# kromoss-image

An image model inside a capsule: [Kromoss](https://huggingface.co/lodestones/Kromoss)
(a MOSS-Image fine-tune, Apache-2.0) with its 8-step DMD2 LoRA, on diffusers.
Text in, 1024x1024 PNG out, about 19 s per image on an NVIDIA GB10.

## What it shows

- **The `image.generate` kind**: m64's front lets `POST /v1/images/generations`
  through and only `prompt` and `seed` in its body. `steps`, `guidance` and the
  like get a 400 that names the field.
- **Big weights as `[[artifacts]]`**: about 40 GB from one Hugging Face repo at a
  fixed commit, only the files the pipeline needs. The capsule folder stays a few
  KB.
- **`[params]`**: the settings the server bakes in (size, steps, guidance, shift,
  LoRA), published in the manifest and returned as `params_used` with every
  image, so a result can be reproduced from its seed.
- **`[routes] extra`**: one more path, `/v1/config`, let through by the front.
- **A Dockerfile on NVIDIA's PyTorch image**, pinned by digest, with every pip
  package at an exact version.
- **Progress in `$M64_STATUS`**: loading, warming (one short generation), ready.

## Where the weights go

The first serve downloads them before the container starts. To do that ahead of
time, without starting anything:

```sh
m64 install examples/kromoss-image
```

m64 checks each file against the repo's commit and mounts the folder read-only at
`/weights/kromoss`; the container itself has no network. By default artifacts
live under `~/.cache/m64`. To keep them on a bigger disk or a NAS, shared by every
capsule:

```sh
m64 config set models_dir /mnt/models
```

## Run it

```sh
m64 check examples/kromoss-image
m64 examples/kromoss-image          # serve it and open /human/
```

Loading takes a few minutes (40 GB to read and move to the GPU). It needs about
40 GB of GPU memory: a GB10, or an x86 card with that much (then set `platform`
and `gpu` in `capsule.toml` to what `m64 doctor` prints).

## Try it

```sh
curl -s <url>/v1/images/generations \
  -H 'Content-Type: application/json' \
  -d '{"prompt": "a tabby cat holding a sign that says MOSS", "seed": 42}' \
  | python3 -c 'import sys,json,base64; d=json.load(sys.stdin); open("out.png","wb").write(base64.b64decode(d["data"][0]["b64_json"])); print(d["seed"], d["params_used"])'
```

The same prompt and seed give the same image, byte for byte.