Merge branch 'sd3' into no-grad-block-swap

2026-04-08 06:28:48 +00:00 · 2025-05-24 18:50:25 +09:00
parent c898e4e536 5753b8ff6b
commit 0e8b5d4af9
25 changed files with 1111 additions and 158 deletions
--- a/.github/FUNDING.yml
+++ b/.github/FUNDING.yml
@@ -0,0 +1,3 @@
+# These are supported funding model platforms
+
+github: kohya-ss
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -12,6 +12,9 @@ on:
      - dev
      - sd3

+# CKV2_GHA_1: "Ensure top-level permissions are not set to write-all"
+permissions: read-all
+
 jobs:
  build:
    runs-on: ${{ matrix.os }}
@@ -40,7 +43,7 @@ jobs:
      - name: Install dependencies
        run: |
          # Pre-install torch to pin version (requirements.txt has dependencies like transformers which requires pytorch)
-          pip install dadaptation==3.2 torch==${{ matrix.pytorch-version }} torchvision==0.19.0 pytest==8.3.4
+          pip install dadaptation==3.2 torch==${{ matrix.pytorch-version }} torchvision pytest==8.3.4
          pip install -r requirements.txt

      - name: Test with pytest
--- a/.github/workflows/typos.yml
+++ b/.github/workflows/typos.yml
@@ -12,6 +12,9 @@ on:
      - synchronize
      - reopened

+# CKV2_GHA_1: "Ensure top-level permissions are not set to write-all"
+permissions: read-all
+
 jobs:
  build:
    runs-on: ubuntu-latest
--- a/README.md
+++ b/README.md
@@ -9,11 +9,35 @@ __Please update PyTorch to 2.4.0. We have tested with `torch==2.4.0` and `torchv
 The command to install PyTorch is as follows:
 `pip3 install torch==2.4.0 torchvision==0.19.0 --index-url https://download.pytorch.org/whl/cu124`

+If you are using DeepSpeed, please install DeepSpeed with `pip install deepspeed==0.16.7`.
+
 - [FLUX.1 training](#flux1-training)
 - [SD3 training](#sd3-training)

 ### Recent Updates

+May 1, 2025:
+- The error when training FLUX.1 with mixed precision in flux_train.py with DeepSpeed enabled has been resolved. Thanks to sharlynxy for PR [#2060](https://github.com/kohya-ss/sd-scripts/pull/2060). Please refer to the PR for details.
+  - If you enable DeepSpeed, please install DeepSpeed with `pip install deepspeed==0.16.7`.
+
+Apr 27, 2025:
+- FLUX.1 training now supports CFG scale in the sample generation during training. Please use `--g` option, to specify the CFG scale (note that `--l` is used as the embedded guidance scale.) PR [#2064](https://github.com/kohya-ss/sd-scripts/pull/2064).
+    - See [here](#sample-image-generation-during-training) for details.
+    - If you have any issues with this, please let us know.
+
+Apr 6, 2025:
+- IP noise gamma has been enabled in FLUX.1. Thanks to rockerBOO for PR [#1992](https://github.com/kohya-ss/sd-scripts/pull/1992). See the PR for details.
+    - `--ip_noise_gamma` and `--ip_noise_gamma_random_strength` are available.
+  
+Mar 30, 2025:
+- LoRA-GGPO is added for FLUX.1 LoRA training. Thank you to rockerBOO for PR [#1974](https://github.com/kohya-ss/sd-scripts/pull/1974). 
+  - Specify `--network_args ggpo_sigma=0.03 ggpo_beta=0.01` in the command line or `network_args = ["ggpo_sigma=0.03", "ggpo_beta=0.01"]` in .toml file. See PR for details.
+- The interpolation method for resizing the original image to the training size can now be specified. Thank you to rockerBOO for PR [#1936](https://github.com/kohya-ss/sd-scripts/pull/1936).
+
+Mar 20, 2025:
+- `pytorch-optimizer` is added to requirements.txt. Thank you to gesen2egee for PR [#1985](https://github.com/kohya-ss/sd-scripts/pull/1985). 
+  - For example, you can use CAME optimizer with `--optimizer_type "pytorch_optimizer.CAME" --optimizer_args "weight_decay=0.01"`.
+
 Mar 6, 2025:

 - Added a utility script to merge the weights of SD3's DiT, VAE (optional), CLIP-L, CLIP-G, and T5XXL into a single .safetensors file. Run `tools/merge_sd3_safetensors.py`. See `--help` for usage. PR [#1960](https://github.com/kohya-ss/sd-scripts/pull/1960)
@@ -748,6 +772,8 @@ Not available yet.
 [__Change History__](#change-history) is moved to the bottom of the page. 
 更新履歴は[ページ末尾](#change-history)に移しました。

+Latest update: 2025-03-21 (Version 0.9.1)
+
 [日本語版READMEはこちら](./README-ja.md)

 The development version is in the `dev` branch. Please check the dev branch for the latest changes.
@@ -855,6 +881,14 @@ Note: Some user reports ``ValueError: fp16 mixed precision requires a GPU`` is o

 (Single GPU with id `0` will be used.)

+## DeepSpeed installation (experimental, Linux or WSL2 only)
+  
+To install DeepSpeed, run the following command in your activated virtual environment:
+
+```bash
+pip install deepspeed==0.16.7 
+```
+
 ## Upgrade

 When a new release comes out you can upgrade your repo with the following command:
@@ -891,6 +925,11 @@ The majority of scripts is licensed under ASL 2.0 (including codes from Diffuser

 ## Change History

+### Mar 21, 2025 /  2025-03-21 Version 0.9.1
+
+- Fixed a bug where some of LoRA modules for CLIP Text Encoder were not trained. Thank you Nekotekina for PR [#1964](https://github.com/kohya-ss/sd-scripts/pull/1964)
+  - The LoRA modules for CLIP Text Encoder are now 264 modules, which is the same as before. Only 88 modules were trained in the previous version. 
+
 ### Jan 17, 2025 /  2025-01-17 Version 0.9.0

 - __important__ The dependent libraries are updated. Please see [Upgrade](#upgrade) and update the libraries.
@@ -1324,11 +1363,13 @@ masterpiece, best quality, 1boy, in business suit, standing at street, looking b

  Lines beginning with `#` are comments. You can specify options for the generated image with options like `--n` after the prompt. The following can be used.

-  * `--n` Negative prompt up to the next option.
+  * `--n` Negative prompt up to the next option. Ignored when CFG scale is `1.0`.
  * `--w` Specifies the width of the generated image.
  * `--h` Specifies the height of the generated image.
  * `--d` Specifies the seed of the generated image.
  * `--l` Specifies the CFG scale of the generated image.
+    * In guidance distillation models like FLUX.1, this value is used as the embedded guidance scale for backward compatibility.
+  * `--g` Specifies the CFG scale for the models with embedded guidance scale. The default is `1.0`, `1.0` means no CFG. In general, should not be changed unless you train the un-distilled FLUX.1 models.
  * `--s` Specifies the number of steps in the generation.

  The prompt weighting such as `( )` and `[ ]` are working.
--- a/docs/config_README-en.md
+++ b/docs/config_README-en.md
@@ -152,6 +152,7 @@ These options are related to subset configuration.
 | `keep_tokens_separator` | `“|||”` | o | o | o |
 | `secondary_separator` | `“;;;”` | o | o | o |
 | `enable_wildcard` | `true` | o | o | o |
+| `resize_interpolation` | (not specified) | o | o | o |

 * `num_repeats`
    * Specifies the number of repeats for images in a subset. This is equivalent to `--dataset_repeats` in fine-tuning but can be specified for any training method.
@@ -165,6 +166,8 @@ These options are related to subset configuration.
    * Specifies an additional separator. The part separated by this separator is treated as one tag and is shuffled and dropped. It is then replaced by `caption_separator`. For example, if you specify `aaa;;;bbb;;;ccc`, it will be replaced by `aaa,bbb,ccc` or dropped together.
 * `enable_wildcard`
    * Enables wildcard notation. This will be explained later.
+* `resize_interpolation`
+    * Specifies the interpolation method used when resizing images. Normally, there is no need to specify this. The following options can be specified: `lanczos`, `nearest`, `bilinear`, `linear`, `bicubic`, `cubic`, `area`, `box`. By default (when not specified), `area` is used for downscaling, and `lanczos` is used for upscaling. If this option is specified, the same interpolation method will be used for both upscaling and downscaling. When `lanczos` or `box` is specified, PIL is used; for other options, OpenCV is used.

 ### DreamBooth-specific options

--- a/docs/config_README-ja.md
+++ b/docs/config_README-ja.md
@@ -144,6 +144,7 @@ DreamBooth の手法と fine tuning の手法の両方とも利用可能な学
 | `keep_tokens_separator` | `“|||”` | o | o | o |
 | `secondary_separator` | `“;;;”` | o | o | o |
 | `enable_wildcard` | `true` | o | o | o |
+| `resize_interpolation` |（通常は設定しません） | o | o | o |

 * `num_repeats`
    * サブセットの画像の繰り返し回数を指定します。fine tuning における `--dataset_repeats` に相当しますが、`num_repeats` はどの学習方法でも指定可能です。
@@ -162,6 +163,9 @@ DreamBooth の手法と fine tuning の手法の両方とも利用可能な学
 * `enable_wildcard`
    * ワイルドカード記法および複数行キャプションを有効にします。ワイルドカード記法、複数行キャプションについては後述します。

+* `resize_interpolation`
+    * 画像のリサイズ時に使用する補間方法を指定します。通常は指定しなくて構いません。`lanczos`, `nearest`, `bilinear`, `linear`, `bicubic`, `cubic`, `area`, `box` が指定可能です。デフォルト（未指定時）は、縮小時は `area`、拡大時は `lanczos` になります。このオプションを指定すると、拡大時・縮小時とも同じ補間方法が使用されます。`lanczos`、`box`を指定するとPILが、それ以外を指定するとOpenCVが使用されます。
+
 ### DreamBooth 方式専用のオプション

 DreamBooth 方式のオプションは、サブセット向けオプションのみ存在します。
--- a/finetune/tag_images_by_wd14_tagger.py
+++ b/finetune/tag_images_by_wd14_tagger.py
@@ -11,7 +11,7 @@ from PIL import Image
 from tqdm import tqdm

 import library.train_util as train_util
-from library.utils import setup_logging, pil_resize
+from library.utils import setup_logging, resize_image

 setup_logging()
 import logging
@@ -42,10 +42,7 @@ def preprocess_image(image):
    pad_t = pad_y // 2
    image = np.pad(image, ((pad_t, pad_y - pad_t), (pad_l, pad_x - pad_l), (0, 0)), mode="constant", constant_values=255)

-    if size > IMAGE_SIZE:
-        image = cv2.resize(image, (IMAGE_SIZE, IMAGE_SIZE), cv2.INTER_AREA)
-    else:
-        image = pil_resize(image, (IMAGE_SIZE, IMAGE_SIZE))
+    image = resize_image(image, image.shape[0], image.shape[1], IMAGE_SIZE, IMAGE_SIZE)

    image = image.astype(np.float32)
    return image
@@ -100,15 +97,19 @@ def main(args):
        else:
            for file in SUB_DIR_FILES:
                hf_hub_download(
-                    args.repo_id,
-                    file,
+                    repo_id=args.repo_id,
+                    filename=file,
                    subfolder=SUB_DIR,
-                    cache_dir=os.path.join(model_location, SUB_DIR),
+                    local_dir=os.path.join(model_location, SUB_DIR),
                    force_download=True,
-                    force_filename=file,
                )
        for file in files:
-            hf_hub_download(args.repo_id, file, cache_dir=model_location, force_download=True, force_filename=file)
+            hf_hub_download(
+                repo_id=args.repo_id,
+                filename=file,
+                local_dir=model_location,
+                force_download=True,
+            )
    else:
        logger.info("using existing wd14 tagger model")

--- a/library/config_util.py
+++ b/library/config_util.py
@@ -75,6 +75,7 @@ class BaseSubsetParams:
    custom_attributes: Optional[Dict[str, Any]] = None
    validation_seed: int = 0
    validation_split: float = 0.0
+    resize_interpolation: Optional[str] = None


@dataclass
@@ -106,7 +107,7 @@ class BaseDatasetParams:
    debug_dataset: bool = False
    validation_seed: Optional[int] = None
    validation_split: float = 0.0
-
+    resize_interpolation: Optional[str] = None

@dataclass
 class DreamBoothDatasetParams(BaseDatasetParams):
@@ -196,6 +197,7 @@ class ConfigSanitizer:
        "caption_prefix": str,
        "caption_suffix": str,
        "custom_attributes": dict,
+        "resize_interpolation": str,
    }
    # DO means DropOut
    DO_SUBSET_ASCENDABLE_SCHEMA = {
@@ -241,6 +243,7 @@ class ConfigSanitizer:
        "validation_split": float,
        "resolution": functools.partial(__validate_and_convert_scalar_or_twodim.__func__, int),
        "network_multiplier": float,
+        "resize_interpolation": str,
    }

    # options handled by argparse but not handled by user config
@@ -525,6 +528,7 @@ def generate_dataset_group_by_blueprint(dataset_group_blueprint: DatasetGroupBlu
                [{dataset_type} {i}]
                  batch_size: {dataset.batch_size}
                  resolution: {(dataset.width, dataset.height)}
+                  resize_interpolation: {dataset.resize_interpolation}
                  enable_bucket: {dataset.enable_bucket}
            """)

@@ -558,6 +562,7 @@ def generate_dataset_group_by_blueprint(dataset_group_blueprint: DatasetGroupBlu
                    token_warmup_min: {subset.token_warmup_min},
                    token_warmup_step: {subset.token_warmup_step},
                    alpha_mask: {subset.alpha_mask}
+                    resize_interpolation: {subset.resize_interpolation}
                    custom_attributes: {subset.custom_attributes}
                """), "  ")

--- a/library/deepspeed_utils.py
+++ b/library/deepspeed_utils.py
@@ -5,6 +5,8 @@ from accelerate import DeepSpeedPlugin, Accelerator

 from .utils import setup_logging

+from .device_utils import get_preferred_device
+
 setup_logging()
 import logging

@@ -94,6 +96,7 @@ def prepare_deepspeed_plugin(args: argparse.Namespace):
    deepspeed_plugin.deepspeed_config["train_batch_size"] = (
        args.train_batch_size * args.gradient_accumulation_steps * int(os.environ["WORLD_SIZE"])
    )
+    
    deepspeed_plugin.set_mixed_precision(args.mixed_precision)
    if args.mixed_precision.lower() == "fp16":
        deepspeed_plugin.deepspeed_config["fp16"]["initial_scale_power"] = 0  # preventing overflow.
@@ -122,18 +125,56 @@ def prepare_deepspeed_model(args: argparse.Namespace, **models):
    class DeepSpeedWrapper(torch.nn.Module):
        def __init__(self, **kw_models) -> None:
            super().__init__()
+            
            self.models = torch.nn.ModuleDict()
+            
+            wrap_model_forward_with_torch_autocast = args.mixed_precision is not "no"

            for key, model in kw_models.items():
                if isinstance(model, list):
                    model = torch.nn.ModuleList(model)
+                                            
+                if wrap_model_forward_with_torch_autocast:
+                    model = self.__wrap_model_with_torch_autocast(model)  
+                
                assert isinstance(
                    model, torch.nn.Module
                ), f"model must be an instance of torch.nn.Module, but got {key} is {type(model)}"
+
                self.models.update(torch.nn.ModuleDict({key: model}))

+        def __wrap_model_with_torch_autocast(self, model):
+            if isinstance(model, torch.nn.ModuleList):
+                model = torch.nn.ModuleList([self.__wrap_model_forward_with_torch_autocast(m) for m in model])
+            else:
+                model = self.__wrap_model_forward_with_torch_autocast(model)
+            return model
+
+        def __wrap_model_forward_with_torch_autocast(self, model):
+            
+            assert hasattr(model, "forward"), f"model must have a forward method."
+
+            forward_fn = model.forward
+
+            def forward(*args, **kwargs):
+                try:
+                    device_type = model.device.type
+                except AttributeError:
+                    logger.warning(
+                            "[DeepSpeed] model.device is not available. Using get_preferred_device() "
+                            "to determine the device_type for torch.autocast()."
+                    )                    
+                    device_type = get_preferred_device().type
+
+                with torch.autocast(device_type = device_type):
+                    return forward_fn(*args, **kwargs)
+
+            model.forward = forward
+            return model
+        
        def get_models(self):
            return self.models
+        

    ds_model = DeepSpeedWrapper(**models)
    return ds_model
--- a/library/flux_train_utils.py
+++ b/library/flux_train_utils.py
@@ -40,7 +40,7 @@ def sample_images(
    text_encoders,
    sample_prompts_te_outputs,
    prompt_replacement=None,
-    controlnet=None
+    controlnet=None,
 ):
    if steps == 0:
        if not args.sample_at_first:
@@ -101,7 +101,7 @@ def sample_images(
                    steps,
                    sample_prompts_te_outputs,
                    prompt_replacement,
-                    controlnet
+                    controlnet,
                )
    else:
        # Creating list with N elements, where each element is a list of prompt_dicts, and N is the number of processes available (number of devices available)
@@ -125,7 +125,7 @@ def sample_images(
                        steps,
                        sample_prompts_te_outputs,
                        prompt_replacement,
-                        controlnet
+                        controlnet,
                    )

    torch.set_rng_state(rng_state)
@@ -147,14 +147,16 @@ def sample_image_inference(
    steps,
    sample_prompts_te_outputs,
    prompt_replacement,
-    controlnet
+    controlnet,
 ):
    assert isinstance(prompt_dict, dict)
-    # negative_prompt = prompt_dict.get("negative_prompt")
+    negative_prompt = prompt_dict.get("negative_prompt")
    sample_steps = prompt_dict.get("sample_steps", 20)
    width = prompt_dict.get("width", 512)
    height = prompt_dict.get("height", 512)
-    scale = prompt_dict.get("scale", 3.5)
+    # TODO refactor variable names
+    cfg_scale = prompt_dict.get("guidance_scale", 1.0)
+    emb_guidance_scale = prompt_dict.get("scale", 3.5)
    seed = prompt_dict.get("seed")
    controlnet_image = prompt_dict.get("controlnet_image")
    prompt: str = prompt_dict.get("prompt", "")
@@ -162,8 +164,8 @@ def sample_image_inference(

    if prompt_replacement is not None:
        prompt = prompt.replace(prompt_replacement[0], prompt_replacement[1])
-        # if negative_prompt is not None:
-        #     negative_prompt = negative_prompt.replace(prompt_replacement[0], prompt_replacement[1])
+        if negative_prompt is not None:
+            negative_prompt = negative_prompt.replace(prompt_replacement[0], prompt_replacement[1])

    if seed is not None:
        torch.manual_seed(seed)
@@ -173,16 +175,21 @@ def sample_image_inference(
        torch.seed()
        torch.cuda.seed()

-    # if negative_prompt is None:
-    #     negative_prompt = ""
+    if negative_prompt is None:
+        negative_prompt = ""
    height = max(64, height - height % 16)  # round to divisible by 16
    width = max(64, width - width % 16)  # round to divisible by 16
    logger.info(f"prompt: {prompt}")
-    # logger.info(f"negative_prompt: {negative_prompt}")
+    if cfg_scale != 1.0:
+        logger.info(f"negative_prompt: {negative_prompt}")
+    elif negative_prompt != "":
+        logger.info(f"negative prompt is ignored because scale is 1.0")
    logger.info(f"height: {height}")
    logger.info(f"width: {width}")
    logger.info(f"sample_steps: {sample_steps}")
-    logger.info(f"scale: {scale}")
+    logger.info(f"embedded guidance scale: {emb_guidance_scale}")
+    if cfg_scale != 1.0:
+        logger.info(f"CFG scale: {cfg_scale}")
    # logger.info(f"sample_sampler: {sampler_name}")
    if seed is not None:
        logger.info(f"seed: {seed}")
@@ -191,26 +198,37 @@ def sample_image_inference(
    tokenize_strategy = strategy_base.TokenizeStrategy.get_strategy()
    encoding_strategy = strategy_base.TextEncodingStrategy.get_strategy()

-    text_encoder_conds = []
-    if sample_prompts_te_outputs and prompt in sample_prompts_te_outputs:
-        text_encoder_conds = sample_prompts_te_outputs[prompt]
-        print(f"Using cached text encoder outputs for prompt: {prompt}")
-    if text_encoders is not None:
-        print(f"Encoding prompt: {prompt}")
-        tokens_and_masks = tokenize_strategy.tokenize(prompt)
-        # strategy has apply_t5_attn_mask option
-        encoded_text_encoder_conds = encoding_strategy.encode_tokens(tokenize_strategy, text_encoders, tokens_and_masks)
+    def encode_prompt(prpt):
+        text_encoder_conds = []
+        if sample_prompts_te_outputs and prpt in sample_prompts_te_outputs:
+            text_encoder_conds = sample_prompts_te_outputs[prpt]
+            print(f"Using cached text encoder outputs for prompt: {prpt}")
+        if text_encoders is not None:
+            print(f"Encoding prompt: {prpt}")
+            tokens_and_masks = tokenize_strategy.tokenize(prpt)
+            # strategy has apply_t5_attn_mask option
+            encoded_text_encoder_conds = encoding_strategy.encode_tokens(tokenize_strategy, text_encoders, tokens_and_masks)

-        # if text_encoder_conds is not cached, use encoded_text_encoder_conds
-        if len(text_encoder_conds) == 0:
-            text_encoder_conds = encoded_text_encoder_conds
-        else:
-            # if encoded_text_encoder_conds is not None, update cached text_encoder_conds
-            for i in range(len(encoded_text_encoder_conds)):
-                if encoded_text_encoder_conds[i] is not None:
-                    text_encoder_conds[i] = encoded_text_encoder_conds[i]
+            # if text_encoder_conds is not cached, use encoded_text_encoder_conds
+            if len(text_encoder_conds) == 0:
+                text_encoder_conds = encoded_text_encoder_conds
+            else:
+                # if encoded_text_encoder_conds is not None, update cached text_encoder_conds
+                for i in range(len(encoded_text_encoder_conds)):
+                    if encoded_text_encoder_conds[i] is not None:
+                        text_encoder_conds[i] = encoded_text_encoder_conds[i]
+        return text_encoder_conds

-    l_pooled, t5_out, txt_ids, t5_attn_mask = text_encoder_conds
+    l_pooled, t5_out, txt_ids, t5_attn_mask = encode_prompt(prompt)
+    # encode negative prompts
+    if cfg_scale != 1.0:
+        neg_l_pooled, neg_t5_out, _, neg_t5_attn_mask = encode_prompt(negative_prompt)
+        neg_t5_attn_mask = (
+            neg_t5_attn_mask.to(accelerator.device) if args.apply_t5_attn_mask and neg_t5_attn_mask is not None else None
+        )
+        neg_cond = (cfg_scale, neg_l_pooled, neg_t5_out, neg_t5_attn_mask)
+    else:
+        neg_cond = None

    # sample image
    weight_dtype = ae.dtype  # TOFO give dtype as argument
@@ -235,7 +253,20 @@ def sample_image_inference(
        controlnet_image = controlnet_image.permute(2, 0, 1).unsqueeze(0).to(weight_dtype).to(accelerator.device)

    with accelerator.autocast(), torch.no_grad():
-        x = denoise(flux, noise, img_ids, t5_out, txt_ids, l_pooled, timesteps=timesteps, guidance=scale, t5_attn_mask=t5_attn_mask, controlnet=controlnet, controlnet_img=controlnet_image)
+        x = denoise(
+            flux,
+            noise,
+            img_ids,
+            t5_out,
+            txt_ids,
+            l_pooled,
+            timesteps=timesteps,
+            guidance=emb_guidance_scale,
+            t5_attn_mask=t5_attn_mask,
+            controlnet=controlnet,
+            controlnet_img=controlnet_image,
+            neg_cond=neg_cond,
+        )

    x = flux_utils.unpack_latents(x, packed_latent_height, packed_latent_width)

@@ -305,22 +336,24 @@ def denoise(
    model: flux_models.Flux,
    img: torch.Tensor,
    img_ids: torch.Tensor,
-    txt: torch.Tensor,
+    txt: torch.Tensor,  # t5_out
    txt_ids: torch.Tensor,
-    vec: torch.Tensor,
+    vec: torch.Tensor,  # l_pooled
    timesteps: list[float],
    guidance: float = 4.0,
    t5_attn_mask: Optional[torch.Tensor] = None,
    controlnet: Optional[flux_models.ControlNetFlux] = None,
    controlnet_img: Optional[torch.Tensor] = None,
+    neg_cond: Optional[Tuple[float, torch.Tensor, torch.Tensor, torch.Tensor]] = None,
 ):
    # this is ignored for schnell
    guidance_vec = torch.full((img.shape[0],), guidance, device=img.device, dtype=img.dtype)
-
+    do_cfg = neg_cond is not None

    for t_curr, t_prev in zip(tqdm(timesteps[:-1]), timesteps[1:]):
        t_vec = torch.full((img.shape[0],), t_curr, dtype=img.dtype, device=img.device)
        model.prepare_block_swap_before_forward()
+
        if controlnet is not None:
            block_samples, block_single_samples = controlnet(
                img=img,
@@ -336,20 +369,48 @@ def denoise(
        else:
            block_samples = None
            block_single_samples = None
-        pred = model(
-            img=img,
-            img_ids=img_ids,
-            txt=txt,
-            txt_ids=txt_ids,
-            y=vec,
-            block_controlnet_hidden_states=block_samples,
-            block_controlnet_single_hidden_states=block_single_samples,
-            timesteps=t_vec,
-            guidance=guidance_vec,
-            txt_attention_mask=t5_attn_mask,
-        )

-        img = img + (t_prev - t_curr) * pred
+        if not do_cfg:
+            pred = model(
+                img=img,
+                img_ids=img_ids,
+                txt=txt,
+                txt_ids=txt_ids,
+                y=vec,
+                block_controlnet_hidden_states=block_samples,
+                block_controlnet_single_hidden_states=block_single_samples,
+                timesteps=t_vec,
+                guidance=guidance_vec,
+                txt_attention_mask=t5_attn_mask,
+            )
+
+            img = img + (t_prev - t_curr) * pred
+        else:
+            cfg_scale, neg_l_pooled, neg_t5_out, neg_t5_attn_mask = neg_cond
+            nc_c_t5_attn_mask = None if t5_attn_mask is None else torch.cat([neg_t5_attn_mask, t5_attn_mask], dim=0)
+
+            # TODO is it ok to use the same block samples for both cond and uncond?
+            block_samples = None if block_samples is None else torch.cat([block_samples, block_samples], dim=0)
+            block_single_samples = (
+                None if block_single_samples is None else torch.cat([block_single_samples, block_single_samples], dim=0)
+            )
+
+            nc_c_pred = model(
+                img=torch.cat([img, img], dim=0),
+                img_ids=torch.cat([img_ids, img_ids], dim=0),
+                txt=torch.cat([neg_t5_out, txt], dim=0),
+                txt_ids=torch.cat([txt_ids, txt_ids], dim=0),
+                y=torch.cat([neg_l_pooled, vec], dim=0),
+                block_controlnet_hidden_states=block_samples,
+                block_controlnet_single_hidden_states=block_single_samples,
+                timesteps=t_vec,
+                guidance=guidance_vec,
+                txt_attention_mask=nc_c_t5_attn_mask,
+            )
+            neg_pred, pred = torch.chunk(nc_c_pred, 2, dim=0)
+            pred = neg_pred + (pred - neg_pred) * cfg_scale
+
+            img = img + (t_prev - t_curr) * pred

    model.prepare_block_swap_before_forward()
    return img
@@ -366,8 +427,6 @@ def get_sigmas(noise_scheduler, timesteps, device, n_dim=4, dtype=torch.float32)
    step_indices = [(schedule_timesteps == t).nonzero().item() for t in timesteps]

    sigma = sigmas[step_indices].flatten()
-    while len(sigma.shape) < n_dim:
-        sigma = sigma.unsqueeze(-1)
    return sigma


@@ -410,42 +469,34 @@ def compute_loss_weighting_for_sd3(weighting_scheme: str, sigmas=None):


 def get_noisy_model_input_and_timesteps(
-    args, noise_scheduler, latents, noise, device, dtype
+    args, noise_scheduler, latents: torch.Tensor, noise: torch.Tensor, device, dtype
 ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
    bsz, _, h, w = latents.shape
-    sigmas = None
-
+    assert bsz > 0, "Batch size not large enough"
+    num_timesteps = noise_scheduler.config.num_train_timesteps
    if args.timestep_sampling == "uniform" or args.timestep_sampling == "sigmoid":
-        # Simple random t-based noise sampling
+        # Simple random sigma-based noise sampling
        if args.timestep_sampling == "sigmoid":
            # https://github.com/XLabs-AI/x-flux/tree/main
-            t = torch.sigmoid(args.sigmoid_scale * torch.randn((bsz,), device=device))
+            sigmas = torch.sigmoid(args.sigmoid_scale * torch.randn((bsz,), device=device))
        else:
-            t = torch.rand((bsz,), device=device)
+            sigmas = torch.rand((bsz,), device=device)

-        timesteps = t * 1000.0
-        t = t.view(-1, 1, 1, 1)
-        noisy_model_input = (1 - t) * latents + t * noise
+        timesteps = sigmas * num_timesteps
    elif args.timestep_sampling == "shift":
        shift = args.discrete_flow_shift
-        logits_norm = torch.randn(bsz, device=device)
-        logits_norm = logits_norm * args.sigmoid_scale  # larger scale for more uniform sampling
-        timesteps = logits_norm.sigmoid()
-        timesteps = (timesteps * shift) / (1 + (shift - 1) * timesteps)
-
-        t = timesteps.view(-1, 1, 1, 1)
-        timesteps = timesteps * 1000.0
-        noisy_model_input = (1 - t) * latents + t * noise
+        sigmas = torch.randn(bsz, device=device)
+        sigmas = sigmas * args.sigmoid_scale  # larger scale for more uniform sampling
+        sigmas = sigmas.sigmoid()
+        sigmas = (sigmas * shift) / (1 + (shift - 1) * sigmas)
+        timesteps = sigmas * num_timesteps
    elif args.timestep_sampling == "flux_shift":
-        logits_norm = torch.randn(bsz, device=device)
-        logits_norm = logits_norm * args.sigmoid_scale  # larger scale for more uniform sampling
-        timesteps = logits_norm.sigmoid()
-        mu = get_lin_function(y1=0.5, y2=1.15)((h // 2) * (w // 2))
-        timesteps = time_shift(mu, 1.0, timesteps)
-
-        t = timesteps.view(-1, 1, 1, 1)
-        timesteps = timesteps * 1000.0
-        noisy_model_input = (1 - t) * latents + t * noise
+        sigmas = torch.randn(bsz, device=device)
+        sigmas = sigmas * args.sigmoid_scale  # larger scale for more uniform sampling
+        sigmas = sigmas.sigmoid()
+        mu = get_lin_function(y1=0.5, y2=1.15)((h // 2) * (w // 2))  # we are pre-packed so must adjust for packed size
+        sigmas = time_shift(mu, 1.0, sigmas)
+        timesteps = sigmas * num_timesteps
    else:
        # Sample a random timestep for each image
        # for weighting schemes where we sample timesteps non-uniformly
@@ -456,12 +507,24 @@ def get_noisy_model_input_and_timesteps(
            logit_std=args.logit_std,
            mode_scale=args.mode_scale,
        )
-        indices = (u * noise_scheduler.config.num_train_timesteps).long()
+        indices = (u * num_timesteps).long()
        timesteps = noise_scheduler.timesteps[indices].to(device=device)
-
-        # Add noise according to flow matching.
        sigmas = get_sigmas(noise_scheduler, timesteps, device, n_dim=latents.ndim, dtype=dtype)
-        noisy_model_input = sigmas * noise + (1.0 - sigmas) * latents
+
+    # Broadcast sigmas to latent shape
+    sigmas = sigmas.view(-1, 1, 1, 1)
+
+    # Add noise to the latents according to the noise magnitude at each timestep
+    # (this is the forward diffusion process)
+    if args.ip_noise_gamma:
+        xi = torch.randn_like(latents, device=latents.device, dtype=dtype)
+        if args.ip_noise_gamma_random_strength:
+            ip_noise_gamma = torch.rand(1, device=latents.device, dtype=dtype) * args.ip_noise_gamma
+        else:
+            ip_noise_gamma = args.ip_noise_gamma
+        noisy_model_input = (1.0 - sigmas) * latents + sigmas * (noise + ip_noise_gamma * xi)
+    else:
+        noisy_model_input = (1.0 - sigmas) * latents + sigmas * noise

    return noisy_model_input.to(dtype), timesteps.to(dtype), sigmas

@@ -567,7 +630,7 @@ def add_flux_train_arguments(parser: argparse.ArgumentParser):
        "--controlnet_model_name_or_path",
        type=str,
        default=None,
-        help="path to controlnet (*.sft or *.safetensors) / controlnetのパス（*.sftまたは*.safetensors）"
+        help="path to controlnet (*.sft or *.safetensors) / controlnetのパス（*.sftまたは*.safetensors）",
    )
    parser.add_argument(
        "--t5xxl_max_token_length",
--- a/library/jpeg_xl_util.py
+++ b/library/jpeg_xl_util.py
@@ -0,0 +1,186 @@
+# Modified from https://github.com/Fraetor/jxl_decode Original license: MIT
+# Added partial read support for up to 200x speedup
+
+import os
+from typing import List, Tuple
+
+class JXLBitstream:
+    """
+    A stream of bits with methods for easy handling.
+    """
+
+    def __init__(self, file, offset: int = 0, offsets: List[List[int]] = None):
+        self.shift = 0
+        self.bitstream = bytearray()
+        self.file = file
+        self.offset = offset
+        self.offsets = offsets
+        if self.offsets:
+            self.offset = self.offsets[0][1]
+            self.previous_data_len = 0
+            self.index = 0
+        self.file.seek(self.offset)
+
+    def get_bits(self, length: int = 1) -> int:
+        if self.offsets and self.shift + length > self.previous_data_len + self.offsets[self.index][2]:
+            self.partial_to_read_length = length
+            if self.shift < self.previous_data_len + self.offsets[self.index][2]:
+                self.partial_read(0, length)
+            self.bitstream.extend(self.file.read(self.partial_to_read_length))
+        else:
+            self.bitstream.extend(self.file.read(length))
+        bitmask = 2**length - 1
+        bits = (int.from_bytes(self.bitstream, "little") >> self.shift) & bitmask
+        self.shift += length
+        return bits
+
+    def partial_read(self, current_length: int, length: int) -> None:
+        self.previous_data_len += self.offsets[self.index][2]
+        to_read_length = self.previous_data_len - (self.shift + current_length)
+        self.bitstream.extend(self.file.read(to_read_length))
+        current_length += to_read_length
+        self.partial_to_read_length -= to_read_length
+        self.index += 1
+        self.file.seek(self.offsets[self.index][1])
+        if self.shift + length > self.previous_data_len + self.offsets[self.index][2]:
+            self.partial_read(current_length, length)
+
+
+def decode_codestream(file, offset: int = 0, offsets: List[List[int]] = None) -> Tuple[int,int]:
+    """
+    Decodes the actual codestream.
+    JXL codestream specification: http://www-internal/2022/18181-1
+    """
+
+    # Convert codestream to int within an object to get some handy methods.
+    codestream = JXLBitstream(file, offset=offset, offsets=offsets)
+
+    # Skip signature
+    codestream.get_bits(16)
+
+    # SizeHeader
+    div8 = codestream.get_bits(1)
+    if div8:
+        height = 8 * (1 + codestream.get_bits(5))
+    else:
+        distribution = codestream.get_bits(2)
+        match distribution:
+            case 0:
+                height = 1 + codestream.get_bits(9)
+            case 1:
+                height = 1 + codestream.get_bits(13)
+            case 2:
+                height = 1 + codestream.get_bits(18)
+            case 3:
+                height = 1 + codestream.get_bits(30)
+    ratio = codestream.get_bits(3)
+    if div8 and not ratio:
+        width = 8 * (1 + codestream.get_bits(5))
+    elif not ratio:
+        distribution = codestream.get_bits(2)
+        match distribution:
+            case 0:
+                width = 1 + codestream.get_bits(9)
+            case 1:
+                width = 1 + codestream.get_bits(13)
+            case 2:
+                width = 1 + codestream.get_bits(18)
+            case 3:
+                width = 1 + codestream.get_bits(30)
+    else:
+        match ratio:
+            case 1:
+                width = height
+            case 2:
+                width = (height * 12) // 10
+            case 3:
+                width = (height * 4) // 3
+            case 4:
+                width = (height * 3) // 2
+            case 5:
+                width = (height * 16) // 9
+            case 6:
+                width = (height * 5) // 4
+            case 7:
+                width = (height * 2) // 1
+    return width, height
+
+
+def decode_container(file) -> Tuple[int,int]:
+    """
+    Parses the ISOBMFF container, extracts the codestream, and decodes it.
+    JXL container specification: http://www-internal/2022/18181-2
+    """
+
+    def parse_box(file, file_start: int) -> dict:
+        file.seek(file_start)
+        LBox = int.from_bytes(file.read(4), "big")
+        XLBox = None
+        if 1 < LBox <= 8:
+            raise ValueError(f"Invalid LBox at byte {file_start}.")
+        if LBox == 1:
+            file.seek(file_start + 8)
+            XLBox = int.from_bytes(file.read(8), "big")
+            if XLBox <= 16:
+                raise ValueError(f"Invalid XLBox at byte {file_start}.")
+        if XLBox:
+            header_length = 16
+            box_length = XLBox
+        else:
+            header_length = 8
+            if LBox == 0:
+                box_length = os.fstat(file.fileno()).st_size - file_start
+            else:
+                box_length = LBox
+        file.seek(file_start + 4)
+        box_type = file.read(4)
+        file.seek(file_start)
+        return {
+            "length": box_length,
+            "type": box_type,
+            "offset": header_length,
+        }
+
+    file.seek(0)
+    # Reject files missing required boxes. These two boxes are required to be at
+    # the start and contain no values, so we can manually check there presence.
+    # Signature box. (Redundant as has already been checked.)
+    if file.read(12) != bytes.fromhex("0000000C 4A584C20 0D0A870A"):
+        raise ValueError("Invalid signature box.")
+    # File Type box.
+    if file.read(20) != bytes.fromhex(
+        "00000014 66747970 6A786C20 00000000 6A786C20"
+    ):
+        raise ValueError("Invalid file type box.")
+
+    offset = 0
+    offsets = []
+    data_offset_not_found = True
+    container_pointer = 32
+    file_size = os.fstat(file.fileno()).st_size
+    while data_offset_not_found:
+        box = parse_box(file, container_pointer)
+        match box["type"]:
+            case b"jxlc":
+                offset = container_pointer + box["offset"]
+                data_offset_not_found = False
+            case b"jxlp":
+                file.seek(container_pointer + box["offset"])
+                index = int.from_bytes(file.read(4), "big")
+                offsets.append([index, container_pointer + box["offset"] + 4, box["length"] - box["offset"] - 4])
+        container_pointer += box["length"]
+        if container_pointer >= file_size:
+            data_offset_not_found = False
+
+    if offsets:
+        offsets.sort(key=lambda i: i[0])
+    file.seek(0)
+
+    return decode_codestream(file, offset=offset, offsets=offsets)
+
+
+def get_jxl_size(path: str) -> Tuple[int,int]:
+    with open(path, "rb") as file:
+        if file.read(2) == bytes.fromhex("FF0A"):
+            return decode_codestream(file)
+        return decode_container(file)
--- a/library/train_util.py
+++ b/library/train_util.py
@@ -74,7 +74,7 @@ import library.model_util as model_util
 import library.huggingface_util as huggingface_util
 import library.sai_model_spec as sai_model_spec
 import library.deepspeed_utils as deepspeed_utils
-from library.utils import setup_logging, pil_resize
+from library.utils import setup_logging, resize_image, validate_interpolation_fn

 setup_logging()
 import logging
@@ -113,14 +113,16 @@ except:
 # JPEG-XL on Linux
 try:
    from jxlpy import JXLImagePlugin
+    from library.jpeg_xl_util import get_jxl_size

    IMAGE_EXTENSIONS.extend([".jxl", ".JXL"])
 except:
    pass

-# JPEG-XL on Windows
+# JPEG-XL on Linux and Windows
 try:
    import pillow_jxl
+    from library.jpeg_xl_util import get_jxl_size

    IMAGE_EXTENSIONS.extend([".jxl", ".JXL"])
 except:
@@ -205,6 +207,7 @@ class ImageInfo:
        self.text_encoder_pool2: Optional[torch.Tensor] = None

        self.alpha_mask: Optional[torch.Tensor] = None  # alpha mask can be flipped in runtime
+        self.resize_interpolation: Optional[str] = None


 class BucketManager:
@@ -429,6 +432,7 @@ class BaseSubset:
        custom_attributes: Optional[Dict[str, Any]] = None,
        validation_seed: Optional[int] = None,
        validation_split: Optional[float] = 0.0,
+        resize_interpolation: Optional[str] = None,
    ) -> None:
        self.image_dir = image_dir
        self.alpha_mask = alpha_mask if alpha_mask is not None else False
@@ -459,6 +463,8 @@ class BaseSubset:
        self.validation_seed = validation_seed
        self.validation_split = validation_split

+        self.resize_interpolation = resize_interpolation
+

 class DreamBoothSubset(BaseSubset):
    def __init__(
@@ -490,6 +496,7 @@ class DreamBoothSubset(BaseSubset):
        custom_attributes: Optional[Dict[str, Any]] = None,
        validation_seed: Optional[int] = None,
        validation_split: Optional[float] = 0.0,
+        resize_interpolation: Optional[str] = None,
    ) -> None:
        assert image_dir is not None, "image_dir must be specified / image_dirは指定が必須です"

@@ -517,6 +524,7 @@ class DreamBoothSubset(BaseSubset):
            custom_attributes=custom_attributes,
            validation_seed=validation_seed,
            validation_split=validation_split,
+            resize_interpolation=resize_interpolation,
        )

        self.is_reg = is_reg
@@ -559,6 +567,7 @@ class FineTuningSubset(BaseSubset):
        custom_attributes: Optional[Dict[str, Any]] = None,
        validation_seed: Optional[int] = None,
        validation_split: Optional[float] = 0.0,
+        resize_interpolation: Optional[str] = None,
    ) -> None:
        assert metadata_file is not None, "metadata_file must be specified / metadata_fileは指定が必須です"

@@ -586,6 +595,7 @@ class FineTuningSubset(BaseSubset):
            custom_attributes=custom_attributes,
            validation_seed=validation_seed,
            validation_split=validation_split,
+            resize_interpolation=resize_interpolation,
        )

        self.metadata_file = metadata_file
@@ -624,6 +634,7 @@ class ControlNetSubset(BaseSubset):
        custom_attributes: Optional[Dict[str, Any]] = None,
        validation_seed: Optional[int] = None,
        validation_split: Optional[float] = 0.0,
+        resize_interpolation: Optional[str] = None,
    ) -> None:
        assert image_dir is not None, "image_dir must be specified / image_dirは指定が必須です"

@@ -651,6 +662,7 @@ class ControlNetSubset(BaseSubset):
            custom_attributes=custom_attributes,
            validation_seed=validation_seed,
            validation_split=validation_split,
+            resize_interpolation=resize_interpolation,
        )

        self.conditioning_data_dir = conditioning_data_dir
@@ -671,6 +683,7 @@ class BaseDataset(torch.utils.data.Dataset):
        resolution: Optional[Tuple[int, int]],
        network_multiplier: float,
        debug_dataset: bool,
+        resize_interpolation: Optional[str] = None
    ) -> None:
        super().__init__()

@@ -705,6 +718,10 @@ class BaseDataset(torch.utils.data.Dataset):

        self.image_transforms = IMAGE_TRANSFORMS

+        if resize_interpolation is not None:
+            assert validate_interpolation_fn(resize_interpolation), f"Resize interpolation \"{resize_interpolation}\" is not a valid interpolation"
+        self.resize_interpolation = resize_interpolation
+
        self.image_data: Dict[str, ImageInfo] = {}
        self.image_to_subset: Dict[str, Union[DreamBoothSubset, FineTuningSubset]] = {}

@@ -1043,8 +1060,11 @@ class BaseDataset(torch.utils.data.Dataset):
                    self.bucket_info["buckets"][i] = {"resolution": reso, "count": len(bucket)}
                    logger.info(f"bucket {i}: resolution {reso}, count: {len(bucket)}")

-            img_ar_errors = np.array(img_ar_errors)
-            mean_img_ar_error = np.mean(np.abs(img_ar_errors))
+            if len(img_ar_errors) == 0:
+                mean_img_ar_error = 0  # avoid NaN
+            else:
+                img_ar_errors = np.array(img_ar_errors)
+                mean_img_ar_error = np.mean(np.abs(img_ar_errors))
            self.bucket_info["mean_img_ar_error"] = mean_img_ar_error
            logger.info(f"mean ar error (without repeats): {mean_img_ar_error}")

@@ -1448,6 +1468,8 @@ class BaseDataset(torch.utils.data.Dataset):
                )

    def get_image_size(self, image_path):
+        if image_path.endswith(".jxl") or image_path.endswith(".JXL"):
+            return get_jxl_size(image_path)
        # return imagesize.get(image_path)
        image_size = imagesize.get(image_path)
        if image_size[0] <= 0:
@@ -1494,7 +1516,7 @@ class BaseDataset(torch.utils.data.Dataset):
        nh = int(height * scale + 0.5)
        nw = int(width * scale + 0.5)
        assert nh >= self.height and nw >= self.width, f"internal error. small scale {scale}, {width}*{height}"
-        image = cv2.resize(image, (nw, nh), interpolation=cv2.INTER_AREA)
+        image = resize_image(image, width, height, nw, nh, subset.resize_interpolation)
        face_cx = int(face_cx * scale + 0.5)
        face_cy = int(face_cy * scale + 0.5)
        height, width = nh, nw
@@ -1591,7 +1613,7 @@ class BaseDataset(torch.utils.data.Dataset):

                if self.enable_bucket:
                    img, original_size, crop_ltrb = trim_and_resize_if_required(
-                        subset.random_crop, img, image_info.bucket_reso, image_info.resized_size
+                        subset.random_crop, img, image_info.bucket_reso, image_info.resized_size, resize_interpolation=image_info.resize_interpolation
                    )
                else:
                    if face_cx > 0:  # 顔位置情報あり
@@ -1852,8 +1874,9 @@ class DreamBoothDataset(BaseDataset):
        debug_dataset: bool,
        validation_split: float,
        validation_seed: Optional[int],
+        resize_interpolation: Optional[str],
    ) -> None:
-        super().__init__(resolution, network_multiplier, debug_dataset)
+        super().__init__(resolution, network_multiplier, debug_dataset, resize_interpolation)

        assert resolution is not None, f"resolution is required / resolution（解像度）指定は必須です"

@@ -2078,6 +2101,7 @@ class DreamBoothDataset(BaseDataset):

            for img_path, caption, size in zip(img_paths, captions, sizes):
                info = ImageInfo(img_path, num_repeats, caption, subset.is_reg, img_path)
+                info.resize_interpolation = subset.resize_interpolation if subset.resize_interpolation is not None else self.resize_interpolation
                if size is not None:
                    info.image_size = size
                if subset.is_reg:
@@ -2133,8 +2157,9 @@ class FineTuningDataset(BaseDataset):
        debug_dataset: bool,
        validation_seed: int,
        validation_split: float,
+        resize_interpolation: Optional[str],
    ) -> None:
-        super().__init__(resolution, network_multiplier, debug_dataset)
+        super().__init__(resolution, network_multiplier, debug_dataset, resize_interpolation)

        self.batch_size = batch_size

@@ -2360,9 +2385,10 @@ class ControlNetDataset(BaseDataset):
        bucket_no_upscale: bool,
        debug_dataset: bool,
        validation_split: float,
-        validation_seed: Optional[int],
+        validation_seed: Optional[int],        
+        resize_interpolation: Optional[str] = None,
    ) -> None:
-        super().__init__(resolution, network_multiplier, debug_dataset)
+        super().__init__(resolution, network_multiplier, debug_dataset, resize_interpolation)

        db_subsets = []
        for subset in subsets:
@@ -2394,6 +2420,7 @@ class ControlNetDataset(BaseDataset):
                subset.caption_suffix,
                subset.token_warmup_min,
                subset.token_warmup_step,
+                resize_interpolation=subset.resize_interpolation,
            )
            db_subsets.append(db_subset)

@@ -2412,6 +2439,7 @@ class ControlNetDataset(BaseDataset):
            debug_dataset,
            validation_split,
            validation_seed,
+            resize_interpolation,
        )

        # config_util等から参照される値をいれておく（若干微妙なのでなんとかしたい）
@@ -2420,7 +2448,8 @@ class ControlNetDataset(BaseDataset):
        self.num_train_images = self.dreambooth_dataset_delegate.num_train_images
        self.num_reg_images = self.dreambooth_dataset_delegate.num_reg_images
        self.validation_split = validation_split
-        self.validation_seed = validation_seed
+        self.validation_seed = validation_seed 
+        self.resize_interpolation = resize_interpolation

        # assert all conditioning data exists
        missing_imgs = []
@@ -2508,9 +2537,8 @@ class ControlNetDataset(BaseDataset):
                assert (
                    cond_img.shape[0] == original_size_hw[0] and cond_img.shape[1] == original_size_hw[1]
                ), f"size of conditioning image is not match / 画像サイズが合いません: {image_info.absolute_path}"
-                cond_img = cv2.resize(
-                    cond_img, image_info.resized_size, interpolation=cv2.INTER_AREA
-                )  # INTER_AREAでやりたいのでcv2でリサイズ
+
+                cond_img = resize_image(cond_img, original_size_hw[1], original_size_hw[0], target_size_hw[1], target_size_hw[0], self.resize_interpolation)

                # TODO support random crop
                # 現在サポートしているcropはrandomではなく中央のみ
@@ -2524,7 +2552,7 @@ class ControlNetDataset(BaseDataset):
                # ), f"image size is small / 画像サイズが小さいようです: {image_info.absolute_path}"
                # resize to target
                if cond_img.shape[0] != target_size_hw[0] or cond_img.shape[1] != target_size_hw[1]:
-                    cond_img = pil_resize(cond_img, (int(target_size_hw[1]), int(target_size_hw[0])))
+                    cond_img = resize_image(cond_img, cond_img.shape[0], cond_img.shape[1], target_size_hw[1], target_size_hw[0], self.resize_interpolation)

            if flipped:
                cond_img = cond_img[:, ::-1, :].copy()  # copy to avoid negative stride
@@ -2921,17 +2949,13 @@ def load_image(image_path, alpha=False):

 # 画像を読み込む。戻り値はnumpy.ndarray,(original width, original height),(crop left, crop top, crop right, crop bottom)
 def trim_and_resize_if_required(
-    random_crop: bool, image: np.ndarray, reso, resized_size: Tuple[int, int]
+    random_crop: bool, image: np.ndarray, reso, resized_size: Tuple[int, int], resize_interpolation: Optional[str] = None
 ) -> Tuple[np.ndarray, Tuple[int, int], Tuple[int, int, int, int]]:
    image_height, image_width = image.shape[0:2]
    original_size = (image_width, image_height)  # size before resize

    if image_width != resized_size[0] or image_height != resized_size[1]:
-        # リサイズする
-        if image_width > resized_size[0] and image_height > resized_size[1]:
-            image = cv2.resize(image, resized_size, interpolation=cv2.INTER_AREA)  # INTER_AREAでやりたいのでcv2でリサイズ
-        else:
-            image = pil_resize(image, resized_size)
+        image = resize_image(image, image_width, image_height, resized_size[0], resized_size[1], resize_interpolation)

    image_height, image_width = image.shape[0:2]

@@ -2976,7 +3000,7 @@ def load_images_and_masks_for_caching(
    for info in image_infos:
        image = load_image(info.absolute_path, use_alpha_mask) if info.image is None else np.array(info.image, np.uint8)
        # TODO 画像のメタデータが壊れていて、メタデータから割り当てたbucketと実際の画像サイズが一致しない場合があるのでチェック追加要
-        image, original_size, crop_ltrb = trim_and_resize_if_required(random_crop, image, info.bucket_reso, info.resized_size)
+        image, original_size, crop_ltrb = trim_and_resize_if_required(random_crop, image, info.bucket_reso, info.resized_size, resize_interpolation=info.resize_interpolation)

        original_sizes.append(original_size)
        crop_ltrbs.append(crop_ltrb)
@@ -3017,7 +3041,7 @@ def cache_batch_latents(
    for info in image_infos:
        image = load_image(info.absolute_path, use_alpha_mask) if info.image is None else np.array(info.image, np.uint8)
        # TODO 画像のメタデータが壊れていて、メタデータから割り当てたbucketと実際の画像サイズが一致しない場合があるのでチェック追加要
-        image, original_size, crop_ltrb = trim_and_resize_if_required(random_crop, image, info.bucket_reso, info.resized_size)
+        image, original_size, crop_ltrb = trim_and_resize_if_required(random_crop, image, info.bucket_reso, info.resized_size, resize_interpolation=info.resize_interpolation)

        info.latents_original_size = original_size
        info.latents_crop_ltrb = crop_ltrb
@@ -4495,7 +4519,13 @@ def add_dataset_arguments(
        action="store_true",
        help="make bucket for each image without upscaling / 画像を拡大せずbucketを作成します",
    )
-
+    parser.add_argument(
+        "--resize_interpolation",
+        type=str,
+        default=None,
+        choices=["lanczos", "nearest", "bilinear", "linear", "bicubic", "cubic", "area"],
+        help="Resize interpolation when required. Default: area Options: lanczos, nearest, bilinear, bicubic, area / 必要に応じてサイズ補間を変更します。デフォルト: area オプション: lanczos, nearest, bilinear, bicubic, area",
+    )
    parser.add_argument(
        "--token_warmup_min",
        type=int,
@@ -5468,6 +5498,11 @@ def load_target_model(args, weight_dtype, accelerator, unet_use_linear_projectio


 def patch_accelerator_for_fp16_training(accelerator):
+    
+    from accelerate import DistributedType
+    if accelerator.distributed_type == DistributedType.DEEPSPEED:
+        return
+    
    org_unscale_grads = accelerator.scaler._unscale_grads_

    def _unscale_grads_replacer(optimizer, inv_scale, found_inf, allow_fp16):
@@ -6151,6 +6186,11 @@ def line_to_prompt_dict(line: str) -> dict:
                prompt_dict["scale"] = float(m.group(1))
                continue

+            m = re.match(r"g ([\d\.]+)", parg, re.IGNORECASE)
+            if m:  # guidance scale
+                prompt_dict["guidance_scale"] = float(m.group(1))
+                continue
+
            m = re.match(r"n (.+)", parg, re.IGNORECASE)
            if m:  # negative prompt
                prompt_dict["negative_prompt"] = m.group(1)
@@ -6533,3 +6573,4 @@ class LossRecorder:
        if losses == 0:
            return 0
        return self.loss_total / losses
+
--- a/library/utils.py
+++ b/library/utils.py
@@ -16,7 +16,6 @@ from PIL import Image
 import numpy as np
 from safetensors.torch import load_file

-
 def fire_in_thread(f, *args, **kwargs):
    threading.Thread(target=f, args=args, kwargs=kwargs).start()

@@ -89,6 +88,8 @@ def setup_logging(args=None, log_level=None, reset=False):
        logger = logging.getLogger(__name__)
        logger.info(msg_init)

+setup_logging()
+logger = logging.getLogger(__name__)

 # endregion

@@ -378,7 +379,7 @@ def load_safetensors(
 # region Image utils


-def pil_resize(image, size, interpolation=Image.LANCZOS):
+def pil_resize(image, size, interpolation):
    has_alpha = image.shape[2] == 4 if len(image.shape) == 3 else False

    if has_alpha:
@@ -386,7 +387,7 @@ def pil_resize(image, size, interpolation=Image.LANCZOS):
    else:
        pil_image = Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))

-    resized_pil = pil_image.resize(size, interpolation)
+    resized_pil = pil_image.resize(size, resample=interpolation)

    # Convert back to cv2 format
    if has_alpha:
@@ -397,6 +398,117 @@ def pil_resize(image, size, interpolation=Image.LANCZOS):
    return resized_cv2


+def resize_image(image: np.ndarray, width: int, height: int, resized_width: int, resized_height: int, resize_interpolation: Optional[str] = None):
+    """
+    Resize image with resize interpolation. Default interpolation to AREA if image is smaller, else LANCZOS.
+
+    Args:
+        image: numpy.ndarray
+        width: int Original image width
+        height: int Original image height
+        resized_width: int Resized image width
+        resized_height: int Resized image height
+        resize_interpolation: Optional[str] Resize interpolation method "lanczos", "area", "bilinear", "bicubic", "nearest", "box"
+
+    Returns:
+        image
+    """
+
+    # Ensure all size parameters are actual integers
+    width = int(width)
+    height = int(height)
+    resized_width = int(resized_width)
+    resized_height = int(resized_height)
+
+    if resize_interpolation is None:
+        if width >= resized_width and height >= resized_height:
+            resize_interpolation = "area"
+        else:
+            resize_interpolation = "lanczos"
+
+    # we use PIL for lanczos (for backward compatibility) and box, cv2 for others
+    use_pil = resize_interpolation in ["lanczos", "lanczos4", "box"]
+
+    resized_size = (resized_width, resized_height)
+    if use_pil:
+        interpolation = get_pil_interpolation(resize_interpolation)
+        image = pil_resize(image, resized_size, interpolation=interpolation)
+        logger.debug(f"resize image using {resize_interpolation} (PIL)")
+    else:
+        interpolation = get_cv2_interpolation(resize_interpolation)
+        image = cv2.resize(image, resized_size, interpolation=interpolation)
+        logger.debug(f"resize image using {resize_interpolation} (cv2)")
+
+    return image
+
+
+def get_cv2_interpolation(interpolation: Optional[str]) -> Optional[int]:
+    """
+    Convert interpolation value to cv2 interpolation integer
+
+    https://docs.opencv.org/3.4/da/d54/group__imgproc__transform.html#ga5bb5a1fea74ea38e1a5445ca803ff121
+    """
+    if interpolation is None:
+        return None 
+
+    if interpolation == "lanczos" or interpolation == "lanczos4":
+        # Lanczos interpolation over 8x8 neighborhood 
+        return cv2.INTER_LANCZOS4
+    elif interpolation == "nearest":
+        # Bit exact nearest neighbor interpolation. This will produce same results as the nearest neighbor method in PIL, scikit-image or Matlab. 
+        return cv2.INTER_NEAREST_EXACT
+    elif interpolation == "bilinear" or interpolation == "linear":
+        # bilinear interpolation
+        return cv2.INTER_LINEAR
+    elif interpolation == "bicubic" or interpolation == "cubic":
+        # bicubic interpolation 
+        return cv2.INTER_CUBIC
+    elif interpolation == "area":
+        # resampling using pixel area relation. It may be a preferred method for image decimation, as it gives moire'-free results. But when the image is zoomed, it is similar to the INTER_NEAREST method. 
+        return cv2.INTER_AREA
+    elif interpolation == "box":
+        # resampling using pixel area relation. It may be a preferred method for image decimation, as it gives moire'-free results. But when the image is zoomed, it is similar to the INTER_NEAREST method. 
+        return cv2.INTER_AREA
+    else:
+        return None
+
+def get_pil_interpolation(interpolation: Optional[str]) -> Optional[Image.Resampling]:
+    """
+    Convert interpolation value to PIL interpolation
+
+    https://pillow.readthedocs.io/en/stable/handbook/concepts.html#concept-filters
+    """
+    if interpolation is None:
+        return None 
+
+    if interpolation == "lanczos":
+        return Image.Resampling.LANCZOS
+    elif interpolation == "nearest":
+        # Pick one nearest pixel from the input image. Ignore all other input pixels.
+        return Image.Resampling.NEAREST
+    elif interpolation == "bilinear" or interpolation == "linear":
+        # For resize calculate the output pixel value using linear interpolation on all pixels that may contribute to the output value. For other transformations linear interpolation over a 2x2 environment in the input image is used.
+        return Image.Resampling.BILINEAR
+    elif interpolation == "bicubic" or interpolation == "cubic":
+        # For resize calculate the output pixel value using cubic interpolation on all pixels that may contribute to the output value. For other transformations cubic interpolation over a 4x4 environment in the input image is used.
+        return Image.Resampling.BICUBIC
+    elif interpolation == "area":
+        # Image.Resampling.BOX may be more appropriate if upscaling 
+        # Area interpolation is related to cv2.INTER_AREA
+        # Produces a sharper image than Resampling.BILINEAR, doesn’t have dislocations on local level like with Resampling.BOX.
+        return Image.Resampling.HAMMING
+    elif interpolation == "box":
+        # Each pixel of source image contributes to one pixel of the destination image with identical weights. For upscaling is equivalent of Resampling.NEAREST.
+        return Image.Resampling.BOX
+    else:
+        return None
+
+def validate_interpolation_fn(interpolation_str: str) -> bool:
+    """
+    Check if a interpolation function is supported
+    """
+    return interpolation_str in ["lanczos", "nearest", "bilinear", "linear", "bicubic", "cubic", "area", "box"]
+
 # endregion

 # TODO make inf_utils.py
--- a/networks/dylora.py
+++ b/networks/dylora.py
@@ -268,7 +268,7 @@ def create_network_from_weights(multiplier, file, vae, text_encoder, unet, weigh
 class DyLoRANetwork(torch.nn.Module):
    UNET_TARGET_REPLACE_MODULE = ["Transformer2DModel"]
    UNET_TARGET_REPLACE_MODULE_CONV2D_3X3 = ["ResnetBlock2D", "Downsample2D", "Upsample2D"]
-    TEXT_ENCODER_TARGET_REPLACE_MODULE = ["CLIPAttention", "CLIPMLP"]
+    TEXT_ENCODER_TARGET_REPLACE_MODULE = ["CLIPAttention", "CLIPSdpaAttention", "CLIPMLP"]
    LORA_PREFIX_UNET = "lora_unet"
    LORA_PREFIX_TEXT_ENCODER = "lora_te"

--- a/networks/lora.py
+++ b/networks/lora.py
@@ -866,7 +866,7 @@ class LoRANetwork(torch.nn.Module):

    UNET_TARGET_REPLACE_MODULE = ["Transformer2DModel"]
    UNET_TARGET_REPLACE_MODULE_CONV2D_3X3 = ["ResnetBlock2D", "Downsample2D", "Upsample2D"]
-    TEXT_ENCODER_TARGET_REPLACE_MODULE = ["CLIPAttention", "CLIPMLP"]
+    TEXT_ENCODER_TARGET_REPLACE_MODULE = ["CLIPAttention", "CLIPSdpaAttention", "CLIPMLP"]
    LORA_PREFIX_UNET = "lora_unet"
    LORA_PREFIX_TEXT_ENCODER = "lora_te"

--- a/networks/lora_diffusers.py
+++ b/networks/lora_diffusers.py
@@ -278,7 +278,7 @@ def merge_lora_weights(pipe, weights_sd: Dict, multiplier: float = 1.0):
 class LoRANetwork(torch.nn.Module):
    UNET_TARGET_REPLACE_MODULE = ["Transformer2DModel"]
    UNET_TARGET_REPLACE_MODULE_CONV2D_3X3 = ["ResnetBlock2D", "Downsample2D", "Upsample2D"]
-    TEXT_ENCODER_TARGET_REPLACE_MODULE = ["CLIPAttention", "CLIPMLP"]
+    TEXT_ENCODER_TARGET_REPLACE_MODULE = ["CLIPAttention", "CLIPSdpaAttention", "CLIPMLP"]
    LORA_PREFIX_UNET = "lora_unet"
    LORA_PREFIX_TEXT_ENCODER = "lora_te"

--- a/networks/lora_fa.py
+++ b/networks/lora_fa.py
@@ -755,7 +755,7 @@ class LoRANetwork(torch.nn.Module):

    UNET_TARGET_REPLACE_MODULE = ["Transformer2DModel"]
    UNET_TARGET_REPLACE_MODULE_CONV2D_3X3 = ["ResnetBlock2D", "Downsample2D", "Upsample2D"]
-    TEXT_ENCODER_TARGET_REPLACE_MODULE = ["CLIPAttention", "CLIPMLP"]
+    TEXT_ENCODER_TARGET_REPLACE_MODULE = ["CLIPAttention", "CLIPSdpaAttention", "CLIPMLP"]
    LORA_PREFIX_UNET = "lora_unet"
    LORA_PREFIX_TEXT_ENCODER = "lora_te"

--- a/networks/lora_flux.py
+++ b/networks/lora_flux.py
@@ -9,11 +9,13 @@

 import math
 import os
+from contextlib import contextmanager
 from typing import Dict, List, Optional, Tuple, Type, Union
 from diffusers import AutoencoderKL
 from transformers import CLIPTextModel
 import numpy as np
 import torch
+from torch import Tensor
 import re
 from library.utils import setup_logging
 from library.sdxl_original_unet import SdxlUNet2DConditionModel
@@ -44,6 +46,8 @@ class LoRAModule(torch.nn.Module):
        rank_dropout=None,
        module_dropout=None,
        split_dims: Optional[List[int]] = None,
+        ggpo_beta: Optional[float] = None,
+        ggpo_sigma: Optional[float] = None,
    ):
        """
        if alpha == 0 or None, alpha is rank (no scaling).
@@ -103,9 +107,20 @@ class LoRAModule(torch.nn.Module):
        self.rank_dropout = rank_dropout
        self.module_dropout = module_dropout

+        self.ggpo_sigma = ggpo_sigma
+        self.ggpo_beta = ggpo_beta
+
+        if self.ggpo_beta is not None and self.ggpo_sigma is not None:
+            self.combined_weight_norms = None
+            self.grad_norms = None
+            self.perturbation_norm_factor = 1.0 / math.sqrt(org_module.weight.shape[0])
+            self.initialize_norm_cache(org_module.weight)
+            self.org_module_shape: tuple[int] = org_module.weight.shape
+
    def apply_to(self):
        self.org_forward = self.org_module.forward
        self.org_module.forward = self.forward
+
        del self.org_module

    def forward(self, x):
@@ -140,7 +155,17 @@ class LoRAModule(torch.nn.Module):

            lx = self.lora_up(lx)

-            return org_forwarded + lx * self.multiplier * scale
+            # LoRA Gradient-Guided Perturbation Optimization
+            if self.training and self.ggpo_sigma is not None and self.ggpo_beta is not None and self.combined_weight_norms is not None and self.grad_norms is not None:
+                with torch.no_grad():
+                    perturbation_scale = (self.ggpo_sigma * torch.sqrt(self.combined_weight_norms ** 2)) + (self.ggpo_beta * (self.grad_norms ** 2))
+                    perturbation_scale_factor = (perturbation_scale * self.perturbation_norm_factor).to(self.device)
+                    perturbation = torch.randn(self.org_module_shape,  dtype=self.dtype, device=self.device)
+                    perturbation.mul_(perturbation_scale_factor)
+                    perturbation_output = x @ perturbation.T  # Result: (batch × n)
+                return org_forwarded + (self.multiplier * scale * lx) + perturbation_output
+            else:
+                return org_forwarded + lx * self.multiplier * scale
        else:
            lxs = [lora_down(x) for lora_down in self.lora_down]

@@ -167,6 +192,116 @@ class LoRAModule(torch.nn.Module):

            return org_forwarded + torch.cat(lxs, dim=-1) * self.multiplier * scale

+    @torch.no_grad()
+    def initialize_norm_cache(self, org_module_weight: Tensor):
+        # Choose a reasonable sample size
+        n_rows = org_module_weight.shape[0]
+        sample_size = min(1000, n_rows)  # Cap at 1000 samples or use all if smaller
+        
+        # Sample random indices across all rows
+        indices = torch.randperm(n_rows)[:sample_size]
+        
+        # Convert to a supported data type first, then index
+        # Use float32 for indexing operations
+        weights_float32 = org_module_weight.to(dtype=torch.float32)
+        sampled_weights = weights_float32[indices].to(device=self.device)
+        
+        # Calculate sampled norms
+        sampled_norms = torch.norm(sampled_weights, dim=1, keepdim=True)
+        
+        # Store the mean norm as our estimate
+        self.org_weight_norm_estimate = sampled_norms.mean()
+        
+        # Optional: store standard deviation for confidence intervals
+        self.org_weight_norm_std = sampled_norms.std()
+        
+        # Free memory
+        del sampled_weights, weights_float32
+
+    @torch.no_grad()
+    def validate_norm_approximation(self, org_module_weight: Tensor, verbose=True):
+        # Calculate the true norm (this will be slow but it's just for validation)
+        true_norms = []
+        chunk_size = 1024  # Process in chunks to avoid OOM
+        
+        for i in range(0, org_module_weight.shape[0], chunk_size):
+            end_idx = min(i + chunk_size, org_module_weight.shape[0])
+            chunk = org_module_weight[i:end_idx].to(device=self.device, dtype=self.dtype)
+            chunk_norms = torch.norm(chunk, dim=1, keepdim=True)
+            true_norms.append(chunk_norms.cpu())
+            del chunk
+            
+        true_norms = torch.cat(true_norms, dim=0)
+        true_mean_norm = true_norms.mean().item()
+        
+        # Compare with our estimate
+        estimated_norm = self.org_weight_norm_estimate.item()
+        
+        # Calculate error metrics
+        absolute_error = abs(true_mean_norm - estimated_norm)
+        relative_error = absolute_error / true_mean_norm * 100  # as percentage
+        
+        if verbose:
+            logger.info(f"True mean norm: {true_mean_norm:.6f}")
+            logger.info(f"Estimated norm: {estimated_norm:.6f}")
+            logger.info(f"Absolute error: {absolute_error:.6f}")
+            logger.info(f"Relative error: {relative_error:.2f}%")
+            
+        return {
+            'true_mean_norm': true_mean_norm,
+            'estimated_norm': estimated_norm,
+            'absolute_error': absolute_error,
+            'relative_error': relative_error
+        }
+
+
+    @torch.no_grad()
+    def update_norms(self):
+        # Not running GGPO so not currently running update norms
+        if self.ggpo_beta is None or self.ggpo_sigma is None:
+            return
+
+        # only update norms when we are training 
+        if self.training is False:
+            return
+
+        module_weights = self.lora_up.weight @ self.lora_down.weight
+        module_weights.mul(self.scale)
+
+        self.weight_norms = torch.norm(module_weights, dim=1, keepdim=True)
+        self.combined_weight_norms = torch.sqrt((self.org_weight_norm_estimate**2) + 
+                                           torch.sum(module_weights**2, dim=1, keepdim=True))
+
+    @torch.no_grad()
+    def update_grad_norms(self):
+        if self.training is False:
+            print(f"skipping update_grad_norms for {self.lora_name}")
+            return
+
+        lora_down_grad = None
+        lora_up_grad = None
+
+        for name, param in self.named_parameters():
+            if name == "lora_down.weight":
+                lora_down_grad = param.grad
+            elif name == "lora_up.weight":
+                lora_up_grad = param.grad
+
+        # Calculate gradient norms if we have both gradients
+        if lora_down_grad is not None and lora_up_grad is not None:
+            with torch.autocast(self.device.type):
+                approx_grad = self.scale * ((self.lora_up.weight @ lora_down_grad) + (lora_up_grad @ self.lora_down.weight))
+                self.grad_norms = torch.norm(approx_grad, dim=1, keepdim=True)
+
+
+    @property
+    def device(self):
+        return next(self.parameters()).device
+
+    @property
+    def dtype(self):
+        return next(self.parameters()).dtype
+

 class LoRAInfModule(LoRAModule):
    def __init__(
@@ -420,6 +555,16 @@ def create_network(
    if split_qkv is not None:
        split_qkv = True if split_qkv == "True" else False

+    ggpo_beta = kwargs.get("ggpo_beta", None)
+    ggpo_sigma = kwargs.get("ggpo_sigma", None)
+
+    if ggpo_beta is not None:
+        ggpo_beta = float(ggpo_beta)
+
+    if ggpo_sigma is not None:
+        ggpo_sigma = float(ggpo_sigma)
+
+
    # train T5XXL
    train_t5xxl = kwargs.get("train_t5xxl", False)
    if train_t5xxl is not None:
@@ -449,6 +594,8 @@ def create_network(
        in_dims=in_dims,
        train_double_block_indices=train_double_block_indices,
        train_single_block_indices=train_single_block_indices,
+        ggpo_beta=ggpo_beta,
+        ggpo_sigma=ggpo_sigma,
        verbose=verbose,
    )

@@ -561,6 +708,8 @@ class LoRANetwork(torch.nn.Module):
        in_dims: Optional[List[int]] = None,
        train_double_block_indices: Optional[List[bool]] = None,
        train_single_block_indices: Optional[List[bool]] = None,
+        ggpo_beta: Optional[float] = None,
+        ggpo_sigma: Optional[float] = None,
        verbose: Optional[bool] = False,
    ) -> None:
        super().__init__()
@@ -599,10 +748,16 @@ class LoRANetwork(torch.nn.Module):
            #     logger.info(
            #         f"apply LoRA to Conv2d with kernel size (3,3). dim (rank): {self.conv_lora_dim}, alpha: {self.conv_alpha}"
            #     )
+
+        if ggpo_beta is not None and ggpo_sigma is not None:
+            logger.info(f"LoRA-GGPO training sigma: {ggpo_sigma} beta: {ggpo_beta}")
+
        if self.split_qkv:
            logger.info(f"split qkv for LoRA")
        if self.train_blocks is not None:
            logger.info(f"train {self.train_blocks} blocks only")
+
+
        if train_t5xxl:
            logger.info(f"train T5XXL as well")

@@ -722,6 +877,8 @@ class LoRANetwork(torch.nn.Module):
                                rank_dropout=rank_dropout,
                                module_dropout=module_dropout,
                                split_dims=split_dims,
+                                ggpo_beta=ggpo_beta,
+                                ggpo_sigma=ggpo_sigma,
                            )
                            loras.append(lora)

@@ -790,6 +947,36 @@ class LoRANetwork(torch.nn.Module):
        for lora in self.text_encoder_loras + self.unet_loras:
            lora.enabled = is_enabled

+    def update_norms(self):
+        for lora in self.text_encoder_loras + self.unet_loras:
+            lora.update_norms()
+
+    def update_grad_norms(self):
+        for lora in self.text_encoder_loras + self.unet_loras:
+            lora.update_grad_norms()
+
+    def grad_norms(self) -> Tensor | None:
+        grad_norms = []
+        for lora in self.text_encoder_loras + self.unet_loras:
+            if hasattr(lora, "grad_norms") and lora.grad_norms is not None:
+                grad_norms.append(lora.grad_norms.mean(dim=0))
+        return torch.stack(grad_norms) if len(grad_norms) > 0 else None
+
+    def weight_norms(self) -> Tensor | None:
+        weight_norms = []
+        for lora in self.text_encoder_loras + self.unet_loras:
+            if hasattr(lora, "weight_norms") and lora.weight_norms is not None:
+                weight_norms.append(lora.weight_norms.mean(dim=0))
+        return torch.stack(weight_norms) if len(weight_norms) > 0 else None
+
+    def combined_weight_norms(self) -> Tensor | None:
+        combined_weight_norms = []
+        for lora in self.text_encoder_loras + self.unet_loras:
+            if hasattr(lora, "combined_weight_norms") and lora.combined_weight_norms is not None:
+                combined_weight_norms.append(lora.combined_weight_norms.mean(dim=0))
+        return torch.stack(combined_weight_norms) if len(combined_weight_norms) > 0 else None
+
+
    def load_weights(self, file):
        if os.path.splitext(file)[1] == ".safetensors":
            from safetensors.torch import load_file
--- a/pytest.ini
+++ b/pytest.ini
@@ -6,3 +6,4 @@ filterwarnings =
    ignore::DeprecationWarning
    ignore::UserWarning
 		ignore::FutureWarning
+pythonpath = .
--- a/requirements.txt
+++ b/requirements.txt
@@ -7,9 +7,11 @@ opencv-python==4.8.1.78
 einops==0.7.0
 pytorch-lightning==1.9.0
 bitsandbytes==0.44.0
-prodigyopt==1.0
 lion-pytorch==0.0.6
 schedulefree==1.4
+pytorch-optimizer==3.5.0
+prodigy-plus-schedule-free==1.9.0
+prodigyopt==1.1.2
 tensorboard
 safetensors==0.4.4
 # gradio==3.16.2
--- a/sdxl_train_network.py
+++ b/sdxl_train_network.py
@@ -24,7 +24,6 @@ class SdxlNetworkTrainer(train_network.NetworkTrainer):
        self.is_sdxl = True

    def assert_extra_args(self, args, train_dataset_group: Union[train_util.DatasetGroup, train_util.MinimalDataset], val_dataset_group: Optional[train_util.DatasetGroup]):
-        super().assert_extra_args(args, train_dataset_group, val_dataset_group)
        sdxl_train_util.verify_sdxl_training_args(args)

        if args.cache_text_encoder_outputs:
--- a/tests/library/test_flux_train_utils.py
+++ b/tests/library/test_flux_train_utils.py
@@ -0,0 +1,220 @@
+import pytest
+import torch
+from unittest.mock import MagicMock, patch
+from library.flux_train_utils import (
+    get_noisy_model_input_and_timesteps,
+)
+
+# Mock classes and functions
+class MockNoiseScheduler:
+    def __init__(self, num_train_timesteps=1000):
+        self.config = MagicMock()
+        self.config.num_train_timesteps = num_train_timesteps
+        self.timesteps = torch.arange(num_train_timesteps, dtype=torch.long)
+
+
+# Create fixtures for commonly used objects
+@pytest.fixture
+def args():
+    args = MagicMock()
+    args.timestep_sampling = "uniform"
+    args.weighting_scheme = "uniform"
+    args.logit_mean = 0.0
+    args.logit_std = 1.0
+    args.mode_scale = 1.0
+    args.sigmoid_scale = 1.0
+    args.discrete_flow_shift = 3.1582
+    args.ip_noise_gamma = None
+    args.ip_noise_gamma_random_strength = False
+    return args
+
+
+@pytest.fixture
+def noise_scheduler():
+    return MockNoiseScheduler(num_train_timesteps=1000)
+
+
+@pytest.fixture
+def latents():
+    return torch.randn(2, 4, 8, 8)
+
+
+@pytest.fixture
+def noise():
+    return torch.randn(2, 4, 8, 8)
+
+
+@pytest.fixture
+def device():
+    # return "cuda" if torch.cuda.is_available() else "cpu"
+    return "cpu"
+
+
+# Mock the required functions
+@pytest.fixture(autouse=True)
+def mock_functions():
+    with (
+        patch("torch.sigmoid", side_effect=torch.sigmoid),
+        patch("torch.rand", side_effect=torch.rand),
+        patch("torch.randn", side_effect=torch.randn),
+    ):
+        yield
+
+
+# Test different timestep sampling methods
+def test_uniform_sampling(args, noise_scheduler, latents, noise, device):
+    args.timestep_sampling = "uniform"
+    dtype = torch.float32
+
+    noisy_input, timesteps, sigmas = get_noisy_model_input_and_timesteps(args, noise_scheduler, latents, noise, device, dtype)
+
+    assert noisy_input.shape == latents.shape
+    assert timesteps.shape == (latents.shape[0],)
+    assert sigmas.shape == (latents.shape[0], 1, 1, 1)
+    assert noisy_input.dtype == dtype
+    assert timesteps.dtype == dtype
+
+
+def test_sigmoid_sampling(args, noise_scheduler, latents, noise, device):
+    args.timestep_sampling = "sigmoid"
+    args.sigmoid_scale = 1.0
+    dtype = torch.float32
+
+    noisy_input, timesteps, sigmas = get_noisy_model_input_and_timesteps(args, noise_scheduler, latents, noise, device, dtype)
+
+    assert noisy_input.shape == latents.shape
+    assert timesteps.shape == (latents.shape[0],)
+    assert sigmas.shape == (latents.shape[0], 1, 1, 1)
+
+
+def test_shift_sampling(args, noise_scheduler, latents, noise, device):
+    args.timestep_sampling = "shift"
+    args.sigmoid_scale = 1.0
+    args.discrete_flow_shift = 3.1582
+    dtype = torch.float32
+
+    noisy_input, timesteps, sigmas = get_noisy_model_input_and_timesteps(args, noise_scheduler, latents, noise, device, dtype)
+
+    assert noisy_input.shape == latents.shape
+    assert timesteps.shape == (latents.shape[0],)
+    assert sigmas.shape == (latents.shape[0], 1, 1, 1)
+
+
+def test_flux_shift_sampling(args, noise_scheduler, latents, noise, device):
+    args.timestep_sampling = "flux_shift"
+    args.sigmoid_scale = 1.0
+    dtype = torch.float32
+
+    noisy_input, timesteps, sigmas = get_noisy_model_input_and_timesteps(args, noise_scheduler, latents, noise, device, dtype)
+
+    assert noisy_input.shape == latents.shape
+    assert timesteps.shape == (latents.shape[0],)
+    assert sigmas.shape == (latents.shape[0], 1, 1, 1)
+
+
+def test_weighting_scheme(args, noise_scheduler, latents, noise, device):
+    # Mock the necessary functions for this specific test
+    with patch("library.flux_train_utils.compute_density_for_timestep_sampling", 
+               return_value=torch.tensor([0.3, 0.7], device=device)), \
+         patch("library.flux_train_utils.get_sigmas", 
+               return_value=torch.tensor([[0.3], [0.7]], device=device).view(-1, 1, 1, 1)):
+               
+        args.timestep_sampling = "other"  # Will trigger the weighting scheme path
+        args.weighting_scheme = "uniform"
+        args.logit_mean = 0.0
+        args.logit_std = 1.0
+        args.mode_scale = 1.0
+        dtype = torch.float32
+        
+        noisy_input, timesteps, sigmas = get_noisy_model_input_and_timesteps(
+            args, noise_scheduler, latents, noise, device, dtype
+        )
+        
+        assert noisy_input.shape == latents.shape
+        assert timesteps.shape == (latents.shape[0],)
+        assert sigmas.shape == (latents.shape[0], 1, 1, 1)
+
+
+# Test IP noise options
+def test_with_ip_noise(args, noise_scheduler, latents, noise, device):
+    args.ip_noise_gamma = 0.5
+    args.ip_noise_gamma_random_strength = False
+    dtype = torch.float32
+
+    noisy_input, timesteps, sigmas = get_noisy_model_input_and_timesteps(args, noise_scheduler, latents, noise, device, dtype)
+
+    assert noisy_input.shape == latents.shape
+    assert timesteps.shape == (latents.shape[0],)
+    assert sigmas.shape == (latents.shape[0], 1, 1, 1)
+
+
+def test_with_random_ip_noise(args, noise_scheduler, latents, noise, device):
+    args.ip_noise_gamma = 0.1
+    args.ip_noise_gamma_random_strength = True
+    dtype = torch.float32
+
+    noisy_input, timesteps, sigmas = get_noisy_model_input_and_timesteps(args, noise_scheduler, latents, noise, device, dtype)
+
+    assert noisy_input.shape == latents.shape
+    assert timesteps.shape == (latents.shape[0],)
+    assert sigmas.shape == (latents.shape[0], 1, 1, 1)
+
+
+# Test different data types
+def test_float16_dtype(args, noise_scheduler, latents, noise, device):
+    dtype = torch.float16
+
+    noisy_input, timesteps, sigmas = get_noisy_model_input_and_timesteps(args, noise_scheduler, latents, noise, device, dtype)
+
+    assert noisy_input.dtype == dtype
+    assert timesteps.dtype == dtype
+
+
+# Test different batch sizes
+def test_different_batch_size(args, noise_scheduler, device):
+    latents = torch.randn(5, 4, 8, 8)  # batch size of 5
+    noise = torch.randn(5, 4, 8, 8)
+    dtype = torch.float32
+
+    noisy_input, timesteps, sigmas = get_noisy_model_input_and_timesteps(args, noise_scheduler, latents, noise, device, dtype)
+
+    assert noisy_input.shape == latents.shape
+    assert timesteps.shape == (5,)
+    assert sigmas.shape == (5, 1, 1, 1)
+
+
+# Test different image sizes
+def test_different_image_size(args, noise_scheduler, device):
+    latents = torch.randn(2, 4, 16, 16)  # larger image size
+    noise = torch.randn(2, 4, 16, 16)
+    dtype = torch.float32
+
+    noisy_input, timesteps, sigmas = get_noisy_model_input_and_timesteps(args, noise_scheduler, latents, noise, device, dtype)
+
+    assert noisy_input.shape == latents.shape
+    assert timesteps.shape == (2,)
+    assert sigmas.shape == (2, 1, 1, 1)
+
+
+# Test edge cases
+def test_zero_batch_size(args, noise_scheduler, device):
+    with pytest.raises(AssertionError):  # expecting an error with zero batch size
+        latents = torch.randn(0, 4, 8, 8)
+        noise = torch.randn(0, 4, 8, 8)
+        dtype = torch.float32
+
+        get_noisy_model_input_and_timesteps(args, noise_scheduler, latents, noise, device, dtype)
+
+
+def test_different_timestep_count(args, device):
+    noise_scheduler = MockNoiseScheduler(num_train_timesteps=500)  # different timestep count
+    latents = torch.randn(2, 4, 8, 8)
+    noise = torch.randn(2, 4, 8, 8)
+    dtype = torch.float32
+
+    noisy_input, timesteps, sigmas = get_noisy_model_input_and_timesteps(args, noise_scheduler, latents, noise, device, dtype)
+
+    assert noisy_input.shape == latents.shape
+    assert timesteps.shape == (2,)
+    # Check that timesteps are within the proper range
+    assert torch.all(timesteps < 500)
--- a/tools/detect_face_rotate.py
+++ b/tools/detect_face_rotate.py
@@ -15,7 +15,7 @@ import os
 from anime_face_detector import create_detector
 from tqdm import tqdm
 import numpy as np
-from library.utils import setup_logging, pil_resize
+from library.utils import setup_logging, resize_image
 setup_logging()
 import logging
 logger = logging.getLogger(__name__)
@@ -170,12 +170,9 @@ def process(args):
            scale = max(cur_crop_width / w, cur_crop_height / h)

        if scale != 1.0:
-          w = int(w * scale + .5)
-          h = int(h * scale + .5)
-          if scale < 1.0:
-            face_img = cv2.resize(face_img, (w, h), interpolation=cv2.INTER_AREA)
-          else:
-            face_img = pil_resize(face_img, (w, h))
+          rw = int(w * scale + .5)
+          rh = int(h * scale + .5)
+          face_img = resize_image(face_img, w, h, rw, rh)
          cx = int(cx * scale + .5)
          cy = int(cy * scale + .5)
          fw = int(fw * scale + .5)
--- a/tools/resize_images_to_resolution.py
+++ b/tools/resize_images_to_resolution.py
@@ -6,7 +6,7 @@ import shutil
 import math
 from PIL import Image
 import numpy as np
-from library.utils import setup_logging, pil_resize
+from library.utils import setup_logging, resize_image
 setup_logging()
 import logging
 logger = logging.getLogger(__name__)
@@ -22,14 +22,6 @@ def resize_images(src_img_folder, dst_img_folder, max_resolution="512x512", divi
  if not os.path.exists(dst_img_folder):
    os.makedirs(dst_img_folder)

-  # Select interpolation method
-  if interpolation == 'lanczos4':
-    pil_interpolation = Image.LANCZOS
-  elif interpolation == 'cubic':
-    pil_interpolation = Image.BICUBIC
-  else:
-    cv2_interpolation = cv2.INTER_AREA
-
  # Iterate through all files in src_img_folder
  img_exts = (".png", ".jpg", ".jpeg", ".webp", ".bmp")                   # copy from train_util.py
  for filename in os.listdir(src_img_folder):
@@ -63,11 +55,7 @@ def resize_images(src_img_folder, dst_img_folder, max_resolution="512x512", divi
        new_height = int(img.shape[0] * math.sqrt(scale_factor))
        new_width = int(img.shape[1] * math.sqrt(scale_factor))

-        # Resize image
-        if cv2_interpolation:
-          img = cv2.resize(img, (new_width, new_height), interpolation=cv2_interpolation)
-        else:
-          img = pil_resize(img, (new_width, new_height), interpolation=pil_interpolation)
+        img = resize_image(img,  img.shape[0], img.shape[1], new_height, new_width, interpolation)
      else:
        new_height, new_width = img.shape[0:2]

@@ -113,8 +101,8 @@ def setup_parser() -> argparse.ArgumentParser:
                      help='Maximum resolution(s) in the format "512x512,384x384, etc, etc" / 最大画像サイズをカンマ区切りで指定 ("512x512,384x384, etc, etc" など)', default="512x512,384x384,256x256,128x128")
  parser.add_argument('--divisible_by', type=int,
                      help='Ensure new dimensions are divisible by this value / リサイズ後の画像のサイズをこの値で割り切れるようにします', default=1)
-  parser.add_argument('--interpolation', type=str, choices=['area', 'cubic', 'lanczos4'],
-                      default='area', help='Interpolation method for resizing / リサイズ時の補完方法')
+  parser.add_argument('--interpolation', type=str, choices=['area', 'cubic', 'lanczos4', 'nearest', 'linear', 'box'],
+                      default=None, help='Interpolation method for resizing. Default to area if smaller, lanczos if larger / サイズ変更の補間方法。小さい場合はデフォルトでエリア、大きい場合はランチョスになります。')
  parser.add_argument('--save_as_png', action='store_true', help='Save as png format / png形式で保存')
  parser.add_argument('--copy_associated_files', action='store_true',
                      help='Copy files with same base name to images (captions etc) / 画像と同じファイル名（拡張子を除く）のファイルもコピーする')
--- a/train_network.py
+++ b/train_network.py
@@ -69,13 +69,20 @@ class NetworkTrainer:
        keys_scaled=None,
        mean_norm=None,
        maximum_norm=None,
+        mean_grad_norm=None,
+        mean_combined_norm=None,
    ):
        logs = {"loss/current": current_loss, "loss/average": avr_loss}

        if keys_scaled is not None:
            logs["max_norm/keys_scaled"] = keys_scaled
-            logs["max_norm/average_key_norm"] = mean_norm
            logs["max_norm/max_key_norm"] = maximum_norm
+        if mean_norm is not None:
+            logs["norm/avg_key_norm"] = mean_norm
+        if mean_grad_norm is not None:
+            logs["norm/avg_grad_norm"] = mean_grad_norm
+        if mean_combined_norm is not None:
+            logs["norm/avg_combined_norm"] = mean_combined_norm

        lrs = lr_scheduler.get_last_lr()
        for i, lr in enumerate(lrs):
@@ -382,7 +389,18 @@ class NetworkTrainer:
                latents = typing.cast(torch.FloatTensor, batch["latents"].to(accelerator.device))
            else:
                # latentに変換
-                latents = self.encode_images_to_latents(args, vae, batch["images"].to(accelerator.device, dtype=vae_dtype))
+                if args.vae_batch_size is None or len(batch["images"]) <= args.vae_batch_size:
+                    latents = self.encode_images_to_latents(args, vae, batch["images"].to(accelerator.device, dtype=vae_dtype))
+                else:
+                    chunks = [
+                        batch["images"][i : i + args.vae_batch_size] for i in range(0, len(batch["images"]), args.vae_batch_size)
+                    ]
+                    list_latents = []
+                    for chunk in chunks:
+                        with torch.no_grad():
+                            chunk = self.encode_images_to_latents(args, vae, chunk.to(accelerator.device, dtype=vae_dtype))
+                            list_latents.append(chunk)
+                    latents = torch.cat(list_latents, dim=0)

                # NaNが含まれていれば警告を表示し0に置き換える
                if torch.any(torch.isnan(latents)):
@@ -651,6 +669,10 @@ class NetworkTrainer:
            return
        network_has_multiplier = hasattr(network, "set_multiplier")

+        # TODO remove `hasattr`s by setting up methods if not defined in the network like (hacky but works):
+        # if not hasattr(network, "prepare_network"):
+        #    network.prepare_network = lambda args: None
+
        if hasattr(network, "prepare_network"):
            network.prepare_network(args)
        if args.scale_weight_norms and not hasattr(network, "apply_max_norm_regularization"):
@@ -1017,6 +1039,7 @@ class NetworkTrainer:
            "ss_max_validation_steps": args.max_validation_steps,
            "ss_validate_every_n_epochs": args.validate_every_n_epochs,
            "ss_validate_every_n_steps": args.validate_every_n_steps,
+            "ss_resize_interpolation": args.resize_interpolation,
        }

        self.update_metadata(metadata, args)  # architecture specific metadata
@@ -1042,6 +1065,7 @@ class NetworkTrainer:
                    "max_bucket_reso": dataset.max_bucket_reso,
                    "tag_frequency": dataset.tag_frequency,
                    "bucket_info": dataset.bucket_info,
+                    "resize_interpolation": dataset.resize_interpolation,
                }

                subsets_metadata = []
@@ -1059,6 +1083,7 @@ class NetworkTrainer:
                        "enable_wildcard": bool(subset.enable_wildcard),
                        "caption_prefix": subset.caption_prefix,
                        "caption_suffix": subset.caption_suffix,
+                        "resize_interpolation": subset.resize_interpolation,
                    }

                    image_dir_or_metadata_file = None
@@ -1400,6 +1425,11 @@ class NetworkTrainer:
                            params_to_clip = accelerator.unwrap_model(network).get_trainable_params()
                            accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)

+                        if hasattr(network, "update_grad_norms"):
+                            network.update_grad_norms()
+                        if hasattr(network, "update_norms"):
+                            network.update_norms()
+
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad(set_to_none=True)
@@ -1408,9 +1438,25 @@ class NetworkTrainer:
                    keys_scaled, mean_norm, maximum_norm = accelerator.unwrap_model(network).apply_max_norm_regularization(
                        args.scale_weight_norms, accelerator.device
                    )
+                    mean_grad_norm = None
+                    mean_combined_norm = None
                    max_mean_logs = {"Keys Scaled": keys_scaled, "Average key norm": mean_norm}
                else:
-                    keys_scaled, mean_norm, maximum_norm = None, None, None
+                    if hasattr(network, "weight_norms"):
+                        weight_norms = network.weight_norms()
+                        mean_norm = weight_norms.mean().item() if weight_norms is not None else None
+                        grad_norms = network.grad_norms()
+                        mean_grad_norm = grad_norms.mean().item() if grad_norms is not None else None
+                        combined_weight_norms = network.combined_weight_norms()
+                        mean_combined_norm = combined_weight_norms.mean().item() if combined_weight_norms is not None else None
+                        maximum_norm = weight_norms.max().item() if weight_norms is not None else None
+                        keys_scaled = None
+                        max_mean_logs = {}
+                    else:
+                        keys_scaled, mean_norm, maximum_norm = None, None, None
+                        mean_grad_norm = None
+                        mean_combined_norm = None
+                        max_mean_logs = {}

                # Checks if the accelerator has performed an optimization step behind the scenes
                if accelerator.sync_gradients:
@@ -1442,14 +1488,21 @@ class NetworkTrainer:
                loss_recorder.add(epoch=epoch, step=step, loss=current_loss)
                avr_loss: float = loss_recorder.moving_average
                logs = {"avr_loss": avr_loss}  # , "lr": lr_scheduler.get_last_lr()[0]}
-                progress_bar.set_postfix(**logs)
-
-                if args.scale_weight_norms:
-                    progress_bar.set_postfix(**{**max_mean_logs, **logs})
+                progress_bar.set_postfix(**{**max_mean_logs, **logs})

                if is_tracking:
                    logs = self.generate_step_logs(
-                        args, current_loss, avr_loss, lr_scheduler, lr_descriptions, optimizer, keys_scaled, mean_norm, maximum_norm
+                        args,
+                        current_loss,
+                        avr_loss,
+                        lr_scheduler,
+                        lr_descriptions,
+                        optimizer,
+                        keys_scaled,
+                        mean_norm,
+                        maximum_norm,
+                        mean_grad_norm,
+                        mean_combined_norm,
                    )
                    self.step_logging(accelerator, logs, global_step, epoch + 1)