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68 lines
3.4 KiB
Python
68 lines
3.4 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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from dataclasses import dataclass, field
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from sglang.multimodal_gen.configs.models.dits.base import DiTArchConfig, DiTConfig
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from sglang.multimodal_gen.configs.models.fsdp import is_blocks_or_double_blocks
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@dataclass
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class JoyImageArchConfig(DiTArchConfig):
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_fsdp_shard_conditions: list = field(
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default_factory=lambda: [is_blocks_or_double_blocks]
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)
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param_names_mapping: dict = field(
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default_factory=lambda: {
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# Condition embedder mappings
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r"^condition_embedder\.text_embedder\.linear_1\.(.*)$": r"condition_embedder.text_embedder.fc_in.\1",
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r"^condition_embedder\.text_embedder\.linear_2\.(.*)$": r"condition_embedder.text_embedder.fc_out.\1",
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r"^condition_embedder\.time_embedder\.linear_1\.(.*)$": r"condition_embedder.time_embedder.mlp.fc_in.\1",
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r"^condition_embedder\.time_embedder\.linear_2\.(.*)$": r"condition_embedder.time_embedder.mlp.fc_out.\1",
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r"^condition_embedder\.time_proj\.(.*)$": r"condition_embedder.time_modulation.linear.\1",
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# Double blocks mappings
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r"^double_blocks\.(\d+)\.attn\.(.*)$": r"double_blocks.\1.\2",
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r"^double_blocks\.(\d+)\.img_mlp\.net\.0\.proj\.(.*)$": r"double_blocks.\1.img_mlp.fc_in.\2",
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r"^double_blocks\.(\d+)\.img_mlp\.net\.2\.(.*)$": r"double_blocks.\1.img_mlp.fc_out.\2",
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r"^double_blocks\.(\d+)\.txt_mlp\.net\.0\.proj\.(.*)$": r"double_blocks.\1.txt_mlp.fc_in.\2",
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r"^double_blocks\.(\d+)\.txt_mlp\.net\.2\.(.*)$": r"double_blocks.\1.txt_mlp.fc_out.\2",
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r"^double_blocks\.(\d+)\.img_attn_qkv\.(.*)$": r"double_blocks.\1.img_attn_qkv.\2",
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r"^double_blocks\.(\d+)\.txt_attn_qkv\.(.*)$": r"double_blocks.\1.txt_attn_qkv.\2",
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r"^double_blocks\.(\d+)\.img_attn_proj\.(.*)$": r"double_blocks.\1.img_attn_proj.\2",
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r"^double_blocks\.(\d+)\.txt_attn_proj\.(.*)$": r"double_blocks.\1.txt_attn_proj.\2",
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r"^double_blocks\.(\d+)\.img_mod\.(.*)$": r"double_blocks.\1.img_mod.\2",
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r"^double_blocks\.(\d+)\.txt_mod\.(.*)$": r"double_blocks.\1.txt_mod.\2",
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r"^double_blocks\.(\d+)\.img_attn_q_norm\.(.*)$": r"double_blocks.\1.img_attn_q_norm.\2",
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r"^double_blocks\.(\d+)\.img_attn_k_norm\.(.*)$": r"double_blocks.\1.img_attn_k_norm.\2",
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r"^double_blocks\.(\d+)\.txt_attn_q_norm\.(.*)$": r"double_blocks.\1.txt_attn_q_norm.\2",
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r"^double_blocks\.(\d+)\.txt_attn_k_norm\.(.*)$": r"double_blocks.\1.txt_attn_k_norm.\2",
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}
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)
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reverse_param_names_mapping: dict = field(default_factory=lambda: {})
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# Model architecture parameters
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patch_size: tuple[int, int, int] = (1, 2, 2)
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num_attention_heads: int = 32
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attention_head_dim: int = 128
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in_channels: int = 16
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out_channels: int = 16
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mm_double_blocks_depth: int = 40
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freq_dim: int = 256
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text_states_dim: int = 4096
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mlp_width_ratio: float = 4.0
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rope_theta: int = 10000
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rope_dim_list: list[int] = field(default_factory=lambda: [16, 56, 56])
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def __post_init__(self):
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super().__post_init__()
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self.out_channels = self.out_channels or self.in_channels
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self.hidden_size = self.num_attention_heads * self.attention_head_dim
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self.num_channels_latents = self.out_channels
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@dataclass
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class JoyImageDiTConfig(DiTConfig):
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arch_config: DiTArchConfig = field(default_factory=JoyImageArchConfig)
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prefix: str = "JoyImage"
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