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v1: curated production adapters of the diffusion aleph line (amoe.diffusion.anchor safetensors, provenance in metadata; sd15 core eps + lune flow + anima NC)
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---
license: mit
base_model:
- stable-diffusion-v1-5/stable-diffusion-v1-5
tags:
- adapters
- aleph
- amoe-lora
- diffusion
- stable-diffusion
---
# aleph-diffusion-adapters β€” the production artifacts of the diffusion aleph line
**This is the curated adapters repo.** The full research record (16
experiment packages, raw ledgers, 2-seed program) is
[geolip-aleph-diffusion](https://huggingface.co/AbstractPhil/geolip-aleph-diffusion);
the framework that loads everything here is
[amoe-lora](https://huggingface.co/AbstractPhil/amoe-lora) (`pip install
amoe-lora[diffusion]`); the article is
[Part 3-D](https://huggingface.co/AbstractPhil/geolip-aleph-diffusion/blob/main/article_ft3d.md).
Every file is an `amoe.diffusion.anchor` safetensors (canonical
`blocks.{site}.{param}` layout, full provenance in the metadata,
content-hash verified). Nothing here modifies trunk weights: adapters
attach to a frozen model, toggle off bit-exactly, and detach with
verification.
## The artifacts
| file | trunk | what it is | evidence status |
|---|---|---|---|
| `sd15/relay_all16_s0/.s1` | SD1.5 core (eps) | the certified 16-site relay stack β€” grounding +0.1809β†’+0.2172 over zero-shot, beats matched LoRA which traded grounding away | exp006, candidate s0 + s1 replication |
| `sd15/mb3_s0/.s1` | SD1.5 core (eps) | the multiband stack: 3 band experts/site on the sigma axis; band lesions SURGICAL (own-band damage 50–200Γ— cross-band); the step-gated controller moved image-space grounding +0.089 over frozen | exp008 2-seed + exp010 battery |
| `sd15-lune-flow/relay_all16_s0` | SD15-Lune flow | the first certified diffusion relay (βˆ’0.85% paired val, LoRA control WORSE than frozen) | exp001, candidate s0 (s1 replicated vs frozen) |
| `sd15-lune-flow/blob_mb3_s0/.s1` | SD15-Lune flow | the conditioning-law stack: blob-supervised HIGH band at Ξ»β‰ˆ1 (βˆ’8.3% on the role-aligned foreground gauge, common gauge in bound) | exp013, 2-seed |
| `anima/aleph_relay_epoch4` | Anima 2B DiT (flow) | 28-block bf16 relay trained through the diffusion-pipe fork (native LoRA control DEGRADED while this improved) | exp004, candidate s0 Β· **NC** (CircleStone NC + NVIDIA OML β€” derived checkpoint) |
Honest scope: these are research-grade adapters from a four-day 2-seed
campaign, certified on their gauges (paired flow/eps-MSE, CLIP round-trip
grounding, band lesions, the role-aligned foreground gauge) β€” not
aesthetics-tuned style adapters. What they buy is measured and cited row
by row; read the linked packages before deploying.
## Quickstart
```python
# pip install amoe-lora[diffusion]
import torch, amoe.diffusion as ad
from diffusers import StableDiffusionPipeline
from huggingface_hub import hf_hub_download
pipe = StableDiffusionPipeline.from_pretrained(
"stable-diffusion-v1-5/stable-diffusion-v1-5",
torch_dtype=torch.float32).to("cuda")
h = ad.attach(pipe.unet, hf_hub_download(
"AbstractPhil/aleph-diffusion-adapters", "sd15/mb3_s0.safetensors"))
img = ad.sample(pipe, h, "a lighthouse at dusk", seed=7) # step-gated
with h.lesion_band(2): # generate WITHOUT the HIGH/structure band
img2 = ad.sample(pipe, h, "a lighthouse at dusk", seed=7)
base = h.detach() # verified bit-exact, or it raises
```
The relay stacks attach the same way (no windows needed). The
`sd15-lune-flow/` artifacts belong on the
[Lune flow trunk](https://huggingface.co/AbstractPhil/sd15-flow-lune-json-prompt)
(ckpt-2500) with `objective="flow"` sampling β€” the metadata records the
trunk id; `ad.attach` warns on substrate mismatch.
## ComfyUI
Planned as `comfyui-amoe` (next release): loader / attach / band-toggle /
detach nodes consuming exactly these files, with step gating installed as
a pre-forward hook so any stock KSampler becomes step-gated. The node
asserts each anchor's site count + width signature from the metadata
before patching β€” a silent enumeration mismatch loads plausibly and
corrupts quietly, so it ships only behind an image-parity check.
## Training your own
Single GPU / DDP: `amoe.diffusion.train` (five lines β€” see the
[amoe-lora card](https://huggingface.co/AbstractPhil/amoe-lora)).
Multi-GPU production: the
[diffusion-pipe fork](https://github.com/AbstractEyes/diffusion-pipe)
with `aleph_relay = true` (relay + multiband modes; saves land in this
repo's format).
## Licenses
MIT for this repo's own content and the SD1.5-family adapters. The
`anima/` adapter is a DERIVED checkpoint of NC-licensed weights
(CircleStone NC + NVIDIA Open Model License) β€” non-commercial use only.