Text-to-Image
Diffusers
Safetensors
StableDiffusionPipeline
clover-image
diffusion
stable-diffusion
knowledge-distillation
compact
local-inference
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
Link verified Clover Image Tiny ZeroGPU demo
Browse files- README.md +14 -6
- checksums.json +2 -2
- research_preview.json +3 -3
README.md
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outputs, exact lineage, and local runner make it useful today for creative
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experimentation and compact-model research.
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## Exact artifact identity
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| Resolved-config SHA-256 | `80cf9395d1f587dc0c1d440d9f5b55c55c20703187998509bb306d19d463f597` |
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| Dataset-manifest SHA-256 | `50c1249f1cb0d8d690a9acc451ca10c9432eb5a7f4e26f34acb5462096e72322` |
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| Denoiser parameters | `323,384,964` |
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| Package bytes | `
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| Package files | `30` |
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| Validated Stage B source-package checksums SHA-256 | `d9a28d5fe6f5b675ee1b9db52e6d0493c8d3d357bb824eac590911acbd5c3ebc` |
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| Builder source commit | `
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The checkpoint completed 500 optimizer steps, 4,000 microsteps, and 4,000
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sample presentations. All 500 recorded training rows were finite and had a
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Only **PNDM + 50 steps + guidance 7.5 + 512×512 + an empty negative
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prompt** is the validated gallery and local-MPS recipe. Other combinations are
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deliberately available for exploration, but no comparative quality or speed
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claim is attached to them. These are conventional
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checkpoint into a distilled four- or eight-step model.
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For example, this asks for two 512×512 images using 30 Euler steps, stronger
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guidance, and an explicit negative prompt:
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outputs, exact lineage, and local runner make it useful today for creative
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experimentation and compact-model research.
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## Try the live demo
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[**Open Clover Image Tiny in the public ZeroGPU demo →**](https://huggingface.co/spaces/neonforestmist/Clover-Image-Tiny-Demo)
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The hosted demo exposes prompt, negative prompt, seed, guidance, dimensions,
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scheduler, and 4–100 conventional Diffusers inference steps. It generates one
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image per request, defaults to the 50-step PNDM recipe, and keeps the packaged
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safety checker enabled.
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## Exact artifact identity
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| Field | Value |
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| Resolved-config SHA-256 | `80cf9395d1f587dc0c1d440d9f5b55c55c20703187998509bb306d19d463f597` |
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| Dataset-manifest SHA-256 | `50c1249f1cb0d8d690a9acc451ca10c9432eb5a7f4e26f34acb5462096e72322` |
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| Denoiser parameters | `323,384,964` |
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| Package bytes | `1671502952` |
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| Package files | `30` |
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| Validated Stage B source-package checksums SHA-256 | `d9a28d5fe6f5b675ee1b9db52e6d0493c8d3d357bb824eac590911acbd5c3ebc` |
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| Builder source commit | `6675345aed2734128cfb441c81d65a644332ca59` |
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The checkpoint completed 500 optimizer steps, 4,000 microsteps, and 4,000
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sample presentations. All 500 recorded training rows were finite and had a
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Only **PNDM + 50 steps + guidance 7.5 + 512×512 + an empty negative
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prompt** is the validated gallery and local-MPS recipe. Other combinations are
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deliberately available for exploration, but no comparative quality or speed
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claim is attached to them. These controls are conventional Diffusers scheduler
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iterations for this checkpoint; selecting four or eight requests that many
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iterations.
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For example, this asks for two 512×512 images using 30 Euler steps, stronger
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guidance, and an explicit negative prompt:
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checksums.json
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"LICENSE-CODE": "e26a0375b714267325c27905ba947fdc41ebc0b7a36940a49ae12379ed6208d6",
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"LICENSE-MODEL-CREATIVEML-OPENRAIL-M.txt": "be351ebe7ac01bcdbb018639aadcfd38f136b7dc3f2a3d4d3a24db51d1b210ef",
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"README.md": "
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"requirements.txt": "9dbb74869a34e3b610f737bce8a9d2a20691fd0fa416cf6bb42354b78e0a3861",
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"research_preview.json": "
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"safety_checker/config.json": "241bd8fb1ba6feff0d7421f7af0f85743711c5d76353626f71f7e2cc8009ac7c",
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"safety_checker/model.safetensors": "57ecdfa243b170f9b4cb3eefaf0f64552ef78fc0bf0eb1c5b9675308447184f6",
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"scheduler/scheduler_config.json": "184bc33ed887951dd86e37f20c878710c4e18e1c5fd4461256a3825679b53d15",
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"LICENSE-CODE": "e26a0375b714267325c27905ba947fdc41ebc0b7a36940a49ae12379ed6208d6",
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"LICENSE-MODEL-CREATIVEML-OPENRAIL-M.txt": "be351ebe7ac01bcdbb018639aadcfd38f136b7dc3f2a3d4d3a24db51d1b210ef",
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"MODEL_DATA_LICENSES.md": "8f6b712826f9904561fb32038635612722be61cfd776da58fb61a4848a03148e",
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"README.md": "880346a5eb2d7f1726c215d1214853f5938a3adb80645537236456b22af5c797",
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"assets/clover-image-tiny-local-mps-library-seed-1469.png": "f8830346f2a9c2b9a8c2a01d8f90e6925c93d667c1bcf998aa904a150589a742",
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"assets/clover-image-tiny-paired-contact-sheet.png": "8653d1105b7b0c56e2385a127336dbe9b3a1a03c8518edb4934353a9d8e13bfa",
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"evidence/clover-image-tiny-local-mps-library-seed-1469.json": "1ddd86de0be992987214266720e0f341f25791402aae637065d6d09a6ea67b27",
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"feature_extractor/preprocessor_config.json": "205d45875859846dc1ca211b9a19f5db72f47e953b396954ee34d48d8e95c3c6",
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"model_index.json": "792073e269e5caff40b3f8c553387540c17a5d900ed0e9cbfab309d650e708dd",
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"requirements.txt": "9dbb74869a34e3b610f737bce8a9d2a20691fd0fa416cf6bb42354b78e0a3861",
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"research_preview.json": "bbc4ccd5811a976f1178588d4e3bf7ab64fdba04d84e3f1a1569bd622146ca2b",
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"safety_checker/config.json": "241bd8fb1ba6feff0d7421f7af0f85743711c5d76353626f71f7e2cc8009ac7c",
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"safety_checker/model.safetensors": "57ecdfa243b170f9b4cb3eefaf0f64552ef78fc0bf0eb1c5b9675308447184f6",
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"scheduler/scheduler_config.json": "184bc33ed887951dd86e37f20c878710c4e18e1c5fd4461256a3825679b53d15",
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research_preview.json
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{
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"repository_commit": "
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"sources": {
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"LICENSE-CODE": {
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"sha256": "e26a0375b714267325c27905ba947fdc41ebc0b7a36940a49ae12379ed6208d6",
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"tracked_at_commit": true
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"hf/stage_b_preview/README.template.md": {
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"sha256": "
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"tracked_at_commit": true
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"hf/stage_b_preview/examples/generate.py": {
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"human_review": "not_measured",
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"quality_evaluation": "not_measured"
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},
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"metadata_sha256": "
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"release_ready": false,
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"runtime_contract": {
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"denoiser_calls": 51,
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{
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"build": {
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"repository_commit": "6675345aed2734128cfb441c81d65a644332ca59",
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"sources": {
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"LICENSE-CODE": {
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"sha256": "e26a0375b714267325c27905ba947fdc41ebc0b7a36940a49ae12379ed6208d6",
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"tracked_at_commit": true
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},
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"hf/stage_b_preview/README.template.md": {
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"sha256": "f2a571eec6d039a52b27dbab2b354ceeda3f7b92c4fa0ab70b42636eb65fbfc5",
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"tracked_at_commit": true
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"hf/stage_b_preview/examples/generate.py": {
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"human_review": "not_measured",
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"quality_evaluation": "not_measured"
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},
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"metadata_sha256": "fc6c8420017ca2ca0c0774d0d47ca5d9769e05a4b4eea1e6e9408d3039572391",
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"release_ready": false,
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"runtime_contract": {
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"denoiser_calls": 51,
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