---
license: creativeml-openrail-m
library_name: onnx
tags:
- onnx
- stable-diffusion
- ip-adapter
- text-to-image
- mobile
inference: false
base_model:
- runwayml/stable-diffusion-v1-5
- h94/IP-Adapter
---

# Sona Forge — SD 1.5 IP-Adapter UNet (ONNX FP16)

A single fused ONNX FP16 graph combining the SD 1.5 UNet with IP-Adapter image-conditioning weights baked into the cross-attention layers. Used by the Sona Forge Android app for identity-preserving avatar generation. Pair with [`sona-forge/clip-vit-h-14-image-fp16`](https://huggingface.co/sona-forge/clip-vit-h-14-image-fp16).

**Revision 1.1.0 (2026-05-01)** adds 13 optional ControlNet residual inputs (12 down-block residuals + 1 mid-block residual) so the same UNet drives both Phase 6 (IP-Adapter only — pass zero-filled residuals or rely on the residual-aware export's pass-through-when-empty semantics) and Phase 7 (IP-Adapter + ControlNet Canny — pass the residuals from [`sona-forge/sd15-controlnet-canny-fp16`](https://huggingface.co/sona-forge/sd15-controlnet-canny-fp16)).

## ONNX shape

| Input | Shape | dtype | Notes |
|---|---|---|---|
| `sample` | `[batch, 4, 64, 64]` | FP16 | latent state at step t |
| `timestep` | `[batch]` | FP16 | scheduler timestep |
| `encoder_hidden_states` | `[batch, 77, 768]` | FP16 | text embeds (e.g. from CLIP text encoder) |
| `image_embeds` | `[batch, num_images, 1024]` | FP16 | **rank-3** per diffusers 0.27.2's `MultiIPAdapterImageProjection`. On-device path uses `num_images=1`. |
| `down_residual_0..11` | 12 tensors | FP16 | ControlNet down-block residuals (canonical SD 1.5 shapes). Pass zeros for Phase-6-only inference. |
| `mid_residual` | `[batch, 1280, 8, 8]` | FP16 | ControlNet mid-block residual. Pass zeros for Phase-6-only inference. |

| Output | Shape | dtype |
|---|---|---|
| `noise_pred` | `[batch, 4, 64, 64]` | FP16 |

Down-block residual canonical shapes (SD 1.5):
`[batch, 320, 64, 64]` ×3, `[batch, 320, 32, 32]`, `[batch, 640, 32, 32]` ×2, `[batch, 640, 16, 16]`, `[batch, 1280, 16, 16]` ×2, `[batch, 1280, 8, 8]` ×3.

## How it was made

Pinned conversion environment:

| Package | Version |
|---|---|
| diffusers | 0.27.2 |
| transformers | 4.40.0 |
| torch | 2.3.0 |
| onnx | 1.16.0 |
| onnxruntime | 1.18.0 |
| numpy | <2 (ABI compat) |

Conversion sequence:
1. Load `runwayml/stable-diffusion-v1-5` UNet at FP16.
2. Download `h94/IP-Adapter`'s `models/ip-adapter_sd15.bin` checkpoint (image-projection MLP + cross-attn K/V).
3. Apply weights via `unet._load_ip_adapter_weights([state_dict])` (the diffusers 0.27.2 internal — public `unet.load_ip_adapter()` doesn't exist on `UNet2DConditionModel` in this version).
4. Set `attn_processor.scale = [1.0, ...]` on each `IPAdapterAttnProcessor` / `IPAdapterAttnProcessor2_0`.
5. Wrap the UNet so `added_cond_kwargs={"image_embeds": [image_embeds]}` is positional and `down_block_additional_residuals` / `mid_block_additional_residual` flow through to the UNet forward call. Then `torch.onnx.export` at opset 17 with FP16 dummy inputs at canonical SD 1.5 shapes.

Re-running the conversion from the same pinned environment produces byte-identical output (same sha256). Conversion artefacts include a spike report with full TracerWarning output, validation metrics, and round-trip checks.

## Files

| File | Size | sha256 | Revision |
|---|---|---|---|
| `model.onnx` | 1,764,924,739 B (1683 MB) | `a0287f119d85b8028d9673850322247b5978ed9b504077bc04d433f4c9fadcb7` | 1.1.0 (current — residual-accepting, Phase 7) |
| ~~`model.onnx`~~ | ~~1,764,923,048 B (1683 MB)~~ | ~~`29e749b2c8dfdd6953a9165eca42e11489f8f90d43fac66c333cfdf6aae0014f`~~ | 1.0.0 (Phase 6 — superseded by 1.1.0; signature was the 4-input subset) |

No external-data sidecar — graph + weights fit under the 2 GB protobuf single-file limit.

The 1.1.0 export is a strict superset of the 1.0.0 input signature: zero-filled residual inputs reproduce the 1.0.0 numerical output (verified during the Phase 7 spike).

## Licence

The fused ONNX is a composite of two upstream artefacts:

- SD 1.5 base UNet — [CreativeML OpenRAIL-M](https://huggingface.co/spaces/CompVis/stable-diffusion-license) (use-based restrictions; permits redistribution + modification).
- IP-Adapter weights — [Apache-2.0](https://github.com/tencent-ailab/IP-Adapter/blob/main/LICENSE).

The composite is distributed under the most restrictive of these terms — **CreativeML OpenRAIL-M**.

## Memory footprint

ORT CPU EP promotes FP16 to FP32 at session load (Phase 6 spike measured ~3.5 GB resident just for this UNet). On Android, NNAPI / XNNPack execute FP16 natively and the on-device working set is closer to the FP16 disk size + activation buffers. Sona Forge gates this pack to Tier B+ devices (≥ 7 GB total RAM).

## Usage

```python
import onnxruntime as ort
import numpy as np

session = ort.InferenceSession("model.onnx", providers=["CPUExecutionProvider"])

# CFG batch=2; uncond at index 0, cond at index 1.
sample = np.random.randn(2, 4, 64, 64).astype(np.float16)
timestep = np.array([999.0, 999.0], dtype=np.float16)
encoder_hidden_states = np.random.randn(2, 77, 768).astype(np.float16)

# image_embeds: zeros for uncond branch, scaled CLIP embeds for cond branch.
clip_emb = np.random.randn(1, 1024).astype(np.float16)  # one reference image
ip_scale = 0.7
image_embeds = np.stack([
    np.zeros_like(clip_emb),
    clip_emb * ip_scale,
]).astype(np.float16)  # shape (2, 1, 1024)

noise_pred = session.run(None, {
    "sample": sample,
    "timestep": timestep,
    "encoder_hidden_states": encoder_hidden_states,
    "image_embeds": image_embeds,
})[0]
```

## Provenance

- Original SD 1.5 weights: [`runwayml/stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5). (The official repo was removed from HF in early 2024; community mirrors persist.)
- Original IP-Adapter checkpoint: [`h94/IP-Adapter/models/ip-adapter_sd15.bin`](https://huggingface.co/h94/IP-Adapter/blob/main/models/ip-adapter_sd15.bin).