mirror of https://github.com/vladmandic/automatic
65 lines
2.2 KiB
Python
65 lines
2.2 KiB
Python
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# Copyright 2024 Alpha-VLLM Authors 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 typing import Optional, Tuple
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from torch.nn import RMSNorm
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from diffusers.models.embeddings import Timesteps, TimestepEmbedding
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# Makes timestep_scale configurable
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# Omnigen 2 uses timestep_scale=1000
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class Lumina2CombinedTimestepCaptionEmbedding(nn.Module):
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def __init__(
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self,
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hidden_size: int = 4096,
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text_feat_dim: int = 2048,
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frequency_embedding_size: int = 256,
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norm_eps: float = 1e-5,
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timestep_scale: float = 1.0,
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) -> None:
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super().__init__()
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self.time_proj = Timesteps(
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num_channels=frequency_embedding_size, flip_sin_to_cos=True, downscale_freq_shift=0.0, scale=timestep_scale
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)
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self.timestep_embedder = TimestepEmbedding(
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in_channels=frequency_embedding_size, time_embed_dim=min(hidden_size, 1024)
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)
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self.caption_embedder = nn.Sequential(
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RMSNorm(text_feat_dim, eps=norm_eps),
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nn.Linear(text_feat_dim, hidden_size, bias=True),
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)
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self._initialize_weights()
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def _initialize_weights(self):
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nn.init.trunc_normal_(self.caption_embedder[1].weight, std=0.02)
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nn.init.zeros_(self.caption_embedder[1].bias)
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def forward(
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self, timestep: torch.Tensor, text_hidden_states: torch.Tensor, dtype: torch.dtype
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) -> Tuple[torch.Tensor, torch.Tensor]:
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timestep_proj = self.time_proj(timestep).to(dtype=dtype)
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time_embed = self.timestep_embedder(timestep_proj)
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caption_embed = self.caption_embedder(text_hidden_states)
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return time_embed, caption_embed
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