SenseNova-U1 is a unified Qwen3-based LLM with Mixture-of-Tokenizers (MoT) attention. Unlike two-stage pipelines (e.g., BAGEL), it handles text encoding, optional reasoning ("think mode"), and flow-matching-based image denoising entirely within a single diffusion stage.
vision encoder (`vision_model`) and the Qwen3 LLM (`language_model`) are
swapped between CPU and GPU on demand:
- During text/vision encoding, the LLM is on CPU.
- During the diffusion loop, the vision encoder is on CPU.
- Lightweight FM modules stay resident on GPU.
This lowers peak VRAM at the cost of extra CPU<->GPU transfers (use pinned
memory) — useful for running SenseNova-U1 on consumer-grade GPUs.
```bash
# Text-to-image with CPU offload
python end2end.py \
--prompt "A cute cat sitting on a windowsill"\
--width 2048 --height 2048\
--enable-cpu-offload --think
# Image-to-image editing with CPU offload
python end2end.py \
--prompt "Turn this into an oil painting"\
--image input.png \
--width 2048 --height 2048\
--enable-cpu-offload --think
# Image understanding (img2text) with CPU offload
python end2end.py \
--modality img2text \
--prompt "Describe this image in detail"\
--image photo.jpg \
--enable-cpu-offload
```
> **Notes**
> - CPU offload is **single-GPU only** (incompatible with `--tensor-parallel-size > 1`).
> - First-step latency is higher because of the cold-start CPU<->GPU transfers.
> - For more details and other offloading strategies, see
> [CPU Offloading for Diffusion Models](../../../docs/user_guide/diffusion/cpu_offload_diffusion.md).
## Reproducing the E2E Test
The following command reproduces the pixel-validated CI test case:
```bash
python end2end.py \
--prompt "Close portrait of an elderly woman by a farmhouse window, textured skin, gentle smile, warm natural light, emotional documentary look. The portrait should feel polished and natural, with sharp eyes, realistic skin texture, accurate facial anatomy, and premium lighting that keeps the face as the main focus."\
