metadata
license: mit
tags:
- text-to-image
- latent-consistency-model
- coreml
- onnx
base_model: SimianLuo/LCM_Dreamshaper_v7
CV Studio illustrator (on-device)
Ready-to-run conversions used by the CV Studio desktop app to draw one illustration per sentence of a video, on the customer's own machine (no PyTorch at run time):
| Folder | Runtime | Contents |
|---|---|---|
macos/ |
Core ML (macOS 14+, Apple Silicon) | text_encoder.mlpackage (fp16), unet_6bit.mlpackage (6-bit palettised), taesd_decoder.mlpackage, tokenizer.json |
windows/ |
ONNX Runtime (CPU / DirectML) | text_encoder_int8.onnx, unet_int8.onnx (dynamic int8), taesd_decoder.onnx, tokenizer.json |
Inputs: UNet (sample 1×4×64×64, timestep 1, encoder_hidden_states 1×77×768) → noise_pred, with the LCM guidance
embedding (w = 8) built in; 4 LCM steps; the TAESD decoder returns RGB in [0, 1] at 512×512. manifest.json lists
every file with its size and SHA-256.
Origin and licences
- LCM_Dreamshaper_v7 by Simian Luo et al. (https://huggingface.co/SimianLuo/LCM_Dreamshaper_v7), published under the MIT licence; it is a latent consistency model distilled from DreamShaper v7 (Lykon), a Stable Diffusion 1.5 fine-tune released under the CreativeML OpenRAIL-M licence. To respect the upstream terms, the use restrictions of OpenRAIL-M (Attachment A: no illegal, harmful, discriminatory, defamatory or deceptive use, no medical advice, no exploitation of minors, etc.) are passed on to users of these files.
- TAESD (Tiny AutoEncoder for Stable Diffusion) by Ollin Boer Bohan (https://github.com/madebyollin/taesd), MIT.
- The conversions (Core ML palettisation, ONNX export and int8 quantisation) were made by the CV Studio team; no weights were retrained.