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@@ -69,6 +69,7 @@ from ..models.flux_value_control import SingleValueEncoder
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from ..lora.flux_lora import FluxLoraPatcher
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from ..models.flux_lora_encoder import FluxLoRAEncoder
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from ..models.nexus_gen_projector import NexusGenAdapter
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model_loader_configs = [
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# These configs are provided for detecting model type automatically.
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@@ -152,6 +153,7 @@ model_loader_configs = [
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(None, "d30fb9e02b1dbf4e509142f05cf7dd50", ["flux_dit", "step1x_connector"], [FluxDiT, Qwen2Connector], "civitai"),
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(None, "30143afb2dea73d1ac580e0787628f8c", ["flux_lora_patcher"], [FluxLoraPatcher], "civitai"),
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(None, "77c2e4dd2440269eb33bfaa0d004f6ab", ["flux_lora_encoder"], [FluxLoRAEncoder], "civitai"),
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(None, "3e6c61b0f9471135fc9c6d6a98e98b6d", ["flux_dit", "nexus-gen_adapter"], [FluxDiT, NexusGenAdapter], "civitai"),
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]
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huggingface_model_loader_configs = [
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# These configs are provided for detecting model type automatically.
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@@ -2,7 +2,7 @@ import torch
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from .sd3_dit import TimestepEmbeddings, AdaLayerNorm, RMSNorm
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from einops import rearrange
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from .tiler import TileWorker
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from .utils import init_weights_on_device
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from .utils import init_weights_on_device, hash_state_dict_keys
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def interact_with_ipadapter(hidden_states, q, ip_k, ip_v, scale=1.0):
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batch_size, num_tokens = hidden_states.shape[0:2]
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@@ -662,6 +662,9 @@ class FluxDiTStateDictConverter:
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return state_dict_
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def from_civitai(self, state_dict):
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if hash_state_dict_keys(state_dict, with_shape=True) == "3e6c61b0f9471135fc9c6d6a98e98b6d":
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dit_state_dict = {key.replace("pipe.dit.", ""): value for key, value in state_dict.items() if not key.startswith('adapter.')}
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return dit_state_dict
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rename_dict = {
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"time_in.in_layer.bias": "time_embedder.timestep_embedder.0.bias",
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"time_in.in_layer.weight": "time_embedder.timestep_embedder.0.weight",
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