10Eros-Max

About

What 10Eros-Max is

10Eros-Max (GGUF Quants) This repository contains highly optimized GGUF quantizations of TenStrip/10Eros-Max (Test4 Pruned). These files allow users to run this massive 40GB+ experimental multi-model merge on consumer hardware with as little as 12GB of VRAM, significantly reducing memory overhead while preserving the model's unique visual fidelity, temporal coherence, and prompt adherence. 📦 Available Files & VRAM Guide 🚀 How to Use in ComfyUI To run these GGUF files in ComfyUI, you must use a GGUF-compatible loader. 1. Install ComfyUI-GGUF: Install the City96/ComfyUI-GGUF custom node pack via the ComfyUI Manager. 2. Download the File: Download your preferred .gguf file from the list above and place it in your ComfyUI/models/unet/ folder. 3. Load the Model: In your workflow, replace the standard Load Checkpoint or Load Diffusion Model node with the Unet Loader (GGUF) node. Select the 10Eros-Max GGUF file. 4. Text Encoders: You will still need to load the separate text encoders natively (the H3 truncated version of Qwen3-VL-32B) and the MiniMax VAE to decode the final video/audio. 📖 About the Original 10Eros-Max Model (The following is from the original creator, TenStrip) So far, this release is an experiment on MiniMax H3 base model that combines learned patterns from two other video diffusion models — LTX 2.3 and Wan 2.2, and in test3 and later, one image diffusion model — Krea 2 — into H3's transformer architecture. A unified transformer with modality-specific input projectors (video, audio, condition) feeding into a 52-block transformer stack (2-block tokenrefiner for text conditioning plus 50 main blocks), with modality-specific output heads. The transformer stack uses identical block architecture throughout, which is what makes the attention and MLP layers architecturally-appropriate targets for character transfer between models — modality routing lives at the pipeline edges, not within the transformer. The LTX pass was applied first to H3's front blocks, followed by a Wan pass targeting H3's middle-to-back blocks. Each pass modified attention weights directly; the second pass also modified the feed-forward layers in a broader block range. Together, these t

Summarised from the published model card. Read the full card on the HuggingFace links below.

Specifications

What it is

How it works

How video models work

Variants

Sizes & precisions

Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys 10eros-max for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (10eros-max below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/videos/generations \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"10eros-max","prompt":"a drone shot over a forest"}'

Details

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Licence

Commercial licence needed

Sources

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