THIRD_PARTY_NOTICES.md

# Attribution and dependencies

**Built with Qwen.** Model weights and configurations derive from
Qwen/Qwen-Image-2.1, revision 790c92633540aa0cb11d9abf19eb46d861714758.
The original Qwen Research License is reproduced without changes in LICENSE,
with the required notice in NOTICE. The license applies to the model and its
quantized derivatives. This package does not grant additional model rights.

EliovpAI modified the weight storage to the balanced MXFP4 profile and provides
the Python loader, single-frame cache handling, CLI, API and validation tools.
The adapter code is distributed under LICENSES/Apache-2.0.txt. Dependency and
model licenses remain separate.

Diffusers, Transformers, Accelerate and Safetensors retain their Apache-2.0
licenses and upstream notices. PyTorch and Torchvision retain their BSD-style
licenses, NumPy its BSD license, and Pillow its HPND license. Preserve the
notices included with installed distributions. No dependency source or native
extension binary is bundled here.

AMD Quark supplied the original offline weight conversion using OCP E2M1 and
E8M0 formats. It is not an inference dependency. The packed tensors were reused
without requantization for this portable consumer. The decoder implements that
format with eager framework operations and BF16 reconstruction.

The optional quality tools use OpenAI CLIP ViT-B/32, CLIPScore's scoring formula
and Richard Zhang and collaborators' LPIPS AlexNet v0.1 model. Their model
identities and hashes are in evaluation/metric-manifest.json. Metric weights
are downloaded separately and retain their respective upstream notices.