README.md

---
license: other
license_name: krea-2-research
license_link: https://huggingface.co/dataautogpt3/Krea2-weights-experiments/blob/main/LICENSE
language:
  - en
library_name: diffusers
tags:
  - krea-2
  - turbo
  - weight-editing
  - diffusion
  - dit
  - mmdit
  - safetensors
  - comfyui
  - experimental
pipeline_tag: text-to-image
---

# Krea 2 Turbo — Hand-Edited Weight Experiments

![Comparison Grid](comparison_ALL.png)

## Overview

This repository contains **weight-edited variants** of the Krea 2 Turbo diffusion model. Each variant was created by surgically scaling specific transformer block weights in the 12.8B parameter single-stream MMDiT, producing artistic and functional model variations without any retraining.

These are **research artifacts** from hand-editing diffusion model weights using the methodology described below. The base models (Krea 2 Turbo and Krea 2 Raw) are NOT included — only the edited variants.

## Method

All variants use the core formula:

```
theta_new = theta_original * (1 - 2 * alpha)
```

Where `alpha` controls the inversion strength:
- `alpha=0.05` → scale 0.90 (subtle)
- `alpha=0.10` → scale 0.80 (artistic sweet spot)
- `alpha=0.15` → scale 0.70 (strong)
- `alpha=0.20` → scale 0.60 (aggressive but functional)

Full negation (`alpha=0.5`, scale=-1.0) **breaks the model** and is excluded from this repository.

## Architecture: Krea 2 Turbo

- **Type**: Single-stream MMDiT (Diffusion Transformer)
- **Parameters**: 12.8B
- **File size**: ~25GB per variant (BF16 + F32 tensors)
- **Structure**: 28 uniform transformer blocks
- **Block sub-layers**:
  - `blocks.N.attn.*` (7 tensors): gate, qknorm, wq, wk, wv, wo
  - `blocks.N.mlp.*` (3 tensors): gate, up, down (SwiGLU)
  - `blocks.N.mod.lin` (1 tensor): conditioning modulation
  - `blocks.N.prenorm.scale` / `blocks.N.postnorm.scale`

## Variants

### B1 — Partial Inversion (Most Artistic)
| Property | Value |
|---|---|
| File | `Krea_2_turbo_inv_B1_partial10.safetensors` |
| Blocks | 12-14 (mid) |
| Layers | ALL (39 tensors per block group) |
| Alpha | 0.10 (scale=0.80) |
| Result | **Most artistic variant** — strong style/content shift while remaining coherent |

### B3 — Attention-Only Partial Inversion
| Property | Value |
|---|---|
| File | `Krea_2_turbo_inv_B3_attn_p10.safetensors` |
| Blocks | 12-14 (mid) |
| Layers | attn only (21 tensors) |
| Alpha | 0.10 (scale=0.80) |
| Result | Functional, subtler than B1 — attention-specific perturbation |

### D — Gate Scaling (All Blocks)
| Property | Value |
|---|---|
| File | `Krea_2_turbo_inv_D_gate_p20.safetensors` |
| Blocks | 0-27 (all) |
| Layers | attn.gate only (28 tensors) |
| Alpha | 0.20 (scale=0.60) |
| Result | Functional, moderate effect — gate weights are more tolerant of aggressive scaling |

### F — Early/Late Block Inversion
| Property | Value |
|---|---|
| File | `Krea_2_turbo_F_early_a10.safetensors` |
| Blocks | 0-2 (early) |
| Layers | ALL |
| Alpha | 0.10 (scale=0.80) |
| Result | Affects structure, composition, spatial layout |

| Property | Value |
|---|---|
| File | `Krea_2_turbo_F_late_a10.safetensors` |
| Blocks | 25-27 (late) |
| Layers | ALL |
| Alpha | 0.10 (scale=0.80) |
| Result | Affects style, color, detail, texture refinement |

### G — Mid-Block Alpha Sweep
Three variants at different inversion strengths on the same block zone:

| File | Alpha | Scale | Notes |
|---|---|---|---|
| `Krea_2_turbo_G_mid_a05.safetensors` | 0.05 | 0.90 | Subtle |
| `Krea_2_turbo_G_mid_a15.safetensors` | 0.15 | 0.70 | Strong |
| `Krea_2_turbo_G_mid_a20.safetensors` | 0.20 | 0.60 | Aggressive but functional |

All target blocks 12-14, ALL layers.

### H — Layer-Selective Mid-Block
| File | Blocks | Layers | Alpha |
|---|---|---|---|
| `Krea_2_turbo_H_mid_attn_a10.safetensors` | 12-14 | attn only | 0.10 |
| `Krea_2_turbo_H_mid_mlp_a10.safetensors` | 12-14 | mlp only | 0.10 |

Isolates the effect of attention vs MLP perturbation on the same block zone.

### I — Gradient Alpha
| Property | Value |
|---|---|
| File | `Krea_2_turbo_I_gradient.safetensors` |
| Blocks | 0-27 (all) |
| Layers | ALL |
| Alpha | 0.03 → 0.17 (gradient across blocks) |
| Scale | 0.94 → 0.66 |
| Result | Smooth global perturbation — early blocks barely touched, late blocks aggressively inverted |

## Excluded Variants (Broken)

The following variants were created but are **broken** (model produces noise/garbage) and are NOT included:

| Variant | What was done | Why it broke |
|---|---|---|
| B2_attn_full | attn weights * -1.0 | Full negation destroys attention computation |
| D_wv_all | wv weights * -1.0 | Full negation of value projection |
| E_ties_mid | TIES-style sign flip on mid blocks | Full negation variant |

## Usage

### ComfyUI

1. Place `.safetensors` files in `ComfyUI/models/diffusion_models/`
2. Load via `UNETLoader` node
3. Use the same VAE, CLIP, and text encoder as Krea 2 Turbo
4. Generate with your standard Krea 2 workflow

### Diffusers

```python
from diffusers import DiffusionPipeline
import torch

pipe = DiffusionPipeline.from_pretrained(
    "dataautogpt3/Krea2-weights-experiments",
    torch_dtype=torch.bfloat16,
    variant="bf16"
).to("cuda")
```

> Note: These are diffusion model weights only. You need the corresponding VAE, text encoders, and tokenizer from the original Krea 2 Turbo release.

## Key Findings

1. **Scaling works, full negation breaks.** Partial inversion (scale 0.60-0.90) produces functional, artistic variants. Full negation (scale=-1.0) breaks the model.

2. **10% inversion is the sweet spot.** Alpha=0.10 (scale=0.80) on mid blocks 12-14 produces the most artistically interesting results.

3. **Mid blocks are safest to modify.** Blocks 12-14 are the most redundant and tolerate perturbation best.

4. **Gate weights are most tolerant.** Attention gate weights can be scaled to 0.60 across all blocks while remaining functional — other layers break sooner.

5. **The artistic effects come from compensation.** Partial perturbation triggers creative reorganization in unedited blocks — the compensatory masquerade effect.

## Research Context

This work draws on findings from:
- **Task Arithmetic** (Ilharco et al., ICLR 2023) — formal basis for weight negation
- **weights2weights** (NeurIPS 2024) — diffusion weight space as meta-latent
- **Unraveling MMDiT Blocks** (2025) — per-block role mapping for MMDiT
- **C3: Creative Concept Catalyst** (CVPR 2025) — low-frequency amplification in shallow blocks
- **ConceptPrune** (ICLR 2025) — tiny weight changes shift semantic output

## Credits

- Base model: Krea 2 Turbo (Krea AI)
- Weight editing: DataPlusEngine
- Methodology: Hand-editing diffusion weights via mmap-based surgical tensor scaling