402 lines
16 KiB
Python
402 lines
16 KiB
Python
# Copyright 2024 HunyuanDiT Authors, Qixun Wang and The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from dataclasses import dataclass
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from typing import Dict, Optional, Union
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import torch
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from torch import nn
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from ...configuration_utils import ConfigMixin, register_to_config
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from ...utils import BaseOutput, logging
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from ..attention_processor import AttentionProcessor
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from ..embeddings import (
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HunyuanCombinedTimestepTextSizeStyleEmbedding,
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PatchEmbed,
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PixArtAlphaTextProjection,
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)
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from ..modeling_utils import ModelMixin
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from ..transformers.hunyuan_transformer_2d import HunyuanDiTBlock
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from .controlnet import Tuple, zero_module
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logger = logging.get_logger(__name__) # pylint: disable=invalid-name
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@dataclass
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class HunyuanControlNetOutput(BaseOutput):
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controlnet_block_samples: Tuple[torch.Tensor]
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class HunyuanDiT2DControlNetModel(ModelMixin, ConfigMixin):
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@register_to_config
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def __init__(
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self,
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conditioning_channels: int = 3,
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num_attention_heads: int = 16,
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attention_head_dim: int = 88,
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in_channels: Optional[int] = None,
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patch_size: Optional[int] = None,
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activation_fn: str = "gelu-approximate",
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sample_size=32,
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hidden_size=1152,
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transformer_num_layers: int = 40,
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mlp_ratio: float = 4.0,
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cross_attention_dim: int = 1024,
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cross_attention_dim_t5: int = 2048,
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pooled_projection_dim: int = 1024,
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text_len: int = 77,
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text_len_t5: int = 256,
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use_style_cond_and_image_meta_size: bool = True,
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):
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super().__init__()
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self.num_heads = num_attention_heads
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self.inner_dim = num_attention_heads * attention_head_dim
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self.text_embedder = PixArtAlphaTextProjection(
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in_features=cross_attention_dim_t5,
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hidden_size=cross_attention_dim_t5 * 4,
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out_features=cross_attention_dim,
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act_fn="silu_fp32",
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)
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self.text_embedding_padding = nn.Parameter(
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torch.randn(text_len + text_len_t5, cross_attention_dim, dtype=torch.float32)
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)
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self.pos_embed = PatchEmbed(
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height=sample_size,
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width=sample_size,
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in_channels=in_channels,
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embed_dim=hidden_size,
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patch_size=patch_size,
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pos_embed_type=None,
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)
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self.time_extra_emb = HunyuanCombinedTimestepTextSizeStyleEmbedding(
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hidden_size,
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pooled_projection_dim=pooled_projection_dim,
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seq_len=text_len_t5,
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cross_attention_dim=cross_attention_dim_t5,
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use_style_cond_and_image_meta_size=use_style_cond_and_image_meta_size,
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)
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# controlnet_blocks
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self.controlnet_blocks = nn.ModuleList([])
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# HunyuanDiT Blocks
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self.blocks = nn.ModuleList(
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[
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HunyuanDiTBlock(
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dim=self.inner_dim,
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num_attention_heads=self.config.num_attention_heads,
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activation_fn=activation_fn,
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ff_inner_dim=int(self.inner_dim * mlp_ratio),
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cross_attention_dim=cross_attention_dim,
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qk_norm=True, # See https://huggingface.co/papers/2302.05442 for details.
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skip=False, # always False as it is the first half of the model
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)
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for layer in range(transformer_num_layers // 2 - 1)
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]
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)
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self.input_block = zero_module(nn.Linear(hidden_size, hidden_size))
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for _ in range(len(self.blocks)):
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controlnet_block = nn.Linear(hidden_size, hidden_size)
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controlnet_block = zero_module(controlnet_block)
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self.controlnet_blocks.append(controlnet_block)
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@property
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def attn_processors(self) -> Dict[str, AttentionProcessor]:
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r"""
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Returns:
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`dict` of attention processors: A dictionary containing all attention processors used in the model with
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indexed by its weight name.
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"""
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# set recursively
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processors = {}
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def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):
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if hasattr(module, "get_processor"):
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processors[f"{name}.processor"] = module.get_processor(return_deprecated_lora=True)
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for sub_name, child in module.named_children():
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fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
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return processors
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for name, module in self.named_children():
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fn_recursive_add_processors(name, module, processors)
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return processors
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def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
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r"""
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Sets the attention processor to use to compute attention.
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Parameters:
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processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
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The instantiated processor class or a dictionary of processor classes that will be set as the processor
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for **all** `Attention` layers. If `processor` is a dict, the key needs to define the path to the
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corresponding cross attention processor. This is strongly recommended when setting trainable attention
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processors.
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"""
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count = len(self.attn_processors.keys())
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if isinstance(processor, dict) and len(processor) != count:
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raise ValueError(
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f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
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f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
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)
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def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
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if hasattr(module, "set_processor"):
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if not isinstance(processor, dict):
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module.set_processor(processor)
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else:
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module.set_processor(processor.pop(f"{name}.processor"))
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for sub_name, child in module.named_children():
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fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
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for name, module in self.named_children():
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fn_recursive_attn_processor(name, module, processor)
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@classmethod
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def from_transformer(
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cls, transformer, conditioning_channels=3, transformer_num_layers=None, load_weights_from_transformer=True
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):
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config = transformer.config
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activation_fn = config.activation_fn
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attention_head_dim = config.attention_head_dim
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cross_attention_dim = config.cross_attention_dim
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cross_attention_dim_t5 = config.cross_attention_dim_t5
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hidden_size = config.hidden_size
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in_channels = config.in_channels
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mlp_ratio = config.mlp_ratio
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num_attention_heads = config.num_attention_heads
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patch_size = config.patch_size
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sample_size = config.sample_size
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text_len = config.text_len
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text_len_t5 = config.text_len_t5
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conditioning_channels = conditioning_channels
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transformer_num_layers = transformer_num_layers or config.transformer_num_layers
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controlnet = cls(
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conditioning_channels=conditioning_channels,
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transformer_num_layers=transformer_num_layers,
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activation_fn=activation_fn,
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attention_head_dim=attention_head_dim,
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cross_attention_dim=cross_attention_dim,
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cross_attention_dim_t5=cross_attention_dim_t5,
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hidden_size=hidden_size,
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in_channels=in_channels,
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mlp_ratio=mlp_ratio,
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num_attention_heads=num_attention_heads,
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patch_size=patch_size,
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sample_size=sample_size,
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text_len=text_len,
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text_len_t5=text_len_t5,
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)
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if load_weights_from_transformer:
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key = controlnet.load_state_dict(transformer.state_dict(), strict=False)
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logger.warning(f"controlnet load from Hunyuan-DiT. missing_keys: {key[0]}")
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return controlnet
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def forward(
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self,
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hidden_states,
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timestep,
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controlnet_cond: torch.Tensor,
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conditioning_scale: float = 1.0,
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encoder_hidden_states=None,
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text_embedding_mask=None,
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encoder_hidden_states_t5=None,
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text_embedding_mask_t5=None,
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image_meta_size=None,
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style=None,
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image_rotary_emb=None,
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return_dict=True,
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):
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"""
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The [`HunyuanDiT2DControlNetModel`] forward method.
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Args:
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hidden_states (`torch.Tensor` of shape `(batch size, dim, height, width)`):
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The input tensor.
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timestep ( `torch.LongTensor`, *optional*):
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Used to indicate denoising step.
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controlnet_cond ( `torch.Tensor` ):
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The conditioning input to ControlNet.
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conditioning_scale ( `float` ):
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Indicate the conditioning scale.
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encoder_hidden_states ( `torch.Tensor` of shape `(batch size, sequence len, embed dims)`, *optional*):
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Conditional embeddings for cross attention layer. This is the output of `BertModel`.
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text_embedding_mask: torch.Tensor
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An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. This is the output
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of `BertModel`.
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encoder_hidden_states_t5 ( `torch.Tensor` of shape `(batch size, sequence len, embed dims)`, *optional*):
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Conditional embeddings for cross attention layer. This is the output of T5 Text Encoder.
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text_embedding_mask_t5: torch.Tensor
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An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. This is the output
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of T5 Text Encoder.
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image_meta_size (torch.Tensor):
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Conditional embedding indicate the image sizes
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style: torch.Tensor:
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Conditional embedding indicate the style
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image_rotary_emb (`torch.Tensor`):
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The image rotary embeddings to apply on query and key tensors during attention calculation.
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return_dict: bool
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Whether to return a dictionary.
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"""
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height, width = hidden_states.shape[-2:]
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hidden_states = self.pos_embed(hidden_states) # b,c,H,W -> b, N, C
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# 2. pre-process
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hidden_states = hidden_states + self.input_block(self.pos_embed(controlnet_cond))
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temb = self.time_extra_emb(
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timestep, encoder_hidden_states_t5, image_meta_size, style, hidden_dtype=timestep.dtype
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) # [B, D]
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# text projection
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batch_size, sequence_length, _ = encoder_hidden_states_t5.shape
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encoder_hidden_states_t5 = self.text_embedder(
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encoder_hidden_states_t5.view(-1, encoder_hidden_states_t5.shape[-1])
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)
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encoder_hidden_states_t5 = encoder_hidden_states_t5.view(batch_size, sequence_length, -1)
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encoder_hidden_states = torch.cat([encoder_hidden_states, encoder_hidden_states_t5], dim=1)
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text_embedding_mask = torch.cat([text_embedding_mask, text_embedding_mask_t5], dim=-1)
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text_embedding_mask = text_embedding_mask.unsqueeze(2).bool()
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encoder_hidden_states = torch.where(text_embedding_mask, encoder_hidden_states, self.text_embedding_padding)
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block_res_samples = ()
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for layer, block in enumerate(self.blocks):
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hidden_states = block(
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hidden_states,
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temb=temb,
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encoder_hidden_states=encoder_hidden_states,
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image_rotary_emb=image_rotary_emb,
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) # (N, L, D)
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block_res_samples = block_res_samples + (hidden_states,)
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controlnet_block_res_samples = ()
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for block_res_sample, controlnet_block in zip(block_res_samples, self.controlnet_blocks):
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block_res_sample = controlnet_block(block_res_sample)
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controlnet_block_res_samples = controlnet_block_res_samples + (block_res_sample,)
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# 6. scaling
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controlnet_block_res_samples = [sample * conditioning_scale for sample in controlnet_block_res_samples]
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if not return_dict:
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return (controlnet_block_res_samples,)
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return HunyuanControlNetOutput(controlnet_block_samples=controlnet_block_res_samples)
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class HunyuanDiT2DMultiControlNetModel(ModelMixin):
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r"""
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`HunyuanDiT2DMultiControlNetModel` wrapper class for Multi-HunyuanDiT2DControlNetModel
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This module is a wrapper for multiple instances of the `HunyuanDiT2DControlNetModel`. The `forward()` API is
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designed to be compatible with `HunyuanDiT2DControlNetModel`.
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Args:
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controlnets (`List[HunyuanDiT2DControlNetModel]`):
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Provides additional conditioning to the unet during the denoising process. You must set multiple
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`HunyuanDiT2DControlNetModel` as a list.
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"""
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def __init__(self, controlnets):
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super().__init__()
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self.nets = nn.ModuleList(controlnets)
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def forward(
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self,
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hidden_states,
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timestep,
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controlnet_cond: torch.Tensor,
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conditioning_scale: float = 1.0,
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encoder_hidden_states=None,
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text_embedding_mask=None,
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encoder_hidden_states_t5=None,
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text_embedding_mask_t5=None,
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image_meta_size=None,
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style=None,
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image_rotary_emb=None,
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return_dict=True,
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):
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"""
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The [`HunyuanDiT2DControlNetModel`] forward method.
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Args:
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hidden_states (`torch.Tensor` of shape `(batch size, dim, height, width)`):
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The input tensor.
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timestep ( `torch.LongTensor`, *optional*):
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Used to indicate denoising step.
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controlnet_cond ( `torch.Tensor` ):
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The conditioning input to ControlNet.
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conditioning_scale ( `float` ):
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Indicate the conditioning scale.
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encoder_hidden_states ( `torch.Tensor` of shape `(batch size, sequence len, embed dims)`, *optional*):
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Conditional embeddings for cross attention layer. This is the output of `BertModel`.
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text_embedding_mask: torch.Tensor
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An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. This is the output
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of `BertModel`.
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encoder_hidden_states_t5 ( `torch.Tensor` of shape `(batch size, sequence len, embed dims)`, *optional*):
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Conditional embeddings for cross attention layer. This is the output of T5 Text Encoder.
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text_embedding_mask_t5: torch.Tensor
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An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. This is the output
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of T5 Text Encoder.
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image_meta_size (torch.Tensor):
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Conditional embedding indicate the image sizes
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style: torch.Tensor:
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Conditional embedding indicate the style
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image_rotary_emb (`torch.Tensor`):
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The image rotary embeddings to apply on query and key tensors during attention calculation.
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return_dict: bool
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Whether to return a dictionary.
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"""
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for i, (image, scale, controlnet) in enumerate(zip(controlnet_cond, conditioning_scale, self.nets)):
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block_samples = controlnet(
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hidden_states=hidden_states,
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timestep=timestep,
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controlnet_cond=image,
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conditioning_scale=scale,
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encoder_hidden_states=encoder_hidden_states,
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text_embedding_mask=text_embedding_mask,
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encoder_hidden_states_t5=encoder_hidden_states_t5,
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text_embedding_mask_t5=text_embedding_mask_t5,
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image_meta_size=image_meta_size,
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style=style,
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image_rotary_emb=image_rotary_emb,
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return_dict=return_dict,
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)
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# merge samples
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if i == 0:
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control_block_samples = block_samples
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else:
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control_block_samples = [
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control_block_sample + block_sample
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for control_block_sample, block_sample in zip(control_block_samples[0], block_samples[0])
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]
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control_block_samples = (control_block_samples,)
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return control_block_samples
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