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To engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or other professional practices. diff --git a/HunYuanDiT/conf.py b/HunYuanDiT/conf.py deleted file mode 100644 index 4322753..0000000 --- a/HunYuanDiT/conf.py +++ /dev/null @@ -1,61 +0,0 @@ -""" -List of all HYDiT model types / settings -""" -from argparse import Namespace -hydit_args = Namespace(**{ # normally from argparse - "infer_mode": "torch", - "norm": "layer", - "learn_sigma": True, - "text_states_dim": 1024, - "text_states_dim_t5": 2048, - "text_len": 77, - "text_len_t5": 256, -}) - -hydit_conf = { - "G/2": { # Seems to be the main one - "unet_config": { - "depth" : 40, - "num_heads" : 16, - "patch_size" : 2, - "hidden_size" : 1408, - "mlp_ratio" : 4.3637, - "input_size": (1024//8, 1024//8), - "args": hydit_args, - }, - "sampling_settings" : { - "beta_schedule" : "linear", - "linear_start" : 0.00085, - "linear_end" : 0.03, - "timesteps" : 1000, - }, - }, - "G/2-1.2": { - "unet_config": { - "depth" : 40, - "num_heads" : 16, - "patch_size" : 2, - "hidden_size" : 1408, - "mlp_ratio" : 4.3637, - "input_size": (1024//8, 1024//8), - "cond_style": False, - "cond_res" : False, - "args": hydit_args, - }, - "sampling_settings" : { - "beta_schedule" : "linear", - "linear_start" : 0.00085, - "linear_end" : 0.018, - "timesteps" : 1000, - }, - } -} - -# these are the same as regular DiT, I think -from ..DiT.conf import dit_conf -for name in ["XL/2", "L/2", "B/2"]: - hydit_conf[name] = { - "unet_config": dit_conf[name]["unet_config"].copy(), - "sampling_settings": hydit_conf["G/2"]["sampling_settings"], - } - hydit_conf[name]["unet_config"]["args"] = hydit_args diff --git a/HunYuanDiT/config_clip.json b/HunYuanDiT/config_clip.json deleted file mode 100644 index f629874..0000000 --- a/HunYuanDiT/config_clip.json +++ /dev/null @@ -1,34 +0,0 @@ -{ - "_name_or_path": "hfl/chinese-roberta-wwm-ext-large", - "architectures": [ - "BertModel" - ], - "attention_probs_dropout_prob": 0.1, - "bos_token_id": 0, - "classifier_dropout": null, - "directionality": "bidi", - "eos_token_id": 2, - "hidden_act": "gelu", - "hidden_dropout_prob": 0.1, - "hidden_size": 1024, - "initializer_range": 0.02, - "intermediate_size": 4096, - "layer_norm_eps": 1e-12, - "max_position_embeddings": 512, - "model_type": "bert", - "num_attention_heads": 16, - "num_hidden_layers": 24, - "output_past": true, - "pad_token_id": 0, - "pooler_fc_size": 768, - "pooler_num_attention_heads": 12, - "pooler_num_fc_layers": 3, - "pooler_size_per_head": 128, - "pooler_type": "first_token_transform", - "position_embedding_type": "absolute", - "torch_dtype": "float32", - "transformers_version": "4.22.1", - "type_vocab_size": 2, - "use_cache": true, - "vocab_size": 47020 -} diff --git a/HunYuanDiT/config_mt5.json b/HunYuanDiT/config_mt5.json deleted file mode 100644 index 825cb35..0000000 --- a/HunYuanDiT/config_mt5.json +++ /dev/null @@ -1,33 +0,0 @@ -{ - "_name_or_path": "mt5", - "architectures": [ - "MT5EncoderModel" - ], - "classifier_dropout": 0.0, - "d_ff": 5120, - "d_kv": 64, - "d_model": 2048, - "decoder_start_token_id": 0, - "dense_act_fn": "gelu_new", - "dropout_rate": 0.1, - "eos_token_id": 1, - "feed_forward_proj": "gated-gelu", - "initializer_factor": 1.0, - "is_encoder_decoder": true, - "is_gated_act": true, - "layer_norm_epsilon": 1e-06, - "model_type": "mt5", - "num_decoder_layers": 24, - "num_heads": 32, - "num_layers": 24, - "output_past": true, - "pad_token_id": 0, - "relative_attention_max_distance": 128, - "relative_attention_num_buckets": 32, - "tie_word_embeddings": false, - "tokenizer_class": "T5Tokenizer", - "torch_dtype": "float16", - "transformers_version": "4.40.2", - "use_cache": true, - "vocab_size": 250112 -} diff --git a/HunYuanDiT/loader.py b/HunYuanDiT/loader.py deleted file mode 100644 index 23be529..0000000 --- a/HunYuanDiT/loader.py +++ /dev/null @@ -1,80 +0,0 @@ -import comfy.supported_models_base -import comfy.latent_formats -import comfy.model_patcher -import comfy.model_base -import comfy.utils -import comfy.conds -import torch -from comfy import model_management -from tqdm import tqdm - -class EXM_HYDiT(comfy.supported_models_base.BASE): - unet_config = {} - unet_extra_config = {} - latent_format = comfy.latent_formats.SDXL - - def __init__(self, model_conf): - self.unet_config = model_conf.get("unet_config", {}) - self.sampling_settings = model_conf.get("sampling_settings", {}) - self.latent_format = self.latent_format() - # UNET is handled by extension - self.unet_config["disable_unet_model_creation"] = True - - def model_type(self, state_dict, prefix=""): - return comfy.model_base.ModelType.V_PREDICTION - -class EXM_HYDiT_Model(comfy.model_base.BaseModel): - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) - - def extra_conds(self, **kwargs): - out = super().extra_conds(**kwargs) - - for name in ["context_t5", "context_mask", "context_t5_mask"]: - out[name] = comfy.conds.CONDRegular(kwargs[name]) - - src_size_cond = kwargs.get("src_size_cond", None) - if src_size_cond is not None: - out["src_size_cond"] = comfy.conds.CONDRegular(torch.tensor(src_size_cond)) - - return out - -def load_hydit(model_path, model_conf): - state_dict = comfy.utils.load_torch_file(model_path) - state_dict = state_dict.get("model", state_dict) - - parameters = comfy.utils.calculate_parameters(state_dict) - unet_dtype = model_management.unet_dtype(model_params=parameters) - load_device = comfy.model_management.get_torch_device() - offload_device = comfy.model_management.unet_offload_device() - - # ignore fp8/etc and use directly for now - manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device) - if manual_cast_dtype: - print(f"HunYuanDiT: falling back to {manual_cast_dtype}") - unet_dtype = manual_cast_dtype - - model_conf = EXM_HYDiT(model_conf) - model = EXM_HYDiT_Model( - model_conf, - model_type=comfy.model_base.ModelType.V_PREDICTION, - device=model_management.get_torch_device() - ) - - from .models.models import HunYuanDiT - model.diffusion_model = HunYuanDiT( - **model_conf.unet_config, - log_fn=tqdm.write, - ) - - model.diffusion_model.load_state_dict(state_dict) - model.diffusion_model.dtype = unet_dtype - model.diffusion_model.eval() - model.diffusion_model.to(unet_dtype) - - model_patcher = comfy.model_patcher.ModelPatcher( - model, - load_device = load_device, - offload_device = offload_device, - ) - return model_patcher diff --git a/HunYuanDiT/models/attn_layers.py b/HunYuanDiT/models/attn_layers.py deleted file mode 100644 index b767d83..0000000 --- a/HunYuanDiT/models/attn_layers.py +++ /dev/null @@ -1,374 +0,0 @@ -import torch -import torch.nn as nn -from typing import Tuple, Union, Optional - -try: - import flash_attn - if hasattr(flash_attn, '__version__') and int(flash_attn.__version__[0]) == 2: - from flash_attn.flash_attn_interface import flash_attn_kvpacked_func - from flash_attn.modules.mha import FlashSelfAttention, FlashCrossAttention - else: - from flash_attn.flash_attn_interface import flash_attn_unpadded_kvpacked_func - from flash_attn.modules.mha import FlashSelfAttention, FlashCrossAttention -except Exception as e: - print(f'flash_attn import failed: {e}') - - -def reshape_for_broadcast(freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]], x: torch.Tensor, head_first=False): - """ - Reshape frequency tensor for broadcasting it with another tensor. - - This function reshapes the frequency tensor to have the same shape as the target tensor 'x' - for the purpose of broadcasting the frequency tensor during element-wise operations. - - Args: - freqs_cis (Union[torch.Tensor, Tuple[torch.Tensor]]): Frequency tensor to be reshaped. - x (torch.Tensor): Target tensor for broadcasting compatibility. - head_first (bool): head dimension first (except batch dim) or not. - - Returns: - torch.Tensor: Reshaped frequency tensor. - - Raises: - AssertionError: If the frequency tensor doesn't match the expected shape. - AssertionError: If the target tensor 'x' doesn't have the expected number of dimensions. - """ - ndim = x.ndim - assert 0 <= 1 < ndim - - if isinstance(freqs_cis, tuple): - # freqs_cis: (cos, sin) in real space - if head_first: - assert freqs_cis[0].shape == (x.shape[-2], x.shape[-1]), f'freqs_cis shape {freqs_cis[0].shape} does not match x shape {x.shape}' - shape = [d if i == ndim - 2 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)] - else: - assert freqs_cis[0].shape == (x.shape[1], x.shape[-1]), f'freqs_cis shape {freqs_cis[0].shape} does not match x shape {x.shape}' - shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)] - return freqs_cis[0].view(*shape), freqs_cis[1].view(*shape) - else: - # freqs_cis: values in complex space - if head_first: - assert freqs_cis.shape == (x.shape[-2], x.shape[-1]), f'freqs_cis shape {freqs_cis.shape} does not match x shape {x.shape}' - shape = [d if i == ndim - 2 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)] - else: - assert freqs_cis.shape == (x.shape[1], x.shape[-1]), f'freqs_cis shape {freqs_cis.shape} does not match x shape {x.shape}' - shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)] - return freqs_cis.view(*shape) - - -def rotate_half(x): - x_real, x_imag = x.float().reshape(*x.shape[:-1], -1, 2).unbind(-1) # [B, S, H, D//2] - return torch.stack([-x_imag, x_real], dim=-1).flatten(3) - - -def apply_rotary_emb( - xq: torch.Tensor, - xk: Optional[torch.Tensor], - freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]], - head_first: bool = False, -) -> Tuple[torch.Tensor, torch.Tensor]: - """ - Apply rotary embeddings to input tensors using the given frequency tensor. - - This function applies rotary embeddings to the given query 'xq' and key 'xk' tensors using the provided - frequency tensor 'freqs_cis'. The input tensors are reshaped as complex numbers, and the frequency tensor - is reshaped for broadcasting compatibility. The resulting tensors contain rotary embeddings and are - returned as real tensors. - - Args: - xq (torch.Tensor): Query tensor to apply rotary embeddings. [B, S, H, D] - xk (torch.Tensor): Key tensor to apply rotary embeddings. [B, S, H, D] - freqs_cis (Union[torch.Tensor, Tuple[torch.Tensor]]): Precomputed frequency tensor for complex exponentials. - head_first (bool): head dimension first (except batch dim) or not. - - Returns: - Tuple[torch.Tensor, torch.Tensor]: Tuple of modified query tensor and key tensor with rotary embeddings. - - """ - xk_out = None - if isinstance(freqs_cis, tuple): - cos, sin = reshape_for_broadcast(freqs_cis, xq, head_first) # [S, D] - cos, sin = cos.to(xq.device), sin.to(xq.device) - xq_out = (xq.float() * cos + rotate_half(xq.float()) * sin).type_as(xq) - if xk is not None: - xk_out = (xk.float() * cos + rotate_half(xk.float()) * sin).type_as(xk) - else: - xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2)) # [B, S, H, D//2] - freqs_cis = reshape_for_broadcast(freqs_cis, xq_, head_first).to(xq.device) # [S, D//2] --> [1, S, 1, D//2] - xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(3).type_as(xq) - if xk is not None: - xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2)) # [B, S, H, D//2] - xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(3).type_as(xk) - - return xq_out, xk_out - - -class FlashSelfMHAModified(nn.Module): - """ - Use QK Normalization. - """ - def __init__(self, - dim, - num_heads, - qkv_bias=True, - qk_norm=False, - attn_drop=0.0, - proj_drop=0.0, - device=None, - dtype=None, - norm_layer=nn.LayerNorm, - ): - factory_kwargs = {'device': device, 'dtype': dtype} - super().__init__() - self.dim = dim - self.num_heads = num_heads - assert self.dim % num_heads == 0, "self.kdim must be divisible by num_heads" - self.head_dim = self.dim // num_heads - assert self.head_dim % 8 == 0 and self.head_dim <= 128, "Only support head_dim <= 128 and divisible by 8" - - self.Wqkv = nn.Linear(dim, 3 * dim, bias=qkv_bias, **factory_kwargs) - # TODO: eps should be 1 / 65530 if using fp16 - self.q_norm = norm_layer(self.head_dim, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity() - self.k_norm = norm_layer(self.head_dim, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity() - self.inner_attn = FlashSelfAttention(attention_dropout=attn_drop) - self.out_proj = nn.Linear(dim, dim, bias=qkv_bias, **factory_kwargs) - self.proj_drop = nn.Dropout(proj_drop) - - def forward(self, x, freqs_cis_img=None): - """ - Parameters - ---------- - x: torch.Tensor - (batch, seqlen, hidden_dim) (where hidden_dim = num heads * head dim) - freqs_cis_img: torch.Tensor - (batch, hidden_dim // 2), RoPE for image - """ - b, s, d = x.shape - - qkv = self.Wqkv(x) - qkv = qkv.view(b, s, 3, self.num_heads, self.head_dim) # [b, s, 3, h, d] - q, k, v = qkv.unbind(dim=2) # [b, s, h, d] - q = self.q_norm(q).half() # [b, s, h, d] - k = self.k_norm(k).half() - - # Apply RoPE if needed - if freqs_cis_img is not None: - qq, kk = apply_rotary_emb(q, k, freqs_cis_img) - assert qq.shape == q.shape and kk.shape == k.shape, f'qq: {qq.shape}, q: {q.shape}, kk: {kk.shape}, k: {k.shape}' - q, k = qq, kk - - qkv = torch.stack([q, k, v], dim=2) # [b, s, 3, h, d] - context = self.inner_attn(qkv) - out = self.out_proj(context.view(b, s, d)) - out = self.proj_drop(out) - - out_tuple = (out,) - - return out_tuple - - -class FlashCrossMHAModified(nn.Module): - """ - Use QK Normalization. - """ - def __init__(self, - qdim, - kdim, - num_heads, - qkv_bias=True, - qk_norm=False, - attn_drop=0.0, - proj_drop=0.0, - device=None, - dtype=None, - norm_layer=nn.LayerNorm, - ): - factory_kwargs = {'device': device, 'dtype': dtype} - super().__init__() - self.qdim = qdim - self.kdim = kdim - self.num_heads = num_heads - assert self.qdim % num_heads == 0, "self.qdim must be divisible by num_heads" - self.head_dim = self.qdim // num_heads - assert self.head_dim % 8 == 0 and self.head_dim <= 128, "Only support head_dim <= 128 and divisible by 8" - - self.scale = self.head_dim ** -0.5 - - self.q_proj = nn.Linear(qdim, qdim, bias=qkv_bias, **factory_kwargs) - self.kv_proj = nn.Linear(kdim, 2 * qdim, bias=qkv_bias, **factory_kwargs) - - # TODO: eps should be 1 / 65530 if using fp16 - self.q_norm = norm_layer(self.head_dim, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity() - self.k_norm = norm_layer(self.head_dim, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity() - - self.inner_attn = FlashCrossAttention(attention_dropout=attn_drop) - self.out_proj = nn.Linear(qdim, qdim, bias=qkv_bias, **factory_kwargs) - self.proj_drop = nn.Dropout(proj_drop) - - def forward(self, x, y, freqs_cis_img=None): - """ - Parameters - ---------- - x: torch.Tensor - (batch, seqlen1, hidden_dim) (where hidden_dim = num_heads * head_dim) - y: torch.Tensor - (batch, seqlen2, hidden_dim2) - freqs_cis_img: torch.Tensor - (batch, hidden_dim // num_heads), RoPE for image - """ - b, s1, _ = x.shape # [b, s1, D] - _, s2, _ = y.shape # [b, s2, 1024] - - q = self.q_proj(x).view(b, s1, self.num_heads, self.head_dim) # [b, s1, h, d] - kv = self.kv_proj(y).view(b, s2, 2, self.num_heads, self.head_dim) # [b, s2, 2, h, d] - k, v = kv.unbind(dim=2) # [b, s2, h, d] - q = self.q_norm(q).half() # [b, s1, h, d] - k = self.k_norm(k).half() # [b, s2, h, d] - - # Apply RoPE if needed - if freqs_cis_img is not None: - qq, _ = apply_rotary_emb(q, None, freqs_cis_img) - assert qq.shape == q.shape, f'qq: {qq.shape}, q: {q.shape}' - q = qq # [b, s1, h, d] - kv = torch.stack([k, v], dim=2) # [b, s1, 2, h, d] - context = self.inner_attn(q, kv) # [b, s1, h, d] - context = context.view(b, s1, -1) # [b, s1, D] - - out = self.out_proj(context) - out = self.proj_drop(out) - - out_tuple = (out,) - - return out_tuple - - -class CrossAttention(nn.Module): - """ - Use QK Normalization. - """ - def __init__(self, - qdim, - kdim, - num_heads, - qkv_bias=True, - qk_norm=False, - attn_drop=0.0, - proj_drop=0.0, - device=None, - dtype=None, - norm_layer=nn.LayerNorm, - ): - factory_kwargs = {'device': device, 'dtype': dtype} - super().__init__() - self.qdim = qdim - self.kdim = kdim - self.num_heads = num_heads - assert self.qdim % num_heads == 0, "self.qdim must be divisible by num_heads" - self.head_dim = self.qdim // num_heads - assert self.head_dim % 8 == 0 and self.head_dim <= 128, "Only support head_dim <= 128 and divisible by 8" - self.scale = self.head_dim ** -0.5 - - self.q_proj = nn.Linear(qdim, qdim, bias=qkv_bias, **factory_kwargs) - self.kv_proj = nn.Linear(kdim, 2 * qdim, bias=qkv_bias, **factory_kwargs) - - # TODO: eps should be 1 / 65530 if using fp16 - self.q_norm = norm_layer(self.head_dim, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity() - self.k_norm = norm_layer(self.head_dim, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity() - self.attn_drop = nn.Dropout(attn_drop) - self.out_proj = nn.Linear(qdim, qdim, bias=qkv_bias, **factory_kwargs) - self.proj_drop = nn.Dropout(proj_drop) - - def forward(self, x, y, freqs_cis_img=None): - """ - Parameters - ---------- - x: torch.Tensor - (batch, seqlen1, hidden_dim) (where hidden_dim = num heads * head dim) - y: torch.Tensor - (batch, seqlen2, hidden_dim2) - freqs_cis_img: torch.Tensor - (batch, hidden_dim // 2), RoPE for image - """ - b, s1, c = x.shape # [b, s1, D] - _, s2, c = y.shape # [b, s2, 1024] - - q = self.q_proj(x).view(b, s1, self.num_heads, self.head_dim) # [b, s1, h, d] - kv = self.kv_proj(y).view(b, s2, 2, self.num_heads, self.head_dim) # [b, s2, 2, h, d] - k, v = kv.unbind(dim=2) # [b, s, h, d] - q = self.q_norm(q) - k = self.k_norm(k) - - # Apply RoPE if needed - if freqs_cis_img is not None: - qq, _ = apply_rotary_emb(q, None, freqs_cis_img) - assert qq.shape == q.shape, f'qq: {qq.shape}, q: {q.shape}' - q = qq - - q = q * self.scale - q = q.transpose(-2, -3).contiguous() # q -> B, L1, H, C - B, H, L1, C - k = k.permute(0, 2, 3, 1).contiguous() # k -> B, L2, H, C - B, H, C, L2 - attn = q @ k # attn -> B, H, L1, L2 - attn = attn.softmax(dim=-1) # attn -> B, H, L1, L2 - attn = self.attn_drop(attn) - x = attn @ v.transpose(-2, -3) # v -> B, L2, H, C - B, H, L2, C x-> B, H, L1, C - context = x.transpose(1, 2) # context -> B, H, L1, C - B, L1, H, C - - context = context.contiguous().view(b, s1, -1) - - out = self.out_proj(context) # context.reshape - B, L1, -1 - out = self.proj_drop(out) - - out_tuple = (out,) - - return out_tuple - - -class Attention(nn.Module): - """ - We rename some layer names to align with flash attention - """ - def __init__(self, dim, num_heads, qkv_bias=True, qk_norm=False, attn_drop=0., proj_drop=0., - norm_layer=nn.LayerNorm, - ): - super().__init__() - self.dim = dim - self.num_heads = num_heads - assert self.dim % num_heads == 0, 'dim should be divisible by num_heads' - self.head_dim = self.dim // num_heads - # This assertion is aligned with flash attention - assert self.head_dim % 8 == 0 and self.head_dim <= 128, "Only support head_dim <= 128 and divisible by 8" - self.scale = self.head_dim ** -0.5 - - # qkv --> Wqkv - self.Wqkv = nn.Linear(dim, dim * 3, bias=qkv_bias) - # TODO: eps should be 1 / 65530 if using fp16 - self.q_norm = norm_layer(self.head_dim, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity() - self.k_norm = norm_layer(self.head_dim, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity() - self.attn_drop = nn.Dropout(attn_drop) - self.out_proj = nn.Linear(dim, dim) - self.proj_drop = nn.Dropout(proj_drop) - - def forward(self, x, freqs_cis_img=None): - B, N, C = x.shape - qkv = self.Wqkv(x).reshape(B, N, 3, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4) # [3, b, h, s, d] - q, k, v = qkv.unbind(0) # [b, h, s, d] - q = self.q_norm(q) # [b, h, s, d] - k = self.k_norm(k) # [b, h, s, d] - - # Apply RoPE if needed - if freqs_cis_img is not None: - qq, kk = apply_rotary_emb(q, k, freqs_cis_img, head_first=True) - assert qq.shape == q.shape and kk.shape == k.shape, \ - f'qq: {qq.shape}, q: {q.shape}, kk: {kk.shape}, k: {k.shape}' - q, k = qq, kk - - # just use SDP here for now - x = torch.nn.functional.scaled_dot_product_attention( - q, k, v, - ).permute(0, 2, 1, 3).contiguous().reshape(B, N, C) - x = self.out_proj(x) - x = self.proj_drop(x) - - out_tuple = (x,) - - return out_tuple diff --git a/HunYuanDiT/models/embedders.py b/HunYuanDiT/models/embedders.py deleted file mode 100644 index 9fe08cb..0000000 --- a/HunYuanDiT/models/embedders.py +++ /dev/null @@ -1,111 +0,0 @@ -import math -import torch -import torch.nn as nn -from einops import repeat - -from timm.models.layers import to_2tuple - - -class PatchEmbed(nn.Module): - """ 2D Image to Patch Embedding - - Image to Patch Embedding using Conv2d - - A convolution based approach to patchifying a 2D image w/ embedding projection. - - Based on the impl in https://github.com/google-research/vision_transformer - - Hacked together by / Copyright 2020 Ross Wightman - - Remove the _assert function in forward function to be compatible with multi-resolution images. - """ - def __init__( - self, - img_size=224, - patch_size=16, - in_chans=3, - embed_dim=768, - norm_layer=None, - flatten=True, - bias=True, - ): - super().__init__() - if isinstance(img_size, int): - img_size = to_2tuple(img_size) - elif isinstance(img_size, (tuple, list)) and len(img_size) == 2: - img_size = tuple(img_size) - else: - raise ValueError(f"img_size must be int or tuple/list of length 2. Got {img_size}") - patch_size = to_2tuple(patch_size) - self.img_size = img_size - self.patch_size = patch_size - self.grid_size = (img_size[0] // patch_size[0], img_size[1] // patch_size[1]) - self.num_patches = self.grid_size[0] * self.grid_size[1] - self.flatten = flatten - - self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=bias) - self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity() - - def update_image_size(self, img_size): - self.img_size = img_size - self.grid_size = (img_size[0] // self.patch_size[0], img_size[1] // self.patch_size[1]) - self.num_patches = self.grid_size[0] * self.grid_size[1] - - def forward(self, x): - # B, C, H, W = x.shape - # _assert(H == self.img_size[0], f"Input image height ({H}) doesn't match model ({self.img_size[0]}).") - # _assert(W == self.img_size[1], f"Input image width ({W}) doesn't match model ({self.img_size[1]}).") - x = self.proj(x) - if self.flatten: - x = x.flatten(2).transpose(1, 2) # BCHW -> BNC - x = self.norm(x) - return x - - -def timestep_embedding(t, dim, max_period=10000, repeat_only=False): - """ - Create sinusoidal timestep embeddings. - :param t: a 1-D Tensor of N indices, one per batch element. - These may be fractional. - :param dim: the dimension of the output. - :param max_period: controls the minimum frequency of the embeddings. - :return: an (N, D) Tensor of positional embeddings. - """ - # https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py - if not repeat_only: - half = dim // 2 - freqs = torch.exp( - -math.log(max_period) - * torch.arange(start=0, end=half, dtype=torch.float32) - / half - ).to(device=t.device) # size: [dim/2], 一个指数衰减的曲线 - args = t[:, None].float() * freqs[None] - embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) - if dim % 2: - embedding = torch.cat( - [embedding, torch.zeros_like(embedding[:, :1])], dim=-1 - ) - else: - embedding = repeat(t, "b -> b d", d=dim) - return embedding - - -class TimestepEmbedder(nn.Module): - """ - Embeds scalar timesteps into vector representations. - """ - def __init__(self, hidden_size, frequency_embedding_size=256, out_size=None): - super().__init__() - if out_size is None: - out_size = hidden_size - self.mlp = nn.Sequential( - nn.Linear(frequency_embedding_size, hidden_size, bias=True), - nn.SiLU(), - nn.Linear(hidden_size, out_size, bias=True), - ) - self.frequency_embedding_size = frequency_embedding_size - - def forward(self, t): - t_freq = timestep_embedding(t, self.frequency_embedding_size).type(self.mlp[0].weight.dtype) - t_emb = self.mlp(t_freq) - return t_emb diff --git a/HunYuanDiT/models/models.py b/HunYuanDiT/models/models.py deleted file mode 100644 index 7dae413..0000000 --- a/HunYuanDiT/models/models.py +++ /dev/null @@ -1,439 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F -from timm.models.vision_transformer import Mlp - -from .attn_layers import Attention, FlashCrossMHAModified, FlashSelfMHAModified, CrossAttention -from .embedders import TimestepEmbedder, PatchEmbed, timestep_embedding -from .norm_layers import RMSNorm -from .poolers import AttentionPool -from .posemb_layers import get_2d_rotary_pos_embed, get_fill_resize_and_crop - -def modulate(x, shift, scale): - return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1) - - -class FP32_Layernorm(nn.LayerNorm): - def forward(self, inputs: torch.Tensor) -> torch.Tensor: - origin_dtype = inputs.dtype - return F.layer_norm(inputs.float(), self.normalized_shape, self.weight.float(), self.bias.float(), - self.eps).to(origin_dtype) - - -class FP32_SiLU(nn.SiLU): - def forward(self, inputs: torch.Tensor) -> torch.Tensor: - return torch.nn.functional.silu(inputs.float(), inplace=False).to(inputs.dtype) - - -class HunYuanDiTBlock(nn.Module): - """ - A HunYuanDiT block with `add` conditioning. - """ - def __init__(self, - hidden_size, - c_emb_size, - num_heads, - mlp_ratio=4.0, - text_states_dim=1024, - use_flash_attn=False, - qk_norm=False, - norm_type="layer", - skip=False, - ): - super().__init__() - self.use_flash_attn = use_flash_attn - use_ele_affine = True - - if norm_type == "layer": - norm_layer = FP32_Layernorm - elif norm_type == "rms": - norm_layer = RMSNorm - else: - raise ValueError(f"Unknown norm_type: {norm_type}") - - # ========================= Self-Attention ========================= - self.norm1 = norm_layer(hidden_size, elementwise_affine=use_ele_affine, eps=1e-6) - if use_flash_attn: - self.attn1 = FlashSelfMHAModified(hidden_size, num_heads=num_heads, qkv_bias=True, qk_norm=qk_norm) - else: - self.attn1 = Attention(hidden_size, num_heads=num_heads, qkv_bias=True, qk_norm=qk_norm) - - # ========================= FFN ========================= - self.norm2 = norm_layer(hidden_size, elementwise_affine=use_ele_affine, eps=1e-6) - mlp_hidden_dim = int(hidden_size * mlp_ratio) - approx_gelu = lambda: nn.GELU(approximate="tanh") - self.mlp = Mlp(in_features=hidden_size, hidden_features=mlp_hidden_dim, act_layer=approx_gelu, drop=0) - - # ========================= Add ========================= - # Simply use add like SDXL. - self.default_modulation = nn.Sequential( - FP32_SiLU(), - nn.Linear(c_emb_size, hidden_size, bias=True) - ) - - # ========================= Cross-Attention ========================= - if use_flash_attn: - self.attn2 = FlashCrossMHAModified(hidden_size, text_states_dim, num_heads=num_heads, qkv_bias=True, - qk_norm=qk_norm) - else: - self.attn2 = CrossAttention(hidden_size, text_states_dim, num_heads=num_heads, qkv_bias=True, - qk_norm=qk_norm) - self.norm3 = norm_layer(hidden_size, elementwise_affine=True, eps=1e-6) - - # ========================= Skip Connection ========================= - if skip: - self.skip_norm = norm_layer(2 * hidden_size, elementwise_affine=True, eps=1e-6) - self.skip_linear = nn.Linear(2 * hidden_size, hidden_size) - else: - self.skip_linear = None - - def forward(self, x, c=None, text_states=None, freq_cis_img=None, skip=None): - # Long Skip Connection - if self.skip_linear is not None: - cat = torch.cat([x, skip], dim=-1) - cat = self.skip_norm(cat) - x = self.skip_linear(cat) - - # Self-Attention - shift_msa = self.default_modulation(c).unsqueeze(dim=1) - attn_inputs = ( - self.norm1(x) + shift_msa, freq_cis_img, - ) - x = x + self.attn1(*attn_inputs)[0] - - # Cross-Attention - cross_inputs = ( - self.norm3(x), text_states, freq_cis_img - ) - x = x + self.attn2(*cross_inputs)[0] - - # FFN Layer - mlp_inputs = self.norm2(x) - x = x + self.mlp(mlp_inputs) - - return x - - -class FinalLayer(nn.Module): - """ - The final layer of HunYuanDiT. - """ - def __init__(self, final_hidden_size, c_emb_size, patch_size, out_channels): - super().__init__() - self.norm_final = nn.LayerNorm(final_hidden_size, elementwise_affine=False, eps=1e-6) - self.linear = nn.Linear(final_hidden_size, patch_size * patch_size * out_channels, bias=True) - self.adaLN_modulation = nn.Sequential( - FP32_SiLU(), - nn.Linear(c_emb_size, 2 * final_hidden_size, bias=True) - ) - - def forward(self, x, c): - shift, scale = self.adaLN_modulation(c).chunk(2, dim=1) - x = modulate(self.norm_final(x), shift, scale) - x = self.linear(x) - return x - - -class HunYuanDiT(nn.Module): - """ - HunYuanDiT: Diffusion model with a Transformer backbone. - - Parameters - ---------- - args: argparse.Namespace - The arguments parsed by argparse. - input_size: tuple - The size of the input image. - patch_size: int - The size of the patch. - in_channels: int - The number of input channels. - hidden_size: int - The hidden size of the transformer backbone. - depth: int - The number of transformer blocks. - num_heads: int - The number of attention heads. - mlp_ratio: float - The ratio of the hidden size of the MLP in the transformer block. - log_fn: callable - The logging function. - """ - def __init__( - self, args, - input_size=(32, 32), - patch_size=2, - in_channels=4, - hidden_size=1152, - depth=28, - num_heads=16, - mlp_ratio=4.0, - log_fn=print, - cond_style=True, - cond_res=True, - **kwargs, - ): - super().__init__() - self.args = args - self.log_fn = log_fn - self.depth = depth - self.learn_sigma = args.learn_sigma - self.in_channels = in_channels - self.out_channels = in_channels * 2 if args.learn_sigma else in_channels - self.patch_size = patch_size - self.num_heads = num_heads - self.hidden_size = hidden_size - self.head_size = hidden_size // num_heads - self.text_states_dim = args.text_states_dim - self.text_states_dim_t5 = args.text_states_dim_t5 - self.text_len = args.text_len - self.text_len_t5 = args.text_len_t5 - self.norm = args.norm - self.cond_res = cond_res - self.cond_style = cond_style - - use_flash_attn = args.infer_mode == 'fa' - if use_flash_attn: - log_fn(f" Enable Flash Attention.") - qk_norm = True # See http://arxiv.org/abs/2302.05442 for details. - - self.mlp_t5 = nn.Sequential( - nn.Linear(self.text_states_dim_t5, self.text_states_dim_t5 * 4, bias=True), - FP32_SiLU(), - nn.Linear(self.text_states_dim_t5 * 4, self.text_states_dim, bias=True), - ) - # learnable replace - self.text_embedding_padding = nn.Parameter( - torch.randn(self.text_len + self.text_len_t5, self.text_states_dim, dtype=torch.float32)) - - # Attention pooling - self.pooler = AttentionPool(self.text_len_t5, self.text_states_dim_t5, num_heads=8, output_dim=1024) - - - self.extra_in_dim = 0 - if self.cond_res: - # Image size and crop size conditions - self.extra_in_dim += 256 * 6 - if self.cond_style: - # Here we use a default learned embedder layer for future extension. - self.style_embedder = nn.Embedding(1, hidden_size) - self.extra_in_dim += hidden_size - - # Text embedding for `add` - self.last_size = input_size - self.x_embedder = PatchEmbed(input_size, patch_size, in_channels, hidden_size) - self.t_embedder = TimestepEmbedder(hidden_size) - self.extra_in_dim += 1024 - self.extra_embedder = nn.Sequential( - nn.Linear(self.extra_in_dim, hidden_size * 4), - FP32_SiLU(), - nn.Linear(hidden_size * 4, hidden_size, bias=True), - ) - - # Image embedding - num_patches = self.x_embedder.num_patches - log_fn(f" Number of tokens: {num_patches}") - - # HUnYuanDiT Blocks - self.blocks = nn.ModuleList([ - HunYuanDiTBlock(hidden_size=hidden_size, - c_emb_size=hidden_size, - num_heads=num_heads, - mlp_ratio=mlp_ratio, - text_states_dim=self.text_states_dim, - use_flash_attn=use_flash_attn, - qk_norm=qk_norm, - norm_type=self.norm, - skip=layer > depth // 2, - ) - for layer in range(depth) - ]) - - self.final_layer = FinalLayer(hidden_size, hidden_size, patch_size, self.out_channels) - self.unpatchify_channels = self.out_channels - - def forward_raw(self, - x, - t, - encoder_hidden_states=None, - text_embedding_mask=None, - encoder_hidden_states_t5=None, - text_embedding_mask_t5=None, - image_meta_size=None, - style=None, - cos_cis_img=None, - sin_cis_img=None, - return_dict=False, - ): - """ - Forward pass of the encoder. - - Parameters - ---------- - x: torch.Tensor - (B, D, H, W) - t: torch.Tensor - (B) - encoder_hidden_states: torch.Tensor - CLIP text embedding, (B, L_clip, D) - text_embedding_mask: torch.Tensor - CLIP text embedding mask, (B, L_clip) - encoder_hidden_states_t5: torch.Tensor - T5 text embedding, (B, L_t5, D) - text_embedding_mask_t5: torch.Tensor - T5 text embedding mask, (B, L_t5) - image_meta_size: torch.Tensor - (B, 6) - style: torch.Tensor - (B) - cos_cis_img: torch.Tensor - sin_cis_img: torch.Tensor - return_dict: bool - Whether to return a dictionary. - """ - - text_states = encoder_hidden_states # 2,77,1024 - text_states_t5 = encoder_hidden_states_t5 # 2,256,2048 - text_states_mask = text_embedding_mask.bool() # 2,77 - text_states_t5_mask = text_embedding_mask_t5.bool() # 2,256 - b_t5, l_t5, c_t5 = text_states_t5.shape - text_states_t5 = self.mlp_t5(text_states_t5.view(-1, c_t5)) - text_states = torch.cat([text_states, text_states_t5.view(b_t5, l_t5, -1)], dim=1) # 2,205，1024 - clip_t5_mask = torch.cat([text_states_mask, text_states_t5_mask], dim=-1) - - clip_t5_mask = clip_t5_mask - text_states = torch.where(clip_t5_mask.unsqueeze(2), text_states, self.text_embedding_padding.to(text_states)) - - _, _, oh, ow = x.shape - th, tw = oh // self.patch_size, ow // self.patch_size - - # ========================= Build time and image embedding ========================= - t = self.t_embedder(t) - x = self.x_embedder(x) - - # Get image RoPE embedding according to `reso`lution. - freqs_cis_img = (cos_cis_img, sin_cis_img) - - # ========================= Concatenate all extra vectors ========================= - # Build text tokens with pooling - extra_vec = self.pooler(encoder_hidden_states_t5) - - if self.cond_res: - # Build image meta size tokens - image_meta_size = timestep_embedding(image_meta_size.view(-1), 256) # [B * 6, 256] - # if self.args.use_fp16: - # image_meta_size = image_meta_size.half() - - image_meta_size = image_meta_size.view(-1, 6 * 256) - extra_vec = torch.cat([extra_vec, image_meta_size], dim=1) # [B, D + 6 * 256] - - if self.cond_style: - # Build style tokens - style_embedding = self.style_embedder(style) - extra_vec = torch.cat([extra_vec, style_embedding], dim=1) - - # Concatenate all extra vectors - c = t + self.extra_embedder(extra_vec.to(self.dtype)) # [B, D] - - # ========================= Forward pass through HunYuanDiT blocks ========================= - skips = [] - for layer, block in enumerate(self.blocks): - if layer > self.depth // 2: - skip = skips.pop() - x = block(x, c, text_states, freqs_cis_img, skip) # (N, L, D) - else: - x = block(x, c, text_states, freqs_cis_img) # (N, L, D) - - if layer < (self.depth // 2 - 1): - skips.append(x) - - # ========================= Final layer ========================= - x = self.final_layer(x, c) # (N, L, patch_size ** 2 * out_channels) - x = self.unpatchify(x, th, tw) # (N, out_channels, H, W) - - if return_dict: - return {'x': x} - return x - - def calc_rope(self, height, width): - """ - Probably not the best in terms of perf to have this here - """ - th = height // 8 // self.patch_size - tw = width // 8 // self.patch_size - base_size = 512 // 8 // self.patch_size - start, stop = get_fill_resize_and_crop((th, tw), base_size) - sub_args = [start, stop, (th, tw)] - rope = get_2d_rotary_pos_embed(self.head_size, *sub_args) - return rope - - def forward(self, x, timesteps, context, context_mask=None, context_t5=None, context_t5_mask=None, src_size_cond=(1024,1024), **kwargs): - """ - Forward pass that adapts comfy input to original forward function - x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images) - timesteps: (N,) tensor of diffusion timesteps - context: (N, 1, 77, C) CLIP conditioning - context_t5: (N, 1, 256, C) MT5 conditioning - """ - # context_mask = torch.zeros(x.shape[0], 77, device=x.device) - # context_t5_mask = torch.zeros(x.shape[0], 256, device=x.device) - - # style - style = torch.as_tensor([0] * (x.shape[0]), device=x.device) - - # image size - todo separate for cond/uncond when batched - if torch.is_tensor(src_size_cond): - src_size_cond = (int(src_size_cond[0][0]), int(src_size_cond[0][1])) - - image_size = (x.shape[2]//2*16, x.shape[3]//2*16) - size_cond = list(src_size_cond) + [image_size[1], image_size[0], 0, 0] - image_meta_size = torch.as_tensor([size_cond] * x.shape[0], device=x.device) - - # RoPE - rope = self.calc_rope(*image_size) - - # Update x_embedder if image size changed - if self.last_size != image_size: - from tqdm import tqdm - tqdm.write(f"HyDiT: New image size {image_size}") - self.x_embedder.update_image_size( - (image_size[0]//8, image_size[1]//8), - ) - self.last_size = image_size - - # Run original forward pass - out = self.forward_raw( - x = x.to(self.dtype), - t = timesteps.to(self.dtype), - encoder_hidden_states = context.to(self.dtype), - text_embedding_mask = context_mask.to(self.dtype), - encoder_hidden_states_t5 = context_t5.to(self.dtype), - text_embedding_mask_t5 = context_t5_mask.to(self.dtype), - image_meta_size = image_meta_size.to(self.dtype), - style = style, - cos_cis_img = rope[0], - sin_cis_img = rope[1], - ) - - # return - out = out.to(torch.float) - if self.learn_sigma: - eps, rest = out[:, :self.in_channels], out[:, self.in_channels:] - return eps - else: - return out - - def unpatchify(self, x, h, w): - """ - x: (N, T, patch_size**2 * C) - imgs: (N, H, W, C) - """ - c = self.unpatchify_channels - p = self.x_embedder.patch_size[0] - # h = w = int(x.shape[1] ** 0.5) - assert h * w == x.shape[1] - - x = x.reshape(shape=(x.shape[0], h, w, p, p, c)) - x = torch.einsum('nhwpqc->nchpwq', x) - imgs = x.reshape(shape=(x.shape[0], c, h * p, w * p)) - return imgs diff --git a/HunYuanDiT/models/norm_layers.py b/HunYuanDiT/models/norm_layers.py deleted file mode 100644 index 5204ad9..0000000 --- a/HunYuanDiT/models/norm_layers.py +++ /dev/null @@ -1,68 +0,0 @@ -import torch -import torch.nn as nn - - -class RMSNorm(nn.Module): - def __init__(self, dim: int, elementwise_affine=True, eps: float = 1e-6): - """ - Initialize the RMSNorm normalization layer. - - Args: - dim (int): The dimension of the input tensor. - eps (float, optional): A small value added to the denominator for numerical stability. Default is 1e-6. - - Attributes: - eps (float): A small value added to the denominator for numerical stability. - weight (nn.Parameter): Learnable scaling parameter. - - """ - super().__init__() - self.eps = eps - if elementwise_affine: - self.weight = nn.Parameter(torch.ones(dim)) - - def _norm(self, x): - """ - Apply the RMSNorm normalization to the input tensor. - - Args: - x (torch.Tensor): The input tensor. - - Returns: - torch.Tensor: The normalized tensor. - - """ - return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps) - - def forward(self, x): - """ - Forward pass through the RMSNorm layer. - - Args: - x (torch.Tensor): The input tensor. - - Returns: - torch.Tensor: The output tensor after applying RMSNorm. - - """ - output = self._norm(x.float()).type_as(x) - if hasattr(self, "weight"): - output = output * self.weight - return output - - -class GroupNorm32(nn.GroupNorm): - def __init__(self, num_groups, num_channels, eps=1e-5, dtype=None): - super().__init__(num_groups=num_groups, num_channels=num_channels, eps=eps, dtype=dtype) - - def forward(self, x): - y = super().forward(x).to(x.dtype) - return y - -def normalization(channels, dtype=None): - """ - Make a standard normalization layer. - :param channels: number of input channels. - :return: an nn.Module for normalization. - """ - return GroupNorm32(num_channels=channels, num_groups=32, dtype=dtype) diff --git a/HunYuanDiT/models/poolers.py b/HunYuanDiT/models/poolers.py deleted file mode 100644 index a4adcac..0000000 --- a/HunYuanDiT/models/poolers.py +++ /dev/null @@ -1,39 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F - - -class AttentionPool(nn.Module): - def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None): - super().__init__() - self.positional_embedding = nn.Parameter(torch.randn(spacial_dim + 1, embed_dim) / embed_dim ** 0.5) - self.k_proj = nn.Linear(embed_dim, embed_dim) - self.q_proj = nn.Linear(embed_dim, embed_dim) - self.v_proj = nn.Linear(embed_dim, embed_dim) - self.c_proj = nn.Linear(embed_dim, output_dim or embed_dim) - self.num_heads = num_heads - - def forward(self, x): - x = x.permute(1, 0, 2) # NLC -> LNC - x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0) # (L+1)NC - x = x + self.positional_embedding[:, None, :].to(x.dtype) # (L+1)NC - x, _ = F.multi_head_attention_forward( - query=x[:1], key=x, value=x, - embed_dim_to_check=x.shape[-1], - num_heads=self.num_heads, - q_proj_weight=self.q_proj.weight, - k_proj_weight=self.k_proj.weight, - v_proj_weight=self.v_proj.weight, - in_proj_weight=None, - in_proj_bias=torch.cat([self.q_proj.bias, self.k_proj.bias, self.v_proj.bias]), - bias_k=None, - bias_v=None, - add_zero_attn=False, - dropout_p=0, - out_proj_weight=self.c_proj.weight, - out_proj_bias=self.c_proj.bias, - use_separate_proj_weight=True, - training=self.training, - need_weights=False - ) - return x.squeeze(0) diff --git a/HunYuanDiT/models/posemb_layers.py b/HunYuanDiT/models/posemb_layers.py deleted file mode 100644 index 62c83df..0000000 --- a/HunYuanDiT/models/posemb_layers.py +++ /dev/null @@ -1,225 +0,0 @@ -import torch -import numpy as np -from typing import Union - - -def _to_tuple(x): - if isinstance(x, int): - return x, x - else: - return x - - -def get_fill_resize_and_crop(src, tgt): # src 来源的分辨率 tgt base 分辨率 - th, tw = _to_tuple(tgt) - h, w = _to_tuple(src) - - tr = th / tw # base 分辨率 - r = h / w # 目标分辨率 - - # resize - if r > tr: - resize_height = th - resize_width = int(round(th / h * w)) - else: - resize_width = tw - resize_height = int(round(tw / w * h)) # 根据base分辨率，将目标分辨率resize下来 - - crop_top = int(round((th - resize_height) / 2.0)) - crop_left = int(round((tw - resize_width) / 2.0)) - - return (crop_top, crop_left), (crop_top + resize_height, crop_left + resize_width) - - -def get_meshgrid(start, *args): - if len(args) == 0: - # start is grid_size - num = _to_tuple(start) - start = (0, 0) - stop = num - elif len(args) == 1: - # start is start, args[0] is stop, step is 1 - start = _to_tuple(start) - stop = _to_tuple(args[0]) - num = (stop[0] - start[0], stop[1] - start[1]) - elif len(args) == 2: - # start is start, args[0] is stop, args[1] is num - start = _to_tuple(start) # 左上角 eg: 12,0 - stop = _to_tuple(args[0]) # 右下角 eg: 20,32 - num = _to_tuple(args[1]) # 目标大小 eg: 32,124 - else: - raise ValueError(f"len(args) should be 0, 1 or 2, but got {len(args)}") - - grid_h = np.linspace(start[0], stop[0], num[0], endpoint=False, dtype=np.float32) # 12-20 中间差值32份 0-32 中间差值124份 - grid_w = np.linspace(start[1], stop[1], num[1], endpoint=False, dtype=np.float32) - grid = np.meshgrid(grid_w, grid_h) # here w goes first - grid = np.stack(grid, axis=0) # [2, W, H] - return grid - -################################################################################# -# Sine/Cosine Positional Embedding Functions # -################################################################################# -# https://github.com/facebookresearch/mae/blob/main/util/pos_embed.py - -def get_2d_sincos_pos_embed(embed_dim, start, *args, cls_token=False, extra_tokens=0): - """ - grid_size: int of the grid height and width - return: - pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token) - """ - grid = get_meshgrid(start, *args) # [2, H, w] - # grid_h = np.arange(grid_size, dtype=np.float32) - # grid_w = np.arange(grid_size, dtype=np.float32) - # grid = np.meshgrid(grid_w, grid_h) # here w goes first - # grid = np.stack(grid, axis=0) # [2, W, H] - - grid = grid.reshape([2, 1, *grid.shape[1:]]) - pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid) - if cls_token and extra_tokens > 0: - pos_embed = np.concatenate([np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0) - return pos_embed - - -def get_2d_sincos_pos_embed_from_grid(embed_dim, grid): - assert embed_dim % 2 == 0 - - # use half of dimensions to encode grid_h - emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) # (H*W, D/2) - emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) # (H*W, D/2) - - emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D) - return emb - - -def get_1d_sincos_pos_embed_from_grid(embed_dim, pos): - """ - embed_dim: output dimension for each position - pos: a list of positions to be encoded: size (W,H) - out: (M, D) - """ - assert embed_dim % 2 == 0 - omega = np.arange(embed_dim // 2, dtype=np.float64) - omega /= embed_dim / 2. - omega = 1. / 10000**omega # (D/2,) - - pos = pos.reshape(-1) # (M,) - out = np.einsum('m,d->md', pos, omega) # (M, D/2), outer product - - emb_sin = np.sin(out) # (M, D/2) - emb_cos = np.cos(out) # (M, D/2) - - emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D) - return emb - - -################################################################################# -# Rotary Positional Embedding Functions # -################################################################################# -# https://github.com/facebookresearch/llama/blob/main/llama/model.py#L443 - -def get_2d_rotary_pos_embed(embed_dim, start, *args, use_real=True): - """ - This is a 2d version of precompute_freqs_cis, which is a RoPE for image tokens with 2d structure. - - Parameters - ---------- - embed_dim: int - embedding dimension size - start: int or tuple of int - If len(args) == 0, start is num; If len(args) == 1, start is start, args[0] is stop, step is 1; - If len(args) == 2, start is start, args[0] is stop, args[1] is num. - use_real: bool - If True, return real part and imaginary part separately. Otherwise, return complex numbers. - - Returns - ------- - pos_embed: torch.Tensor - [HW, D/2] - """ - grid = get_meshgrid(start, *args) # [2, H, w] - grid = grid.reshape([2, 1, *grid.shape[1:]]) # 返回一个采样矩阵 分辨率与目标分辨率一致 - pos_embed = get_2d_rotary_pos_embed_from_grid(embed_dim, grid, use_real=use_real) - return pos_embed - - -def get_2d_rotary_pos_embed_from_grid(embed_dim, grid, use_real=False): - assert embed_dim % 4 == 0 - - # use half of dimensions to encode grid_h - emb_h = get_1d_rotary_pos_embed(embed_dim // 2, grid[0].reshape(-1), use_real=use_real) # (H*W, D/4) - emb_w = get_1d_rotary_pos_embed(embed_dim // 2, grid[1].reshape(-1), use_real=use_real) # (H*W, D/4) - - if use_real: - cos = torch.cat([emb_h[0], emb_w[0]], dim=1) # (H*W, D/2) - sin = torch.cat([emb_h[1], emb_w[1]], dim=1) # (H*W, D/2) - return cos, sin - else: - emb = torch.cat([emb_h, emb_w], dim=1) # (H*W, D/2) - return emb - - -def get_1d_rotary_pos_embed(dim: int, pos: Union[np.ndarray, int], theta: float = 10000.0, use_real=False): - """ - Precompute the frequency tensor for complex exponentials (cis) with given dimensions. - - This function calculates a frequency tensor with complex exponentials using the given dimension 'dim' - and the end index 'end'. The 'theta' parameter scales the frequencies. - The returned tensor contains complex values in complex64 data type. - - Args: - dim (int): Dimension of the frequency tensor. - pos (np.ndarray, int): Position indices for the frequency tensor. [S] or scalar - theta (float, optional): Scaling factor for frequency computation. Defaults to 10000.0. - use_real (bool, optional): If True, return real part and imaginary part separately. - Otherwise, return complex numbers. - - Returns: - torch.Tensor: Precomputed frequency tensor with complex exponentials. [S, D/2] - - """ - if isinstance(pos, int): - pos = np.arange(pos) - freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim)) # [D/2] - t = torch.from_numpy(pos).to(freqs.device) # type: ignore # [S] - freqs = torch.outer(t, freqs).float() # type: ignore # [S, D/2] - if use_real: - freqs_cos = freqs.cos().repeat_interleave(2, dim=1) # [S, D] - freqs_sin = freqs.sin().repeat_interleave(2, dim=1) # [S, D] - return freqs_cos, freqs_sin - else: - freqs_cis = torch.polar(torch.ones_like(freqs), freqs) # complex64 # [S, D/2] - return freqs_cis - - - -def calc_sizes(rope_img, patch_size, th, tw): - """ 计算 RoPE 的尺寸. """ - if rope_img == 'extend': - # 拓展模式 - sub_args = [(th, tw)] - elif rope_img.startswith('base'): - # 基于一个尺寸, 其他尺寸插值获得. - base_size = int(rope_img[4:]) // 8 // patch_size # 基于512作为base，其他根据512差值得到 - start, stop = get_fill_resize_and_crop((th, tw), base_size) # 需要在32x32里面 crop的左上角和右下角 - sub_args = [start, stop, (th, tw)] - else: - raise ValueError(f"Unknown rope_img: {rope_img}") - return sub_args - - -def init_image_posemb(rope_img, - resolutions, - patch_size, - hidden_size, - num_heads, - log_fn, - rope_real=True, - ): - freqs_cis_img = {} - for reso in resolutions: - th, tw = reso.height // 8 // patch_size, reso.width // 8 // patch_size - sub_args = calc_sizes(rope_img, patch_size, th, tw) # [左上角, 右下角, 目标高宽] 需要在32x32里面 crop的左上角和右下角 - freqs_cis_img[str(reso)] = get_2d_rotary_pos_embed(hidden_size // num_heads, *sub_args, use_real=rope_real) - log_fn(f" Using image RoPE ({rope_img}) ({'real' if rope_real else 'complex'}): {sub_args} | ({reso}) " - f"{freqs_cis_img[str(reso)][0].shape if rope_real else freqs_cis_img[str(reso)].shape}") - return freqs_cis_img diff --git a/HunYuanDiT/mt5_tokenizer/config.json b/HunYuanDiT/mt5_tokenizer/config.json deleted file mode 100644 index d5c7028..0000000 --- a/HunYuanDiT/mt5_tokenizer/config.json +++ /dev/null @@ -1,33 +0,0 @@ -{ - "_name_or_path": "mt5", - "architectures": [ - "MT5ForConditionalGeneration" - ], - "classifier_dropout": 0.0, - "d_ff": 5120, - "d_kv": 64, - "d_model": 2048, - "decoder_start_token_id": 0, - "dense_act_fn": "gelu_new", - "dropout_rate": 0.1, - "eos_token_id": 1, - "feed_forward_proj": "gated-gelu", - "initializer_factor": 1.0, - "is_encoder_decoder": true, - "is_gated_act": true, - "layer_norm_epsilon": 1e-06, - "model_type": "mt5", - "num_decoder_layers": 24, - "num_heads": 32, - "num_layers": 24, - "output_past": true, - "pad_token_id": 0, - "relative_attention_max_distance": 128, - "relative_attention_num_buckets": 32, - "tie_word_embeddings": false, - "tokenizer_class": "T5Tokenizer", - "torch_dtype": "float16", - "transformers_version": "4.40.2", - "use_cache": true, - "vocab_size": 250112 -} diff --git a/HunYuanDiT/mt5_tokenizer/special_tokens_map.json b/HunYuanDiT/mt5_tokenizer/special_tokens_map.json deleted file mode 100644 index 6dc4d43..0000000 --- a/HunYuanDiT/mt5_tokenizer/special_tokens_map.json +++ /dev/null @@ -1 +0,0 @@ -{"eos_token": "", "unk_token": "", "pad_token": ""} \ No newline at end of file diff --git a/HunYuanDiT/mt5_tokenizer/spiece.model b/HunYuanDiT/mt5_tokenizer/spiece.model deleted file mode 100644 index 26a2a78..0000000 Binary files a/HunYuanDiT/mt5_tokenizer/spiece.model and /dev/null differ diff --git a/HunYuanDiT/mt5_tokenizer/tokenizer_config.json b/HunYuanDiT/mt5_tokenizer/tokenizer_config.json deleted file mode 100644 index 712e82c..0000000 --- a/HunYuanDiT/mt5_tokenizer/tokenizer_config.json +++ /dev/null @@ -1 +0,0 @@ -{"eos_token": "", "unk_token": "", "pad_token": "", "extra_ids": 0, "additional_special_tokens": null, "special_tokens_map_file": "/home/patrick/.cache/torch/transformers/685ac0ca8568ec593a48b61b0a3c272beee9bc194a3c7241d15dcadb5f875e53.f76030f3ec1b96a8199b2593390c610e76ca8028ef3d24680000619ffb646276", "tokenizer_file": null, "name_or_path": "google/mt5-small"} \ No newline at end of file diff --git a/HunYuanDiT/nodes.py b/HunYuanDiT/nodes.py deleted file mode 100644 index dbce038..0000000 --- a/HunYuanDiT/nodes.py +++ /dev/null @@ -1,198 +0,0 @@ -import os -import folder_paths -from copy import deepcopy - -from .conf import hydit_conf -from .loader import load_hydit - -class HYDiTCheckpointLoader: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "ckpt_name": (folder_paths.get_filename_list("checkpoints"),), - "model": (list(hydit_conf.keys()),{"default":"G/2"}), - } - } - RETURN_TYPES = ("MODEL",) - RETURN_NAMES = ("model",) - FUNCTION = "load_checkpoint" - CATEGORY = "ExtraModels/HunyuanDiT" - TITLE = "Hunyuan DiT Checkpoint Loader" - - def load_checkpoint(self, ckpt_name, model): - ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name) - model_conf = hydit_conf[model] - model = load_hydit( - model_path = ckpt_path, - model_conf = model_conf, - ) - return (model,) - -#### temp stuff for the text encoder #### -import torch -from .tenc import load_clip, load_t5 -from ..utils.dtype import string_to_dtype -dtypes = [ - "default", - "auto (comfy)", - "FP32", - "FP16", - "BF16" -] - -class HYDiTTextEncoderLoader: - @classmethod - def INPUT_TYPES(s): - devices = ["auto", "cpu", "gpu"] - # hack for using second GPU as offload - for k in range(1, torch.cuda.device_count()): - devices.append(f"cuda:{k}") - return { - "required": { - "clip_name": (folder_paths.get_filename_list("clip"),), - "mt5_name": (folder_paths.get_filename_list("t5"),), - "device": (devices, {"default":"cpu"}), - "dtype": (dtypes,), - } - } - - RETURN_TYPES = ("CLIP", "T5") - FUNCTION = "load_model" - CATEGORY = "ExtraModels/HunyuanDiT" - TITLE = "Hunyuan DiT Text Encoder Loader" - - def load_model(self, clip_name, mt5_name, device, dtype): - dtype = string_to_dtype(dtype, "text_encoder") - if device == "cpu": - assert dtype in [None, torch.float32, torch.bfloat16], f"Can't use dtype '{dtype}' with CPU! Set dtype to 'default' or 'bf16'." - - clip = load_clip( - model_path = folder_paths.get_full_path("clip", clip_name), - device = device, - dtype = dtype, - ) - t5 = load_t5( - model_path = folder_paths.get_full_path("t5", mt5_name), - device = device, - dtype = dtype, - ) - return(clip, t5) - -class HYDiTTextEncode: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "text": ("STRING", {"multiline": True}), - "text_t5": ("STRING", {"multiline": True}), - "CLIP": ("CLIP",), - "T5": ("T5",), - } - } - - RETURN_TYPES = ("CONDITIONING",) - FUNCTION = "encode" - CATEGORY = "ExtraModels/HunyuanDiT" - TITLE = "Hunyuan DiT Text Encode" - - def encode(self, text, text_t5, CLIP, T5): - # T5 - T5.load_model() - t5_pre = T5.tokenizer( - text_t5, - max_length = T5.cond_stage_model.max_length, - padding = 'max_length', - truncation = True, - return_attention_mask = True, - add_special_tokens = True, - return_tensors = 'pt' - ) - t5_mask = t5_pre["attention_mask"] - with torch.no_grad(): - t5_outs = T5.cond_stage_model.transformer( - input_ids = t5_pre["input_ids"].to(T5.load_device), - attention_mask = t5_mask.to(T5.load_device), - output_hidden_states = True, - ) - # to-do: replace -1 for clip skip - t5_embs = t5_outs["hidden_states"][-1].float().cpu() - - # "clip" - CLIP.load_model() - clip_pre = CLIP.tokenizer( - text, - max_length = CLIP.cond_stage_model.max_length, - padding = 'max_length', - truncation = True, - return_attention_mask = True, - add_special_tokens = True, - return_tensors = 'pt' - ) - clip_mask = clip_pre["attention_mask"] - with torch.no_grad(): - clip_outs = CLIP.cond_stage_model.transformer( - input_ids = clip_pre["input_ids"].to(CLIP.load_device), - attention_mask = clip_mask.to(CLIP.load_device), - ) - # to-do: add hidden states - clip_embs = clip_outs[0].float().cpu() - - # combined cond - return ([[ - clip_embs, { - "context_t5": t5_embs, - "context_mask": clip_mask.float(), - "context_t5_mask": t5_mask.float() - } - ]],) - -class HYDiTTextEncodeSimple(HYDiTTextEncode): - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "text": ("STRING", {"multiline": True}), - "CLIP": ("CLIP",), - "T5": ("T5",), - } - } - - FUNCTION = "encode_simple" - TITLE = "Hunyuan DiT Text Encode (simple)" - - def encode_simple(self, text, **args): - return self.encode(text=text, text_t5=text, **args) - -class HYDiTSrcSizeCond: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "cond": ("CONDITIONING", ), - "width": ("INT", {"default": 1024.0, "min": 0, "max": 8192, "step": 16}), - "height": ("INT", {"default": 1024.0, "min": 0, "max": 8192, "step": 16}), - } - } - - RETURN_TYPES = ("CONDITIONING",) - RETURN_NAMES = ("cond",) - FUNCTION = "add_cond" - CATEGORY = "ExtraModels/HunyuanDiT" - TITLE = "Hunyuan DiT Size Conditioning (advanced)" - - def add_cond(self, cond, width, height): - cond = deepcopy(cond) - for c in range(len(cond)): - cond[c][1].update({ - "src_size_cond": [[height, width]], - }) - return (cond,) - -NODE_CLASS_MAPPINGS = { - "HYDiTCheckpointLoader": HYDiTCheckpointLoader, - "HYDiTTextEncoderLoader": HYDiTTextEncoderLoader, - "HYDiTTextEncode": HYDiTTextEncode, - "HYDiTTextEncodeSimple": HYDiTTextEncodeSimple, - "HYDiTSrcSizeCond": HYDiTSrcSizeCond, -} diff --git a/HunYuanDiT/tenc.py b/HunYuanDiT/tenc.py deleted file mode 100644 index a2d7107..0000000 --- a/HunYuanDiT/tenc.py +++ /dev/null @@ -1,180 +0,0 @@ -# This is for loading the CLIP (bert?) + mT5 encoder for HunYuanDiT -import os -import torch -from transformers import AutoTokenizer, modeling_utils -from transformers import T5Config, T5EncoderModel, BertConfig, BertModel - -from comfy import model_management -import comfy.model_patcher -import comfy.utils - -class mT5Model(torch.nn.Module): - def __init__(self, textmodel_json_config=None, device="cpu", max_length=256, freeze=True, dtype=None): - super().__init__() - self.device = device - self.dtype = dtype - self.max_length = max_length - if textmodel_json_config is None: - textmodel_json_config = os.path.join( - os.path.dirname(os.path.realpath(__file__)), - f"config_mt5.json" - ) - config = T5Config.from_json_file(textmodel_json_config) - with modeling_utils.no_init_weights(): - self.transformer = T5EncoderModel(config) - self.to(dtype) - if freeze: - self.freeze() - - def freeze(self): - self.transformer = self.transformer.eval() - for param in self.parameters(): - param.requires_grad = False - - def load_sd(self, sd): - return self.transformer.load_state_dict(sd, strict=False) - - def to(self, *args, **kwargs): - return self.transformer.to(*args, **kwargs) - -class hyCLIPModel(torch.nn.Module): - def __init__(self, textmodel_json_config=None, device="cpu", max_length=77, freeze=True, dtype=None): - super().__init__() - self.device = device - self.dtype = dtype - self.max_length = max_length - if textmodel_json_config is None: - textmodel_json_config = os.path.join( - os.path.dirname(os.path.realpath(__file__)), - f"config_clip.json" - ) - config = BertConfig.from_json_file(textmodel_json_config) - with modeling_utils.no_init_weights(): - self.transformer = BertModel(config) - self.to(dtype) - if freeze: - self.freeze() - - def freeze(self): - self.transformer = self.transformer.eval() - for param in self.parameters(): - param.requires_grad = False - - def load_sd(self, sd): - return self.transformer.load_state_dict(sd, strict=False) - - def to(self, *args, **kwargs): - return self.transformer.to(*args, **kwargs) - -class EXM_HyDiT_Tenc_Temp: - def __init__(self, no_init=False, device="cpu", dtype=None, model_class="mT5", *kwargs): - if no_init: - return - - size = 8 if model_class == "mT5" else 2 - if dtype == torch.float32: - size *= 2 - size *= (1024**3) - - if device == "auto": - self.load_device = model_management.text_encoder_device() - self.offload_device = model_management.text_encoder_offload_device() - self.init_device = "cpu" - elif device == "cpu": - size = 0 # doesn't matter - self.load_device = "cpu" - self.offload_device = "cpu" - self.init_device="cpu" - elif device.startswith("cuda"): - print("Direct CUDA device override!\nVRAM will not be freed by default.") - size = 0 # not used - self.load_device = device - self.offload_device = device - self.init_device = device - else: - self.load_device = model_management.get_torch_device() - self.offload_device = "cpu" - self.init_device="cpu" - - self.dtype = dtype - self.device = self.load_device - if model_class == "mT5": - self.cond_stage_model = mT5Model( - device = self.load_device, - dtype = self.dtype, - ) - tokenizer_args = {"subfolder": "t2i/mt5"} # web - tokenizer_path = os.path.join( # local - os.path.dirname(os.path.realpath(__file__)), - "mt5_tokenizer", - ) - else: - self.cond_stage_model = hyCLIPModel( - device = self.load_device, - dtype = self.dtype, - ) - tokenizer_args = {"subfolder": "t2i/tokenizer",} # web - tokenizer_path = os.path.join( # local - os.path.dirname(os.path.realpath(__file__)), - "tokenizer", - ) - # self.tokenizer = AutoTokenizer.from_pretrained( - # "Tencent-Hunyuan/HunyuanDiT", - # **tokenizer_args - # ) - self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_path) - self.patcher = comfy.model_patcher.ModelPatcher( - self.cond_stage_model, - load_device = self.load_device, - offload_device = self.offload_device, - size = size, - ) - - def clone(self): - n = EXM_HyDiT_Tenc_Temp(no_init=True) - n.patcher = self.patcher.clone() - n.cond_stage_model = self.cond_stage_model - n.tokenizer = self.tokenizer - return n - - def load_sd(self, sd): - return self.cond_stage_model.load_sd(sd) - - def get_sd(self): - return self.cond_stage_model.state_dict() - - def load_model(self): - if self.load_device != "cpu": - model_management.load_model_gpu(self.patcher) - return self.patcher - - def add_patches(self, patches, strength_patch=1.0, strength_model=1.0): - return self.patcher.add_patches(patches, strength_patch, strength_model) - - def get_key_patches(self): - return self.patcher.get_key_patches() - -def load_clip(model_path, **kwargs): - model = EXM_HyDiT_Tenc_Temp(model_class="clip", **kwargs) - sd = comfy.utils.load_torch_file(model_path) - - prefix = "bert." - state_dict = {} - for key in sd: - nkey = key - if key.startswith(prefix): - nkey = key[len(prefix):] - state_dict[nkey] = sd[key] - - m, e = model.load_sd(state_dict) - if len(m) > 0 or len(e) > 0: - print(f"HYDiT: clip missing {len(m)} keys ({len(e)} extra)") - return model - -def load_t5(model_path, **kwargs): - model = EXM_HyDiT_Tenc_Temp(model_class="mT5", **kwargs) - sd = comfy.utils.load_torch_file(model_path) - m, e = model.load_sd(sd) - if len(m) > 0 or len(e) > 0: - print(f"HYDiT: mT5 missing {len(m)} keys ({len(e)} extra)") - return model diff --git a/HunYuanDiT/tokenizer/config.json b/HunYuanDiT/tokenizer/config.json deleted file mode 100644 index f629874..0000000 --- a/HunYuanDiT/tokenizer/config.json +++ /dev/null @@ -1,34 +0,0 @@ -{ - "_name_or_path": "hfl/chinese-roberta-wwm-ext-large", - "architectures": [ - "BertModel" - ], - "attention_probs_dropout_prob": 0.1, - "bos_token_id": 0, - "classifier_dropout": null, - "directionality": "bidi", - "eos_token_id": 2, - "hidden_act": "gelu", - "hidden_dropout_prob": 0.1, - "hidden_size": 1024, - "initializer_range": 0.02, - "intermediate_size": 4096, - "layer_norm_eps": 1e-12, - "max_position_embeddings": 512, - "model_type": "bert", - "num_attention_heads": 16, - "num_hidden_layers": 24, - "output_past": true, - "pad_token_id": 0, - "pooler_fc_size": 768, - "pooler_num_attention_heads": 12, - "pooler_num_fc_layers": 3, - "pooler_size_per_head": 128, - "pooler_type": "first_token_transform", - "position_embedding_type": "absolute", - "torch_dtype": "float32", - "transformers_version": "4.22.1", - "type_vocab_size": 2, - "use_cache": true, - "vocab_size": 47020 -} diff --git a/HunYuanDiT/tokenizer/special_tokens_map.json b/HunYuanDiT/tokenizer/special_tokens_map.json deleted file mode 100644 index a8b3208..0000000 --- a/HunYuanDiT/tokenizer/special_tokens_map.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "cls_token": "[CLS]", - "mask_token": "[MASK]", - "pad_token": "[PAD]", - "sep_token": "[SEP]", - "unk_token": "[UNK]" -} diff --git a/HunYuanDiT/tokenizer/tokenizer_config.json b/HunYuanDiT/tokenizer/tokenizer_config.json deleted file mode 100644 index a143560..0000000 --- a/HunYuanDiT/tokenizer/tokenizer_config.json +++ /dev/null @@ -1,16 +0,0 @@ -{ - "cls_token": "[CLS]", - "do_basic_tokenize": true, - "do_lower_case": true, - "mask_token": "[MASK]", - "name_or_path": "hfl/chinese-roberta-wwm-ext", - "never_split": null, - "pad_token": "[PAD]", - "sep_token": "[SEP]", - "special_tokens_map_file": "/home/chenweifeng/.cache/huggingface/hub/models--hfl--chinese-roberta-wwm-ext/snapshots/5c58d0b8ec1d9014354d691c538661bf00bfdb44/special_tokens_map.json", - "strip_accents": null, - "tokenize_chinese_chars": true, - "tokenizer_class": "BertTokenizer", - "unk_token": "[UNK]", - "model_max_length": 77 -} diff --git a/HunYuanDiT/tokenizer/vocab.txt b/HunYuanDiT/tokenizer/vocab.txt deleted file mode 100644 index 6246906..0000000 --- a/HunYuanDiT/tokenizer/vocab.txt +++ /dev/null @@ -1,47020 +0,0 @@ -[PAD] -[unused1] -[unused2] -[unused3] -[unused4] -[unused5] -[unused6] -[unused7] -[unused8] -[unused9] -[unused10] -[unused11] -[unused12] -[unused13] -[unused14] -[unused15] -[unused16] -[unused17] -[unused18] -[unused19] -[unused20] -[unused21] -[unused22] -[unused23] -[unused24] -[unused25] -[unused26] -[unused27] -[unused28] -[unused29] -[unused30] -[unused31] -[unused32] -[unused33] -[unused34] -[unused35] -[unused36] -[unused37] -[unused38] -[unused39] -[unused40] -[unused41] -[unused42] -[unused43] -[unused44] -[unused45] -[unused46] -[unused47] -[unused48] -[unused49] -[unused50] -[unused51] -[unused52] -[unused53] -[unused54] -[unused55] -[unused56] -[unused57] -[unused58] -[unused59] -[unused60] -[unused61] -[unused62] -[unused63] -[unused64] -[unused65] -[unused66] -[unused67] -[unused68] -[unused69] -[unused70] -[unused71] -[unused72] -[unused73] -[unused74] -[unused75] -[unused76] -[unused77] -[unused78] -[unused79] -[unused80] -[unused81] -[unused82] -[unused83] -[unused84] -[unused85] -[unused86] -[unused87] -[unused88] -[unused89] -[unused90] -[unused91] -[unused92] -[unused93] -[unused94] -[unused95] -[unused96] -[unused97] -[unused98] -[unused99] -[UNK] -[CLS] -[SEP] -[MASK] - 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