diff --git a/.github/FUNDING.yml b/.github/FUNDING.yml new file mode 100644 index 00000000..3b8943c3 --- /dev/null +++ b/.github/FUNDING.yml @@ -0,0 +1,3 @@ +# These are supported funding model platforms + +github: kohya-ss diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index 2eddedc7..9e037e53 100644 --- 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 diff --git a/.github/workflows/typos.yml b/.github/workflows/typos.yml index f53cda21..b9d6acc9 100644 --- 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 diff --git a/README.md b/README.md index 426eaed8..497969ab 100644 --- 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. diff --git a/docs/config_README-en.md b/docs/config_README-en.md index 66a50dc0..8c55903d 100644 --- 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 diff --git a/docs/config_README-ja.md b/docs/config_README-ja.md index 0ed95e0e..aec0eca5 100644 --- 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。 diff --git a/finetune/tag_images_by_wd14_tagger.py b/finetune/tag_images_by_wd14_tagger.py index cbc3d2d6..d41b8a11 100644 --- 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") diff --git a/library/config_util.py b/library/config_util.py index a2e07dc6..53727f25 100644 --- 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} """), " ") diff --git a/library/deepspeed_utils.py b/library/deepspeed_utils.py index 99a7b2b3..a8a05c3a 100644 --- 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 diff --git a/library/flux_train_utils.py b/library/flux_train_utils.py index f7f06c5c..5f6867a8 100644 --- 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", diff --git a/library/jpeg_xl_util.py b/library/jpeg_xl_util.py new file mode 100644 index 00000000..c2e3a393 --- /dev/null +++ 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) diff --git a/library/train_util.py b/library/train_util.py index 1f591c42..36d419fd 100644 --- 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 + diff --git a/library/utils.py b/library/utils.py index 4df8bd32..d0586b84 100644 --- 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 diff --git a/networks/dylora.py b/networks/dylora.py index b0925453..82d96f59 100644 --- 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" diff --git a/networks/lora.py b/networks/lora.py index 6f33f1a1..1699a60f 100644 --- 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" diff --git a/networks/lora_diffusers.py b/networks/lora_diffusers.py index b99b0244..56b74d10 100644 --- 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" diff --git a/networks/lora_fa.py b/networks/lora_fa.py index 919222ce..5fe778b4 100644 --- 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" diff --git a/networks/lora_flux.py b/networks/lora_flux.py index 91e9cd77..0b30f1b8 100644 --- 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 diff --git a/pytest.ini b/pytest.ini index 484d3aef..34b7e9c1 100644 --- a/pytest.ini +++ b/pytest.ini @@ -6,3 +6,4 @@ filterwarnings = ignore::DeprecationWarning ignore::UserWarning ignore::FutureWarning +pythonpath = . diff --git a/requirements.txt b/requirements.txt index de39f588..767d9e8e 100644 --- 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 diff --git a/sdxl_train_network.py b/sdxl_train_network.py index eb09831e..d56c76b0 100644 --- 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: diff --git a/tests/library/test_flux_train_utils.py b/tests/library/test_flux_train_utils.py new file mode 100644 index 00000000..2ad7ce4e --- /dev/null +++ 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) diff --git a/tools/detect_face_rotate.py b/tools/detect_face_rotate.py index d2a4d9cf..16fd7d0b 100644 --- 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) diff --git a/tools/resize_images_to_resolution.py b/tools/resize_images_to_resolution.py index 0f9e00b1..f5fbae2b 100644 --- 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') diff --git a/train_network.py b/train_network.py index 2d279b3b..1336a0b1 100644 --- 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)