Text-to-Image
Transformers
Safetensors
Hunyuan
text-generation
hunyuan
quantization
int8
comfyui
custom-nodes
autoregressive
DiT
HunyuanImage-3.0
bitsandbytes
custom_code
8-bit precision
Instructions to use EricRollei/HunyuanImage-3-INT8-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EricRollei/HunyuanImage-3-INT8-v2 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("EricRollei/HunyuanImage-3-INT8-v2", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download quantization_metadata.json from EricRollei/HunyuanImage-3-INT8-v2: direct link, hf CLI and curl.
- Browser
- Download file 422 Bytes
-
https://huggingface.co/EricRollei/HunyuanImage-3-INT8-v2/resolve/main/quantization_metadata.json
- Command line
-
hf download hf://EricRollei/HunyuanImage-3-INT8-v2/quantization_metadata.json
-
curl -L -o quantization_metadata.json https://huggingface.co/EricRollei/HunyuanImage-3-INT8-v2/resolve/main/quantization_metadata.json
422 Bytes
| { | |
| "quantization_method": "bitsandbytes_int8", | |
| "load_in_8bit": true, | |
| "llm_int8_threshold": 6.0, | |
| "expected_vram_gb": 95, | |
| "expected_total_memory_gb": 100, | |
| "notes": "Load with BitsAndBytesConfig for INT8 quantization. VAE and attention layers kept in full precision.", | |
| "attention_layers_quantized": false, | |
| "quality_vs_nf4": "Significantly better - approximately 2x memory for ~98% quality retention" | |
| } |