README.md

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

# TokForge SD1.5-LCM — Photoreal (Realistic Vision V5.1)

A **Realistic Vision V5.1 + LCM** SD1.5 MNN bundle — the TokForge `SD15_LCM_MNN` fast few-step
image model, but with the photoreal **Realistic Vision V5.1** checkpoint as the base instead of
DreamShaper-7. Renders a coherent, photorealistic 512px image in 4 steps on the in-process MNN
diffusion engine — full MNN speed, the photoreal look baked into the UNet weights.

This is the **Photoreal** tier of the TokForge styled image catalog: natural skin, lifelike
texture and lighting, true-to-life portraits and scenes. Verified on-device (OnePlus D9500 CPU,
4 steps, ~36 s) rendering a clean photorealistic candid portrait where the DreamShaper base
renders a more stylized/cinematic look.

9-file MNN bundle: `unet.mnn(.weight)` (INT8, Realistic Vision V5.1 + LCM fused), reference SD1.5
f16 `text_encoder.mnn(.weight)` + `vae_decoder.mnn(.weight)`, `vocab.json`, `merges.txt`,
`alphas.txt`.

## Provenance & licenses
- **Base model:** [SG161222/Realistic_Vision_V5.1_noVAE](https://huggingface.co/SG161222/Realistic_Vision_V5.1_noVAE)
  — **CreativeML-OpenRAIL-M** (commercial-OK; redistribution of derivatives is licensed under
  OpenRAIL-M §III.4). RealisticVision V5.1 by **SG_161222**.
- **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. Realistic Vision V5.1 ships
  "noVAE", so the standard SD1.5 VAE is used (the same VAE referenced by the DreamShaper bundle).

> **Note:** The Lykon "Add More Details" detail-enhancer LoRA (Civitai 82098) was evaluated for
> additional skin/texture detail but is distributed in kohya format with text-encoder
> (`lora_te_*`) keys that the diffusers fuse converter cannot apply (raises `IndexError`); it is
> **not** included. Realistic Vision V5.1 already biases strongly photoreal on its own.

## Modifications (OpenRAIL-M §III "mark modified")
This bundle is a **modified derivative** of Realistic Vision V5.1:
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 Realistic Vision'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
- Realistic Vision V5.1 — © SG_161222, CreativeML-OpenRAIL-M.
- Latent Consistency Model LoRA (SD1.5) — Latent Consistency team, openrail++.
- Packaged for on-device MNN inference by TokForge (dev.tokforge).