This model takes xlm-roberta-large and fine-tunes it on a combination of NLI data in 15 languages. It is intended to be used for zero-shot text classification, such as with the Hugging Face ZeroShotClassificationPipeline. This model is intended to be used for zero-shot text classification, especially in languages other than English. It is fine-tuned on XNLI, which is a multilingual NLI dataset. The model can therefore be used with any of the languages in the XNLI corpus: - English - French - Spanish - German - Greek - Bulgarian - Russian - Turkish - Arabic - Vietnamese - Thai - Chinese - Hindi - Swahili - Urdu
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LTX-2.5 generates picture and sound in one pass. It is also 42 GB of bf16 weights, and the text encoder beside it is another 26 GB. This repo is the set of weights that make it run on a card that does not have that. Sizes are measured, not estimated. "Fits 16 GB" assumes the text encoder is not resident at the same time — ComfyUI frees it before sampling — and tiled VAE decode. File sizes are decimal GB (bytes / 10^9); card capacities are the usual binary GiB, so a 15.9 GB file occupies 14.8 GiB of a 16 GiB card. The gap is real and it is where your activation pool lives. On the text encoder: at bf16 it is 26.3 GB and at Lightricks' int8 15.4 GB, which on a 16 GB card means the encoder alone owns the machine for the one step that just makes an embedding. **No GGUF of the LTX-2.5 encoder exists** — ComfyUI-GGUF gates text encoders on `TXT_ARCH_LIST`, which carries `gemma3` but not `gemma4`, so LTX-2.3's gemma3 encoder does have GGUFs and this one cannot. The open request (#450, 2026-05-28) has not been picked up — but comfy-native quantisations of it do exist and landed the same day as ours (Winnougan's w4a8-convrot at 10.6 GB, DmitryDB's nvfp4 at 11.2, vonkaiser's nvfp4-torchao at 8.9). Ours is w4a8 at 10.60 GB, within 2 MB of Winnougan's — the same format at the same size, published independently. Treat it as a mirror, not a first.
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The `fl2va` half of the comfy-native set. If you want `ref2va`, it is over here — same formats, same method, different job. **Stock ComfyUI 0.32+ loads these directly.** No ComfyUI-GGUF, no custom loader node, no dequantise-on-the-fly. ComfyUI carries its own quantisation system (`comfy/quant_ops.py` plus the `comfy_kitchen` kernels) and these files use it, so the kernels run in the quantised domain instead of unpacking to bf16 first. `fl2va` is the first/last-frame variant. It has **no reference rows**, so voice anchoring, the identity bank and reference images do nothing on it. In exchange it does the one thing `ref2va` cannot:
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GGUF quantizations of MiniMax-H3's 33B video+audio DiTs, built from MiniMax's **pruned** checkpoints. Same model, same quant tiers as the original-form repo, **about 40% smaller** - and a Q8_0 that fits a 24 GB card. `fl2va` = text/first-last-frame to video+audio (T2V and I2V). `ref2va` = reference-conditioned generation (identity from up to 9 images, 3 videos with soundtracks, 3 voice clips). Runs in ComfyUI with ComfyUI-GGUF plus a one-line architecture patch (node pack: **ComfyUI-H3-Multishot**; workflows: **MiniMax-H3-Multishot-Workflow**).
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GGUF quantizations of the **Qwen3-VL-32B vision-language text encoder** used by MiniMax-H3 in ComfyUI. The H3 DiT quants are here: **joeygambino/MiniMax-H3-GGUF**. You need **one file from each repo** to run H3 — the DiT alone will not generate anything. Load these encoders with **H3 Clip Loader (Any)** from ComfyUI-H3-Multishot, **not** the stock `CLIPLoaderGGUF` node.
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GGUF quantizations of MiniMax-H3's 33B video+audio DiTs, quantized from the Comfy-Org bf16 repackages. Runs in ComfyUI with ComfyUI-GGUF plus a one-line architecture patch (node pack: **ComfyUI-H3-Multishot**; workflows: **MiniMax-H3-Multishot-Workflow**). > **Want smaller files?** There is a second set built from MiniMax's pruned > checkpoints, ~40% smaller at the same quant tier, in > **MiniMax-H3-curve-GGUF**. > They need **ComfyUI 0.30.0+**, and they are a different trade, not a straight > replacement - so this repo stays exactly as it is. Stay here if you are on an > older ComfyUI, or if you would rather run the larger quants. `fl2va` = text/first-last-frame to video+audio (T2V and I2V). `ref2va` = reference-conditioned generation (identity from up to 9 images, 3 videos with soundtracks, 3 voice clips).
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The comfy-native cuts of the MiniMax-H3 × Z-Image graft: Z-Image's spatial-attention profile on H3's engine — richer sets and textures, same identity, no per-shot sharpening creep. Full story, demos and verification on the GGUF page. Load with the plain **Load Diffusion Model** node, ComfyUI 0.32+. Files are the `pruned` H3 builds with the graft baked in (`zs05` = late-block gains, dose 0.5): - `bf16` — the master (ref2va) - `comfy-fp8` / `fp8e5m2` — fp8 scaled - `comfy-int8` / `int8_convrot` — the fast pick on RTX 50 - `comfy-w4a8` / `w4a4` / `nvfp4` — 4-bit family for 16 GB cards (`w4a8` is the quality pick; `nvfp4` is Blackwell-native, emulated elsewhere) - `comfy-mxfp8` — 8-bit microscaling, Blackwell-specialized; little benefit on older architectures - fl2va and ref2va variants where both exist; picking rules are identical to stock H3
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