Instructions to use neonforestmist/Clover-Image-Tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use neonforestmist/Clover-Image-Tiny with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("neonforestmist/Clover-Image-Tiny", torch_dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Data Provenance — Clover Image Tiny Research Preview
This document records the portable data identity for the exact Stage B calibration checkpoint in this package. It deliberately contains no local machine paths, private storage locations, or source-image payloads.
Clover fine-tuning corpus
| Field | Value |
|---|---|
| Dataset | Spawning/PD3M |
| Pinned revision | 2a5eb24a8dccf245acd8e56341761aee06da0bdf |
| Accepted records | 1,000 image-caption pairs |
| Split | 973 train / 17 validation / 10 test |
| Shards | 1 deterministic checksummed shard |
| Dataset gate license | CDLA-Permissive-2.0 |
| Item status | CC0-1.0 or Public Domain Mark 1.0, recorded per item |
| Manifest SHA-256 | 50c1249f1cb0d8d690a9acc451ca10c9432eb5a7f4e26f34acb5462096e72322 |
| Preprocessing version | clover-pd3m-center-crop-512-jpeg95-v1 |
Every accepted manifest row records a stable item ID, caption, split, source organization and URL, item-level status, source MD5, transformed SHA-256, original dimensions and MIME type, crop/resize details, filtering result, and removal status. Preprocessing applies EXIF transpose, a deterministic square center crop, Lanczos resize to 512×512, and JPEG encoding with stripped metadata, quality 95, and subsampling 0.
The captions paired with these accepted items were used as the Stage B training text. They are not represented as a separate, untracked prompt corpus. Neither the source images nor the gated training shard is distributed in this model package.
Checkpoint binding
The data identity above is bound to:
- experiment
clover-kd-20260712T050925Z-01KXABNHP0; - optimizer step 500, microstep 4,000, sample position 4,000;
- checkpoint SHA-256
4a5b99ff18478742528a0d31c97dcee939b166a51be858721d40ad5984110893; - checkpoint-bundle SHA-256
384b6515f5f26838aea33ec9a941e06610a20764f0b8637c8b7b0667bfc0d447; - resolved-config SHA-256
80cf9395d1f587dc0c1d440d9f5b55c55c20703187998509bb306d19d463f597.
The bundle digest includes checkpoint metadata in addition to the format-2 resume artifacts, preventing the data identity from being relabeled without changing the approved bundle identity.
Engineering gallery prompts
The bundled paired contact sheet uses eight fixed project-authored prompts from
the 256-prompt clover-eval-v1 suite. It is evaluation input, not training
data. Each row fixes the prompt and seed and compares the pinned starting model
on the left with this Stage B checkpoint on the right. The contact sheet
SHA-256 is
8653d1105b7b0c56e2385a127336dbe9b3a1a03c8518edb4934353a9d8e13bfa.
This subset checks that the pipeline produces finite, nonblank images. It does not establish representative quality, prompt alignment, diversity, fairness, or human preference.
Foundational upstream provenance limitation
The 1,000-pair manifest fully describes only the additional Clover calibration data. It does not replace or erase the pretraining lineage already embedded in the starting student and frozen teacher.
The pinned BK-SDM-Tiny-2M model card names LAION-Aesthetics V2 6.25+ and 2,256,472 pairs, but does not supply the per-record license and provenance evidence required by Clover's full release policy. Complete per-record provenance for all foundational data behind the upstream student and teacher is therefore not available to this project. The upstream checkpoints and their declared CreativeML OpenRAIL-M licenses are pinned and disclosed, but that is not equivalent to a complete foundational dataset audit.
This inherited limitation is material. The weights are distributed only as a RESEARCH PREVIEW · PRODUCTION PILOT · NOT RELEASE-READY artifact.