---
license: mit
base_model: stabilityai/stable-diffusion-3.5-medium
library_name: diffusers
tags:
  - sd3.5
  - adapter
  - higgsfield
inference: false
---

# phenomenalai/sd3-token-mod-1024

Adapter for `stabilityai/stable-diffusion-3.5-medium` trained with Higgsfield.

## Usage

```python
import torch
from diffusers import StableDiffusion3Pipeline
from higgsfield.adapters.token_mod import GlobalTokenModulator
from huggingface_hub import hf_hub_download

base_model = "stabilityai/stable-diffusion-3.5-medium"
repo_id = "phenomenalai/sd3-token-mod-1024"

pipe = StableDiffusion3Pipeline.from_pretrained(base_model, torch_dtype=torch.bfloat16).to("cuda")
# download adapter
adapter_path = hf_hub_download(repo_id, filename="token_mod.pt")
# load adapter
mod = GlobalTokenModulator(num_tokens=333, embed_dim=4096, out_channels=pipe.transformer.config.in_channels)
mod.load_state_dict(torch.load(adapter_path, map_location="cpu"))
mod.to("cuda").eval()
# Now use your own inference loop adding bias from `mod` like in your codebase.
```

## Files
- `token_mod.pt` adapter weights
- `run.json` metadata
- `manifest.md` human summary