICTION		—
scale_B	FLOAT	0.500–1	—

Interpolate Predictions

Inputs (3)

Outputs (1)