Skip to content

docs: enhance text encoder CPU usage instructions for HunyuanImage-2.… #2213

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 1 commit into from
Sep 28, 2025
+1 −1
Merged

docs: enhance text encoder CPU usage instructions for HunyuanImage-2.… #2213

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
2 changes: 1 addition & 1 deletion docs/hunyuan_image_train_network.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -190,7 +190,7 @@ The script adds HunyuanImage-2.1 specific arguments. For common arguments (like
* `--fp8_vl`
- Use FP8 for the VLM (Qwen2.5-VL) text encoder.
* `--text_encoder_cpu`
- Runs the text encoders on CPU to reduce VRAM usage. This is useful when VRAM is insufficient (less than 12GB). Encoding one text may take a few minutes (depending on CPU). It is highly recommended to use this option with `--cache_text_encoder_outputs_to_disk` to avoid repeated encoding every time training starts.
- Runs the text encoders on CPU to reduce VRAM usage. This is useful when VRAM is insufficient (less than 12GB). Encoding one text may take a few minutes (depending on CPU). It is highly recommended to use this option with `--cache_text_encoder_outputs_to_disk` to avoid repeated encoding every time training starts. **In addition, increasing `--num_cpu_threads_per_process` in the `accelerate launch` command, like `--num_cpu_threads_per_process=8` or `16`, can speed up encoding in some environments.**
* `--blocks_to_swap=<integer>` **[Experimental Feature]**
- Setting to reduce VRAM usage by swapping parts of the model (Transformer blocks) between CPU and GPU. Specify the number of blocks to swap as an integer (e.g., `18`). Larger values reduce VRAM usage but decrease training speed. Adjust according to your GPU's VRAM capacity. Can be used with `gradient_checkpointing`.
* `--cache_text_encoder_outputs`
Expand Down