From 9f432eaf2261cc7ead8b3a958357dd83399f93c1 Mon Sep 17 00:00:00 2001 From: Kohaku-Blueleaf <59680068+KohakuBlueleaf@users.noreply.github.com> Date: Fri, 21 Jun 2024 18:12:31 +0800 Subject: [PATCH 01/21] Init implementation --- hunyuan_test.py | 112 ++++ library/hunyuan_models.py | 1159 +++++++++++++++++++++++++++++++++++++ library/hunyuan_utils.py | 481 +++++++++++++++ 3 files changed, 1752 insertions(+) create mode 100644 hunyuan_test.py create mode 100644 library/hunyuan_models.py create mode 100644 library/hunyuan_utils.py diff --git a/hunyuan_test.py b/hunyuan_test.py new file mode 100644 index 000000000..7775ad38a --- /dev/null +++ b/hunyuan_test.py @@ -0,0 +1,112 @@ +import numpy as np +import torch + +from k_diffusion.external import DiscreteVDDPMDenoiser +from k_diffusion.sampling import sample_euler_ancestral + +from PIL import Image +from pytorch_lightning import seed_everything + +from library.hunyuan_models import * +from library.hunyuan_utils import * + + +PROMPT = """ +Anime-style illustration of a young girl with long black hair, red eyes, and small red horns, +wearing a traditional white kimono with delicate blue floral patterns and a red obi sash. +She's standing in shallow, reflective water at night, waving with one hand. +Her hair is adorned with red and white flowers. +In the background, a large red torii gate stands prominently, silhouetted against a misty blue night sky. +Multiple glowing paper lanterns float on the water, creating a warm, magical atmosphere. +A ethereal blue waterfall cascades in the distance, surrounded by shadowy trees. +The scene is illuminated by a soft, mystical light, +highlighting the water's surface and creating a dreamy, fantastical mood. +The color palette focuses on deep blues, vibrant reds, and soft whites. +Highly detailed in anime art style with vibrant colors and smooth linework. +""" +NEG_PROMPT = "错误的眼睛,糟糕的人脸,毁容,糟糕的艺术,变形,多余的肢体,模糊的颜色,模糊,重复,病态,残缺" +CLIP_TOKENS = 75*1 + 2 +ATTN_MODE = "xformers" +STEPS = 32 +CFG_SCALE = 4 +DEVICE = "cuda" +DTYPE = torch.bfloat16 + + +if __name__ == "__main__": + seed_everything(0) + with torch.inference_mode(True): + alphas, sigmas = load_scheduler_sigmas() + denoiser, patch_size, head_dim, clip_tokenizer, clip_encoder, mt5_embedder, vae = ( + load_model("./model", dtype=DTYPE, device=DEVICE) + ) + denoiser.enable_gradient_checkpointing() + denoiser.set_attn_mode(ATTN_MODE) + vae.requires_grad_(False) + + with torch.autocast("cuda"): + clip_h, clip_m, mt5_h, mt5_m = get_cond( + PROMPT, + mt5_embedder, + clip_tokenizer, + clip_encoder, + # Should be same as original implemention with max_length_clip=77 + # Support 75*n + 2 + max_length_clip=CLIP_TOKENS + ) + neg_clip_h, neg_clip_m, neg_mt5_h, neg_mt5_m = get_cond( + NEG_PROMPT, + mt5_embedder, + clip_tokenizer, + clip_encoder, + max_length_clip=CLIP_TOKENS + ) + torch.cuda.empty_cache() + + style = torch.as_tensor([0], device=DEVICE) + # src hw, dst hw, 0, 0 + size_cond = [1024, 1024, 1024, 1024, 0, 0] + image_meta_size = torch.as_tensor([size_cond], device=DEVICE) + freqs_cis_img = calc_rope(1024, 1024, patch_size, head_dim) + + denoiser_wrapper = DiscreteVDDPMDenoiser( + # A quick patch for learn_sigma + lambda *args, **kwargs: denoiser(*args, **kwargs).chunk(2, dim=1)[0], + alphas, + False, + ).to(DEVICE) + + def cfg_denoise_func(x, sigma): + cond, uncond = denoiser_wrapper( + x.repeat(2, 1, 1, 1), + sigma.repeat(2), + encoder_hidden_states=torch.concat([clip_h, neg_clip_h], dim=0), + text_embedding_mask=torch.concat([clip_m, neg_clip_m], dim=0), + encoder_hidden_states_t5=torch.concat([mt5_h, neg_mt5_h], dim=0), + text_embedding_mask_t5=torch.concat([mt5_m, neg_mt5_m], dim=0), + image_meta_size=torch.concat([image_meta_size, image_meta_size], dim=0), + style=torch.concat([style, style], dim=0), + cos_cis_img=freqs_cis_img[0], + sin_cis_img=freqs_cis_img[1], + ).chunk(2, dim=0) + return uncond + (cond - uncond) * CFG_SCALE + + sigmas = denoiser_wrapper.get_sigmas(STEPS).to(DEVICE) + x1 = torch.randn(1, 4, 128, 128, dtype=torch.float16, device=DEVICE) + + with torch.autocast("cuda"): + sample = sample_euler_ancestral( + cfg_denoise_func, + x1 * sigmas[0], + sigmas, + ) + torch.cuda.empty_cache() + with torch.no_grad(): + latent = sample / 0.13025 + image = vae.decode(latent).sample + image = (image / 2 + 0.5).clamp(0, 1) + image = image.permute(0, 2, 3, 1).cpu().numpy() + image = (image * 255).round().astype(np.uint8) + image = [Image.fromarray(im) for im in image] + for im in image: + im.save("test.png") diff --git a/library/hunyuan_models.py b/library/hunyuan_models.py new file mode 100644 index 000000000..9100e1c32 --- /dev/null +++ b/library/hunyuan_models.py @@ -0,0 +1,1159 @@ +import math +from typing import Tuple, Union, Optional, Any + +import torch +import torch.utils +import torch.nn as nn +import torch.nn.functional as F +import torch.utils.checkpoint as checkpoint +import einops +from einops import repeat + +from diffusers import AutoencoderKL +from timm.models.vision_transformer import Mlp +from timm.models.layers import to_2tuple +from transformers import ( + AutoTokenizer, + MT5EncoderModel, + BertModel, +) + +memory_efficient_attention = None +try: + import xformers +except: + pass + +try: + from xformers.ops import memory_efficient_attention +except: + memory_efficient_attention = None + + +class MT5Embedder(nn.Module): + def __init__( + self, + model_dir="t5-v1_1-xxl", + model_kwargs=None, + torch_dtype=None, + use_tokenizer_only=False, + max_length=128, + ): + super().__init__() + self.device = "cuda" if torch.cuda.is_available() else "cpu" + self.torch_dtype = torch_dtype or torch.bfloat16 + self.max_length = max_length + if model_kwargs is None: + model_kwargs = { + # "low_cpu_mem_usage": True, + "torch_dtype": self.torch_dtype, + } + model_kwargs["device_map"] = {"shared": self.device, "encoder": self.device} + self.tokenizer = AutoTokenizer.from_pretrained(model_dir) + if use_tokenizer_only: + return + self.model = ( + MT5EncoderModel.from_pretrained(model_dir, **model_kwargs) + .eval() + .to(self.torch_dtype) + ) + + def get_tokens_and_mask(self, texts): + text_tokens_and_mask = self.tokenizer( + texts, + max_length=self.max_length, + padding="max_length", + truncation=True, + return_attention_mask=True, + add_special_tokens=True, + return_tensors="pt", + ) + tokens = text_tokens_and_mask["input_ids"][0] + mask = text_tokens_and_mask["attention_mask"][0] + return tokens, mask + + def get_text_embeddings(self, texts, attention_mask=True, layer_index=-1): + text_tokens_and_mask = self.tokenizer( + texts, + max_length=self.max_length, + padding="max_length", + truncation=True, + return_attention_mask=True, + add_special_tokens=True, + return_tensors="pt", + ) + + outputs = self.model( + input_ids=text_tokens_and_mask["input_ids"].to(self.device), + attention_mask=( + text_tokens_and_mask["attention_mask"].to(self.device) + if attention_mask + else None + ), + output_hidden_states=True, + ) + text_encoder_embs = outputs["hidden_states"][layer_index].detach() + + return text_encoder_embs, text_tokens_and_mask["attention_mask"].to(self.device) + + +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 + + +MEMORY_LAYOUTS = { + "torch": ( + lambda x, head_dim: x.transpose(1, 2), + lambda x: x.transpose(1, 2), + lambda x: (1, x, 1, 1), + ), + "xformers": ( + lambda x, head_dim: x, + lambda x: x.reshape(x.shape[0], x.shape[1], -1), + lambda x: (1, 1, x, 1), + ), + "math": ( + lambda x, head_dim: x.transpose(1, 2), + lambda x: x.transpose(1, 2), + lambda x: (1, x, 1, 1), + ), +} + + +def vanilla_attention(q, k, v, mask, dropout_p, scale=None): + if scale is None: + scale = math.sqrt(q.size(-1)) + scores = torch.bmm(q, k.transpose(-1, -2)) / scale + if mask is not None: + mask = einops.rearrange(mask, "b ... -> b (...)") + max_neg_value = -torch.finfo(scores.dtype).max + mask = einops.repeat(mask, "b j -> (b h) j", h=q.size(-3)) + scores = scores.masked_fill(~mask, max_neg_value) + p_attn = F.softmax(scores, dim=-1) + if dropout_p != 0: + scores = F.dropout(p_attn, p=dropout_p, training=True) + return torch.bmm(p_attn, v) + + +def attention(q, k, v, head_dim, dropout_p=0, mask=None, scale=None, mode="xformers"): + """ + q, k, v: [B, L, H, D] + """ + pre_attn_layout = MEMORY_LAYOUTS[mode][0] + post_attn_layout = MEMORY_LAYOUTS[mode][1] + q = pre_attn_layout(q, head_dim) + k = pre_attn_layout(k, head_dim) + v = pre_attn_layout(v, head_dim) + + # scores = ATTN_FUNCTION[mode](q, k.to(q), v.to(q), mask, scale=scale) + if mode == "torch": + assert scale is None + scores = F.scaled_dot_product_attention(q, k.to(q), v.to(q), mask, dropout_p) # , scale=scale) + elif mode == "xformers": + scores = memory_efficient_attention(q, k.to(q), v.to(q), mask, dropout_p, scale=scale) + else: + scores = vanilla_attention(q, k.to(q), v.to(q), mask, dropout_p, scale=scale) + + scores = post_attn_layout(scores) + return scores + + +class SelfAttention(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, + attn_mode="xformers", + ): + 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.out_proj = nn.Linear(dim, dim, bias=qkv_bias, **factory_kwargs) + self.proj_drop = nn.Dropout(proj_drop) + self.attn_drop = attn_drop + self.attn_mode = attn_mode + + def set_attn_mode(self, mode): + self.attn_mode = mode + + 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) + context = attention(q, k, v, self.head_dim, self.attn_drop, mode=self.attn_mode) + out = self.out_proj(context.view(b, s, d)) + 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, + attn_mode="xformers", + ): + 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.out_proj = nn.Linear(qdim, qdim, bias=qkv_bias, **factory_kwargs) + self.proj_drop = nn.Dropout(proj_drop) + self.attn_drop = attn_drop + self.attn_mode = attn_mode + + def set_attn_mode(self, mode): + self.attn_mode = mode + + 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 = attention(q, k, v, self.head_dim, self.attn_drop, mode=self.attn_mode) + 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 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 + + +class RMSNorm(nn.Module): + def __init__(self, dim: int, elementwise_affine=True, eps: float = 1e-6): + super().__init__() + self.eps = eps + if elementwise_affine: + self.weight = nn.Parameter(torch.ones(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()).type_as(x) + if hasattr(self, "weight"): + output = output * self.weight + return output + + +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) + + +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, + qk_norm=False, + norm_type="layer", + skip=False, + attn_mode="xformers", + ): + super().__init__() + self.attn_mode = attn_mode + 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 + ) + self.attn1 = SelfAttention( + hidden_size, num_heads=num_heads, qkv_bias=True, qk_norm=qk_norm, attn_mode=attn_mode + ) + + # ========================= 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 ========================= + self.attn2 = CrossAttention( + hidden_size, + text_states_dim, + num_heads=num_heads, + qkv_bias=True, + qk_norm=qk_norm,attn_mode=attn_mode + ) + 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 + + self.gradient_checkpointing = False + + def set_attn_mode(self, attn_mode): + self.attn1.set_attn_mode(attn_mode) + self.attn2.set_attn_mode(attn_mode) + + 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 + + def forward(self, *args, **kwargs): + if self.gradient_checkpointing and self.training: + return checkpoint.checkpoint(self._forward, *args, **kwargs) + else: + return self._forward(*args, **kwargs) + + +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. + + Inherit ModelMixin and ConfigMixin to be compatible with the sampler StableDiffusionPipeline of diffusers. + + Inherit PeftAdapterMixin to be compatible with the PEFT training pipeline. + + 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, + qk_norm=True, + norm="layer", + text_states_dim=1024, + text_len=77, + text_states_dim_t5=2048, + text_len_t5=256, + learn_sigma=True, + 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, + attn_mode="xformers" + ): + super().__init__() + self.log_fn = log_fn + self.depth = depth + self.learn_sigma = learn_sigma + self.in_channels = in_channels + self.out_channels = in_channels * 2 if learn_sigma else in_channels + self.patch_size = patch_size + self.num_heads = num_heads + self.hidden_size = hidden_size + self.text_states_dim = text_states_dim + self.text_states_dim_t5 = text_states_dim_t5 + self.text_len = text_len + self.text_len_t5 = text_len_t5 + self.norm = norm + + log_fn(f" Use {attn_mode} attention implementation.") + qk_norm = qk_norm # 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 + ) + + # Here we use a default learned embedder layer for future extension. + self.style_embedder = nn.Embedding(1, hidden_size) + + # Image size and crop size conditions + self.extra_in_dim = 256 * 6 + hidden_size + + # Text embedding for `add` + 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, + qk_norm=qk_norm, + norm_type=self.norm, + skip=layer > depth // 2, + attn_mode=attn_mode + ) + for layer in range(depth) + ] + ) + + self.final_layer = FinalLayer( + hidden_size, hidden_size, patch_size, self.out_channels + ) + self.unpatchify_channels = self.out_channels + + self.initialize_weights() + + def enable_gradient_checkpointing(self): + for block in self.blocks: + block.gradient_checkpointing = True + + def disable_gradient_checkpointing(self): + for block in self.blocks: + block.gradient_checkpointing = False + + def set_attn_mode(self, attn_mode): + for block in self.blocks: + block.set_attn_mode(attn_mode) + + def forward( + 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, + ): + """ + 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 + """ + # MODIFIED BEGIN + 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_cl, l_cl, c_cl = text_states.shape + 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_t5 = text_states_t5.view(b_t5, l_t5, -1) + + # Support for "CLIP Concat" trick + # We allow user to use multiple CLIP embed concat together + # Which means the learanble pad for CLIP only apply on last 76 tokens + padding = self.text_embedding_padding.to(text_states) + padding_clip = torch.concat( + [padding[:1], padding[1:76].repeat((l_cl - 2) // 75, 1), padding[76:77]], + dim=0, + ) + text_states = torch.where( + text_states_mask.unsqueeze(2), + text_states, + padding_clip, + ) + text_states_t5 = torch.where( + text_states_t5_mask.unsqueeze(2), + text_states_t5, + padding[77:], + ) + text_states = torch.cat([text_states, text_states_t5], dim=1) + # MODIFIED END + + _, _, 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) + + # Build image meta size tokens + image_meta_size = timestep_embedding( + image_meta_size.view(-1), 256 + ) # [B * 6, 256] + image_meta_size = image_meta_size.to(x) + 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] + + # 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) # [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) + return x + + 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) + + # 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])) + nn.init.constant_(self.x_embedder.proj.bias, 0) + + # Initialize label embedding table: + nn.init.normal_(self.extra_embedder[0].weight, std=0.02) + nn.init.normal_(self.extra_embedder[2].weight, std=0.02) + + # 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) + + # Zero-out adaLN modulation layers in HunYuanDiT blocks: + for block in self.blocks: + nn.init.constant_(block.default_modulation[-1].weight, 0) + nn.init.constant_(block.default_modulation[-1].bias, 0) + + # Zero-out output layers: + nn.init.constant_(self.final_layer.adaLN_modulation[-1].weight, 0) + nn.init.constant_(self.final_layer.adaLN_modulation[-1].bias, 0) + nn.init.constant_(self.final_layer.linear.weight, 0) + nn.init.constant_(self.final_layer.linear.bias, 0) + + 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 + + +################################################################################# +# HunYuanDiT Configs # +################################################################################# + +HUNYUAN_DIT_CONFIG = { + "DiT-g/2": { + "depth": 40, + "hidden_size": 1408, + "patch_size": 2, + "num_heads": 16, + "mlp_ratio": 4.3637, + }, + "DiT-XL/2": {"depth": 28, "hidden_size": 1152, "patch_size": 2, "num_heads": 16}, +} + + +def DiT_g_2(**kwargs): + return ( + HunYuanDiT( + depth=40, + hidden_size=1408, + patch_size=2, + num_heads=16, + mlp_ratio=4.3637, + **kwargs, + ), + 2, + 88, + ) + + +def DiT_XL_2(**kwargs): + return ( + HunYuanDiT(depth=28, hidden_size=1152, patch_size=2, num_heads=16, **kwargs), + 2, + 72, + ) + + +HUNYUAN_DIT_MODELS = { + "DiT-g/2": DiT_g_2, + "DiT-XL/2": DiT_XL_2, +} + + +if __name__ == "__main__": + denoiser: HunYuanDiT = DiT_g_2(input_size=(128, 128)) + sd = torch.load("./model/denoiser/pytorch_model_module.pt") + denoiser.load_state_dict(sd) + denoiser.half().cuda() + denoiser.enable_gradient_checkpointing() + + clip_tokenizer = AutoTokenizer.from_pretrained("./model/clip") + clip_encoder = BertModel.from_pretrained("./model/clip").half().cuda() + + mt5_embedder = MT5Embedder("./model/mt5", torch_dtype=torch.float16, max_length=256) + + vae = AutoencoderKL.from_pretrained("./model/vae").half().cuda() + + print(sum(p.numel() for p in denoiser.parameters()) / 1e6) + print(sum(p.numel() for p in mt5_embedder.parameters()) / 1e6) + print(sum(p.numel() for p in clip_encoder.parameters()) / 1e6) + print(sum(p.numel() for p in vae.parameters()) / 1e6) diff --git a/library/hunyuan_utils.py b/library/hunyuan_utils.py new file mode 100644 index 000000000..ede8fbb4a --- /dev/null +++ b/library/hunyuan_utils.py @@ -0,0 +1,481 @@ +import os +from typing import Tuple, Union, Optional, Any + +import numpy as np +import torch +import torch.utils + +from diffusers import AutoencoderKL, LMSDiscreteScheduler +from transformers import ( + AutoTokenizer, + BertModel, + BertTokenizer, +) + +from .hunyuan_models import MT5Embedder, HunYuanDiT, BertModel, DiT_g_2 + + +def get_input_ids(caption, tokenizer, tokenizer_max_length=225): + tokens = tokenizer( + caption, + padding="max_length", + truncation=True, + max_length=tokenizer_max_length, + return_tensors="pt", + ) + input_ids = tokens["input_ids"] + masks = tokens["attention_mask"] + + if tokenizer_max_length > tokenizer.model_max_length: + input_ids = input_ids.squeeze(0) + masks = masks.squeeze(0) + iids_list = [] + mask_list = [] + for i in range( + 1, + tokenizer_max_length - tokenizer.model_max_length + 2, + tokenizer.model_max_length - 2, + ): + ids_chunk = ( + input_ids[0].unsqueeze(0), # BOS + input_ids[i : i + tokenizer.model_max_length - 2], + input_ids[-1].unsqueeze(0), + ) # PAD or EOS + ids_chunk = torch.cat(ids_chunk) + mask_chunk = ( + masks[0].unsqueeze(0), + masks[i : i + tokenizer.model_max_length - 2], + masks[-1].unsqueeze(0), + ) + mask_chunk = torch.cat(mask_chunk) + + # 末尾が または の場合は、何もしなくてよい + # 末尾が x の場合は末尾を に変える(x なら結果的に変化なし) + if ( + ids_chunk[-2] != tokenizer.eos_token_id + and ids_chunk[-2] != tokenizer.pad_token_id + ): + ids_chunk[-1] = tokenizer.eos_token_id + # 先頭が ... の場合は ... に変える + if ids_chunk[1] == tokenizer.pad_token_id: + ids_chunk[1] = tokenizer.eos_token_id + + iids_list.append(ids_chunk) + mask_list.append(mask_chunk) + + input_ids = torch.stack(iids_list) # 3,77 + masks = torch.stack(mask_list) # 3,77 + return input_ids, masks + + +def get_hidden_states( + input_ids, + masks, + tokenizer, + text_encoder: BertModel, + max_token_length=225, + weight_dtype=None, +): + # with no_token_padding, the length is not max length, return result immediately + if input_ids.size()[-1] != tokenizer.model_max_length: + return text_encoder(input_ids)[0] + + # input_ids: b,n,77 + b_size = input_ids.size(0) + input_ids = input_ids.reshape((-1, tokenizer.model_max_length)) # batch_size*3, 77 + masks = masks.reshape((-1, tokenizer.model_max_length)) + + encoder_hidden_states = text_encoder(input_ids, attention_mask=masks)[0] + + # bs*3, 77, 768 or 1024 + encoder_hidden_states = encoder_hidden_states.reshape( + (b_size, -1, encoder_hidden_states.shape[-1]) + ) + masks = masks.reshape((b_size, -1)) + + if max_token_length is not None: + states_list = [encoder_hidden_states[:, 0].unsqueeze(1)] # + mask_list = [masks[:, 0].unsqueeze(1)] + for i in range(1, max_token_length, tokenizer.model_max_length): + states_list.append( + encoder_hidden_states[:, i : i + tokenizer.model_max_length - 2] + ) # の後から の前まで + mask_list.append(masks[:, i : i + tokenizer.model_max_length - 2]) + states_list.append(encoder_hidden_states[:, -1].unsqueeze(1)) # + mask_list.append(masks[:, -1].unsqueeze(1)) + + masks = torch.cat(mask_list, dim=1) + encoder_hidden_states = torch.cat(states_list, dim=1) + + if weight_dtype is not None: + # this is required for additional network training + encoder_hidden_states = encoder_hidden_states.to(weight_dtype) + + return encoder_hidden_states.reshape((b_size, -1, encoder_hidden_states.shape[-1])), masks + + +def get_cond( + prompt: str, + mt5_embedder: MT5Embedder, + clip_tokenizer: BertTokenizer, + clip_encoder: BertModel, + max_length_clip: int = 75 * 3 + 2, + dtype=None, + device="cuda", +): + ''' + Get CLIP and mT5 embeddings for HunYuan DiT + Note that this function support "CLIP Concat" trick. + with max_length_clip = 152/227 or higher. + ''' + prompt = prompt.strip() + clip_input_ids, mask = get_input_ids(prompt, clip_tokenizer, max_length_clip) + clip_hidden_states, clip_mask = get_hidden_states( + clip_input_ids.unsqueeze(0).to(device), + mask.to(device), + clip_tokenizer, + clip_encoder, + max_token_length=max_length_clip, + ) + + mt5_hidden_states, mt5_mask = mt5_embedder.get_text_embeddings(prompt) + + return ( + clip_hidden_states.to(dtype), + clip_mask.long().to(device), + mt5_hidden_states.to(dtype), + mt5_mask.long().to(device), + ) + + +def load_scheduler_sigmas(): + scheduler: LMSDiscreteScheduler = LMSDiscreteScheduler.from_pretrained( + "Tencent-Hunyuan/HunyuanDiT-v1.1-Diffusers", + subfolder="scheduler", + ) + return scheduler.alphas_cumprod, scheduler.sigmas + + +def load_model(model_path: str, dtype=torch.float16, device="cuda"): + denoiser: HunYuanDiT + denoiser, patch_size, head_dim = DiT_g_2(input_size=(128, 128)) + sd = torch.load(os.path.join(model_path, "denoiser/pytorch_model_module.pt")) + denoiser.load_state_dict(sd) + denoiser.to(device).to(dtype) + + clip_tokenizer = AutoTokenizer.from_pretrained(os.path.join(model_path, "clip")) + clip_tokenizer.eos_token_id = 2 + clip_encoder = ( + BertModel.from_pretrained(os.path.join(model_path, "clip")).to(device).to(dtype) + ) + + mt5_embedder = ( + MT5Embedder(os.path.join(model_path, "mt5"), torch_dtype=dtype, max_length=256) + .to(device) + .to(dtype) + ) + mt5_embedder.device = device + + vae = ( + AutoencoderKL.from_pretrained(os.path.join(model_path, "vae")) + .to(device) + .to(dtype) + ) + vae.requires_grad_(False) + return ( + denoiser, + patch_size, + head_dim, + clip_tokenizer, + clip_encoder, + mt5_embedder, + vae, + ) + + +def _to_tuple(x): + if isinstance(x, int): + return x, x + else: + return x + + +def get_fill_resize_and_crop(src, tgt): + th, tw = _to_tuple(tgt) + h, w = _to_tuple(src) + + tr = th / tw # base resolution + r = h / w # target resolution + + # 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) + ) # resize the target resolution down based on the base resolution + + 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) + stop = _to_tuple(args[0]) + num = _to_tuple(args[1]) + 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) + 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 / 2.0 + omega = 1.0 / 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:]] + ) # Returns a sampling matrix with the same resolution as the target resolution + 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): + if rope_img == "extend": + # Expansion mode + sub_args = [(th, tw)] + elif rope_img.startswith("base"): + # Based on the specified dimensions, other dimensions are obtained through interpolation. + base_size = int(rope_img[4:]) // 8 // patch_size + start, stop = get_fill_resize_and_crop((th, tw), base_size) + 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) + 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 + + +def calc_rope(height, width, patch_size=2, head_size=64): + th = height // 8 // patch_size + tw = width // 8 // patch_size + base_size = 512 // 8 // 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(head_size, *sub_args) + return rope + + +if __name__ == "__main__": + clip_tokenizer = AutoTokenizer.from_pretrained("./model/clip") + clip_tokenizer.eos_token_id = 2 + clip_encoder = BertModel.from_pretrained("./model/clip").half().cuda() + print(clip_tokenizer.eos_token_id, clip_tokenizer.eos_token) + + mt5_embedder = MT5Embedder( + "./model/mt5", torch_dtype=torch.float16, max_length=256 + ).cuda() + mt5_embedder.device = "cuda" + + clip_h, clip_m, mt5_h, mt5_m = get_cond("""anime style, illustration, masterpiece, +1girl, + +ciloranko, maccha (mochancc), lobelia (saclia), welchino, yanyo (ogino atsuki), + +solo, loli, purple eyes, hair ornament, dragon wings, wings, indoors, dragon girl, +dragon tail, pointy ears, long hair, two side up, earrings, medium breasts, +cleavage, swimsuit, sitting, breasts, jewelry, barefoot, bikini, tail, horns, +looking at viewer, smile, dragon horns, collarbone, feet, navel, purple hair, full body, bare legs, + +masterpiece, newest, absurdres, sensitive +""", + mt5_embedder, + clip_tokenizer, + clip_encoder, + 75*3+2 + ) + print(clip_h.dtype, clip_m.dtype, mt5_h.dtype, mt5_m.dtype) + print(clip_h.shape, clip_m.shape, mt5_h.shape, mt5_m.shape) + print(mt5_m) + print(clip_m) From dabb22fc6e3d5c70873bc9d25a4bb3d4194db6db Mon Sep 17 00:00:00 2001 From: Kohaku-Blueleaf <59680068+KohakuBlueleaf@users.noreply.github.com> Date: Fri, 21 Jun 2024 18:26:51 +0800 Subject: [PATCH 02/21] typo --- hunyuan_test.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/hunyuan_test.py b/hunyuan_test.py index 7775ad38a..b12ff1bbe 100644 --- a/hunyuan_test.py +++ b/hunyuan_test.py @@ -50,7 +50,7 @@ mt5_embedder, clip_tokenizer, clip_encoder, - # Should be same as original implemention with max_length_clip=77 + # Should be same as original implementation with max_length_clip=77 # Support 75*n + 2 max_length_clip=CLIP_TOKENS ) From 148756db004c0a429814e76fc92d1370196968d7 Mon Sep 17 00:00:00 2001 From: Kohaku-Blueleaf <59680068+KohakuBlueleaf@users.noreply.github.com> Date: Fri, 21 Jun 2024 18:38:15 +0800 Subject: [PATCH 03/21] Remove redundant reshape --- library/hunyuan_models.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/library/hunyuan_models.py b/library/hunyuan_models.py index 9100e1c32..1d39f3e54 100644 --- a/library/hunyuan_models.py +++ b/library/hunyuan_models.py @@ -224,7 +224,7 @@ def apply_rotary_emb( ), "xformers": ( lambda x, head_dim: x, - lambda x: x.reshape(x.shape[0], x.shape[1], -1), + lambda x: x, lambda x: (1, 1, x, 1), ), "math": ( From 48f7739f0eec14f72d9975c1d87991f84e1ba507 Mon Sep 17 00:00:00 2001 From: Kohaku-Blueleaf <59680068+KohakuBlueleaf@users.noreply.github.com> Date: Fri, 21 Jun 2024 22:53:58 +0800 Subject: [PATCH 04/21] add get_input_ids/get_hidden_states for t5 --- library/hunyuan_models.py | 37 +++++++++++++++++++++++++++++++------ 1 file changed, 31 insertions(+), 6 deletions(-) diff --git a/library/hunyuan_models.py b/library/hunyuan_models.py index 1d39f3e54..b5af5645d 100644 --- a/library/hunyuan_models.py +++ b/library/hunyuan_models.py @@ -96,6 +96,22 @@ def get_text_embeddings(self, texts, attention_mask=True, layer_index=-1): return text_encoder_embs, text_tokens_and_mask["attention_mask"].to(self.device) + def get_input_ids(self, caption): + return self.tokenizer( + caption, + padding="max_length", + truncation=True, + max_length=self.max_length, + return_tensors="pt", + ).input_ids + + def get_hidden_states(self, input_ids, layer_index=-1): + mask = (input_ids != 0).long() + outputs = self.model( + input_ids=input_ids, attention_mask=mask, output_hidden_states=True + ) + return outputs["hidden_states"][layer_index] + def reshape_for_broadcast( freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]], @@ -263,9 +279,13 @@ def attention(q, k, v, head_dim, dropout_p=0, mask=None, scale=None, mode="xform # scores = ATTN_FUNCTION[mode](q, k.to(q), v.to(q), mask, scale=scale) if mode == "torch": assert scale is None - scores = F.scaled_dot_product_attention(q, k.to(q), v.to(q), mask, dropout_p) # , scale=scale) + scores = F.scaled_dot_product_attention( + q, k.to(q), v.to(q), mask, dropout_p + ) # , scale=scale) elif mode == "xformers": - scores = memory_efficient_attention(q, k.to(q), v.to(q), mask, dropout_p, scale=scale) + scores = memory_efficient_attention( + q, k.to(q), v.to(q), mask, dropout_p, scale=scale + ) else: scores = vanilla_attention(q, k.to(q), v.to(q), mask, dropout_p, scale=scale) @@ -685,7 +705,11 @@ def __init__( hidden_size, elementwise_affine=use_ele_affine, eps=1e-6 ) self.attn1 = SelfAttention( - hidden_size, num_heads=num_heads, qkv_bias=True, qk_norm=qk_norm, attn_mode=attn_mode + hidden_size, + num_heads=num_heads, + qkv_bias=True, + qk_norm=qk_norm, + attn_mode=attn_mode, ) # ========================= FFN ========================= @@ -713,7 +737,8 @@ def __init__( text_states_dim, num_heads=num_heads, qkv_bias=True, - qk_norm=qk_norm,attn_mode=attn_mode + qk_norm=qk_norm, + attn_mode=attn_mode, ) self.norm3 = norm_layer(hidden_size, elementwise_affine=True, eps=1e-6) @@ -835,7 +860,7 @@ def __init__( num_heads=16, mlp_ratio=4.0, log_fn=print, - attn_mode="xformers" + attn_mode="xformers", ): super().__init__() self.log_fn = log_fn @@ -906,7 +931,7 @@ def __init__( qk_norm=qk_norm, norm_type=self.norm, skip=layer > depth // 2, - attn_mode=attn_mode + attn_mode=attn_mode, ) for layer in range(depth) ] From dc6c97ef8f73d5592e5c24b4ebb2153116e25cd1 Mon Sep 17 00:00:00 2001 From: Kohaku-Blueleaf <59680068+KohakuBlueleaf@users.noreply.github.com> Date: Fri, 21 Jun 2024 23:19:05 +0800 Subject: [PATCH 05/21] better impl to fit Kohya style --- library/hunyuan_models.py | 2 +- library/hunyuan_utils.py | 117 +++++++++++++++++++++++++++++--------- 2 files changed, 92 insertions(+), 27 deletions(-) diff --git a/library/hunyuan_models.py b/library/hunyuan_models.py index b5af5645d..ff14ecd7a 100644 --- a/library/hunyuan_models.py +++ b/library/hunyuan_models.py @@ -110,7 +110,7 @@ def get_hidden_states(self, input_ids, layer_index=-1): outputs = self.model( input_ids=input_ids, attention_mask=mask, output_hidden_states=True ) - return outputs["hidden_states"][layer_index] + return outputs["hidden_states"][layer_index], mask def reshape_for_broadcast( diff --git a/library/hunyuan_utils.py b/library/hunyuan_utils.py index ede8fbb4a..eb3f722d7 100644 --- a/library/hunyuan_utils.py +++ b/library/hunyuan_utils.py @@ -15,7 +15,7 @@ from .hunyuan_models import MT5Embedder, HunYuanDiT, BertModel, DiT_g_2 -def get_input_ids(caption, tokenizer, tokenizer_max_length=225): +def clip_get_input_ids(caption, tokenizer, tokenizer_max_length=225): tokens = tokenizer( caption, padding="max_length", @@ -24,13 +24,10 @@ def get_input_ids(caption, tokenizer, tokenizer_max_length=225): return_tensors="pt", ) input_ids = tokens["input_ids"] - masks = tokens["attention_mask"] if tokenizer_max_length > tokenizer.model_max_length: input_ids = input_ids.squeeze(0) - masks = masks.squeeze(0) iids_list = [] - mask_list = [] for i in range( 1, tokenizer_max_length - tokenizer.model_max_length + 2, @@ -42,12 +39,6 @@ def get_input_ids(caption, tokenizer, tokenizer_max_length=225): input_ids[-1].unsqueeze(0), ) # PAD or EOS ids_chunk = torch.cat(ids_chunk) - mask_chunk = ( - masks[0].unsqueeze(0), - masks[i : i + tokenizer.model_max_length - 2], - masks[-1].unsqueeze(0), - ) - mask_chunk = torch.cat(mask_chunk) # 末尾が または の場合は、何もしなくてよい # 末尾が x の場合は末尾を に変える(x なら結果的に変化なし) @@ -61,16 +52,13 @@ def get_input_ids(caption, tokenizer, tokenizer_max_length=225): ids_chunk[1] = tokenizer.eos_token_id iids_list.append(ids_chunk) - mask_list.append(mask_chunk) input_ids = torch.stack(iids_list) # 3,77 - masks = torch.stack(mask_list) # 3,77 - return input_ids, masks + return input_ids -def get_hidden_states( +def clip_get_hidden_states( input_ids, - masks, tokenizer, text_encoder: BertModel, max_token_length=225, @@ -83,7 +71,7 @@ def get_hidden_states( # input_ids: b,n,77 b_size = input_ids.size(0) input_ids = input_ids.reshape((-1, tokenizer.model_max_length)) # batch_size*3, 77 - masks = masks.reshape((-1, tokenizer.model_max_length)) + masks = (input_ids != tokenizer.pad_token_id).long() encoder_hidden_states = text_encoder(input_ids, attention_mask=masks)[0] @@ -92,6 +80,7 @@ def get_hidden_states( (b_size, -1, encoder_hidden_states.shape[-1]) ) masks = masks.reshape((b_size, -1)) + input_ids = input_ids.reshape((b_size, -1)) if max_token_length is not None: states_list = [encoder_hidden_states[:, 0].unsqueeze(1)] # @@ -100,7 +89,11 @@ def get_hidden_states( states_list.append( encoder_hidden_states[:, i : i + tokenizer.model_max_length - 2] ) # の後から の前まで - mask_list.append(masks[:, i : i + tokenizer.model_max_length - 2]) + ids = input_ids[:, i : i + tokenizer.model_max_length - 2] + mask_list.append( + masks[:, i : i + tokenizer.model_max_length - 2] + * (ids[:, :1] != tokenizer.eos_token_id) + ) states_list.append(encoder_hidden_states[:, -1].unsqueeze(1)) # mask_list.append(masks[:, -1].unsqueeze(1)) @@ -111,7 +104,60 @@ def get_hidden_states( # this is required for additional network training encoder_hidden_states = encoder_hidden_states.to(weight_dtype) - return encoder_hidden_states.reshape((b_size, -1, encoder_hidden_states.shape[-1])), masks + return ( + encoder_hidden_states.reshape((b_size, -1, encoder_hidden_states.shape[-1])), + masks, + ) + + +def hunyuan_get_input_ids( + caption: str, + max_token_length: int, + tokenizer1: BertTokenizer, + tokenizer2, +): + input_ids1 = clip_get_input_ids(caption, tokenizer1, max_token_length) + input_ids2 = tokenizer2( + caption, + padding="max_length", + truncation=True, + max_length=256, + return_tensors="pt", + ).input_ids + return input_ids1, input_ids2 + + +def hunyuan_get_hidden_states( + max_token_length: int, + input_ids1: torch.Tensor, + input_ids2: torch.Tensor, + tokenizer1: BertTokenizer, + tokenizer2, + text_encoder1: BertModel, + text_encoder2: MT5Embedder, + weight_dtype: Optional[str] = None, + accelerator=None, +): + device = ( + accelerator.device + if accelerator is not None + else next(text_encoder1.parameters()).device + ) + input_ids1 = input_ids1.to(device) + input_ids2 = input_ids2.to(device) + clip_hidden_states, clip_mask = clip_get_hidden_states( + input_ids1.unsqueeze(0).to(device), + tokenizer1, + clip_encoder, + max_token_length=max_token_length + 2, + ) + mt5_hidden_states, mt5_mask = text_encoder2.get_hidden_states(input_ids2) + return ( + clip_hidden_states.to(weight_dtype), + clip_mask.long().to(device), + mt5_hidden_states.to(weight_dtype), + mt5_mask.long().to(device), + ) def get_cond( @@ -123,16 +169,15 @@ def get_cond( dtype=None, device="cuda", ): - ''' + """ Get CLIP and mT5 embeddings for HunYuan DiT Note that this function support "CLIP Concat" trick. with max_length_clip = 152/227 or higher. - ''' + """ prompt = prompt.strip() - clip_input_ids, mask = get_input_ids(prompt, clip_tokenizer, max_length_clip) - clip_hidden_states, clip_mask = get_hidden_states( + clip_input_ids = clip_get_input_ids(prompt, clip_tokenizer, max_length_clip) + clip_hidden_states, clip_mask = clip_get_hidden_states( clip_input_ids.unsqueeze(0).to(device), - mask.to(device), clip_tokenizer, clip_encoder, max_token_length=max_length_clip, @@ -451,14 +496,17 @@ def calc_rope(height, width, patch_size=2, head_size=64): clip_tokenizer = AutoTokenizer.from_pretrained("./model/clip") clip_tokenizer.eos_token_id = 2 clip_encoder = BertModel.from_pretrained("./model/clip").half().cuda() - print(clip_tokenizer.eos_token_id, clip_tokenizer.eos_token) mt5_embedder = MT5Embedder( "./model/mt5", torch_dtype=torch.float16, max_length=256 ).cuda() mt5_embedder.device = "cuda" - clip_h, clip_m, mt5_h, mt5_m = get_cond("""anime style, illustration, masterpiece, + print(clip_tokenizer.pad_token_id, mt5_embedder.tokenizer.pad_token_id) + print(clip_tokenizer.eos_token_id, mt5_embedder.tokenizer.eos_token_id) + + clip_h, clip_m, mt5_h, mt5_m = get_cond( + """anime style, illustration, masterpiece, 1girl, ciloranko, maccha (mochancc), lobelia (saclia), welchino, yanyo (ogino atsuki), @@ -473,7 +521,24 @@ def calc_rope(height, width, patch_size=2, head_size=64): mt5_embedder, clip_tokenizer, clip_encoder, - 75*3+2 + 75 * 3 + 2, + ) + print(clip_h.dtype, clip_m.dtype, mt5_h.dtype, mt5_m.dtype) + print(clip_h.shape, clip_m.shape, mt5_h.shape, mt5_m.shape) + print(mt5_m) + print(clip_m) + + input_ids1, input_ids2 = hunyuan_get_input_ids( + "Hello HunYuan DiT", 77, clip_tokenizer, mt5_embedder.tokenizer + ) + clip_h, clip_m, mt5_h, mt5_m = hunyuan_get_hidden_states( + 77, + input_ids1, + input_ids2, + clip_tokenizer, + mt5_embedder.tokenizer, + clip_encoder, + mt5_embedder, ) print(clip_h.dtype, clip_m.dtype, mt5_h.dtype, mt5_m.dtype) print(clip_h.shape, clip_m.shape, mt5_h.shape, mt5_m.shape) From efe49437042d4c0f34d4d69fc89f5ef1d06a7e74 Mon Sep 17 00:00:00 2001 From: Kohaku-Blueleaf <59680068+KohakuBlueleaf@users.noreply.github.com> Date: Fri, 21 Jun 2024 23:48:12 +0800 Subject: [PATCH 06/21] Init model loader --- library/hunyuan_models.py | 4 ++ library/hunyuan_utils.py | 75 ++++++++++++++++++++++++++++++++++++++ test.png | Bin 0 -> 1601001 bytes 3 files changed, 79 insertions(+) create mode 100644 test.png diff --git a/library/hunyuan_models.py b/library/hunyuan_models.py index ff14ecd7a..0cad2c0d3 100644 --- a/library/hunyuan_models.py +++ b/library/hunyuan_models.py @@ -30,6 +30,10 @@ memory_efficient_attention = None +VAE_SCALE_FACTOR = 0.13025 +MODEL_VERSION_HUNYUAN_V1_1 = "HunyuanDiT-v1.1" + + class MT5Embedder(nn.Module): def __init__( self, diff --git a/library/hunyuan_utils.py b/library/hunyuan_utils.py index eb3f722d7..548672991 100644 --- a/library/hunyuan_utils.py +++ b/library/hunyuan_utils.py @@ -8,11 +8,23 @@ from diffusers import AutoencoderKL, LMSDiscreteScheduler from transformers import ( AutoTokenizer, + T5Tokenizer, BertModel, BertTokenizer, ) +from library.device_utils import init_ipex, clean_memory_on_device from .hunyuan_models import MT5Embedder, HunYuanDiT, BertModel, DiT_g_2 +from .utils import setup_logging + +setup_logging() +import logging + +logger = logging.getLogger(__name__) + +BASE_PATH = "Tencent-Hunyuan/HunyuanDiT-v1.1-Diffusers" +TOKENIZER1_PATH = "tokenizer" +TOKENIZER2_PATH = "tokenizer_2" def clip_get_input_ids(caption, tokenizer, tokenizer_max_length=225): @@ -193,6 +205,19 @@ def get_cond( ) +def load_tokenizers(): + tokenizer = AutoTokenizer.from_pretrained( + BASE_PATH, + subfolder=TOKENIZER1_PATH, + ) + tokenizer.eos_token_id = tokenizer.sep_token_id + tokenizer2 = T5Tokenizer( + BASE_PATH, + subfolder=TOKENIZER2_PATH, + ) + return tokenizer, tokenizer2 + + def load_scheduler_sigmas(): scheduler: LMSDiscreteScheduler = LMSDiscreteScheduler.from_pretrained( "Tencent-Hunyuan/HunyuanDiT-v1.1-Diffusers", @@ -238,6 +263,56 @@ def load_model(model_path: str, dtype=torch.float16, device="cuda"): ) +def match_mixed_precision(args, weight_dtype): + if args.full_fp16: + assert ( + weight_dtype == torch.float16 + ), "full_fp16 requires mixed precision='fp16' / full_fp16を使う場合はmixed_precision='fp16'を指定してください。" + return weight_dtype + elif args.full_bf16: + assert ( + weight_dtype == torch.bfloat16 + ), "full_bf16 requires mixed precision='bf16' / full_bf16を使う場合はmixed_precision='bf16'を指定してください。" + return weight_dtype + else: + return None + + +def load_target_model(args, accelerator, model_version: str, weight_dtype): + model_dtype = match_mixed_precision(args, weight_dtype) # prepare fp16/bf16 + for pi in range(accelerator.state.num_processes): + if pi == accelerator.state.local_process_index: + logger.info( + f"loading model for process {accelerator.state.local_process_index}/{accelerator.state.num_processes}" + ) + + ( + denoiser, + patch_size, + head_dim, + clip_tokenizer, + clip_encoder, + mt5_embedder, + vae, + ) = load_model( + args.pretrained_model_name_or_path, + model_dtype, + accelerator.device if args.lowram else "cpu", + ) + + # work on low-ram device + if args.lowram: + clip_encoder.to(accelerator.device) + mt5_embedder.to(accelerator.device) + denoiser.to(accelerator.device) + vae.to(accelerator.device) + + clean_memory_on_device(accelerator.device) + accelerator.wait_for_everyone() + + return False, clip_encoder, mt5_embedder, vae, denoiser, None, None + + def _to_tuple(x): if isinstance(x, int): return x, x diff --git a/test.png b/test.png new file mode 100644 index 0000000000000000000000000000000000000000..3414a78a5ddcd4910311517cff0b2512953390b4 GIT binary patch literal 1601001 zcmV)1K+V62P)WMYy}aIo|GbfN-OWE^`NEqJsckaJftHF*SBcHf}E;e{$a)F2LOZ z2_QgFW&%JG6bxj32NT%0CWj_y2H&CkoVy@WLR3`Ubc&`8D~0Y=l5 z1H-2vQIfmo`cJ2IL+f_gyct@PQJ{!9WdtM%nvx5GoN&fNOQIOh1A#&Vn`A%-4K#q5 z1V|YLFqbZ_3B=U}7ub@(GASY@G7#Lg>2Gq}&NIww8hj0um_1 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From e30c503fca8ca07ec0dca851afefdaf1316eb965 Mon Sep 17 00:00:00 2001 From: Kohaku-Blueleaf <59680068+KohakuBlueleaf@users.noreply.github.com> Date: Sat, 22 Jun 2024 00:10:54 +0800 Subject: [PATCH 08/21] Some improvements --- hunyuan_test.py | 22 +++++----------- library/hunyuan_models.py | 55 ++++++++++++++++++++++++++------------- 2 files changed, 44 insertions(+), 33 deletions(-) diff --git a/hunyuan_test.py b/hunyuan_test.py index b12ff1bbe..4db051e88 100644 --- a/hunyuan_test.py +++ b/hunyuan_test.py @@ -12,25 +12,15 @@ PROMPT = """ -Anime-style illustration of a young girl with long black hair, red eyes, and small red horns, -wearing a traditional white kimono with delicate blue floral patterns and a red obi sash. -She's standing in shallow, reflective water at night, waving with one hand. -Her hair is adorned with red and white flowers. -In the background, a large red torii gate stands prominently, silhouetted against a misty blue night sky. -Multiple glowing paper lanterns float on the water, creating a warm, magical atmosphere. -A ethereal blue waterfall cascades in the distance, surrounded by shadowy trees. -The scene is illuminated by a soft, mystical light, -highlighting the water's surface and creating a dreamy, fantastical mood. -The color palette focuses on deep blues, vibrant reds, and soft whites. -Highly detailed in anime art style with vibrant colors and smooth linework. +Very beautiful Steampunk lady, long silver hair, steampunk outfit and weapon, hyperrealism, photorealistic, 8k, unreal engine """ NEG_PROMPT = "错误的眼睛,糟糕的人脸,毁容,糟糕的艺术,变形,多余的肢体,模糊的颜色,模糊,重复,病态,残缺" CLIP_TOKENS = 75*1 + 2 ATTN_MODE = "xformers" -STEPS = 32 -CFG_SCALE = 4 +STEPS = 16 +CFG_SCALE = 7 DEVICE = "cuda" -DTYPE = torch.bfloat16 +DTYPE = torch.float16 if __name__ == "__main__": @@ -40,8 +30,10 @@ denoiser, patch_size, head_dim, clip_tokenizer, clip_encoder, mt5_embedder, vae = ( load_model("./model", dtype=DTYPE, device=DEVICE) ) - denoiser.enable_gradient_checkpointing() + # denoiser.enable_gradient_checkpointing() denoiser.set_attn_mode(ATTN_MODE) + denoiser.disable_fp32_layer_norm() + denoiser.disable_fp32_silu vae.requires_grad_(False) with torch.autocast("cuda"): diff --git a/library/hunyuan_models.py b/library/hunyuan_models.py index 0cad2c0d3..6b0a819d5 100644 --- a/library/hunyuan_models.py +++ b/library/hunyuan_models.py @@ -359,8 +359,8 @@ def forward(self, x, freqs_cis_img=None): 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() + q = self.q_norm(q).to(q) # [b, s, h, d] + k = self.k_norm(k).to(q) # Apply RoPE if needed if freqs_cis_img is not None: @@ -373,7 +373,7 @@ def forward(self, x, freqs_cis_img=None): # qkv = torch.stack([q, k, v], dim=2) # [b, s, 3, h, d] # context = self.inner_attn(qkv) context = attention(q, k, v, self.head_dim, self.attn_drop, mode=self.attn_mode) - out = self.out_proj(context.view(b, s, d)) + out = self.out_proj(context.reshape(b, s, d)) out = self.proj_drop(out) out_tuple = (out,) @@ -455,8 +455,8 @@ def forward(self, x, y, freqs_cis_img=None): 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] + q = self.q_norm(q).to(q) # [b, s1, h, d] + k = self.k_norm(k).to(k) # [b, s2, h, d] # Apply RoPE if needed if freqs_cis_img is not None: @@ -466,7 +466,7 @@ def forward(self, x, y, freqs_cis_img=None): # kv = torch.stack([k, v], dim=2) # [b, s1, 2, h, d] # context = self.inner_attn(q, kv) # [b, s1, h, d] context = attention(q, k, v, self.head_dim, self.attn_drop, mode=self.attn_mode) - context = context.view(b, s1, -1) # [b, s1, D] + context = context.reshape(b, s1, -1) # [b, s1, D] out = self.out_proj(context) out = self.proj_drop(out) @@ -553,9 +553,8 @@ def timestep_embedding(t, dim, max_period=10000, repeat_only=False): if not repeat_only: half = dim // 2 freqs = torch.exp( - -math.log(max_period) + (-math.log(max_period) / half) * 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 @@ -566,7 +565,7 @@ def timestep_embedding(t, dim, max_period=10000, repeat_only=False): [embedding, torch.zeros_like(embedding[:, :1])], dim=-1 ) else: - embedding = repeat(t, "b -> b d", d=dim) + embedding = t.unsqueeze(-1).repeat(1, dim) return embedding @@ -660,20 +659,28 @@ def modulate(x, shift, scale): class FP32_Layernorm(nn.LayerNorm): + enable_fp32 = True 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) + if self.enable_fp32: + return F.layer_norm( + inputs.float(), + self.normalized_shape, + self.weight.float(), + self.bias.float(), + self.eps, + ).to(inputs.dtype) + else: + return F.layer_norm( + inputs, self.normalized_shape, self.weight, self.bias, self.eps + ) class FP32_SiLU(nn.SiLU): + enable_fp32 = True def forward(self, inputs: torch.Tensor) -> torch.Tensor: - return torch.nn.functional.silu(inputs.float(), inplace=False).to(inputs.dtype) + if self.enable_fp32: + return torch.nn.functional.silu(inputs.float(), inplace=False).to(inputs.dtype) + return torch.nn.functional.silu(inputs, inplace=False).to(inputs.dtype) class HunYuanDiTBlock(nn.Module): @@ -960,6 +967,18 @@ def set_attn_mode(self, attn_mode): for block in self.blocks: block.set_attn_mode(attn_mode) + def enable_fp32_layer_norm(self): + FP32_Layernorm.enable_fp32 = True + + def disable_fp32_layer_norm(self): + FP32_Layernorm.enable_fp32 = False + + def enable_fp32_silu(self): + FP32_SiLU.enable_fp32 = True + + def disable_fp32_silu(self): + FP32_SiLU.enable_fp32 = False + def forward( self, x, From 09620b4a00952136ccc8d780c789f07d2b7e53f3 Mon Sep 17 00:00:00 2001 From: Kohaku-Blueleaf <59680068+KohakuBlueleaf@users.noreply.github.com> Date: Sat, 22 Jun 2024 00:18:00 +0800 Subject: [PATCH 09/21] Update hunyuan_test.py --- hunyuan_test.py | 26 ++++++++++++++------------ 1 file changed, 14 insertions(+), 12 deletions(-) diff --git a/hunyuan_test.py b/hunyuan_test.py index 4db051e88..42907a076 100644 --- a/hunyuan_test.py +++ b/hunyuan_test.py @@ -25,15 +25,13 @@ if __name__ == "__main__": seed_everything(0) - with torch.inference_mode(True): + with torch.inference_mode(True), torch.no_grad(): alphas, sigmas = load_scheduler_sigmas() denoiser, patch_size, head_dim, clip_tokenizer, clip_encoder, mt5_embedder, vae = ( load_model("./model", dtype=DTYPE, device=DEVICE) ) - # denoiser.enable_gradient_checkpointing() + denoiser.eval() denoiser.set_attn_mode(ATTN_MODE) - denoiser.disable_fp32_layer_norm() - denoiser.disable_fp32_silu vae.requires_grad_(False) with torch.autocast("cuda"): @@ -53,12 +51,16 @@ clip_encoder, max_length_clip=CLIP_TOKENS ) + clip_h = torch.concat([clip_h, neg_clip_h], dim=0) + clip_m = torch.concat([clip_m, neg_clip_m], dim=0) + mt5_h = torch.concat([mt5_h, neg_mt5_h], dim=0) + mt5_m = torch.concat([mt5_m, neg_mt5_m], dim=0) torch.cuda.empty_cache() - style = torch.as_tensor([0], device=DEVICE) + style = torch.as_tensor([0]*2, device=DEVICE) # src hw, dst hw, 0, 0 size_cond = [1024, 1024, 1024, 1024, 0, 0] - image_meta_size = torch.as_tensor([size_cond], device=DEVICE) + image_meta_size = torch.as_tensor([size_cond]*2, device=DEVICE) freqs_cis_img = calc_rope(1024, 1024, patch_size, head_dim) denoiser_wrapper = DiscreteVDDPMDenoiser( @@ -72,12 +74,12 @@ def cfg_denoise_func(x, sigma): cond, uncond = denoiser_wrapper( x.repeat(2, 1, 1, 1), sigma.repeat(2), - encoder_hidden_states=torch.concat([clip_h, neg_clip_h], dim=0), - text_embedding_mask=torch.concat([clip_m, neg_clip_m], dim=0), - encoder_hidden_states_t5=torch.concat([mt5_h, neg_mt5_h], dim=0), - text_embedding_mask_t5=torch.concat([mt5_m, neg_mt5_m], dim=0), - image_meta_size=torch.concat([image_meta_size, image_meta_size], dim=0), - style=torch.concat([style, style], dim=0), + encoder_hidden_states=clip_h, + text_embedding_mask=clip_m, + encoder_hidden_states_t5=mt5_h, + text_embedding_mask_t5=mt5_m, + image_meta_size=image_meta_size, + style=style, cos_cis_img=freqs_cis_img[0], sin_cis_img=freqs_cis_img[1], ).chunk(2, dim=0) From 982cf79d43c4d8619476a3683e9e1844d859eba7 Mon Sep 17 00:00:00 2001 From: Kohaku-Blueleaf <59680068+KohakuBlueleaf@users.noreply.github.com> Date: Sat, 22 Jun 2024 01:07:14 +0800 Subject: [PATCH 10/21] A quick conversion of train_network from sdxl ver --- hunyuan_train_network.py | 199 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 199 insertions(+) create mode 100644 hunyuan_train_network.py diff --git a/hunyuan_train_network.py b/hunyuan_train_network.py new file mode 100644 index 000000000..977c738ee --- /dev/null +++ b/hunyuan_train_network.py @@ -0,0 +1,199 @@ +import argparse + +import torch +from library.device_utils import init_ipex, clean_memory_on_device + +init_ipex() + +from library import ( + hunyuan_models, + hunyuan_utils, + sdxl_model_util, + sdxl_train_util, + train_util, +) +import train_network +from library.utils import setup_logging + +setup_logging() +import logging + +logger = logging.getLogger(__name__) + + +class HunYuanNetworkTrainer(train_network.NetworkTrainer): + def __init__(self): + super().__init__() + self.vae_scale_factor = sdxl_model_util.VAE_SCALE_FACTOR + self.is_sdxl = True + + def assert_extra_args(self, args, train_dataset_group): + super().assert_extra_args(args, train_dataset_group) + sdxl_train_util.verify_sdxl_training_args(args) + + if args.cache_text_encoder_outputs: + assert ( + train_dataset_group.is_text_encoder_output_cacheable() + ), "when caching Text Encoder output, either caption_dropout_rate, shuffle_caption, token_warmup_step or caption_tag_dropout_rate cannot be used / Text Encoderの出力をキャッシュするときはcaption_dropout_rate, shuffle_caption, token_warmup_step, caption_tag_dropout_rateは使えません" + + assert ( + args.network_train_unet_only or not args.cache_text_encoder_outputs + ), "network for Text Encoder cannot be trained with caching Text Encoder outputs / Text Encoderの出力をキャッシュしながらText Encoderのネットワークを学習することはできません" + + train_dataset_group.verify_bucket_reso_steps(16) + + def load_target_model(self, args, weight_dtype, accelerator): + ( + load_stable_diffusion_format, + text_encoder1, + text_encoder2, + vae, + unet, + logit_scale, + ckpt_info, + ) = hunyuan_utils.load_target_model( + args, + accelerator, + hunyuan_models.MODEL_VERSION_HUNYUAN_V1_1, + weight_dtype, + ) + + self.load_stable_diffusion_format = load_stable_diffusion_format + self.logit_scale = logit_scale + self.ckpt_info = ckpt_info + + return ( + hunyuan_models.MODEL_VERSION_HUNYUAN_V1_1, + [text_encoder1, text_encoder2], + vae, + unet, + ) + + def load_tokenizer(self, args): + tokenizer = hunyuan_utils.load_tokenizers(args) + return tokenizer + + def is_text_encoder_outputs_cached(self, args): + return args.cache_text_encoder_outputs + + def cache_text_encoder_outputs_if_needed( + self, + args, + accelerator, + unet, + vae, + tokenizers, + text_encoders, + dataset: train_util.DatasetGroup, + weight_dtype, + ): + if args.cache_text_encoder_outputs: + raise NotImplementedError + else: + # Text Encoderから毎回出力を取得するので、GPUに乗せておく + text_encoders[0].to(accelerator.device, dtype=weight_dtype) + text_encoders[1].to(accelerator.device, dtype=weight_dtype) + + def get_text_cond( + self, args, accelerator, batch, tokenizers, text_encoders, weight_dtype + ): + if ( + "text_encoder_outputs1_list" not in batch + or batch["text_encoder_outputs1_list"] is None + ): + input_ids1 = batch["input_ids"] + input_ids2 = batch["input_ids2"] + with torch.enable_grad(): + input_ids1 = input_ids1.to(accelerator.device) + input_ids2 = input_ids2.to(accelerator.device) + encoder_hidden_states1, mask1, encoder_hidden_states2, mask2 = ( + hunyuan_utils.hunyuan_get_hidden_states( + args.max_token_length, + input_ids1, + input_ids2, + tokenizers[0], + tokenizers[1], + text_encoders[0], + text_encoders[1], + None if not args.full_fp16 else weight_dtype, + accelerator=accelerator, + ) + ) + else: + raise NotImplementedError + return encoder_hidden_states1, mask1, encoder_hidden_states2, mask2 + + def call_unet( + self, + args, + accelerator, + unet, + noisy_latents, + timesteps, + text_conds, + batch, + weight_dtype, + ): + noisy_latents = noisy_latents.to( + weight_dtype + ) # TODO check why noisy_latents is not weight_dtype + + # get size embeddings + orig_size = batch["original_sizes_hw"] + crop_size = batch["crop_top_lefts"] + target_size = batch["target_sizes_hw"] + B, C, H, W = noisy_latents.shape + + # TODO implement correct meta_size info + style = torch.as_tensor([0] * B, device=accelerator.device) + # src hw, dst hw, 0, 0 + size_cond = [1024, 1024, 1024, 1024, 0, 0] + image_meta_size = torch.as_tensor([size_cond], device=accelerator.device) + freqs_cis_img = hunyuan_utils.calc_rope(H*8, W*8, 2, 88) + + # concat embeddings + encoder_hidden_states1, mask1, encoder_hidden_states2, mask2 = text_conds + noise_pred = unet( + noisy_latents, + timesteps, + encoder_hidden_states=encoder_hidden_states1, + text_embedding_mask=mask1, + encoder_hidden_states_t5=encoder_hidden_states2, + text_embedding_mask_t5=mask2, + image_meta_size=None, + style=style, + cos_cis_img=freqs_cis_img[0], + sin_cis_img=freqs_cis_img[1], + ) + return noise_pred + + def sample_images( + self, + accelerator, + args, + epoch, + global_step, + device, + vae, + tokenizer, + text_encoder, + unet, + ): + raise NotImplementedError + + +def setup_parser() -> argparse.ArgumentParser: + parser = train_network.setup_parser() + sdxl_train_util.add_sdxl_training_arguments(parser) + return parser + + +if __name__ == "__main__": + parser = setup_parser() + + args = parser.parse_args() + train_util.verify_command_line_training_args(args) + args = train_util.read_config_from_file(args, parser) + + trainer = HunYuanNetworkTrainer() + trainer.train(args) From fb3e8a7acdaf19484e943a80854fbd1be9b06ea1 Mon Sep 17 00:00:00 2001 From: Kohaku-Blueleaf <59680068+KohakuBlueleaf@users.noreply.github.com> Date: Sat, 22 Jun 2024 13:08:39 +0800 Subject: [PATCH 11/21] initial support of lycoris/lora + hunyuan dit --- hunyuan_test.py | 26 ++++++++----- hunyuan_train_network.py | 54 +++++++++++++++++++------- library/hunyuan_models.py | 79 ++++++++++++++++++++++++++++++++++++--- library/hunyuan_utils.py | 11 +++--- library/train_util.py | 17 ++++++++- requirements.txt | 4 ++ train_network.py | 14 +++++-- 7 files changed, 167 insertions(+), 38 deletions(-) diff --git a/hunyuan_test.py b/hunyuan_test.py index 42907a076..0c02473d9 100644 --- a/hunyuan_test.py +++ b/hunyuan_test.py @@ -15,9 +15,9 @@ Very beautiful Steampunk lady, long silver hair, steampunk outfit and weapon, hyperrealism, photorealistic, 8k, unreal engine """ NEG_PROMPT = "错误的眼睛,糟糕的人脸,毁容,糟糕的艺术,变形,多余的肢体,模糊的颜色,模糊,重复,病态,残缺" -CLIP_TOKENS = 75*1 + 2 +CLIP_TOKENS = 75 * 1 + 2 ATTN_MODE = "xformers" -STEPS = 16 +STEPS = 50 CFG_SCALE = 7 DEVICE = "cuda" DTYPE = torch.float16 @@ -27,10 +27,18 @@ seed_everything(0) with torch.inference_mode(True), torch.no_grad(): alphas, sigmas = load_scheduler_sigmas() - denoiser, patch_size, head_dim, clip_tokenizer, clip_encoder, mt5_embedder, vae = ( - load_model("./model", dtype=DTYPE, device=DEVICE) - ) + ( + denoiser, + patch_size, + head_dim, + clip_tokenizer, + clip_encoder, + mt5_embedder, + vae, + ) = load_model("./model", dtype=DTYPE, device=DEVICE) denoiser.eval() + denoiser.disable_fp32_silu() + denoiser.disable_fp32_layer_norm() denoiser.set_attn_mode(ATTN_MODE) vae.requires_grad_(False) @@ -42,14 +50,14 @@ clip_encoder, # Should be same as original implementation with max_length_clip=77 # Support 75*n + 2 - max_length_clip=CLIP_TOKENS + max_length_clip=CLIP_TOKENS, ) neg_clip_h, neg_clip_m, neg_mt5_h, neg_mt5_m = get_cond( NEG_PROMPT, mt5_embedder, clip_tokenizer, clip_encoder, - max_length_clip=CLIP_TOKENS + max_length_clip=CLIP_TOKENS, ) clip_h = torch.concat([clip_h, neg_clip_h], dim=0) clip_m = torch.concat([clip_m, neg_clip_m], dim=0) @@ -57,10 +65,10 @@ mt5_m = torch.concat([mt5_m, neg_mt5_m], dim=0) torch.cuda.empty_cache() - style = torch.as_tensor([0]*2, device=DEVICE) + style = torch.as_tensor([0] * 2, device=DEVICE) # src hw, dst hw, 0, 0 size_cond = [1024, 1024, 1024, 1024, 0, 0] - image_meta_size = torch.as_tensor([size_cond]*2, device=DEVICE) + image_meta_size = torch.as_tensor([size_cond] * 2, device=DEVICE) freqs_cis_img = calc_rope(1024, 1024, patch_size, head_dim) denoiser_wrapper = DiscreteVDDPMDenoiser( diff --git a/hunyuan_train_network.py b/hunyuan_train_network.py index 977c738ee..40dcd4953 100644 --- a/hunyuan_train_network.py +++ b/hunyuan_train_network.py @@ -29,7 +29,7 @@ def __init__(self): def assert_extra_args(self, args, train_dataset_group): super().assert_extra_args(args, train_dataset_group) - sdxl_train_util.verify_sdxl_training_args(args) + # sdxl_train_util.verify_sdxl_training_args(args) if args.cache_text_encoder_outputs: assert ( @@ -70,7 +70,7 @@ def load_target_model(self, args, weight_dtype, accelerator): ) def load_tokenizer(self, args): - tokenizer = hunyuan_utils.load_tokenizers(args) + tokenizer = hunyuan_utils.load_tokenizers() return tokenizer def is_text_encoder_outputs_cached(self, args): @@ -103,6 +103,8 @@ def get_text_cond( ): input_ids1 = batch["input_ids"] input_ids2 = batch["input_ids2"] + print("input_ids1", input_ids1.shape) + print("input_ids2", input_ids2.shape) with torch.enable_grad(): input_ids1 = input_ids1.to(accelerator.device) input_ids2 = input_ids2.to(accelerator.device) @@ -119,6 +121,8 @@ def get_text_cond( accelerator=accelerator, ) ) + print("encoder_hidden_states1", encoder_hidden_states1.shape) + print("encoder_hidden_states2", encoder_hidden_states2.shape) else: raise NotImplementedError return encoder_hidden_states1, mask1, encoder_hidden_states2, mask2 @@ -139,17 +143,22 @@ def call_unet( ) # TODO check why noisy_latents is not weight_dtype # get size embeddings - orig_size = batch["original_sizes_hw"] - crop_size = batch["crop_top_lefts"] - target_size = batch["target_sizes_hw"] + orig_size = batch["original_sizes_hw"] # B, 2 + crop_size = batch["crop_top_lefts"] # B, 2 + target_size = batch["target_sizes_hw"] # B, 2 B, C, H, W = noisy_latents.shape - - # TODO implement correct meta_size info + style = torch.as_tensor([0] * B, device=accelerator.device) - # src hw, dst hw, 0, 0 - size_cond = [1024, 1024, 1024, 1024, 0, 0] - image_meta_size = torch.as_tensor([size_cond], device=accelerator.device) - freqs_cis_img = hunyuan_utils.calc_rope(H*8, W*8, 2, 88) + image_meta_size = torch.concat( + [ + orig_size, + target_size, + # Not following SDXL but following HunYuan's Implementation + # TODO examine if this is correct + torch.zeros_like(target_size), + ] + ) + freqs_cis_img = hunyuan_utils.calc_rope(H * 8, W * 8, 2, 88) # concat embeddings encoder_hidden_states1, mask1, encoder_hidden_states2, mask2 = text_conds @@ -160,12 +169,13 @@ def call_unet( text_embedding_mask=mask1, encoder_hidden_states_t5=encoder_hidden_states2, text_embedding_mask_t5=mask2, - image_meta_size=None, + image_meta_size=image_meta_size, style=style, cos_cis_img=freqs_cis_img[0], sin_cis_img=freqs_cis_img[1], ) - return noise_pred + # TODO Handle learned sigma correctly + return noise_pred.chunk(2, dim=1)[0] def sample_images( self, @@ -179,7 +189,23 @@ def sample_images( text_encoder, unet, ): - raise NotImplementedError + steps = global_step + if steps == 0: + if not args.sample_at_first: + return + else: + if args.sample_every_n_steps is None and args.sample_every_n_epochs is None: + return + if args.sample_every_n_epochs is not None: + # sample_every_n_steps は無視する + if epoch is None or epoch % args.sample_every_n_epochs != 0: + return + else: + if ( + steps % args.sample_every_n_steps != 0 or epoch is not None + ): # steps is not divisible or end of epoch + return + logger.warning("Sampling images not supported yet.") def setup_parser() -> argparse.ArgumentParser: diff --git a/library/hunyuan_models.py b/library/hunyuan_models.py index 6b0a819d5..1cceb06aa 100644 --- a/library/hunyuan_models.py +++ b/library/hunyuan_models.py @@ -44,7 +44,6 @@ def __init__( max_length=128, ): super().__init__() - self.device = "cuda" if torch.cuda.is_available() else "cpu" self.torch_dtype = torch_dtype or torch.bfloat16 self.max_length = max_length if model_kwargs is None: @@ -52,7 +51,6 @@ def __init__( # "low_cpu_mem_usage": True, "torch_dtype": self.torch_dtype, } - model_kwargs["device_map"] = {"shared": self.device, "encoder": self.device} self.tokenizer = AutoTokenizer.from_pretrained(model_dir) if use_tokenizer_only: return @@ -61,6 +59,53 @@ def __init__( .eval() .to(self.torch_dtype) ) + self.register_buffer("device", torch.tensor(0.0), persistent=False) + + def get_token_embedding(self): + return self.model.shared + + def gradient_checkpointing_enable(self): + for block in self.model.encoder.block: + block.org_forward = block.forward + + def mt5_block_forward( + hidden_states, + attention_mask=None, + position_bias=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + encoder_decoder_position_bias=None, + layer_head_mask=None, + cross_attn_layer_head_mask=None, + past_key_value=None, + use_cache=False, + output_attentions=False, + return_dict=True, + ): + return checkpoint.checkpoint( + block.org_forward, + hidden_states, + attention_mask, + position_bias, + encoder_hidden_states, + encoder_attention_mask, + encoder_decoder_position_bias, + layer_head_mask, + cross_attn_layer_head_mask, + past_key_value, + use_cache, + output_attentions, + return_dict, + use_reentrant=False, + ) + + block.forward = mt5_block_forward + + def gradient_checkpointing_disable(self): + for block in self.model.encoder.block: + if hasattr(block, "org_forward"): + block.forward = block.org_forward + delattr(block, "org_forward") def get_tokens_and_mask(self, texts): text_tokens_and_mask = self.tokenizer( @@ -110,6 +155,8 @@ def get_input_ids(self, caption): ).input_ids def get_hidden_states(self, input_ids, layer_index=-1): + if input_ids.dim() == 3: + input_ids = input_ids.view(input_ids.size(0), -1) mask = (input_ids != 0).long() outputs = self.model( input_ids=input_ids, attention_mask=mask, output_hidden_states=True @@ -660,6 +707,7 @@ def modulate(x, shift, scale): class FP32_Layernorm(nn.LayerNorm): enable_fp32 = True + def forward(self, inputs: torch.Tensor) -> torch.Tensor: if self.enable_fp32: return F.layer_norm( @@ -677,9 +725,12 @@ def forward(self, inputs: torch.Tensor) -> torch.Tensor: class FP32_SiLU(nn.SiLU): enable_fp32 = True + def forward(self, inputs: torch.Tensor) -> torch.Tensor: if self.enable_fp32: - return torch.nn.functional.silu(inputs.float(), inplace=False).to(inputs.dtype) + return torch.nn.functional.silu(inputs.float(), inplace=False).to( + inputs.dtype + ) return torch.nn.functional.silu(inputs, inplace=False).to(inputs.dtype) @@ -793,11 +844,19 @@ def _forward(self, x, c=None, text_states=None, freq_cis_img=None, skip=None): return x - def forward(self, *args, **kwargs): + def forward(self, x, c=None, text_states=None, freq_cis_img=None, skip=None): if self.gradient_checkpointing and self.training: - return checkpoint.checkpoint(self._forward, *args, **kwargs) + return checkpoint.checkpoint( + self._forward, + x, + c, + text_states, + freq_cis_img, + skip, + use_reentrant=False, + ) else: - return self._forward(*args, **kwargs) + return self._forward(x, c, text_states, freq_cis_img, skip) class FinalLayer(nn.Module): @@ -967,6 +1026,14 @@ def set_attn_mode(self, attn_mode): for block in self.blocks: block.set_attn_mode(attn_mode) + def set_use_memory_efficient_attention(self, xformers, mem_eff): + if xformers: + self.set_attn_mode("xformers") + elif mem_eff: + self.set_attn_mode("torch") + else: + self.set_attn_mode("vanilla") + def enable_fp32_layer_norm(self): FP32_Layernorm.enable_fp32 = True diff --git a/library/hunyuan_utils.py b/library/hunyuan_utils.py index 548672991..1d4a0d334 100644 --- a/library/hunyuan_utils.py +++ b/library/hunyuan_utils.py @@ -157,10 +157,12 @@ def hunyuan_get_hidden_states( ) input_ids1 = input_ids1.to(device) input_ids2 = input_ids2.to(device) + if input_ids1.dim() == 2: + input_ids1 = input_ids1.unsqueeze(0) clip_hidden_states, clip_mask = clip_get_hidden_states( - input_ids1.unsqueeze(0).to(device), + input_ids1.to(device), tokenizer1, - clip_encoder, + text_encoder1, max_token_length=max_token_length + 2, ) mt5_hidden_states, mt5_mask = text_encoder2.get_hidden_states(input_ids2) @@ -211,11 +213,11 @@ def load_tokenizers(): subfolder=TOKENIZER1_PATH, ) tokenizer.eos_token_id = tokenizer.sep_token_id - tokenizer2 = T5Tokenizer( + tokenizer2 = T5Tokenizer.from_pretrained( BASE_PATH, subfolder=TOKENIZER2_PATH, ) - return tokenizer, tokenizer2 + return [tokenizer, tokenizer2] def load_scheduler_sigmas(): @@ -244,7 +246,6 @@ def load_model(model_path: str, dtype=torch.float16, device="cuda"): .to(device) .to(dtype) ) - mt5_embedder.device = device vae = ( AutoencoderKL.from_pretrained(os.path.join(model_path, "vae")) diff --git a/library/train_util.py b/library/train_util.py index 4736ff4ff..2a027b1bd 100644 --- a/library/train_util.py +++ b/library/train_util.py @@ -40,7 +40,7 @@ from torch.nn.parallel import DistributedDataParallel as DDP from torch.optim import Optimizer from torchvision import transforms -from transformers import CLIPTokenizer, CLIPTextModel, CLIPTextModelWithProjection +from transformers import CLIPTokenizer, CLIPTextModel, CLIPTextModelWithProjection, T5Tokenizer import transformers from diffusers.optimization import SchedulerType, TYPE_TO_SCHEDULER_FUNCTION from diffusers import ( @@ -68,6 +68,7 @@ import safetensors.torch from library.lpw_stable_diffusion import StableDiffusionLongPromptWeightingPipeline import library.model_util as model_util +import library.hunyuan_utils as hunyuan_utils import library.huggingface_util as huggingface_util import library.sai_model_spec as sai_model_spec import library.deepspeed_utils as deepspeed_utils @@ -812,6 +813,20 @@ def get_input_ids(self, caption, tokenizer=None): if tokenizer is None: tokenizer = self.tokenizers[0] + # HunYuan DiT + if not isinstance(tokenizer, CLIPTokenizer): + if isinstance(tokenizer, T5Tokenizer): + result = tokenizer( + caption, + padding="max_length", + truncation=True, + max_length=256, + return_tensors="pt", + ).input_ids + else: + result = hunyuan_utils.clip_get_input_ids(caption, tokenizer, self.tokenizer_max_length) + return result + input_ids = tokenizer( caption, padding="max_length", truncation=True, max_length=self.tokenizer_max_length, return_tensors="pt" ).input_ids diff --git a/requirements.txt b/requirements.txt index e99775b8a..38288f31d 100644 --- a/requirements.txt +++ b/requirements.txt @@ -32,6 +32,10 @@ imagesize==1.4.1 # for cuda 12.1(default 11.8) # onnxruntime-gpu --extra-index-url https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/onnxruntime-cuda-12/pypi/simple/ +# for HunYuanDiT +sentencepiece==0.2.0 +timm==1.0.7 + # this is for onnx: # protobuf==3.20.3 # open clip for SDXL diff --git a/train_network.py b/train_network.py index 7ba073855..b68296d5a 100644 --- a/train_network.py +++ b/train_network.py @@ -427,8 +427,15 @@ def train(self, args): # in case of cpu, dtype is already set to fp32 because cpu does not support fp8/fp16/bf16 if t_enc.device.type != "cpu": t_enc.to(dtype=te_weight_dtype) - # nn.Embedding not support FP8 - t_enc.text_model.embeddings.to(dtype=(weight_dtype if te_weight_dtype != weight_dtype else te_weight_dtype)) + # nn.Embedding not support FP8 + if hasattr(t_enc, "text_model"): + t_enc.text_model.embeddings.to(dtype=(weight_dtype if te_weight_dtype != weight_dtype else te_weight_dtype)) + elif hasattr(t_enc, "embeddings"): + # HunYuan Bert(CLIP) + t_enc.embeddings.to(dtype=(weight_dtype if te_weight_dtype != weight_dtype else te_weight_dtype)) + elif hasattr(t_enc, "get_token_embedding"): + # Others (mT5 or other encoder, will have custom method to get the correct embedding) + t_enc.get_token_embedding().to(dtype=(weight_dtype if te_weight_dtype != weight_dtype else te_weight_dtype)) # acceleratorがなんかよろしくやってくれるらしい / accelerator will do something good if args.deepspeed: @@ -960,7 +967,8 @@ def remove_model(old_ckpt_name): for x in noisy_latents: x.requires_grad_(True) for t in text_encoder_conds: - t.requires_grad_(True) + if t.dtype in {torch.float16, torch.bfloat16, torch.float32}: + t.requires_grad_(True) # Predict the noise residual with accelerator.autocast(): From 32f15c9093f9d2e96fb12a133c4a4951ef93583c Mon Sep 17 00:00:00 2001 From: Kohaku-Blueleaf <59680068+KohakuBlueleaf@users.noreply.github.com> Date: Sat, 22 Jun 2024 13:10:00 +0800 Subject: [PATCH 12/21] Remove debug print --- hunyuan_train_network.py | 4 ---- 1 file changed, 4 deletions(-) diff --git a/hunyuan_train_network.py b/hunyuan_train_network.py index 40dcd4953..bfaca869e 100644 --- a/hunyuan_train_network.py +++ b/hunyuan_train_network.py @@ -103,8 +103,6 @@ def get_text_cond( ): input_ids1 = batch["input_ids"] input_ids2 = batch["input_ids2"] - print("input_ids1", input_ids1.shape) - print("input_ids2", input_ids2.shape) with torch.enable_grad(): input_ids1 = input_ids1.to(accelerator.device) input_ids2 = input_ids2.to(accelerator.device) @@ -121,8 +119,6 @@ def get_text_cond( accelerator=accelerator, ) ) - print("encoder_hidden_states1", encoder_hidden_states1.shape) - print("encoder_hidden_states2", encoder_hidden_states2.shape) else: raise NotImplementedError return encoder_hidden_states1, mask1, encoder_hidden_states2, mask2 From 6155efcc34b611b52f214863eba3d3a476955bfa Mon Sep 17 00:00:00 2001 From: Kohaku-Blueleaf <59680068+KohakuBlueleaf@users.noreply.github.com> Date: Sat, 22 Jun 2024 13:33:25 +0800 Subject: [PATCH 13/21] Support train TE --- hunyuan_train_network.py | 8 ++++++++ train_network.py | 10 +++++++++- 2 files changed, 17 insertions(+), 1 deletion(-) diff --git a/hunyuan_train_network.py b/hunyuan_train_network.py index bfaca869e..5b9e5d07e 100644 --- a/hunyuan_train_network.py +++ b/hunyuan_train_network.py @@ -1,3 +1,7 @@ +import warnings + +warnings.filterwarnings("ignore") + import argparse import torch @@ -103,6 +107,8 @@ def get_text_cond( ): input_ids1 = batch["input_ids"] input_ids2 = batch["input_ids2"] + logger.debug("input_ids1", input_ids1.shape) + logger.debug("input_ids2", input_ids2.shape) with torch.enable_grad(): input_ids1 = input_ids1.to(accelerator.device) input_ids2 = input_ids2.to(accelerator.device) @@ -119,6 +125,8 @@ def get_text_cond( accelerator=accelerator, ) ) + logger.debug("encoder_hidden_states1", encoder_hidden_states1.shape) + logger.debug("encoder_hidden_states2", encoder_hidden_states2.shape) else: raise NotImplementedError return encoder_hidden_states1, mask1, encoder_hidden_states2, mask2 diff --git a/train_network.py b/train_network.py index b68296d5a..650a84ff5 100644 --- a/train_network.py +++ b/train_network.py @@ -477,7 +477,15 @@ def train(self, args): # set top parameter requires_grad = True for gradient checkpointing works if train_text_encoder: - t_enc.text_model.embeddings.requires_grad_(True) + if hasattr(t_enc, "text_model"): + t_enc.text_model.embeddings.requires_grad_(True) + elif hasattr(t_enc, "embeddings"): + # HunYuan Bert(CLIP) + t_enc.embeddings.requires_grad_(True) + elif hasattr(t_enc, "get_token_embedding"): + # Others (mT5 or other encoder, will have custom method to get the correct embedding) + t_enc.get_token_embedding().requires_grad_(True) + else: unet.eval() From 580b48978817a43d6d33e6ca3241d0bc353c20fc Mon Sep 17 00:00:00 2001 From: Kohaku-Blueleaf <59680068+KohakuBlueleaf@users.noreply.github.com> Date: Sat, 22 Jun 2024 14:57:19 +0800 Subject: [PATCH 14/21] Fix NaN error --- hunyuan_test.py | 3 +- library/hunyuan_models.py | 84 +++++++++++++++++++++++---------------- train_network.py | 1 - 3 files changed, 52 insertions(+), 36 deletions(-) diff --git a/hunyuan_test.py b/hunyuan_test.py index 0c02473d9..e04aa4f28 100644 --- a/hunyuan_test.py +++ b/hunyuan_test.py @@ -17,7 +17,7 @@ NEG_PROMPT = "错误的眼睛,糟糕的人脸,毁容,糟糕的艺术,变形,多余的肢体,模糊的颜色,模糊,重复,病态,残缺" CLIP_TOKENS = 75 * 1 + 2 ATTN_MODE = "xformers" -STEPS = 50 +STEPS = 16 CFG_SCALE = 7 DEVICE = "cuda" DTYPE = torch.float16 @@ -41,6 +41,7 @@ denoiser.disable_fp32_layer_norm() denoiser.set_attn_mode(ATTN_MODE) vae.requires_grad_(False) + mt5_embedder.to(torch.float16) with torch.autocast("cuda"): clip_h, clip_m, mt5_h, mt5_m = get_cond( diff --git a/library/hunyuan_models.py b/library/hunyuan_models.py index 1cceb06aa..db5aadaaf 100644 --- a/library/hunyuan_models.py +++ b/library/hunyuan_models.py @@ -56,7 +56,6 @@ def __init__( return self.model = ( MT5EncoderModel.from_pretrained(model_dir, **model_kwargs) - .eval() .to(self.torch_dtype) ) self.register_buffer("device", torch.tensor(0.0), persistent=False) @@ -68,38 +67,55 @@ def gradient_checkpointing_enable(self): for block in self.model.encoder.block: block.org_forward = block.forward - def mt5_block_forward( - hidden_states, - attention_mask=None, - position_bias=None, - encoder_hidden_states=None, - encoder_attention_mask=None, - encoder_decoder_position_bias=None, - layer_head_mask=None, - cross_attn_layer_head_mask=None, - past_key_value=None, - use_cache=False, - output_attentions=False, - return_dict=True, - ): - return checkpoint.checkpoint( - block.org_forward, + def wrapper(block): + def mt5_block_forward( hidden_states, - attention_mask, - position_bias, - encoder_hidden_states, - encoder_attention_mask, - encoder_decoder_position_bias, - layer_head_mask, - cross_attn_layer_head_mask, - past_key_value, - use_cache, - output_attentions, - return_dict, - use_reentrant=False, - ) - - block.forward = mt5_block_forward + attention_mask=None, + position_bias=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + encoder_decoder_position_bias=None, + layer_head_mask=None, + cross_attn_layer_head_mask=None, + past_key_value=None, + use_cache=False, + output_attentions=False, + return_dict=True, + ): + if not block.training: + return block.org_forward( + hidden_states, + attention_mask, + position_bias, + encoder_hidden_states, + encoder_attention_mask, + encoder_decoder_position_bias, + layer_head_mask, + cross_attn_layer_head_mask, + past_key_value, + use_cache, + output_attentions, + return_dict, + ) + return checkpoint.checkpoint( + block.org_forward, + hidden_states, + attention_mask, + position_bias, + encoder_hidden_states, + encoder_attention_mask, + encoder_decoder_position_bias, + layer_head_mask, + cross_attn_layer_head_mask, + past_key_value, + use_cache, + output_attentions, + return_dict, + use_reentrant=False, + ) + return mt5_block_forward + + block.forward = wrapper(block) def gradient_checkpointing_disable(self): for block in self.model.encoder.block: @@ -133,9 +149,9 @@ def get_text_embeddings(self, texts, attention_mask=True, layer_index=-1): ) outputs = self.model( - input_ids=text_tokens_and_mask["input_ids"].to(self.device), + input_ids=text_tokens_and_mask["input_ids"].to(self.device.device), attention_mask=( - text_tokens_and_mask["attention_mask"].to(self.device) + text_tokens_and_mask["attention_mask"].to(self.device.device) if attention_mask else None ), diff --git a/train_network.py b/train_network.py index 650a84ff5..781671241 100644 --- a/train_network.py +++ b/train_network.py @@ -486,7 +486,6 @@ def train(self, args): # Others (mT5 or other encoder, will have custom method to get the correct embedding) t_enc.get_token_embedding().requires_grad_(True) - else: unet.eval() for t_enc in text_encoders: From 945688dafd7935be9cb0118686c8eebac869de39 Mon Sep 17 00:00:00 2001 From: Kohaku-Blueleaf <59680068+KohakuBlueleaf@users.noreply.github.com> Date: Sat, 22 Jun 2024 19:04:59 +0800 Subject: [PATCH 15/21] Add lycoris sample code --- hunyuan_test.py | 49 ++++++++++++++++++++++++++++++++++++++++++------- 1 file changed, 42 insertions(+), 7 deletions(-) diff --git a/hunyuan_test.py b/hunyuan_test.py index e04aa4f28..65dc4f5ac 100644 --- a/hunyuan_test.py +++ b/hunyuan_test.py @@ -2,7 +2,7 @@ import torch from k_diffusion.external import DiscreteVDDPMDenoiser -from k_diffusion.sampling import sample_euler_ancestral +from k_diffusion.sampling import sample_euler_ancestral, get_sigmas_exponential from PIL import Image from pytorch_lightning import seed_everything @@ -10,15 +10,32 @@ from library.hunyuan_models import * from library.hunyuan_utils import * +from lycoris.kohya import create_network_from_weights + PROMPT = """ -Very beautiful Steampunk lady, long silver hair, steampunk outfit and weapon, hyperrealism, photorealistic, 8k, unreal engine +2girls, multiple girls, + +cesario (umamusume), daring tact (umamusume), umamusume, + +shino (ponjiyuusu), + +aged down, ahoge, animal ears, black hair, blue hair, blue sky, braid, cloud, crown braid, +day, green eyes, horse ears, horse girl, horse tail, loafers, long hair, mole, mole under eye, +multicolored hair, outdoors, pleated skirt, purple eyes, purple sailor collar, purple shirt, +purple skirt, purple thighhighs, sailor collar, school uniform, shirt, shoes, short hair, +skirt, sky, tail, thighhighs, time paradox, tracen school uniform, two-tone hair, white hair, +winter uniform, + +masterpiece, absurdres, latest, safe """ NEG_PROMPT = "错误的眼睛,糟糕的人脸,毁容,糟糕的艺术,变形,多余的肢体,模糊的颜色,模糊,重复,病态,残缺" -CLIP_TOKENS = 75 * 1 + 2 +CLIP_TOKENS = 75 * 3 + 2 ATTN_MODE = "xformers" +H = 1024 +W = 1024 STEPS = 16 -CFG_SCALE = 7 +CFG_SCALE = 5 DEVICE = "cuda" DTYPE = torch.float16 @@ -43,6 +60,21 @@ vae.requires_grad_(False) mt5_embedder.to(torch.float16) + lycoris_net, state_dict = create_network_from_weights( + multiplier=1.0, + file="./output/lokr-hy-test-step00000600.safetensors", + vae=vae, + text_encoder=[clip_encoder, mt5_embedder], + unet=denoiser, + ) + lycoris_net.merge_to( + text_encoder=[clip_encoder, mt5_embedder], + unet=denoiser, + weights_sd=state_dict, + dtype=DTYPE, + device=DEVICE, + ) + with torch.autocast("cuda"): clip_h, clip_m, mt5_h, mt5_m = get_cond( PROMPT, @@ -68,9 +100,9 @@ style = torch.as_tensor([0] * 2, device=DEVICE) # src hw, dst hw, 0, 0 - size_cond = [1024, 1024, 1024, 1024, 0, 0] + size_cond = [H, W, H, W, 0, 0] image_meta_size = torch.as_tensor([size_cond] * 2, device=DEVICE) - freqs_cis_img = calc_rope(1024, 1024, patch_size, head_dim) + freqs_cis_img = calc_rope(H, W, patch_size, head_dim) denoiser_wrapper = DiscreteVDDPMDenoiser( # A quick patch for learn_sigma @@ -95,7 +127,10 @@ def cfg_denoise_func(x, sigma): return uncond + (cond - uncond) * CFG_SCALE sigmas = denoiser_wrapper.get_sigmas(STEPS).to(DEVICE) - x1 = torch.randn(1, 4, 128, 128, dtype=torch.float16, device=DEVICE) + sigmas = get_sigmas_exponential( + STEPS, denoiser_wrapper.sigma_min, denoiser_wrapper.sigma_max, DEVICE + ) + x1 = torch.randn(1, 4, H//8, W//8, dtype=torch.float16, device=DEVICE) with torch.autocast("cuda"): sample = sample_euler_ancestral( From c0a936e7f2e9dbcd864a725b79ec467f1a48be3d Mon Sep 17 00:00:00 2001 From: Kohaku-Blueleaf <59680068+KohakuBlueleaf@users.noreply.github.com> Date: Wed, 26 Jun 2024 03:42:55 +0800 Subject: [PATCH 16/21] move load scheduler to method --- hunyuan_train_network.py | 11 +++++++++++ train_network.py | 10 +++++++--- 2 files changed, 18 insertions(+), 3 deletions(-) diff --git a/hunyuan_train_network.py b/hunyuan_train_network.py index 5b9e5d07e..85e448497 100644 --- a/hunyuan_train_network.py +++ b/hunyuan_train_network.py @@ -5,6 +5,7 @@ import argparse import torch +from diffusers import DDPMScheduler from library.device_utils import init_ipex, clean_memory_on_device init_ipex() @@ -77,6 +78,16 @@ def load_tokenizer(self, args): tokenizer = hunyuan_utils.load_tokenizers() return tokenizer + def load_noise_scheduler(self): + return DDPMScheduler( + beta_start=0.00085, + beta_end=0.03, + beta_schedule="scaled_linear", + num_train_timesteps=1000, + clip_sample=False, + steps_offset=1 + ) + def is_text_encoder_outputs_cached(self, args): return args.cache_text_encoder_outputs diff --git a/train_network.py b/train_network.py index 781671241..0b9ab100f 100644 --- a/train_network.py +++ b/train_network.py @@ -105,6 +105,12 @@ def load_tokenizer(self, args): tokenizer = train_util.load_tokenizer(args) return tokenizer + def load_noise_scheduler(self): + noise_scheduler = DDPMScheduler( + beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", num_train_timesteps=1000, clip_sample=False + ) + return noise_scheduler + def is_text_encoder_outputs_cached(self, args): return False @@ -839,9 +845,7 @@ def load_model_hook(models, input_dir): global_step = 0 - noise_scheduler = DDPMScheduler( - beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", num_train_timesteps=1000, clip_sample=False - ) + noise_scheduler = self.load_noise_scheduler() prepare_scheduler_for_custom_training(noise_scheduler, accelerator.device) if args.zero_terminal_snr: custom_train_functions.fix_noise_scheduler_betas_for_zero_terminal_snr(noise_scheduler) From 5598cfd6be8261cd67a4e9b8f7c5d74b1d9480cb Mon Sep 17 00:00:00 2001 From: Jiahao Li <56757835+xljh0520@users.noreply.github.com> Date: Fri, 28 Jun 2024 22:58:20 +0800 Subject: [PATCH 17/21] We add lora in DiT blocks and CLIP text encoder layers. (#5) * support hunyuan lora in CLIP text encoder and DiT blocks * add hunyuan lora test script * append lora blocks in target module --------- Co-authored-by: leoriojhli --- hunyuan_test_lora.py | 138 +++++++++++++++++++++++++++++++++++++++++++ networks/lora.py | 4 +- 2 files changed, 140 insertions(+), 2 deletions(-) create mode 100644 hunyuan_test_lora.py diff --git a/hunyuan_test_lora.py b/hunyuan_test_lora.py new file mode 100644 index 000000000..1d901dcb3 --- /dev/null +++ b/hunyuan_test_lora.py @@ -0,0 +1,138 @@ +import numpy as np +import torch + +from k_diffusion.external import DiscreteVDDPMDenoiser +from k_diffusion.sampling import sample_euler_ancestral, get_sigmas_exponential + +from PIL import Image +from pytorch_lightning import seed_everything + +from library.hunyuan_models import * +from library.hunyuan_utils import * + +# from lycoris.kohya import create_network_from_weights +from networks.lora import create_network_from_weights + +from copy import deepcopy +PROMPT = "青花瓷风格, 在白色背景上,一只小狗在追逐蝴蝶" +NEG_PROMPT = "" +CLIP_TOKENS = 75 * 2 + 2 +ATTN_MODE = "xformers" +H = 1024 +W = 1024 +STEPS = 100 +CFG_SCALE = 6 +DEVICE = "cuda" +DTYPE = torch.float16 + + +if __name__ == "__main__": + seed_everything(777) + with torch.inference_mode(True), torch.no_grad(): + alphas, sigmas = load_scheduler_sigmas() + ( + denoiser, + patch_size, + head_dim, + clip_tokenizer, + clip_encoder, + mt5_embedder, + vae, + ) = load_model("/root/albertxyu/HunYuanDiT-V1.1-fp16-pruned", dtype=DTYPE, device=DEVICE) + # breakpoint() + denoiser.eval() + denoiser.disable_fp32_silu() + denoiser.disable_fp32_layer_norm() + denoiser.set_attn_mode(ATTN_MODE) + vae.requires_grad_(False) + mt5_embedder.to(torch.float16) + + lora_net, state_dict = create_network_from_weights( + multiplier=1.0, + file="test_lora_unet_clip_0628/last-step00001600.safetensors", + vae=vae, + text_encoder=[clip_encoder, mt5_embedder], + unet=denoiser, + ) + lora_net.apply_to( + text_encoder=[clip_encoder, mt5_embedder], + unet=denoiser, + ) + lora_net.load_state_dict(state_dict) + lora_net = lora_net.to(DEVICE, dtype=DTYPE) + + + + with torch.autocast("cuda"): + clip_h, clip_m, mt5_h, mt5_m = get_cond( + PROMPT, + mt5_embedder, + clip_tokenizer, + clip_encoder, + # Should be same as original implementation with max_length_clip=77 + # Support 75*n + 2 + max_length_clip=CLIP_TOKENS, + ) + neg_clip_h, neg_clip_m, neg_mt5_h, neg_mt5_m = get_cond( + NEG_PROMPT, + mt5_embedder, + clip_tokenizer, + clip_encoder, + max_length_clip=CLIP_TOKENS, + ) + clip_h = torch.concat([clip_h, neg_clip_h], dim=0) + clip_m = torch.concat([clip_m, neg_clip_m], dim=0) + mt5_h = torch.concat([mt5_h, neg_mt5_h], dim=0) + mt5_m = torch.concat([mt5_m, neg_mt5_m], dim=0) + torch.cuda.empty_cache() + + style = torch.as_tensor([0] * 2, device=DEVICE) + # src hw, dst hw, 0, 0 + size_cond = [H, W, H, W, 0, 0] + image_meta_size = torch.as_tensor([size_cond] * 2, device=DEVICE) + freqs_cis_img = calc_rope(H, W, patch_size, head_dim) + + denoiser_wrapper = DiscreteVDDPMDenoiser( + # A quick patch for learn_sigma + lambda *args, **kwargs: denoiser(*args, **kwargs).chunk(2, dim=1)[0], + alphas, + False, + ).to(DEVICE) + + def cfg_denoise_func(x, sigma): + cond, uncond = denoiser_wrapper( + x.repeat(2, 1, 1, 1), + sigma.repeat(2), + encoder_hidden_states=clip_h, + text_embedding_mask=clip_m, + encoder_hidden_states_t5=mt5_h, + text_embedding_mask_t5=mt5_m, + image_meta_size=image_meta_size, + style=style, + cos_cis_img=freqs_cis_img[0], + sin_cis_img=freqs_cis_img[1], + ).chunk(2, dim=0) + return uncond + (cond - uncond) * CFG_SCALE + + sigmas = denoiser_wrapper.get_sigmas(STEPS).to(DEVICE) + sigmas = get_sigmas_exponential( + STEPS, denoiser_wrapper.sigma_min, denoiser_wrapper.sigma_max, DEVICE + ) + x1 = torch.randn(1, 4, H//8, W//8, dtype=torch.float16, device=DEVICE) + + with torch.autocast("cuda"): + sample = sample_euler_ancestral( + cfg_denoise_func, + x1 * sigmas[0], + sigmas, + ) + torch.cuda.empty_cache() + with torch.no_grad(): + latent = sample / 0.13025 + image = vae.decode(latent).sample + image = (image / 2 + 0.5).clamp(0, 1) + image = image.permute(0, 2, 3, 1).cpu().numpy() + image = (image * 255).round().astype(np.uint8) + image = [Image.fromarray(im) for im in image] + for im in image: + im.save("test_1600_unet_clip.png") diff --git a/networks/lora.py b/networks/lora.py index 82b8b5b47..5ac88ae23 100644 --- a/networks/lora.py +++ b/networks/lora.py @@ -857,9 +857,9 @@ class LoRANetwork(torch.nn.Module): SDXL_NUM_OF_BLOCKS = 9 # SDXLのモデルでのinput/outputの層の数 total=1(base) 9(input) + 3(mid) + 9(output) + 1(out) = 23 SDXL_NUM_OF_MID_BLOCKS = 3 - UNET_TARGET_REPLACE_MODULE = ["Transformer2DModel"] + UNET_TARGET_REPLACE_MODULE = ["Transformer2DModel", "HunYuanDiTBlock"] UNET_TARGET_REPLACE_MODULE_CONV2D_3X3 = ["ResnetBlock2D", "Downsample2D", "Upsample2D"] - TEXT_ENCODER_TARGET_REPLACE_MODULE = ["CLIPAttention", "CLIPMLP"] + TEXT_ENCODER_TARGET_REPLACE_MODULE = ["CLIPAttention", "CLIPMLP", "BertLayer"] LORA_PREFIX_UNET = "lora_unet" LORA_PREFIX_TEXT_ENCODER = "lora_te" From 4141c7a99bbd9376887a566e06fb6d98dceb7cb3 Mon Sep 17 00:00:00 2001 From: Jian-Wei Zhang Date: Sun, 30 Jun 2024 20:21:41 +0800 Subject: [PATCH 18/21] Support HunyuanDiT finetuning all parameters (#6) --- hunyuan_test_ft.py | 143 ++++++ hunyuan_train.py | 889 ++++++++++++++++++++++++++++++++++++++ library/hunyuan_models.py | 45 +- library/hunyuan_utils.py | 175 +++++++- library/sai_model_spec.py | 18 +- library/train_util.py | 2 + 6 files changed, 1245 insertions(+), 27 deletions(-) create mode 100644 hunyuan_test_ft.py create mode 100644 hunyuan_train.py diff --git a/hunyuan_test_ft.py b/hunyuan_test_ft.py new file mode 100644 index 000000000..d79a725e8 --- /dev/null +++ b/hunyuan_test_ft.py @@ -0,0 +1,143 @@ +import numpy as np +import torch +from pathlib import Path + +from k_diffusion.external import DiscreteVDDPMDenoiser +from k_diffusion.sampling import sample_euler_ancestral, get_sigmas_exponential + +from PIL import Image +from pytorch_lightning import seed_everything + +from library.hunyuan_models import * +from library.hunyuan_utils import * + + +PROMPT = """ +qinglongshengzhe, 1girl, solo, breasts, looking at viewer, smile, open mouth, bangs, hair between eyes, bare shoulders, collarbone, upper body, detached sleeves, midriff, crop top, black background +""" +NEG_PROMPT = "错误的眼睛,糟糕的人脸,毁容,糟糕的艺术,变形,多余的肢体,模糊的颜色,模糊,重复,病态,残缺" +CLIP_TOKENS = 75 * 3 + 2 +ATTN_MODE = "xformers" +H = 1024 +W = 1024 +STEPS = 30 +CFG_SCALE = 5 +DEVICE = "cuda" +DTYPE = torch.float16 +USE_EXTRA_COND = False +BETA_END = 0.02 + + +def load_scheduler_sigmas(beta_start=0.00085, beta_end=0.018, num_train_timesteps=1000): + betas = torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2 + alphas = 1.0 - betas + alphas_cumprod = torch.cumprod(alphas, dim=0) + + sigmas = np.array(((1 - alphas_cumprod) / alphas_cumprod) ** 0.5) + sigmas = np.concatenate([sigmas[::-1], [0.0]]).astype(np.float32) + sigmas = torch.from_numpy(sigmas) + return alphas_cumprod, sigmas + + +if __name__ == "__main__": + seed_everything(0) + with torch.inference_mode(True), torch.no_grad(): + alphas, sigmas = load_scheduler_sigmas(beta_end=BETA_END) + ( + denoiser, + patch_size, + head_dim, + clip_tokenizer, + clip_encoder, + mt5_embedder, + vae, + ) = load_model("/root/albertxyu/HunYuanDiT-V1.2-fp16-pruned", dtype=DTYPE, device=DEVICE, + # dit_path="./output_pro/debug-000006.ckpt", + ) + + denoiser.eval() + denoiser.disable_fp32_silu() + denoiser.disable_fp32_layer_norm() + denoiser.set_attn_mode(ATTN_MODE) + vae.requires_grad_(False) + mt5_embedder.to(torch.float16) + + + with torch.autocast("cuda"): + clip_h, clip_m, mt5_h, mt5_m = get_cond( + PROMPT, + mt5_embedder, + clip_tokenizer, + clip_encoder, + # Should be same as original implementation with max_length_clip=77 + # Support 75*n + 2 + max_length_clip=CLIP_TOKENS, + ) + neg_clip_h, neg_clip_m, neg_mt5_h, neg_mt5_m = get_cond( + NEG_PROMPT, + mt5_embedder, + clip_tokenizer, + clip_encoder, + max_length_clip=CLIP_TOKENS, + ) + clip_h = torch.concat([clip_h, neg_clip_h], dim=0) + clip_m = torch.concat([clip_m, neg_clip_m], dim=0) + mt5_h = torch.concat([mt5_h, neg_mt5_h], dim=0) + mt5_m = torch.concat([mt5_m, neg_mt5_m], dim=0) + torch.cuda.empty_cache() + + if USE_EXTRA_COND: + style = torch.as_tensor([0] * 2, device=DEVICE) + # src hw, dst hw, 0, 0 + size_cond = [H, W, H, W, 0, 0] + image_meta_size = torch.as_tensor([size_cond] * 2, device=DEVICE) + else: + style = None + image_meta_size = None + freqs_cis_img = calc_rope(H, W, patch_size, head_dim) + + denoiser_wrapper = DiscreteVDDPMDenoiser( + # A quick patch for learn_sigma + lambda *args, **kwargs: denoiser(*args, **kwargs).chunk(2, dim=1)[0], + alphas, + False, + ).to(DEVICE) + + def cfg_denoise_func(x, sigma): + cond, uncond = denoiser_wrapper( + x.repeat(2, 1, 1, 1), + sigma.repeat(2), + encoder_hidden_states=clip_h, + text_embedding_mask=clip_m, + encoder_hidden_states_t5=mt5_h, + text_embedding_mask_t5=mt5_m, + image_meta_size=image_meta_size, + style=style, + cos_cis_img=freqs_cis_img[0], + sin_cis_img=freqs_cis_img[1], + ).chunk(2, dim=0) + return uncond + (cond - uncond) * CFG_SCALE + + sigmas = denoiser_wrapper.get_sigmas(STEPS).to(DEVICE) + sigmas = get_sigmas_exponential( + STEPS, denoiser_wrapper.sigma_min, denoiser_wrapper.sigma_max, DEVICE + ) + x1 = torch.randn(1, 4, H//8, W//8, dtype=torch.float16, device=DEVICE) + + Path('imgs').mkdir(exist_ok=True, parents=True) + with torch.autocast("cuda"): + sample = sample_euler_ancestral( + cfg_denoise_func, + x1 * sigmas[0], + sigmas, + ) + torch.cuda.empty_cache() + with torch.no_grad(): + latent = sample / 0.13025 + image = vae.decode(latent).sample + image = (image / 2 + 0.5).clamp(0, 1) + image = image.permute(0, 2, 3, 1).cpu().numpy() + image = (image * 255).round().astype(np.uint8) + image = [Image.fromarray(im) for im in image] + for im in image: + im.save("imgs/test_opro.png") diff --git a/hunyuan_train.py b/hunyuan_train.py new file mode 100644 index 000000000..2afaf0058 --- /dev/null +++ b/hunyuan_train.py @@ -0,0 +1,889 @@ +# ================================================ +# HunyuanDiT training scripts (with captions) + +import argparse +import math +import os +from multiprocessing import Value +from typing import List +import toml + +from tqdm import tqdm + +import torch +from library.device_utils import init_ipex, clean_memory_on_device + + +init_ipex() + +from accelerate.utils import set_seed +from diffusers import DDPMScheduler +from library import deepspeed_utils, sdxl_model_util + +import library.train_util as train_util + +from library.utils import setup_logging, add_logging_arguments + +setup_logging() +import logging + +logger = logging.getLogger(__name__) + +import library.config_util as config_util +import library.sdxl_train_util as sdxl_train_util +from library.config_util import ( + ConfigSanitizer, + BlueprintGenerator, +) +import library.custom_train_functions as custom_train_functions +from library.custom_train_functions import ( + apply_snr_weight, + prepare_scheduler_for_custom_training, + scale_v_prediction_loss_like_noise_prediction, + add_v_prediction_like_loss, + apply_debiased_estimation, + apply_masked_loss, +) +import library.hunyuan_utils as hunyuan_utils + +UNET_NUM_BLOCKS_FOR_BLOCK_LR = 23 + + +def train(args): + train_util.verify_training_args(args) + train_util.prepare_dataset_args(args, True) + sdxl_train_util.verify_sdxl_training_args(args) + deepspeed_utils.prepare_deepspeed_args(args) + setup_logging(args, reset=True) + + assert ( + not args.weighted_captions + ), "weighted_captions is not supported currently / weighted_captionsは現在サポートされていません" + assert ( + not args.train_text_encoder or not args.cache_text_encoder_outputs + ), "cache_text_encoder_outputs is not supported when training text encoder / text encoderを学習するときはcache_text_encoder_outputsはサポートされていません" + + if args.block_lr: + block_lrs = [float(lr) for lr in args.block_lr.split(",")] + assert ( + len(block_lrs) == UNET_NUM_BLOCKS_FOR_BLOCK_LR + ), f"block_lr must have {UNET_NUM_BLOCKS_FOR_BLOCK_LR} values / block_lrは{UNET_NUM_BLOCKS_FOR_BLOCK_LR}個の値を指定してください" + else: + block_lrs = None + + cache_latents = args.cache_latents + use_dreambooth_method = args.in_json is None + + if args.seed is not None: + set_seed(args.seed) # 乱数系列を初期化する + + tokenizer1, tokenizer2 = hunyuan_utils.load_tokenizers() + + # Prepare datasets + if args.dataset_class is None: + blueprint_generator = BlueprintGenerator(ConfigSanitizer(True, True, args.masked_loss, True)) + if args.dataset_config is not None: + logger.info(f"Load dataset config from {args.dataset_config}") + user_config = config_util.load_user_config(args.dataset_config) + ignored = ["train_data_dir", "in_json"] + if any(getattr(args, attr) is not None for attr in ignored): + logger.warning( + "ignore following options because config file is found: {0} / 設定ファイルが利用されるため以下のオプションは無視されます: {0}".format( + ", ".join(ignored) + ) + ) + else: + if use_dreambooth_method: + logger.info("Using DreamBooth method.") + user_config = { + "datasets": [ + { + "subsets": config_util.generate_dreambooth_subsets_config_by_subdirs( + args.train_data_dir, args.reg_data_dir + ) + } + ] + } + else: + logger.info("Training with captions.") + user_config = { + "datasets": [ + { + "subsets": [ + { + "image_dir": args.train_data_dir, + "metadata_file": args.in_json, + } + ] + } + ] + } + + blueprint = blueprint_generator.generate(user_config, args, tokenizer=[tokenizer1, tokenizer2]) + train_dataset_group = config_util.generate_dataset_group_by_blueprint(blueprint.dataset_group) + else: + train_dataset_group = train_util.load_arbitrary_dataset(args, [tokenizer1, tokenizer2]) + + current_epoch = Value("i", 0) + current_step = Value("i", 0) + ds_for_collator = train_dataset_group if args.max_data_loader_n_workers == 0 else None + collator = train_util.collator_class(current_epoch, current_step, ds_for_collator) + + train_dataset_group.verify_bucket_reso_steps(32) + + if args.debug_dataset: + train_util.debug_dataset(train_dataset_group, True) + return + if len(train_dataset_group) == 0: + logger.error( + "No data found. Please verify the metadata file and train_data_dir option. / 画像がありません。メタデータおよびtrain_data_dirオプションを確認してください。" + ) + return + + if cache_latents: + assert ( + train_dataset_group.is_latent_cacheable() + ), "when caching latents, either color_aug or random_crop cannot be used / latentをキャッシュするときはcolor_augとrandom_cropは使えません" + + if args.cache_text_encoder_outputs: + assert ( + train_dataset_group.is_text_encoder_output_cacheable() + ), "when caching text encoder output, either caption_dropout_rate, shuffle_caption, token_warmup_step or caption_tag_dropout_rate cannot be used / text encoderの出力をキャッシュするときはcaption_dropout_rate, shuffle_caption, token_warmup_step, caption_tag_dropout_rateは使えません" + + # Prepare accelerator + logger.info("prepare accelerator") + accelerator = train_util.prepare_accelerator(args) + + # Prepare types that supports mixed precision and casts as needed. + weight_dtype, save_dtype = train_util.prepare_dtype(args) + vae_dtype = torch.float32 if args.no_half_vae else weight_dtype + + # Load models + ( + load_stable_diffusion_format, + text_encoder1, + text_encoder2, + vae, + hydit, + logit_scale, + ckpt_info, + ) = hunyuan_utils.load_target_model(args, accelerator, "hydit", weight_dtype, args.use_extra_cond) + if args.use_extra_cond: + hydit_version = 'v1.1' + else: + hydit_version = 'v1.2' + + # verify load/save model formats + if load_stable_diffusion_format: + src_stable_diffusion_ckpt = args.pretrained_model_name_or_path + src_diffusers_model_path = None + else: + src_stable_diffusion_ckpt = None + src_diffusers_model_path = args.pretrained_model_name_or_path + + if args.save_model_as is None: + save_stable_diffusion_format = load_stable_diffusion_format + use_safetensors = args.use_safetensors + else: + save_stable_diffusion_format = args.save_model_as.lower() == "ckpt" or args.save_model_as.lower() == "safetensors" + use_safetensors = args.use_safetensors or ("safetensors" in args.save_model_as.lower()) + # assert save_stable_diffusion_format, "save_model_as must be ckpt or safetensors / save_model_asはckptかsafetensorsである必要があります" + + # Setting the flag for using Diffusers version of xformers function + def set_diffusers_xformers_flag(model, valid): + def fn_recursive_set_mem_eff(module: torch.nn.Module): + if hasattr(module, "set_use_memory_efficient_attention_xformers"): + module.set_use_memory_efficient_attention_xformers(valid) + + for child in module.children(): + fn_recursive_set_mem_eff(child) + + fn_recursive_set_mem_eff(model) + + # Integrate xformers and memory efficient attention into the model + if args.diffusers_xformers: + # もうU-Netを独自にしたので動かないけどVAEのxformersは動くはず + accelerator.print("Use xformers by Diffusers") + # set_diffusers_xformers_flag(hydit, True) + set_diffusers_xformers_flag(vae, True) + else: + # The Windows version of xformers may not be able to train with float, so there is a need to enable settings that don't use xformers. + accelerator.print("Disable Diffusers' xformers") + train_util.replace_unet_modules(hydit, args.mem_eff_attn, args.xformers, args.sdpa) + if torch.__version__ >= "2.0.0": # The following can be used with xformers compatible with PyTorch 2.0.0 and above. + vae.set_use_memory_efficient_attention_xformers(args.xformers) + + # Prepare vae latents + if cache_latents: + vae.to(accelerator.device, dtype=vae_dtype) + vae.requires_grad_(False) + vae.eval() + with torch.no_grad(): + train_dataset_group.cache_latents(vae, args.vae_batch_size, args.cache_latents_to_disk, accelerator.is_main_process) + vae.to("cpu") + clean_memory_on_device(accelerator.device) + + accelerator.wait_for_everyone() + + # Prepare for learning: Get the model into a proper state + if args.gradient_checkpointing: + hydit.enable_gradient_checkpointing() + train_hydit = args.learning_rate != 0 + train_text_encoder1 = False + train_text_encoder2 = False + + if args.train_text_encoder: + raise NotImplementedError("Training text encoder is not supported yet for HunyuanDiT") + else: + text_encoder1.to(weight_dtype) + text_encoder2.to(weight_dtype) + text_encoder1.requires_grad_(False) + text_encoder2.requires_grad_(False) + text_encoder1.eval() + text_encoder2.eval() + + # Cache the output of Textencoder + if args.cache_text_encoder_outputs: + raise NotImplementedError("Caching text encoder outputs in HunyuanDiT is not supported yet") + # TODO: We just copy the code from sdxl_train.py, need to rewrite `cache_text_encoder_outputs` + # for supporting SDXL and HunyuanDiT at the same time. + # Text Encodes are eval and no grad + with torch.no_grad(), accelerator.autocast(): + train_dataset_group.cache_text_encoder_outputs( + (tokenizer1, tokenizer2), + (text_encoder1, text_encoder2), + accelerator.device, + None, + args.cache_text_encoder_outputs_to_disk, + accelerator.is_main_process, + ) + accelerator.wait_for_everyone() + + if not cache_latents: + vae.requires_grad_(False) + vae.eval() + vae.to(accelerator.device, dtype=vae_dtype) + + hydit.requires_grad_(train_hydit) + if not train_hydit: + hydit.to(accelerator.device, dtype=weight_dtype) # because of hydit is not prepared + + training_models = [] + params_to_optimize = [] + if train_hydit: + training_models.append(hydit) + if block_lrs is None: + params_to_optimize.append({"params": list(hydit.parameters()), "lr": args.learning_rate}) + else: + raise NotImplementedError("block_lr is not supported yet for HunyuanDiT") + + if train_text_encoder1: + training_models.append(text_encoder1) + params_to_optimize.append({"params": list(text_encoder1.parameters()), "lr": args.learning_rate_te1 or args.learning_rate}) + if train_text_encoder2: + training_models.append(text_encoder2) + params_to_optimize.append({"params": list(text_encoder2.parameters()), "lr": args.learning_rate_te2 or args.learning_rate}) + + # calculate number of trainable parameters + n_params = 0 + for group in params_to_optimize: + for p in group["params"]: + n_params += p.numel() + + accelerator.print(f"train hydit: {train_hydit}, text_encoder1: {train_text_encoder1}, text_encoder2: {train_text_encoder2}") + accelerator.print(f"number of models: {len(training_models)}") + accelerator.print(f"number of trainable parameters: {n_params}") + + # Prepare the tools necessary for training + accelerator.print("prepare optimizer, data loader etc.") + + if args.fused_optimizer_groups: + # fused backward pass: https://pytorch.org/tutorials/intermediate/optimizer_step_in_backward_tutorial.html + # Instead of creating an optimizer for all parameters as in the tutorial, we create an optimizer for each group of parameters. + # This balances memory usage and management complexity. + + # calculate total number of parameters + n_total_params = sum(len(params["params"]) for params in params_to_optimize) + params_per_group = math.ceil(n_total_params / args.fused_optimizer_groups) + + # split params into groups, keeping the learning rate the same for all params in a group + # this will increase the number of groups if the learning rate is different for different params (e.g. U-Net and text encoders) + grouped_params = [] + param_group = [] + param_group_lr = -1 + for group in params_to_optimize: + lr = group["lr"] + for p in group["params"]: + # if the learning rate is different for different params, start a new group + if lr != param_group_lr: + if param_group: + grouped_params.append({"params": param_group, "lr": param_group_lr}) + param_group = [] + param_group_lr = lr + + param_group.append(p) + + # if the group has enough parameters, start a new group + if len(param_group) == params_per_group: + grouped_params.append({"params": param_group, "lr": param_group_lr}) + param_group = [] + param_group_lr = -1 + + if param_group: + grouped_params.append({"params": param_group, "lr": param_group_lr}) + + # prepare optimizers for each group + optimizers = [] + for group in grouped_params: + _, _, optimizer = train_util.get_optimizer(args, trainable_params=[group]) + optimizers.append(optimizer) + optimizer = optimizers[0] # avoid error in the following code + + logger.info(f"using {len(optimizers)} optimizers for fused optimizer groups") + + else: + _, _, optimizer = train_util.get_optimizer(args, trainable_params=params_to_optimize) + + # Prepare the DataLoader + # Note that the number of DataLoader processes: 0 cannot use persistent_workers + n_workers = min(args.max_data_loader_n_workers, os.cpu_count()) # cpu_count or max_data_loader_n_workers + train_dataloader = torch.utils.data.DataLoader( + train_dataset_group, + batch_size=1, + shuffle=True, + collate_fn=collator, + num_workers=n_workers, + persistent_workers=args.persistent_data_loader_workers, + ) + + # Calculate the number of training steps + if args.max_train_epochs is not None: + args.max_train_steps = args.max_train_epochs * math.ceil( + len(train_dataloader) / accelerator.num_processes / args.gradient_accumulation_steps + ) + accelerator.print( + f"override steps. steps for {args.max_train_epochs} epochs is / 指定エポックまでのステップ数: {args.max_train_steps}" + ) + + # Send the training steps to the dataset side + train_dataset_group.set_max_train_steps(args.max_train_steps) + + # Prepare a learning rate scheduler + if args.fused_optimizer_groups: + # prepare lr schedulers for each optimizer + lr_schedulers = [train_util.get_scheduler_fix(args, optimizer, accelerator.num_processes) for optimizer in optimizers] + lr_scheduler = lr_schedulers[0] # avoid error in the following code + else: + lr_scheduler = train_util.get_scheduler_fix(args, optimizer, accelerator.num_processes) + + # Experimental Feature: Conducting fp16/bf16 learning, including gradients, converting the entire model to fp16/bf16. + if args.full_fp16: + assert ( + args.mixed_precision == "fp16" + ), "full_fp16 requires mixed precision='fp16' / full_fp16を使う場合はmixed_precision='fp16'を指定してください。" + accelerator.print("enable full fp16 training.") + hydit.to(weight_dtype) + text_encoder1.to(weight_dtype) + text_encoder2.to(weight_dtype) + elif args.full_bf16: + assert ( + args.mixed_precision == "bf16" + ), "full_bf16 requires mixed precision='bf16' / full_bf16を使う場合はmixed_precision='bf16'を指定してください。" + accelerator.print("enable full bf16 training.") + hydit.to(weight_dtype) + text_encoder1.to(weight_dtype) + text_encoder2.to(weight_dtype) + + if args.deepspeed: + ds_model = deepspeed_utils.prepare_deepspeed_model( + args, + hydit=hydit if train_hydit else None, + text_encoder1=text_encoder1 if train_text_encoder1 else None, + text_encoder2=text_encoder2 if train_text_encoder2 else None, + ) + # most of ZeRO stage uses optimizer partitioning, so we have to prepare optimizer and ds_model at the same time. # pull/1139#issuecomment-1986790007 + ds_model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( + ds_model, optimizer, train_dataloader, lr_scheduler + ) + training_models = [ds_model] + + else: + # acceleratorがなんかよろしくやってくれるらしい + if train_hydit: + hydit = accelerator.prepare(hydit) + if train_text_encoder1: + text_encoder1 = accelerator.prepare(text_encoder1) + if train_text_encoder2: + text_encoder2 = accelerator.prepare(text_encoder2) + optimizer, train_dataloader, lr_scheduler = accelerator.prepare(optimizer, train_dataloader, lr_scheduler) + + # TextEncoderの出力をキャッシュするときにはCPUへ移動する + if args.cache_text_encoder_outputs: + # move Text Encoders for sampling images. Text Encoder doesn't work on CPU with fp16 + text_encoder1.to("cpu", dtype=torch.float32) + text_encoder2.to("cpu", dtype=torch.float32) + clean_memory_on_device(accelerator.device) + else: + # make sure Text Encoders are on GPU + text_encoder1.to(accelerator.device) + text_encoder2.to(accelerator.device) + + # 実験的機能:勾配も含めたfp16学習を行う PyTorchにパッチを当ててfp16でのgrad scaleを有効にする + if args.full_fp16: + # During deepseed training, accelerate not handles fp16/bf16|mixed precision directly via scaler. Let deepspeed engine do. + # -> But we think it's ok to patch accelerator even if deepspeed is enabled. + train_util.patch_accelerator_for_fp16_training(accelerator) + + # resumeする + train_util.resume_from_local_or_hf_if_specified(accelerator, args) + + if args.fused_backward_pass: + # use fused optimizer for backward pass: other optimizers will be supported in the future + import library.adafactor_fused + + library.adafactor_fused.patch_adafactor_fused(optimizer) + for param_group in optimizer.param_groups: + for parameter in param_group["params"]: + if parameter.requires_grad: + + def __grad_hook(tensor: torch.Tensor, param_group=param_group): + if accelerator.sync_gradients and args.max_grad_norm != 0.0: + accelerator.clip_grad_norm_(tensor, args.max_grad_norm) + optimizer.step_param(tensor, param_group) + tensor.grad = None + + parameter.register_post_accumulate_grad_hook(__grad_hook) + + elif args.fused_optimizer_groups: + # prepare for additional optimizers and lr schedulers + for i in range(1, len(optimizers)): + optimizers[i] = accelerator.prepare(optimizers[i]) + lr_schedulers[i] = accelerator.prepare(lr_schedulers[i]) + + # counters are used to determine when to step the optimizer + global optimizer_hooked_count + global num_parameters_per_group + global parameter_optimizer_map + + optimizer_hooked_count = {} + num_parameters_per_group = [0] * len(optimizers) + parameter_optimizer_map = {} + + for opt_idx, optimizer in enumerate(optimizers): + for param_group in optimizer.param_groups: + for parameter in param_group["params"]: + if parameter.requires_grad: + + def optimizer_hook(parameter: torch.Tensor): + if accelerator.sync_gradients and args.max_grad_norm != 0.0: + accelerator.clip_grad_norm_(parameter, args.max_grad_norm) + + i = parameter_optimizer_map[parameter] + optimizer_hooked_count[i] += 1 + if optimizer_hooked_count[i] == num_parameters_per_group[i]: + optimizers[i].step() + optimizers[i].zero_grad(set_to_none=True) + + parameter.register_post_accumulate_grad_hook(optimizer_hook) + parameter_optimizer_map[parameter] = opt_idx + num_parameters_per_group[opt_idx] += 1 + + # epoch数を計算する + num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) + num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) + if (args.save_n_epoch_ratio is not None) and (args.save_n_epoch_ratio > 0): + args.save_every_n_epochs = math.floor(num_train_epochs / args.save_n_epoch_ratio) or 1 + + # 学習する + # total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps + accelerator.print("running training / 学習開始") + accelerator.print(f" num examples / サンプル数: {train_dataset_group.num_train_images}") + accelerator.print(f" num batches per epoch / 1epochのバッチ数: {len(train_dataloader)}") + accelerator.print(f" num epochs / epoch数: {num_train_epochs}") + accelerator.print( + f" batch size per device / バッチサイズ: {', '.join([str(d.batch_size) for d in train_dataset_group.datasets])}" + ) + # accelerator.print( + # f" total train batch size (with parallel & distributed & accumulation) / 総バッチサイズ(並列学習、勾配合計含む): {total_batch_size}" + # ) + accelerator.print(f" gradient accumulation steps / 勾配を合計するステップ数 = {args.gradient_accumulation_steps}") + accelerator.print(f" total optimization steps / 学習ステップ数: {args.max_train_steps}") + + progress_bar = tqdm(range(args.max_train_steps), smoothing=0, disable=not accelerator.is_local_main_process, desc="steps") + global_step = 0 + + train_noise_scheduler = DDPMScheduler( + beta_start=0.00085, beta_end=args.beta_end, beta_schedule="scaled_linear", num_train_timesteps=1000, clip_sample=False, + steps_offset=1, + ) + prepare_scheduler_for_custom_training(train_noise_scheduler, accelerator.device) + if args.zero_terminal_snr: + custom_train_functions.fix_noise_scheduler_betas_for_zero_terminal_snr(train_noise_scheduler) + + if accelerator.is_main_process: + init_kwargs = {} + if args.wandb_run_name: + init_kwargs["wandb"] = {"name": args.wandb_run_name} + if args.log_tracker_config is not None: + init_kwargs = toml.load(args.log_tracker_config) + accelerator.init_trackers( + "finetuning" if args.log_tracker_name is None else args.log_tracker_name, + config=train_util.get_sanitized_config_or_none(args), + init_kwargs=init_kwargs, + ) + + # For --sample_at_first + sdxl_train_util.sample_images( + accelerator, args, 0, global_step, accelerator.device, vae, [tokenizer1, tokenizer2], [text_encoder1, text_encoder2], hydit + ) + + loss_recorder = train_util.LossRecorder() + for epoch in range(num_train_epochs): + accelerator.print(f"\nepoch {epoch+1}/{num_train_epochs}") + current_epoch.value = epoch + 1 + + for m in training_models: + m.train() + + for step, batch in enumerate(train_dataloader): + current_step.value = global_step + + if args.fused_optimizer_groups: + optimizer_hooked_count = {i: 0 for i in range(len(optimizers))} # reset counter for each step + + with accelerator.accumulate(*training_models): + if "latents" in batch and batch["latents"] is not None: + latents = batch["latents"].to(accelerator.device).to(dtype=weight_dtype) + else: + with torch.no_grad(): + # latentに変換 + latents = vae.encode(batch["images"].to(vae_dtype)).latent_dist.sample().to(weight_dtype) + + # NaNが含まれていれば警告を表示し0に置き換える + if torch.any(torch.isnan(latents)): + accelerator.print("NaN found in latents, replacing with zeros") + latents = torch.nan_to_num(latents, 0, out=latents) + latents = latents * sdxl_model_util.VAE_SCALE_FACTOR + + if "text_encoder_outputs1_list" not in batch or batch["text_encoder_outputs1_list"] is None: + input_ids1 = batch["input_ids"] + input_ids2 = batch["input_ids2"] + with torch.set_grad_enabled(args.train_text_encoder): + input_ids1 = input_ids1.to(accelerator.device) + input_ids2 = input_ids2.to(accelerator.device) + encoder_hidden_states1, mask1, encoder_hidden_states2, mask2 = ( + hunyuan_utils.hunyuan_get_hidden_states( + args.max_token_length, + input_ids1, + input_ids2, + tokenizer1, + tokenizer2, + text_encoder1, + text_encoder2, + None if not args.full_fp16 else weight_dtype, + accelerator=accelerator, + ) + ) + logger.debug("encoder_hidden_states1", encoder_hidden_states1.shape) + logger.debug("encoder_hidden_states2", encoder_hidden_states2.shape) + else: + raise NotImplementedError + + # Sample noise, sample a random timestep for each image, and add noise to the latents, + # with noise offset and/or multires noise if specified + noise, noisy_latents, timesteps, huber_c = train_util.get_noise_noisy_latents_and_timesteps( + args, train_noise_scheduler, latents + ) + + noisy_latents = noisy_latents.to(weight_dtype) # TODO check why noisy_latents is not weight_dtype + B, C, H, W = noisy_latents.shape + + if args.use_extra_cond: + # get size embeddings + orig_size = batch["original_sizes_hw"] + crop_size = batch["crop_top_lefts"] + target_size = batch["target_sizes_hw"] + style = torch.as_tensor([0] * B, device=accelerator.device) + image_meta_size = torch.concat([orig_size, target_size, crop_size]) + else: + style = None + image_meta_size = None + + # RoPE embeddings + freqs_cis_img = hunyuan_utils.calc_rope(H * 8, W * 8, 2, 88) + + # Predict the noise residual + with accelerator.autocast(): + noise_pred = hydit( + noisy_latents, + timesteps, + encoder_hidden_states=encoder_hidden_states1, + text_embedding_mask=mask1, + encoder_hidden_states_t5=encoder_hidden_states2, + text_embedding_mask_t5=mask2, + image_meta_size=image_meta_size, + style=style, + cos_cis_img=freqs_cis_img[0], + sin_cis_img=freqs_cis_img[1], + ) + # `noise_pred` has 8 channels. The first four channels are used for the noise prediction, and the + # last four channels are used for the variance prediction. During inference, we found that the + # predicted variance has imperceptible affect on the quality of the generated images. Therefore, we + # only use the first four channels for the noise prediction. See the following link for details. + # https://github.com/Tencent/HunyuanDiT/blob/5657364143e44ac90f72aeb47b81bd505a95665d/hydit/diffusion/gaussian_diffusion.py#L562 + noise_pred, _ = noise_pred.chunk(2, dim=1) + + if args.v_parameterization: + # v-parameterization training + target = train_noise_scheduler.get_velocity(latents, noise, timesteps) + else: + target = noise + + if ( + args.min_snr_gamma + or args.scale_v_pred_loss_like_noise_pred + or args.v_pred_like_loss + or args.debiased_estimation_loss + or args.masked_loss + ): + # do not mean over batch dimension for snr weight or scale v-pred loss + loss = train_util.conditional_loss( + noise_pred.float(), target.float(), reduction="none", loss_type=args.loss_type, huber_c=huber_c + ) + if args.masked_loss or ("alpha_masks" in batch and batch["alpha_masks"] is not None): + loss = apply_masked_loss(loss, batch) + loss = loss.mean([1, 2, 3]) + + if args.min_snr_gamma: + loss = apply_snr_weight(loss, timesteps, train_noise_scheduler, args.min_snr_gamma) + if args.scale_v_pred_loss_like_noise_pred: + loss = scale_v_prediction_loss_like_noise_prediction(loss, timesteps, train_noise_scheduler) + if args.v_pred_like_loss: + loss = add_v_prediction_like_loss(loss, timesteps, train_noise_scheduler, args.v_pred_like_loss) + if args.debiased_estimation_loss: + loss = apply_debiased_estimation(loss, timesteps, train_noise_scheduler) + + loss = loss.mean() # mean over batch dimension + else: + loss = train_util.conditional_loss( + noise_pred.float(), target.float(), reduction="mean", loss_type=args.loss_type, huber_c=huber_c + ) + + accelerator.backward(loss) + + if not (args.fused_backward_pass or args.fused_optimizer_groups): + if accelerator.sync_gradients and args.max_grad_norm != 0.0: + params_to_clip = [] + for m in training_models: + params_to_clip.extend(m.parameters()) + accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm) + + optimizer.step() + lr_scheduler.step() + optimizer.zero_grad(set_to_none=True) + else: + # optimizer.step() and optimizer.zero_grad() are called in the optimizer hook + lr_scheduler.step() + if args.fused_optimizer_groups: + for i in range(1, len(optimizers)): + lr_schedulers[i].step() + + # Checks if the accelerator has performed an optimization step behind the scenes + if accelerator.sync_gradients: + progress_bar.update(1) + global_step += 1 + + sdxl_train_util.sample_images( + accelerator, + args, + None, + global_step, + accelerator.device, + vae, + [tokenizer1, tokenizer2], + [text_encoder1, text_encoder2], + hydit, + ) + + # 指定ステップごとにモデルを保存 + if args.save_every_n_steps is not None and global_step % args.save_every_n_steps == 0: + accelerator.wait_for_everyone() + if accelerator.is_main_process: + src_path = src_stable_diffusion_ckpt if save_stable_diffusion_format else src_diffusers_model_path + hunyuan_utils.save_hydit_model_on_epoch_end_or_stepwise( + args, + False, + accelerator, + src_path, + save_stable_diffusion_format, + use_safetensors, + save_dtype, + epoch, + num_train_epochs, + global_step, + accelerator.unwrap_model(text_encoder1), + accelerator.unwrap_model(text_encoder2), + accelerator.unwrap_model(hydit), + vae, + logit_scale, + ckpt_info, + hydit_version, + ) + + current_loss = loss.detach().item() # 平均なのでbatch sizeは関係ないはず + if args.logging_dir is not None: + logs = {"loss": current_loss} + if block_lrs is None: + train_util.append_lr_to_logs(logs, lr_scheduler, args.optimizer_type, including_unet=train_hydit) + else: + append_block_lr_to_logs(block_lrs, logs, lr_scheduler, args.optimizer_type) # U-Net is included in block_lrs + + accelerator.log(logs, step=global_step) + + loss_recorder.add(epoch=epoch, step=step, loss=current_loss) + avr_loss: float = loss_recorder.moving_average + logs = {"avr_loss": avr_loss} # , "lr": lr_scheduler.get_last_lr()[0]} + progress_bar.set_postfix(**logs) + + if global_step >= args.max_train_steps: + break + + if args.logging_dir is not None: + logs = {"loss/epoch": loss_recorder.moving_average} + accelerator.log(logs, step=epoch + 1) + + accelerator.wait_for_everyone() + + if args.save_every_n_epochs is not None: + if accelerator.is_main_process: + src_path = src_stable_diffusion_ckpt if save_stable_diffusion_format else src_diffusers_model_path + hunyuan_utils.save_hydit_model_on_epoch_end_or_stepwise( + args, + True, + accelerator, + src_path, + save_stable_diffusion_format, + use_safetensors, + save_dtype, + epoch, + num_train_epochs, + global_step, + accelerator.unwrap_model(text_encoder1), + accelerator.unwrap_model(text_encoder2), + accelerator.unwrap_model(hydit), + vae, + logit_scale, + ckpt_info, + hydit_version, + ) + + sdxl_train_util.sample_images( + accelerator, + args, + epoch + 1, + global_step, + accelerator.device, + vae, + [tokenizer1, tokenizer2], + [text_encoder1, text_encoder2], + hydit, + ) + + is_main_process = accelerator.is_main_process + # if is_main_process: + hydit = accelerator.unwrap_model(hydit) + text_encoder1 = accelerator.unwrap_model(text_encoder1) + text_encoder2 = accelerator.unwrap_model(text_encoder2) + + accelerator.end_training() + + if args.save_state or args.save_state_on_train_end: + train_util.save_state_on_train_end(args, accelerator) + + del accelerator # この後メモリを使うのでこれは消す + + if is_main_process: + src_path = src_stable_diffusion_ckpt if save_stable_diffusion_format else src_diffusers_model_path + hunyuan_utils.save_hydit_model_on_train_end( + args, + src_path, + save_stable_diffusion_format, + use_safetensors, + save_dtype, + epoch, + global_step, + text_encoder1, + text_encoder2, + hydit, + vae, + logit_scale, + ckpt_info, + hydit_version, + ) + logger.info("model saved.") + + +def setup_parser() -> argparse.ArgumentParser: + parser = argparse.ArgumentParser() + + add_logging_arguments(parser) + train_util.add_sd_models_arguments(parser) + train_util.add_dataset_arguments(parser, True, True, True) + train_util.add_training_arguments(parser, False) + train_util.add_masked_loss_arguments(parser) + deepspeed_utils.add_deepspeed_arguments(parser) + train_util.add_sd_saving_arguments(parser) + train_util.add_optimizer_arguments(parser) + config_util.add_config_arguments(parser) + custom_train_functions.add_custom_train_arguments(parser) + sdxl_train_util.add_sdxl_training_arguments(parser) + + hunyuan_utils.add_hydit_arguments(parser) + + parser.add_argument( + "--learning_rate_te1", + type=float, + default=None, + help="learning rate for text encoder 1 (CLIP) / text encoder 1 (ViT-L)の学習率", + ) + parser.add_argument( + "--learning_rate_te2", + type=float, + default=None, + help="learning rate for text encoder 2 (mT5) / text encoder 2 (BiG-G)の学習率", + ) + + parser.add_argument( + "--diffusers_xformers", action="store_true", help="use xformers by diffusers / Diffusersでxformersを使用する" + ) + parser.add_argument("--train_text_encoder", action="store_true", help="train text encoder / text encoderも学習する") + parser.add_argument( + "--no_half_vae", + action="store_true", + help="do not use fp16/bf16 VAE in mixed precision (use float VAE) / mixed precisionでも fp16/bf16 VAEを使わずfloat VAEを使う", + ) + parser.add_argument( + "--block_lr", + type=str, + default=None, + help=f"learning rates for each block of HunyuanDiT, comma-separated, {UNET_NUM_BLOCKS_FOR_BLOCK_LR} values / " + + f"U-Netの各ブロックの学習率、カンマ区切り、{UNET_NUM_BLOCKS_FOR_BLOCK_LR}個の値", + ) + parser.add_argument( + "--fused_optimizer_groups", + type=int, + default=None, + help="number of optimizers for fused backward pass and optimizer step / fused backward passとoptimizer stepのためのoptimizer数", + ) + return parser + + +if __name__ == '__main__': + parser = setup_parser() + + args = parser.parse_args() + print(args) + train_util.verify_command_line_training_args(args) + args = train_util.read_config_from_file(args, parser) + + train(args) diff --git a/library/hunyuan_models.py b/library/hunyuan_models.py index db5aadaaf..e65a7bd88 100644 --- a/library/hunyuan_models.py +++ b/library/hunyuan_models.py @@ -947,6 +947,7 @@ def __init__( mlp_ratio=4.0, log_fn=print, attn_mode="xformers", + use_extra_cond=False, ): super().__init__() self.log_fn = log_fn @@ -962,6 +963,7 @@ def __init__( self.text_len = text_len self.text_len_t5 = text_len_t5 self.norm = norm + self.use_extra_cond = use_extra_cond log_fn(f" Use {attn_mode} attention implementation.") qk_norm = qk_norm # See http://arxiv.org/abs/2302.05442 for details. @@ -981,20 +983,24 @@ def __init__( ) # Attention pooling + pooler_out_dim = 1024 self.pooler = AttentionPool( - self.text_len_t5, self.text_states_dim_t5, num_heads=8, output_dim=1024 + self.text_len_t5, self.text_states_dim_t5, num_heads=8, output_dim=pooler_out_dim ) - # Here we use a default learned embedder layer for future extension. - self.style_embedder = nn.Embedding(1, hidden_size) + # Dimension of the extra input vectors + self.extra_in_dim = pooler_out_dim - # Image size and crop size conditions - self.extra_in_dim = 256 * 6 + hidden_size + if self.use_extra_cond: + # Image source size, image target size and crop size conditions + self.extra_in_dim += 6 * 256 + # 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.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(), @@ -1092,9 +1098,9 @@ def forward( 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 + image_meta_size: None or torch.Tensor (B, 6) - style: torch.Tensor + style: None or torch.Tensor (B) cos_cis_img: torch.Tensor sin_cis_img: torch.Tensor @@ -1144,17 +1150,18 @@ def forward( # Build text tokens with pooling extra_vec = self.pooler(encoder_hidden_states_t5) - # Build image meta size tokens - image_meta_size = timestep_embedding( - image_meta_size.view(-1), 256 - ) # [B * 6, 256] - image_meta_size = image_meta_size.to(x) - 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] - - # Build style tokens - style_embedding = self.style_embedder(style) - extra_vec = torch.cat([extra_vec, style_embedding], dim=1) + if self.use_extra_cond: + # Build image meta size tokens + image_meta_size = timestep_embedding( + image_meta_size.view(-1), 256 + ) # [B * 6, 256] + image_meta_size = image_meta_size.to(x) + 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] + + # 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) # [B, D] diff --git a/library/hunyuan_utils.py b/library/hunyuan_utils.py index 1d4a0d334..40105b07d 100644 --- a/library/hunyuan_utils.py +++ b/library/hunyuan_utils.py @@ -1,5 +1,6 @@ import os from typing import Tuple, Union, Optional, Any +import argparse import numpy as np import torch @@ -13,9 +14,12 @@ BertTokenizer, ) +from library import model_util from library.device_utils import init_ipex, clean_memory_on_device from .hunyuan_models import MT5Embedder, HunYuanDiT, BertModel, DiT_g_2 from .utils import setup_logging +import library.train_util as train_util +from safetensors.torch import load_file, save_file setup_logging() import logging @@ -222,17 +226,22 @@ def load_tokenizers(): def load_scheduler_sigmas(): scheduler: LMSDiscreteScheduler = LMSDiscreteScheduler.from_pretrained( - "Tencent-Hunyuan/HunyuanDiT-v1.1-Diffusers", + "Tencent-Hunyuan/HunyuanDiT-v1.2-Diffusers", subfolder="scheduler", ) return scheduler.alphas_cumprod, scheduler.sigmas -def load_model(model_path: str, dtype=torch.float16, device="cuda"): +def load_model(model_path: str, dtype=torch.float16, device="cuda", use_extra_cond=False, dit_path=None): denoiser: HunYuanDiT - denoiser, patch_size, head_dim = DiT_g_2(input_size=(128, 128)) - sd = torch.load(os.path.join(model_path, "denoiser/pytorch_model_module.pt")) - denoiser.load_state_dict(sd) + denoiser, patch_size, head_dim = DiT_g_2(input_size=(128, 128), use_extra_cond=use_extra_cond) + if dit_path is not None: + state_dict = torch.load(dit_path) + if 'state_dict' in state_dict: + state_dict = state_dict['state_dict'] + else: + state_dict = torch.load(os.path.join(model_path, "denoiser/pytorch_model_module.pt")) + denoiser.load_state_dict(state_dict) denoiser.to(device).to(dtype) clip_tokenizer = AutoTokenizer.from_pretrained(os.path.join(model_path, "clip")) @@ -279,7 +288,8 @@ def match_mixed_precision(args, weight_dtype): return None -def load_target_model(args, accelerator, model_version: str, weight_dtype): +def load_target_model(args, accelerator, model_version: str, weight_dtype, use_extra_cond=False): + _ = model_version # unused model_dtype = match_mixed_precision(args, weight_dtype) # prepare fp16/bf16 for pi in range(accelerator.state.num_processes): if pi == accelerator.state.local_process_index: @@ -299,6 +309,7 @@ def load_target_model(args, accelerator, model_version: str, weight_dtype): args.pretrained_model_name_or_path, model_dtype, accelerator.device if args.lowram else "cpu", + use_extra_cond, ) # work on low-ram device @@ -568,6 +579,158 @@ def calc_rope(height, width, patch_size=2, head_size=64): return rope +def add_hydit_arguments(parser: argparse.ArgumentParser): + parser.add_argument( + "--use_extra_cond", action="store_true", help="Use image_meta_size and style conditions for the model" + ) + parser.add_argument( + "--beta_end", type=float, default=0.018, help="End value of beta for DDPM training" + ) + + +def save_hydit_checkpoint( + output_file, + text_encoder1, + text_encoder2, + hydit, + epochs, + steps, + ckpt_info, + vae, + logit_scale, + metadata, + save_dtype=None, +): + state_dict = {} + + def update_state(prefix, sd): + for k, v in sd.items(): + key = prefix + k + if save_dtype is not None: + v = v.detach().clone().to("cpu").to(save_dtype) + state_dict[key] = v + + # Convert the hydit model + update_state('', hydit.state_dict()) + + # Put together new checkpoint + key_count = len(state_dict.keys()) + new_ckpt = {"state_dict": state_dict} + + # epoch and global_step are sometimes not int + if ckpt_info is not None: + epochs += ckpt_info[0] + steps += ckpt_info[1] + + new_ckpt["epoch"] = epochs + new_ckpt["global_step"] = steps + + if model_util.is_safetensors(output_file): + save_file(state_dict, output_file, metadata) + else: + torch.save(new_ckpt, output_file) + + return key_count + + +def save_hydit_model_on_train_end( + args: argparse.Namespace, + src_path: str, + save_stable_diffusion_format: bool, + use_safetensors: bool, + save_dtype: torch.dtype, + epoch: int, + global_step: int, + text_encoder1, + text_encoder2, + unet, + vae, + logit_scale, + ckpt_info, + hydit, +): + def hydit_saver(ckpt_file, epoch_no, global_step): + sai_metadata = train_util.get_sai_model_spec(None, args, False, False, False, is_stable_diffusion_ckpt=False, + hydit=hydit) + save_hydit_checkpoint( + ckpt_file, + text_encoder1, + text_encoder2, + unet, + epoch_no, + global_step, + ckpt_info, + vae, + logit_scale, + sai_metadata, + save_dtype, + ) + + def diffusers_saver(out_dir): + _ = out_dir + raise NotImplementedError("Diffusers saving is not supported yet for HunYuan DiT") + + train_util.save_sd_model_on_train_end_common( + args, save_stable_diffusion_format, use_safetensors, epoch, global_step, hydit_saver, diffusers_saver + ) + + +# Save epochs and steps, integrate because the metadata includes epochs/steps and the arguments are identical. +# on_epoch_end: If true, at the end of epoch, if false, after the steps have been completed. +def save_hydit_model_on_epoch_end_or_stepwise( + args: argparse.Namespace, + on_epoch_end: bool, + accelerator, + src_path, + save_stable_diffusion_format: bool, + use_safetensors: bool, + save_dtype: torch.dtype, + epoch: int, + num_train_epochs: int, + global_step: int, + text_encoder1, + text_encoder2, + unet, + vae, + logit_scale, + ckpt_info, + hydit, +): + def hydit_saver(ckpt_file, epoch_no, global_step): + sai_metadata = train_util.get_sai_model_spec(None, args, False, False, False, is_stable_diffusion_ckpt=False, + hydit=hydit) + save_hydit_checkpoint( + ckpt_file, + text_encoder1, + text_encoder2, + unet, + epoch_no, + global_step, + ckpt_info, + vae, + logit_scale, + sai_metadata, + save_dtype, + ) + + def diffusers_saver(out_dir): + _ = out_dir + raise NotImplementedError("Diffusers saving is not supported yet for HunYuan DiT") + + train_util.save_sd_model_on_epoch_end_or_stepwise_common( + args, + on_epoch_end, + accelerator, + save_stable_diffusion_format, + use_safetensors, + epoch, + num_train_epochs, + global_step, + hydit_saver, + diffusers_saver, + ) + + if __name__ == "__main__": clip_tokenizer = AutoTokenizer.from_pretrained("./model/clip") clip_tokenizer.eos_token_id = 2 diff --git a/library/sai_model_spec.py b/library/sai_model_spec.py index a63bd82ec..7535fe02c 100644 --- a/library/sai_model_spec.py +++ b/library/sai_model_spec.py @@ -56,11 +56,15 @@ ARCH_SD_V2_768_V = "stable-diffusion-v2-768-v" ARCH_SD_XL_V1_BASE = "stable-diffusion-xl-v1-base" +ARCH_HYDIT_V1_1 = "hunyuan-dit-g2-v1_1" +ARCH_HYDIT_V1_2 = "hunyuan-dit-g2-v1_2" + ADAPTER_LORA = "lora" ADAPTER_TEXTUAL_INVERSION = "textual-inversion" IMPL_STABILITY_AI = "https://github.com/Stability-AI/generative-models" IMPL_DIFFUSERS = "diffusers" +IMPL_HUNYUAN_DIT = "https://github.com/Tencent/HunyuanDiT" PRED_TYPE_EPSILON = "epsilon" PRED_TYPE_V = "v" @@ -113,6 +117,7 @@ def build_metadata( merged_from: Optional[str] = None, timesteps: Optional[Tuple[int, int]] = None, clip_skip: Optional[int] = None, + hydit: Optional[str] = None, ): # if state_dict is None, hash is not calculated @@ -124,7 +129,14 @@ def build_metadata( # hash = precalculate_safetensors_hashes(state_dict) # metadata["modelspec.hash_sha256"] = hash - if sdxl: + if hydit: + if hydit == 'v1.1': + arch = ARCH_HYDIT_V1_1 + elif hydit == 'v1.2': + arch = ARCH_HYDIT_V1_2 + else: + raise ValueError(f"Invalid hydit version: {hydit}") + elif sdxl: arch = ARCH_SD_XL_V1_BASE elif v2: if v_parameterization: @@ -144,7 +156,9 @@ def build_metadata( if not lora and not textual_inversion and is_stable_diffusion_ckpt is None: is_stable_diffusion_ckpt = True # default is stable diffusion ckpt if not lora and not textual_inversion - if (lora and sdxl) or textual_inversion or is_stable_diffusion_ckpt: + if hydit: + impl = IMPL_HUNYUAN_DIT + elif (lora and sdxl) or textual_inversion or is_stable_diffusion_ckpt: # Stable Diffusion ckpt, TI, SDXL LoRA impl = IMPL_STABILITY_AI else: diff --git a/library/train_util.py b/library/train_util.py index 2a027b1bd..07c3502ce 100644 --- a/library/train_util.py +++ b/library/train_util.py @@ -2922,6 +2922,7 @@ def get_sai_model_spec( lora: bool, textual_inversion: bool, is_stable_diffusion_ckpt: Optional[bool] = None, # None for TI and LoRA + hydit: str = None, ): timestamp = time.time() @@ -2955,6 +2956,7 @@ def get_sai_model_spec( tags=args.metadata_tags, timesteps=timesteps, clip_skip=args.clip_skip, # None or int + hydit=hydit, ) return metadata From ee724d3c4fd3e89def60e662ee4d8228950c69d5 Mon Sep 17 00:00:00 2001 From: Jiahao Li <56757835+xljh0520@users.noreply.github.com> Date: Sun, 30 Jun 2024 22:24:56 +0800 Subject: [PATCH 19/21] Support HunYuanDiT v1.1 and v1.2 lora (#8) * support hunyuan lora in CLIP text encoder and DiT blocks * add hunyuan lora test script * append lora blocks in target module * Support HunYuanDiT v1.1 and v1.2 lora --------- Co-authored-by: leoriojhli --- hunyuan_test_lora.py | 36 ++++++++++++++++++++++++++++++------ hunyuan_train_network.py | 5 +++-- train_network.py | 4 ++-- 3 files changed, 35 insertions(+), 10 deletions(-) diff --git a/hunyuan_test_lora.py b/hunyuan_test_lora.py index 1d901dcb3..f57e24968 100644 --- a/hunyuan_test_lora.py +++ b/hunyuan_test_lora.py @@ -25,11 +25,36 @@ DEVICE = "cuda" DTYPE = torch.float16 +VERSION = "1.2" + +if VERSION == "1.1": + BETA_END = 0.03 + USE_EXTRA_COND = True + MODEL_PATH = '/root/albertxyu/HunYuanDiT-V1.1-fp16-pruned' +elif VERSION == "1.2": + BETA_END = 0.018 + USE_EXTRA_COND = False + MODEL_PATH = '/root/albertxyu/HunYuanDiT-V1.2-fp16-pruned' +else: + raise ValueError(f"Invalid version: {VERSION}") + +LORA_WEIGHT = '/apdcephfs_cq10/share_1367250/jiahaoli/0630_gui_output_v1.2/last-000001.ckpt' +save_path = './test_lora_v1.2_epoch1_lora.png' + +def load_scheduler_sigmas(beta_start=0.00085, beta_end=0.018, num_train_timesteps=1000): + betas = torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2 + alphas = 1.0 - betas + alphas_cumprod = torch.cumprod(alphas, dim=0) + + sigmas = np.array(((1 - alphas_cumprod) / alphas_cumprod) ** 0.5) + sigmas = np.concatenate([sigmas[::-1], [0.0]]).astype(np.float32) + sigmas = torch.from_numpy(sigmas) + return alphas_cumprod, sigmas if __name__ == "__main__": seed_everything(777) with torch.inference_mode(True), torch.no_grad(): - alphas, sigmas = load_scheduler_sigmas() + alphas, sigmas = load_scheduler_sigmas(beta_end=BETA_END) ( denoiser, patch_size, @@ -38,7 +63,7 @@ clip_encoder, mt5_embedder, vae, - ) = load_model("/root/albertxyu/HunYuanDiT-V1.1-fp16-pruned", dtype=DTYPE, device=DEVICE) + ) = load_model(MODEL_PATH, dtype=DTYPE, device=DEVICE, use_extra_cond=USE_EXTRA_COND) # breakpoint() denoiser.eval() denoiser.disable_fp32_silu() @@ -49,7 +74,7 @@ lora_net, state_dict = create_network_from_weights( multiplier=1.0, - file="test_lora_unet_clip_0628/last-step00001600.safetensors", + file=LORA_WEIGHT, vae=vae, text_encoder=[clip_encoder, mt5_embedder], unet=denoiser, @@ -61,8 +86,6 @@ lora_net.load_state_dict(state_dict) lora_net = lora_net.to(DEVICE, dtype=DTYPE) - - with torch.autocast("cuda"): clip_h, clip_m, mt5_h, mt5_m = get_cond( PROMPT, @@ -135,4 +158,5 @@ def cfg_denoise_func(x, sigma): image = (image * 255).round().astype(np.uint8) image = [Image.fromarray(im) for im in image] for im in image: - im.save("test_1600_unet_clip.png") + # im.save(f"test_1600_{VERSION}_lora.png") + im.save(save_path) diff --git a/hunyuan_train_network.py b/hunyuan_train_network.py index 85e448497..76ebf3fb3 100644 --- a/hunyuan_train_network.py +++ b/hunyuan_train_network.py @@ -61,6 +61,7 @@ def load_target_model(self, args, weight_dtype, accelerator): accelerator, hunyuan_models.MODEL_VERSION_HUNYUAN_V1_1, weight_dtype, + use_extra_cond=args.use_extra_cond, ) self.load_stable_diffusion_format = load_stable_diffusion_format @@ -78,10 +79,10 @@ def load_tokenizer(self, args): tokenizer = hunyuan_utils.load_tokenizers() return tokenizer - def load_noise_scheduler(self): + def load_noise_scheduler(self, args): return DDPMScheduler( beta_start=0.00085, - beta_end=0.03, + beta_end=args.beta_end, beta_schedule="scaled_linear", num_train_timesteps=1000, clip_sample=False, diff --git a/train_network.py b/train_network.py index 0b9ab100f..c9b4caf9c 100644 --- a/train_network.py +++ b/train_network.py @@ -105,7 +105,7 @@ def load_tokenizer(self, args): tokenizer = train_util.load_tokenizer(args) return tokenizer - def load_noise_scheduler(self): + def load_noise_scheduler(self, args): noise_scheduler = DDPMScheduler( beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", num_train_timesteps=1000, clip_sample=False ) @@ -845,7 +845,7 @@ def load_model_hook(models, input_dir): global_step = 0 - noise_scheduler = self.load_noise_scheduler() + noise_scheduler = self.load_noise_scheduler(args) prepare_scheduler_for_custom_training(noise_scheduler, accelerator.device) if args.zero_terminal_snr: custom_train_functions.fix_noise_scheduler_betas_for_zero_terminal_snr(noise_scheduler) From b69cc546c7df73c5132549556171555d3a743d41 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E9=9D=92=E9=BE=8D=E8=81=96=E8=80=85=40bdsqlsz?= <865105819@qq.com> Date: Sun, 30 Jun 2024 22:25:22 +0800 Subject: [PATCH 20/21] Add extra cond for Hunyuan_train_network (#7) * add use_extra_cond for hy_train_network * change model version * Update hunyuan_train_network.py --- hunyuan_train.py | 26 ++++++++++++++------------ hunyuan_train_network.py | 32 +++++++++++++++----------------- library/hunyuan_models.py | 1 + library/hunyuan_utils.py | 4 ++-- library/sai_model_spec.py | 4 ++-- 5 files changed, 34 insertions(+), 33 deletions(-) diff --git a/hunyuan_train.py b/hunyuan_train.py index 2afaf0058..8553aa9b8 100644 --- a/hunyuan_train.py +++ b/hunyuan_train.py @@ -29,8 +29,14 @@ logger = logging.getLogger(__name__) -import library.config_util as config_util -import library.sdxl_train_util as sdxl_train_util +from library import ( + hunyuan_models, + hunyuan_utils, + sdxl_model_util, + sdxl_train_util, + train_util, + config_util, +) from library.config_util import ( ConfigSanitizer, BlueprintGenerator, @@ -44,7 +50,6 @@ apply_debiased_estimation, apply_masked_loss, ) -import library.hunyuan_utils as hunyuan_utils UNET_NUM_BLOCKS_FOR_BLOCK_LR = 23 @@ -158,6 +163,7 @@ def train(args): weight_dtype, save_dtype = train_util.prepare_dtype(args) vae_dtype = torch.float32 if args.no_half_vae else weight_dtype + hydit_version = hunyuan_models.MODEL_VERSION_HUNYUAN_V1_1 if args.use_extra_cond else hunyuan_models.MODEL_VERSION_HUNYUAN_V1_2 # Load models ( load_stable_diffusion_format, @@ -167,11 +173,7 @@ def train(args): hydit, logit_scale, ckpt_info, - ) = hunyuan_utils.load_target_model(args, accelerator, "hydit", weight_dtype, args.use_extra_cond) - if args.use_extra_cond: - hydit_version = 'v1.1' - else: - hydit_version = 'v1.2' + ) = hunyuan_utils.load_target_model(args, accelerator, hydit_version, weight_dtype) # verify load/save model formats if load_stable_diffusion_format: @@ -733,10 +735,10 @@ def optimizer_hook(parameter: torch.Tensor): current_loss = loss.detach().item() # 平均なのでbatch sizeは関係ないはず if args.logging_dir is not None: logs = {"loss": current_loss} - if block_lrs is None: - train_util.append_lr_to_logs(logs, lr_scheduler, args.optimizer_type, including_unet=train_hydit) - else: - append_block_lr_to_logs(block_lrs, logs, lr_scheduler, args.optimizer_type) # U-Net is included in block_lrs + # if block_lrs is None: + train_util.append_lr_to_logs(logs, lr_scheduler, args.optimizer_type, including_unet=train_hydit) + # else: + # train_util.append_block_lr_to_logs(block_lrs, logs, lr_scheduler, args.optimizer_type) # U-Net is included in block_lrs accelerator.log(logs, step=global_step) diff --git a/hunyuan_train_network.py b/hunyuan_train_network.py index 76ebf3fb3..25541f198 100644 --- a/hunyuan_train_network.py +++ b/hunyuan_train_network.py @@ -59,7 +59,7 @@ def load_target_model(self, args, weight_dtype, accelerator): ) = hunyuan_utils.load_target_model( args, accelerator, - hunyuan_models.MODEL_VERSION_HUNYUAN_V1_1, + hunyuan_models.MODEL_VERSION_HUNYUAN_V1_1 if args.use_extra_cond else hunyuan_models.MODEL_VERSION_HUNYUAN_V1_2, weight_dtype, use_extra_cond=args.use_extra_cond, ) @@ -69,7 +69,7 @@ def load_target_model(self, args, weight_dtype, accelerator): self.ckpt_info = ckpt_info return ( - hunyuan_models.MODEL_VERSION_HUNYUAN_V1_1, + hunyuan_models.MODEL_VERSION_HUNYUAN_V1_1 if args.use_extra_cond else hunyuan_models.MODEL_VERSION_HUNYUAN_V1_2, [text_encoder1, text_encoder2], vae, unet, @@ -157,23 +157,20 @@ def call_unet( noisy_latents = noisy_latents.to( weight_dtype ) # TODO check why noisy_latents is not weight_dtype - - # get size embeddings - orig_size = batch["original_sizes_hw"] # B, 2 - crop_size = batch["crop_top_lefts"] # B, 2 - target_size = batch["target_sizes_hw"] # B, 2 B, C, H, W = noisy_latents.shape - style = torch.as_tensor([0] * B, device=accelerator.device) - image_meta_size = torch.concat( - [ - orig_size, - target_size, - # Not following SDXL but following HunYuan's Implementation - # TODO examine if this is correct - torch.zeros_like(target_size), - ] - ) + if args.use_extra_cond: + # get size embeddings + orig_size = batch["original_sizes_hw"] # B, 2 + crop_size = batch["crop_top_lefts"] # B, 2 + target_size = batch["target_sizes_hw"] # B, 2 + + style = torch.as_tensor([0] * B, device=accelerator.device) + image_meta_size = torch.concat([orig_size, target_size, crop_size]) + else: + style = None + image_meta_size = None + freqs_cis_img = hunyuan_utils.calc_rope(H * 8, W * 8, 2, 88) # concat embeddings @@ -227,6 +224,7 @@ def sample_images( def setup_parser() -> argparse.ArgumentParser: parser = train_network.setup_parser() sdxl_train_util.add_sdxl_training_arguments(parser) + hunyuan_utils.add_hydit_arguments(parser) return parser diff --git a/library/hunyuan_models.py b/library/hunyuan_models.py index e65a7bd88..0e1b1737b 100644 --- a/library/hunyuan_models.py +++ b/library/hunyuan_models.py @@ -32,6 +32,7 @@ VAE_SCALE_FACTOR = 0.13025 MODEL_VERSION_HUNYUAN_V1_1 = "HunyuanDiT-v1.1" +MODEL_VERSION_HUNYUAN_V1_2 = "HunyuanDiT-v1.2" class MT5Embedder(nn.Module): diff --git a/library/hunyuan_utils.py b/library/hunyuan_utils.py index 40105b07d..ba3c7341c 100644 --- a/library/hunyuan_utils.py +++ b/library/hunyuan_utils.py @@ -288,7 +288,7 @@ def match_mixed_precision(args, weight_dtype): return None -def load_target_model(args, accelerator, model_version: str, weight_dtype, use_extra_cond=False): +def load_target_model(args, accelerator, model_version: str, weight_dtype): _ = model_version # unused model_dtype = match_mixed_precision(args, weight_dtype) # prepare fp16/bf16 for pi in range(accelerator.state.num_processes): @@ -309,7 +309,7 @@ def load_target_model(args, accelerator, model_version: str, weight_dtype, use_e args.pretrained_model_name_or_path, model_dtype, accelerator.device if args.lowram else "cpu", - use_extra_cond, + args.use_extra_cond, ) # work on low-ram device diff --git a/library/sai_model_spec.py b/library/sai_model_spec.py index 7535fe02c..d588830c9 100644 --- a/library/sai_model_spec.py +++ b/library/sai_model_spec.py @@ -130,9 +130,9 @@ def build_metadata( # metadata["modelspec.hash_sha256"] = hash if hydit: - if hydit == 'v1.1': + if hydit == 'HunyuanDiT-v1.1': arch = ARCH_HYDIT_V1_1 - elif hydit == 'v1.2': + elif hydit == 'HunyuanDiT-v1.2': arch = ARCH_HYDIT_V1_2 else: raise ValueError(f"Invalid hydit version: {hydit}") From 0dc79edc01f2000de1dad5ad6d20d8b099bfafe2 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E9=9D=92=E9=BE=8D=E8=81=96=E8=80=85=40bdsqlsz?= <865105819@qq.com> Date: Mon, 1 Jul 2024 18:40:04 +0800 Subject: [PATCH 21/21] Final change (#9) * add use_extra_cond for hy_train_network * change model version * Update hunyuan_train_network.py * Update hunyuan_train_network.py * Update hunyuan_train.py --- hunyuan_train.py | 6 +++--- hunyuan_train_network.py | 1 - 2 files changed, 3 insertions(+), 4 deletions(-) diff --git a/hunyuan_train.py b/hunyuan_train.py index 8553aa9b8..3a63ad2aa 100644 --- a/hunyuan_train.py +++ b/hunyuan_train.py @@ -275,16 +275,16 @@ def fn_recursive_set_mem_eff(module: torch.nn.Module): if train_hydit: training_models.append(hydit) if block_lrs is None: - params_to_optimize.append({"params": list(hydit.parameters()), "lr": args.learning_rate}) + params_to_optimize.append({"params": list(filter(lambda p: p.requires_grad, hydit.parameters())), "lr": args.learning_rate}) else: raise NotImplementedError("block_lr is not supported yet for HunyuanDiT") if train_text_encoder1: training_models.append(text_encoder1) - params_to_optimize.append({"params": list(text_encoder1.parameters()), "lr": args.learning_rate_te1 or args.learning_rate}) + params_to_optimize.append({"params": list(filter(lambda p: p.requires_grad, text_encoder1.parameters())), "lr": args.learning_rate_te1 or args.learning_rate}) if train_text_encoder2: training_models.append(text_encoder2) - params_to_optimize.append({"params": list(text_encoder2.parameters()), "lr": args.learning_rate_te2 or args.learning_rate}) + params_to_optimize.append({"params": list(filter(lambda p: p.requires_grad, text_encoder2.parameters())), "lr": args.learning_rate_te2 or args.learning_rate}) # calculate number of trainable parameters n_params = 0 diff --git a/hunyuan_train_network.py b/hunyuan_train_network.py index 25541f198..5bc698f36 100644 --- a/hunyuan_train_network.py +++ b/hunyuan_train_network.py @@ -61,7 +61,6 @@ def load_target_model(self, args, weight_dtype, accelerator): accelerator, hunyuan_models.MODEL_VERSION_HUNYUAN_V1_1 if args.use_extra_cond else hunyuan_models.MODEL_VERSION_HUNYUAN_V1_2, weight_dtype, - use_extra_cond=args.use_extra_cond, ) self.load_stable_diffusion_format = load_stable_diffusion_format

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