---
library_name: diffusers
pipeline_tag: text-to-image
inference: false
base_model: nota-ai/bk-sdm-tiny-2m
license: creativeml-openrail-m
tags:
  - text-to-image
  - diffusion
  - stable-diffusion
  - knowledge-distillation
  - research-preview
---

# Clover Image Tiny — Research Preview

This is **Clover Image Tiny**, an early conventional knowledge-distillation
checkpoint released as a research preview. It is a production-data pilot, not
a release-ready model. It uses the compact BK-SDM-Tiny-2M architecture, a
conventional 50-step PNDM schedule, 512×512 output, and classifier-free
guidance 7.5.

The checkpoint is useful for inspecting an early, licensed-data quality-refresh
experiment. It is not a finished Clover release, a quality benchmark, or a
claim of uniformly better output than its starting model.

## Exact artifact identity

| Field | Value |
|---|---|
| Repository | `neonforestmist/Clover-Image-Tiny` |
| Status | **RESEARCH PREVIEW · PRODUCTION PILOT · NOT RELEASE-READY** |
| Experiment | `clover-kd-20260712T050925Z-01KXABNHP0` |
| Training stage | Stage B knowledge distillation |
| Optimizer step | 500 |
| Checkpoint SHA-256 | `4a5b99ff18478742528a0d31c97dcee939b166a51be858721d40ad5984110893` |
| Checkpoint-bundle SHA-256 | `384b6515f5f26838aea33ec9a941e06610a20764f0b8637c8b7b0667bfc0d447` |
| Resolved-config SHA-256 | `80cf9395d1f587dc0c1d440d9f5b55c55c20703187998509bb306d19d463f597` |
| Dataset-manifest SHA-256 | `50c1249f1cb0d8d690a9acc451ca10c9432eb5a7f4e26f34acb5462096e72322` |
| Package bytes | `1671492371` |
| Package files | `30` |
| Validated Stage B source-package checksums SHA-256 | `d9a28d5fe6f5b675ee1b9db52e6d0493c8d3d357bb824eac590911acbd5c3ebc` |
| Builder source commit | `e6079eec2b91c5d026bfb461fd6c844a98b888ec` |

The checkpoint completed 500 optimizer steps, 4,000 microsteps, and 4,000
sample presentations. All 500 recorded training rows were finite and had a
nonzero gradient. Those are training-integrity observations, not image-quality
scores.

## Example gallery

![Eight paired baseline and Clover Image Tiny examples](assets/clover-image-tiny-paired-contact-sheet.png)

Each row uses the same prompt and seed. The **left column is the pinned
BK-SDM-Tiny-2M baseline; only the right column is this Stage B checkpoint**.
The gallery was generated on an NVIDIA L4 in bfloat16 with 50 PNDM steps,
guidance 7.5, an empty negative prompt, and 512×512 output.
With the pinned Diffusers 0.39.0 scheduler, those 50 requested PNDM steps use
51 U-Net invocations because PLMS repeats its first retained timestep.

All eight Stage B gallery images were finite, nonblank, nonblack, and returned
clear from the packaged upstream safety checker in that measured run. This
small engineering subset is not a general safety or quality evaluation. The
changes are modest and not uniformly better: the bottle example loses prompt
fidelity, hands remain weak, and some details change without clear improvement.

## Run locally

This is a conventional PyTorch/Diffusers model, so it can run on macOS,
Windows, or Linux. Use Python 3.11 or 3.12 and allow about 2 GB of free disk
space for the model itself. Mac MPS is the only local runtime measured for this
preview; Windows/Linux CUDA and CPU execution are supported by the runner but
their performance is not claimed here.

### macOS Apple silicon — MPS

These commands download a complete local copy, install the pinned runtime in
an isolated environment, and generate through MPS:

```bash
mkdir clover-image-tiny-local
cd clover-image-tiny-local

python3.12 -m venv .venv
source .venv/bin/activate
python -m pip install --upgrade pip
python -m pip install "huggingface-hub==0.36.2"

hf download "neonforestmist/Clover-Image-Tiny" --local-dir model
python -m pip install -r model/requirements.txt

python model/examples/generate.py \
  --model model \
  --device mps \
  --local-files-only \
  --prompt "a compact modern library with arched windows" \
  --seed 1469 \
  --output clover-image-tiny.png

open clover-image-tiny.png
```

If `python3.12` is not installed but Python 3.11 is, use `python3.11` instead.
After the first download, `--local-files-only` keeps inference offline. The
script writes the resolved runtime settings beside the PNG as
`clover-image-tiny.png.json`. This is the PyTorch/Diffusers model; no Core ML or
iPhone package is required or included.

### Windows — PowerShell

On Windows, the same runner automatically selects an available NVIDIA CUDA GPU
and otherwise falls back to CPU:

```powershell
mkdir clover-image-tiny-local
cd clover-image-tiny-local

py -3.12 -m venv .venv
.venv\Scripts\Activate.ps1
python -m pip install --upgrade pip
python -m pip install "huggingface-hub==0.36.2"

hf download "neonforestmist/Clover-Image-Tiny" --local-dir model
python -m pip install -r model\requirements.txt

python model\examples\generate.py `
  --model model `
  --device auto `
  --local-files-only `
  --prompt "a compact modern library with arched windows" `
  --seed 1469 `
  --output clover-image-tiny.png

Invoke-Item .\clover-image-tiny.png
```

Use `py -3.11` if that is the installed supported Python. Check
`python -c "import torch; print(torch.cuda.is_available())"` after installation;
`False` means `--device auto` will use CPU. A Windows AMD/DirectML path is not
included or validated.

### Linux

Use the macOS shell flow with `python3.11` or `python3.12`, and replace
`--device mps` with `--device auto`. It selects CUDA when PyTorch can see an
NVIDIA GPU and otherwise uses CPU.

### Python API

The equivalent conventional configuration, with automatic CUDA/MPS/CPU
selection, is:

```python
import torch
from diffusers import DiffusionPipeline, PNDMScheduler

model_id = "neonforestmist/Clover-Image-Tiny"
if torch.cuda.is_available():
    device = "cuda"
elif torch.backends.mps.is_available():
    device = "mps"
else:
    device = "cpu"
dtype = torch.float16 if device in {"cuda", "mps"} else torch.float32

pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=dtype)
pipe.scheduler = PNDMScheduler.from_config(pipe.scheduler.config)
pipe = pipe.to(device)

generator_device = "cuda" if device == "cuda" else "cpu"
generator = torch.Generator(device=generator_device).manual_seed(1337)
result = pipe(
    prompt="a tiny greenhouse glowing in a moonlit garden",
    negative_prompt="",
    num_inference_steps=50,
    guidance_scale=7.5,
    height=512,
    width=512,
    generator=generator,
)
result.images[0].save("clover-image-tiny.png")
```

The bundled `examples/generate.py` chooses CUDA, MPS, or CPU safely and writes
the resolved settings next to the PNG:

```bash
python examples/generate.py \
  --model "neonforestmist/Clover-Image-Tiny" \
  --prompt "a tiny greenhouse glowing in a moonlit garden" \
  --output clover-image-tiny.png
```

Seeded generation is repeatable only within the limits of the selected runtime.
Different devices, dtypes, kernels, and dependency builds can produce different
pixels. The bundled Mac example below was measured locally on an Apple M4 Pro
with MPS and fp16: 18.21 seconds, exactly 51 U-Net calls, and 631,341,056 bytes
of process-lifetime maximum RSS. It used the prompt “a compact modern library
with arched windows” and seed 1469. The packaged safety checker ran and returned
clear. This does not promise identical pixels or performance on another Mac.

![Clover Image Tiny local MPS library example](assets/clover-image-tiny-local-mps-library-seed-1469.png)

Its machine-readable evidence is bundled at
`evidence/clover-image-tiny-local-mps-library-seed-1469.json`; the image
SHA-256 is
`f8830346f2a9c2b9a8c2a01d8f90e6925c93d667c1bcf998aa904a150589a742`.

## Training lineage

- Starting student:
  `nota-ai/bk-sdm-tiny-2m@aad3e0e8ba61b7cb9f64869dc4e586f8ad9d3665`
- Frozen teacher:
  `CompVis/stable-diffusion-v1-4@133a221b8aa7292a167afc5127cb63fb5005638b`
- Fine-tuning data: exactly 1,000 accepted image-caption pairs from
  `Spawning/PD3M@2a5eb24a8dccf245acd8e56341761aee06da0bdf`
- Split: 973 train, 17 validation, and 10 test records in one checksummed shard
- Data gate: `CDLA-Permissive-2.0`; accepted items retain CC0-1.0 or Public
  Domain Mark 1.0 provenance
- Preprocessing: deterministic center crop and 512×512 JPEG conversion,
  version `clover-pd3m-center-crop-512-jpeg95-v1`

The 1,000 records describe only this Clover fine-tuning run. The starting
student and teacher already contain knowledge learned from much larger upstream
corpora. Their pinned model cards and weight licenses were verified, but the
project does not have complete per-record provenance for all foundational
pretraining behind those weights. In particular, the BK-SDM model card names
LAION-Aesthetics V2 6.25+ and 2,256,472 pairs without the per-record evidence
required by Clover's full release policy. This inherited gap is a material
reason the package is labeled a research preview and not release-ready.

See `DATA_PROVENANCE.md`, `MODEL_DATA_LICENSES.md`, the bundled CreativeML Open
RAIL-M terms, and the package checksums for the portable evidence included here.

## Intended use

- Research and inspection of compact Stable Diffusion knowledge distillation
- Reproducing the fixed conventional 50-step engineering examples
- Comparing this early calibration checkpoint with its pinned starting student
- Non-consequential creative experimentation subject to the model license

## Limitations

- The model was fine-tuned for only 500 optimizer steps on 1,000 pairs.
- The eight-prompt gallery is an engineering-health subset, not a representative
  quality, alignment, diversity, bias, or human-preference evaluation.
- Results may omit requested objects, lose relationships or counts, produce
  malformed anatomy and hands, render text poorly, or preserve/amplify biases
  from upstream models and data.
- The Stage B changes are modest and can make individual prompts worse.
- The included pipeline is the conventional PyTorch/Diffusers path only.

## Safety and out-of-scope use

The upstream safety checker is included and was exercised in the recorded
gallery run, but it is not a complete safety system and can miss harmful output
or over-filter benign output. Applications should add appropriate prompt and
output controls, human review, and policy enforcement.

Do not use this preview for consequential decisions, identity claims, medical
or legal conclusions, harassment, exploitation, illegal activity, or any use
prohibited by CreativeML OpenRAIL-M. Review outputs before sharing them.

## Licenses

The model weights are a derivative under **CreativeML OpenRAIL-M**. The small
example and packaging code is licensed separately under **Apache-2.0**. The
PD3M dataset declaration and each accepted item's public-domain status remain
separate from both licenses. Read the bundled license files and
`MODEL_DATA_LICENSES.md`; this summary is not legal advice.