<h2 id="whats-new">Whats new</h2><h2 id="beta:">beta:</h2>Updated the workflow and custom node (both need to be updated).Thanks to user Hysocs for the tip regarding the Anima <code>timestep_sampling</code> parameter (changed to <code>uniform</code>).The model is now slightly better at understanding natural language prompting; the default style for natural prompting has improved in some areas but not others, and there are some issues with anatomy—the model is still undergoing training (currently trained on 150,000 images).<h2 id="description">Description</h2>an attempt to transition Anime base to an MoE architecture—two 2B models, each trained on a specific noise-reduction range.The model handles natural language prompting quite well, but the resulting style often leaves something to be desired—I'm thinking about how to fix that. (Maybe it's because I recently increased the maximum prompt length from 512 to 2048.)This model was used to make image captions: <a target="_blank" rel="ugc" href="https://huggingface.co/Minthy/ToriiGate-0.5">Minthy/ToriiGate-0.5 · Hugging Face</a>Workflow is included in posted images metadata and optional filesYou need to put this into <code>custom_nodes</code> for the workflow to work.<a target="_blank" rel="ugc" href="https://huggingface.co/femboysLover/anima-looped-test-diffusion-blocks-idx-0/resolve/main/model_range_wrapper.py?download=true">https://huggingface.co/femboysLover/anima-looped-test-diffusion-blocks-idx-0/resolve/main/model_range_wrapper.py?download=true</a>real time training checkpoints are there:low noise<a target="_blank" rel="ugc" href="https://huggingface.co/femboysLover/anima-looped-test-diffusion-blocks-idx-0">https://huggingface.co/femboysLover/anima-looped-test-diffusion-blocks-idx-0</a>high noise<a target="_blank" rel="ugc" href="https://huggingface.co/femboysLover/anima-looped-test-diffusion-blocks-idx-1">https://huggingface.co/femboysLover/anima-looped-test-diffusion-blocks-idx-1</a>the article from which I took the methodology:<a target="_blank" rel="ugc" href="https://arxiv.org/abs/2506.14202">[2506.14202] DiffusionBlocks: Block-wise Neural Network Training via Diffusion Interpretation</a>

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