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support animatediff on sdxl
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103
diffsynth/models/sdxl_motion.py
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103
diffsynth/models/sdxl_motion.py
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from .sd_motion import TemporalBlock
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import torch
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class SDXLMotionModel(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.motion_modules = torch.nn.ModuleList([
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TemporalBlock(8, 320//8, 320, eps=1e-6),
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TemporalBlock(8, 320//8, 320, eps=1e-6),
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TemporalBlock(8, 640//8, 640, eps=1e-6),
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TemporalBlock(8, 640//8, 640, eps=1e-6),
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TemporalBlock(8, 1280//8, 1280, eps=1e-6),
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TemporalBlock(8, 1280//8, 1280, eps=1e-6),
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TemporalBlock(8, 1280//8, 1280, eps=1e-6),
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TemporalBlock(8, 1280//8, 1280, eps=1e-6),
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TemporalBlock(8, 1280//8, 1280, eps=1e-6),
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TemporalBlock(8, 640//8, 640, eps=1e-6),
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TemporalBlock(8, 640//8, 640, eps=1e-6),
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TemporalBlock(8, 640//8, 640, eps=1e-6),
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TemporalBlock(8, 320//8, 320, eps=1e-6),
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TemporalBlock(8, 320//8, 320, eps=1e-6),
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TemporalBlock(8, 320//8, 320, eps=1e-6),
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])
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self.call_block_id = {
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0: 0,
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2: 1,
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7: 2,
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10: 3,
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15: 4,
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18: 5,
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25: 6,
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28: 7,
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31: 8,
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35: 9,
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38: 10,
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41: 11,
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44: 12,
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46: 13,
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48: 14,
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}
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def forward(self):
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pass
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def state_dict_converter(self):
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return SDMotionModelStateDictConverter()
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class SDMotionModelStateDictConverter:
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def __init__(self):
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pass
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def from_diffusers(self, state_dict):
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rename_dict = {
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"norm": "norm",
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"proj_in": "proj_in",
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"transformer_blocks.0.attention_blocks.0.to_q": "transformer_blocks.0.attn1.to_q",
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"transformer_blocks.0.attention_blocks.0.to_k": "transformer_blocks.0.attn1.to_k",
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"transformer_blocks.0.attention_blocks.0.to_v": "transformer_blocks.0.attn1.to_v",
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"transformer_blocks.0.attention_blocks.0.to_out.0": "transformer_blocks.0.attn1.to_out",
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"transformer_blocks.0.attention_blocks.0.pos_encoder": "transformer_blocks.0.pe1",
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"transformer_blocks.0.attention_blocks.1.to_q": "transformer_blocks.0.attn2.to_q",
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"transformer_blocks.0.attention_blocks.1.to_k": "transformer_blocks.0.attn2.to_k",
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"transformer_blocks.0.attention_blocks.1.to_v": "transformer_blocks.0.attn2.to_v",
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"transformer_blocks.0.attention_blocks.1.to_out.0": "transformer_blocks.0.attn2.to_out",
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"transformer_blocks.0.attention_blocks.1.pos_encoder": "transformer_blocks.0.pe2",
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"transformer_blocks.0.norms.0": "transformer_blocks.0.norm1",
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"transformer_blocks.0.norms.1": "transformer_blocks.0.norm2",
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"transformer_blocks.0.ff.net.0.proj": "transformer_blocks.0.act_fn.proj",
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"transformer_blocks.0.ff.net.2": "transformer_blocks.0.ff",
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"transformer_blocks.0.ff_norm": "transformer_blocks.0.norm3",
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"proj_out": "proj_out",
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}
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name_list = sorted([i for i in state_dict if i.startswith("down_blocks.")])
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name_list += sorted([i for i in state_dict if i.startswith("mid_block.")])
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name_list += sorted([i for i in state_dict if i.startswith("up_blocks.")])
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state_dict_ = {}
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last_prefix, module_id = "", -1
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for name in name_list:
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names = name.split(".")
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prefix_index = names.index("temporal_transformer") + 1
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prefix = ".".join(names[:prefix_index])
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if prefix != last_prefix:
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last_prefix = prefix
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module_id += 1
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middle_name = ".".join(names[prefix_index:-1])
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suffix = names[-1]
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if "pos_encoder" in names:
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rename = ".".join(["motion_modules", str(module_id), rename_dict[middle_name]])
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else:
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rename = ".".join(["motion_modules", str(module_id), rename_dict[middle_name], suffix])
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state_dict_[rename] = state_dict[name]
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return state_dict_
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def from_civitai(self, state_dict):
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return self.from_diffusers(state_dict)
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