Release Clover Image Tiny model card and banner

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

Clover Image Tiny 🍀

A compact 512×512 text-to-image model you can run locally on macOS, Windows, or Linux.

323,384,964 denoiser parameters · about 1.67 GB · 4–100 inference steps · PyTorch/Diffusers

Clover Image Tiny is the public PyTorch/Diffusers checkpoint release behind these examples. Its output has a recognizable, playful DALL·E mini-ish character. That is a visual description, not a claim of equivalent architecture, training scale, or benchmark performance.

Try Clover Image Tiny in the live ZeroGPU demo →

The demo exposes prompt, negative prompt, seed, guidance, dimensions, scheduler, and 4–100 conventional Diffusers inference steps. It creates one image per request and keeps the packaged safety checker enabled.

Run locally

Download once, then generate offline with the bundled runner. Python 3.11 and 3.12 are supported.

macOS — Apple silicon

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 tiny glass greenhouse glowing in a moonlit garden, detailed photography" \
  --negative-prompt "blurry, distorted, low detail" \
  --steps 50 \
  --guidance-scale 7.5 \
  --scheduler pndm \
  --seed 1337 \
  --output clover-image-tiny.png

open clover-image-tiny.png

Use python3.11 instead if that is the installed supported Python.

Windows — 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 tiny glass greenhouse glowing in a moonlit garden, detailed photography" `
  --negative-prompt "blurry, distorted, low detail" `
  --steps 50 `
  --guidance-scale 7.5 `
  --scheduler pndm `
  --seed 1337 `
  --output clover-image-tiny.png

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

Use py -3.11 if needed. With --device auto, the runner selects an available NVIDIA CUDA GPU and otherwise uses CPU.

Linux

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 auto \
  --local-files-only \
  --prompt "a tiny glass greenhouse glowing in a moonlit garden, detailed photography" \
  --negative-prompt "blurry, distorted, low detail" \
  --steps 50 \
  --seed 1337 \
  --output clover-image-tiny.png

--device auto selects CUDA when PyTorch can see an NVIDIA GPU and otherwise uses CPU. After the first download, --local-files-only prevents network access during generation.

Generation controls

The command above is ready to copy. Change these flags to explore the model:

Flag	Accepted values	Default	What it controls
`--prompt`	Non-empty text	Required	What to generate
`--negative-prompt`	Text, or empty	Empty	Details to discourage; the starter commands and live demo use `blurry, distorted, low detail`
`--steps`	4–100	`50`	Diffusion iterations; more steps take longer and do not guarantee a better image
`--guidance-scale`	0.0–20.0	`7.5`	How strongly the image follows the prompt
`--scheduler`	`pndm`, `ddim`, `euler`, `euler-a`, `dpmpp-2m`	`pndm`	Sampling method
`--width`	256–768, divisible by 64	`512`	Output width
`--height`	256–768, divisible by 64	`512`	Output height
`--num-images`	1–4	`1`	Images generated in one run
`--seed`	0–(2⁶³−1)	`1337`	Repeatable starting seed
`--device`	`auto`, `cuda`, `mps`, `cpu`	`auto`	Compute backend
`--local-files-only`	Flag	Off	Require an already-downloaded local model

The reference configuration is 50-step PNDM, guidance 7.5, 512×512, one image, seed 1337, and an empty negative prompt. The live demo pre-fills blurry, distorted, low detail; the local runner leaves the field empty unless you pass the flag.

For multiple images, the first uses the requested filename and later images use numbered names such as clover-image-tiny-02.png. Seeds advance from the requested seed. A JSON sidecar beside the first PNG records every resolved setting, output filename, seed, checksum, and safety result. Existing planned outputs are never overwritten.

Run python model/examples/generate.py --help for the complete CLI reference.

Hardware and operating systems

System	Automatic backend	Precision	Current evidence
Apple-silicon Mac	MPS	fp16	Measured locally on an M4 Pro
Windows/Linux with NVIDIA	CUDA	fp16	Supported code path; performance not measured
CPU-only macOS/Windows/Linux	CPU	fp32	Supported code path; performance not measured
Windows AMD/DirectML	—	—	No packaged DirectML path

The model package itself is about 1.67 GB. Keep at least 2 GB free for the model alone and additional room for the Python environment and caches; no formal total-install minimum has been measured. Larger images and batches need more memory; lower --width, --height, or --num-images if necessary.

The measured Mac reference used a 24 GB Apple M4 Pro and completed one 512×512 image in 18.21 seconds with fp16 MPS. Its process-lifetime maximum RSS was 631,341,056 bytes. This is a measured point, not a minimum-RAM claim. No Core ML or iPhone package is required or included.

Example outputs

Each row uses the same prompt and seed. The left column is the pinned BK-SDM-Tiny-2M starting model; the right column is Clover Image Tiny. The gallery used an NVIDIA L4 in bfloat16, 50 PNDM steps, guidance 7.5, an empty negative prompt, and 512×512 output. With Diffusers 0.39.0, 50 requested PNDM steps use 51 U-Net calls because PLMS repeats its first retained timestep.

All eight Clover images were finite, nonblank, nonblack, and cleared by the packaged upstream safety checker in this run. The set covers objects, a person, an animal, a landscape, an interior, food, a product, and a night scene.

The local MPS reference below used “a compact modern library with arched windows,” seed 1469, and the same 50-step configuration:

Python API

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)
image = pipe(
    prompt="a tiny greenhouse glowing in a moonlit garden",
    negative_prompt="blurry, distorted, low detail",
    num_inference_steps=50,
    guidance_scale=7.5,
    height=512,
    width=512,
    generator=generator,
).images[0]
image.save("clover-image-tiny.png")

Seeded generation is repeatable within the selected runtime. Different devices, dtypes, kernels, and dependency builds can produce different pixels.

About this release

Clover Image Tiny is a conventional knowledge-distillation checkpoint trained for 500 optimizer steps on an exact licensed 1,000-pair calibration set. The run recorded 4,000 microsteps and 4,000 sample presentations, with finite training rows and nonzero gradients throughout.

The model was initialized from nota-ai/bk-sdm-tiny-2m@aad3e0e8ba61b7cb9f64869dc4e586f8ad9d3665 and distilled with a frozen CompVis/stable-diffusion-v1-4@133a221b8aa7292a167afc5127cb63fb5005638b teacher. It is a genuinely modified checkpoint, but it was not trained from random initialization.

This checkpoint release covers the conventional PyTorch/Diffusers model shown here. Formal quality acceptance, the separate 1–4 Leaf architecture, Core ML, and iPhone work remain separate workstreams and are not claims of this package.

Quality and known behavior

The included gallery demonstrates recognizable subjects across colorful scenes, products, food, an animal, a landscape, and an interior.
Individual results vary by prompt, seed, scheduler, and step count. More steps increase runtime but do not guarantee a better result.
Hands, anatomy, exact counts and relationships, and readable text can be difficult.
The paired eight-prompt gallery is a reproducible engineering sample, not a controlled benchmark or broad human-preference study.
Resolution and batch size multiply memory use.

Safety

The upstream safety checker is packaged and enabled in both the supported runner and hosted demo. A flagged output may be returned as a black placeholder; the JSON sidecar records nsfw_content_detected so the result is not silent. The checker is useful but not a complete moderation system and can miss harmful content or over-filter benign content.

Applications should add controls appropriate to their audience and review outputs before sharing them. Do not use the model for consequential decisions, identity claims, medical or legal conclusions, harassment, exploitation, illegal activity, or uses prohibited by CreativeML OpenRAIL-M.

Training lineage and data

Clover fine-tuning data: exactly 1,000 accepted image-caption pairs from Spawning/PD3M@2a5eb24a8dccf245acd8e56341761aee06da0bdf
Split: 973 train, 17 validation, and 10 test records
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
Dataset-manifest SHA-256: 50c1249f1cb0d8d690a9acc451ca10c9432eb5a7f4e26f34acb5462096e72322

The 1,000 records describe the Clover fine-tuning run. The student and teacher already contain knowledge from larger upstream corpora. Their pinned model cards and weight licenses are disclosed, while complete item-level provenance for all foundational pretraining is not available to this project.

See DATA_PROVENANCE.md for the portable manifest identity and MODEL_DATA_LICENSES.md for the complete component ledger.

Licenses

The model weights are a derivative under CreativeML OpenRAIL-M. The example runner and packaging code are under Apache-2.0. Dataset and item-level terms remain separate. Read LICENSE, LICENSE-MODEL-CREATIVEML-OPENRAIL-M.txt, LICENSE-CODE, and MODEL_DATA_LICENSES.md before redistribution or use.

The hero mosaic is user-supplied presentation artwork included by explicit request for display in this public model repository. It is not benchmark evidence, its panel-generation provenance is not claimed, and this package does not grant a downstream reuse license for it.

Reproducibility and artifact identity

Field	Value
Repository	`neonforestmist/Clover-Image-Tiny`
Release status	PUBLIC PYTORCH/DIFFUSERS CHECKPOINT RELEASE
Training experiment	`clover-kd-20260712T050925Z-01KXABNHP0`
Optimizer step	500
Checkpoint SHA-256	`4a5b99ff18478742528a0d31c97dcee939b166a51be858721d40ad5984110893`
Checkpoint-bundle SHA-256	`384b6515f5f26838aea33ec9a941e06610a20764f0b8637c8b7b0667bfc0d447`
Resolved-config SHA-256	`80cf9395d1f587dc0c1d440d9f5b55c55c20703187998509bb306d19d463f597`
Denoiser parameters	`323,384,964`
Package bytes	`1676086612`
Package files	`31`
Validated Stage B source-package checksums SHA-256	`d9a28d5fe6f5b675ee1b9db52e6d0493c8d3d357bb824eac590911acbd5c3ebc`
Builder source commit	`9f5ce495fcb88238ec7fdc33204fa42ec9690c37`

The local MPS reference evidence is bundled at evidence/clover-image-tiny-local-mps-library-seed-1469.json. Its image SHA-256 is f8830346f2a9c2b9a8c2a01d8f90e6925c93d667c1bcf998aa904a150589a742. checksums.json covers every packaged file.