---
license: creativeml-openrail-m
tags: [stable-diffusion, sd1.5, lcm, mnn, tokforge, image-generation, cinematic, photoreal, epicrealism]
---

## TokForge

- **Website:** https://tokforge.ai
- **Discord:** https://discord.gg/Acv3CBtfVm
- **Google Play:** https://play.google.com/store/apps/details?id=dev.tokforge
- **iOS TestFlight:** https://testflight.apple.com/join/jnufjzRr

Runs on-device in the TokForge app.

# TokForge SD1.5-LCM — Cinematic (epiCRealism)

An **epiCRealism + LCM** SD1.5 MNN bundle — the TokForge `SD15_LCM_MNN` fast few-step image model,
but with the candid/film-look photoreal **epiCRealism** checkpoint as the base instead of
DreamShaper-7. Renders a coherent, cinematic-photoreal 512px image in 4 steps on the in-process MNN
diffusion engine — full MNN speed, the film look baked into the UNet weights.

This is the **Cinematic** tier of the TokForge styled image catalog — the twin photoreal style to
the Photoreal (Realistic Vision V5.1) tier. epiCRealism leans toward a candid, cinematic film look:
naturalistic lighting and colour, true-to-life skin and texture, lifelike people and scenes. It won
the candid/film-look people category in TokForge's internal photoreal-base A/B. Verified on-device
(OnePlus D9500 CPU, 4 steps) rendering a clean candid/film-look portrait at MNN speed.

9-file MNN bundle: `unet.mnn(.weight)` (INT8, epiCRealism + LCM fused), reference SD1.5
f16 `text_encoder.mnn(.weight)` + `vae_decoder.mnn(.weight)`, `vocab.json`, `merges.txt`,
`alphas.txt`.

## Provenance & licenses
- **Base model:** [emilianJR/epiCRealism](https://huggingface.co/emilianJR/epiCRealism)
  — **CreativeML-OpenRAIL-M** (commercial-OK; redistribution of derivatives is licensed under
  OpenRAIL-M §III.4). epiCRealism is a photoreal/film-look SD1.5 checkpoint.
- **LCM adapter:** [latent-consistency/lcm-lora-sdv1-5](https://huggingface.co/latent-consistency/lcm-lora-sdv1-5)
  — openrail++ (UNet-only consistency adapter, fused to keep the 4-step floor).
- **CLIP / VAE / tokenizer:** the standard SD1.5 reference assets (CLIP ViT-L/14 text encoder +
  SD1.5 VAE) reused verbatim from the TokForge base LCM bundle. This matches the other TokForge
  SD1.5-LCM bundles' 9-file layout and dodges the libDIF CLIP-trace issue with freshly-exported CLIP.

## Modifications (OpenRAIL-M §III "mark modified")
This bundle is a **modified derivative** of epiCRealism:
1. The LCM consistency adapter (`lcm-lora-sdv1-5`) is fused into the UNet (fp32 ΔW via
   `diffusers.fuse_lora()`) so the model is coherent at 4-8 steps with CFG≈1.0.
2. The fused UNet is exported to ONNX and converted to an **INT8-quantized MNN** model
   (`MNNConvert`, asymmetric 8-bit weight quant) for on-device CPU inference.
3. CLIP and VAE are the SD1.5 reference MNN assets, not epiCRealism's own.

No retraining or fine-tuning of the original weights was performed beyond the LCM fuse + quant.

## Use restrictions (OpenRAIL-M Attachment A)
Use of this model is subject to the CreativeML Open RAIL-M license, including the
**Attachment A use-based restrictions**: you agree not to use the model, or any derivative, to
violate any law; to exploit/harm minors; to generate or disseminate verifiably false information
to harm others; to generate or disseminate personal identifiable information to harm someone; to
defame, disparage or harass others; for fully automated decision-making that adversely affects
legal rights or creates binding obligations; for discrimination or harm to individuals or groups
based on protected characteristics; to exploit vulnerabilities of a specific group; to generate
non-consensual or false content about individuals; or to provide medical advice/interpretation of
medical results as a substitute for professional advice. See the full license text:
https://huggingface.co/spaces/CompVis/stable-diffusion-license

These use-based restrictions **propagate** to all who use or redistribute this bundle.

## Attribution
- epiCRealism — © emilianJR, CreativeML-OpenRAIL-M.
- Latent Consistency Model LoRA (SD1.5) — Latent Consistency team, openrail++.
- Packaged for on-device MNN inference by TokForge (dev.tokforge).