---
license: creativeml-openrail-m
tags:
- stable-diffusion
- stable-diffusion-1-5
- realistic-vision
- euler
- axera
- ax-m1
- ax8850
- axmodel
---

# AX-M1 (AX8850) — Realistic Vision (SD 1.5) **Euler 512** (AXMODEL weights)

This repository hosts the compiled **`.axmodel`** weights for running **Realistic Vision (Stable Diffusion 1.5–based)** with **Euler / EulerDiscreteScheduler** at **512×512** on **Radxa AI Core AX-M1 (AX8850)**.

**Runtime / scripts (GitHub):** https://github.com/Mojo24x7/SD1.5_AXM1-AX8850_Euler

> These files are **compiled AXERA artifacts** (`.axmodel`) intended for AX-M1 / AX8850 inference via AXCLRT/axengine. They are not raw PyTorch weights.

---

## What’s inside

Main weights:
- `sd15_text_encoder_sim.axmodel` — CLIP text encoder (prompt → text embeddings)
- `unet.axmodel` — UNet denoiser (latent diffusion core)
- `vae_decoder.axmodel` — VAE decoder (latent → RGB image)

Optional (needed for img2img / masked workflows):
- `vae_encoder.axmodel` — VAE encoder (RGB → latent)

---

## Download

### Option A — Git LFS (recommended)
```bash
git lfs install
git clone https://huggingface.co/Mojo24x7/sd15-axm1-euler512-axmodels
```

### Option B — Hugging Face CLI
```bash
pip install -U "huggingface_hub[cli]"
huggingface-cli download Mojo24x7/sd15-axm1-euler512-axmodels \
  --local-dir sd15-axm1-euler512-axmodels
```

---

## Where to place the files

In the runtime repo, place these into:

```text
./axmodels/
  sd15_text_encoder_sim.axmodel
  unet.axmodel
  vae_decoder.axmodel
  vae_encoder.axmodel   (optional)
```

The runtime/scripts repo also expects supporting assets (tokenizer, scheduler config, VAE config). See the GitHub repo for the full folder layout.

---

## Expected model I/O (Euler 512)

### Text encoder
- input: `input_ids` `[1,77]` `int32`
- output: `last_hidden_state` `[1,77,768]` `fp32`

### UNet
- inputs:
  - `sample` `[1,4,64,64]` `fp32`  *(512/8 = 64 latent resolution)*
  - `timestep` `[1]` `int32`
  - `encoder_hidden_states` `[1,77,768]` `fp32`
- output: `[1,4,64,64]` `fp32`

### VAE decoder
- input: `latent` `[1,4,64,64]` `fp32`
- output: `[1,3,512,512]` `fp32` *(commonly in `[-1..1]` before postprocess)*

### VAE encoder (optional)
- input: image `[1,3,512,512]` `fp32`
- output: latent `[1,4,64,64]` `fp32`

---

## Runtime notes (important)

- The runtime scripts use **EulerDiscreteScheduler**.
- Ensure `input_ids` and `timestep` are **int32** (int64 will fail in many AX pipelines).
- Typical flow:
  1) tokenize → text encoder
  2) scheduler loop → UNet
  3) VAE decode → image postprocess

---

## Base model: Realistic Vision

These compiled weights are derived from **Realistic Vision**, which is **Stable Diffusion 1.5–based**.



---

## Troubleshooting

- If cloning is slow or files look tiny: you likely don’t have LFS installed.
  - Run `git lfs install` and re-clone.
- If the runtime says a model input type is wrong:
  - Verify `timestep` is `int32`
  - Verify `input_ids` is `int32`
- If outputs look washed out:
  - Check VAE postprocess and scaling (model outputs typically need `(x * 0.5 + 0.5)` then clamp to `[0..1]`).

---

## Credits

- **AX-M1 / AX8850** compilation and runtime packaging: Mojo24x7
- Base architecture: Stable Diffusion 1.5 family (Realistic Vision derivative)