FlashVSR v1.1 (tiny) for Nightmare Mobile
FlashVSR v1.1 (Junhao Zhuang et al., Apache 2.0) converted to
one GGUF in the layout the Nightmare Mobile DiT engine reads (a stable-diffusion.cpp fork, dit/058):
| prefix | what | type |
|---|---|---|
dit.* |
the one-step streaming Wan 2.1 1.3B DiT (diffusion_pytorch_model_streaming_dmd) |
linear weights Q8_0 (or Q4_0), the rest f32 |
lq.* |
the causal LQ projector; each 3-D conv split into one 2-D conv a temporal tap (lq.convN.kT) |
f16 |
dec.decoder.* |
TCDecoder; its first conv padded from 784 to 800 input channels (zeros) | f16 |
context |
the fixed prompt embedding (posi_prompt.pth, umT5) |
f32 |
| file | size | note |
|---|---|---|
flashvsr_v11_q8_0.gguf |
2.18 GB | the one that was tested on the phone: 45.6 dB from the PyTorch reference (PC CPU) |
flashvsr_v11_q4_0.gguf |
1.47 GB | DiT linears Q4_0: 29.4 dB from the reference, visibly softer. Kept as a record, not recommended |
Built for an "Upscale 2x" option on the app's Wan video nodes (a finished clip at twice its width and height, on the phone's NPU in 256 px input tiles with dense attention). That option was not shipped: on a Galaxy S25 Ultra a 2 s 832x480 clip took about 5 minutes to upscale, five times as long as making it. The files stay here for anyone who wants to pick it up. It is a conversion only: the weights and their license are the authors'.
- Downloads last month
- 30
Hardware compatibility
Log In to add your hardware
4-bit
8-bit
Inference Providers NEW
This model isn't deployed by any Inference Provider. ๐ Ask for provider support
Model tree for AbrahamPJ/nightmare-flashvsr
Base model
JunhaoZhuang/FlashVSR-v1.1