225 lines
10 KiB
Markdown
225 lines
10 KiB
Markdown
# WAS_Extras
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Experimental nodes, or other random extra helper nodes.
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# Installation
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- Git clone the repo to `ComfyUI/custom_nodes`.
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- Or; install with [ComfyUI Manager](https://github.com/ltdrdata/ComfyUI-Manager)
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- Or; download the individual custom node `.py` files and put them in your `ComfyUI/custom_nodes` folder. **Note:** Still will need to install `requirements.txt` if using `ksampler_sequence.py`
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## Nodes
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<details>
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<summary><code>BLVaeEncode.py</code></summary>
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### Provides
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- VAEEncode (Bundle Latent)
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Encodes an image to a latent like ComfyUI’s normal VAE encode, but can also store that latent directly into the workflow’s embedded metadata.
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This makes it easier to share workflows that include a “starting latent” without requiring a separate latent file, and it can optionally load the previously-stored latent back out on subsequent runs.
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Includes an optional tiled encode mode to reduce VRAM usage on large images.
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Workflow Example:
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</details>
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<details>
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<summary><code>CameraMotionTrajectorySimple.py</code></summary>
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### Provides
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- Camera Motion Trajectory from Images
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Generates a motion “camera path” over a sequence of images using a keyframed trajectory (JSON) describing pan/tilt, rotation, zoom, and optional radial effects.
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Use it to create animated video clips (Ken Burns-style moves, rotations, etc.) from stills or frame sequences while keeping everything inside the ComfyUI graph.
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Includes easing and interpolation so you can smoothly transition between keyframes.
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</details>
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<details>
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<summary><code>ConditioningBlend.py</code></summary>
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### Provides
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- Conditioning (Blend)
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Combines two `CONDITIONING` inputs into a single conditioning using a selectable blend algorithm (lerp, cosine, slerp variants, difference/exclusion, and more).
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This is useful for prompt morphing, soft transitions between prompts, or “mixing” two prompt embeddings without needing to re-encode prompts elsewhere.
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Some modes can be stochastic; a seed input is provided for reproducibility.
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</details>
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<details>
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<summary><code>DebugThis.py</code></summary>
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### Provides
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- Debug Input
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Debug/inspect any incoming value in a workflow.
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Prints the value to the console and, for non-primitive objects, also prints a directory-style listing so you can discover available fields and methods.
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Useful when building custom graphs that pass around complex data structures (options dicts, custom objects, etc.).
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</details>
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<details>
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<summary><code>ksampler_sequence.py</code></summary>
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### Provides
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- CLIP Text Encode Sequence (Advanced)
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- **Note**: *CLIPTextEncode will use BLK Advanced CLIP Text Encode if available.*
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- CLIP Text Encode Sequence (v2)
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- KSampler Sequence
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- KSampler Sequence (v2)
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A prompt + sampling sequencer for running multiple KSampler passes in a controlled loop.
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You can define prompt schedules (including per-frame keyframes), repeat for a number of loops, evolve seeds, and optionally interpolate between latents across iterations to smooth transitions.
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Intended for “prompt progression” workflows where each loop nudges the generation while keeping continuity.
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[KSampler_Sequence_Workflow.zip](https://github.com/WASasquatch/WAS_Extras/files/12840983/KSampler_Sequence_Workflow.zip)
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</details>
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<details>
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<summary><code>TiledUpscaleModel.py</code></summary>
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### Provides
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- Tiled Image Upscale (With Model)
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Runs an upscale model (ESRGAN-style / Comfy upscalers) on large images by splitting the input into tiles and stitching the results back together.
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Designed to reduce VRAM usage and avoid OOM errors by only sending one tile to the GPU at a time, with optional overlap/feathering to minimize seams.
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Includes adaptive behavior that can reduce tile size if a tile upscaling attempt runs out of memory.
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</details>
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<details>
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<summary><code>VAEEncodeForInpaint.py</code></summary>
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### Provides
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- Inpainting VAE Encode (WAS)
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[**Download Inpainting_Example_Workflow_And_Images.zip**](https://github.com/WASasquatch/WAS_Extras/files/12719211/Inpainting_Example_Workflow_And_Images.zip)
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Encodes an image to an inpainting latent while generating a matching `noise_mask`, with a mask-offset control for dilation/erosion.
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The mask is resized and applied to pixels before encoding, which can produce cleaner edges and reduce boundary artifacts when inpainting.
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Use it when you need more control over mask growth/shrink than the default inpaint encode path.
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</details>
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<details>
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<summary><code>VividSharpen.py</code></summary>
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### Provides
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- VividSharpen
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An image post-processing node implementing a “Vivid Sharpen” technique inspired by Photoshop workflows.
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It constructs an inverted/blur high-pass style layer and blends it back to increase perceived detail while keeping a configurable radius and overall strength.
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Useful as a final-pass crispness/detail enhancement after upscaling or before saving.
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</details>
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<details>
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<summary><code>VividSharpenV2.py</code></summary>
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### Provides
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- Vivid Sharpen (V2)
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A torch-only reimplementation of Vivid Sharpen with more granular controls.
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Lets you tune two blur radii, choose blur kernel type, adjust the high-pass brightness/contrast, and independently control vivid-light and overlay blend opacities.
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Supports strength values above 1.0 for more aggressive sharpening.
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</details>
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<details>
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<summary><code>WASEdgeSafeLatentUpscale.py</code></summary>
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### Provides
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- WAS Adaptive Difference Latent Upscale (Damped)
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Latent upscaling that focuses enhancement away from strong edges to reduce ringing/halos.
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It computes an edge/energy-based damp mask (with optional temporal smoothing for video latents) and uses that to modulate how strongly upscaling differences are applied.
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Outputs the upscaled latent plus a mask and a mask preview image so you can see exactly where the node is “protecting” detail.
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</details>
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<details>
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<summary><code>WASHybridLatentUpscale.py</code></summary>
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### Provides
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- Latent Hybrid Upscale
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Hybrid latent upscaling that blends a decoded image-space edge mask back into latent-space upscaling, or providing a donor latent with.
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It uses OpenCV (if available) to build an edge mask (Canny + dilation + feathering) and uses that mask to guide how the upscale blends across edges.
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Includes special handling for video latents and optional tiled decode settings to keep VRAM usage under control.
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</details>
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<details>
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<summary><code>WASLatentContrastLimitedDetailBoost.py</code></summary>
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### Provides
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- WAS Latent Detail Boost
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Adds micro-detail to latents using a contrast-limited band-pass (Difference-of-Gaussians) enhancement.
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Includes RMS-based limiting and optional edge protection to reduce haloing and hard outlines, even at higher gain.
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Outputs both a mask (detail or edge mask) and a preview image so you can dial in settings visually.
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</details>
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<details>
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<summary><code>WASLoraReweight.py</code></summary>
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### Provides
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- WAS Apply Reweighted LoRA
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Reweights a LoRA’s internal tensors based on detected block indices (supports presets like WAN/Qwen/Flux/SD/SDXL and an auto-detect mode).
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This lets you bias a LoRA’s influence toward “front/mid/back” blocks, optionally limit it to a subset of blocks, and choose whether to scale LoRA up weights, down weights, or both.
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Can optionally save the reweighted LoRA as a new `.safetensors` file and then apply it to the current MODEL/CLIP.
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</details>
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<details>
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<summary><code>WASPowerLoraMerger.py</code></summary>
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### Provides
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- WAS Power LoRA Merger
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- WAS Power LoRA Merger Options
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An advanced LoRA merging node that can combine multiple LoRAs into a single output LoRA saved into your `models/loras` folder.
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Supports multiple merge strategies (including SVD recompression, additive stacking, diff-based modes, mixture-of-experts routing, and block-mix recipes) via ComfyUI.
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Optionally takes an existing MODEL/CLIP and applies the newly-created LoRA immediately, so the merge can be part of a single workflow run.
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</details>
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<details>
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<summary><code>WASWanExposureStabilizer.py</code></summary>
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### Provides
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- WAN 2.2 Exposure Stabilizer
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Stabilizes exposure across an image batch (typically video frames) by measuring per-frame log-luma statistics and applying per-frame gain correction.
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Designed to reduce WAN 2.2 style exposure drift at the start and/or end of a sequence, with configurable anchoring (middle window vs tail reference) and settle detection.
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Returns the corrected images plus a human-readable report string with the computed exposure deltas and gains.
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</details>
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<details>
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<summary><code>WanMoESampler.py</code></summary>
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### Provides
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- Wan 2.2 MoE Sampler (WAS)
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- Wan 2.2 MoE Conditioning Append (WAS)
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Utilities and sampler logic intended for WAN 2.2 workflows that need more control than a single straight KSampler pass.
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Includes a MoE-oriented sampler context node and a conditioning “append” helper for building lists of conditionings that can be routed/combined.
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Also contains a set of transition and latent upscaling utilities for smoother multi-stage sampling pipelines.
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</details>
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<details>
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<summary><code>WASLUT.py</code></summary>
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Load and combine LUTs, and visualize RGB waveform with RGB parade view.
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Provides a small LUT pipeline: create/load LUTs (including `.cube` files), blend/combine multiple LUTs, apply LUTs to images, and export LUTs back to `.cube`.
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Also includes a waveform/parade visualization node to help evaluate color and channel balance while grading.
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Intended for quick color grading inside ComfyUI without round-tripping to external tools.
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### Provides
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- WAS Load LUT
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- Create a custom LUT, load a preset, or load *.cube files from your `ComfyUI/models/LUT` folder.
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- WAS LUT Blender
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- Combine multiple LUTs into a single LUT.
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- WAS Apply LUT
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- Apply a LUT to an image.
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- WAS Save LUT (.cube)
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- WAS Channel Waveform (Parade)
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- Visualize the RGB waveform with RGB parade view.
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</details>
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