Comfyui - Anima Experimental Fast Training Node

ComfyUI-AnimaFastTrain is an experimental ComfyUI custom node for quickly training in-memory Anima reference context tokens from one or more reference images. Instead of creating a LoRA, checkpoint, embedding, or safetensors file, AnimaFastTrain learns temporary context tokens during the workflow and injects them into the Anima model’s cross-attention at generation time. This makes it useful for experiments with character consistency, visual reference influence, and style transfer without writing trained weights to disk. Main features: - Trains lightweight reference context tokens directly inside ComfyUI - Keeps everything in memory, no model files are saved - Patches the Anima model during sampling - Supports up to 3 reference images - Adjustable token count, training steps, learning rate, dtype, and runtime strength - Useful for quick style/identity experiments before committing to a full LoRA training run Included nodes: - AnimaFastTrain - Train Context Tokens - AnimaFastTrain - Patch Model Recommended workflow: Checkpoint Loader -> LoRA Loader -> AnimaFastTrain Patch Model -> KSampler Train the context tokens from a reference image, patch the final model after other model/LoRA patches, and then generate normally. Install by cloning the repo or using comfy manager. GitHub: https://github.com/quinteroac/ComfyUI-AnimaFastTrain