--- 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 πŸ€ ![Clover Image Tiny mosaic banner](assets/clover-image-tiny-banner.png) A compact 512Γ—512 text-to-image model you can run locally on macOS, Windows, or Linux. **323,384,964 denoiser parameters Β· about 1.67 GB Β· 4–100 inference steps Β· PyTorch/Diffusers** Clover Image Tiny is the public PyTorch/Diffusers checkpoint release behind these examples. Its output has a recognizable, playful **DALLΒ·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 β†’**](https://huggingface.co/spaces/neonforestmist/Clover-Image-Tiny-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 β€” Apple silicon ~~~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 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 β€” PowerShell ~~~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 ~~~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 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–(2βΆΒ³βˆ’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, 512Γ—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 | β€” | β€” | 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 512Γ—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 ![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 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 512Γ—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 β€œa compact modern library with arched windows,” seed 1469, and the same 50-step configuration: ![Clover Image Tiny local MPS library example](assets/clover-image-tiny-local-mps-library-seed-1469.png) ## Python API ~~~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) 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 512Γ—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.