* Add min_orig_resolution and max_orig_resolution
* Rename min_orig_resolution to skip_image_resolution; remove max_orig_resolution
* Change skip_image_resolution to tuple
* Move filtering to __init__
* Minor fix
* fix: update extend-exclude list in _typos.toml to include configs
* fix: exclude anima tests from pytest
* feat: add entry for 'temperal' in extend-words section of _typos.toml for Qwen-Image VAE
* fix: update default value for --discrete_flow_shift in anima training guide
* feat: add Qwen-Image VAE
* feat: simplify encode_tokens
* feat: use unified attention module, add wrapper for state dict compatibility
* feat: loading with dynamic fp8 optimization and LoRA support
* feat: add anima minimal inference script (WIP)
* format: format
* feat: simplify target module selection by regular expression patterns
* feat: kept caption dropout rate in cache and handle in training script
* feat: update train_llm_adapter and verbose default values to string type
* fix: use strategy instead of using tokenizers directly
* feat: add dtype property and all-zero mask handling in cross-attention in LLMAdapterTransformerBlock
* feat: support 5d tensor in get_noisy_model_input_and_timesteps
* feat: update loss calculation to support 5d tensor
* fix: update argument names in anima_train_utils to align with other archtectures
* feat: simplify Anima training script and update empty caption handling
* feat: support LoRA format without `net.` prefix
* fix: update to work fp8_scaled option
* feat: add regex-based learning rates and dimensions handling in create_network
* fix: improve regex matching for module selection and learning rates in LoRANetwork
* fix: update logging message for regex match in LoRANetwork
* fix: keep latents 4D except DiT call
* feat: enhance block swap functionality for inference and training in Anima model
* feat: refactor Anima training script
* feat: optimize VAE processing by adjusting tensor dimensions and data types
* fix: wait all block trasfer before siwtching offloader mode
* feat: update Anima training guide with new argument specifications and regex-based module selection. Thank you Claude!
* feat: support LORA for Qwen3
* feat: update Anima SAI model spec metadata handling
* fix: remove unused code
* feat: split CFG processing in do_sample function to reduce memory usage
* feat: add VAE chunking and caching options to reduce memory usage
* feat: optimize RMSNorm forward method and remove unused torch_attention_op
* Update library/strategy_anima.py
Use torch.all instead of all.
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* Update library/safetensors_utils.py
Fix duplicated new_key for concat_hook.
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* Update anima_minimal_inference.py
Remove unused code.
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* Update anima_train.py
Remove unused import.
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* Update library/anima_train_utils.py
Remove unused import.
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* fix: review with Copilot
* feat: add script to convert LoRA format to ComfyUI compatible format (WIP, not tested yet)
* feat: add process_escape function to handle escape sequences in prompts
* feat: enhance LoRA weight handling in model loading and add text encoder loading function
* feat: improve ComfyUI conversion script with prefix constants and module name adjustments
* feat: update caption dropout documentation to clarify cache regeneration requirement
* feat: add clarification on learning rate adjustments
* feat: add note on PyTorch version requirement to prevent NaN loss
---------
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* fix: support dataset with metadata
* feat: support another tagger model
* fix: improve handling of image size and caption/tag processing in FineTuningDataset
* fix: enhance metadata loading to support JSONL format in FineTuningDataset
* feat: enhance image loading and processing in ImageLoadingPrepDataset with batch support and output options
* fix: improve image path handling and memory management in dataset classes
* Update finetune/tag_images_by_wd14_tagger.py
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
* fix: add return type annotation for process_tag_replacement function and ensure tags are returned
* feat: add artist category threshold for tagging
* doc: add comment for clarification
---------
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
2、Deprecate and remove the guidance_scale parameter because it used in inference not train
3、Add inference command-line arguments --ct for cfg_trunc_ratio and --rc for renorm_cfg to control CFG truncation and renormalization during inference.
