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
| { | |
| "_class_name": "StableDiffusionPipeline", | |
| "base_components": { | |
| "adaptations": [ | |
| { | |
| "input_sha256": "e089ad92ba36837a0d31433e555c8f45fe601ab5c221d4f607ded32d9f7a4349", | |
| "output_sha256": "4e6a66b6903c12d273a1a6645f761a2de07e239facfc52fd797b83a75014ca78", | |
| "path": "tokenizer/vocab.json", | |
| "reason": "remove machine-specific source paths" | |
| }, | |
| { | |
| "input_sha256": "3ff772e0199995e0dc5c5d5cc0e269483d0e565a2b13a694d6342225d00a08f4", | |
| "output_sha256": "a51192fd731f5826ef0db722dc032b8c6f10456c5f626bc54da0e8514b899b40", | |
| "path": "vae/config.json", | |
| "reason": "remove machine-specific source paths" | |
| }, | |
| { | |
| "input_sha256": "4d81f5325e0a6daba814c9bd2bfd2d9bdbdf294b71bac2ee7456edf172dd6ada", | |
| "output_sha256": "59f07bfeb2e53d480207436ce0ea2a30c0a3608d3bbf44c98458440167eff606", | |
| "path": "unet/config.json", | |
| "reason": "instantiate the independently pinned conventional Stage B architecture and remove its machine-specific source path" | |
| } | |
| ], | |
| "copied_components": [ | |
| "feature_extractor", | |
| "safety_checker", | |
| "scheduler", | |
| "text_encoder", | |
| "tokenizer", | |
| "vae" | |
| ], | |
| "package_checksums_sha256": "8483be0cde3b32493ce30d5c8762ac71303eee015e90535389e846d7ec5aea40", | |
| "source_file_count": 18 | |
| }, | |
| "checkpoint": { | |
| "checkpoint_bundle_checksum": "384b6515f5f26838aea33ec9a941e06610a20764f0b8637c8b7b0667bfc0d447", | |
| "checkpoint_checksum": "4a5b99ff18478742528a0d31c97dcee939b166a51be858721d40ad5984110893", | |
| "config_hash": "80cf9395d1f587dc0c1d440d9f5b55c55c20703187998509bb306d19d463f597", | |
| "dataset_manifest_checksum": "50c1249f1cb0d8d690a9acc451ca10c9432eb5a7f4e26f34acb5462096e72322", | |
| "experiment_id": "clover-kd-20260712T050925Z-01KXABNHP0", | |
| "optimizer_step": 500, | |
| "source_checksums": { | |
| "metadata.json": "7b75556689e047b09da657a001bf49c42198531bd130daff4a44fa3c4afc7299", | |
| "model.safetensors": "d827005c60bb737053795aa12d7aff8215469cbb4b3dc327ab35c217b3863f8c", | |
| "training_state.pt": "f9c7552da28b956044128027e1c216f6350da7e3f7e78d2263caa89c9cde824a" | |
| } | |
| }, | |
| "kind": "clover-stage-b-calibration-local-inference", | |
| "leaf_steps_supported": false, | |
| "limitations": { | |
| "host_output_review_required": true, | |
| "physical_device_validation": "not_measured", | |
| "quality_evaluation": "not_measured" | |
| }, | |
| "parameter_count": 323384964, | |
| "release_ready": false, | |
| "runtime_contract": { | |
| "classifier_free_guidance": true, | |
| "denoiser_calls": 51, | |
| "guidance_scale_default": 7.5, | |
| "height": 512, | |
| "negative_prompt": "", | |
| "num_inference_steps": 50, | |
| "scheduler": "PNDMScheduler", | |
| "width": 512 | |
| }, | |
| "schema_version": 1, | |
| "upstream": { | |
| "model_id": "nota-ai/bk-sdm-tiny-2m", | |
| "revision": "aad3e0e8ba61b7cb9f64869dc4e586f8ad9d3665" | |
| }, | |
| "usage_label": "PRODUCTION PILOT \u2014 NOT RELEASE-READY", | |
| "weight_dtype": "float16" | |
| } | |