Philipp Emanuel Weidmann (p-e-w), creator of the Heretic tool for automatically removing censorship from language models, published a public warning on r/StableDiffusion: models processed by Heretic using the directional ablation method are unsuitable as text encoders for image and video generative models — MiniMax H3, Stable Diffusion, Flux, and others.

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What happened

Weidmann explained the technical limitation of the method in his PSA post. Heretic modifies the internal residual vectors of an LLM using directional ablation (directional ablation / SOMA), forcing potentially "harmful" prompts to project into the "safe" representation space. This does not enrich embeddings with additional details or make them more graphic — it distorts them. Diffusion models receive slightly altered text embeddings they were not trained on, which naturally reduces prompt adherence and causes visual artifacts. The author emphasizes: "it will never, ever remove censorship from the output." The only working scenario for using Heretic models is LLM-based prompt enhancers, which block a request before it is passed to the diffusion generator, where the modification does not participate in the embedding pipeline.

Context

Heretic is a fully automatic tool for removing censorship from language models, with its source code open on GitHub (p-e-w/heretic). The SOMA (directional ablation) method acts on the residual-stream representations of an LLM at the architecture level, not at the output filter level. The Bestiary collection on HuggingFace contains dozens of decensor models created using Heretic, including modified versions of Qwen3-VL and Gemma. These models have become popular in the community, and users have started experimenting with them as text encoders in ComfyUI workflows for MiniMax H3, Flux, SDXL, and Krea 2. Weidmann's publication is a rare case where the creator of a tool publicly and technically justifies closing off a popular misuse. H3's refusal to generate for certain prompts is due to a lack of corresponding knowledge in the training data, not a safety filter — replacing the text encoder will not fix this.

Why this matters for the industry

For the open-source generation ecosystem, this is an important technical clarification of the boundaries of the SOMA/directional ablation method. Developers of diffusion models and pipeline tools (ComfyUI and similar) will receive a formal signal about the incompatibility of modified LLM encoders. It is expected that in the future developers will begin documenting text encoder compatibility at the API and weights level, and that evaluation protocols will emerge that check not only safety benchmarks but also downstream embedding quality for cross-modal tasks. For startups and projects built on the idea that "heretic encoder = better," this is a short-term noise risk requiring a review of workflow architecture.

Why this matters for users

If you are using Heretic versions of models (Qwen3-VL, Gemma) as text encoders in ComfyUI for H3, Flux, SDXL, or Krea 2 — this will not unlock censorship and will degrade generation quality. Switching back to the original text encoders will save VRAM and inference time without a loss in quality. Heretic models remain relevant in only one scenario: as an LLM prompt enhancer that filters requests before passing them to the generator, where the modification of residual vectors does not affect the diffusion model's embedding pipeline.

What is still unknown / limitations

Weidmann's technical analysis focuses on practical incompatibility, but the methodological boundary of the directional ablation method remains understudied in the context of cross-modal tasks. There are no independent benchmarks that quantitatively assess the degree of prompt adherence degradation when using modified embeddings in various diffusion models. It is unclear how strongly the effects depend on the specific combination of LLM and diffusion model — results may vary between Flux, SDXL, and H3.

Sources

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