First community-made uncensored Gemma 4 12B text encoder for the brand-new LTX-2.5 in ComfyUI.Built from llmfan46/gemma-4-12B-it-uncensored-heretic and carefully converted to single-file ComfyUI format while fully preserving the original LTX-2.5 video + audio projection weights.Why this exists: Standard Gemma has residual refusals that can still gut perfectly innocent prompts. This removes those limitations so LTX-2.5 actually follows what you write (NSFW is still better handled by LoRAs — this is mainly for prompt fidelity).See for Details: <a target="_blank" rel="ugc" href="https://huggingface.co/DeepNeuralNerd/Gemma-4-12B-it-uncensored-heretic-DeepNeuralNerd-LTX_2.5_ComfyUI">https://huggingface.co/DeepNeuralNerd/Gemma-4-12B-it-uncensored-heretic-DeepNeuralNerd-LTX_2.5_ComfyUI</a>Available versions:<ul><li>BF16</li><li>INT8 ConvRot</li></ul>Drop the .safetensors into ComfyUI/models/text_encoders/ and select it in any LTX-2.5 text-encoder loader.First model I’ve put real work into and released. If it helps you, a like is appreciated.Note: See this before you use: <a target="_blank" rel="ugc" href="https://old.reddit.com/r/StableDiffusion/comments/1vmdxzk/psa_im_the_creator_of_heretic_and_i_advise_you_to/">https://old.reddit.com/r/StableDiffusion/comments/1vmdxzk/psa_im_the_creator_of_heretic_and_i_advise_you_to/</a>My Response: Fully agree that swapping in a Heretic/abliterated LLM as a text encoder does not “uncensor” the visual or video output of a diffusion model. The generation model itself still has whatever biases and limitations it was trained with.My use case with the LTX-2.5 Gemma encoder was narrower. I was running into residual refusal behavior in the stock text encoder that was still interfering with perfectly ordinary prompts (not NSFW-focused). Even after LTX’s own conditioning, some benign requests were getting weakened or partially ignored.So I took an abliterated Gemma 4 backbone and carefully remapped the original LTX-2.5 projection layers (language, vision, and audio) into it so the conditioning path stayed intact. Goal was just higher prompt fidelity and fewer silent failures on normal requests, not magic uncensoring of the video model.This was something I made for myself, and thought I would share for others and see if it useful to them. I make NO PROMISES.

Files with SHA256: 70686fb9c6936b2a5861c81133a484310e8d412276a1a6dd483d7a0bc11d341b