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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/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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}, - "PixArtMS_Sigma_XL_2": { - "target": "PixArtMSSigma", - "unet_config": { - "input_size" : 1024//8, - "token_num" : 300, - "depth" : 28, - "num_heads" : 16, - "patch_size" : 2, - "hidden_size" : 1152, - "micro_condition": False, - "pe_interpolation": 2, - "model_max_length": 300, - }, - "sampling_settings" : sampling_settings, - }, - "PixArtMS_Sigma_XL_2_900M": { - "target": "PixArtMSSigma", - "unet_config": { - "input_size": 1024 // 8, - "token_num": 300, - "depth": 42, - "num_heads": 16, - "patch_size": 2, - "hidden_size": 1152, - "micro_condition": False, - "pe_interpolation": 2, - "model_max_length": 300, - }, - "sampling_settings": sampling_settings, - }, - "PixArtMS_Sigma_XL_2_2K": { - "target": "PixArtMSSigma", - "unet_config": { - "input_size" : 2048//8, - "token_num" : 300, - "depth" : 28, - "num_heads" : 16, - "patch_size" : 2, - "hidden_size" : 1152, - "micro_condition": False, - "pe_interpolation": 4, - "model_max_length": 300, - }, - "sampling_settings" : sampling_settings, - }, - "PixArt_XL_2": { # models/PixArt - "target": "PixArt", - "unet_config": { - "input_size" : 512//8, - "token_num" : 120, - "depth" : 28, - "num_heads" : 16, - "patch_size" : 2, - "hidden_size" : 1152, - "pe_interpolation": 1, - }, - "sampling_settings" : sampling_settings, - }, -} - -pixart_conf.update({ # controlnet models - "ControlPixArtHalf": { - "target": "ControlPixArtHalf", - "unet_config": pixart_conf["PixArt_XL_2"]["unet_config"], - "sampling_settings": pixart_conf["PixArt_XL_2"]["sampling_settings"], - }, - "ControlPixArtMSHalf": { - "target": "ControlPixArtMSHalf", - "unet_config": pixart_conf["PixArtMS_XL_2"]["unet_config"], - "sampling_settings": pixart_conf["PixArtMS_XL_2"]["sampling_settings"], - } -}) - -pixart_res = { - "PixArtMS_XL_2": { # models/PixArtMS 1024x1024 - '0.25': [512, 2048], '0.26': [512, 1984], '0.27': [512, 1920], '0.28': [512, 1856], - '0.32': [576, 1792], '0.33': [576, 1728], '0.35': [576, 1664], '0.40': [640, 1600], - '0.42': [640, 1536], '0.48': [704, 1472], '0.50': [704, 1408], '0.52': [704, 1344], - '0.57': [768, 1344], '0.60': [768, 1280], '0.68': [832, 1216], '0.72': [832, 1152], - '0.78': [896, 1152], '0.82': [896, 1088], '0.88': [960, 1088], '0.94': [960, 1024], - '1.00': [1024,1024], '1.07': [1024, 960], '1.13': [1088, 960], '1.21': [1088, 896], - '1.29': [1152, 896], '1.38': [1152, 832], '1.46': [1216, 832], '1.67': [1280, 768], - '1.75': [1344, 768], '2.00': [1408, 704], '2.09': [1472, 704], '2.40': [1536, 640], - '2.50': [1600, 640], '2.89': [1664, 576], '3.00': [1728, 576], '3.11': [1792, 576], - '3.62': [1856, 512], '3.75': [1920, 512], '3.88': [1984, 512], '4.00': [2048, 512], - }, - "PixArt_XL_2": { # models/PixArt 512x512 - '0.25': [256,1024], '0.26': [256, 992], '0.27': [256, 960], '0.28': [256, 928], - '0.32': [288, 896], '0.33': [288, 864], '0.35': [288, 832], '0.40': [320, 800], - '0.42': [320, 768], '0.48': [352, 736], '0.50': [352, 704], '0.52': [352, 672], - '0.57': [384, 672], '0.60': [384, 640], '0.68': [416, 608], '0.72': [416, 576], - '0.78': [448, 576], '0.82': [448, 544], '0.88': [480, 544], '0.94': [480, 512], - '1.00': [512, 512], '1.07': [512, 480], '1.13': [544, 480], '1.21': [544, 448], - '1.29': [576, 448], '1.38': [576, 416], '1.46': [608, 416], '1.67': [640, 384], - '1.75': [672, 384], '2.00': [704, 352], '2.09': [736, 352], '2.40': [768, 320], - '2.50': [800, 320], '2.89': [832, 288], '3.00': [864, 288], '3.11': [896, 288], - '3.62': [928, 256], '3.75': [960, 256], '3.88': [992, 256], '4.00': [1024,256] - }, - "PixArtMS_Sigma_XL_2_2K": { - '0.25': [1024, 4096], '0.26': [1024, 3968], '0.27': [1024, 3840], '0.28': [1024, 3712], - '0.32': [1152, 3584], '0.33': [1152, 3456], '0.35': [1152, 3328], '0.40': [1280, 3200], - '0.42': [1280, 3072], '0.48': [1408, 2944], '0.50': [1408, 2816], '0.52': [1408, 2688], - '0.57': [1536, 2688], '0.60': [1536, 2560], '0.68': [1664, 2432], '0.72': [1664, 2304], - '0.78': [1792, 2304], '0.82': [1792, 2176], '0.88': [1920, 2176], '0.94': [1920, 2048], - '1.00': [2048, 2048], '1.07': [2048, 1920], '1.13': [2176, 1920], '1.21': [2176, 1792], - '1.29': [2304, 1792], '1.38': [2304, 1664], '1.46': [2432, 1664], '1.67': [2560, 1536], - '1.75': [2688, 1536], '2.00': [2816, 1408], '2.09': [2944, 1408], '2.40': [3072, 1280], - '2.50': [3200, 1280], '2.89': [3328, 1152], '3.00': [3456, 1152], '3.11': [3584, 1152], - '3.62': [3712, 1024], '3.75': [3840, 1024], '3.88': [3968, 1024], '4.00': [4096, 1024] - } -} -# These should be the same -pixart_res.update({ - "PixArtMS_Sigma_XL_2": pixart_res["PixArtMS_XL_2"], - "PixArtMS_Sigma_XL_2_512": pixart_res["PixArt_XL_2"], -}) diff --git a/PixArt/diffusers_convert.py b/PixArt/diffusers_convert.py index 312ea9d..6013d17 100644 --- a/PixArt/diffusers_convert.py +++ b/PixArt/diffusers_convert.py @@ -4,220 +4,116 @@ import torch conversion_map_ms = [ # for multi_scale_train (MS) - # Resolution - ("csize_embedder.mlp.0.weight", "adaln_single.emb.resolution_embedder.linear_1.weight"), - ("csize_embedder.mlp.0.bias", "adaln_single.emb.resolution_embedder.linear_1.bias"), - ("csize_embedder.mlp.2.weight", "adaln_single.emb.resolution_embedder.linear_2.weight"), - ("csize_embedder.mlp.2.bias", "adaln_single.emb.resolution_embedder.linear_2.bias"), - # Aspect ratio - ("ar_embedder.mlp.0.weight", "adaln_single.emb.aspect_ratio_embedder.linear_1.weight"), - ("ar_embedder.mlp.0.bias", "adaln_single.emb.aspect_ratio_embedder.linear_1.bias"), - ("ar_embedder.mlp.2.weight", "adaln_single.emb.aspect_ratio_embedder.linear_2.weight"), - ("ar_embedder.mlp.2.bias", "adaln_single.emb.aspect_ratio_embedder.linear_2.bias"), + # Resolution + ("csize_embedder.mlp.0.weight", "adaln_single.emb.resolution_embedder.linear_1.weight"), + ("csize_embedder.mlp.0.bias", "adaln_single.emb.resolution_embedder.linear_1.bias"), + ("csize_embedder.mlp.2.weight", "adaln_single.emb.resolution_embedder.linear_2.weight"), + ("csize_embedder.mlp.2.bias", "adaln_single.emb.resolution_embedder.linear_2.bias"), + # Aspect ratio + ("ar_embedder.mlp.0.weight", "adaln_single.emb.aspect_ratio_embedder.linear_1.weight"), + ("ar_embedder.mlp.0.bias", "adaln_single.emb.aspect_ratio_embedder.linear_1.bias"), + ("ar_embedder.mlp.2.weight", "adaln_single.emb.aspect_ratio_embedder.linear_2.weight"), + ("ar_embedder.mlp.2.bias", "adaln_single.emb.aspect_ratio_embedder.linear_2.bias"), ] def get_depth(state_dict): - return sum(key.endswith('.attn1.to_k.bias') for key in state_dict.keys()) + return sum(key.endswith('.attn1.to_k.bias') for key in state_dict.keys()) def get_lora_depth(state_dict): - cnt = max([ - sum(key.endswith('.attn1.to_k.lora_A.weight') for key in state_dict.keys()), - sum(key.endswith('_attn1_to_k.lora_A.weight') for key in state_dict.keys()), - sum(key.endswith('.attn1.to_k.lora_up.weight') for key in state_dict.keys()), - sum(key.endswith('_attn1_to_k.lora_up.weight') for key in state_dict.keys()), - ]) - assert cnt > 0, "Unable to detect model depth!" - return cnt + cnt = max([ + sum(key.endswith('.attn1.to_k.lora_A.weight') for key in state_dict.keys()), + sum(key.endswith('_attn1_to_k.lora_A.weight') for key in state_dict.keys()), + sum(key.endswith('.attn1.to_k.lora_up.weight') for key in state_dict.keys()), + sum(key.endswith('_attn1_to_k.lora_up.weight') for key in state_dict.keys()), + ]) + assert cnt > 0, "Unable to detect model depth!" + return cnt def get_conversion_map(state_dict): - conversion_map = [ # main SD conversion map (PixArt reference, HF Diffusers) - # Patch embeddings - ("x_embedder.proj.weight", "pos_embed.proj.weight"), - ("x_embedder.proj.bias", "pos_embed.proj.bias"), - # Caption projection - ("y_embedder.y_embedding", "caption_projection.y_embedding"), - ("y_embedder.y_proj.fc1.weight", "caption_projection.linear_1.weight"), - ("y_embedder.y_proj.fc1.bias", "caption_projection.linear_1.bias"), - ("y_embedder.y_proj.fc2.weight", "caption_projection.linear_2.weight"), - ("y_embedder.y_proj.fc2.bias", "caption_projection.linear_2.bias"), - # AdaLN-single LN - ("t_embedder.mlp.0.weight", "adaln_single.emb.timestep_embedder.linear_1.weight"), - ("t_embedder.mlp.0.bias", "adaln_single.emb.timestep_embedder.linear_1.bias"), - ("t_embedder.mlp.2.weight", "adaln_single.emb.timestep_embedder.linear_2.weight"), - ("t_embedder.mlp.2.bias", "adaln_single.emb.timestep_embedder.linear_2.bias"), - # Shared norm - ("t_block.1.weight", "adaln_single.linear.weight"), - ("t_block.1.bias", "adaln_single.linear.bias"), - # Final block - ("final_layer.linear.weight", "proj_out.weight"), - ("final_layer.linear.bias", "proj_out.bias"), - ("final_layer.scale_shift_table", "scale_shift_table"), - ] - - # Add actual transformer blocks - for depth in range(get_depth(state_dict)): - # Transformer blocks - conversion_map += [ - (f"blocks.{depth}.scale_shift_table", f"transformer_blocks.{depth}.scale_shift_table"), - # Projection - (f"blocks.{depth}.attn.proj.weight", f"transformer_blocks.{depth}.attn1.to_out.0.weight"), - (f"blocks.{depth}.attn.proj.bias", f"transformer_blocks.{depth}.attn1.to_out.0.bias"), - # Feed-forward - (f"blocks.{depth}.mlp.fc1.weight", f"transformer_blocks.{depth}.ff.net.0.proj.weight"), - (f"blocks.{depth}.mlp.fc1.bias", f"transformer_blocks.{depth}.ff.net.0.proj.bias"), - (f"blocks.{depth}.mlp.fc2.weight", f"transformer_blocks.{depth}.ff.net.2.weight"), - (f"blocks.{depth}.mlp.fc2.bias", f"transformer_blocks.{depth}.ff.net.2.bias"), - # Cross-attention (proj) - (f"blocks.{depth}.cross_attn.proj.weight" ,f"transformer_blocks.{depth}.attn2.to_out.0.weight"), - (f"blocks.{depth}.cross_attn.proj.bias" ,f"transformer_blocks.{depth}.attn2.to_out.0.bias"), - ] - return conversion_map + conversion_map = [ # main SD conversion map (PixArt reference, HF Diffusers) + # Patch embeddings + ("x_embedder.proj.weight", "pos_embed.proj.weight"), + ("x_embedder.proj.bias", "pos_embed.proj.bias"), + # Caption projection + ("y_embedder.y_embedding", "caption_projection.y_embedding"), + ("y_embedder.y_proj.fc1.weight", "caption_projection.linear_1.weight"), + ("y_embedder.y_proj.fc1.bias", "caption_projection.linear_1.bias"), + ("y_embedder.y_proj.fc2.weight", "caption_projection.linear_2.weight"), + ("y_embedder.y_proj.fc2.bias", "caption_projection.linear_2.bias"), + # AdaLN-single LN + ("t_embedder.mlp.0.weight", "adaln_single.emb.timestep_embedder.linear_1.weight"), + ("t_embedder.mlp.0.bias", "adaln_single.emb.timestep_embedder.linear_1.bias"), + ("t_embedder.mlp.2.weight", "adaln_single.emb.timestep_embedder.linear_2.weight"), + ("t_embedder.mlp.2.bias", "adaln_single.emb.timestep_embedder.linear_2.bias"), + # Shared norm + ("t_block.1.weight", "adaln_single.linear.weight"), + ("t_block.1.bias", "adaln_single.linear.bias"), + # Final block + ("final_layer.linear.weight", "proj_out.weight"), + ("final_layer.linear.bias", "proj_out.bias"), + ("final_layer.scale_shift_table", "scale_shift_table"), + ] + + # Add actual transformer blocks + for depth in range(get_depth(state_dict)): + # Transformer blocks + conversion_map += [ + (f"blocks.{depth}.scale_shift_table", f"transformer_blocks.{depth}.scale_shift_table"), + # Projection + (f"blocks.{depth}.attn.proj.weight", f"transformer_blocks.{depth}.attn1.to_out.0.weight"), + (f"blocks.{depth}.attn.proj.bias", f"transformer_blocks.{depth}.attn1.to_out.0.bias"), + # Feed-forward + (f"blocks.{depth}.mlp.fc1.weight", f"transformer_blocks.{depth}.ff.net.0.proj.weight"), + (f"blocks.{depth}.mlp.fc1.bias", f"transformer_blocks.{depth}.ff.net.0.proj.bias"), + (f"blocks.{depth}.mlp.fc2.weight", f"transformer_blocks.{depth}.ff.net.2.weight"), + (f"blocks.{depth}.mlp.fc2.bias", f"transformer_blocks.{depth}.ff.net.2.bias"), + # Cross-attention (proj) + (f"blocks.{depth}.cross_attn.proj.weight" ,f"transformer_blocks.{depth}.attn2.to_out.0.weight"), + (f"blocks.{depth}.cross_attn.proj.bias" ,f"transformer_blocks.{depth}.attn2.to_out.0.bias"), + ] + return conversion_map def find_prefix(state_dict, target_key): - prefix = "" - for k in state_dict.keys(): - if k.endswith(target_key): - prefix = k.split(target_key)[0] - break - return prefix + prefix = "" + for k in state_dict.keys(): + if k.endswith(target_key): + prefix = k.split(target_key)[0] + break + return prefix def convert_state_dict(state_dict): - if "adaln_single.emb.resolution_embedder.linear_1.weight" in state_dict.keys(): - cmap = get_conversion_map(state_dict) + conversion_map_ms - else: - cmap = get_conversion_map(state_dict) - - missing = [k for k,v in cmap if v not in state_dict] - new_state_dict = {k: state_dict[v] for k,v in cmap if k not in missing} - matched = list(v for k,v in cmap if v in state_dict.keys()) - - for depth in range(get_depth(state_dict)): - for wb in ["weight", "bias"]: - # Self Attention - key = lambda a: f"transformer_blocks.{depth}.attn1.to_{a}.{wb}" - new_state_dict[f"blocks.{depth}.attn.qkv.{wb}"] = torch.cat(( - state_dict[key('q')], state_dict[key('k')], state_dict[key('v')] - ), dim=0) - matched += [key('q'), key('k'), key('v')] - - # Cross-attention (linear) - key = lambda a: f"transformer_blocks.{depth}.attn2.to_{a}.{wb}" - new_state_dict[f"blocks.{depth}.cross_attn.q_linear.{wb}"] = state_dict[key('q')] - new_state_dict[f"blocks.{depth}.cross_attn.kv_linear.{wb}"] = torch.cat(( - state_dict[key('k')], state_dict[key('v')] - ), dim=0) - matched += [key('q'), key('k'), key('v')] - - if len(matched) < len(state_dict): - print(f"PixArt: UNET conversion has leftover keys! ({len(matched)} vs {len(state_dict)})") - print(list( set(state_dict.keys()) - set(matched) )) - - if len(missing) > 0: - print(f"PixArt: UNET conversion has missing keys!") - print(missing) - - return new_state_dict - -# Same as above but for LoRA weights: -def convert_lora_state_dict(state_dict, peft=True): - # koyha - rep_ak = lambda x: x.replace(".weight", ".lora_down.weight") - rep_bk = lambda x: x.replace(".weight", ".lora_up.weight") - rep_pk = lambda x: x.replace(".weight", ".alpha") - if peft: # peft - rep_ap = lambda x: x.replace(".weight", ".lora_A.weight") - rep_bp = lambda x: x.replace(".weight", ".lora_B.weight") - rep_pp = lambda x: x.replace(".weight", ".alpha") - - prefix = find_prefix(state_dict, "adaln_single.linear.lora_A.weight") - state_dict = {k[len(prefix):]:v for k,v in state_dict.items()} - else: # OneTrainer - rep_ap = lambda x: x.replace(".", "_")[:-7] + ".lora_down.weight" - rep_bp = lambda x: x.replace(".", "_")[:-7] + ".lora_up.weight" - rep_pp = lambda x: x.replace(".", "_")[:-7] + ".alpha" - - prefix = "lora_transformer_" - t5_marker = "lora_te_encoder" - t5_keys = [] - for key in list(state_dict.keys()): - if key.startswith(prefix): - state_dict[key[len(prefix):]] = state_dict.pop(key) - elif t5_marker in key: - t5_keys.append(state_dict.pop(key)) - if len(t5_keys) > 0: - print(f"Text Encoder not supported for PixArt LoRA, ignoring {len(t5_keys)} keys") - - cmap = [] - cmap_unet = get_conversion_map(state_dict) + conversion_map_ms # todo: 512 model - for k, v in cmap_unet: - if v.endswith(".weight"): - cmap.append((rep_ak(k), rep_ap(v))) - cmap.append((rep_bk(k), rep_bp(v))) - if not peft: - cmap.append((rep_pk(k), rep_pp(v))) - - missing = [k for k,v in cmap if v not in state_dict] - new_state_dict = {k: state_dict[v] for k,v in cmap if k not in missing} - matched = list(v for k,v in cmap if v in state_dict.keys()) - - lora_depth = get_lora_depth(state_dict) - for fp, fk in ((rep_ap, rep_ak),(rep_bp, rep_bk)): - for depth in range(lora_depth): - # Self Attention - key = lambda a: fp(f"transformer_blocks.{depth}.attn1.to_{a}.weight") - new_state_dict[fk(f"blocks.{depth}.attn.qkv.weight")] = torch.cat(( - state_dict[key('q')], state_dict[key('k')], state_dict[key('v')] - ), dim=0) - - matched += [key('q'), key('k'), key('v')] - if not peft: - akey = lambda a: rep_pp(f"transformer_blocks.{depth}.attn1.to_{a}.weight") - new_state_dict[rep_pk((f"blocks.{depth}.attn.qkv.weight"))] = state_dict[akey("q")] - matched += [akey('q'), akey('k'), akey('v')] - - # Self Attention projection? - key = lambda a: fp(f"transformer_blocks.{depth}.attn1.to_{a}.weight") - new_state_dict[fk(f"blocks.{depth}.attn.proj.weight")] = state_dict[key('out.0')] - matched += [key('out.0')] - - # Cross-attention (linear) - key = lambda a: fp(f"transformer_blocks.{depth}.attn2.to_{a}.weight") - new_state_dict[fk(f"blocks.{depth}.cross_attn.q_linear.weight")] = state_dict[key('q')] - new_state_dict[fk(f"blocks.{depth}.cross_attn.kv_linear.weight")] = torch.cat(( - state_dict[key('k')], state_dict[key('v')] - ), dim=0) - matched += [key('q'), key('k'), key('v')] - if not peft: - akey = lambda a: rep_pp(f"transformer_blocks.{depth}.attn2.to_{a}.weight") - new_state_dict[rep_pk((f"blocks.{depth}.cross_attn.q_linear.weight"))] = state_dict[akey("q")] - new_state_dict[rep_pk((f"blocks.{depth}.cross_attn.kv_linear.weight"))] = state_dict[akey("k")] - matched += [akey('q'), akey('k'), akey('v')] - - # Cross Attention projection? - key = lambda a: fp(f"transformer_blocks.{depth}.attn2.to_{a}.weight") - new_state_dict[fk(f"blocks.{depth}.cross_attn.proj.weight")] = state_dict[key('out.0')] - matched += [key('out.0')] - - try: - key = fp(f"transformer_blocks.{depth}.ff.net.0.proj.weight") - new_state_dict[fk(f"blocks.{depth}.mlp.fc1.weight")] = state_dict[key] - matched += [key] - except KeyError: - pass - - try: - key = fp(f"transformer_blocks.{depth}.ff.net.2.weight") - new_state_dict[fk(f"blocks.{depth}.mlp.fc2.weight")] = state_dict[key] - matched += [key] - except KeyError: - pass - - if len(matched) < len(state_dict): - print(f"PixArt: LoRA conversion has leftover keys! ({len(matched)} vs {len(state_dict)})") - print(list( set(state_dict.keys()) - set(matched) )) - - if len(missing) > 0: - print(f"PixArt: LoRA conversion has missing keys! (probably)") - print(missing) - - return new_state_dict + if "adaln_single.emb.resolution_embedder.linear_1.weight" in state_dict.keys(): + cmap = get_conversion_map(state_dict) + conversion_map_ms + else: + cmap = get_conversion_map(state_dict) + + missing = [k for k,v in cmap if v not in state_dict] + new_state_dict = {k: state_dict[v] for k,v in cmap if k not in missing} + matched = list(v for k,v in cmap if v in state_dict.keys()) + + for depth in range(get_depth(state_dict)): + for wb in ["weight", "bias"]: + # Self Attention + key = lambda a: f"transformer_blocks.{depth}.attn1.to_{a}.{wb}" + new_state_dict[f"blocks.{depth}.attn.qkv.{wb}"] = torch.cat(( + state_dict[key('q')], state_dict[key('k')], state_dict[key('v')] + ), dim=0) + matched += [key('q'), key('k'), key('v')] + + # Cross-attention (linear) + key = lambda a: f"transformer_blocks.{depth}.attn2.to_{a}.{wb}" + new_state_dict[f"blocks.{depth}.cross_attn.q_linear.{wb}"] = state_dict[key('q')] + new_state_dict[f"blocks.{depth}.cross_attn.kv_linear.{wb}"] = torch.cat(( + state_dict[key('k')], state_dict[key('v')] + ), dim=0) + matched += [key('q'), key('k'), key('v')] + + if len(matched) < len(state_dict): + print(f"PixArt: UNET conversion has leftover keys! ({len(matched)} vs {len(state_dict)})") + print(list( set(state_dict.keys()) - set(matched) )) + + if len(missing) > 0: + print(f"PixArt: UNET conversion has missing keys!") + print(missing) + + return new_state_dict diff --git a/PixArt/loader.py b/PixArt/loader.py index cedb5fd..4fcee7c 100644 --- a/PixArt/loader.py +++ b/PixArt/loader.py @@ -1,180 +1,155 @@ +import math +import logging + +import comfy.utils +import comfy.model_base +import comfy.model_detection + import comfy.supported_models_base +import comfy.supported_models import comfy.latent_formats -import comfy.model_patcher -import comfy.model_base -import comfy.utils -import comfy.conds -import torch -import math -from comfy import model_management -from .diffusers_convert import convert_state_dict -class EXM_PixArt(comfy.supported_models_base.BASE): - unet_config = {} - unet_extra_config = {} - latent_format = comfy.latent_formats.SD15 - - def __init__(self, model_conf): - self.model_target = model_conf.get("target") - 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.EPS - -class EXM_PixArt_Model(comfy.model_base.BaseModel): - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) - - def extra_conds(self, **kwargs): - out = super().extra_conds(**kwargs) - - img_hw = kwargs.get("img_hw", None) - if img_hw is not None: - out["img_hw"] = comfy.conds.CONDRegular(torch.tensor(img_hw)) - - aspect_ratio = kwargs.get("aspect_ratio", None) - if aspect_ratio is not None: - out["aspect_ratio"] = comfy.conds.CONDRegular(torch.tensor(aspect_ratio)) - - cn_hint = kwargs.get("cn_hint", None) - if cn_hint is not None: - out["cn_hint"] = comfy.conds.CONDRegular(cn_hint) - - return out - -def load_pixart(model_path, model_conf=None): - state_dict = comfy.utils.load_torch_file(model_path) - state_dict = state_dict.get("model", state_dict) - - # prefix - for prefix in ["model.diffusion_model.",]: - if any(True for x in state_dict if x.startswith(prefix)): - state_dict = {k[len(prefix):]:v for k,v in state_dict.items()} - - # diffusers - if "adaln_single.linear.weight" in state_dict: - state_dict = convert_state_dict(state_dict) # Diffusers - - # guess auto config - if model_conf is None: - model_conf = guess_pixart_config(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"PixArt: falling back to {manual_cast_dtype}") - unet_dtype = manual_cast_dtype - - model_conf = EXM_PixArt(model_conf) # convert to object - model = EXM_PixArt_Model( # same as comfy.model_base.BaseModel - model_conf, - model_type=comfy.model_base.ModelType.EPS, - device=model_management.get_torch_device() - ) - - if model_conf.model_target == "PixArtMS": - from .models.PixArtMS import PixArtMS - model.diffusion_model = PixArtMS(**model_conf.unet_config) - elif model_conf.model_target == "PixArt": - from .models.PixArt import PixArt - model.diffusion_model = PixArt(**model_conf.unet_config) - elif model_conf.model_target == "PixArtMSSigma": - from .models.PixArtMS import PixArtMS - model.diffusion_model = PixArtMS(**model_conf.unet_config) - model.latent_format = comfy.latent_formats.SDXL() - elif model_conf.model_target == "ControlPixArtMSHalf": - from .models.PixArtMS import PixArtMS - from .models.pixart_controlnet import ControlPixArtMSHalf - model.diffusion_model = PixArtMS(**model_conf.unet_config) - model.diffusion_model = ControlPixArtMSHalf(model.diffusion_model) - elif model_conf.model_target == "ControlPixArtHalf": - from .models.PixArt import PixArt - from .models.pixart_controlnet import ControlPixArtHalf - model.diffusion_model = PixArt(**model_conf.unet_config) - model.diffusion_model = ControlPixArtHalf(model.diffusion_model) - else: - raise NotImplementedError(f"Unknown model target '{model_conf.model_target}'") - - m, u = model.diffusion_model.load_state_dict(state_dict, strict=False) - if len(m) > 0: print("Missing UNET keys", m) - if len(u) > 0: print("Leftover UNET keys", u) - 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 - -def guess_pixart_config(sd): - """ - Guess config based on converted state dict. - """ - # Shared settings based on DiT_XL_2 - could be enumerated - config = { - "num_heads" : 16, # get from attention - "patch_size" : 2, # final layer I guess? - "hidden_size" : 1152, # pos_embed.shape[2] - } - config["depth"] = sum([key.endswith(".attn.proj.weight") for key in sd.keys()]) or 28 - - try: - # this is not present in the diffusers version for sigma? - config["model_max_length"] = sd["y_embedder.y_embedding"].shape[0] - except KeyError: - # need better logic to guess this - config["model_max_length"] = 300 - - if "pos_embed" in sd: - config["input_size"] = int(math.sqrt(sd["pos_embed"].shape[1])) * config["patch_size"] - config["pe_interpolation"] = config["input_size"] // (512//8) # dumb guess - - target_arch = "PixArtMS" - if config["model_max_length"] == 300: - # Sigma - target_arch = "PixArtMSSigma" - config["micro_condition"] = False - if "input_size" not in config: - # The diffusers weights for 1K/2K are exactly the same...? - # replace patch embed logic with HyDiT? - print(f"PixArt: diffusers weights - 2K model will be broken, use manual loading!") - config["input_size"] = 1024//8 - else: - # Alpha - if "csize_embedder.mlp.0.weight" in sd: - # MS (microconds) - target_arch = "PixArtMS" - config["micro_condition"] = True - if "input_size" not in config: - config["input_size"] = 1024//8 - config["pe_interpolation"] = 2 - else: - # PixArt - target_arch = "PixArt" - if "input_size" not in config: - config["input_size"] = 512//8 - config["pe_interpolation"] = 1 - - print("PixArt guessed config:", target_arch, config) - return { - "target": target_arch, - "unet_config": config, - "sampling_settings": { - "beta_schedule" : "sqrt_linear", - "linear_start" : 0.0001, - "linear_end" : 0.02, - "timesteps" : 1000, - } - } +from .models.pixart import PixArt +from .models.pixartms import PixArtMS +from .diffusers_convert import convert_state_dict +from ..utils.loader import load_state_dict_from_config +from ..text_encoders.pixart.tenc import PixArtTokenizer, PixArtT5XXL + +class PixArtConfig(comfy.supported_models_base.BASE): + unet_class = PixArtMS + unet_config = {} + unet_extra_config = {} + + latent_format = comfy.latent_formats.SD15 + sampling_settings = { + "beta_schedule" : "sqrt_linear", + "linear_start" : 0.0001, + "linear_end" : 0.02, + "timesteps" : 1000, + } + + def model_type(self, state_dict, prefix=""): + return comfy.model_base.ModelType.EPS + + def get_model(self, state_dict, prefix="", device=None): + return PixArtModel(model_config=self, unet_model=self.unet_class, device=device) + + def clip_target(self, state_dict={}): + return comfy.supported_models_base.ClipTarget(PixArtTokenizer, PixArtT5XXL) + +class PixArtModel(comfy.model_base.BaseModel): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + def extra_conds(self, **kwargs): + out = super().extra_conds(**kwargs) + return out + +def load_pixart_state_dict(sd, model_options={}): + # prefix / format + sd = sd.get("model", sd) # ref ckpt + diffusion_model_prefix = comfy.model_detection.unet_prefix_from_state_dict(sd) + temp_sd = comfy.utils.state_dict_prefix_replace(sd, {diffusion_model_prefix: ""}, filter_keys=True) + if len(temp_sd) > 0: + sd = temp_sd + + # diffusers convert + if "adaln_single.linear.weight" in sd: + sd = convert_state_dict(sd) + + # model config + model_config = model_config_from_unet(sd) + return load_state_dict_from_config(model_config, sd, model_options) + +def model_config_from_unet(sd): + """ + Guess config based on (converted) state dict. + """ + # Shared settings based on DiT_XL_2 - could be enumerated + config = { + "num_heads" : 16, # get from attention + "patch_size" : 2, # final layer I guess? + "hidden_size" : 1152, # pos_embed.shape[2] + } + config["depth"] = sum([key.endswith(".attn.proj.weight") for key in sd.keys()]) or 28 + + try: + # this is not present in the diffusers version for sigma? + config["model_max_length"] = sd["y_embedder.y_embedding"].shape[0] + except KeyError: + # need better logic to guess this + config["model_max_length"] = 300 + + if "pos_embed" in sd: + config["input_size"] = int(math.sqrt(sd["pos_embed"].shape[1])) * config["patch_size"] + config["pe_interpolation"] = config["input_size"] // (512//8) # dumb guess + + model_config = PixArtModel + if config["model_max_length"] == 300: + # Sigma + model_class = PixArtMS + model_config.latent_format = comfy.latent_formats.SDXL + config["micro_condition"] = False + if "input_size" not in config: + # The diffusers weights for 1K/2K are exactly the same...? + # replace patch embed logic with HyDiT? + logging.warn(f"PixArt: diffusers weights - 2K model will be broken, use manual loading!") + config["input_size"] = 1024//8 + else: + # Alpha + if "csize_embedder.mlp.0.weight" in sd: + # MS (microconds) + model_class = PixArtMS + config["micro_condition"] = True + if "input_size" not in config: + config["input_size"] = 1024//8 + config["pe_interpolation"] = 2 + else: + # PixArt + model_class = PixArt + if "input_size" not in config: + config["input_size"] = 512//8 + config["pe_interpolation"] = 1 + model_config = PixArtConfig(config) + model_config.unet_class = model_class + logging.debug(f"PixArt config: {model_class}\n{config}") + return model_config + +resolutions = { + "PixArt 512": { + 0.25: [256,1024], 0.26: [256, 992], 0.27: [256, 960], 0.28: [256, 928], + 0.32: [288, 896], 0.33: [288, 864], 0.35: [288, 832], 0.40: [320, 800], + 0.42: [320, 768], 0.48: [352, 736], 0.50: [352, 704], 0.52: [352, 672], + 0.57: [384, 672], 0.60: [384, 640], 0.68: [416, 608], 0.72: [416, 576], + 0.78: [448, 576], 0.82: [448, 544], 0.88: [480, 544], 0.94: [480, 512], + 1.00: [512, 512], 1.07: [512, 480], 1.13: [544, 480], 1.21: [544, 448], + 1.29: [576, 448], 1.38: [576, 416], 1.46: [608, 416], 1.67: [640, 384], + 1.75: [672, 384], 2.00: [704, 352], 2.09: [736, 352], 2.40: [768, 320], + 2.50: [800, 320], 2.89: [832, 288], 3.00: [864, 288], 3.11: [896, 288], + 3.62: [928, 256], 3.75: [960, 256], 3.88: [992, 256], 4.00: [1024,256] + }, + "PixArt 1024": { + 0.25: [512, 2048], 0.26: [512, 1984], 0.27: [512, 1920], 0.28: [512, 1856], + 0.32: [576, 1792], 0.33: [576, 1728], 0.35: [576, 1664], 0.40: [640, 1600], + 0.42: [640, 1536], 0.48: [704, 1472], 0.50: [704, 1408], 0.52: [704, 1344], + 0.57: [768, 1344], 0.60: [768, 1280], 0.68: [832, 1216], 0.72: [832, 1152], + 0.78: [896, 1152], 0.82: [896, 1088], 0.88: [960, 1088], 0.94: [960, 1024], + 1.00: [1024,1024], 1.07: [1024, 960], 1.13: [1088, 960], 1.21: [1088, 896], + 1.29: [1152, 896], 1.38: [1152, 832], 1.46: [1216, 832], 1.67: [1280, 768], + 1.75: [1344, 768], 2.00: [1408, 704], 2.09: [1472, 704], 2.40: [1536, 640], + 2.50: [1600, 640], 2.89: [1664, 576], 3.00: [1728, 576], 3.11: [1792, 576], + 3.62: [1856, 512], 3.75: [1920, 512], 3.88: [1984, 512], 4.00: [2048, 512], + }, + "PixArt 2K": { + 0.25: [1024, 4096], 0.26: [1024, 3968], 0.27: [1024, 3840], 0.28: [1024, 3712], + 0.32: [1152, 3584], 0.33: [1152, 3456], 0.35: [1152, 3328], 0.40: [1280, 3200], + 0.42: [1280, 3072], 0.48: [1408, 2944], 0.50: [1408, 2816], 0.52: [1408, 2688], + 0.57: [1536, 2688], 0.60: [1536, 2560], 0.68: [1664, 2432], 0.72: [1664, 2304], + 0.78: [1792, 2304], 0.82: [1792, 2176], 0.88: [1920, 2176], 0.94: [1920, 2048], + 1.00: [2048, 2048], 1.07: [2048, 1920], 1.13: [2176, 1920], 1.21: [2176, 1792], + 1.29: [2304, 1792], 1.38: [2304, 1664], 1.46: [2432, 1664], 1.67: [2560, 1536], + 1.75: [2688, 1536], 2.00: [2816, 1408], 2.09: [2944, 1408], 2.40: [3072, 1280], + 2.50: [3200, 1280], 2.89: [3328, 1152], 3.00: [3456, 1152], 3.11: [3584, 1152], + 3.62: [3712, 1024], 3.75: [3840, 1024], 3.88: [3968, 1024], 4.00: [4096, 1024] + } +} diff --git a/PixArt/lora.py b/PixArt/lora.py deleted file mode 100644 index fca5931..0000000 --- a/PixArt/lora.py +++ /dev/null @@ -1,146 +0,0 @@ -import os -import copy -import json -import torch -import comfy.lora -import comfy.model_management -from comfy.model_patcher import ModelPatcher -from .diffusers_convert import convert_lora_state_dict - -class EXM_PixArt_ModelPatcher(ModelPatcher): - def calculate_weight(self, patches, weight, key): - """ - This is almost the same as the comfy function, but stripped down to just the LoRA patch code. - The problem with the original code is the q/k/v keys being combined into one for the attention. - In the diffusers code, they're treated as separate keys, but in the reference code they're recombined (q+kv|qkv). - This means, for example, that the [1152,1152] weights become [3456,1152] in the state dict. - The issue with this is that the LoRA weights are [128,1152],[1152,128] and become [384,1162],[3456,128] instead. - - This is the best thing I could think of that would fix that, but it's very fragile. - - Check key shape to determine if it needs the fallback logic - - Cut the input into parts based on the shape (undoing the torch.cat) - - Do the matrix multiplication logic - - Recombine them to match the expected shape - """ - for p in patches: - alpha = p[0] - v = p[1] - strength_model = p[2] - if strength_model != 1.0: - weight *= strength_model - - if isinstance(v, list): - v = (self.calculate_weight(v[1:], v[0].clone(), key), ) - - if len(v) == 2: - patch_type = v[0] - v = v[1] - - if patch_type == "lora": - mat1 = comfy.model_management.cast_to_device(v[0], weight.device, torch.float32) - mat2 = comfy.model_management.cast_to_device(v[1], weight.device, torch.float32) - if v[2] is not None: - alpha *= v[2] / mat2.shape[0] - try: - mat1 = mat1.flatten(start_dim=1) - mat2 = mat2.flatten(start_dim=1) - - ch1 = mat1.shape[0] // mat2.shape[1] - ch2 = mat2.shape[0] // mat1.shape[1] - ### Fallback logic for shape mismatch ### - if mat1.shape[0] != mat2.shape[1] and ch1 == ch2 and (mat1.shape[0]/mat2.shape[1])%1 == 0: - mat1 = mat1.chunk(ch1, dim=0) - mat2 = mat2.chunk(ch1, dim=0) - weight += torch.cat( - [alpha * torch.mm(mat1[x], mat2[x]) for x in range(ch1)], - dim=0, - ).reshape(weight.shape).type(weight.dtype) - else: - weight += (alpha * torch.mm(mat1, mat2)).reshape(weight.shape).type(weight.dtype) - except Exception as e: - print("ERROR", key, e) - return weight - - def clone(self): - n = EXM_PixArt_ModelPatcher(self.model, self.load_device, self.offload_device, self.size, self.current_device, weight_inplace_update=self.weight_inplace_update) - n.patches = {} - for k in self.patches: - n.patches[k] = self.patches[k][:] - - n.object_patches = self.object_patches.copy() - n.model_options = copy.deepcopy(self.model_options) - n.model_keys = self.model_keys - return n - -def replace_model_patcher(model): - n = EXM_PixArt_ModelPatcher( - model = model.model, - size = model.size, - load_device = model.load_device, - offload_device = model.offload_device, - weight_inplace_update = model.weight_inplace_update, - ) - n.patches = {} - for k in model.patches: - n.patches[k] = model.patches[k][:] - - n.object_patches = model.object_patches.copy() - n.model_options = copy.deepcopy(model.model_options) - return n - -def find_peft_alpha(path): - def load_json(json_path): - with open(json_path) as f: - data = json.load(f) - alpha = data.get("lora_alpha") - alpha = alpha or data.get("alpha") - if not alpha: - print(" Found config but `lora_alpha` is missing!") - else: - print(f" Found config at {json_path} [alpha:{alpha}]") - return alpha - - # For some weird reason peft doesn't include the alpha in the actual model - print("PixArt: Warning! This is a PEFT LoRA. Trying to find config...") - files = [ - f"{os.path.splitext(path)[0]}.json", - f"{os.path.splitext(path)[0]}.config.json", - os.path.join(os.path.dirname(path),"adapter_config.json"), - ] - for file in files: - if os.path.isfile(file): - return load_json(file) - - print(" Missing config/alpha! assuming alpha of 8. Consider converting it/adding a config json to it.") - return 8.0 - -def load_pixart_lora(model, lora, lora_path, strength): - k_back = lambda x: x.replace(".lora_up.weight", "") - # need to convert the actual weights for this to work. - if any(True for x in lora.keys() if x.endswith("adaln_single.linear.lora_A.weight")): - lora = convert_lora_state_dict(lora, peft=True) - alpha = find_peft_alpha(lora_path) - lora.update({f"{k_back(x)}.alpha":torch.tensor(alpha) for x in lora.keys() if "lora_up" in x}) - else: # OneTrainer - lora = convert_lora_state_dict(lora, peft=False) - - key_map = {k_back(x):f"diffusion_model.{k_back(x)}.weight" for x in lora.keys() if "lora_up" in x} # fake - - loaded = comfy.lora.load_lora(lora, key_map) - if model is not None: - # switch to custom model patcher when using LoRAs - if isinstance(model, EXM_PixArt_ModelPatcher): - new_modelpatcher = model.clone() - else: - new_modelpatcher = replace_model_patcher(model) - k = new_modelpatcher.add_patches(loaded, strength) - else: - k = () - new_modelpatcher = None - - k = set(k) - for x in loaded: - if (x not in k): - print("NOT LOADED", x) - - return new_modelpatcher diff --git a/T5/LICENSE-T5 b/PixArt/models/LICENSE similarity index 99% rename from T5/LICENSE-T5 rename to PixArt/models/LICENSE index 261eeb9..fb524b1 100644 --- a/T5/LICENSE-T5 +++ b/PixArt/models/LICENSE @@ -186,7 +186,7 @@ same "printed page" as the copyright notice for easier identification within third-party archives. - Copyright [yyyy] [name of copyright owner] + Copyright 2024 Junsong Chen, Jincheng Yu, Enze Xie Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. diff --git a/PixArt/models/__init__.py b/PixArt/models/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/PixArt/models/PixArt_blocks.py b/PixArt/models/blocks.py similarity index 72% rename from PixArt/models/PixArt_blocks.py rename to PixArt/models/blocks.py index df2eae0..d540d96 100644 --- a/PixArt/models/PixArt_blocks.py +++ b/PixArt/models/blocks.py @@ -12,9 +12,11 @@ import torch import torch.nn as nn import torch.nn.functional as F -from timm.models.vision_transformer import Mlp, Attention as Attention_ from einops import rearrange +import comfy.ldm.common_dit +from .utils import to_2tuple + sdpa_32b = None Q_4GB_LIMIT = 32000000 """If q is greater than this, the operation will likely require >4GB VRAM, which will fail on Intel Arc Alchemist GPUs without a workaround.""" @@ -24,11 +26,14 @@ from comfy import model_management if model_management.xformers_enabled(): - import xformers import xformers.ops + if int((xformers.__version__).split(".")[2]) >= 28: + block_diagonal_mask_from_seqlens = xformers.ops.fmha.attn_bias.BlockDiagonalMask.from_seqlens + else: + block_diagonal_mask_from_seqlens = xformers.ops.fmha.BlockDiagonalMask.from_seqlens else: if model_management.xpu_available: - import intel_extension_for_pytorch as ipex + import intel_extension_for_pytorch as ipex # type: ignore import os if not torch.xpu.has_fp64_dtype() and not os.environ.get('IPEX_FORCE_ATTENTION_SLICE', None): from ...utils.IPEX.attention import scaled_dot_product_attention_32_bit @@ -44,7 +49,7 @@ def t2i_modulate(x, shift, scale): return x * (1 + scale) + shift class MultiHeadCrossAttention(nn.Module): - def __init__(self, d_model, num_heads, attn_drop=0., proj_drop=0., **block_kwargs): + def __init__(self, d_model, num_heads, attn_drop=0., proj_drop=0., dtype=None, device=None, operations=None, **block_kwargs): super(MultiHeadCrossAttention, self).__init__() assert d_model % num_heads == 0, "d_model must be divisible by num_heads" @@ -52,10 +57,10 @@ def __init__(self, d_model, num_heads, attn_drop=0., proj_drop=0., **block_kwarg self.num_heads = num_heads self.head_dim = d_model // num_heads - self.q_linear = nn.Linear(d_model, d_model) - self.kv_linear = nn.Linear(d_model, d_model*2) + self.q_linear = operations.Linear(d_model, d_model, dtype=dtype, device=device) + self.kv_linear = operations.Linear(d_model, d_model*2, dtype=dtype, device=device) self.attn_drop = nn.Dropout(attn_drop) - self.proj = nn.Linear(d_model, d_model) + self.proj = operations.Linear(d_model, d_model, dtype=dtype, device=device) self.proj_drop = nn.Dropout(proj_drop) def forward(self, x, cond, mask=None): @@ -69,7 +74,7 @@ def forward(self, x, cond, mask=None): if model_management.xformers_enabled(): attn_bias = None if mask is not None: - attn_bias = xformers.ops.fmha.BlockDiagonalMask.from_seqlens([N] * B, mask) + attn_bias = block_diagonal_mask_from_seqlens([N] * B, mask) x = xformers.ops.memory_efficient_attention( q, k, v, p=self.attn_drop.p, @@ -111,7 +116,7 @@ def forward(self, x, cond, mask=None): return x -class AttentionKVCompress(Attention_): +class AttentionKVCompress(nn.Module): """Multi-head Attention block with KV token compression and qk norm.""" def __init__( @@ -122,6 +127,9 @@ def __init__( sampling='conv', sr_ratio=1, qk_norm=False, + dtype=None, + device=None, + operations=None, **block_kwargs, ): """ @@ -130,19 +138,26 @@ def __init__( num_heads (int): Number of attention heads. qkv_bias (bool: If True, add a learnable bias to query, key, value. """ - super().__init__(dim, num_heads=num_heads, qkv_bias=qkv_bias, **block_kwargs) + super().__init__() + assert dim % num_heads == 0, 'dim should be divisible by num_heads' + self.num_heads = num_heads + self.head_dim = dim // num_heads + self.scale = self.head_dim ** -0.5 + + self.qkv = operations.Linear(dim, dim * 3, bias=qkv_bias, dtype=dtype, device=device) + self.proj = operations.Linear(dim, dim, dtype=dtype, device=device) self.sampling=sampling # ['conv', 'ave', 'uniform', 'uniform_every'] self.sr_ratio = sr_ratio if sr_ratio > 1 and sampling == 'conv': # Avg Conv Init. - self.sr = nn.Conv2d(dim, dim, groups=dim, kernel_size=sr_ratio, stride=sr_ratio) - self.sr.weight.data.fill_(1/sr_ratio**2) - self.sr.bias.data.zero_() - self.norm = nn.LayerNorm(dim) + self.sr = operations.Conv2d(dim, dim, groups=dim, kernel_size=sr_ratio, stride=sr_ratio, dtype=dtype, device=device) + # self.sr.weight.data.fill_(1/sr_ratio**2) + # self.sr.bias.data.zero_() + self.norm = operations.LayerNorm(dim, dtype=dtype, device=device) if qk_norm: - self.q_norm = nn.LayerNorm(dim) - self.k_norm = nn.LayerNorm(dim) + self.q_norm = operations.LayerNorm(dim, dtype=dtype, device=device) + self.k_norm = operations.LayerNorm(dim, dtype=dtype, device=device) else: self.q_norm = nn.Identity() self.k_norm = nn.Identity() @@ -204,14 +219,12 @@ def forward(self, x, mask=None, HW=None, block_id=None): if model_management.xformers_enabled(): x = xformers.ops.memory_efficient_attention( q, k, v, - p=self.attn_drop.p, + p=0, attn_bias=attn_bias ) else: q, k, v = map(lambda t: t.transpose(1, 2),(q, k, v),) - - p = getattr(self.attn_drop, "p", 0) # IPEX.optimize() will turn attn_drop into an Identity() - + p = 0 if sdpa_32b is not None and (q.element_size() * q.nelement()) > Q_4GB_LIMIT: sdpa = sdpa_32b else: @@ -224,30 +237,6 @@ def forward(self, x, mask=None, HW=None, block_id=None): ).transpose(1, 2).contiguous() x = x.view(B, N, C) x = self.proj(x) - x = self.proj_drop(x) - return x - - -################################################################################# -# AMP attention with fp32 softmax to fix loss NaN problem during training # -################################################################################# -class Attention(Attention_): - def forward(self, x): - B, N, C = x.shape - qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) - q, k, v = qkv.unbind(0) # make torchscript happy (cannot use tensor as tuple) - use_fp32_attention = getattr(self, 'fp32_attention', False) - if use_fp32_attention: - q, k = q.float(), k.float() - with torch.cuda.amp.autocast(enabled=not use_fp32_attention): - attn = (q @ k.transpose(-2, -1)) * self.scale - attn = attn.softmax(dim=-1) - - attn = self.attn_drop(attn) - - x = (attn @ v).transpose(1, 2).reshape(B, N, C) - x = self.proj(x) - x = self.proj_drop(x) return x @@ -256,13 +245,13 @@ class FinalLayer(nn.Module): The final layer of PixArt. """ - def __init__(self, hidden_size, patch_size, out_channels): + def __init__(self, hidden_size, patch_size, out_channels, dtype=None, device=None, operations=None): super().__init__() - self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) - self.linear = nn.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True) + self.norm_final = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + self.linear = operations.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True, dtype=dtype, device=device) self.adaLN_modulation = nn.Sequential( nn.SiLU(), - nn.Linear(hidden_size, 2 * hidden_size, bias=True) + operations.Linear(hidden_size, 2 * hidden_size, bias=True, dtype=dtype, device=device) ) def forward(self, x, c): @@ -271,23 +260,23 @@ def forward(self, x, c): x = self.linear(x) return x - class T2IFinalLayer(nn.Module): """ The final layer of PixArt. """ - def __init__(self, hidden_size, patch_size, out_channels): + def __init__(self, hidden_size, patch_size, out_channels, dtype=None, device=None, operations=None): super().__init__() - self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) - self.linear = nn.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True) + self.norm_final = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + self.linear = operations.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True, dtype=dtype, device=device) self.scale_shift_table = nn.Parameter(torch.randn(2, hidden_size) / hidden_size ** 0.5) self.out_channels = out_channels def forward(self, x, t): + dtype = x.dtype shift, scale = (self.scale_shift_table[None] + t[:, None]).chunk(2, dim=1) x = t2i_modulate(self.norm_final(x), shift, scale) - x = self.linear(x) + x = self.linear(x.to(dtype)) return x @@ -296,13 +285,13 @@ class MaskFinalLayer(nn.Module): The final layer of PixArt. """ - def __init__(self, final_hidden_size, c_emb_size, patch_size, out_channels): + def __init__(self, final_hidden_size, c_emb_size, patch_size, out_channels, dtype=None, device=None, operations=None): 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.norm_final = operations.LayerNorm(final_hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + self.linear = operations.Linear(final_hidden_size, patch_size * patch_size * out_channels, bias=True, dtype=dtype, device=device) self.adaLN_modulation = nn.Sequential( nn.SiLU(), - nn.Linear(c_emb_size, 2 * final_hidden_size, bias=True) + operations.Linear(c_emb_size, 2 * final_hidden_size, bias=True, dtype=dtype, device=device) ) def forward(self, x, t): shift, scale = self.adaLN_modulation(t).chunk(2, dim=1) @@ -316,13 +305,13 @@ class DecoderLayer(nn.Module): The final layer of PixArt. """ - def __init__(self, hidden_size, decoder_hidden_size): + def __init__(self, hidden_size, decoder_hidden_size, dtype=None, device=None, operations=None): super().__init__() - self.norm_decoder = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) - self.linear = nn.Linear(hidden_size, decoder_hidden_size, bias=True) + self.norm_decoder = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + self.linear = operations.Linear(hidden_size, decoder_hidden_size, bias=True, dtype=dtype, device=device) self.adaLN_modulation = nn.Sequential( nn.SiLU(), - nn.Linear(hidden_size, 2 * hidden_size, bias=True) + operations.Linear(hidden_size, 2 * hidden_size, bias=True, dtype=dtype, device=device) ) def forward(self, x, t): shift, scale = self.adaLN_modulation(t).chunk(2, dim=1) @@ -339,12 +328,12 @@ class TimestepEmbedder(nn.Module): Embeds scalar timesteps into vector representations. """ - def __init__(self, hidden_size, frequency_embedding_size=256): + def __init__(self, hidden_size, frequency_embedding_size=256, dtype=None, device=None, operations=None): super().__init__() self.mlp = nn.Sequential( - nn.Linear(frequency_embedding_size, hidden_size, bias=True), + operations.Linear(frequency_embedding_size, hidden_size, bias=True, dtype=dtype, device=device), nn.SiLU(), - nn.Linear(hidden_size, hidden_size, bias=True), + operations.Linear(hidden_size, hidden_size, bias=True, dtype=dtype, device=device), ) self.frequency_embedding_size = frequency_embedding_size @@ -368,9 +357,9 @@ def timestep_embedding(t, dim, max_period=10000): embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1) return embedding - def forward(self, t): + def forward(self, t, dtype): t_freq = self.timestep_embedding(t, self.frequency_embedding_size) - t_emb = self.mlp(t_freq.to(t.dtype)) + t_emb = self.mlp(t_freq.to(dtype)) return t_emb @@ -379,12 +368,12 @@ class SizeEmbedder(TimestepEmbedder): Embeds scalar timesteps into vector representations. """ - def __init__(self, hidden_size, frequency_embedding_size=256): - super().__init__(hidden_size=hidden_size, frequency_embedding_size=frequency_embedding_size) + def __init__(self, hidden_size, frequency_embedding_size=256, dtype=None, device=None, operations=None): + super().__init__(hidden_size=hidden_size, frequency_embedding_size=frequency_embedding_size, operations=operations) self.mlp = nn.Sequential( - nn.Linear(frequency_embedding_size, hidden_size, bias=True), + operations.Linear(frequency_embedding_size, hidden_size, bias=True, dtype=dtype, device=device), nn.SiLU(), - nn.Linear(hidden_size, hidden_size, bias=True), + operations.Linear(hidden_size, hidden_size, bias=True, dtype=dtype, device=device), ) self.frequency_embedding_size = frequency_embedding_size self.outdim = hidden_size @@ -409,10 +398,10 @@ class LabelEmbedder(nn.Module): Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance. """ - def __init__(self, num_classes, hidden_size, dropout_prob): + def __init__(self, num_classes, hidden_size, dropout_prob, dtype=None, device=None, operations=None): super().__init__() use_cfg_embedding = dropout_prob > 0 - self.embedding_table = nn.Embedding(num_classes + use_cfg_embedding, hidden_size) + self.embedding_table = operations.Embedding(num_classes + use_cfg_embedding, hidden_size, dtype=dtype, device=device), self.num_classes = num_classes self.dropout_prob = dropout_prob @@ -435,14 +424,35 @@ def forward(self, labels, train, force_drop_ids=None): return embeddings +class Mlp(nn.Module): + def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, bias=True, drop=None, dtype=None, device=None, operations=None) -> None: + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + + self.fc1 = operations.Linear(in_features, hidden_features, bias=bias, dtype=dtype, device=device) + self.act = act_layer() + self.fc2 = operations.Linear(hidden_features, out_features, bias=bias, dtype=dtype, device=device) + + self.drop1 = nn.Identity() + self.drop2 = nn.Identity() + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = self.act(self.fc1(x)) + return self.fc2(x) + + class CaptionEmbedder(nn.Module): """ Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance. """ - def __init__(self, in_channels, hidden_size, uncond_prob, act_layer=nn.GELU(approximate='tanh'), token_num=120): + def __init__(self, in_channels, hidden_size, uncond_prob, act_layer=nn.GELU(approximate='tanh'), token_num=120, dtype=None, device=None, operations=None): super().__init__() - self.y_proj = Mlp(in_features=in_channels, hidden_features=hidden_size, out_features=hidden_size, act_layer=act_layer, drop=0) + self.y_proj = Mlp( + in_features=in_channels, hidden_features=hidden_size, out_features=hidden_size, act_layer=act_layer, + dtype=dtype, device=device, operations=operations, + ) self.register_buffer("y_embedding", nn.Parameter(torch.randn(token_num, in_channels) / in_channels ** 0.5)) self.uncond_prob = uncond_prob @@ -472,9 +482,12 @@ class CaptionEmbedderDoubleBr(nn.Module): Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance. """ - def __init__(self, in_channels, hidden_size, uncond_prob, act_layer=nn.GELU(approximate='tanh'), token_num=120): + def __init__(self, in_channels, hidden_size, uncond_prob, act_layer=nn.GELU(approximate='tanh'), token_num=120, dtype=None, device=None, operations=None): super().__init__() - self.proj = Mlp(in_features=in_channels, hidden_features=hidden_size, out_features=hidden_size, act_layer=act_layer, drop=0) + self.proj = Mlp( + in_features=in_channels, hidden_features=hidden_size, out_features=hidden_size, act_layer=act_layer, + dtype=dtype, device=device, operations=operations, + ) self.embedding = nn.Parameter(torch.randn(1, in_channels) / 10 ** 0.5) self.y_embedding = nn.Parameter(torch.randn(token_num, in_channels) / 10 ** 0.5) self.uncond_prob = uncond_prob diff --git a/PixArt/models/PixArt.py b/PixArt/models/pixart.py similarity index 96% rename from PixArt/models/PixArt.py rename to PixArt/models/pixart.py index 4d6cf93..a5ca872 100644 --- a/PixArt/models/PixArt.py +++ b/PixArt/models/pixart.py @@ -8,17 +8,23 @@ # GLIDE: https://github.com/openai/glide-text2im # MAE: https://github.com/facebookresearch/mae/blob/main/models_mae.py # -------------------------------------------------------- -import math import torch import torch.nn as nn -import os import numpy as np -from timm.models.layers import DropPath -from timm.models.vision_transformer import PatchEmbed, Mlp - from .utils import auto_grad_checkpoint, to_2tuple -from .PixArt_blocks import t2i_modulate, CaptionEmbedder, AttentionKVCompress, MultiHeadCrossAttention, T2IFinalLayer, TimestepEmbedder, LabelEmbedder, FinalLayer +from .blocks import ( + t2i_modulate, + CaptionEmbedder, + AttentionKVCompress, + MultiHeadCrossAttention, + T2IFinalLayer, + TimestepEmbedder, + LabelEmbedder, + FinalLayer, + Mlp +) +from comfy.ldm.modules.diffusionmodules.mmdit import PatchEmbed class PixArtBlock(nn.Module): @@ -37,7 +43,7 @@ def __init__(self, hidden_size, num_heads, mlp_ratio=4.0, drop_path=0, input_siz # to be compatible with lower version pytorch approx_gelu = lambda: nn.GELU(approximate="tanh") self.mlp = Mlp(in_features=hidden_size, hidden_features=int(hidden_size * mlp_ratio), act_layer=approx_gelu, drop=0) - self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() + self.drop_path = nn.Identity() #DropPath(drop_path) if drop_path > 0. else nn.Identity() self.scale_shift_table = nn.Parameter(torch.randn(6, hidden_size) / hidden_size ** 0.5) self.sampling = sampling self.sr_ratio = sr_ratio diff --git a/PixArt/models/pixart_controlnet.py b/PixArt/models/pixart_controlnet.py deleted file mode 100644 index 37fa4c1..0000000 --- a/PixArt/models/pixart_controlnet.py +++ /dev/null @@ -1,312 +0,0 @@ -import re -import torch -import torch.nn as nn - -from copy import deepcopy -from torch import Tensor -from torch.nn import Module, Linear, init -from typing import Any, Mapping - -from .PixArt import PixArt, get_2d_sincos_pos_embed -from .PixArtMS import PixArtMSBlock, PixArtMS -from .utils import auto_grad_checkpoint - -# The implementation of ControlNet-Half architrecture -# https://github.com/lllyasviel/ControlNet/discussions/188 -class ControlT2IDitBlockHalf(Module): - def __init__(self, base_block: PixArtMSBlock, block_index: 0) -> None: - super().__init__() - self.copied_block = deepcopy(base_block) - self.block_index = block_index - - for p in self.copied_block.parameters(): - p.requires_grad_(True) - - self.copied_block.load_state_dict(base_block.state_dict()) - self.copied_block.train() - - self.hidden_size = hidden_size = base_block.hidden_size - if self.block_index == 0: - self.before_proj = Linear(hidden_size, hidden_size) - init.zeros_(self.before_proj.weight) - init.zeros_(self.before_proj.bias) - self.after_proj = Linear(hidden_size, hidden_size) - init.zeros_(self.after_proj.weight) - init.zeros_(self.after_proj.bias) - - def forward(self, x, y, t, mask=None, c=None): - - if self.block_index == 0: - # the first block - c = self.before_proj(c) - c = self.copied_block(x + c, y, t, mask) - c_skip = self.after_proj(c) - else: - # load from previous c and produce the c for skip connection - c = self.copied_block(c, y, t, mask) - c_skip = self.after_proj(c) - - return c, c_skip - - -# The implementation of ControlPixArtHalf net -class ControlPixArtHalf(Module): - # only support single res model - def __init__(self, base_model: PixArt, copy_blocks_num: int = 13) -> None: - super().__init__() - self.dtype = torch.get_default_dtype() - self.base_model = base_model.eval() - self.controlnet = [] - self.copy_blocks_num = copy_blocks_num - self.total_blocks_num = len(base_model.blocks) - for p in self.base_model.parameters(): - p.requires_grad_(False) - - # Copy first copy_blocks_num block - for i in range(copy_blocks_num): - self.controlnet.append(ControlT2IDitBlockHalf(base_model.blocks[i], i)) - self.controlnet = nn.ModuleList(self.controlnet) - - def __getattr__(self, name: str) -> Tensor or Module: - if name in ['forward', 'forward_with_dpmsolver', 'forward_with_cfg', 'forward_c', 'load_state_dict']: - return self.__dict__[name] - elif name in ['base_model', 'controlnet']: - return super().__getattr__(name) - else: - return getattr(self.base_model, name) - - def forward_c(self, c): - self.h, self.w = c.shape[-2]//self.patch_size, c.shape[-1]//self.patch_size - pos_embed = torch.from_numpy(get_2d_sincos_pos_embed(self.pos_embed.shape[-1], (self.h, self.w), lewei_scale=self.lewei_scale, base_size=self.base_size)).unsqueeze(0).to(c.device).to(self.dtype) - return self.x_embedder(c) + pos_embed if c is not None else c - - # def forward(self, x, t, c, **kwargs): - # return self.base_model(x, t, c=self.forward_c(c), **kwargs) - def forward_raw(self, x, timestep, y, mask=None, data_info=None, c=None, **kwargs): - # modify the original PixArtMS forward function - if c is not None: - c = c.to(self.dtype) - c = self.forward_c(c) - """ - Forward pass of PixArt. - x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images) - t: (N,) tensor of diffusion timesteps - y: (N, 1, 120, C) tensor of class labels - """ - x = x.to(self.dtype) - timestep = timestep.to(self.dtype) - y = y.to(self.dtype) - pos_embed = self.pos_embed.to(self.dtype) - self.h, self.w = x.shape[-2]//self.patch_size, x.shape[-1]//self.patch_size - x = self.x_embedder(x) + pos_embed # (N, T, D), where T = H * W / patch_size ** 2 - t = self.t_embedder(timestep.to(x.dtype)) # (N, D) - t0 = self.t_block(t) - y = self.y_embedder(y, self.training) # (N, 1, L, D) - if mask is not None: - if mask.shape[0] != y.shape[0]: - mask = mask.repeat(y.shape[0] // mask.shape[0], 1) - mask = mask.squeeze(1).squeeze(1) - y = y.squeeze(1).masked_select(mask.unsqueeze(-1) != 0).view(1, -1, x.shape[-1]) - y_lens = mask.sum(dim=1).tolist() - else: - y_lens = [y.shape[2]] * y.shape[0] - y = y.squeeze(1).view(1, -1, x.shape[-1]) - - # define the first layer - x = auto_grad_checkpoint(self.base_model.blocks[0], x, y, t0, y_lens, **kwargs) # (N, T, D) #support grad checkpoint - - if c is not None: - # update c - for index in range(1, self.copy_blocks_num + 1): - c, c_skip = auto_grad_checkpoint(self.controlnet[index - 1], x, y, t0, y_lens, c, **kwargs) - x = auto_grad_checkpoint(self.base_model.blocks[index], x + c_skip, y, t0, y_lens, **kwargs) - - # update x - for index in range(self.copy_blocks_num + 1, self.total_blocks_num): - x = auto_grad_checkpoint(self.base_model.blocks[index], x, y, t0, y_lens, **kwargs) - else: - for index in range(1, self.total_blocks_num): - x = auto_grad_checkpoint(self.base_model.blocks[index], x, y, t0, y_lens, **kwargs) - - x = self.final_layer(x, t) # (N, T, patch_size ** 2 * out_channels) - x = self.unpatchify(x) # (N, out_channels, H, W) - return x - - def forward(self, x, timesteps, context, cn_hint=None, **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, 120, C) conditioning - cn_hint: controlnet hint - """ - ## Still accepts the input w/o that dim but returns garbage - if len(context.shape) == 3: - context = context.unsqueeze(1) - - ## run original forward pass - out = self.forward_raw( - x = x.to(self.dtype), - timestep = timesteps.to(self.dtype), - y = context.to(self.dtype), - c = cn_hint, - ) - - ## only return EPS - out = out.to(torch.float) - eps, rest = out[:, :self.in_channels], out[:, self.in_channels:] - return eps - - def forward_with_dpmsolver(self, x, t, y, data_info, c, **kwargs): - model_out = self.forward_raw(x, t, y, data_info=data_info, c=c, **kwargs) - return model_out.chunk(2, dim=1)[0] - - # def forward_with_dpmsolver(self, x, t, y, data_info, c, **kwargs): - # return self.base_model.forward_with_dpmsolver(x, t, y, data_info=data_info, c=self.forward_c(c), **kwargs) - - def forward_with_cfg(self, x, t, y, cfg_scale, data_info, c, **kwargs): - return self.base_model.forward_with_cfg(x, t, y, cfg_scale, data_info, c=self.forward_c(c), **kwargs) - - def load_state_dict(self, state_dict: Mapping[str, Any], strict: bool = True): - if all((k.startswith('base_model') or k.startswith('controlnet')) for k in state_dict.keys()): - return super().load_state_dict(state_dict, strict) - else: - new_key = {} - for k in state_dict.keys(): - new_key[k] = re.sub(r"(blocks\.\d+)(.*)", r"\1.base_block\2", k) - for k, v in new_key.items(): - if k != v: - print(f"replace {k} to {v}") - state_dict[v] = state_dict.pop(k) - - return self.base_model.load_state_dict(state_dict, strict) - - def unpatchify(self, x): - """ - x: (N, T, patch_size**2 * C) - imgs: (N, H, W, C) - """ - c = self.out_channels - p = self.x_embedder.patch_size[0] - assert self.h * self.w == x.shape[1] - - x = x.reshape(shape=(x.shape[0], self.h, self.w, p, p, c)) - x = torch.einsum('nhwpqc->nchpwq', x) - imgs = x.reshape(shape=(x.shape[0], c, self.h * p, self.w * p)) - return imgs - - # @property - # def dtype(self): - ## 返回模型参数的数据类型 - # return next(self.parameters()).dtype - - -# The implementation for PixArtMS_Half + 1024 resolution -class ControlPixArtMSHalf(ControlPixArtHalf): - # support multi-scale res model (multi-scale model can also be applied to single reso training & inference) - def __init__(self, base_model: PixArtMS, copy_blocks_num: int = 13) -> None: - super().__init__(base_model=base_model, copy_blocks_num=copy_blocks_num) - - def forward_raw(self, x, timestep, y, mask=None, data_info=None, c=None, **kwargs): - # modify the original PixArtMS forward function - """ - Forward pass of PixArt. - x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images) - t: (N,) tensor of diffusion timesteps - y: (N, 1, 120, C) tensor of class labels - """ - if c is not None: - c = c.to(self.dtype) - c = self.forward_c(c) - bs = x.shape[0] - x = x.to(self.dtype) - timestep = timestep.to(self.dtype) - y = y.to(self.dtype) - c_size, ar = data_info['img_hw'].to(self.dtype), data_info['aspect_ratio'].to(self.dtype) - self.h, self.w = x.shape[-2]//self.patch_size, x.shape[-1]//self.patch_size - - pos_embed = torch.from_numpy(get_2d_sincos_pos_embed(self.pos_embed.shape[-1], (self.h, self.w), lewei_scale=self.lewei_scale, base_size=self.base_size)).unsqueeze(0).to(x.device).to(self.dtype) - x = self.x_embedder(x) + pos_embed # (N, T, D), where T = H * W / patch_size ** 2 - t = self.t_embedder(timestep) # (N, D) - csize = self.csize_embedder(c_size, bs) # (N, D) - ar = self.ar_embedder(ar, bs) # (N, D) - t = t + torch.cat([csize, ar], dim=1) - t0 = self.t_block(t) - y = self.y_embedder(y, self.training) # (N, D) - if mask is not None: - if mask.shape[0] != y.shape[0]: - mask = mask.repeat(y.shape[0] // mask.shape[0], 1) - mask = mask.squeeze(1).squeeze(1) - y = y.squeeze(1).masked_select(mask.unsqueeze(-1) != 0).view(1, -1, x.shape[-1]) - y_lens = mask.sum(dim=1).tolist() - else: - y_lens = [y.shape[2]] * y.shape[0] - y = y.squeeze(1).view(1, -1, x.shape[-1]) - - # define the first layer - x = auto_grad_checkpoint(self.base_model.blocks[0], x, y, t0, y_lens, **kwargs) # (N, T, D) #support grad checkpoint - - if c is not None: - # update c - for index in range(1, self.copy_blocks_num + 1): - c, c_skip = auto_grad_checkpoint(self.controlnet[index - 1], x, y, t0, y_lens, c, **kwargs) - x = auto_grad_checkpoint(self.base_model.blocks[index], x + c_skip, y, t0, y_lens, **kwargs) - - # update x - for index in range(self.copy_blocks_num + 1, self.total_blocks_num): - x = auto_grad_checkpoint(self.base_model.blocks[index], x, y, t0, y_lens, **kwargs) - else: - for index in range(1, self.total_blocks_num): - x = auto_grad_checkpoint(self.base_model.blocks[index], x, y, t0, y_lens, **kwargs) - - x = self.final_layer(x, t) # (N, T, patch_size ** 2 * out_channels) - x = self.unpatchify(x) # (N, out_channels, H, W) - return x - - def forward(self, x, timesteps, context, img_hw=None, aspect_ratio=None, cn_hint=None, **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, 120, C) conditioning - img_hw: height|width conditioning - aspect_ratio: aspect ratio conditioning - cn_hint: controlnet hint - """ - ## size/ar from cond with fallback based on the latent image shape. - bs = x.shape[0] - data_info = {} - if img_hw is None: - data_info["img_hw"] = torch.tensor( - [[x.shape[2]*8, x.shape[3]*8]], - dtype=self.dtype, - device=x.device - ).repeat(bs, 1) - else: - data_info["img_hw"] = img_hw.to(x.dtype) - if aspect_ratio is None or True: - data_info["aspect_ratio"] = torch.tensor( - [[x.shape[2]/x.shape[3]]], - dtype=self.dtype, - device=x.device - ).repeat(bs, 1) - else: - data_info["aspect_ratio"] = aspect_ratio.to(x.dtype) - - ## Still accepts the input w/o that dim but returns garbage - if len(context.shape) == 3: - context = context.unsqueeze(1) - - ## run original forward pass - out = self.forward_raw( - x = x.to(self.dtype), - timestep = timesteps.to(self.dtype), - y = context.to(self.dtype), - c = cn_hint, - data_info=data_info, - ) - - ## only return EPS - out = out.to(torch.float) - eps, rest = out[:, :self.in_channels], out[:, self.in_channels:] - return eps diff --git a/PixArt/models/PixArtMS.py b/PixArt/models/pixartms.py similarity index 61% rename from PixArt/models/PixArtMS.py rename to PixArt/models/pixartms.py index 908589c..a9e7035 100644 --- a/PixArt/models/PixArtMS.py +++ b/PixArt/models/pixartms.py @@ -11,12 +11,10 @@ import torch import torch.nn as nn from tqdm import tqdm -from timm.models.layers import DropPath -from timm.models.vision_transformer import Mlp from .utils import auto_grad_checkpoint, to_2tuple -from .PixArt_blocks import t2i_modulate, CaptionEmbedder, AttentionKVCompress, MultiHeadCrossAttention, T2IFinalLayer, TimestepEmbedder, SizeEmbedder -from .PixArt import PixArt, get_2d_sincos_pos_embed +from .blocks import t2i_modulate, CaptionEmbedder, AttentionKVCompress, MultiHeadCrossAttention, T2IFinalLayer, TimestepEmbedder, SizeEmbedder, Mlp +from .pixart import PixArt, get_2d_sincos_pos_embed class PatchEmbed(nn.Module): @@ -31,12 +29,15 @@ def __init__( norm_layer=None, flatten=True, bias=True, + dtype=None, + device=None, + operations=None ): super().__init__() patch_size = to_2tuple(patch_size) self.patch_size = patch_size self.flatten = flatten - self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=bias) + self.proj = operations.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=bias, dtype=dtype, device=device) self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity() def forward(self, x): @@ -52,29 +53,34 @@ class PixArtMSBlock(nn.Module): A PixArt block with adaptive layer norm zero (adaLN-Zero) conditioning. """ def __init__(self, hidden_size, num_heads, mlp_ratio=4.0, drop_path=0., input_size=None, - sampling=None, sr_ratio=1, qk_norm=False, **block_kwargs): + sampling=None, sr_ratio=1, qk_norm=False, dtype=None, device=None, operations=None, **block_kwargs): super().__init__() self.hidden_size = hidden_size - self.norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + self.norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) self.attn = AttentionKVCompress( hidden_size, num_heads=num_heads, qkv_bias=True, sampling=sampling, sr_ratio=sr_ratio, - qk_norm=qk_norm, **block_kwargs + qk_norm=qk_norm, dtype=dtype, device=device, operations=operations, **block_kwargs ) - self.cross_attn = MultiHeadCrossAttention(hidden_size, num_heads, **block_kwargs) - self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + self.cross_attn = MultiHeadCrossAttention( + hidden_size, num_heads, dtype=dtype, device=device, operations=operations, **block_kwargs + ) + self.norm2 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) # to be compatible with lower version pytorch approx_gelu = lambda: nn.GELU(approximate="tanh") - self.mlp = Mlp(in_features=hidden_size, hidden_features=int(hidden_size * mlp_ratio), act_layer=approx_gelu, drop=0) - self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() + self.mlp = Mlp( + in_features=hidden_size, hidden_features=int(hidden_size * mlp_ratio), act_layer=approx_gelu, + dtype=dtype, device=device, operations=operations + ) self.scale_shift_table = nn.Parameter(torch.randn(6, hidden_size) / hidden_size ** 0.5) def forward(self, x, y, t, mask=None, HW=None, **kwargs): B, N, C = x.shape + dtype = x.dtype - shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (self.scale_shift_table[None] + t.reshape(B, 6, -1)).chunk(6, dim=1) - x = x + self.drop_path(gate_msa * self.attn(t2i_modulate(self.norm1(x), shift_msa, scale_msa), HW=HW)) + shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (self.scale_shift_table[None].to(x.dtype) + t.reshape(B, 6, -1)).chunk(6, dim=1) + x = x + (gate_msa * self.attn(t2i_modulate(self.norm1(x), shift_msa, scale_msa), HW=HW)) x = x + self.cross_attn(x, y, mask) - x = x + self.drop_path(gate_mlp * self.mlp(t2i_modulate(self.norm2(x), shift_mlp, scale_mlp))) + x = x + (gate_mlp * self.mlp(t2i_modulate(self.norm2(x), shift_mlp, scale_mlp))) return x @@ -105,40 +111,52 @@ def __init__( micro_condition=True, qk_norm=False, kv_compress_config=None, + dtype=None, + device=None, + operations=None, **kwargs, ): - super().__init__( - input_size=input_size, - patch_size=patch_size, - in_channels=in_channels, - hidden_size=hidden_size, - depth=depth, - num_heads=num_heads, - mlp_ratio=mlp_ratio, - class_dropout_prob=class_dropout_prob, - learn_sigma=learn_sigma, - pred_sigma=pred_sigma, - drop_path=drop_path, - pe_interpolation=pe_interpolation, - config=config, - model_max_length=model_max_length, - qk_norm=qk_norm, - kv_compress_config=kv_compress_config, - **kwargs, - ) - self.dtype = torch.get_default_dtype() + nn.Module.__init__(self) + self.dtype = dtype + self.pred_sigma = pred_sigma + self.in_channels = in_channels + self.out_channels = in_channels * 2 if pred_sigma else in_channels + self.patch_size = patch_size + self.num_heads = num_heads + self.pe_interpolation = pe_interpolation + self.pe_precision = pe_precision + self.depth = depth + self.h = self.w = 0 approx_gelu = lambda: nn.GELU(approximate="tanh") self.t_block = nn.Sequential( nn.SiLU(), - nn.Linear(hidden_size, 6 * hidden_size, bias=True) + operations.Linear(hidden_size, 6 * hidden_size, bias=True, dtype=dtype, device=device) + ) + self.x_embedder = PatchEmbed( + patch_size, in_channels, hidden_size, bias=True, + dtype=dtype, device=device, operations=operations + ) + self.t_embedder = TimestepEmbedder( + hidden_size, dtype=dtype, device=device, operations=operations, ) - self.x_embedder = PatchEmbed(patch_size, in_channels, hidden_size, bias=True) - self.y_embedder = CaptionEmbedder(in_channels=caption_channels, hidden_size=hidden_size, uncond_prob=class_dropout_prob, act_layer=approx_gelu, token_num=model_max_length) + self.y_embedder = CaptionEmbedder( + in_channels=caption_channels, hidden_size=hidden_size, uncond_prob=class_dropout_prob, + act_layer=approx_gelu, token_num=model_max_length, + dtype=dtype, device=device, operations=operations, + ) + self.micro_conditioning = micro_condition if self.micro_conditioning: - self.csize_embedder = SizeEmbedder(hidden_size//3) # c_size embed - self.ar_embedder = SizeEmbedder(hidden_size//3) # aspect ratio embed + + self.csize_embedder = SizeEmbedder(hidden_size//3, dtype=dtype, device=device, operations=operations) + self.ar_embedder = SizeEmbedder(hidden_size//3, dtype=dtype, device=device, operations=operations) + + # Will use fixed sin-cos embedding: + num_patches = (input_size // patch_size) * (input_size // patch_size) + self.base_size = input_size // self.patch_size + self.register_buffer("pos_embed", torch.zeros(1, num_patches, hidden_size)) + drop_path = [x.item() for x in torch.linspace(0, drop_path, depth)] # stochastic depth decay rule if kv_compress_config is None: kv_compress_config = { @@ -153,12 +171,17 @@ def __init__( sampling=kv_compress_config['sampling'], sr_ratio=int(kv_compress_config['scale_factor']) if i in kv_compress_config['kv_compress_layer'] else 1, qk_norm=qk_norm, + dtype=dtype, + device=device, + operations=operations, ) for i in range(depth) ]) - self.final_layer = T2IFinalLayer(hidden_size, patch_size, self.out_channels) + self.final_layer = T2IFinalLayer( + hidden_size, patch_size, self.out_channels, dtype=dtype, device=device, operations=operations + ) - def forward_raw(self, x, t, y, mask=None, data_info=None, **kwargs): + def forward_orig(self, x, timestep, y, mask=None, data_info=None, **kwargs): """ Original forward pass of PixArt. x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images) @@ -166,9 +189,6 @@ def forward_raw(self, x, t, y, mask=None, data_info=None, **kwargs): y: (N, 1, 120, C) tensor of class labels """ bs = x.shape[0] - x = x.to(self.dtype) - timestep = t.to(self.dtype) - y = y.to(self.dtype) pe_interpolation = self.pe_interpolation if pe_interpolation is None or self.pe_precision is not None: @@ -181,10 +201,10 @@ def forward_raw(self, x, t, y, mask=None, data_info=None, **kwargs): self.pos_embed.shape[-1], (self.h, self.w), pe_interpolation=pe_interpolation, base_size=self.base_size ) - ).unsqueeze(0).to(device=x.device, dtype=self.dtype) + ).to(device=x.device, dtype=x.dtype).unsqueeze(0) x = self.x_embedder(x) + pos_embed # (N, T, D), where T = H * W / patch_size ** 2 - t = self.t_embedder(timestep) # (N, D) + t = self.t_embedder(timestep, x.dtype) # (N, D) if self.micro_conditioning: c_size, ar = data_info['img_hw'].to(self.dtype), data_info['aspect_ratio'].to(self.dtype) @@ -212,46 +232,29 @@ def forward_raw(self, x, t, y, mask=None, data_info=None, **kwargs): return x - def forward(self, x, timesteps, context, img_hw=None, aspect_ratio=None, **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, 120, C) conditioning - img_hw: height|width conditioning - aspect_ratio: aspect ratio conditioning - """ + def forward(self, x, timesteps, context, width=None, height=None, img_hw=None, aspect_ratio=None, **kwargs): + bs, c, h, w = x.shape + dtype = self.dtype + device = x.device + ## size/ar from cond with fallback based on the latent image shape. bs = x.shape[0] data_info = {} if img_hw is None: - data_info["img_hw"] = torch.tensor( - [[x.shape[2]*8, x.shape[3]*8]], - dtype=self.dtype, - device=x.device - ).repeat(bs, 1) + data_info["img_hw"] = torch.tensor([h*8, w*8], dtype=dtype, device=device).repeat(bs, 1) else: data_info["img_hw"] = img_hw.to(dtype=x.dtype, device=x.device) - if aspect_ratio is None or True: - data_info["aspect_ratio"] = torch.tensor( - [[x.shape[2]/x.shape[3]]], - dtype=self.dtype, - device=x.device - ).repeat(bs, 1) + if aspect_ratio is None: + data_info["aspect_ratio"] = torch.tensor([h/w], dtype=dtype, device=device).repeat(bs, 1) else: - data_info["aspect_ratio"] = aspect_ratio.to(dtype=x.dtype, device=x.device) + data_info["aspect_ratio"] = aspect_ratio.to(dtype=dtype, device=device) ## Still accepts the input w/o that dim but returns garbage if len(context.shape) == 3: context = context.unsqueeze(1) ## run original forward pass - out = self.forward_raw( - x = x.to(self.dtype), - t = timesteps.to(self.dtype), - y = context.to(self.dtype), - data_info=data_info, - ) + out = self.forward_orig(x, timesteps, context, data_info=data_info) ## only return EPS out = out.to(torch.float) diff --git a/PixArt/nodes.py b/PixArt/nodes.py index f027d89..6a1f2dd 100644 --- a/PixArt/nodes.py +++ b/PixArt/nodes.py @@ -1,278 +1,87 @@ -import os -import json -import torch -import folder_paths - -from comfy import utils -from .conf import pixart_conf, pixart_res -from .lora import load_pixart_lora -from .loader import load_pixart - -class PixArtCheckpointLoader: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "ckpt_name": (folder_paths.get_filename_list("checkpoints"),), - "model": (list(pixart_conf.keys()),), - } - } - RETURN_TYPES = ("MODEL",) - RETURN_NAMES = ("model",) - FUNCTION = "load_checkpoint" - CATEGORY = "ExtraModels/PixArt" - TITLE = "PixArt Checkpoint Loader" - - def load_checkpoint(self, ckpt_name, model): - ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name) - model_conf = pixart_conf[model] - model = load_pixart( - model_path = ckpt_path, - model_conf = model_conf, - ) - return (model,) - -class PixArtCheckpointLoaderSimple(PixArtCheckpointLoader): - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "ckpt_name": (folder_paths.get_filename_list("checkpoints"),), - } - } - TITLE = "PixArt Checkpoint Loader (auto)" - - def load_checkpoint(self, ckpt_name): - ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name) - model = load_pixart(model_path=ckpt_path) - return (model,) - -class PixArtResolutionSelect(): - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "model": (list(pixart_res.keys()),), - # keys are the same for both - "ratio": (list(pixart_res["PixArtMS_XL_2"].keys()),{"default":"1.00"}), - } - } - RETURN_TYPES = ("INT","INT") - RETURN_NAMES = ("width","height") - FUNCTION = "get_res" - CATEGORY = "ExtraModels/PixArt" - TITLE = "PixArt Resolution Select" - - def get_res(self, model, ratio): - width, height = pixart_res[model][ratio] - return (width,height) - -class PixArtLoraLoader: - def __init__(self): - self.loaded_lora = None - - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "model": ("MODEL",), - "lora_name": (folder_paths.get_filename_list("loras"), ), - "strength": ("FLOAT", {"default": 1.0, "min": -20.0, "max": 20.0, "step": 0.01}), - } - } - RETURN_TYPES = ("MODEL",) - FUNCTION = "load_lora" - CATEGORY = "ExtraModels/PixArt" - TITLE = "PixArt Load LoRA" - - def load_lora(self, model, lora_name, strength,): - if strength == 0: - return (model) - - lora_path = folder_paths.get_full_path("loras", lora_name) - lora = None - if self.loaded_lora is not None: - if self.loaded_lora[0] == lora_path: - lora = self.loaded_lora[1] - else: - temp = self.loaded_lora - self.loaded_lora = None - del temp - - if lora is None: - lora = utils.load_torch_file(lora_path, safe_load=True) - self.loaded_lora = (lora_path, lora) - - model_lora = load_pixart_lora(model, lora, lora_path, strength,) - return (model_lora,) - class PixArtResolutionCond: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "cond": ("CONDITIONING", ), - "width": ("INT", {"default": 1024.0, "min": 0, "max": 8192}), - "height": ("INT", {"default": 1024.0, "min": 0, "max": 8192}), - } - } - - RETURN_TYPES = ("CONDITIONING",) - RETURN_NAMES = ("cond",) - FUNCTION = "add_cond" - CATEGORY = "ExtraModels/PixArt" - TITLE = "PixArt Resolution Conditioning" - - def add_cond(self, cond, width, height): - for c in range(len(cond)): - cond[c][1].update({ - "img_hw": [[height, width]], - "aspect_ratio": [[height/width]], - }) - return (cond,) - -class PixArtControlNetCond: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "cond": ("CONDITIONING",), - "latent": ("LATENT",), - # "image": ("IMAGE",), - # "vae": ("VAE",), - # "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}) - } - } - - RETURN_TYPES = ("CONDITIONING",) - RETURN_NAMES = ("cond",) - FUNCTION = "add_cond" - CATEGORY = "ExtraModels/PixArt" - TITLE = "PixArt ControlNet Conditioning" - - def add_cond(self, cond, latent): - for c in range(len(cond)): - cond[c][1]["cn_hint"] = latent["samples"] * 0.18215 - return (cond,) - -class PixArtT5TextEncode: - """ - Reference code, mostly to verify compatibility. - Once everything works, this should instead inherit from the - T5 text encode node and simply add the extra conds (res/ar). - """ - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "text": ("STRING", {"multiline": True}), - "T5": ("T5",), - } - } - - RETURN_TYPES = ("CONDITIONING",) - FUNCTION = "encode" - CATEGORY = "ExtraModels/PixArt" - TITLE = "PixArt T5 Text Encode [Reference]" - - def mask_feature(self, emb, mask): - if emb.shape[0] == 1: - keep_index = mask.sum().item() - return emb[:, :, :keep_index, :], keep_index - else: - masked_feature = emb * mask[:, None, :, None] - return masked_feature, emb.shape[2] - - def encode(self, text, T5): - text = text.lower().strip() - tokenizer_out = T5.tokenizer.tokenizer( - text, - max_length = 120, - padding = 'max_length', - truncation = True, - return_attention_mask = True, - add_special_tokens = True, - return_tensors = 'pt' - ) - tokens = tokenizer_out["input_ids"] - mask = tokenizer_out["attention_mask"] - embs = T5.cond_stage_model.transformer( - input_ids = tokens.to(T5.load_device), - attention_mask = mask.to(T5.load_device), - )['last_hidden_state'].float()[:, None] - masked_embs, keep_index = self.mask_feature( - embs.detach().to("cpu"), - mask.detach().to("cpu") - ) - masked_embs = masked_embs.squeeze(0) # match CLIP/internal - print("Encoded T5:", masked_embs.shape) - return ([[masked_embs, {}]], ) + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "cond": ("CONDITIONING", ), + "width": ("INT", {"default": 1024.0, "min": 0, "max": 8192}), + "height": ("INT", {"default": 1024.0, "min": 0, "max": 8192}), + } + } + + RETURN_TYPES = ("CONDITIONING",) + RETURN_NAMES = ("cond",) + FUNCTION = "add_cond" + CATEGORY = "ExtraModels/PixArt" + TITLE = "PixArt Resolution Conditioning" + + def add_cond(self, cond, width, height): + for c in range(len(cond)): + cond[c][1].update({ + "img_hw": [[height, width]], + "aspect_ratio": [[height/width]], + }) + return (cond,) class PixArtT5FromSD3CLIP: - """ - Split the T5 text encoder away from SD3 - """ - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "sd3_clip": ("CLIP",), - "padding": ("INT", {"default": 1, "min": 1, "max": 300}), - } - } - - RETURN_TYPES = ("CLIP",) - RETURN_NAMES = ("t5",) - FUNCTION = "split" - CATEGORY = "ExtraModels/PixArt" - TITLE = "PixArt T5 from SD3 CLIP" - - def split(self, sd3_clip, padding): - try: - from comfy.text_encoders.sd3_clip import SD3Tokenizer, SD3ClipModel - except ImportError: - # fallback for older ComfyUI versions - from comfy.sd3_clip import SD3Tokenizer, SD3ClipModel - import copy - - clip = sd3_clip.clone() - assert clip.cond_stage_model.t5xxl is not None, "CLIP must have T5 loaded!" - - # remove transformer - transformer = clip.cond_stage_model.t5xxl.transformer - clip.cond_stage_model.t5xxl.transformer = None - - # clone object - tmp = SD3ClipModel(clip_l=False, clip_g=False, t5=False) - tmp.t5xxl = copy.deepcopy(clip.cond_stage_model.t5xxl) - # put transformer back - clip.cond_stage_model.t5xxl.transformer = transformer - tmp.t5xxl.transformer = transformer - - # override special tokens - tmp.t5xxl.special_tokens = copy.deepcopy(clip.cond_stage_model.t5xxl.special_tokens) - tmp.t5xxl.special_tokens.pop("end") # make sure empty tokens match - - # add attn mask opt if present in original - if hasattr(sd3_clip.cond_stage_model, "t5_attention_mask"): - tmp.t5_attention_mask = False - - # tokenizer - tok = SD3Tokenizer() - tok.t5xxl.min_length = padding - - clip.cond_stage_model = tmp - clip.tokenizer = tok - - return (clip, ) + """ + Split the T5 text encoder away from SD3 + """ + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "sd3_clip": ("CLIP",), + "padding": ("INT", {"default": 1, "min": 1, "max": 300}), + } + } + + RETURN_TYPES = ("CLIP",) + RETURN_NAMES = ("t5",) + FUNCTION = "split" + CATEGORY = "ExtraModels/PixArt" + TITLE = "PixArt T5 from SD3 CLIP" + + def split(self, sd3_clip, padding): + try: + from comfy.text_encoders.sd3_clip import SD3Tokenizer, SD3ClipModel + except ImportError: + # fallback for older ComfyUI versions + from comfy.sd3_clip import SD3Tokenizer, SD3ClipModel # type: ignore + import copy + + clip = sd3_clip.clone() + assert clip.cond_stage_model.t5xxl is not None, "CLIP must have T5 loaded!" + + # remove transformer + transformer = clip.cond_stage_model.t5xxl.transformer + clip.cond_stage_model.t5xxl.transformer = None + + # clone object + tmp = SD3ClipModel(clip_l=False, clip_g=False, t5=False) + tmp.t5xxl = copy.deepcopy(clip.cond_stage_model.t5xxl) + # put transformer back + clip.cond_stage_model.t5xxl.transformer = transformer + tmp.t5xxl.transformer = transformer + + # override special tokens + tmp.t5xxl.special_tokens = copy.deepcopy(clip.cond_stage_model.t5xxl.special_tokens) + tmp.t5xxl.special_tokens.pop("end") # make sure empty tokens match + + # add attn mask opt if present in original + if hasattr(sd3_clip.cond_stage_model, "t5_attention_mask"): + tmp.t5_attention_mask = False + + # tokenizer + tok = SD3Tokenizer() + tok.t5xxl.min_length = padding + + clip.cond_stage_model = tmp + clip.tokenizer = tok + + return (clip, ) NODE_CLASS_MAPPINGS = { - "PixArtCheckpointLoader" : PixArtCheckpointLoader, - "PixArtCheckpointLoaderSimple" : PixArtCheckpointLoaderSimple, - "PixArtResolutionSelect" : PixArtResolutionSelect, - "PixArtLoraLoader" : PixArtLoraLoader, - "PixArtT5TextEncode" : PixArtT5TextEncode, - "PixArtResolutionCond" : PixArtResolutionCond, - "PixArtControlNetCond" : PixArtControlNetCond, - "PixArtT5FromSD3CLIP": PixArtT5FromSD3CLIP, + "PixArtResolutionCond" : PixArtResolutionCond, + "PixArtT5FromSD3CLIP": PixArtT5FromSD3CLIP, } diff --git a/Sana/conf.py b/Sana/conf.py deleted file mode 100644 index 3719c0f..0000000 --- a/Sana/conf.py +++ /dev/null @@ -1,98 +0,0 @@ -""" -List of all Sana model types / settings -""" - -sampling_settings = { - "shift": 3.0, -} - -sana_conf = { - "SanaMS_600M_P1_D28": { - "target": "SanaMS", - "unet_config": { - "in_channels": 32, - "depth": 28, - "hidden_size": 1152, - "patch_size": 1, - "num_heads": 16, - "linear_head_dim": 32, - "model_max_length": 300, - "y_norm": True, - "attn_type": "linear", - "ffn_type": "glumbconv", - "mlp_ratio": 2.5, - "mlp_acts": ["silu", "silu", None], - "use_pe": False, - "pred_sigma": False, - "learn_sigma": False, - "fp32_attention": True, - }, - "sampling_settings" : sampling_settings, - }, - "SanaMS_1600M_P1_D20": { - "target": "SanaMS", - "unet_config": { - "in_channels": 32, - "depth": 20, - "hidden_size": 2240, - "patch_size": 1, - "num_heads": 20, - "linear_head_dim": 32, - "model_max_length": 300, - "y_norm": True, - "attn_type": "linear", - "ffn_type": "glumbconv", - "mlp_ratio": 2.5, - "mlp_acts": ["silu", "silu", None], - "use_pe": False, - "pred_sigma": False, - "learn_sigma": False, - "fp32_attention": True, - }, - "sampling_settings" : sampling_settings, - }, -} - -sana_res = { - "1024px": { # models/SanaMS 1024x1024 - '0.25': [512, 2048], '0.26': [512, 1984], '0.27': [512, 1920], '0.28': [512, 1856], - '0.32': [576, 1792], '0.33': [576, 1728], '0.35': [576, 1664], '0.40': [640, 1600], - '0.42': [640, 1536], '0.48': [704, 1472], '0.50': [704, 1408], '0.52': [704, 1344], - '0.57': [768, 1344], '0.60': [768, 1280], '0.68': [832, 1216], '0.72': [832, 1152], - '0.78': [896, 1152], '0.82': [896, 1088], '0.88': [960, 1088], '0.94': [960, 1024], - '1.00': [1024,1024], '1.07': [1024, 960], '1.13': [1088, 960], '1.21': [1088, 896], - '1.29': [1152, 896], '1.38': [1152, 832], '1.46': [1216, 832], '1.67': [1280, 768], - '1.75': [1344, 768], '2.00': [1408, 704], '2.09': [1472, 704], '2.40': [1536, 640], - '2.50': [1600, 640], '2.89': [1664, 576], '3.00': [1728, 576], '3.11': [1792, 576], - '3.62': [1856, 512], '3.75': [1920, 512], '3.88': [1984, 512], '4.00': [2048, 512], - }, - "512px": { # models/SanaMS 512x512 - '0.25': [256,1024], '0.26': [256, 992], '0.27': [256, 960], '0.28': [256, 928], - '0.32': [288, 896], '0.33': [288, 864], '0.35': [288, 832], '0.40': [320, 800], - '0.42': [320, 768], '0.48': [352, 736], '0.50': [352, 704], '0.52': [352, 672], - '0.57': [384, 672], '0.60': [384, 640], '0.68': [416, 608], '0.72': [416, 576], - '0.78': [448, 576], '0.82': [448, 544], '0.88': [480, 544], '0.94': [480, 512], - '1.00': [512, 512], '1.07': [512, 480], '1.13': [544, 480], '1.21': [544, 448], - '1.29': [576, 448], '1.38': [576, 416], '1.46': [608, 416], '1.67': [640, 384], - '1.75': [672, 384], '2.00': [704, 352], '2.09': [736, 352], '2.40': [768, 320], - '2.50': [800, 320], '2.89': [832, 288], '3.00': [864, 288], '3.11': [896, 288], - '3.62': [928, 256], '3.75': [960, 256], '3.88': [992, 256], '4.00': [1024,256] - }, - "2K": { - '0.25': [1024, 4096], '0.26': [1024, 3968], '0.27': [1024, 3840], '0.28': [1024, 3712], - '0.32': [1152, 3584], '0.33': [1152, 3456], '0.35': [1152, 3328], '0.40': [1280, 3200], - '0.42': [1280, 3072], '0.48': [1408, 2944], '0.50': [1408, 2816], '0.52': [1408, 2688], - '0.57': [1536, 2688], '0.60': [1536, 2560], '0.68': [1664, 2432], '0.72': [1664, 2304], - '0.78': [1792, 2304], '0.82': [1792, 2176], '0.88': [1920, 2176], '0.94': [1920, 2048], - '1.00': [2048, 2048], '1.07': [2048, 1920], '1.13': [2176, 1920], '1.21': [2176, 1792], - '1.29': [2304, 1792], '1.38': [2304, 1664], '1.46': [2432, 1664], '1.67': [2560, 1536], - '1.75': [2688, 1536], '2.00': [2816, 1408], '2.09': [2944, 1408], '2.40': [3072, 1280], - '2.50': [3200, 1280], '2.89': [3328, 1152], '3.00': [3456, 1152], '3.11': [3584, 1152], - '3.62': [3712, 1024], '3.75': [3840, 1024], '3.88': [3968, 1024], '4.00': [4096, 1024] - } -} -# These should be the same -sana_res.update({ - "SanaMS_600M_P1_D28": sana_res["1024px"], - "SanaMS_1600M_P1_D20": sana_res["1024px"], -}) diff --git a/Sana/loader.py b/Sana/loader.py index 806ca64..413ffb0 100644 --- a/Sana/loader.py +++ b/Sana/loader.py @@ -1,100 +1,146 @@ +import math +import logging + +import comfy.utils +import comfy.model_base +import comfy.model_detection + import comfy.supported_models_base +import comfy.supported_models import comfy.latent_formats -import comfy.model_patcher -import comfy.model_base -import comfy.utils -import comfy.conds -import torch -import math -from comfy import model_management -from comfy.latent_formats import LatentFormat -from .diffusers_convert import convert_state_dict +from .models.sana import Sana +from .models.sana_multi_scale import SanaMS +from .diffusers_convert import convert_state_dict +from ..utils.loader import load_state_dict_from_config -class SanaLatent(LatentFormat): +class SanaLatent(comfy.latent_formats.LatentFormat): latent_channels = 32 def __init__(self): self.scale_factor = 0.41407 - - -class EXM_Sana(comfy.supported_models_base.BASE): - unet_config = {} - unet_extra_config = {} - latent_format = SanaLatent - - def __init__(self, model_conf): - self.model_target = model_conf.get("target") - 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.FLOW - - -class EXM_Sana_Model(comfy.model_base.BaseModel): - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) - - def extra_conds(self, **kwargs): - out = super().extra_conds(**kwargs) - - cn_hint = kwargs.get("cn_hint", None) - if cn_hint is not None: - out["cn_hint"] = comfy.conds.CONDRegular(cn_hint) - - return out - - -def load_sana(model_path, model_conf): - state_dict = comfy.utils.load_torch_file(model_path) - state_dict = state_dict.get("model", state_dict) - - # prefix - for prefix in ["model.diffusion_model.",]: - if any(True for x in state_dict if x.startswith(prefix)): - state_dict = {k[len(prefix):]:v for k,v in state_dict.items()} - - # diffusers - if "adaln_single.linear.weight" in state_dict: - state_dict = convert_state_dict(state_dict) # Diffusers - - parameters = comfy.utils.calculate_parameters(state_dict) - unet_dtype = comfy.model_management.unet_dtype() - 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"Sana: falling back to {manual_cast_dtype}") - unet_dtype = manual_cast_dtype - - model_conf = EXM_Sana(model_conf) # convert to object - model = EXM_Sana_Model( # same as comfy.model_base.BaseModel - model_conf, - model_type=comfy.model_base.ModelType.FLOW, - device=model_management.get_torch_device() - ) - - if model_conf.model_target == "SanaMS": - from .models.sana_multi_scale import SanaMS - model.diffusion_model = SanaMS(**model_conf.unet_config) - else: - raise NotImplementedError(f"Unknown model target '{model_conf.model_target}'") - - m, u = model.diffusion_model.load_state_dict(state_dict, strict=False) - if len(m) > 0: print("Missing UNET keys", m) - if len(u) > 0: print("Leftover UNET keys", u) - 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 + self.latent_rgb_factors = latent_rgb_factors.copy() + self.latent_rgb_factors_bias = latent_rgb_factors_bias.copy() + +class SanaConfig(comfy.supported_models_base.BASE): + unet_class = SanaMS + unet_config = {} + unet_extra_config = {} + + latent_format = SanaLatent + sampling_settings = { + "shift": 3.0, + } + + def model_type(self, state_dict, prefix=""): + return comfy.model_base.ModelType.FLOW + + def get_model(self, state_dict, prefix="", device=None): + return SanaModel(model_config=self, unet_model=self.unet_class, device=device) + +class SanaModel(comfy.model_base.BaseModel): + def __init__(self, *args, model_type=comfy.model_base.ModelType.FLOW, unet_model=SanaMS, **kwargs): + super().__init__(*args, model_type=model_type, unet_model=unet_model, **kwargs) + +def load_sana_state_dict(sd, model_options={}): + # prefix / format + sd = sd.get("model", sd) # ref ckpt + diffusion_model_prefix = comfy.model_detection.unet_prefix_from_state_dict(sd) + temp_sd = comfy.utils.state_dict_prefix_replace(sd, {diffusion_model_prefix: ""}, filter_keys=True) + if len(temp_sd) > 0: + sd = temp_sd + + # diffusers convert + if "adaln_single.linear.weight" in sd: + sd = convert_state_dict(sd) + + # model config + model_config = model_config_from_unet(sd) + return load_state_dict_from_config(model_config, sd, model_options) + +def model_config_from_unet(sd): + """ + Guess config based on (converted) state dict. + """ + # shared settings that match between all models + # TODO: some can (should) be enumerated + config = { + "in_channels": 32, + "linear_head_dim": 32, + "model_max_length": 300, + "y_norm": True, + "attn_type": "linear", + "ffn_type": "glumbconv", + "mlp_ratio": 2.5, + "mlp_acts": ["silu", "silu", None], + "use_pe": False, + "pred_sigma": False, + "learn_sigma": False, + "fp32_attention": True, + "patch_size": 1, + } + config["depth"] = sum([key.endswith(".point_conv.conv.weight") for key in sd.keys()]) or 28 + + if "pos_embed" in sd: + config["input_size"] = int(math.sqrt(sd["pos_embed"].shape[1])) * config["patch_size"] + else: + # TODO: this isn't optimal though most models don't use it + config["use_pe"] = False + + if "x_embedder.proj.bias" in sd: + config["hidden_size"] = sd["x_embedder.proj.bias"].shape[0] + + if config["hidden_size"] == 1152: + config["num_heads"] = 16 + elif config["hidden_size"] == 2240: + config["num_heads"] = 20 + else: + raise RuntimeError(f"Unknown model config.") + + model_config = SanaConfig(config) + logging.debug(f"Sana config:\n{config}") + return model_config + +# for fast latent preview +latent_rgb_factors = [ + [-2.0022e-03, -6.0736e-03, -1.7096e-03], + [ 1.4221e-03, 3.6703e-03, 4.1083e-03], + [ 1.0081e-02, 2.6456e-04, -1.4333e-02], + [-2.4253e-03, 3.0967e-03, -1.0301e-03], + [ 2.2158e-03, 7.7274e-03, -1.3151e-02], + [ 1.1235e-02, 5.7630e-03, 3.6146e-03], + [-7.2899e-02, 1.1062e-02, 3.6103e-02], + [ 3.2346e-02, 2.8678e-02, 2.5014e-02], + [ 1.6469e-03, -1.1364e-03, 2.8366e-03], + [-3.5597e-02, -2.3447e-02, -3.1172e-03], + [-1.9985e-04, -2.0647e-03, -1.2702e-02], + [ 2.1318e-04, 1.2196e-03, -8.3461e-04], + [ 1.3766e-02, 2.7559e-03, 7.3567e-03], + [ 1.3027e-02, 2.6365e-03, 3.0405e-03], + [ 1.5335e-02, 9.4682e-03, 6.7312e-03], + [ 5.1827e-03, -9.4865e-03, 8.5080e-03], + [ 1.4365e-02, -3.2867e-03, 9.5108e-03], + [-4.1216e-03, -1.9177e-03, -3.3726e-03], + [-2.4757e-03, 5.1739e-04, 2.0280e-03], + [-3.5950e-03, 1.0720e-03, 5.3043e-03], + [-5.1758e-03, 8.1040e-03, -3.7564e-02], + [-3.8555e-03, -1.5529e-03, -3.5799e-03], + [-6.6175e-03, -6.8484e-03, -9.9609e-03], + [-2.1656e-03, 5.5770e-05, 1.4936e-03], + [-9.2857e-02, -1.1379e-01, -1.0919e-01], + [ 7.7044e-04, 5.5594e-03, 3.4755e-02], + [ 1.2714e-02, 2.9729e-02, 3.1989e-03], + [-1.1805e-03, 9.0548e-03, -4.1063e-04], + [ 8.3309e-04, 4.9694e-03, 2.3087e-03], + [ 7.8456e-03, 3.9750e-03, 3.5655e-03], + [-1.7552e-03, 4.9306e-03, 1.4210e-02], + [-1.4790e-03, 2.8837e-03, -4.5687e-03] +] +latent_rgb_factors_bias = [0.4358, 0.3814, 0.3388] + +# 512/1024/2K match, TODO: 4K is new, add on release +from ..PixArt.loader import resolutions as pixart_res +resolutions = { + "Sana 512": pixart_res["PixArt 512"], + "Sana 1024": pixart_res["PixArt 1024"], + "Sana 2K": pixart_res["PixArt 2K"], +} diff --git a/Sana/models/__init__.py b/Sana/models/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/Sana/models/act.py b/Sana/models/act.py index 9df6a7a..7993065 100644 --- a/Sana/models/act.py +++ b/Sana/models/act.py @@ -37,7 +37,7 @@ } -def build_act(name: str or None, **kwargs) -> nn.Module or None: +def build_act(name, **kwargs): if name in REGISTERED_ACT_DICT: act_cls, default_args = copy.deepcopy(REGISTERED_ACT_DICT[name]) for key in default_args: @@ -50,7 +50,7 @@ def build_act(name: str or None, **kwargs) -> nn.Module or None: raise ValueError(f"do not support: {name}") -def get_act_name(act: nn.Module or None) -> str or None: +def get_act_name(act): if act is None: return None module2name = {} diff --git a/Sana/models/basic_modules.py b/Sana/models/basic_modules.py index ece579a..6b279a4 100644 --- a/Sana/models/basic_modules.py +++ b/Sana/models/basic_modules.py @@ -17,13 +17,31 @@ # This file is modified from https://github.com/PixArt-alpha/PixArt-sigma import torch import torch.nn as nn -from timm.models.vision_transformer import Mlp +#from timm.models.vision_transformer import Mlp from .act import build_act, get_act_name from .norms import build_norm, get_norm_name from .utils import get_same_padding, val2tuple +class Mlp(nn.Module): + def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, bias=True, drop=None, dtype=None, device=None, operations=None) -> None: + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + + self.fc1 = operations.Linear(in_features, hidden_features, bias=bias, dtype=dtype, device=device) + self.act = act_layer() + self.fc2 = operations.Linear(hidden_features, out_features, bias=bias, dtype=dtype, device=device) + + self.drop1 = nn.Identity() + self.drop2 = nn.Identity() + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = self.act(self.fc1(x)) + return self.fc2(x) + + class ConvLayer(nn.Module): def __init__( self, @@ -33,11 +51,14 @@ def __init__( stride=1, dilation=1, groups=1, - padding: int or None = None, + padding=None, use_bias=False, dropout=0.0, norm="bn2d", act="relu", + dtype=None, + device=None, + operations=None, ): super().__init__() if padding is None: @@ -54,7 +75,7 @@ def __init__( self.use_bias = use_bias self.dropout = nn.Dropout2d(dropout, inplace=False) if dropout > 0 else None - self.conv = nn.Conv2d( + self.conv = operations.Conv2d( in_dim, out_dim, kernel_size=(kernel_size, kernel_size), @@ -63,6 +84,8 @@ def __init__( dilation=(dilation, dilation), groups=groups, bias=use_bias, + dtype=dtype, + device=device, ) self.norm = build_norm(norm, num_features=out_dim) self.act = build_act(act) @@ -86,11 +109,14 @@ def __init__( out_feature=None, kernel_size=3, stride=1, - padding: int or None = None, + padding=None, use_bias=False, norm=(None, None, None), act=("silu", "silu", None), dilation=1, + dtype=None, + device=None, + operations=None, ): out_feature = out_feature or in_features super().__init__() @@ -106,6 +132,9 @@ def __init__( use_bias=use_bias[0], norm=norm[0], act=act[0], + dtype=dtype, + device=device, + operations=operations, ) self.depth_conv = ConvLayer( hidden_features * 2, @@ -118,6 +147,9 @@ def __init__( norm=norm[1], act=None, dilation=dilation, + dtype=dtype, + device=device, + operations=operations, ) self.point_conv = ConvLayer( hidden_features, @@ -126,6 +158,9 @@ def __init__( use_bias=use_bias[2], norm=norm[2], act=act[2], + dtype=dtype, + device=device, + operations=operations, ) # from IPython import embed; embed(header='debug dilate conv') @@ -189,10 +224,13 @@ def __init__( stride=1, mid_dim=None, expand=6, - padding: int or None = None, + padding=None, use_bias=False, norm=(None, None, "ln2d"), act=("silu", "silu", None), + dtype=None, + device=None, + operations=None, ): super().__init__() use_bias = val2tuple(use_bias, 3) @@ -208,6 +246,9 @@ def __init__( use_bias=use_bias[0], norm=norm[0], act=None, + dtype=dtype, + device=device, + operations=operations, ) self.glu_act = build_act(act[0], inplace=False) self.depth_conv = ConvLayer( @@ -220,6 +261,9 @@ def __init__( use_bias=use_bias[1], norm=norm[1], act=act[1], + dtype=dtype, + device=device, + operations=operations, ) self.point_conv = ConvLayer( mid_dim, @@ -228,6 +272,9 @@ def __init__( use_bias=use_bias[2], norm=norm[2], act=act[2], + dtype=dtype, + device=device, + operations=operations, ) def forward(self, x: torch.Tensor, HW=None) -> torch.Tensor: @@ -283,6 +330,9 @@ def __init__( stride=1, dilation=1, padding=None, + dtype=None, + device=None, + operations=None, ): super().__init__( in_features=in_features, @@ -291,6 +341,9 @@ def __init__( act_layer=act_layer, bias=bias, drop=drop, + dtype=dtype, + device=device, + operations=operations, ) hidden_features = hidden_features or in_features self.hidden_features = hidden_features @@ -298,7 +351,7 @@ def __init__( padding = get_same_padding(kernel_size) padding *= dilation - self.conv = nn.Conv2d( + self.conv = operations.Conv2d( hidden_features, hidden_features, kernel_size=(kernel_size, kernel_size), @@ -307,6 +360,8 @@ def __init__( dilation=(dilation, dilation), groups=hidden_features, bias=bias, + dtype=dtype, + device=device, ) def forward(self, x, HW=None): @@ -324,38 +379,3 @@ def forward(self, x, HW=None): x = self.fc2(x) x = self.drop2(x) return x - - -class Mlp(Mlp): - """MLP as used in Vision Transformer, MLP-Mixer and related networks""" - - def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, bias=True, drop=0.0): - super().__init__( - in_features=in_features, - hidden_features=hidden_features, - out_features=out_features, - act_layer=act_layer, - bias=bias, - drop=drop, - ) - - def forward(self, x, HW=None): - x = self.fc1(x) - x = self.act(x) - x = self.drop1(x) - x = self.fc2(x) - x = self.drop2(x) - return x - - -if __name__ == "__main__": - model = GLUMBConv( - 1152, - 1152 * 4, - 1152, - use_bias=(True, True, False), - norm=(None, None, None), - act=("silu", "silu", None), - ).cuda() - input = torch.randn(4, 256, 1152).cuda() - output = model(input) diff --git a/Sana/models/norms.py b/Sana/models/norms.py index 4731d69..5174fdc 100644 --- a/Sana/models/norms.py +++ b/Sana/models/norms.py @@ -48,7 +48,7 @@ def extra_repr(self) -> str: } -def build_norm(name="bn2d", num_features=None, affine=True, **kwargs) -> nn.Module or None: +def build_norm(name="bn2d", num_features=None, affine=True, **kwargs): if name in ["ln", "ln2d"]: kwargs["normalized_shape"] = num_features kwargs["elementwise_affine"] = affine @@ -67,7 +67,7 @@ def build_norm(name="bn2d", num_features=None, affine=True, **kwargs) -> nn.Modu raise ValueError("do not support: %s" % name) -def get_norm_name(norm: nn.Module or None) -> str or None: +def get_norm_name(norm): if norm is None: return None module2name = {} @@ -171,7 +171,7 @@ def remove_bn(model: nn.Module) -> None: m.forward = lambda x: x -def set_norm_eps(model: nn.Module, eps: float or None = None, momentum: float or None = None) -> None: +def set_norm_eps(model, eps=None, momentum=None): for m in model.modules(): if isinstance(m, (nn.GroupNorm, nn.LayerNorm, _BatchNorm)): if eps is not None: diff --git a/Sana/models/sana.py b/Sana/models/sana.py index 81c2211..113adfb 100644 --- a/Sana/models/sana.py +++ b/Sana/models/sana.py @@ -20,7 +20,6 @@ import numpy as np import torch import torch.nn as nn -from timm.models.layers import DropPath from .basic_modules import DWMlp, GLUMBConv, MBConvPreGLU, Mlp from .sana_blocks import ( @@ -35,7 +34,7 @@ t2i_modulate, ) from .norms import RMSNorm -from .utils import auto_grad_checkpoint, to_2tuple +from .utils import to_2tuple class SanaBlock(nn.Module): @@ -55,10 +54,13 @@ def __init__( ffn_type="mlp", mlp_acts=("silu", "silu", None), linear_head_dim=32, + dtype=None, + device=None, + operations=None, **block_kwargs, ): super().__init__() - self.norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + self.norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) if attn_type == "flash": # flash self attention self.attn = FlashAttention( @@ -66,20 +68,28 @@ def __init__( num_heads=num_heads, qkv_bias=True, qk_norm=qk_norm, + dtype=dtype, + device=device, + operations=operations, **block_kwargs, ) elif attn_type == "linear": # linear self attention # TODO: Here the num_heads set to 36 for tmp used self_num_heads = hidden_size // linear_head_dim - self.attn = LiteLA(hidden_size, hidden_size, heads=self_num_heads, eps=1e-8, qk_norm=qk_norm) + self.attn = LiteLA( + hidden_size, hidden_size, heads=self_num_heads, eps=1e-8, qk_norm=qk_norm, + dtype=dtype, device=device, operations=operations, + ) elif attn_type == "vanilla": # vanilla self attention - self.attn = Attention(hidden_size, num_heads=num_heads, qkv_bias=True) + self.attn = Attention( + hidden_size, num_heads=num_heads, qkv_bias=True, dtype=dtype, device=device, operations=operations, + ) else: raise ValueError(f"{attn_type} type is not defined.") - self.cross_attn = MultiHeadCrossAttention(hidden_size, num_heads, **block_kwargs) + self.cross_attn = MultiHeadCrossAttention(hidden_size, num_heads, dtype=dtype, device=device, operations=operations, **block_kwargs) self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) # to be compatible with lower version pytorch if ffn_type == "dwmlp": @@ -94,6 +104,9 @@ def __init__( use_bias=(True, True, False), norm=(None, None, None), act=mlp_acts, + dtype=dtype, + device=device, + operations=operations, ) elif ffn_type == "glumbconv_dilate": self.mlp = GLUMBConv( @@ -103,6 +116,9 @@ def __init__( norm=(None, None, None), act=mlp_acts, dilation=2, + dtype=dtype, + device=device, + operations=operations, ) elif ffn_type == "mbconvpreglu": self.mlp = MBConvPreGLU( @@ -112,15 +128,19 @@ def __init__( use_bias=(True, True, False), norm=None, act=("silu", "silu", None), + dtype=dtype, + device=device, + operations=operations, ) elif ffn_type == "mlp": approx_gelu = lambda: nn.GELU(approximate="tanh") self.mlp = Mlp( - in_features=hidden_size, hidden_features=int(hidden_size * mlp_ratio), act_layer=approx_gelu, drop=0 + in_features=hidden_size, hidden_features=int(hidden_size * mlp_ratio), act_layer=approx_gelu, drop=0, + # dtype=dtype, device=device, operations=operations, ) else: raise ValueError(f"{ffn_type} type is not defined.") - self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() + self.drop_path = nn.Identity() #DropPath(drop_path) if drop_path > 0.0 else nn.Identity() self.scale_shift_table = nn.Parameter(torch.randn(6, hidden_size) / hidden_size**0.5) def forward(self, x, y, t, mask=None, **kwargs): @@ -146,7 +166,7 @@ class Sana(nn.Module): def __init__( self, - input_size=32, + input_size=None, patch_size=1, in_channels=32, hidden_size=1152, @@ -170,9 +190,13 @@ def __init__( patch_embed_kernel=None, mlp_acts=("silu", "silu", None), linear_head_dim=32, + dtype=None, + device=None, + operations=None, **kwargs, ): super().__init__() + self.dtype = dtype self.pred_sigma = pred_sigma self.in_channels = in_channels self.out_channels = in_channels * 2 if pred_sigma else in_channels @@ -187,22 +211,36 @@ def __init__( kernel_size = patch_embed_kernel or patch_size self.x_embedder = PatchEmbed( - input_size, patch_size, in_channels, hidden_size, kernel_size=kernel_size, bias=True + input_size, patch_size, in_channels, hidden_size, kernel_size=kernel_size, bias=True, + dtype=dtype, device=device, operations=operations ) - self.t_embedder = TimestepEmbedder(hidden_size) + self.t_embedder = TimestepEmbedder(hidden_size, dtype=dtype, device=device, operations=operations) + + if input_size is not None: + self.base_size = input_size // self.patch_size + else: + self.base_size = None + num_patches = self.x_embedder.num_patches - self.base_size = input_size // self.patch_size - # Will use fixed sin-cos embedding: - self.register_buffer("pos_embed", torch.zeros(1, num_patches, hidden_size)) + if self.use_pe and num_patches is not None: + #Will use fixed sin-cos embedding: + self.register_buffer("pos_embed", torch.zeros(1, num_patches, hidden_size)) + else: + self.pos_embed = None approx_gelu = lambda: nn.GELU(approximate="tanh") - self.t_block = nn.Sequential(nn.SiLU(), nn.Linear(hidden_size, 6 * hidden_size, bias=True)) + self.t_block = nn.Sequential( + nn.SiLU(), operations.Linear(hidden_size, 6 * hidden_size, bias=True, dtype=dtype, device=device) + ) self.y_embedder = CaptionEmbedder( in_channels=caption_channels, hidden_size=hidden_size, uncond_prob=class_dropout_prob, act_layer=approx_gelu, token_num=model_max_length, + dtype=dtype, + device=device, + operations=operations ) if self.y_norm: self.attention_y_norm = RMSNorm(hidden_size, scale_factor=y_norm_scale_factor, eps=norm_eps) @@ -220,29 +258,32 @@ def __init__( ffn_type=ffn_type, mlp_acts=mlp_acts, linear_head_dim=linear_head_dim, + dtype=dtype, + device=device, + operations=operations ) for i in range(depth) ] ) - self.final_layer = T2IFinalLayer(hidden_size, patch_size, self.out_channels) + self.final_layer = T2IFinalLayer( + hidden_size, patch_size, self.out_channels, dtype=dtype, device=device, operations=operations + ) - def forward(self, x, timestep, y, mask=None, data_info=None, **kwargs): + def forward(self, x, timestep, context, mask=None, data_info=None, **kwargs): """ Forward pass of Sana. x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images) t: (N,) tensor of diffusion timesteps y: (N, 1, 120, C) tensor of class labels """ - x = x.to(self.dtype) - timestep = timestep.to(self.dtype) - y = y.to(self.dtype) - pos_embed = self.pos_embed.to(self.dtype) + y = context # remap comfy cond name self.h, self.w = x.shape[-2] // self.patch_size, x.shape[-1] // self.patch_size if self.use_pe: + pos_embed = self.pos_embed.to(x.dtype) x = self.x_embedder(x) + pos_embed # (N, T, D), where T = H * W / patch_size ** 2 else: x = self.x_embedder(x) - t = self.t_embedder(timestep.to(x.dtype)) # (N, D) + t = self.t_embedder(timestep, x.dtype) # (N, D) t0 = self.t_block(t) y = self.y_embedder(y, self.training) # (N, 1, L, D) if self.y_norm: @@ -257,7 +298,7 @@ def forward(self, x, timestep, y, mask=None, data_info=None, **kwargs): y_lens = [y.shape[2]] * y.shape[0] y = y.squeeze(1).view(1, -1, x.shape[-1]) for block in self.blocks: - x = auto_grad_checkpoint(block, x, y, t0, y_lens) # (N, T, D) #support grad checkpoint + x = block(x, y, t0, y_lens) # (N, T, D) x = self.final_layer(x, t) # (N, T, patch_size ** 2 * out_channels) x = self.unpatchify(x) # (N, out_channels, H, W) return x @@ -292,39 +333,6 @@ def unpatchify(self, x): imgs = x.reshape(shape=(x.shape[0], c, h * p, h * p)) return imgs - def initialize_weights(self): - # Initialize transformer layers: - def _basic_init(module): - if isinstance(module, nn.Linear): - torch.nn.init.xavier_uniform_(module.weight) - if module.bias is not None: - nn.init.constant_(module.bias, 0) - - self.apply(_basic_init) - - if self.use_pe: - # Initialize (and freeze) pos_embed by sin-cos embedding: - pos_embed = get_2d_sincos_pos_embed( - self.pos_embed.shape[-1], - int(self.x_embedder.num_patches**0.5), - pe_interpolation=self.pe_interpolation, - base_size=self.base_size, - ) - self.pos_embed.data.copy_(torch.from_numpy(pos_embed).float().unsqueeze(0)) - - # Initialize patch_embed like nn.Linear (instead of nn.Conv2d): - w = self.x_embedder.proj.weight.data - nn.init.xavier_uniform_(w.view([w.shape[0], -1])) - - # Initialize timestep embedding MLP: - nn.init.normal_(self.t_embedder.mlp[0].weight, std=0.02) - nn.init.normal_(self.t_embedder.mlp[2].weight, std=0.02) - nn.init.normal_(self.t_block[1].weight, std=0.02) - - # Initialize caption embedding MLP: - nn.init.normal_(self.y_embedder.y_proj.fc1.weight, std=0.02) - nn.init.normal_(self.y_embedder.y_proj.fc2.weight, std=0.02) - def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False, extra_tokens=0, pe_interpolation=1.0, base_size=16): """ diff --git a/Sana/models/sana_blocks.py b/Sana/models/sana_blocks.py index cdd37e4..894b23c 100644 --- a/Sana/models/sana_blocks.py +++ b/Sana/models/sana_blocks.py @@ -22,12 +22,13 @@ import torch.nn as nn import torch.nn.functional as F from einops import rearrange -from timm.models.vision_transformer import Attention as Attention_ -from timm.models.vision_transformer import Mlp from transformers import AutoModelForCausalLM from .norms import RMSNorm from .utils import get_same_padding, to_2tuple +from .basic_modules import Mlp + +import comfy.ldm.common_dit sdpa_32b = None Q_4GB_LIMIT = 32000000 @@ -38,11 +39,14 @@ from comfy import model_management if model_management.xformers_enabled(): - import xformers import xformers.ops + if int((xformers.__version__).split(".")[2]) >= 28: + block_diagonal_mask_from_seqlens = xformers.ops.fmha.attn_bias.BlockDiagonalMask.from_seqlens + else: + block_diagonal_mask_from_seqlens = xformers.ops.fmha.BlockDiagonalMask.from_seqlens else: if model_management.xpu_available: - import intel_extension_for_pytorch as ipex + import intel_extension_for_pytorch as ipex # type: ignore import os if not torch.xpu.has_fp64_dtype() and not os.environ.get('IPEX_FORCE_ATTENTION_SLICE', None): from ...utils.IPEX.attention import scaled_dot_product_attention_32_bit @@ -61,7 +65,7 @@ def t2i_modulate(x, shift, scale): class MultiHeadCrossAttention(nn.Module): - def __init__(self, d_model, num_heads, attn_drop=0.0, proj_drop=0.0, qk_norm=False, **block_kwargs): + def __init__(self, d_model, num_heads, attn_drop=0.0, proj_drop=0.0, qk_norm=False, dtype=None, device=None, operations=None, **block_kwargs): super().__init__() assert d_model % num_heads == 0, "d_model must be divisible by num_heads" @@ -69,10 +73,10 @@ def __init__(self, d_model, num_heads, attn_drop=0.0, proj_drop=0.0, qk_norm=Fal self.num_heads = num_heads self.head_dim = d_model // num_heads - self.q_linear = nn.Linear(d_model, d_model) - self.kv_linear = nn.Linear(d_model, d_model * 2) + self.q_linear = operations.Linear(d_model, d_model, dtype=dtype, device=device) + self.kv_linear = operations.Linear(d_model, d_model * 2, dtype=dtype, device=device) self.attn_drop = nn.Dropout(attn_drop) - self.proj = nn.Linear(d_model, d_model) + self.proj = operations.Linear(d_model, d_model, dtype=dtype, device=device) self.proj_drop = nn.Dropout(proj_drop) if qk_norm: # not used for now @@ -93,7 +97,7 @@ def forward(self, x, cond, mask=None): if model_management.xformers_enabled(): attn_bias = None if mask is not None: - attn_bias = xformers.ops.fmha.BlockDiagonalMask.from_seqlens([N] * B, mask) + attn_bias = block_diagonal_mask_from_seqlens([N] * B, mask) x = xformers.ops.memory_efficient_attention( q, k, v, p=self.attn_drop.p, @@ -135,7 +139,7 @@ def forward(self, x, cond, mask=None): return x -class LiteLA(Attention_): +class LiteLA(torch.nn.Module): # from attention r"""Lightweight linear attention""" PAD_VAL = 1 @@ -151,9 +155,20 @@ def __init__( use_bias=False, qk_norm=False, norm_eps=1e-5, + dtype=None, + device=None, + operations=None, ): + super().__init__() heads = heads or int(out_dim // dim * heads_ratio) - super().__init__(in_dim, num_heads=heads, qkv_bias=use_bias) + + # assert dim % heads == 0, 'dim should be divisible by num_heads' + self.num_heads = heads + self.head_dim = in_dim // heads + self.scale = self.head_dim ** -0.5 + + self.qkv = operations.Linear(in_dim, in_dim * 3, bias=use_bias, dtype=dtype, device=device) + self.proj = operations.Linear(in_dim, in_dim, dtype=dtype, device=device) self.in_dim = in_dim self.out_dim = out_dim @@ -346,7 +361,7 @@ def __call__(self, x: torch.Tensor, mask=None, HW=None, block_id=None) -> torch. return out -class FlashAttention(Attention_): +class FlashAttention(torch.nn.Module): # from attention """Multi-head Flash Attention block with qk norm.""" def __init__( @@ -395,7 +410,7 @@ def forward(self, x, mask=None, HW=None, block_id=None): attn_bias = torch.zeros([B * self.num_heads, q.shape[1], k.shape[1]], dtype=q.dtype, device=q.device) attn_bias.masked_fill_(mask.squeeze(1).repeat(self.num_heads, 1, 1) == 0, float("-inf")) - if _xformers_available: + if model_management.xformers_enabled(): x = xformers.ops.memory_efficient_attention(q, k, v, p=self.attn_drop.p, attn_bias=attn_bias) else: q, k, v = q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2) @@ -418,7 +433,31 @@ def forward(self, x, mask=None, HW=None, block_id=None): ################################################################################# # AMP attention with fp32 softmax to fix loss NaN problem during training # ################################################################################# -class Attention(Attention_): +class Attention(torch.nn.Module): + def __init__( + self, + dim, + num_heads=8, + qkv_bias=True, + sampling='conv', + sr_ratio=1, + qk_norm=False, + dtype=None, + device=None, + operations=None, + **block_kwargs, + ): + super().__init__() + assert dim % num_heads == 0, 'dim should be divisible by num_heads' + self.num_heads = num_heads + self.head_dim = dim // num_heads + self.scale = self.head_dim ** -0.5 + + self.qkv = operations.Linear(dim, dim * 3, bias=qkv_bias, dtype=dtype, device=device) + self.q_norm = nn.Identity() + self.k_norm = nn.Identity() + self.proj = operations.Linear(dim, dim, dtype=dtype, device=device) + def forward(self, x, HW=None): B, N, C = x.shape qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) @@ -432,11 +471,11 @@ def forward(self, x, HW=None): attn = (q @ k.transpose(-2, -1)) * self.scale attn = attn.softmax(dim=-1) - attn = self.attn_drop(attn) + #attn = self.attn_drop(attn) x = (attn @ v).transpose(1, 2).reshape(B, N, C) x = self.proj(x) - x = self.proj_drop(x) + #x = self.proj_drop(x) return x @@ -445,11 +484,14 @@ class FinalLayer(nn.Module): The final layer of Sana. """ - def __init__(self, hidden_size, patch_size, out_channels): + def __init__(self, hidden_size, patch_size, out_channels, dtype=None, device=None, operations=None): super().__init__() - self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) - self.linear = nn.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True) - self.adaLN_modulation = nn.Sequential(nn.SiLU(), nn.Linear(hidden_size, 2 * hidden_size, bias=True)) + self.norm_final = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + self.linear = operations.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True, dtype=dtype, device=device) + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), + operations.Linear(hidden_size, 2 * hidden_size, bias=True, dtype=dtype, device=device) + ) def forward(self, x, c): shift, scale = self.adaLN_modulation(c).chunk(2, dim=1) @@ -463,17 +505,18 @@ class T2IFinalLayer(nn.Module): The final layer of Sana. """ - def __init__(self, hidden_size, patch_size, out_channels): + def __init__(self, hidden_size, patch_size, out_channels, dtype=None, device=None, operations=None): super().__init__() - self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) - self.linear = nn.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True) - self.scale_shift_table = nn.Parameter(torch.randn(2, hidden_size) / hidden_size**0.5) + self.norm_final = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + self.linear = operations.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True, dtype=dtype, device=device) + self.scale_shift_table = nn.Parameter(torch.randn(2, hidden_size) / hidden_size ** 0.5) self.out_channels = out_channels def forward(self, x, t): + dtype = x.dtype shift, scale = (self.scale_shift_table[None] + t[:, None]).chunk(2, dim=1) x = t2i_modulate(self.norm_final(x), shift, scale) - x = self.linear(x) + x = self.linear(x.to(dtype)) return x @@ -482,12 +525,14 @@ class MaskFinalLayer(nn.Module): The final layer of Sana. """ - def __init__(self, final_hidden_size, c_emb_size, patch_size, out_channels): + def __init__(self, final_hidden_size, c_emb_size, patch_size, out_channels, dtype=None, device=None, operations=None): 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(nn.SiLU(), nn.Linear(c_emb_size, 2 * final_hidden_size, bias=True)) - + self.norm_final = operations.LayerNorm(final_hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + self.linear = operations.Linear(final_hidden_size, patch_size * patch_size * out_channels, bias=True, dtype=dtype, device=device) + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), + operations.Linear(c_emb_size, 2 * final_hidden_size, bias=True, dtype=dtype, device=device) + ) def forward(self, x, t): shift, scale = self.adaLN_modulation(t).chunk(2, dim=1) x = modulate(self.norm_final(x), shift, scale) @@ -500,12 +545,14 @@ class DecoderLayer(nn.Module): The final layer of Sana. """ - def __init__(self, hidden_size, decoder_hidden_size): + def __init__(self, hidden_size, decoder_hidden_size, dtype=None, device=None, operations=None): super().__init__() - self.norm_decoder = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) - self.linear = nn.Linear(hidden_size, decoder_hidden_size, bias=True) - self.adaLN_modulation = nn.Sequential(nn.SiLU(), nn.Linear(hidden_size, 2 * hidden_size, bias=True)) - + self.norm_decoder = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + self.linear = operations.Linear(hidden_size, decoder_hidden_size, bias=True, dtype=dtype, device=device) + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), + operations.Linear(hidden_size, 2 * hidden_size, bias=True, dtype=dtype, device=device) + ) def forward(self, x, t): shift, scale = self.adaLN_modulation(t).chunk(2, dim=1) x = modulate(self.norm_decoder(x), shift, scale) @@ -521,12 +568,12 @@ class TimestepEmbedder(nn.Module): Embeds scalar timesteps into vector representations. """ - def __init__(self, hidden_size, frequency_embedding_size=256): + def __init__(self, hidden_size, frequency_embedding_size=256, dtype=None, device=None, operations=None): super().__init__() self.mlp = nn.Sequential( - nn.Linear(frequency_embedding_size, hidden_size, bias=True), + operations.Linear(frequency_embedding_size, hidden_size, bias=True, dtype=dtype, device=device), nn.SiLU(), - nn.Linear(hidden_size, hidden_size, bias=True), + operations.Linear(hidden_size, hidden_size, bias=True, dtype=dtype, device=device), ) self.frequency_embedding_size = frequency_embedding_size @@ -551,9 +598,9 @@ def timestep_embedding(t, dim, max_period=10000): embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1) return embedding - def forward(self, t): - t_freq = self.timestep_embedding(t, self.frequency_embedding_size).to(self.dtype) - t_emb = self.mlp(t_freq) + def forward(self, t, dtype): + t_freq = self.timestep_embedding(t, self.frequency_embedding_size) + t_emb = self.mlp(t_freq.to(dtype)) return t_emb @property @@ -644,10 +691,14 @@ def __init__( uncond_prob, act_layer=nn.GELU(approximate="tanh"), token_num=120, + dtype=None, + device=None, + operations=None, ): super().__init__() self.y_proj = Mlp( - in_features=in_channels, hidden_features=hidden_size, out_features=hidden_size, act_layer=act_layer, drop=0 + in_features=in_channels, hidden_features=hidden_size, out_features=hidden_size, act_layer=act_layer, drop=0, + dtype=dtype, device=device, operations=operations ) self.register_buffer("y_embedding", nn.Parameter(torch.randn(token_num, in_channels) / in_channels**0.5)) self.uncond_prob = uncond_prob @@ -734,30 +785,46 @@ def __init__( norm_layer=None, flatten=True, bias=True, + dynamic_img_pad=True, + padding_mode='circular', + dtype=None, + device=None, + operations=None, ): super().__init__() kernel_size = kernel_size or patch_size img_size = to_2tuple(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] + + if img_size is not None and False: + self.img_size = img_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] + else: + self.img_size = None + self.grid_size = None + self.num_patches = None + self.flatten = flatten + self.dynamic_img_pad = dynamic_img_pad + self.padding_mode = padding_mode if not padding and kernel_size % 2 > 0: padding = get_same_padding(kernel_size) - self.proj = nn.Conv2d( - in_chans, embed_dim, kernel_size=kernel_size, stride=patch_size, padding=padding, bias=bias + self.proj = operations.Conv2d( + in_chans, embed_dim, kernel_size=kernel_size, stride=patch_size, padding=padding, bias=bias, dtype=dtype, device=device ) self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity() 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]}).") + # 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]}).") + if self.dynamic_img_pad: + x = comfy.ldm.common_dit.pad_to_patch_size(x, self.patch_size, padding_mode=self.padding_mode) x = self.proj(x) if self.flatten: - x = x.flatten(2).transpose(1, 2) # BCHW -> BNC + x = x.flatten(2).transpose(1, 2) # BCHW -> BNC x = self.norm(x) return x @@ -775,20 +842,29 @@ def __init__( norm_layer=None, flatten=True, bias=True, + dynamic_img_pad=True, + padding_mode='circular', + dtype=None, + device=None, + operations=None, ): super().__init__() kernel_size = kernel_size or patch_size patch_size = to_2tuple(patch_size) self.patch_size = patch_size self.flatten = flatten + self.dynamic_img_pad = dynamic_img_pad + self.padding_mode = padding_mode if not padding and kernel_size % 2 > 0: padding = get_same_padding(kernel_size) - self.proj = nn.Conv2d( - in_chans, embed_dim, kernel_size=kernel_size, stride=patch_size, padding=padding, bias=bias + self.proj = operations.Conv2d( + in_chans, embed_dim, kernel_size=kernel_size, stride=patch_size, padding=padding, bias=bias, dtype=dtype, device=device ) self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity() def forward(self, x): + if self.dynamic_img_pad: + x = comfy.ldm.common_dit.pad_to_patch_size(x, self.patch_size, padding_mode=self.padding_mode) x = self.proj(x) if self.flatten: x = x.flatten(2).transpose(1, 2) # BCHW -> BNC diff --git a/Sana/models/sana_multi_scale.py b/Sana/models/sana_multi_scale.py index 2d5452c..bd5bab8 100644 --- a/Sana/models/sana_multi_scale.py +++ b/Sana/models/sana_multi_scale.py @@ -17,13 +17,13 @@ # This file is modified from https://github.com/PixArt-alpha/PixArt-sigma import torch import torch.nn as nn -from timm.models.layers import DropPath from .basic_modules import DWMlp, GLUMBConv, MBConvPreGLU, Mlp from .sana import Sana, get_2d_sincos_pos_embed from .sana_blocks import ( Attention, CaptionEmbedder, + TimestepEmbedder, FlashAttention, LiteLA, MultiHeadCrossAttention, @@ -31,8 +31,7 @@ T2IFinalLayer, t2i_modulate, ) -from .utils import auto_grad_checkpoint - +from .norms import RMSNorm class SanaMSBlock(nn.Module): """ @@ -52,11 +51,14 @@ def __init__( mlp_acts=("silu", "silu", None), linear_head_dim=32, cross_norm=False, + dtype=None, + device=None, + operations=None, **block_kwargs, ): super().__init__() self.hidden_size = hidden_size - self.norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + self.norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) if attn_type == "flash": # flash self attention self.attn = FlashAttention( @@ -64,25 +66,34 @@ def __init__( num_heads=num_heads, qkv_bias=True, qk_norm=qk_norm, + dtype=dtype, + device=device, + operations=operations, **block_kwargs, ) elif attn_type == "linear": # linear self attention # TODO: Here the num_heads set to 36 for tmp used self_num_heads = hidden_size // linear_head_dim - self.attn = LiteLA(hidden_size, hidden_size, heads=self_num_heads, eps=1e-8, qk_norm=qk_norm) + self.attn = LiteLA( + hidden_size, hidden_size, heads=self_num_heads, eps=1e-8, qk_norm=qk_norm, + dtype=dtype, device=device, operations=operations, + ) elif attn_type == "vanilla": # vanilla self attention - self.attn = Attention(hidden_size, num_heads=num_heads, qkv_bias=True) + self.attn = Attention( + hidden_size, num_heads=num_heads, qkv_bias=True, dtype=dtype, device=device, operations=operations, + ) else: raise ValueError(f"{attn_type} type is not defined.") - self.cross_attn = MultiHeadCrossAttention(hidden_size, num_heads, qk_norm=cross_norm, **block_kwargs) + self.cross_attn = MultiHeadCrossAttention(hidden_size, num_heads, qk_norm=cross_norm, dtype=dtype, device=device, operations=operations, **block_kwargs) self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) if ffn_type == "dwmlp": approx_gelu = lambda: nn.GELU(approximate="tanh") self.mlp = DWMlp( - in_features=hidden_size, hidden_features=int(hidden_size * mlp_ratio), act_layer=approx_gelu, drop=0 + in_features=hidden_size, hidden_features=int(hidden_size * mlp_ratio), act_layer=approx_gelu, drop=0, + dtype=dtype, device=device, operations=operations, ) elif ffn_type == "glumbconv": self.mlp = GLUMBConv( @@ -91,6 +102,9 @@ def __init__( use_bias=(True, True, False), norm=(None, None, None), act=mlp_acts, + dtype=dtype, + device=device, + operations=operations, ) elif ffn_type == "glumbconv_dilate": self.mlp = GLUMBConv( @@ -100,11 +114,15 @@ def __init__( norm=(None, None, None), act=mlp_acts, dilation=2, + dtype=dtype, + device=device, + operations=operations, ) elif ffn_type == "mlp": approx_gelu = lambda: nn.GELU(approximate="tanh") self.mlp = Mlp( - in_features=hidden_size, hidden_features=int(hidden_size * mlp_ratio), act_layer=approx_gelu, drop=0 + in_features=hidden_size, hidden_features=int(hidden_size * mlp_ratio), act_layer=approx_gelu, drop=0, + dtype=dtype, device=device, operations=operations, ) elif ffn_type == "mbconvpreglu": self.mlp = MBConvPreGLU( @@ -114,17 +132,20 @@ def __init__( use_bias=(True, True, False), norm=None, act=mlp_acts, + dtype=dtype, + device=device, + operations=operations, ) else: raise ValueError(f"{ffn_type} type is not defined.") - self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() + self.drop_path = nn.Identity() # DropPath(drop_path) if drop_path > 0.0 else nn.Identity() self.scale_shift_table = nn.Parameter(torch.randn(6, hidden_size) / hidden_size**0.5) def forward(self, x, y, t, mask=None, HW=None, **kwargs): B, N, C = x.shape shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = ( - self.scale_shift_table[None] + t.reshape(B, 6, -1) + self.scale_shift_table[None].to(x.dtype) + t.reshape(B, 6, -1) ).chunk(6, dim=1) x = x + self.drop_path(gate_msa * self.attn(t2i_modulate(self.norm1(x), shift_msa, scale_msa), HW=HW)) x = x + self.cross_attn(x, y, mask) @@ -169,51 +190,57 @@ def __init__( mlp_acts=("silu", "silu", None), linear_head_dim=32, cross_norm=False, + dtype=None, + device=None, + operations=None, **kwargs, ): - super().__init__( - input_size=input_size, - patch_size=patch_size, - in_channels=in_channels, - hidden_size=hidden_size, - depth=depth, - num_heads=num_heads, - mlp_ratio=mlp_ratio, - class_dropout_prob=class_dropout_prob, - learn_sigma=learn_sigma, - pred_sigma=pred_sigma, - drop_path=drop_path, - caption_channels=caption_channels, - pe_interpolation=pe_interpolation, - config=config, - model_max_length=model_max_length, - qk_norm=qk_norm, - y_norm=y_norm, - norm_eps=norm_eps, - attn_type=attn_type, - ffn_type=ffn_type, - use_pe=use_pe, - y_norm_scale_factor=y_norm_scale_factor, - patch_embed_kernel=patch_embed_kernel, - mlp_acts=mlp_acts, - linear_head_dim=linear_head_dim, - **kwargs, - ) - self.dtype = torch.get_default_dtype() + nn.Module.__init__(self) + self.dtype = dtype + self.pred_sigma = pred_sigma + self.in_channels = in_channels + self.out_channels = in_channels * 2 if pred_sigma else in_channels + self.patch_size = patch_size + self.num_heads = num_heads + self.pe_interpolation = pe_interpolation + self.depth = depth + self.use_pe = use_pe + self.y_norm = y_norm + self.model_max_length = model_max_length + self.fp32_attention = kwargs.get("use_fp32_attention", False) self.h = self.w = 0 + approx_gelu = lambda: nn.GELU(approximate="tanh") - self.t_block = nn.Sequential(nn.SiLU(), nn.Linear(hidden_size, 6 * hidden_size, bias=True)) + self.t_block = nn.Sequential( + nn.SiLU(), operations.Linear(hidden_size, 6 * hidden_size, bias=True, dtype=dtype, device=device) + ) + + self.t_embedder = TimestepEmbedder(hidden_size, dtype=dtype, device=device, operations=operations) + self.pos_embed_ms = None + if input_size is not None: + self.base_size = input_size // self.patch_size + else: + self.base_size = None kernel_size = patch_embed_kernel or patch_size - self.x_embedder = PatchEmbedMS(patch_size, in_channels, hidden_size, kernel_size=kernel_size, bias=True) + self.x_embedder = PatchEmbedMS( + patch_size, in_channels, hidden_size, kernel_size=kernel_size, bias=True, + dtype=dtype, device=device, operations=operations, + ) self.y_embedder = CaptionEmbedder( in_channels=caption_channels, hidden_size=hidden_size, uncond_prob=class_dropout_prob, act_layer=approx_gelu, token_num=model_max_length, + dtype=dtype, + device=device, + operations=operations, ) + if self.y_norm: + self.attention_y_norm = RMSNorm(hidden_size, scale_factor=y_norm_scale_factor, eps=norm_eps) + drop_path = [x.item() for x in torch.linspace(0, drop_path, depth)] # stochastic depth decay rule self.blocks = nn.ModuleList( [ @@ -229,13 +256,16 @@ def __init__( mlp_acts=mlp_acts, linear_head_dim=linear_head_dim, cross_norm=cross_norm, + dtype=dtype, + device=device, + operations=operations, ) for i in range(depth) ] ) - self.final_layer = T2IFinalLayer(hidden_size, patch_size, self.out_channels) - - self.initialize() + self.final_layer = T2IFinalLayer( + hidden_size, patch_size, self.out_channels, dtype=dtype, device=device, operations=operations + ) def forward(self, x, timesteps, context, **kwargs): """ @@ -251,7 +281,7 @@ def forward(self, x, timesteps, context, **kwargs): context = context.unsqueeze(1) ## run original forward pass - out = self.forward_raw( + out = self.forward_orig( x = x.to(self.dtype), timestep = timesteps.to(self.dtype), y = context.to(self.dtype), @@ -262,7 +292,7 @@ def forward(self, x, timesteps, context, **kwargs): return out - def forward_raw(self, x, timestep, y, mask=None, data_info=None, **kwargs): + def forward(self, x, timestep, context, mask=None, data_info=None, **kwargs): """ Forward pass of Sana. x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images) @@ -270,9 +300,9 @@ def forward_raw(self, x, timestep, y, mask=None, data_info=None, **kwargs): y: (N, 1, 120, C) tensor of class labels """ bs = x.shape[0] - x = x.to(self.dtype) - timestep = timestep.to(self.dtype) - y = y.to(self.dtype) + y = context + if len(y.shape) == 3: + y = y.unsqueeze(1) self.h, self.w = x.shape[-2] // self.patch_size, x.shape[-1] // self.patch_size if self.use_pe: x = self.x_embedder(x) @@ -285,16 +315,13 @@ def forward_raw(self, x, timestep, y, mask=None, data_info=None, **kwargs): pe_interpolation=self.pe_interpolation, base_size=self.base_size, ) - ) - .unsqueeze(0) - .to(x.device) - .to(self.dtype) + ).unsqueeze(0).to(x.device).to(x.dtype) ) x += self.pos_embed_ms # (N, T, D), where T = H * W / patch_size ** 2 else: x = self.x_embedder(x) - t = self.t_embedder(timestep) # (N, D) + t = self.t_embedder(timestep, x.dtype) # (N, D) y_lens = ((y != 0).sum(dim=3) > 0).sum(dim=2).squeeze().tolist() y_lens = [y_lens[1]] * bs @@ -311,9 +338,7 @@ def forward_raw(self, x, timestep, y, mask=None, data_info=None, **kwargs): y = y.squeeze(1).masked_select(mask.unsqueeze(-1).bool()).view(1, -1, y.shape[-1]) for block in self.blocks: - x = auto_grad_checkpoint( - block, x, y, t0, y_lens, (self.h, self.w), **kwargs - ) # (N, T, D) #support grad checkpoint + x = block(x, y, t0, y_lens, (self.h, self.w), **kwargs) # (N, T, D) # x = self.final_layer(x, t) # (N, T, patch_size ** 2 * out_channels) x = self.unpatchify(x) # (N, out_channels, H, W) @@ -348,26 +373,3 @@ def unpatchify(self, x): x = torch.einsum("nhwpqc->nchpwq", x) imgs = x.reshape(shape=(x.shape[0], c, self.h * p, self.w * p)) return imgs - - def initialize(self): - # Initialize transformer layers: - def _basic_init(module): - if isinstance(module, nn.Linear): - torch.nn.init.xavier_uniform_(module.weight) - if module.bias is not None: - nn.init.constant_(module.bias, 0) - - self.apply(_basic_init) - - # Initialize patch_embed like nn.Linear (instead of nn.Conv2d): - w = self.x_embedder.proj.weight.data - nn.init.xavier_uniform_(w.view([w.shape[0], -1])) - - # Initialize timestep embedding MLP: - nn.init.normal_(self.t_embedder.mlp[0].weight, std=0.02) - nn.init.normal_(self.t_embedder.mlp[2].weight, std=0.02) - nn.init.normal_(self.t_block[1].weight, std=0.02) - - # Initialize caption embedding MLP: - nn.init.normal_(self.y_embedder.y_proj.fc1.weight, std=0.02) - nn.init.normal_(self.y_embedder.y_proj.fc2.weight, std=0.02) diff --git a/Sana/nodes.py b/Sana/nodes.py index abce9ba..ca1628b 100644 --- a/Sana/nodes.py +++ b/Sana/nodes.py @@ -2,151 +2,66 @@ import folder_paths from nodes import EmptyLatentImage -from .conf import sana_conf, sana_res -from .loader import load_sana - -dtypes = [ - "auto", - "FP32", - "FP16", - "BF16" -] - -class SanaCheckpointLoader: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "ckpt_name": (folder_paths.get_filename_list("checkpoints"),), - "model": (list(sana_conf.keys()),), - } - } - RETURN_TYPES = ("MODEL",) - RETURN_NAMES = ("model",) - FUNCTION = "load_checkpoint" - CATEGORY = "ExtraModels/Sana" - TITLE = "Sana Checkpoint Loader" - - def load_checkpoint(self, ckpt_name, model): - ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name) - model_conf = sana_conf[model] - model = load_sana( - model_path = ckpt_path, - model_conf = model_conf, - ) - return (model,) - - class EmptySanaLatentImage(EmptyLatentImage): - CATEGORY = "ExtraModels/Sana" - TITLE = "Empty Sana Latent Image" - - def generate(self, width, height, batch_size=1): - latent = torch.zeros([batch_size, 32, height // 32, width // 32], device=self.device) - return ({"samples":latent}, ) - - -class SanaResolutionSelect(): - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "model": (list(sana_res.keys()),), - "ratio": (list(sana_res["1024px"].keys()),{"default":"1.00"}), - } - } - RETURN_TYPES = ("INT","INT") - RETURN_NAMES = ("width","height") - FUNCTION = "get_res" - CATEGORY = "ExtraModels/Sana" - TITLE = "Sana Resolution Select" - - def get_res(self, model, ratio): - width, height = sana_res[model][ratio] - return (width,height) - - -class SanaResolutionCond: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "cond": ("CONDITIONING", ), - "width": ("INT", {"default": 1024.0, "min": 0, "max": 8192}), - "height": ("INT", {"default": 1024.0, "min": 0, "max": 8192}), - } - } - - RETURN_TYPES = ("CONDITIONING",) - RETURN_NAMES = ("cond",) - FUNCTION = "add_cond" - CATEGORY = "ExtraModels/Sana" - TITLE = "Sana Resolution Conditioning" - - def add_cond(self, cond, width, height): - for c in range(len(cond)): - cond[c][1].update({ - "img_hw": [[height, width]], - "aspect_ratio": [[height/width]], - }) - return (cond,) + CATEGORY = "ExtraModels/Sana" + TITLE = "Empty Sana Latent Image" + def generate(self, width, height, batch_size=1): + latent = torch.zeros([batch_size, 32, height // 32, width // 32], device=self.device) + return ({"samples":latent}, ) class SanaTextEncode: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "text": ("STRING", {"multiline": True}), - "GEMMA": ("GEMMA",), - } - } - - RETURN_TYPES = ("CONDITIONING",) - FUNCTION = "encode" - CATEGORY = "ExtraModels/Sana" - TITLE = "Sana Text Encode" - - def encode(self, text, GEMMA=None): - tokenizer = GEMMA["tokenizer"] - text_encoder = GEMMA["text_encoder"] - - with torch.no_grad(): - chi_prompt = "\n".join(preset_te_prompt) - full_prompt = chi_prompt + text - num_chi_tokens = len(tokenizer.encode(chi_prompt)) - max_length = num_chi_tokens + 300 - 2 - - tokens = tokenizer( - [full_prompt], - max_length=max_length, - padding="max_length", - truncation=True, - return_tensors="pt" - ).to(text_encoder.device) - - select_idx = [0] + list(range(-300 + 1, 0)) - embs = text_encoder(tokens.input_ids, tokens.attention_mask)[0][:, None][:, :, select_idx] - emb_masks = tokens.attention_mask[:, select_idx] - embs = embs * emb_masks.unsqueeze(-1) - - return ([[embs, {}]], ) + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "text": ("STRING", {"multiline": True}), + "GEMMA": ("GEMMA",), + } + } + + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "encode" + CATEGORY = "ExtraModels/Sana" + TITLE = "Sana Text Encode" + + def encode(self, text, GEMMA=None): + tokenizer = GEMMA["tokenizer"] + text_encoder = GEMMA["text_encoder"] + + with torch.no_grad(): + chi_prompt = "\n".join(preset_te_prompt) + full_prompt = chi_prompt + text + num_chi_tokens = len(tokenizer.encode(chi_prompt)) + max_length = num_chi_tokens + 300 - 2 + + tokens = tokenizer( + [full_prompt], + max_length=max_length, + padding="max_length", + truncation=True, + return_tensors="pt" + ).to(text_encoder.device) + + select_idx = [0] + list(range(-300 + 1, 0)) + embs = text_encoder(tokens.input_ids, tokens.attention_mask)[0][:, None][:, :, select_idx] + emb_masks = tokens.attention_mask[:, select_idx] + embs = embs * emb_masks.unsqueeze(-1) + + return ([[embs, {}]], ) preset_te_prompt = [ - 'Given a user prompt, generate an "Enhanced prompt" that provides detailed visual descriptions suitable for image generation. Evaluate the level of detail in the user prompt:', - '- If the prompt is simple, focus on adding specifics about colors, shapes, sizes, textures, and spatial relationships to create vivid and concrete scenes.', - '- If the prompt is already detailed, refine and enhance the existing details slightly without overcomplicating.', - 'Here are examples of how to transform or refine prompts:', - '- User Prompt: A cat sleeping -> Enhanced: A small, fluffy white cat curled up in a round shape, sleeping peacefully on a warm sunny windowsill, surrounded by pots of blooming red flowers.', - '- User Prompt: A busy city street -> Enhanced: A bustling city street scene at dusk, featuring glowing street lamps, a diverse crowd of people in colorful clothing, and a double-decker bus passing by towering glass skyscrapers.', - 'Please generate only the enhanced description for the prompt below and avoid including any additional commentary or evaluations:', - 'User Prompt: ' + 'Given a user prompt, generate an "Enhanced prompt" that provides detailed visual descriptions suitable for image generation. Evaluate the level of detail in the user prompt:', + '- If the prompt is simple, focus on adding specifics about colors, shapes, sizes, textures, and spatial relationships to create vivid and concrete scenes.', + '- If the prompt is already detailed, refine and enhance the existing details slightly without overcomplicating.', + 'Here are examples of how to transform or refine prompts:', + '- User Prompt: A cat sleeping -> Enhanced: A small, fluffy white cat curled up in a round shape, sleeping peacefully on a warm sunny windowsill, surrounded by pots of blooming red flowers.', + '- User Prompt: A busy city street -> Enhanced: A bustling city street scene at dusk, featuring glowing street lamps, a diverse crowd of people in colorful clothing, and a double-decker bus passing by towering glass skyscrapers.', + 'Please generate only the enhanced description for the prompt below and avoid including any additional commentary or evaluations:', + 'User Prompt: ' ] NODE_CLASS_MAPPINGS = { - "SanaCheckpointLoader" : SanaCheckpointLoader, - "SanaResolutionSelect" : SanaResolutionSelect, - "SanaTextEncode" : SanaTextEncode, - "SanaResolutionCond" : SanaResolutionCond, - "EmptySanaLatentImage": EmptySanaLatentImage, + "SanaTextEncode" : SanaTextEncode, + "EmptySanaLatentImage": EmptySanaLatentImage, } diff --git a/T5/LICENSE-ComfyUI b/T5/LICENSE-ComfyUI deleted file mode 100644 index f288702..0000000 --- a/T5/LICENSE-ComfyUI +++ /dev/null @@ -1,674 +0,0 @@ - GNU GENERAL PUBLIC LICENSE - Version 3, 29 June 2007 - - Copyright (C) 2007 Free Software Foundation, Inc. - Everyone is permitted to copy and distribute verbatim copies - of this license document, but changing it is not allowed. - - Preamble - - The GNU General Public License is a free, copyleft license for -software and other kinds of works. - - The licenses for most software and other practical works are designed -to take away your freedom to share and change the works. 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But first, please read -. diff --git a/T5/loader.py b/T5/loader.py deleted file mode 100644 index db321ab..0000000 --- a/T5/loader.py +++ /dev/null @@ -1,118 +0,0 @@ -import os -import torch -import comfy.utils -import comfy.model_patcher -from comfy import model_management -import folder_paths - -from .t5v11 import T5v11Model, T5v11Tokenizer - -class EXM_T5v11: - def __init__(self, textmodel_ver="xxl", embedding_directory=None, textmodel_path=None, no_init=False, device="cpu", dtype=None): - if no_init: - return - - if device == "auto": - size = 0 - self.load_device = model_management.text_encoder_device() - self.offload_device = model_management.text_encoder_offload_device() - self.init_device = "cpu" - elif dtype == "bnb8bit": - # BNB doesn't support size enum - size = 12.4 * (1024**3) - # Or moving between devices - self.load_device = model_management.get_torch_device() - self.offload_device = self.load_device - self.init_device = self.load_device - elif dtype == "bnb4bit": - # This seems to use the same VRAM as 8bit on Pascal? - size = 6.2 * (1024**3) - self.load_device = model_management.get_torch_device() - self.offload_device = self.load_device - self.init_device = self.load_device - elif device == "cpu": - size = 0 - 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 - self.load_device = device - self.offload_device = device - self.init_device = device - else: - size = 0 - self.load_device = model_management.get_torch_device() - self.offload_device = "cpu" - self.init_device="cpu" - - self.cond_stage_model = T5v11Model( - textmodel_ver = textmodel_ver, - textmodel_path = textmodel_path, - device = device, - dtype = dtype, - ) - self.tokenizer = T5v11Tokenizer(embedding_directory=embedding_directory) - 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 = T5(no_init=True) - n.patcher = self.patcher.clone() - n.cond_stage_model = self.cond_stage_model - n.tokenizer = self.tokenizer - return n - - def tokenize(self, text, return_word_ids=False): - return self.tokenizer.tokenize_with_weights(text, return_word_ids) - - def encode_from_tokens(self, tokens): - self.load_model() - return self.cond_stage_model.encode_token_weights(tokens) - - def encode(self, text): - tokens = self.tokenize(text) - return self.encode_from_tokens(tokens) - - 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_t5(model_type, model_ver, model_path, path_type="file", device="cpu", dtype=None): - assert model_type in ["t5v11"] # Only supported model for now - model_args = { - "textmodel_ver" : model_ver, - "device" : device, - "dtype" : dtype, - } - - if path_type == "folder": - # pass directly to transformers and initialize there - # this is to avoid having to handle multi-file state dict loading for now. - model_args["textmodel_path"] = os.path.dirname(model_path) - return EXM_T5v11(**model_args) - else: - # for some reason this returns garbage with torch.int8 weights, or just OOMs - model = EXM_T5v11(**model_args) - sd = comfy.utils.load_torch_file(model_path) - model.load_sd(sd) - return model diff --git a/T5/nodes.py b/T5/nodes.py deleted file mode 100644 index 021386e..0000000 --- a/T5/nodes.py +++ /dev/null @@ -1,95 +0,0 @@ -import os -import json -import torch -import folder_paths - -from .loader import load_t5 -from ..utils.dtype import string_to_dtype - -# initialize custom folder path -os.makedirs( - os.path.join(folder_paths.models_dir,"t5"), - exist_ok = True, -) -folder_paths.folder_names_and_paths["t5"] = ( - [ - os.path.join(folder_paths.models_dir,"t5"), - *folder_paths.folder_names_and_paths.get("t5", [[],set()])[0] - ], - folder_paths.supported_pt_extensions -) - -dtypes = [ - "default", - "auto (comfy)", - "FP32", - "FP16", - # Note: remove these at some point - "bnb8bit", - "bnb4bit", -] -try: torch.float8_e5m2 -except AttributeError: print("Torch version too old for FP8") -else: dtypes += ["FP8 E4M3", "FP8 E5M2"] - -class T5v11Loader: - @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": { - "t5v11_name": (folder_paths.get_filename_list("t5"),), - "t5v11_ver": (["xxl"],), - "path_type": (["folder", "file"],), - "device": (devices, {"default":"cpu"}), - "dtype": (dtypes,), - } - } - RETURN_TYPES = ("T5",) - FUNCTION = "load_model" - CATEGORY = "ExtraModels/T5" - TITLE = "T5v1.1 Loader" - - def load_model(self, t5v11_name, t5v11_ver, path_type, device, dtype): - if "bnb" in dtype: - assert device == "gpu" or device.startswith("cuda"), "BitsAndBytes only works on CUDA! Set device to 'gpu'." - dtype = string_to_dtype(dtype, "text_encoder") - if device == "cpu": - assert dtype in [None, torch.float32], f"Can't use dtype '{dtype}' with CPU! Set dtype to 'default'." - - return (load_t5( - model_type = "t5v11", - model_ver = t5v11_ver, - model_path = folder_paths.get_full_path("t5", t5v11_name), - path_type = path_type, - device = device, - dtype = dtype, - ),) - -class T5TextEncode: - @classmethod - def INPUT_TYPES(s): - return { - "required": { - "text": ("STRING", {"multiline": True}), - "T5": ("T5",), - } - } - - RETURN_TYPES = ("CONDITIONING",) - FUNCTION = "encode" - CATEGORY = "ExtraModels/T5" - TITLE = "T5 Text Encode" - - def encode(self, text, T5=None): - tokens = T5.tokenize(text) - cond = T5.encode_from_tokens(tokens) - return ([[cond, {}]], ) - -NODE_CLASS_MAPPINGS = { - "T5v11Loader" : T5v11Loader, - "T5TextEncode" : T5TextEncode, -} diff --git a/T5/t5v11-xxl_config.json b/T5/t5v11-xxl_config.json deleted file mode 100644 index d133daa..0000000 --- a/T5/t5v11-xxl_config.json +++ /dev/null @@ -1,31 +0,0 @@ -{ - "_name_or_path": "google/t5-v1_1-xxl", - "architectures": [ - "T5EncoderModel" - ], - "d_ff": 10240, - "d_kv": 64, - "d_model": 4096, - "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": "t5", - "num_decoder_layers": 24, - "num_heads": 64, - "num_layers": 24, - "output_past": true, - "pad_token_id": 0, - "relative_attention_max_distance": 128, - "relative_attention_num_buckets": 32, - "tie_word_embeddings": false, - "torch_dtype": "float32", - "transformers_version": "4.21.1", - "use_cache": true, - "vocab_size": 32128 -} diff --git a/T5/t5v11.py b/T5/t5v11.py deleted file mode 100644 index 76cec9c..0000000 --- a/T5/t5v11.py +++ /dev/null @@ -1,227 +0,0 @@ -""" -Adapted from comfyui CLIP code. -https://github.com/comfyanonymous/ComfyUI/blob/master/comfy/sd1_clip.py -""" - -import os - -from transformers import T5Tokenizer, T5EncoderModel, T5Config, modeling_utils -import torch -import traceback -import zipfile -from comfy import model_management - -from comfy.sd1_clip import parse_parentheses, token_weights, escape_important, unescape_important, safe_load_embed_zip, expand_directory_list, load_embed - -class T5v11Model(torch.nn.Module): - def __init__(self, textmodel_ver="xxl", textmodel_json_config=None, textmodel_path=None, device="cpu", max_length=120, freeze=True, dtype=None): - super().__init__() - - self.num_layers = 24 - self.max_length = max_length - self.bnb = False - - if textmodel_path is not None: - model_args = {} - model_args["low_cpu_mem_usage"] = True # Don't take 2x system ram on cpu - if dtype == "bnb8bit": - self.bnb = True - model_args["load_in_8bit"] = True - elif dtype == "bnb4bit": - self.bnb = True - model_args["load_in_4bit"] = True - else: - if dtype: model_args["torch_dtype"] = dtype - self.bnb = False - # second GPU offload hack part 2 - if device.startswith("cuda"): - model_args["device_map"] = device - print(f"Loading T5 from '{textmodel_path}'") - self.transformer = T5EncoderModel.from_pretrained(textmodel_path, **model_args) - else: - if textmodel_json_config is None: - textmodel_json_config = os.path.join( - os.path.dirname(os.path.realpath(__file__)), - f"t5v11-{textmodel_ver}_config.json" - ) - config = T5Config.from_json_file(textmodel_json_config) - self.num_layers = config.num_hidden_layers - with modeling_utils.no_init_weights(): - self.transformer = T5EncoderModel(config) - - if freeze: - self.freeze() - self.empty_tokens = [[0] * self.max_length] # token - - def freeze(self): - self.transformer = self.transformer.eval() - for param in self.parameters(): - param.requires_grad = False - - def forward(self, tokens): - device = self.transformer.get_input_embeddings().weight.device - tokens = torch.LongTensor(tokens).to(device) - attention_mask = torch.zeros_like(tokens) - max_token = 1 # token - for x in range(attention_mask.shape[0]): - for y in range(attention_mask.shape[1]): - attention_mask[x, y] = 1 - if tokens[x, y] == max_token: - break - - outputs = self.transformer(input_ids=tokens, attention_mask=attention_mask) - - z = outputs['last_hidden_state'] - z.detach().cpu().float() - return z - - def encode(self, tokens): - return self(tokens) - - def load_sd(self, sd): - return self.transformer.load_state_dict(sd, strict=False) - - def to(self, *args, **kwargs): - """BNB complains if you try to change the device or dtype""" - if self.bnb: - print("Thanks to BitsAndBytes, T5 becomes an immovable rock.", args, kwargs) - else: - self.transformer.to(*args, **kwargs) - - def encode_token_weights(self, token_weight_pairs, return_padded=False): - to_encode = list(self.empty_tokens) - for x in token_weight_pairs: - tokens = list(map(lambda a: a[0], x)) - to_encode.append(tokens) - - out = self.encode(to_encode) - z_empty = out[0:1] - - output = [] - for k in range(1, out.shape[0]): - z = out[k:k+1] - for i in range(len(z)): - for j in range(len(z[i])): - weight = token_weight_pairs[k - 1][j][1] - z[i][j] = (z[i][j] - z_empty[0][j]) * weight + z_empty[0][j] - output.append(z) - - if (len(output) == 0): - return z_empty.cpu() - - out = torch.cat(output, dim=-2) - if not return_padded: - # Count number of tokens that aren't , then use that number as an index. - keep_index = sum([sum([1 for y in x if y[0] != 0]) for x in token_weight_pairs]) - out = out[:, :keep_index, :] - return out - - -class T5v11Tokenizer: - """ - This is largely just based on the ComfyUI CLIP code. - """ - def __init__(self, tokenizer_path=None, max_length=120, embedding_directory=None, embedding_size=4096, embedding_key='t5'): - if tokenizer_path is None: - tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer") - self.tokenizer = T5Tokenizer.from_pretrained(tokenizer_path) - self.max_length = max_length - self.max_tokens_per_section = self.max_length - 1 # but no - - self.pad_token = self.tokenizer("", add_special_tokens=False)["input_ids"][0] - self.end_token = self.tokenizer("", add_special_tokens=False)["input_ids"][0] - vocab = self.tokenizer.get_vocab() - self.inv_vocab = {v: k for k, v in vocab.items()} - self.embedding_directory = embedding_directory - self.max_word_length = 8 # haven't verified this - self.embedding_identifier = "embedding:" - self.embedding_size = embedding_size - self.embedding_key = embedding_key - - def _try_get_embedding(self, embedding_name:str): - ''' - Takes a potential embedding name and tries to retrieve it. - Returns a Tuple consisting of the embedding and any leftover string, embedding can be None. - ''' - embed = load_embed(embedding_name, self.embedding_directory, self.embedding_size, self.embedding_key) - if embed is None: - stripped = embedding_name.strip(',') - if len(stripped) < len(embedding_name): - embed = load_embed(stripped, self.embedding_directory, self.embedding_size, self.embedding_key) - return (embed, embedding_name[len(stripped):]) - return (embed, "") - - def tokenize_with_weights(self, text:str, return_word_ids=False): - ''' - Takes a prompt and converts it to a list of (token, weight, word id) elements. - Tokens can both be integer tokens and pre computed T5 tensors. - Word id values are unique per word and embedding, where the id 0 is reserved for non word tokens. - Returned list has the dimensions NxM where M is the input size of T5 - ''' - pad_token = self.pad_token - text = escape_important(text) - parsed_weights = token_weights(text, 1.0) - - #tokenize words - tokens = [] - for weighted_segment, weight in parsed_weights: - to_tokenize = unescape_important(weighted_segment).replace("\n", " ").split(' ') - to_tokenize = [x for x in to_tokenize if x != ""] - for word in to_tokenize: - #if we find an embedding, deal with the embedding - if word.startswith(self.embedding_identifier) and self.embedding_directory is not None: - embedding_name = word[len(self.embedding_identifier):].strip('\n') - embed, leftover = self._try_get_embedding(embedding_name) - if embed is None: - print(f"warning, embedding:{embedding_name} does not exist, ignoring") - else: - if len(embed.shape) == 1: - tokens.append([(embed, weight)]) - else: - tokens.append([(embed[x], weight) for x in range(embed.shape[0])]) - #if we accidentally have leftover text, continue parsing using leftover, else move on to next word - if leftover != "": - word = leftover - else: - continue - #parse word - tokens.append([(t, weight) for t in self.tokenizer(word, add_special_tokens=False)["input_ids"]]) - - #reshape token array to T5 input size - batched_tokens = [] - batch = [] - batched_tokens.append(batch) - for i, t_group in enumerate(tokens): - #determine if we're going to try and keep the tokens in a single batch - is_large = len(t_group) >= self.max_word_length - - while len(t_group) > 0: - if len(t_group) + len(batch) > self.max_length - 1: - remaining_length = self.max_length - len(batch) - 1 - #break word in two and add end token - if is_large: - batch.extend([(t,w,i+1) for t,w in t_group[:remaining_length]]) - batch.append((self.end_token, 1.0, 0)) - t_group = t_group[remaining_length:] - #add end token and pad - else: - batch.append((self.end_token, 1.0, 0)) - batch.extend([(self.pad_token, 1.0, 0)] * (remaining_length)) - #start new batch - batch = [] - batched_tokens.append(batch) - else: - batch.extend([(t,w,i+1) for t,w in t_group]) - t_group = [] - - # fill last batch - batch.extend([(self.end_token, 1.0, 0)] + [(self.pad_token, 1.0, 0)] * (self.max_length - len(batch) - 1)) - # instead of filling, just add EOS (DEBUG) - # batch.extend([(self.end_token, 1.0, 0)]) - - if not return_word_ids: - batched_tokens = [[(t, w) for t, w,_ in x] for x in batched_tokens] - return batched_tokens - - def untokenize(self, token_weight_pair): - return list(map(lambda a: (a, self.inv_vocab[a[0]]), token_weight_pair)) diff --git a/__init__.py b/__init__.py index 1fff84c..2f9bee0 100644 --- a/__init__.py +++ b/__init__.py @@ -1,51 +1,42 @@ # only import if running as a custom node try: - import comfy.utils + import comfy.utils except ImportError: - pass + pass else: - NODE_CLASS_MAPPINGS = {} + NODE_CLASS_MAPPINGS = {} - # Deci Diffusion - # from .DeciDiffusion.nodes import NODE_CLASS_MAPPINGS as DeciDiffusion_Nodes - # NODE_CLASS_MAPPINGS.update(DeciDiffusion_Nodes) + # Generic/universal nodes + from .nodes import NODE_CLASS_MAPPINGS as Base_Nodes + NODE_CLASS_MAPPINGS.update(Base_Nodes) - # DiT - from .DiT.nodes import NODE_CLASS_MAPPINGS as DiT_Nodes - NODE_CLASS_MAPPINGS.update(DiT_Nodes) + # DiT + from .DiT.nodes import NODE_CLASS_MAPPINGS as DiT_Nodes + NODE_CLASS_MAPPINGS.update(DiT_Nodes) - # PixArt - from .PixArt.nodes import NODE_CLASS_MAPPINGS as PixArt_Nodes - NODE_CLASS_MAPPINGS.update(PixArt_Nodes) + # PixArt + from .PixArt.nodes import NODE_CLASS_MAPPINGS as PixArt_Nodes + NODE_CLASS_MAPPINGS.update(PixArt_Nodes) - # T5 - from .T5.nodes import NODE_CLASS_MAPPINGS as T5_Nodes - NODE_CLASS_MAPPINGS.update(T5_Nodes) - - # HYDiT - from .HunYuanDiT.nodes import NODE_CLASS_MAPPINGS as HunYuanDiT_Nodes - NODE_CLASS_MAPPINGS.update(HunYuanDiT_Nodes) - - # VAE - from .VAE.nodes import NODE_CLASS_MAPPINGS as VAE_Nodes - NODE_CLASS_MAPPINGS.update(VAE_Nodes) + # VAE + from .VAE.nodes import NODE_CLASS_MAPPINGS as VAE_Nodes + NODE_CLASS_MAPPINGS.update(VAE_Nodes) - # MiaoBi - from .MiaoBi.nodes import NODE_CLASS_MAPPINGS as MiaoBi_Nodes - NODE_CLASS_MAPPINGS.update(MiaoBi_Nodes) - - # Extra - from .utils.nodes import NODE_CLASS_MAPPINGS as Extra_Nodes - NODE_CLASS_MAPPINGS.update(Extra_Nodes) + # MiaoBi + from .MiaoBi.nodes import NODE_CLASS_MAPPINGS as MiaoBi_Nodes + NODE_CLASS_MAPPINGS.update(MiaoBi_Nodes) - # Sana - from .Sana.nodes import NODE_CLASS_MAPPINGS as Sana_Nodes - NODE_CLASS_MAPPINGS.update(Sana_Nodes) + # Extra + from .utils.nodes import NODE_CLASS_MAPPINGS as Extra_Nodes + NODE_CLASS_MAPPINGS.update(Extra_Nodes) - # Gemma - from .Gemma.nodes import NODE_CLASS_MAPPINGS as Gemma_Nodes - NODE_CLASS_MAPPINGS.update(Gemma_Nodes) + # Sana + from .Sana.nodes import NODE_CLASS_MAPPINGS as Sana_Nodes + NODE_CLASS_MAPPINGS.update(Sana_Nodes) - NODE_DISPLAY_NAME_MAPPINGS = {k:v.TITLE for k,v in NODE_CLASS_MAPPINGS.items()} - __all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS'] + # Gemma + from .Gemma.nodes import NODE_CLASS_MAPPINGS as Gemma_Nodes + NODE_CLASS_MAPPINGS.update(Gemma_Nodes) + NODE_DISPLAY_NAME_MAPPINGS = {k:v.TITLE for k,v in NODE_CLASS_MAPPINGS.items()} + __all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS'] diff --git a/nodes.py b/nodes.py new file mode 100644 index 0000000..c7ba2f5 --- /dev/null +++ b/nodes.py @@ -0,0 +1,70 @@ +import folder_paths +import comfy.utils + +from .PixArt.loader import load_pixart_state_dict +from .Sana.loader import load_sana_state_dict +from .text_encoders.tenc import load_text_encoder, tenc_names + +loaders = { + "PixArt": load_pixart_state_dict, + "Sana": load_sana_state_dict, +} + +class EXMUnetLoader: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "unet_name": (folder_paths.get_filename_list("unet"),), + "model_type": (list(loaders.keys()),) + } + } + + RETURN_TYPES = ("MODEL",) + FUNCTION = "load_unet" + CATEGORY = "ExtraModels" + TITLE = "Load Diffusion Model (ExtraModels)" + + def load_unet(self, unet_name, model_type): + model_options = {} + unet_path = folder_paths.get_full_path("diffusion_models", unet_name) + loader_fn = loaders[model_type] + sd = comfy.utils.load_torch_file(unet_path) + return (loader_fn(sd),) + +class EXMCLIPLoader: + @classmethod + def INPUT_TYPES(s): + files = [] + files += folder_paths.get_filename_list("clip") + # if "clip_gguf" in folder_paths.folder_names_and_paths: + # files += folder_paths.get_filename_list("clip_gguf") + return { + "required": { + "clip_name": (files, ), + "type": (["PixArt", "MiaoBi", "Sana"],), + } + } + + RETURN_TYPES = ("CLIP",) + FUNCTION = "load_clip" + CATEGORY = "ExtraModels" + TITLE = "CLIPLoader (ExtraModels)" + + def load_clip(self, clip_name, type): + clip_path = folder_paths.get_full_path("clip", clip_name) + clip_type = tenc_names.get(type, None) + + clip = load_text_encoder( + ckpt_paths =[clip_path], + embedding_directory = folder_paths.get_folder_paths("embeddings"), + clip_type = clip_type + ) + return (clip,) + +#class EXMResolutionSelect: + +NODE_CLASS_MAPPINGS = { + "EXMUnetLoader": EXMUnetLoader, + "EXMCLIPLoader": EXMCLIPLoader, +} diff --git a/requirements.txt b/requirements.txt index 1e54893..017b5b8 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,3 @@ -timm>=0.6.13 sentencepiece>=0.1.97 transformers>=4.34.1 accelerate>=0.23.0 diff --git a/text_encoders/pixart/tenc.py b/text_encoders/pixart/tenc.py new file mode 100644 index 0000000..04ab372 --- /dev/null +++ b/text_encoders/pixart/tenc.py @@ -0,0 +1,43 @@ +import os +import torch + +from comfy import sd1_clip +import comfy.text_encoders.t5 +import comfy.text_encoders.sd3_clip +import comfy.model_management + +from transformers import T5TokenizerFast + +class T5XXLModel(comfy.text_encoders.sd3_clip.T5XXLModel): + def __init__(self, **kwargs): + super().__init__(**kwargs) + # make sure empty tokens match + self.special_tokens.pop("end") + +class PixArtT5XXL(sd1_clip.SD1ClipModel): + def __init__(self, device="cpu", dtype=None, model_options={}): + super().__init__(device=device, dtype=dtype, name="t5xxl", clip_model=T5XXLModel, model_options=model_options) + +class T5XXLTokenizer(sd1_clip.SDTokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + tokenizer_path = os.path.join( + os.path.dirname(os.path.dirname(os.path.realpath(__file__))), + "tokenizers", "t5_tokenizer", + ) + super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=1) + +class PixArtTokenizer(sd1_clip.SD1Tokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="t5xxl", tokenizer=T5XXLTokenizer) + +# TODO: don't duplicate this? +def pixart_te(dtype_t5=None, t5xxl_scaled_fp8=None): + class PixArtTEModel_(PixArtT5XXL): + def __init__(self, device="cpu", dtype=None, model_options={}): + if t5xxl_scaled_fp8 is not None and "t5xxl_scaled_fp8" not in model_options: + model_options = model_options.copy() + model_options["t5xxl_scaled_fp8"] = t5xxl_scaled_fp8 + if dtype is None: + dtype = dtype_t5 + super().__init__(device=device, dtype=dtype, model_options=model_options) + return PixArtTEModel_ diff --git a/text_encoders/sana/config.json b/text_encoders/sana/config.json new file mode 100644 index 0000000..05131f6 --- /dev/null +++ b/text_encoders/sana/config.json @@ -0,0 +1,35 @@ +{ + "architectures": [ + "Gemma2ForCausalLM" + ], + "attention_bias": false, + "attention_dropout": 0.0, + "attn_logit_softcapping": 50.0, + "bos_token_id": 2, + "cache_implementation": "hybrid", + "eos_token_id": [ + 1, + 107 + ], + "final_logit_softcapping": 30.0, + "head_dim": 256, + "hidden_act": "gelu_pytorch_tanh", + "hidden_activation": "gelu_pytorch_tanh", + "hidden_size": 2304, + "initializer_range": 0.02, + "intermediate_size": 9216, + "max_position_embeddings": 8192, + "model_type": "gemma2", + "num_attention_heads": 8, + "num_hidden_layers": 26, + "num_key_value_heads": 4, + "pad_token_id": 0, + "query_pre_attn_scalar": 256, + "rms_norm_eps": 1e-06, + "rope_theta": 10000.0, + "sliding_window": 4096, + "torch_dtype": "bfloat16", + "transformers_version": "4.42.4", + "use_cache": true, + "vocab_size": 256000 +} diff --git a/text_encoders/sana/gemma.py b/text_encoders/sana/gemma.py new file mode 100644 index 0000000..3b6679c --- /dev/null +++ b/text_encoders/sana/gemma.py @@ -0,0 +1,379 @@ +# Copyright 2024 Google Inc. HuggingFace Inc. team. All rights reserved. +# +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import torch +import torch.nn as nn +import importlib + +def rotate_half(x): + x1 = x[..., : x.shape[-1] // 2] + x2 = x[..., x.shape[-1] // 2 :] + return torch.cat((-x2, x1), dim=-1) + +def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1): + cos = cos.unsqueeze(unsqueeze_dim) + sin = sin.unsqueeze(unsqueeze_dim) + q_embed = (q * cos) + (rotate_half(q) * sin) + k_embed = (k * cos) + (rotate_half(k) * sin) + return q_embed, k_embed + +def repeat_kv(hidden_states, n_rep): + batch, num_key_value_heads, slen, head_dim = hidden_states.shape + if n_rep == 1: + return hidden_states + hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim) + return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) + +def sdpa_attention_forward(config, query, key, value, mask=None, **kwargs): + key = repeat_kv(key, config["num_key_value_groups"]) + value = repeat_kv(value, config["num_key_value_groups"]) + + causal_mask = mask + if mask is not None: + causal_mask = causal_mask[:, :, :, : key.shape[-2]] + + # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask, + # Reference: https://github.com/pytorch/pytorch/issues/112577. + if query.device.type == "cuda" and causal_mask is not None: + query = query.contiguous() + key = key.contiguous() + value = value.contiguous() + + # We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment + # in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling. + is_causal = True if causal_mask is None and query.shape[1] > 1 else False + + attn_output = torch.nn.functional.scaled_dot_product_attention( + query, + key, + value, + attn_mask=causal_mask, + dropout_p=0.0, + is_causal=is_causal, + scale=config["scaling"], + ) + attn_output = attn_output.transpose(1, 2).contiguous() + return attn_output, None + +def eager_attention_forward(config, query, key, value, mask, **kwargs): + key_states = repeat_kv(key, config["num_key_value_groups"]) + value_states = repeat_kv(value, config["num_key_value_groups"]) + + attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * config["scaling"] + + if config["attn_logit_softcapping"] is not None: + attn_weights = attn_weights / config["attn_logit_softcapping"] + attn_weights = torch.tanh(attn_weights) + attn_weights = attn_weights * config["attn_logit_softcapping"] + if mask is not None: # no matter the length, we just slice it + causal_mask = mask[:, :, :, : key_states.shape[-2]] + attn_weights = attn_weights + causal_mask + + # upcast attention to fp32 + attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype) + #attn_weights = nn.functional.dropout(attn_weights, p=0, training=config.training) + attn_output = torch.matmul(attn_weights, value_states) + attn_output = attn_output.transpose(1, 2).contiguous() + return attn_output, attn_weights + +# torch 2.0 can't pass scale arg to sdpa +if int((torch.__version__).split(".")[1]) >= 1: + attention_forward = sdpa_attention_forward +else: + attention_forward = eager_attention_forward + +class Gemma2RMSNorm(nn.Module): + def __init__(self, dim, eps=1e-6): + super().__init__() + self.eps = eps + self.weight = nn.Parameter(torch.zeros(dim)) + + def _norm(self, x): + return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps) + + def forward(self, x): + output = self._norm(x.float()) + # Llama does x.to(float16) * w whilst Gemma2 is (x * w).to(float16) + # See https://github.com/huggingface/transformers/pull/29402 + output = output * (1.0 + self.weight.float()) + return output.type_as(x) + + def extra_repr(self): + return f"{tuple(self.weight.shape)}, eps={self.eps}" + +class Gemma2MLP(nn.Module): + def __init__(self, config, dtype=None, device=None, operations=None): + super().__init__() + self.config = config + self.hidden_size = config["hidden_size"] + self.intermediate_size = config["intermediate_size"] + self.gate_proj = operations.Linear(self.hidden_size, self.intermediate_size, bias=False, dtype=dtype, device=device) + self.up_proj = operations.Linear(self.hidden_size, self.intermediate_size, bias=False, dtype=dtype, device=device) + self.down_proj = operations.Linear(self.intermediate_size, self.hidden_size, bias=False, dtype=dtype, device=device) + if config["hidden_activation"] != "gelu_pytorch_tanh": + raise NotImplementedError("Unknown act mode") + self.act_fn = torch.nn.GELU() + + def forward(self, x): + return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) + +class Gemma2RotaryEmbedding(nn.Module): + def __init__(self, dim, max_position_embeddings=2048, base=10000): + super().__init__() + + self.dim = dim + self.max_position_embeddings = max_position_embeddings + self.base = base + inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float() / self.dim)) + self.register_buffer("inv_freq", tensor=inv_freq, persistent=False) + + @torch.no_grad() + def forward(self, x, position_ids, seq_len=None): + # x: [bs, num_attention_heads, seq_len, head_size] + self.inv_freq.to(x.device) + inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1) + position_ids_expanded = position_ids[:, None, :].float() + # Force float32 since bfloat16 loses precision on long contexts + # See https://github.com/huggingface/transformers/pull/29285 + device_type = x.device.type + device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu" + with torch.autocast(device_type=device_type, enabled=False): + freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) + emb = torch.cat((freqs, freqs), dim=-1) + cos = emb.cos() + sin = emb.sin() + return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype) + +class Gemma2Attention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + def __init__(self, config, layer_idx=None, dtype=None, device=None, operations=None): + super().__init__() + self.config = config + self.layer_idx = layer_idx + + self.attention_dropout = config["attention_dropout"] + self.hidden_size = config["hidden_size"] + self.num_heads = config["num_attention_heads"] + self.head_dim = config["head_dim"] + self.num_key_value_heads = config["num_key_value_heads"] + self.num_key_value_groups = self.num_heads // self.num_key_value_heads + self.max_position_embeddings = config["max_position_embeddings"] + self.rope_theta = config["rope_theta"] + self.is_causal = True + self.scaling = config["query_pre_attn_scalar"]**-0.5 + self.sliding_window = config["sliding_window"] if not bool(layer_idx % 2) else None + self.attn_logit_softcapping = config["attn_logit_softcapping"] + if self.hidden_size % self.num_heads != 0: + raise ValueError( + f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}" + f" and `num_heads`: {self.num_heads})." + ) + + self.q_proj = operations.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config["attention_bias"], dtype=dtype, device=device) + self.k_proj = operations.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config["attention_bias"], dtype=dtype, device=device) + self.v_proj = operations.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config["attention_bias"], dtype=dtype, device=device) + self.o_proj = operations.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config["attention_bias"], dtype=dtype, device=device) + self.rotary_emb = Gemma2RotaryEmbedding( + self.head_dim, + max_position_embeddings=self.max_position_embeddings, + base=self.rope_theta, + ) + + def forward(self, hidden_states, attention_mask=None, position_ids=None, past_key_value=None, output_attentions=False, use_cache=False, cache_position= None): + bsz, q_len, _ = hidden_states.size() + + query_states = self.q_proj(hidden_states) + key_states = self.k_proj(hidden_states) + value_states = self.v_proj(hidden_states) + + query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + + cos, sin = self.rotary_emb(value_states, position_ids) + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) + + if past_key_value is not None: + # sin and cos are specific to RoPE models; cache_position needed for the static cache + cache_kwargs = { + "sin": sin, + "cos": cos, + "sliding_window": self.sliding_window, + "cache_position": cache_position, + } + key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) + + config = { + "scaling": self.scaling, + "num_key_value_groups": self.num_key_value_groups, + "max_position_embeddings": self.max_position_embeddings, + "attn_logit_softcapping": self.attn_logit_softcapping, + } + attn_output, attn_weights = attention_forward(config, query_states, key_states, value_states, attention_mask, output_attentions=output_attentions) + + attn_output = attn_output.reshape(bsz, q_len, -1).contiguous() + attn_output = self.o_proj(attn_output) + + if not output_attentions: + attn_weights = None + + return attn_output, attn_weights, past_key_value + +class Gemma2DecoderLayer(nn.Module): + def __init__(self, config, layer_idx, dtype=None, device=None, operations=None): + super().__init__() + self.hidden_size = config["hidden_size"] + self.config = config + self.is_sliding = not bool(layer_idx % 2) + + self.self_attn = Gemma2Attention(config=config, layer_idx=layer_idx, dtype=dtype, device=device, operations=operations) + self.mlp = Gemma2MLP(config, dtype=dtype, device=device, operations=operations) + + self.input_layernorm = Gemma2RMSNorm(config["hidden_size"], eps=config["rms_norm_eps"]) + self.post_attention_layernorm = Gemma2RMSNorm(config["hidden_size"], eps=config["rms_norm_eps"]) + + self.pre_feedforward_layernorm = Gemma2RMSNorm(config["hidden_size"], eps=config["rms_norm_eps"]) + self.post_feedforward_layernorm = Gemma2RMSNorm(config["hidden_size"], eps=config["rms_norm_eps"]) + self.sliding_window = config["sliding_window"] + + def forward(self, hidden_states, attention_mask=None, position_ids=None, past_key_value=None, output_attentions=False, use_cache=False, cache_position=None): + if self.is_sliding and attention_mask is not None: # efficient SDPA and no padding + # # Flash-attn is a 2D tensor + # if self.config["_attn_implementation == "flash_attention_2": + # if past_key_value is not None: # when decoding + # attention_mask = attention_mask[:, -self.sliding_window :] + # else: + min_dtype = torch.finfo(hidden_states.dtype).min + sliding_window_mask = torch.tril( + torch.ones_like(attention_mask, dtype=torch.bool), diagonal=-self.sliding_window + ) + attention_mask = torch.where(sliding_window_mask, min_dtype, attention_mask) + if attention_mask.shape[-1] <= 1: # when decoding + attention_mask = attention_mask[:, :, :, -self.sliding_window :] + + residual = hidden_states + hidden_states = self.input_layernorm(hidden_states) + + # Self Attention + hidden_states, self_attn_weights, present_key_value = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_value=past_key_value, + output_attentions=output_attentions, + use_cache=use_cache, + cache_position=cache_position, + ) + hidden_states = self.post_attention_layernorm(hidden_states) + hidden_states = residual + hidden_states + + residual = hidden_states + hidden_states = self.pre_feedforward_layernorm(hidden_states) + hidden_states = self.mlp(hidden_states) + hidden_states = self.post_feedforward_layernorm(hidden_states) + hidden_states = residual + hidden_states + + outputs = (hidden_states,) + + if output_attentions: + outputs += (self_attn_weights,) + + if use_cache: + outputs += (present_key_value,) + + return outputs + +def prepare_causal_mask(input_tensor, attention_mask): + dtype, device = input_tensor.dtype, input_tensor.device + batch_size=input_tensor.shape[0] + sequence_length = input_tensor.shape[1] + target_length = attention_mask.shape[-1] if attention_mask is not None else input_tensor.shape[1] + + min_dtype = torch.finfo(dtype).min + causal_mask = torch.full( + (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device + ) + if sequence_length != 1: + causal_mask = torch.triu(causal_mask, diagonal=1) + #causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1) + causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1) + if attention_mask is not None: + causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit + mask_length = attention_mask.shape[-1] + padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :] + padding_mask = padding_mask == 0 + causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill( + padding_mask, min_dtype + ) + + return causal_mask + +class Gemma2Model(torch.nn.Module): + def __init__(self, config_dict, dtype, device, operations): + super().__init__() + self.padding_idx = 0 + self.hidden_size = config_dict["hidden_size"] + self.embed_tokens = operations.Embedding(config_dict["vocab_size"], self.hidden_size, self.padding_idx, device=device, dtype=dtype) + self.num_layers = config_dict["num_hidden_layers"] + self.layers = nn.ModuleList( + [Gemma2DecoderLayer(config_dict, layer_idx, dtype=dtype, device=device, operations=operations) for layer_idx in range(config_dict["num_hidden_layers"])] + ) + self.norm = Gemma2RMSNorm(self.hidden_size, eps=config_dict["rms_norm_eps"]) + + def get_input_embeddings(self): + return self.embed_tokens + + def set_input_embeddings(self, value): + self.embed_tokens = value + + def forward(self, input_ids=None, attention_mask=None, position_ids=None, intermediate_output=None, final_layer_norm_intermediate=False, *args, **kwargs): + inputs_embeds = self.embed_tokens(input_ids, out_dtype=kwargs.get("dtype", torch.float32)) + hidden_states = inputs_embeds + intermediate = None + + if attention_mask is not None and position_ids is None: + position_ids = attention_mask.long().cumsum(-1) - 1 + position_ids.masked_fill_(attention_mask == 0, 1) + + # normalized + # Gemma2 downcasts the below to float16, causing sqrt(3072)=55.4256 to become 55.5 + # See https://github.com/huggingface/transformers/pull/29402 + normalizer = torch.tensor(self.hidden_size**0.5, dtype=hidden_states.dtype) + hidden_states = hidden_states * normalizer + + causal_mask = prepare_causal_mask(inputs_embeds, attention_mask) + + if intermediate_output is not None: + if intermediate_output < 0: + intermediate_output = len(self.layers) + intermediate_output + + for i, decoder_layer in enumerate(self.layers): + layer_outputs = decoder_layer( + hidden_states, + attention_mask=causal_mask, + position_ids=position_ids, + past_key_value=None, + output_attentions=False, + use_cache=False, + ) + if i == intermediate_output: + intermediate = hidden_states.clone() + hidden_states = layer_outputs[0] + + hidden_states = self.norm(hidden_states) + if intermediate is not None and final_layer_norm_intermediate: + intermediate = self.norm(intermediate) + + return hidden_states, intermediate diff --git a/text_encoders/sana/tenc.py b/text_encoders/sana/tenc.py new file mode 100644 index 0000000..45da847 --- /dev/null +++ b/text_encoders/sana/tenc.py @@ -0,0 +1,46 @@ +import os +import torch + +from comfy import sd1_clip +import comfy.model_management +from .gemma import Gemma2Model + +from transformers import GemmaTokenizer as TFGemmaTokenizer + +class GemmaClipModel(sd1_clip.SDClipModel): + def __init__(self, device="cpu", dtype=None, model_options={}, **kwargs): + textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "config.json") + special_tokens = {"start": 2, "end": 1, "pad": 0} + super().__init__(device=device, layer="last", layer_idx=None, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens=special_tokens, model_class=Gemma2Model, enable_attention_masks=True, return_attention_masks=False, model_options=model_options) + +class SanaClipModel(sd1_clip.SD1ClipModel): + def __init__(self, device="cpu", dtype=None, model_options={}): + super().__init__(device=device, dtype=dtype, name="gemma", clip_model=GemmaClipModel, model_options=model_options) + +class GemmaTokenizer(sd1_clip.SDTokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + tokenizer_path = os.path.join( + os.path.dirname(os.path.dirname(os.path.realpath(__file__))), + "tokenizers", "gemma_tokenizer", + ) + # TODO: reenable proper logic here - needs comfy version 44db978 or higher + super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=2304, embedding_key='gemma', tokenizer_class=TFGemmaTokenizer, has_start_token=False, pad_to_max_length=True, max_length=300, min_length=1) + self.start_token = 2 + self.end_token = 1 + self.pad_token = 0 + + def tokenize_with_weights(self, text, return_word_ids=False): + # TODO: see above, this is still just a wrapper for now + tokens = self.tokenizer( + text, + max_length=300, + padding="max_length", + truncation=True, + return_tensors="pt" + ) + batched_tokens = [(x.item(), 1.0) for x in tokens.input_ids[0]] + return [batched_tokens] + +class SanaTokenizer(sd1_clip.SD1Tokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="gemma", tokenizer=GemmaTokenizer) diff --git a/text_encoders/tenc.py b/text_encoders/tenc.py new file mode 100644 index 0000000..8ec8acc --- /dev/null +++ b/text_encoders/tenc.py @@ -0,0 +1,81 @@ +import logging +from enum import Enum + +import comfy.sd +import comfy.utils +import comfy.text_encoders +import comfy.model_management + +from .pixart.tenc import pixart_te, PixArtTokenizer +from .sana.tenc import SanaClipModel, SanaTokenizer + +class TencType(Enum): + # offset in case we ever integrate w/ original + PixArt = 1001 + MiaoBi = 1002 + # HunYuan = 1003 # deprecated + Sana = 1004 + +tenc_names = { + # for node readout + "PixArt": TencType.PixArt, + "MiaoBi": TencType.MiaoBi, + # "HunYuan": TencType.HunYuan, + "Sana": TencType.Sana, +} + + +def load_text_encoder(ckpt_paths, embedding_directory=None, clip_type=TencType.PixArt, model_options={}): + # Partial duplicate of ComfyUI/comfy/sd:load_clip + clip_data = [] + for p in ckpt_paths: + if p.lower().endswith(".gguf"): + # TODO: cross-node call w/o code duplication + raise NotImplementedError("Planned!") + else: + clip_data.append(comfy.utils.load_torch_file(p, safe_load=True)) + return load_text_encoder_state_dicts(clip_data, embedding_directory=embedding_directory, clip_type=clip_type, model_options=model_options) + +def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip_type=TencType.PixArt, model_options={}): + # Partial duplicate of ComfyUI/comfy/sd:load_text_encoder_state_dicts + clip_data = state_dicts + + class EmptyClass: + pass + + for i in range(len(clip_data)): + if "transformer.resblocks.0.ln_1.weight" in clip_data[i]: + clip_data[i] = comfy.utils.clip_text_transformers_convert(clip_data[i], "", "") + elif "model.layers.25.post_feedforward_layernorm.weight" in clip_data[i]: + clip_data[i] = {k[len("model."):]:v for k,v in clip_data[i].items()} + else: + if "text_projection" in clip_data[i]: + clip_data[i]["text_projection.weight"] = clip_data[i]["text_projection"].transpose(0, 1) #old models saved with the CLIPSave node + + clip_target = EmptyClass() + clip_target.params = {} + + if clip_type == TencType.PixArt: + clip_target.clip = pixart_te(**comfy.sd.t5xxl_detect(clip_data)) + clip_target.tokenizer = PixArtTokenizer + elif clip_type == TencType.Sana: + clip_target.clip = SanaClipModel + clip_target.tokenizer = SanaTokenizer + else: + raise NotImplementedError(f"Unknown tenc: {clip_type}") + + parameters = 0 + tokenizer_data = {} + for c in clip_data: + parameters += comfy.utils.calculate_parameters(c) + tokenizer_data, model_options = comfy.text_encoders.long_clipl.model_options_long_clip(c, tokenizer_data, model_options) + + clip = comfy.sd.CLIP(clip_target, embedding_directory=embedding_directory, parameters=parameters, tokenizer_data=tokenizer_data, model_options=model_options) + for c in clip_data: + m, u = clip.load_sd(c) + if len(m) > 0: + logging.warning("clip missing: {}".format(m)) + + if len(u) > 0: + logging.debug("clip unexpected: {}".format(u)) + return clip diff --git a/text_encoders/tokenizers/gemma_tokenizer/special_tokens_map.json b/text_encoders/tokenizers/gemma_tokenizer/special_tokens_map.json new file mode 100644 index 0000000..8d6368f --- /dev/null +++ b/text_encoders/tokenizers/gemma_tokenizer/special_tokens_map.json @@ -0,0 +1,34 @@ +{ + "additional_special_tokens": [ + "", + "" + ], + "bos_token": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "eos_token": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "pad_token": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "unk_token": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + } +} diff --git a/HunYuanDiT/mt5_tokenizer/spiece.model b/text_encoders/tokenizers/gemma_tokenizer/tokenizer.model similarity index 63% rename from HunYuanDiT/mt5_tokenizer/spiece.model rename to text_encoders/tokenizers/gemma_tokenizer/tokenizer.model index 26a2a78..14a2422 100644 Binary files a/HunYuanDiT/mt5_tokenizer/spiece.model and b/text_encoders/tokenizers/gemma_tokenizer/tokenizer.model differ diff --git a/text_encoders/tokenizers/gemma_tokenizer/tokenizer_config.json b/text_encoders/tokenizers/gemma_tokenizer/tokenizer_config.json new file mode 100644 index 0000000..f23acea --- /dev/null +++ b/text_encoders/tokenizers/gemma_tokenizer/tokenizer_config.json @@ -0,0 +1,1516 @@ +{ + "add_bos_token": true, + "add_eos_token": false, + "added_tokens_decoder": { + "0": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "1": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "2": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "3": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "4": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": false + }, + "5": { + "content": "<2mass>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": false + }, + "6": { + "content": "[@BOS@]", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": 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"single_word": false, + "special": false + }, + "207": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": false + }, + "208": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": false + }, + "209": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": false + }, + "210": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": false + }, + "211": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": false + }, + "212": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": false + }, + "213": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": false + }, + "214": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": false + }, + "215": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": false + }, + "216": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": false + } + }, + "additional_special_tokens": [ + "", + "" + ], + "bos_token": "", + "clean_up_tokenization_spaces": false, + "eos_token": "", + "model_max_length": 1000000000000000019884624838656, + "pad_token": "", + "sp_model_kwargs": {}, + "spaces_between_special_tokens": false, + "tokenizer_class": "GemmaTokenizer", + "unk_token": "", + "use_default_system_prompt": false +} diff --git a/T5/t5_tokenizer/special_tokens_map.json b/text_encoders/tokenizers/t5_tokenizer/special_tokens_map.json similarity index 100% rename from T5/t5_tokenizer/special_tokens_map.json rename to text_encoders/tokenizers/t5_tokenizer/special_tokens_map.json diff --git a/T5/t5_tokenizer/spiece.model b/text_encoders/tokenizers/t5_tokenizer/spiece.model similarity index 100% rename from T5/t5_tokenizer/spiece.model rename to text_encoders/tokenizers/t5_tokenizer/spiece.model diff --git a/T5/t5_tokenizer/tokenizer_config.json b/text_encoders/tokenizers/t5_tokenizer/tokenizer_config.json similarity index 100% rename from T5/t5_tokenizer/tokenizer_config.json rename to text_encoders/tokenizers/t5_tokenizer/tokenizer_config.json diff --git a/utils/loader.py b/utils/loader.py new file mode 100644 index 0000000..f3f1b77 --- /dev/null +++ b/utils/loader.py @@ -0,0 +1,35 @@ +import logging +import comfy.utils +import comfy.model_patcher +from comfy import model_management + +def load_state_dict_from_config(model_config, sd, model_options={}): + parameters = comfy.utils.calculate_parameters(sd) + load_device = model_management.get_torch_device() + offload_device = comfy.model_management.unet_offload_device() + + dtype = model_options.get("dtype", None) + weight_dtype = comfy.utils.weight_dtype(sd) + unet_weight_dtype = list(model_config.supported_inference_dtypes) + + if weight_dtype is not None and model_config.scaled_fp8 is None: + unet_weight_dtype.append(weight_dtype) + + if dtype is None: + unet_dtype = model_management.unet_dtype(model_params=parameters, supported_dtypes=unet_weight_dtype) + else: + unet_dtype = dtype + + manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device, model_config.supported_inference_dtypes) + model_config.set_inference_dtype(unet_dtype, manual_cast_dtype) + model_config.custom_operations = model_options.get("custom_operations", model_config.custom_operations) + if model_options.get("fp8_optimizations", False): + model_config.optimizations["fp8"] = True + + model = model_config.get_model(sd, "") + model = model.to(offload_device).eval() + model.load_model_weights(sd, "") + left_over = sd.keys() + if len(left_over) > 0: + logging.info("left over keys in unet: {}".format(left_over)) + return comfy.model_patcher.ModelPatcher(model, load_device=load_device, offload_device=offload_device)