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---
license: other
license_name: nvidia-open-model-license
license_link: >-
https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license
language:
- en
pipeline_tag: text-to-image
tags:
- comfyui
- diffusion-single-file
base_model:
- nvidia/Cosmos-Predict2-14B-Text2Image
base_model_relation: quantized
---
For more information (including how to compress models yourself), check out https://huggingface.co/DFloat11 and https://github.com/LeanModels/DFloat11
Feel free to request for other models for compression as well, although models whose architecture I am unfamiliar with might be slightly tricky for me.
### How to Use
#### ComfyUI
Install my own fork of the DF11 ComfyUI custom node: https://github.com/mingyi456/ComfyUI-DFloat11-Extended. After installing the DF11 custom node, use the provided workflow [json](cosmos_predict2_14B_t2i-DF11-workflow.json), or simply replace the "Load Diffusion Model" node of an existing Kontext workflow with the "DFloat11 Model Loader" node. If you run into any issues, feel free to leave a comment. The workflow is also embedded in the below [png](cosmos_predict2_14B_t2i-DF11-workflow.png) image.
#### `diffusers`
Refer to this [model](https://huggingface.co/mingyi456/Cosmos-Predict2-14B-Text2Image-DF11) instead.
### Compression Details
This is the `pattern_dict` for compression:
```python
pattern_dict_comfyui = {
"t_embedder\.1": (
"linear_1",
"linear_2",
),
r"blocks\.\d+": (
"self_attn.q_proj",
"self_attn.k_proj",
"self_attn.v_proj",
"self_attn.output_proj",
"cross_attn.q_proj",
"cross_attn.k_proj",
"cross_attn.v_proj",
"cross_attn.output_proj",
"mlp.layer1",
"mlp.layer2",
"adaln_modulation_self_attn.1",
"adaln_modulation_self_attn.2",
"adaln_modulation_cross_attn.1",
"adaln_modulation_cross_attn.2",
"adaln_modulation_mlp.1",
"adaln_modulation_mlp.2",
)
}
``` |