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fixed save quantization

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DeepBeepMeep
2025-06-13 18:41:35 +02:00
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@@ -66,14 +66,20 @@ If a model is not quantized, it is assumed to be mostly 16 bits (with maybe a fe
If a model is quantized the term *quanto* should also be included since WanGP supports for the moment only *quanto* quantized model, most specically you should replace *fp16* by *quanto_fp16_int8* or *bf6* by *quanto_bf16_int8*.
Please note it is important than *bf16", "fp16* and *quanto* are all in lower cases letters.
## Creating a Quanto Quantized file
If you launch the app with the *--save-quantized* switch, WanGP will create a quantized file in the **ckpts** subfolder just after the model has been loaded. Please note that the model will *bf16* or *fp16* quantized depending on what you chose in the configuration menu.
1) Make sure that in the finetune definition json file there is only a URL or filepath that points to the non quantized model
2) Launch WanGP *python wgp.py --save-quantized*
3) In the configuration menu *Transformer Data Type* property choose either *BF16* of *FP16*
4) Launch a generation (settings used do not matter). As soon as the model is loaded, a new quantized model will be created in the **ckpts** subfolder it doesn't already exist.
4) Launch a video generation (settings used do not matter). As soon as the model is loaded, a new quantized model will be created in the **ckpts** subfolder if it doesn't already exist.
5) To test that this works properly set the local path in the "URLs" key of the finetune definition file. For instance *URLs = ["ckpts/finetune_quanto_fp16_int8.safetensors"]*
6) Restart WanGP and select *Scaled Int8 Quantization* in the *Transformer Model Quantization* property
7) Launch a new generation an verify in the terminal window that the right quantized model is loaded
8) In order to share the finetune definition file will need to store the fine model weights in the cloud. You can upload them for instance on *Huggingface*. You can now replace in the definition file the local path by a URL (on Huggingface to get the URL of the model file click *Copy download link* when accessing the model properties)
6) Remove *--save-quantized*, restart WanGP and select *Scaled Int8 Quantization* in the *Transformer Model Quantization* property
7) Launch a new generation and verify in the terminal window that the right quantized model is loaded
8) In order to share the finetune definition file you will need to store the fine model weights in the cloud. You can upload them for instance on *Huggingface*. You can now replace in the definition file the local path by a URL (on Huggingface to get the URL of the model file click *Copy download link* when accessing the model properties)
You need to create a quantized model specifically for *bf16* or *fp16* as they can not converted on the fly. However there is no need for a non quantized model as they can be converted on the fly while being loaded.
Wan models supports both *fp16* and *bf16* data types albeit *fp16* delivers in theory better quality. On the contrary Hunyuan and LTXV supports only *bf16*.