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AbstractPhil
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https://civitai.com/user/AbstractPhila

AI & ML interests

datasets, research papers, experimentation, vision, classification, text encoders, tokenization, llms, diffusion, distillation, and more.

Recent Activity

published a dataset about 1 hour ago
AbstractPhil/anima-90k-cache
published an article about 3 hours ago
Subject Bucketing: Teaching a Diffusion Model New Prompt Languages Without Forgetting
posted an update about 3 hours ago
Anima - Brent JSON (PREVIEW) - Subject Bucketing Full article available https://huggingface.co/blog/AbstractPhil/subject-bucketing. https://huggingface.co/AbstractPhil/anima-prelim-1k-r64 The JSON multi-prompt diffusion model prototype using Anima 1.0 base as the pretrain to finetune into the JSON target. The upcoming JSON lora is being cached and trained with 40,000 of the full 83,000 valid images from the qwen set. This first preview version is ready to use as a ComfyUI capable LORA, so you can just load up the epoch you want without anything special in comfyui and have at it. You can currently use plain English in conjunction with tagging to produce useful and meaningful prompt targets without the JSON. https://huggingface.co/AbstractPhil/anima-prelim-1k-r64/tree/main/comfy-qwen-json The comfyui nodes are present and work for testing use-case, but they are not ready for production use just yet. -- Technical -- Primarily the target was the VLM json target followed by the AnimeTIMM vit processed through the VLM json processor as the followup. First 12 epochs VLM experienced images with json formatting, last 8 epochs were finetuning from epoch 12 onward to 20 using the AnimeTIMM captions turned into JSON instead. The Anima model itself accepted the 1000 image and the json prompting works quite well. In the process I set up a couple comfyui nodes that can translate base prompts into the same language the model is learning. Those are present in the repo.
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