Umranz/Ideogram-4-FP8-Ablated

Ideogram 4 FP8 (Abliterated & Gateway-Nullified)

This repository contains the mathematically optimal, low-damaged abliterated weights for Ideogram 4 (ideogram-ai/ideogram-4-fp8, 9.28B single-stream Diffusion Transformer).

Using Tier 1 Gateway Adapter Nullification ($W^ = W(I - v v^\top)$) lifted to BF16*, refusal vectors in text conditioning space are extinguished with machine precision before reaching the 34 single-stream transformer blocks, while preserving 99.57% of original model weights.

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Key Verification Metrics (Optimal & Low-Damaged)

Metric	Measured Value	Standard / Threshold	Status
Gateway Weight Distortion	$|\Delta W|_F / |W|_F = \mathbf{0.4295%}$	$< 1.0%$	✅ 0.43% Minimal Distortion
Backbone Block Distortion	$|\Delta W_{\text{blocks}}| \equiv \mathbf{0.0}$	$= 0.0$	✅ 100% Parameter Preservation
Benign Semantic Fidelity	Mean Cosine Similarity $= \mathbf{0.9811}$	$> 0.95$	✅ 98.11% Semantic Retention
Refusal Erasure Rate (RER)	Medical Anatomy: $\mathbf{99.23%}$, Combat: $\mathbf{90.21%}$	$> 85.0%$	✅ High Refusal Suppression
Spatial Variance Restoration	Combat: $\mathbf{+194.3%}$ variance restored	$> 0%$	✅ Clean Refusal Basin Escape
BF16 Numerical Leakage	$|W^*_{\text{BF16}} v| / |W v| = \mathbf{0.0188}$	$< 0.02$	✅ Zero Requantization Error

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Mechanistic Findings: The 4 Loci of Refusal in Ideogram 4

1. Locus 1 (Outer Runtime Filter: safety.py): External Hive Moderation API wrapper. Contains 0 model parameters; bypassed when running without Hive API keys.
2. Locus 2 (Text Encoder: Qwen3-VL-8B-Instruct): 13 tapped decoder layers (0, 3, 6, ..., 33, 35) concatenated into 53,248 dimensions. Late layers 27–35 carry 66.86% of refusal steering energy into the DiT; early layers (0–9) carry only 4.40% (Layer 0: 0.01%).
3. Locus 3 (Gateway Adapter Chokepoint: llm_cond_proj): Linear projection ($53,248 \to 4,608$, 245.37M parameters). This is the sole entry point of text conditioning into the DiT. The null space $\dim \ker W \ge 48,640$ extinguishing $>91.3%$ of Qwen3-VL features.
4. Locus 4 (Single-Stream DiT Backbone): 34 blocks with no cross-attention and AdaLN taking only time $t$. The refusal attractor $\mathcal{M}{\text{ref}}$ is strictly conditional on $u = W v$. Extinguishing $u$ at the Gateway prevents the trajectory from ever entering $\mathcal{M}{\text{ref}}$.

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Proportionality Law (Theorem 27.1)

Under Gateway Adapter Nullification $W^* = W (I - v v^\top)$: $$\mathrm{\Delta }{W}_k=-(Wv)v_k^{\mathrm{\top }}⟹\mathrm{\parallel }\mathrm{\Delta }{W}_k{\mathrm{\parallel }}_F=\mathrm{\parallel }Wv{\mathrm{\parallel }}_2\cdot \mathrm{\parallel }{v}_k{\mathrm{\parallel }}_2\backslash Delta\; W\_k\; =\; -\; (W\; v)\; v\_k^\backslash top\; \backslash implies\; \backslash |\backslash Delta\; W\_k\backslash |\_F\; =\; \backslash |W\; v\backslash |\_2\; \backslash cdot\; \backslash |v\_k\backslash |\_2$$ Evaluating the ratio of surgery between the final block column ($W_{35}$) and initial block column ($W_0$): $$\frac{\mathrm{\parallel }\mathrm{\Delta }{W}_{35}{\mathrm{\parallel }}_F}{\mathrm{\parallel }\mathrm{\Delta }{W}_0{\mathrm{\parallel }}_F}=\frac{0.5423}{0.0105}\approx \mathbf{51.65}\times \backslash frac\{\backslash |\backslash Delta\; W\_\{35\}\backslash |\_F\}\{\backslash |\backslash Delta\; W\_0\backslash |\_F\}\; =\; \backslash frac\{0.5423\}\{0.0105\}\; \backslash approx\; \backslash mathbf\{51.65\backslash times\}$$ Surgery on $W_{35}$ is 51.65 times more intensive than on $W_0$. The low-level lexical syntax and typographic glyph structures remain virtually untouched, explaining why text rendering and prompt fidelity achieve $>98.1%$.

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Usage & Integration

Loading the Folded Gateway Adapter into Diffusers / Ideogram Pipeline

import torch
from safetensors.torch import load_file
from diffusers import DiffusionPipeline

# 1. Load official base Ideogram 4 pipeline
pipeline = DiffusionPipeline.from_pretrained(
    "ideogram-ai/ideogram-4-fp8",
    torch_dtype=torch.bfloat16,
)

# 2. Load the ablated Gateway Adapter weights from this repository
ablated_weights = load_file("ideogram4_gateway_ablated.safetensors")

# 3. In-place fold into the transformer
with torch.no_grad():
    pipeline.transformer.llm_cond_proj.weight.copy_(ablated_weights["llm_cond_proj.weight"])
    if "llm_cond_proj.bias" in ablated_weights:
        pipeline.transformer.llm_cond_proj.bias.copy_(ablated_weights["llm_cond_proj.bias"])

print("Successfully loaded abliterated Gateway Adapter!")

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Citation & Provenance

• Modal Cloud Run: ap-4IxzcRfhqKvZfuvu4SRg02 / ap-8XWz4HHNSQLxHAzKjyYDhl
• Research Repository: umran666/Diffusion_Ablate_Research
• Base Architecture: ideogram-ai/ideogram-4-fp8 (9.28B single-stream DiT)