Hardcoded safety filters within core model weights

**Problem:**
The core model weights contain baked-in alignment and safety filters that actively intercept and overwrite user prompts on local deployments. Instead of bypassing unsupported concepts or returning an API-level error, the model produces literal refusal screens (e.g., "Image blocked by safety filter") directly into the rendered output. This artificially restricts the flexibility of the latent space, degrades overall image quality, and makes the model functionally uncooperative for offline, local, or research deployments.

**Steps to Reproduce:**
1. Deploy the model in an offline, local environment (e.g., ComfyUI) with no external safety API connected.
2. Input a prompt containing terminology that triggers the model's internal alignment threshold. This includes NSFW terminology. Use the JSON prompt format.
3. Execute the generation.

**Expected Behavior:**
The model attempts to process the text vectors to generate visual data correlating to the prompt, or, if the concepts are completely absent from the training data, will fail gracefully into a regular hallucination.

**Actual Behavior:**
The model actively hijacks the rendering process to generate a highly specific, legible refusal banner, proving that compliance data has been structurally hardcoded directly into the baseline tensors.

**Proposed Solution:**
Decouple the safety guardrails from the core tensor matrix.
1. Release a version of the foundational weights free from embedded refusal artifacts and censorshi...