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+Collecting transformers>=4.43.0
+  Downloading transformers-4.53.1-py3-none-any.whl.metadata (40 kB)
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+Collecting accelerate>=0.30.0
+  Downloading accelerate-1.8.1-py3-none-any.whl.metadata (19 kB)
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+  Downloading bitsandbytes-0.46.1-py3-none-manylinux_2_24_x86_64.whl.metadata (10 kB)
+Collecting peft>=0.11.1
+  Downloading peft-0.16.0-py3-none-any.whl.metadata (14 kB)
+Collecting datasets
+  Downloading datasets-3.6.0-py3-none-any.whl.metadata (19 kB)
+Collecting wandb
+  Downloading wandb-0.21.0-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (10 kB)
+Collecting deepspeed
+  Downloading deepspeed-0.17.1.tar.gz (1.5 MB)
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+  Preparing metadata (setup.py): started
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+Collecting huggingface_hub[cli]
+  Downloading huggingface_hub-0.33.2-py3-none-any.whl.metadata (14 kB)
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+Collecting regex!=2019.12.17 (from transformers>=4.43.0)
+  Downloading regex-2024.11.6-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (40 kB)
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+Collecting tokenizers<0.22,>=0.21 (from transformers>=4.43.0)
+  Downloading tokenizers-0.21.2-cp39-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (6.8 kB)
+Collecting safetensors>=0.4.3 (from transformers>=4.43.0)
+  Downloading safetensors-0.5.3-cp38-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (3.8 kB)
+Collecting tqdm>=4.27 (from transformers>=4.43.0)
+  Downloading tqdm-4.67.1-py3-none-any.whl.metadata (57 kB)
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+Collecting pyarrow>=15.0.0 (from datasets)
+  Downloading pyarrow-20.0.0-cp310-cp310-manylinux_2_28_x86_64.whl.metadata (3.3 kB)
+Collecting dill<0.3.9,>=0.3.0 (from datasets)
+  Downloading dill-0.3.8-py3-none-any.whl.metadata (10 kB)
+Collecting pandas (from datasets)
+  Downloading pandas-2.3.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (91 kB)
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+Collecting requests (from transformers>=4.43.0)
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+Collecting xxhash (from datasets)
+  Downloading xxhash-3.5.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (12 kB)
+Collecting multiprocess<0.70.17 (from datasets)
+  Downloading multiprocess-0.70.16-py310-none-any.whl.metadata (7.2 kB)
+Requirement already satisfied: fsspec<=2025.3.0,>=2023.1.0 in /usr/local/lib/python3.10/dist-packages (from fsspec[http]<=2025.3.0,>=2023.1.0->datasets) (2023.4.0)
+Collecting click!=8.0.0,>=7.1 (from wandb)
+  Downloading click-8.2.1-py3-none-any.whl.metadata (2.5 kB)
+Collecting gitpython!=3.1.29,>=1.0.0 (from wandb)
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+Collecting protobuf!=4.21.0,!=5.28.0,<7,>=3.19.0 (from wandb)
+  Downloading protobuf-6.31.1-cp39-abi3-manylinux2014_x86_64.whl.metadata (593 bytes)
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+Collecting sentry-sdk>=2.0.0 (from wandb)
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+Collecting einops (from deepspeed)
+  Downloading einops-0.8.1-py3-none-any.whl.metadata (13 kB)
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+  Downloading hjson-3.1.0-py3-none-any.whl.metadata (2.6 kB)
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+Collecting py-cpuinfo (from deepspeed)
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+Collecting nvidia-ml-py (from deepspeed)
+  Downloading nvidia_ml_py-12.575.51-py3-none-any.whl.metadata (9.3 kB)
+Collecting fsspec<=2025.3.0,>=2023.1.0 (from fsspec[http]<=2025.3.0,>=2023.1.0->datasets)
+  Downloading fsspec-2025.3.0-py3-none-any.whl.metadata (11 kB)
+Collecting hf-xet<2.0.0,>=1.1.2 (from huggingface_hub[cli])
+  Downloading hf_xet-1.1.5-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (879 bytes)
+Collecting InquirerPy==0.3.4 (from huggingface_hub[cli])
+  Downloading InquirerPy-0.3.4-py3-none-any.whl.metadata (8.1 kB)
+Collecting pfzy<0.4.0,>=0.3.1 (from InquirerPy==0.3.4->huggingface_hub[cli])
+  Downloading pfzy-0.3.4-py3-none-any.whl.metadata (4.9 kB)
+Requirement already satisfied: prompt-toolkit<4.0.0,>=3.0.1 in /usr/local/lib/python3.10/dist-packages (from InquirerPy==0.3.4->huggingface_hub[cli]) (3.0.39)
+INFO: pip is looking at multiple versions of fsspec[http] to determine which version is compatible with other requirements. This could take a while.
+Collecting aiohttp!=4.0.0a0,!=4.0.0a1 (from fsspec[http]<=2025.3.0,>=2023.1.0->datasets)
+  Downloading aiohttp-3.12.13-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (7.6 kB)
+Collecting gitdb<5,>=4.0.1 (from gitpython!=3.1.29,>=1.0.0->wandb)
+  Downloading gitdb-4.0.12-py3-none-any.whl.metadata (1.2 kB)
+Collecting annotated-types>=0.6.0 (from pydantic<3->wandb)
+  Downloading annotated_types-0.7.0-py3-none-any.whl.metadata (15 kB)
+Collecting pydantic-core==2.33.2 (from pydantic<3->wandb)
+  Downloading pydantic_core-2.33.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (6.8 kB)
+Collecting typing-inspection>=0.4.0 (from pydantic<3->wandb)
+  Downloading typing_inspection-0.4.1-py3-none-any.whl.metadata (2.6 kB)
+Requirement already satisfied: charset_normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers>=4.43.0) (2.1.1)
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+Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /usr/local/lib/python3.10/dist-packages (from torch>=2.0.0->accelerate>=0.30.0) (8.9.2.26)
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+Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /usr/local/lib/python3.10/dist-packages (from torch>=2.0.0->accelerate>=0.30.0) (11.0.2.54)
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+Collecting pytz>=2020.1 (from pandas->datasets)
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+Collecting tzdata>=2022.7 (from pandas->datasets)
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+Collecting aiohappyeyeballs>=2.5.0 (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets)
+  Downloading aiohappyeyeballs-2.6.1-py3-none-any.whl.metadata (5.9 kB)
+Collecting aiosignal>=1.1.2 (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets)
+  Downloading aiosignal-1.4.0-py3-none-any.whl.metadata (3.7 kB)
+Collecting async-timeout<6.0,>=4.0 (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets)
+  Downloading async_timeout-5.0.1-py3-none-any.whl.metadata (5.1 kB)
+Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets) (23.1.0)
+Collecting frozenlist>=1.1.1 (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets)
+  Downloading frozenlist-1.7.0-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (18 kB)
+Collecting multidict<7.0,>=4.5 (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets)
+  Downloading multidict-6.6.3-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl.metadata (5.3 kB)
+Collecting propcache>=0.2.0 (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets)
+  Downloading propcache-0.3.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (12 kB)
+Collecting yarl<2.0,>=1.17.0 (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets)
+  Downloading yarl-1.20.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (73 kB)
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+Collecting smmap<6,>=3.0.1 (from gitdb<5,>=4.0.1->gitpython!=3.1.29,>=1.0.0->wandb)
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+Downloading transformers-4.53.1-py3-none-any.whl (10.8 MB)
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DeepSeek-V3-AWQ/.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
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DeepSeek-V3-AWQ/README.md

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+---
+license: mit
+language:
+- en
+- zh
+base_model:
+- deepseek-ai/DeepSeek-V3
+pipeline_tag: text-generation
+library_name: transformers
+---
+# DeepSeek V3 AWQ
+AWQ of DeepSeek V3.
+
+Quantized by [Eric Hartford](https://huggingface.co/ehartford) and [v2ray](https://huggingface.co/v2ray).
+
+This quant modified some of the model code to fix an overflow issue when using float16.
+
+To serve using vLLM with 8x 80GB GPUs, use the following command:
+```sh
+VLLM_USE_V1=0 VLLM_WORKER_MULTIPROC_METHOD=spawn VLLM_MARLIN_USE_ATOMIC_ADD=1 python -m vllm.entrypoints.openai.api_server --host 0.0.0.0 --port 12345 --max-model-len 65536 --max-seq-len-to-capture 65536 --enable-chunked-prefill --enable-prefix-caching --trust-remote-code --tensor-parallel-size 8 --gpu-memory-utilization 0.95 --served-model-name deepseek-chat --model cognitivecomputations/DeepSeek-V3-AWQ
+```
+You can download the wheel I built for PyTorch 2.6, Python 3.12 by clicking [here](https://huggingface.co/x2ray/wheels/resolve/main/vllm-0.8.3.dev250%2Bg10afedcfd.cu128-cp312-cp312-linux_x86_64.whl), the benchmark below was done with this wheel, it contains [2 PR merges](https://github.com/vllm-project/vllm/issues?q=is%3Apr+is%3Aopen+author%3Ajinzhen-lin) and an unoptimized FlashMLA (still faster than Triton) for A100 which boosted performance a lot. The vLLM repo which contained A100 FlashMLA can be found at [LagPixelLOL/vllm@sm80_flashmla](https://github.com/LagPixelLOL/vllm/tree/sm80_flashmla), which is a fork of [vllm-project/vllm](https://github.com/vllm-project/vllm). The A100 FlashMLA it used is based on [LagPixelLOL/FlashMLA@vllm](https://github.com/LagPixelLOL/FlashMLA/tree/vllm), which is a fork of [pzhao-eng/FlashMLA](https://github.com/pzhao-eng/FlashMLA).
+
+## TPS Per Request
+| GPU \ Batch Input Output | B: 1 I: 2 O: 2K | B: 32 I: 4K O: 256 | B: 1 I: 63K O: 2K | Prefill |
+|:-:|:-:|:-:|:-:|:-:|
+| **8x H100/H200** | 61.5 | 30.1 | 54.3 | 4732.2 |
+| **4x H200** | 58.4 | 19.8 | 53.7 | 2653.1 |
+| **8x A100 80GB** | 46.8 | 12.8 | 30.4 | 2442.4 |
+| **8x L40S** | 46.3 | OOM | OOM | 688.5 |
+
+Note:
+- The A100 config uses an unoptimized FlashMLA implementation, which is only superior than Triton during high context inference, it would be faster if it's optimized.
+- The L40S config doesn't support FlashMLA, thus the Triton implementation is used, this makes it extremely slow with high context. But the L40S doesn't have much VRAM, so it can't really have that much context anyway, and it also doesn't have the fast GPU to GPU interconnection bandwidth, making it even slower. It is not recommended to serve with this config, as you must limit the context to <= 4096, `--gpu-memory-utilization` to 0.98, and `--max-num-seqs` to 4.
+- All types of GPU used during benchmark are SXM form factor except L40S.
+- Inference speed will be better than FP8 at low batch size but worse than FP8 at high batch size, this is the nature of low bit quantization.
+- vLLM supports MLA for AWQ now, you can run this model with full context length on just 8x 80GB GPUs.

DeepSeek-V3-AWQ/configuration_deepseek.py

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+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+DEEPSEEK_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
+class DeepseekV3Config(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`DeepseekV3Model`]. It is used to instantiate an DeepSeek
+    model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
+    defaults will yield a similar configuration to that of the DeepSeek-V3.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 129280):
+            Vocabulary size of the Deep model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`DeepseekV3Model`]
+        hidden_size (`int`, *optional*, defaults to 4096):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 11008):
+            Dimension of the MLP representations.
+        moe_intermediate_size (`int`, *optional*, defaults to 1407):
+            Dimension of the MoE representations.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer decoder.
+        num_nextn_predict_layers (`int`, *optional*, defaults to 1):
+            Number of nextn predict layers in the DeepSeekV3 Model.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer decoder.
+        n_shared_experts (`int`, *optional*, defaults to None):
+            Number of shared experts, None means dense model.
+        n_routed_experts (`int`, *optional*, defaults to None):
+            Number of routed experts, None means dense model.
+        routed_scaling_factor (`float`, *optional*, defaults to 1.0):
+            Scaling factor or routed experts.
+        topk_method (`str`, *optional*, defaults to `gready`):
+            Topk method used in routed gate.
+        n_group (`int`, *optional*, defaults to None):
+            Number of groups for routed experts.
+        topk_group (`int`, *optional*, defaults to None):
+            Number of selected groups for each token(for each token, ensuring the selected experts is only within `topk_group` groups).
+        num_experts_per_tok (`int`, *optional*, defaults to None):
+            Number of selected experts, None means dense model.
+        moe_layer_freq (`int`, *optional*, defaults to 1):
+            The frequency of the MoE layer: one expert layer for every `moe_layer_freq - 1` dense layers.
+        first_k_dense_replace (`int`, *optional*, defaults to 0):
+            Number of dense layers in shallow layers(embed->dense->dense->...->dense->moe->moe...->lm_head).
+                                                            \--k dense layers--/
+        norm_topk_prob (`bool`, *optional*, defaults to False):
+            Whether to normalize the weights of the routed experts.
+        scoring_func (`str`, *optional*, defaults to 'softmax'):
+            Method of computing expert weights.
+        aux_loss_alpha (`float`, *optional*, defaults to 0.001):
+            Auxiliary loss weight coefficient.
+        seq_aux = (`bool`, *optional*, defaults to True):
+            Whether to compute the auxiliary loss for each individual sample.
+        num_key_value_heads (`int`, *optional*):
+            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+            `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+            by meanpooling all the original heads within that group. For more details checkout [this
+            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
+            `num_attention_heads`.
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function (function or string) in the decoder.
+        max_position_embeddings (`int`, *optional*, defaults to 2048):
+            The maximum sequence length that this model might ever be used with.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-06):
+            The epsilon used by the rms normalization layers.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if `config.is_decoder=True`.
+        pad_token_id (`int`, *optional*):
+            Padding token id.
+        bos_token_id (`int`, *optional*, defaults to 1):
+            Beginning of stream token id.
+        eos_token_id (`int`, *optional*, defaults to 2):
+            End of stream token id.
+        pretraining_tp (`int`, *optional*, defaults to 1):
+            Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
+            document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is
+            necessary to ensure exact reproducibility of the pretraining results. Please refer to [this
+            issue](https://github.com/pytorch/pytorch/issues/76232).
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether to tie weight embeddings
+        rope_theta (`float`, *optional*, defaults to 10000.0):
+            The base period of the RoPE embeddings.
+        rope_scaling (`Dict`, *optional*):
+            Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
+            strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
+            `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
+            `max_position_embeddings` to the expected new maximum.
+        attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
+            Whether to use a bias in the query, key, value and output projection layers during self-attention.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+
+    ```python
+    >>> from transformers import DeepseekV3Model, DeepseekV3Config
+
+    >>> # Initializing a Deepseek-V3 style configuration
+    >>> configuration = DeepseekV3Config()
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "deepseek_v3"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=129280,
+        hidden_size=7168,
+        intermediate_size=18432,
+        moe_intermediate_size = 2048,
+        num_hidden_layers=61,
+        num_nextn_predict_layers=1,
+        num_attention_heads=128,
+        num_key_value_heads=128,
+        n_shared_experts = 1,
+        n_routed_experts = 256,
+        ep_size = 1,
+        routed_scaling_factor = 2.5,
+        kv_lora_rank = 512,
+        q_lora_rank = 1536,
+        qk_rope_head_dim = 64,
+        v_head_dim = 128,
+        qk_nope_head_dim = 128,
+        topk_method = 'noaux_tc',
+        n_group = 8,
+        topk_group = 4,
+        num_experts_per_tok = 8,
+        moe_layer_freq = 1,
+        first_k_dense_replace = 3,
+        norm_topk_prob = True,
+        scoring_func = 'sigmoid',
+        aux_loss_alpha = 0.001,
+        seq_aux = True,
+        hidden_act="silu",
+        max_position_embeddings=4096,
+        initializer_range=0.02,
+        rms_norm_eps=1e-6,
+        use_cache=True,
+        pad_token_id=None,
+        bos_token_id=0,
+        eos_token_id=1,
+        pretraining_tp=1,
+        tie_word_embeddings=False,
+        rope_theta=10000.0,
+        rope_scaling=None,
+        attention_bias=False,
+        attention_dropout=0.0,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.moe_intermediate_size = moe_intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_nextn_predict_layers = num_nextn_predict_layers
+        self.num_attention_heads = num_attention_heads
+        self.n_shared_experts = n_shared_experts
+        self.n_routed_experts = n_routed_experts
+        self.ep_size = ep_size
+        self.routed_scaling_factor = routed_scaling_factor
+        self.kv_lora_rank = kv_lora_rank
+        self.q_lora_rank = q_lora_rank
+        self.qk_rope_head_dim = qk_rope_head_dim
+        self.v_head_dim = v_head_dim
+        self.qk_nope_head_dim = qk_nope_head_dim
+        self.topk_method = topk_method
+        self.n_group = n_group
+        self.topk_group = topk_group
+        self.num_experts_per_tok = num_experts_per_tok
+        self.moe_layer_freq = moe_layer_freq
+        self.first_k_dense_replace = first_k_dense_replace
+        self.norm_topk_prob = norm_topk_prob
+        self.scoring_func = scoring_func
+        self.aux_loss_alpha = aux_loss_alpha
+        self.seq_aux = seq_aux
+        # for backward compatibility
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.pretraining_tp = pretraining_tp
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        self.attention_bias = attention_bias
+        self.attention_dropout = attention_dropout
+
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )

DeepSeek-V3-AWQ/generation_config.json

@@ -0,0 +1,7 @@
+{
+  "_from_model_config": true,
+  "bos_token_id": 0,
+  "do_sample": true,
+  "eos_token_id": 1,
+  "transformers_version": "4.48.0.dev0"
+}

download.py

@@ -0,0 +1,150 @@
+#!/usr/bin/env python
+# DeepSeek-V3 (MoE) + LoRA + ZeRO-3, 10 k samples, r=16, ep_size=8
+#  – weights created empty on CPU, then loaded & sharded with Accelerate
+#  – GPU memory capped at ~50 GB per device; everything else off-loaded to RAM
+
+import os, time, logging
+from pathlib import Path
+
+import torch
+from accelerate import (
+    Accelerator,
+    init_empty_weights,
+    infer_auto_device_map,
+    load_checkpoint_and_dispatch,   # ← this is the right util
+)
+from datasets import load_dataset, load_from_disk, DatasetDict
+from transformers import AutoConfig, AutoTokenizer, AutoModelForCausalLM
+from peft import LoraConfig, get_peft_model
+from tqdm import tqdm
+
+os.environ["PYTORCH_CUDA_ALLOC_CONF"] = (
+    "garbage_collection_threshold:0.6,max_split_size_mb:128"
+)
+
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s — %(name)s — %(levelname)s — %(message)s",
+)
+logger = logging.getLogger("train_qlora")
+
+
+def main() -> None:
+    accel = Accelerator(mixed_precision="bf16")
+    logger.info(f"Accelerate device: {accel.device}")
+
+    # ─── hyper-params & paths ─────────────────────────────────────────────
+    epochs, batch_size, grad_accum, lr = 1, 1, 16, 1e-4
+    n_samples   = 10_000
+    MODEL_DIR   = "/workspace/DeepSeek-V3-Base"        # local snapshot
+    DATA_FILE   = "/workspace/data/splits/train.jsonl"
+    CACHE_DIR   = "/workspace/data/processed_10k"
+
+    # ─── build *empty* skeleton (no GPU mem yet) ──────────────────────────
+    logger.info("Building empty model skeleton in CPU RAM…")
+    cfg = AutoConfig.from_pretrained(
+        MODEL_DIR, trust_remote_code=True, local_files_only=True
+    )
+    cfg.ep_size = 8
+
+    with init_empty_weights():
+        model = AutoModelForCausalLM.from_config(cfg, trust_remote_code=True)
+
+    # tokenizer (loaded once per rank, tiny)
+    tok = AutoTokenizer.from_pretrained(
+        MODEL_DIR, use_fast=False, trust_remote_code=True, local_files_only=True
+    )
+
+    # ─── device-map & checkpoint dispatch ─────────────────────────────────
+    logger.info("Inferring device_map for dispatch + offload…")
+    max_mem = {i: "50GB" for i in range(accel.num_processes)}  # GPU_i → 50 GB
+    max_mem["cpu"] = "2000GB"
+    dmap = infer_auto_device_map(model, max_memory=max_mem)
+
+    logger.info("Loading checkpoint shards & dispatching…")
+    t0 = time.time()
+    model = load_checkpoint_and_dispatch(
+        model,
+        MODEL_DIR,                   # root with *.bin / *.safetensors
+        device_map=dmap,
+        offload_folder="hf_offload", # gets created automatically
+        dtype=torch.bfloat16,
+    )
+    model.gradient_checkpointing_enable()
+    logger.info(f"✅ Model ready in {time.time()-t0:.1f}s")
+
+    # ─── LoRA adapters ────────────────────────────────────────────────────
+    logger.info("Attaching LoRA adapters (r=16)…")
+    lora_cfg = LoraConfig(
+        r=16, lora_alpha=16, bias="none",
+        target_modules=["q_proj","v_proj","o_proj","up_proj","down_proj"],
+        task_type="CAUSAL_LM",
+    )
+    model = get_peft_model(model, lora_cfg)
+    logger.info("✅ LoRA attached")
+
+    # ─── dataset (tokenised or cached) ────────────────────────────────────
+    logger.info("Preparing 10 k-sample dataset…")
+    if os.path.isdir(CACHE_DIR) and os.listdir(CACHE_DIR):
+        ds = load_from_disk(CACHE_DIR)
+        logger.info("Loaded cached subset")
+    else:
+        raw = load_dataset("json", data_files={"data": DATA_FILE}, split="data")
+        raw = raw.shuffle(seed=42).select(range(n_samples))
+        split = int(n_samples * 0.95)
+        tr, va = raw.select(range(split)), raw.select(range(split, n_samples))
+
+        def tok_fn(batch):
+            inp = [f"<|begin_of_sentence|>User: {p}\nAssistant:" for p in batch["prompt"]]
+            out = [f"{r}<|end_of_sentence|>" for r in batch["response"]]
+            enc = tok(inp, max_length=1024, truncation=True,
+                      padding="max_length", return_tensors="pt")
+            dec = tok(out, max_length=1024, truncation=True,
+                      padding="max_length", return_tensors="pt")
+            enc["labels"] = dec.input_ids
+            return enc
+
+        tr = tr.map(tok_fn, batched=True, remove_columns=["prompt","response"])
+        va = va.map(tok_fn, batched=True, remove_columns=["prompt","response"])
+        tr.set_format(type="torch", columns=["input_ids","attention_mask","labels"])
+        va.set_format(type="torch", columns=["input_ids","attention_mask","labels"])
+        ds = DatasetDict({"train": tr, "validation": va})
+        ds.save_to_disk(CACHE_DIR)
+        logger.info("✅ Tokenised subset cached")
+
+    # ─── loaders & ZeRO prep ──────────────────────────────────────────────
+    train_loader = torch.utils.data.DataLoader(ds["train"], batch_size=batch_size, shuffle=True)
+    valid_loader = torch.utils.data.DataLoader(ds["validation"], batch_size=batch_size)
+
+    logger.info("Preparing for ZeRO-3 distributed training…")
+    model, train_loader, valid_loader = accel.prepare(model, train_loader, valid_loader)
+    optim = torch.optim.AdamW(model.parameters(), lr=lr)
+
+    # ─── training ─────────────────────────────────────────────────────────
+    logger.info("🚀 Starting training…")
+    model.train()
+    for epoch in range(epochs):
+        t0, tot = time.time(), 0.0
+        loop = tqdm(enumerate(train_loader), total=len(train_loader),
+                    desc=f"Epoch {epoch}", disable=not accel.is_local_main_process)
+        for step, batch in loop:
+            loss = model(**batch).loss / grad_accum
+            accel.backward(loss)
+            if (step+1) % grad_accum == 0:
+                optim.step(); optim.zero_grad()
+            tot += loss.item() * grad_accum
+            if accel.is_local_main_process and step % 50 == 0:
+                loop.set_postfix(loss=loss.item()*grad_accum)
+
+        if accel.is_local_main_process:
+            logger.info(f"Epoch {epoch} finished in {time.time()-t0:.1f}s ─ avg loss {tot/len(train_loader):.4f}")
+
+    # ─── save LoRA adapter ────────────────────────────────────────────────
+    if accel.is_main_process:
+        out = Path("./ckpt/final_adapter"); out.mkdir(parents=True, exist_ok=True)
+        model.save_pretrained(out)
+        logger.info(f"✅ LoRA adapter saved to {out}")
+
+
+if __name__ == "__main__":
+    main()

eichi_utils/tensor_combiner.py

@@ -0,0 +1,194 @@
+import os
+import sys
+import argparse
+import torch
+import traceback
+import safetensors.torch as sf
+from datetime import datetime
+import gradio as gr
+
+# ルートパスをシステムパスに追加
+root_path = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+if root_path not in sys.path:
+    sys.path.append(root_path)
+
+# ルートパスを追加した後でインポート
+from locales.i18n_extended import translate
+
+def combine_tensor_files(file1_path, file2_path, output_path=None):
+    """2つのsafetensorsファイルを読み込み、結合して新しいファイルに保存する
+
+    Args:
+        file1_path (str): 1つ目のsafetensorsファイルパス
+        file2_path (str): 2つ目のsafetensorsファイルパス
+        output_path (str, optional): 出力ファイルパス。指定しない場合は自動生成
+
+    Returns:
+        tuple: (成功したかどうかのbool, 出力ファイルパス, 結果メッセージ)
+    """
+    try:
+        # ファイル1を読み込み
+        print(translate("ファイル1を読み込み中: {0}").format(os.path.basename(file1_path)))
+        tensor_dict1 = sf.load_file(file1_path)
+
+        # ファイル2を読み込み
+        print(translate("ファイル2を読み込み中: {0}").format(os.path.basename(file2_path)))
+        tensor_dict2 = sf.load_file(file2_path)
+
+        # テンソルを取得
+        if "history_latents" in tensor_dict1 and "history_latents" in tensor_dict2:
+            tensor1 = tensor_dict1["history_latents"]
+            tensor2 = tensor_dict2["history_latents"]
+
+            # テンソル情報の表示
+            print(translate("テンソル1: shape={0}, dtype={1}, フレーム数={2}").format(tensor1.shape, tensor1.dtype, tensor1.shape[2]))
+            print(translate("テンソル2: shape={0}, dtype={1}, フレーム数={2}").format(tensor2.shape, tensor2.dtype, tensor2.shape[2]))
+
+            # サイズチェック
+            if tensor1.shape[3] != tensor2.shape[3] or tensor1.shape[4] != tensor2.shape[4]:
+                error_msg = translate("エラー: テンソルサイズが異なります: {0} vs {1}").format(tensor1.shape, tensor2.shape)
+                print(error_msg)
+                return False, None, error_msg
+
+            # データ型とデバイスの調整
+            if tensor1.dtype != tensor2.dtype:
+                print(translate("データ型の変換: {0} → {1}").format(tensor2.dtype, tensor1.dtype))
+                tensor2 = tensor2.to(dtype=tensor1.dtype)
+
+            # 両方CPUに移動
+            tensor1 = tensor1.cpu()
+            tensor2 = tensor2.cpu()
+
+            # 結合（テンソル1の後にテンソル2を追加）
+            combined_tensor = torch.cat([tensor1, tensor2], dim=2)
+
+            # 結合されたテンソルの情報を表示
+            tensor1_frames = tensor1.shape[2]
+            tensor2_frames = tensor2.shape[2]
+            combined_frames = combined_tensor.shape[2]
+            print(translate("結合成功: 結合後のフレーム数={0} ({1}+{2}フレーム)").format(combined_frames, tensor1_frames, tensor2_frames))
+
+            # メタデータを更新
+            height, width = tensor1.shape[3], tensor1.shape[4]
+            metadata = torch.tensor([height, width, combined_frames], dtype=torch.int32)
+
+            # 出力ファイルパスが指定されていない場合は自動生成
+            if output_path is None:
+                timestamp = datetime.now().strftime("%y%m%d_%H%M%S")
+                output_dir = os.path.dirname(file1_path)
+                output_path = os.path.join(output_dir, f"combined_{timestamp}.safetensors")
+
+            # 結合したテンソルをファイルに保存
+            tensor_dict = {
+                "history_latents": combined_tensor,
+                "metadata": metadata
+            }
+
+            # ファイル保存
+            sf.save_file(tensor_dict, output_path)
+
+            # テンソルデータの保存サイズの概算
+            tensor_size_mb = (combined_tensor.element_size() * combined_tensor.nelement()) / (1024 * 1024)
+
+            success_msg = translate("結合テンソルを保存しました: {0}\n").format(os.path.basename(output_path))
+            success_msg += translate("フレーム数: {0}フレーム ({1}+{2}フレーム)\n").format(combined_frames, tensor1_frames, tensor2_frames)
+            success_msg += translate("サイズ: {0:.2f}MB, 形状: {1}").format(tensor_size_mb, combined_tensor.shape)
+            print(success_msg)
+
+            return True, output_path, success_msg
+        else:
+            error_msg = translate("エラー: テンソルファイルに必要なキー'history_latents'がありません")
+            print(error_msg)
+            return False, None, error_msg
+
+    except Exception as e:
+        error_msg = translate("テンソル結合中にエラーが発生: {0}").format(e)
+        print(error_msg)
+        traceback.print_exc()
+        return False, None, error_msg
+
+def create_ui():
+    """Gradio UIを作成"""
+    with gr.Blocks(title=translate("テンソル結合ツール")) as app:
+        gr.Markdown(translate("## テンソルデータ結合ツール"))
+        gr.Markdown(translate("safetensors形式のテンソルデータファイルを2つ選択して結合します。結合順序は「テンソル1 + テンソル2」です。"))
+
+        with gr.Row():
+            with gr.Column(scale=1):
+                tensor_file1 = gr.File(label=translate("テンソルファイル1 (.safetensors)"), file_types=[".safetensors"])
+            with gr.Column(scale=1):
+                tensor_file2 = gr.File(label=translate("テンソルファイル2 (.safetensors)"), file_types=[".safetensors"])
+
+        with gr.Row():
+            output_file = gr.Textbox(label=translate("出力ファイル名 (空欄で自動生成)"), placeholder=translate("例: combined.safetensors"))
+
+        with gr.Row():
+            combine_btn = gr.Button(translate("テンソルファイルを結合"), variant="primary")
+
+        with gr.Row():
+            result_output = gr.Textbox(label=translate("結果"), lines=5)
+
+        def combine_tensors(file1, file2, output_path):
+            if file1 is None or file2 is None:
+                return translate("エラー: 2つのテンソルファイルを選択してください")
+
+            file1_path = file1.name
+            file2_path = file2.name
+
+            # 出力パスの決定
+            if output_path and output_path.strip():
+                # 拡張子のチェックと追加
+                if not output_path.lower().endswith('.safetensors'):
+                    output_path += '.safetensors'
+                # ディレクトリパスの決定（入力ファイルと同じ場所）
+                output_dir = os.path.dirname(file1_path)
+                full_output_path = os.path.join(output_dir, output_path)
+            else:
+                # 自動生成の場合はNoneのまま（関数内で自動生成）
+                full_output_path = None
+
+            success, result_path, message = combine_tensor_files(file1_path, file2_path, full_output_path)
+            if success:
+                return message
+            else:
+                return translate("結合失敗: {0}").format(message)
+
+        combine_btn.click(
+            fn=combine_tensors,
+            inputs=[tensor_file1, tensor_file2, output_file],
+            outputs=[result_output]
+        )
+
+    return app
+
+def main():
+    """コマンドライン引数を解析して実行"""
+    parser = argparse.ArgumentParser(description=translate("2つのsafetensorsファイルを結合するツール"))
+    parser.add_argument('--file1', type=str, help=translate("1つ目のsafetensorsファイルパス"))
+    parser.add_argument('--file2', type=str, help=translate("2つ目のsafetensorsファイルパス"))
+    parser.add_argument('--output', type=str, default=None, help=translate("出力ファイルパス (省略可能)"))
+    parser.add_argument('--ui', action='store_true', help=translate("GradioのUIモードで起動"))
+
+    args = parser.parse_args()
+
+    if args.ui:
+        # UIモードで起動
+        app = create_ui()
+        app.launch()
+    elif args.file1 and args.file2:
+        # コマンドラインモードで実行
+        success, output_path, message = combine_tensor_files(args.file1, args.file2, args.output)
+        if success:
+            print(translate("結合成功:"))
+            print(message)
+            return 0
+        else:
+            print(translate("結合失敗:"))
+            print(message)
+            return 1
+    else:
+        parser.print_help()
+        return 1
+
+if __name__ == "__main__":
+    sys.exit(main())

eichi_utils/ui_styles.py

@@ -0,0 +1,210 @@
+"""
+UI関連のスタイルを定義するモジュール
+"""
+from diffusers_helper.gradio.progress_bar import make_progress_bar_css
+
+from locales.i18n import translate
+
+def get_app_css():
+    """
+    アプリケーションのCSSスタイルを返す
+
+    Returns:
+        str: CSSスタイル定義
+    """
+    return make_progress_bar_css() + """
+    .title-suffix {
+        color: currentColor;
+        opacity: 0.05;
+    }
+
+    /* 赤枠のキーフレーム - 偶数パターン用 */
+    .highlighted-keyframe-red {
+        border: 4px solid #ff3860 !important;
+        box-shadow: 0 0 10px rgba(255, 56, 96, 0.5) !important;
+        background-color: rgba(255, 56, 96, 0.05) !important;
+        position: relative;
+    }
+
+    /* 赤枠キーフレームに「偶数番号」のラベルを追加 */
+    .highlighted-keyframe-red::after {
+    """ + 'content: "' + translate("偶数セクションのコピー元") + '"' + """;
+        position: absolute;
+        top: 5px;
+        right: 5px;
+        background: rgba(255, 56, 96, 0.8);
+        color: white;
+        padding: 2px 6px;
+        font-size: 10px;
+        border-radius: 4px;
+        pointer-events: none;
+    }
+
+    /* 青枠のキーフレーム - 奇数パターン用 */
+    .highlighted-keyframe-blue {
+        border: 4px solid #3273dc !important;
+        box-shadow: 0 0 10px rgba(50, 115, 220, 0.5) !important;
+        background-color: rgba(50, 115, 220, 0.05) !important;
+        position: relative;
+    }
+
+    /* 青枠キーフレームに「奇数番号」のラベルを追加 */
+    .highlighted-keyframe-blue::after {
+    """ + 'content: "' + translate("奇数セクションのコピー元") + '"' + """;
+        position: absolute;
+        top: 5px;
+        right: 5px;
+        background: rgba(50, 115, 220, 0.8);
+        color: white;
+        padding: 2px 6px;
+        font-size: 10px;
+        border-radius: 4px;
+        pointer-events: none;
+    }
+
+    /* 引き続きサポート（古いクラス名）- 前方互換性用 */
+    .highlighted-keyframe {
+        border: 4px solid #ff3860 !important;
+        box-shadow: 0 0 10px rgba(255, 56, 96, 0.5) !important;
+        background-color: rgba(255, 56, 96, 0.05) !important;
+    }
+
+    /* 赤枠用セクション番号ラベル */
+    .highlighted-label-red label {
+        color: #ff3860 !important;
+        font-weight: bold !important;
+    }
+
+    /* 青枠用セクション番号ラベル */
+    .highlighted-label-blue label {
+        color: #3273dc !important;
+        font-weight: bold !important;
+    }
+
+    /* 引き続きサポート（古いクラス名）- 前方互換性用 */
+    .highlighted-label label {
+        color: #ff3860 !important;
+        font-weight: bold !important;
+    }
+
+    /* オールパディングの高さ調整 */
+    #all_padding_checkbox {
+        padding-top: 1.5rem;
+        min-height: 5.8rem;
+    }
+
+    #all_padding_checkbox .wrap {
+        align-items: flex-start;
+    }
+
+    #all_padding_checkbox .label-wrap {
+        margin-bottom: 0.8rem;
+        font-weight: 500;
+        font-size: 14px;
+    }
+
+    #all_padding_checkbox .info {
+        margin-top: 0.2rem;
+    }
+
+    /* セクション間の区切り線を太くする */
+    .section-row {
+        border-bottom: 4px solid #3273dc;
+        margin-bottom: 20px;
+        padding-bottom: 15px;
+        margin-top: 10px;
+        position: relative;
+    }
+
+    /* セクション番号を目立たせる */
+    .section-row .gr-form:first-child label {
+        font-weight: bold;
+        font-size: 1.1em;
+        color: #3273dc;
+        background-color: rgba(50, 115, 220, 0.1);
+        padding: 5px 10px;
+        border-radius: 4px;
+        margin-bottom: 10px;
+        display: inline-block;
+    }
+
+    /* セクションの背景を少し強調 */
+    .section-row {
+        background-color: rgba(50, 115, 220, 0.03);
+        border-radius: 8px;
+        box-shadow: 0 1px 3px rgba(0, 0, 0, 0.1);
+    }
+
+    /* セクション間の余白を増やす */
+    .section-container > .gr-block:not(:first-child) {
+        margin-top: 10px;
+    }
+
+    /* アコーディオンセクションのスタイル */
+    .section-accordion {
+        margin-top: 15px;
+        margin-bottom: 15px;
+        border-left: 4px solid #3273dc;
+        padding-left: 10px;
+    }
+
+    .section-accordion h3 button {
+        font-weight: bold;
+        color: #3273dc;
+    }
+
+    .section-accordion .gr-block {
+        border-radius: 8px;
+    }
+
+    /* 保存対象の設定項目を薄い青色でハイライト（ライト/ダークモード対応） */
+    .saveable-setting {
+        background-color: rgba(240, 248, 255, 0.5) !important; /* 薄い青色を透過指定（ライトモード） */
+        border-left: 3px solid #90caf9 !important; /* 薄いボーダー色 */
+    }
+    
+    /* システムのダークモード対応 */
+    @media (prefers-color-scheme: dark) {
+        .saveable-setting {
+            background-color: rgba(25, 35, 60, 0.4) !important; /* ダークモードでの背景色 */
+            border-left: 3px solid #64b5f6 !important; /* ダークモードでのボーダー色（少し明るめ） */
+        }
+    }
+    
+    /* Gradioのダークテーマ対応 */
+    .dark .saveable-setting {
+        background-color: rgba(25, 35, 60, 0.4) !important; /* ダークモードでの背景色 */
+        border-left: 3px solid #64b5f6 !important; /* ダークモードでのボーダー色（少し明るめ） */
+    }
+    
+    /* 保存対象項目のラベルにアイコンを追加 */
+    .saveable-setting label::before {
+        content: "💾 ";
+        margin-right: 5px;
+    }
+    
+    /* ダークモードでのラベル色調整 */
+    .dark .saveable-setting label {
+        color: #90caf9 !important; /* ダークモードで少し明るい青に */
+    }
+
+    /* markdownタイトル用 */
+    .markdown-title {
+        padding: 3px;
+    }
+
+    /* markdownサブタイトル用 */
+    .markdown-subtitle {
+        padding: 2px;
+    }
+
+    /* markdown領域用 */
+    .markdown-desc {
+        padding: 2px;
+    }
+
+    /* グルーピング用ボーダー */
+    .group-border {
+        border: solid 1px;
+    }
+    """

eichi_utils/vae_cache.py

@@ -0,0 +1,305 @@
+"""
+VAE Cache Utility for FramePack-eichi
+
+1フレームずつVAEデコードを行うためのキャッシュ機能を提供するモジュール。
+Hunyuan VideoのVAEに対して、フレームごとに処理しながらキャッシュを活用することで
+メモリ使用効率と処理速度を改善します。
+"""
+
+import torch
+import torch.nn.functional as F
+from typing import Optional
+import time
+
+def hook_forward_conv3d(self):
+    """HunyuanVideoCausalConv3dのforwardをフック置換する関数"""
+    def forward(hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = F.pad(hidden_states, self.time_causal_padding, mode=self.pad_mode)
+        if self.time_causal_padding[4] > 0:
+            if hasattr(self, "cache") and self.cache is not None:
+                hidden_states[:, :, :self.time_causal_padding[4]] = self.cache.clone() # 先頭フレームにキャッシュをコピー
+            self.cache = hidden_states[:, :, -self.time_causal_padding[4]:].clone() # 末尾フレームをキャッシュ
+        return self.conv(hidden_states)
+    return forward
+
+def hook_forward_upsample(self):
+    """HunyuanVideoUpsampleCausal3Dのforwardをフック置換する関数"""
+    def forward(hidden_states: torch.Tensor) -> torch.Tensor:
+        if hasattr(self.conv, "cache") and self.conv.cache is not None:
+            # キャッシュを使用している場合は全フレームをアップサンプリング
+            hidden_states = F.interpolate(hidden_states.contiguous(), scale_factor=self.upsample_factor, mode="nearest")
+        else:
+            num_frames = hidden_states.size(2)
+
+            first_frame, other_frames = hidden_states.split((1, num_frames - 1), dim=2)
+            first_frame = F.interpolate(
+                first_frame.squeeze(2), scale_factor=self.upsample_factor[1:], mode="nearest"
+            ).unsqueeze(2)
+
+            if num_frames > 1:
+                other_frames = other_frames.contiguous()
+                other_frames = F.interpolate(other_frames, scale_factor=self.upsample_factor, mode="nearest")
+                hidden_states = torch.cat((first_frame, other_frames), dim=2)
+            else:
+                hidden_states = first_frame
+
+        hidden_states = self.conv(hidden_states)
+        return hidden_states
+    return forward
+
+# Attention用のKVキャッシュプロセッサ
+class AttnProcessor2_0_KVCache:
+    """KVキャッシュを使用するAttentionプロセッサ"""
+    
+    def __init__(self):
+        self.k_cache = None
+        self.v_cache = None
+        if not hasattr(F, "scaled_dot_product_attention"):
+            raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
+
+    def __call__(
+        self,
+        attn,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        temb: Optional[torch.Tensor] = None,
+        *args,
+        **kwargs,
+    ) -> torch.Tensor:
+
+        residual = hidden_states
+        if attn.spatial_norm is not None:
+            hidden_states = attn.spatial_norm(hidden_states, temb)
+
+        input_ndim = hidden_states.ndim
+
+        if input_ndim == 4:
+            batch_size, channel, height, width = hidden_states.shape
+            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
+
+        batch_size, sequence_length, _ = (
+            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
+        )
+
+        if attention_mask is not None:
+            attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
+            # scaled_dot_product_attention expects attention_mask shape to be
+            # (batch, heads, source_length, target_length)
+            attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
+
+        if attn.group_norm is not None:
+            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
+
+        query = attn.to_q(hidden_states)
+
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states
+        elif attn.norm_cross:
+            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
+
+        key = attn.to_k(encoder_hidden_states)
+        value = attn.to_v(encoder_hidden_states)
+
+        inner_dim = key.shape[-1]
+        head_dim = inner_dim // attn.heads
+
+        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        if attn.norm_q is not None:
+            query = attn.norm_q(query)
+        if attn.norm_k is not None:
+            key = attn.norm_k(key)
+
+        # KVキャッシュの統合
+        if self.k_cache is not None:
+            key = torch.cat([self.k_cache, key], dim=2)
+            value = torch.cat([self.v_cache, value], dim=2)
+            attention_mask = torch.cat(
+                [torch.zeros(attention_mask.shape[0], attention_mask.shape[1], attention_mask.shape[2], self.k_cache.shape[2]).to(attention_mask), attention_mask], dim=3
+            )
+        
+        # 現在のKVをキャッシュとして保存
+        self.k_cache = key.clone()
+        self.v_cache = value.clone()
+
+        # Scaled Dot-Product Attention
+        hidden_states = F.scaled_dot_product_attention(
+            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
+        )
+
+        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
+        hidden_states = hidden_states.to(query.dtype)
+
+        # 線形変換
+        hidden_states = attn.to_out[0](hidden_states)
+        # ドロップアウト
+        hidden_states = attn.to_out[1](hidden_states)
+
+        if input_ndim == 4:
+            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
+
+        if attn.residual_connection:
+            hidden_states = hidden_states + residual
+
+        hidden_states = hidden_states / attn.rescale_output_factor
+
+        return hidden_states
+
+def hook_vae(vae):
+    """VAEをキャッシュモードに変更"""
+    # 元の設定を保存
+    vae._original_use_framewise_decoding = vae.use_framewise_decoding
+    vae._original_use_slicing = vae.use_slicing
+    vae._original_use_tiling = vae.use_tiling
+    
+    # キャッシュモード用の設定に変更
+    vae.use_framewise_decoding = False
+    vae.use_slicing = False
+    vae.use_tiling = False
+    
+    # 各モジュールをフック
+    for module in vae.decoder.modules():
+        if module.__class__.__name__ == "HunyuanVideoCausalConv3d":
+            module._orginal_forward = module.forward
+            module.forward = hook_forward_conv3d(module)
+        if module.__class__.__name__ == "HunyuanVideoUpsampleCausal3D":
+            module._orginal_forward = module.forward
+            module.forward = hook_forward_upsample(module)
+        if module.__class__.__name__ == "Attention":
+            module._orginal_processor = module.processor
+            module.processor = AttnProcessor2_0_KVCache()
+
+def restore_vae(vae):
+    """VAEを元の状態に戻す"""
+    # 設定を元に戻す
+    vae.use_framewise_decoding = vae._original_use_framewise_decoding
+    vae.use_slicing = vae._original_use_slicing
+    vae.use_tiling = vae._original_use_tiling
+
+    # キャッシュをクリアして元の実装に戻す
+    for module in vae.decoder.modules():
+        if module.__class__.__name__ == "HunyuanVideoCausalConv3d":
+            module.forward = module._orginal_forward
+            if hasattr(module, "cache"):
+                module.cache = None
+        if module.__class__.__name__ == "HunyuanVideoUpsampleCausal3D":
+            module.forward = module._orginal_forward
+            if hasattr(module.conv, "cache"):
+                module.conv.cache = None
+        if module.__class__.__name__ == "Attention":
+            if hasattr(module.processor, "k_cache"):
+                module.processor.k_cache = None
+                module.processor.v_cache = None
+            module.processor = module._orginal_processor
+
+@torch.no_grad()
+def vae_decode_cache(latents, vae):
+    """1フレームずつVAEデコードを行う関数"""
+    # デバッグログを追加
+    print("=== VAEキャッシュデコード開始 ===")
+    print(f"入力latents形状: {latents.shape}, デバイス: {latents.device}, 型: {latents.dtype}")
+    
+    # スケーリング係数の適用
+    latents = latents / vae.config.scaling_factor
+    frames = latents.shape[2]
+    print(f"処理フレーム数: {frames}")
+    
+    # VAEにフックを適用
+    print("VAEにフックを適用...")
+    hook_vae(vae)
+    print("フック適用完了")
+    
+    # 1フレームずつ処理
+    image = None
+    try:
+        for i in range(frames):
+            print(f"フレーム {i+1}/{frames} 処理中...")
+            latents_slice = latents[:, :, i:i+1, :, :]
+            # デコード処理（内部でキャッシュを活用）
+            image_slice = vae.decode(latents_slice.to(device=vae.device, dtype=vae.dtype)).sample
+            print(f"フレーム {i+1} デコード完了: 形状 {image_slice.shape}")
+            
+            # 結果の結合
+            if image is None:
+                image = image_slice
+            else:
+                image = torch.cat((image, image_slice), dim=2)
+            print(f"現在の結合結果形状: {image.shape}")
+    except Exception as e:
+        print(f"VAEキャッシュデコード中のエラー: {e}")
+        print(f"エラー詳細: {type(e).__name__}")
+        import traceback
+        traceback.print_exc()
+        # エラーが発生した場合、VAEを元の状態に戻してから例外を再スロー
+        restore_vae(vae)
+        raise e
+    
+    # VAEを元の状態に戻す
+    print("VAEを元の状態に戻しています...")
+    restore_vae(vae)
+    print("VAEを元の状態に戻しました")
+    
+    print(f"出力image形状: {image.shape}, デバイス: {image.device}, 型: {image.dtype}")
+    print("=== VAEキャッシュデコード完了 ===")
+    return image
+
+# 元のデコード関数（比較用）
+@torch.no_grad()
+def vae_decode(latents, vae):
+    """通常のVAEデコード処理（全フレーム一括）"""
+    latents = latents / vae.config.scaling_factor
+    # 一括でデコード
+    image = vae.decode(latents.to(device=vae.device, dtype=vae.dtype)).sample
+    return image
+
+# メモリ・速度のベンチマーク関数
+def benchmark_vae_decode(latents, vae, method="both"):
+    """VAEデコードのベンチマーク関数"""
+    results = {}
+    
+    if method in ["original", "both"]:
+        # 通常のデコード
+        torch.cuda.reset_peak_memory_stats()
+        torch.cuda.empty_cache()
+        with torch.no_grad():
+            start = time.time()
+            images_o = vae_decode(latents, vae)
+            torch.cuda.synchronize()
+            end = time.time()
+            
+            mem_o = torch.cuda.max_memory_allocated()
+            results["original"] = {
+                "images": images_o,
+                "memory": mem_o / (1024**2),
+                "time": end - start
+            }
+            print(f"vae_decode() メモリ使用量: {mem_o / (1024**2):.2f} MB 実行時間: {end - start:.4f} 秒")
+    
+    if method in ["cache", "both"]:
+        # キャッシュを使用したデコード
+        torch.cuda.reset_peak_memory_stats()
+        torch.cuda.empty_cache()
+        with torch.no_grad():
+            start = time.time()
+            images_c = vae_decode_cache(latents, vae)
+            torch.cuda.synchronize()
+            end = time.time()
+            
+            mem_c = torch.cuda.max_memory_allocated()
+            results["cache"] = {
+                "images": images_c,
+                "memory": mem_c / (1024**2),
+                "time": end - start
+            }
+            print(f"vae_decode_cache() メモリ使用量: {mem_c / (1024**2):.2f} MB 実行時間: {end - start:.4f} 秒")
+    
+    # 両方のメソッドを実行した場合に結果の差異を表示
+    if method == "both":
+        diff = (results["original"]["images"] - results["cache"]["images"]).abs().mean()
+        print(f"出力画像の平均差異: {diff.item():.6f}")
+    
+    return results

hf_download/hub/models--hunyuanvideo-community--HunyuanVideo/blobs/22f91ac3aeb401be0a10d294e00bb1d6293bc4c5

@@ -0,0 +1,31 @@
+{
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "49406": {
+      "content": "<|startoftext|>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "49407": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<|startoftext|>",
+  "clean_up_tokenization_spaces": false,
+  "do_lower_case": true,
+  "eos_token": "<|endoftext|>",
+  "errors": "replace",
+  "extra_special_tokens": {},
+  "model_max_length": 77,
+  "pad_token": "<|endoftext|>",
+  "tokenizer_class": "CLIPTokenizer",
+  "unk_token": "<|endoftext|>"
+}

hf_download/hub/models--hunyuanvideo-community--HunyuanVideo/blobs/747d2159aaebc628e8105b91c2ab77d50a289f17

@@ -0,0 +1,2096 @@
+{
+  "add_bos_token": true,
+  "add_eos_token": false,
+  "add_prefix_space": null,
+  "added_tokens_decoder": {
+    "128000": {
+      "content": "<|begin_of_text|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128001": {
+      "content": "<|end_of_text|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128002": {
+      "content": "<|reserved_special_token_0|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128003": {
+      "content": "<|reserved_special_token_1|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128004": {
+      "content": "<|reserved_special_token_2|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128005": {
+      "content": "<|reserved_special_token_3|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128006": {
+      "content": "<|start_header_id|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128007": {
+      "content": "<|end_header_id|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
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hf_download/hub/models--hunyuanvideo-community--HunyuanVideo/blobs/7c6fa7065265909bd500cafb38cc939b81b1b018

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hf_download/hub/models--hunyuanvideo-community--HunyuanVideo/blobs/b6e7a9e010002205834fd4f2808ca042bad4a246

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hf_download/hub/models--hunyuanvideo-community--HunyuanVideo/blobs/b70acd51d20aeee27af7a81cea7d68f5288b8f4b

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hf_download/hub/models--hunyuanvideo-community--HunyuanVideo/blobs/cf0682d6de72c1547f41b4f6d7c59f62deffef94

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hf_download/hub/models--hunyuanvideo-community--HunyuanVideo/blobs/f5f2205251eb0b863c5b0f9a60cd9fad069c5872

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hf_download/hub/models--hunyuanvideo-community--HunyuanVideo/snapshots/e8c2aaa66fe3742a32c11a6766aecbf07c56e773/text_encoder/config.json

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hf_download/hub/models--hunyuanvideo-community--HunyuanVideo/snapshots/e8c2aaa66fe3742a32c11a6766aecbf07c56e773/text_encoder_2/config.json

@@ -0,0 +1,25 @@
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hf_download/hub/models--hunyuanvideo-community--HunyuanVideo/snapshots/e8c2aaa66fe3742a32c11a6766aecbf07c56e773/tokenizer/special_tokens_map.json

@@ -0,0 +1,30 @@
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hf_download/hub/models--hunyuanvideo-community--HunyuanVideo/snapshots/e8c2aaa66fe3742a32c11a6766aecbf07c56e773/tokenizer/tokenizer_config.json

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+    },
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+    },
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+    },
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+    },
+    "128245": {
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+    },
+    "128247": {
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+    },
+    "128248": {
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+    },
+    "128249": {
+      "content": "<|reserved_special_token_244|>",
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+    },
+    "128250": {
+      "content": "<|reserved_special_token_245|>",
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+    },
+    "128251": {
+      "content": "<|reserved_special_token_246|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
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+      "special": true
+    },
+    "128252": {
+      "content": "<|reserved_special_token_247|>",
+      "lstrip": false,
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+    },
+    "128253": {
+      "content": "<|reserved_special_token_248|>",
+      "lstrip": false,
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+    },
+    "128254": {
+      "content": "<|reserved_special_token_249|>",
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+    },
+    "128255": {
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128256": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128257": {
+      "content": "<image>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128258": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<|begin_of_text|>",
+  "chat_template": "{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "<|end_of_text|>",
+  "extra_special_tokens": {},
+  "legacy": true,
+  "model_input_names": [
+    "input_ids",
+    "attention_mask"
+  ],
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "<pad>",
+  "padding_side": "right",
+  "processor_class": "LlavaProcessor",
+  "tokenizer_class": "LlamaTokenizer",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false
+}

hf_download/hub/models--hunyuanvideo-community--HunyuanVideo/snapshots/e8c2aaa66fe3742a32c11a6766aecbf07c56e773/tokenizer_2/special_tokens_map.json

@@ -0,0 +1,30 @@
+{
+  "bos_token": {
+    "content": "<|startoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

hf_download/hub/models--hunyuanvideo-community--HunyuanVideo/snapshots/e8c2aaa66fe3742a32c11a6766aecbf07c56e773/tokenizer_2/tokenizer_config.json

@@ -0,0 +1,31 @@
+{
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "49406": {
+      "content": "<|startoftext|>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "49407": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<|startoftext|>",
+  "clean_up_tokenization_spaces": false,
+  "do_lower_case": true,
+  "eos_token": "<|endoftext|>",
+  "errors": "replace",
+  "extra_special_tokens": {},
+  "model_max_length": 77,
+  "pad_token": "<|endoftext|>",
+  "tokenizer_class": "CLIPTokenizer",
+  "unk_token": "<|endoftext|>"
+}

hf_download/hub/models--hunyuanvideo-community--HunyuanVideo/snapshots/e8c2aaa66fe3742a32c11a6766aecbf07c56e773/vae/config.json

@@ -0,0 +1,32 @@
+{
+  "_class_name": "AutoencoderKLHunyuanVideo",
+  "_diffusers_version": "0.32.0.dev0",
+  "act_fn": "silu",
+  "block_out_channels": [
+    128,
+    256,
+    512,
+    512
+  ],
+  "down_block_types": [
+    "HunyuanVideoDownBlock3D",
+    "HunyuanVideoDownBlock3D",
+    "HunyuanVideoDownBlock3D",
+    "HunyuanVideoDownBlock3D"
+  ],
+  "in_channels": 3,
+  "latent_channels": 16,
+  "layers_per_block": 2,
+  "mid_block_add_attention": true,
+  "norm_num_groups": 32,
+  "out_channels": 3,
+  "scaling_factor": 0.476986,
+  "spatial_compression_ratio": 8,
+  "temporal_compression_ratio": 4,
+  "up_block_types": [
+    "HunyuanVideoUpBlock3D",
+    "HunyuanVideoUpBlock3D",
+    "HunyuanVideoUpBlock3D",
+    "HunyuanVideoUpBlock3D"
+  ]
+}

hf_download/hub/models--lllyasviel--flux_redux_bfl/refs/main

@@ -0,0 +1 @@
+45b801affc54ff2af4e5daf1b282e0921901db87

kaggle.json

@@ -0,0 +1 @@
+{"username":"kiranparthiban123","key":"51e8f0ad0a0c4ea1b633c66adb55b17e"}

locales/i18n.py

@@ -0,0 +1,126 @@
+import json
+import os.path
+
+# デフォルト言語設定
+# 注意: init関数を呼び出すまでは翻訳機能は使用できません
+lang = "ja"  # 明示的にデフォルト言語を日本語(ja)に設定
+translateContext = None
+
+class I18nString(str):
+    def __new__(cls, value):
+        result = translateContext.get(lang, {}).get(value, value)
+        return result
+
+    def __init__(self, value):
+        if isinstance(value, I18nString):
+            self.add_values = value.add_values
+            self.radd_values = value.radd_values
+        else:
+            self.add_values = []
+            self.radd_values = []
+
+    def __str__(self):
+        result = translateContext.get(lang, {}).get(self, super().__str__())
+
+        for v in self.radd_values:
+            result = str(v) + result
+
+        for v in self.add_values:
+            result = result + str(v)
+
+        # hotfix, remove unexpected single quotes
+        while len(result) >= 2 and result.startswith("'") and result.endswith("'"):
+            result = result[1:-1]
+
+        return result
+
+    def __add__(self, other):
+        v = str(self)
+        if isinstance(v, I18nString):
+            self.add_values.append(other)
+            return self
+        return v.__add__(other)
+
+    def __radd__(self, other):
+        v = str(self)
+        if isinstance(v, I18nString):
+            self.radd_values.append(other)
+            return self
+        return other.__add__(v)
+
+    def __hash__(self) -> int:
+        return super().__hash__()
+
+    def format(self, *args, **kwargs) -> str:
+        v = str(self)
+        if isinstance(v, I18nString):
+            return super().format(*args, **kwargs)
+        return v.format(*args, **kwargs)
+
+    def unwrap(self):
+        return super().__str__()
+
+    @staticmethod
+    def unwrap_strings(obj):
+        """Unwrap all keys in I18nStrings in the object"""
+        if isinstance(obj, I18nString):
+            yield obj.unwrap()
+            for v in obj.add_values:
+                yield from I18nString.unwrap_strings(v)
+            for v in obj.radd_values:
+                yield from I18nString.unwrap_strings(v)
+            return
+        yield obj
+
+def translate(key: str):
+    """指定されたキーに対応する翻訳文字列を返します。
+    
+    Args:
+        key: 翻訳したい文字列のキー
+        
+    Returns:
+        I18nString: 現在の言語設定に基づいた翻訳文字列
+    """
+    # デバッグ用：translateContextがロードされていない場合に自動的にロード
+    global translateContext
+    if translateContext is None:
+        # 自動的にinitializeを呼び出す
+        init(lang)
+    
+    return I18nString(key)
+
+def load_translations():
+    translations = {}
+    locales_dir = os.path.join(os.path.dirname(__file__), './')
+
+    for locale in ["en", "ja", "zh-tw", "ru"]:
+        json_file = os.path.join(locales_dir, f"{locale}.json")
+        if os.path.exists(json_file):
+            with open(json_file, 'r', encoding='utf-8') as f:
+                translations[locale] = json.load(f)
+        else:
+            print("Warning: Translation file {0} not found".format(json_file))
+            translations[locale] = {}
+
+    return translations
+
+def init(locale="ja"):
+    """言語を初期化します。
+    
+    Args:
+        locale: 使用する言語コード（例: 'ja', 'en', 'zh-tw'）。
+               未対応の言語の場合は自動的に'ja'が使用されます。
+    """
+    global lang
+    global translateContext
+    
+    # 対応言語のリスト
+    supported_locales = ["ja", "en", "zh-tw", "ru"]
+    
+    # 対応していない言語の場合はデフォルト言語(ja)を使用
+    if locale not in supported_locales:
+        print("Unsupported language: {0}. Falling back to 'ja'".format(locale))
+        locale = "ja"
+    
+    lang = locale
+    translateContext = load_translations()

lora_utils/__init__.py

@@ -0,0 +1,46 @@
+# FramePack-eichi LoRA Utilities
+# 
+# LoRAの適用、FP8最適化、LoRAフォーマット検出と変換のための機能を提供します。
+
+from .lora_utils import (
+    merge_lora_to_state_dict,
+    load_safetensors_with_lora_and_fp8,
+    load_safetensors_with_fp8_optimization,
+    convert_hunyuan_to_framepack,
+    convert_from_diffusion_pipe_or_something
+)
+
+from .fp8_optimization_utils import (
+    calculate_fp8_maxval,
+    quantize_tensor_to_fp8,
+    optimize_state_dict_with_fp8_on_the_fly,
+    fp8_linear_forward_patch,
+    apply_fp8_monkey_patch,
+    check_fp8_support
+)
+
+from .lora_loader import (
+    load_and_apply_lora
+)
+
+from .safetensors_utils import (
+    MemoryEfficientSafeOpen
+)
+
+# 国際化対応ヘルパー
+try:
+    from locales import i18n
+    HAS_I18N = True
+except ImportError:
+    HAS_I18N = False
+    print("Warning: i18n module not found, using fallback translations")
+
+# 翻訳ヘルパー関数
+def _(text):
+    """国際化対応のためのヘルパー関数"""
+    if HAS_I18N:
+        return i18n.translate(text)
+    return text
+
+# バージョン情報
+__version__ = "1.0.0"

lora_utils/dynamic_swap_lora.py

@@ -0,0 +1,76 @@
+# FramePack-eichi Dynamic Swap LoRA
+#
+# このモジュールは後方互換性のために残されていますが、
+# 実際にはdirect_applicationによるLoRA適用が使用されています。
+
+import os
+import torch
+import warnings
+
+# 国際化対応
+from locales.i18n_extended import translate as _
+
+
+class DynamicSwapLoRAManager:
+    """
+    この旧式のLoRA管理クラスは後方互換性のために残されていますが、
+    実際の処理では使用されません。代わりに直接的なLoRA適用が行われます。
+    """
+
+    def __init__(self):
+        """初期化"""
+        self.is_active = False
+        self.lora_path = None
+        self.lora_scale = 0.8
+        warnings.warn(
+            _("DynamicSwapLoRAManagerは非推奨です。代わりにlora_loader.load_and_apply_lora()を使用してください。"),
+            DeprecationWarning,
+            stacklevel=2
+        )
+
+    def load_lora(self, lora_path, is_diffusers=False):
+        """
+        LoRAファイルをロードする (実際には、パスの記録のみ)
+
+        Args:
+            lora_path: LoRAファイルのパス
+            is_diffusers: 互換性のために残されたパラメータ（使用されない）
+        """
+        if not os.path.exists(lora_path):
+            raise FileNotFoundError(_("LoRAファイルが見つかりません: {0}").format(lora_path))
+
+        self.lora_path = lora_path
+        self.is_active = True
+
+        print(_("LoRAファイルがロードされました (非推奨インターフェース): {0}").format(lora_path))
+        print(_("注意: ") + _("DynamicSwapLoRAManagerは非推奨です。代わりにlora_loader.load_and_apply_lora()を使用してください。"))
+
+    def set_scale(self, scale):
+        """
+        LoRA適用スケールを設定する
+
+        Args:
+            scale: LoRAの適用強度
+        """
+        self.lora_scale = scale
+
+    def install_hooks(self, model):
+        """
+        モデルにLoRAフックをインストールする (実際には、直接適用を行う)
+
+        Args:
+            model: フックをインストールするモデル
+        """
+        # 直接適用モードを使用してLoRAを適用
+        from .lora_loader import load_and_apply_lora
+
+        print(_("警告: DynamicSwapLoRAManagerは非推奨です。直接適用モードにリダイレクトします。"))
+
+        load_and_apply_lora(
+            model,
+            self.lora_path,
+            self.lora_scale,
+            device=torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        )
+
+        print(_("LoRAは直接適用モードで適用されました。"))