DreamLite-Mobile ONNX (WebGPU browser bundle)

Browser-ready ONNX fp16 weights for running DreamLite-Mobile diffusion in the web via ONNX Runtime Web and WebGPU. This repo is the Option A bundle: UNet + VAE only (~750 MB uncompressed).

What is in this repository

File	Approx. size	Role
`unet_fp16.onnx`	~746 MB	4-step distilled diffusion UNet
`vae_decoder_fp16.onnx`	~2.4 MB	VAE decode (latents to image)
`vae_encoder_fp16.onnx`	~2.4 MB	VAE encode (image to latents)

4-step DreamLite-Mobile distilled sampling (match your app scheduler settings to the mobile export).
Text encoder is not included. You still need:
- encode_server (or equivalent) to produce text embeddings at runtime, and
- The full DreamLite-Mobile setup / weights — see carlofkl/DreamLite-mobile.

Base model

Derived from ByteVisionLab/DreamLite (DreamLite family). This upload packages the mobile ONNX slice for in-browser inference, not the full training checkpoint.

Using these files in a web UI

Local / static hosting: copy the three .onnx files into your app public/models/ (or another static URL your bundler serves).
Configurable base URL: set VITE_MODEL_BASE to the directory URL where the three files live (trailing slash behavior depends on your app; point at the folder that contains the .onnx names above).

Example (conceptual):

VITE_MODEL_BASE=https://huggingface.co/dror201031/DreamLite-mobile-ONNX-WebGPU/resolve/main/

Your frontend should request unet_fp16.onnx, vae_decoder_fp16.onnx, and vae_encoder_fp16.onnx under that base.

What is not included

Text encoder ONNX
Validation images or test fixtures
node_modules or application source

License

CC BY-NC 4.0 — consistent with the DreamLite model family. See the license for commercial use restrictions.

Citation / links

Base: ByteVisionLab/DreamLite
Mobile weights reference: carlofkl/DreamLite-mobile

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