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README.md

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+---
+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