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DiffSynth-Studio/diffsynth/utils/state_dict_converters/flux_vae.py
Artiprocher 0b72c2b3ba z-image
2025-11-27 22:43:43 +08:00

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def FluxVAEEncoderStateDictConverter(state_dict):
rename_dict = {
"encoder.conv_in.bias": "conv_in.bias",
"encoder.conv_in.weight": "conv_in.weight",
"encoder.conv_out.bias": "conv_out.bias",
"encoder.conv_out.weight": "conv_out.weight",
"encoder.down.0.block.0.conv1.bias": "blocks.0.conv1.bias",
"encoder.down.0.block.0.conv1.weight": "blocks.0.conv1.weight",
"encoder.down.0.block.0.conv2.bias": "blocks.0.conv2.bias",
"encoder.down.0.block.0.conv2.weight": "blocks.0.conv2.weight",
"encoder.down.0.block.0.norm1.bias": "blocks.0.norm1.bias",
"encoder.down.0.block.0.norm1.weight": "blocks.0.norm1.weight",
"encoder.down.0.block.0.norm2.bias": "blocks.0.norm2.bias",
"encoder.down.0.block.0.norm2.weight": "blocks.0.norm2.weight",
"encoder.down.0.block.1.conv1.bias": "blocks.1.conv1.bias",
"encoder.down.0.block.1.conv1.weight": "blocks.1.conv1.weight",
"encoder.down.0.block.1.conv2.bias": "blocks.1.conv2.bias",
"encoder.down.0.block.1.conv2.weight": "blocks.1.conv2.weight",
"encoder.down.0.block.1.norm1.bias": "blocks.1.norm1.bias",
"encoder.down.0.block.1.norm1.weight": "blocks.1.norm1.weight",
"encoder.down.0.block.1.norm2.bias": "blocks.1.norm2.bias",
"encoder.down.0.block.1.norm2.weight": "blocks.1.norm2.weight",
"encoder.down.0.downsample.conv.bias": "blocks.2.conv.bias",
"encoder.down.0.downsample.conv.weight": "blocks.2.conv.weight",
"encoder.down.1.block.0.conv1.bias": "blocks.3.conv1.bias",
"encoder.down.1.block.0.conv1.weight": "blocks.3.conv1.weight",
"encoder.down.1.block.0.conv2.bias": "blocks.3.conv2.bias",
"encoder.down.1.block.0.conv2.weight": "blocks.3.conv2.weight",
"encoder.down.1.block.0.nin_shortcut.bias": "blocks.3.conv_shortcut.bias",
"encoder.down.1.block.0.nin_shortcut.weight": "blocks.3.conv_shortcut.weight",
"encoder.down.1.block.0.norm1.bias": "blocks.3.norm1.bias",
"encoder.down.1.block.0.norm1.weight": "blocks.3.norm1.weight",
"encoder.down.1.block.0.norm2.bias": "blocks.3.norm2.bias",
"encoder.down.1.block.0.norm2.weight": "blocks.3.norm2.weight",
"encoder.down.1.block.1.conv1.bias": "blocks.4.conv1.bias",
"encoder.down.1.block.1.conv1.weight": "blocks.4.conv1.weight",
"encoder.down.1.block.1.conv2.bias": "blocks.4.conv2.bias",
"encoder.down.1.block.1.conv2.weight": "blocks.4.conv2.weight",
"encoder.down.1.block.1.norm1.bias": "blocks.4.norm1.bias",
"encoder.down.1.block.1.norm1.weight": "blocks.4.norm1.weight",
"encoder.down.1.block.1.norm2.bias": "blocks.4.norm2.bias",
"encoder.down.1.block.1.norm2.weight": "blocks.4.norm2.weight",
"encoder.down.1.downsample.conv.bias": "blocks.5.conv.bias",
"encoder.down.1.downsample.conv.weight": "blocks.5.conv.weight",
"encoder.down.2.block.0.conv1.bias": "blocks.6.conv1.bias",
"encoder.down.2.block.0.conv1.weight": "blocks.6.conv1.weight",
"encoder.down.2.block.0.conv2.bias": "blocks.6.conv2.bias",
"encoder.down.2.block.0.conv2.weight": "blocks.6.conv2.weight",
"encoder.down.2.block.0.nin_shortcut.bias": "blocks.6.conv_shortcut.bias",
"encoder.down.2.block.0.nin_shortcut.weight": "blocks.6.conv_shortcut.weight",
"encoder.down.2.block.0.norm1.bias": "blocks.6.norm1.bias",
"encoder.down.2.block.0.norm1.weight": "blocks.6.norm1.weight",
"encoder.down.2.block.0.norm2.bias": "blocks.6.norm2.bias",
"encoder.down.2.block.0.norm2.weight": "blocks.6.norm2.weight",
"encoder.down.2.block.1.conv1.bias": "blocks.7.conv1.bias",
"encoder.down.2.block.1.conv1.weight": "blocks.7.conv1.weight",
"encoder.down.2.block.1.conv2.bias": "blocks.7.conv2.bias",
"encoder.down.2.block.1.conv2.weight": "blocks.7.conv2.weight",
"encoder.down.2.block.1.norm1.bias": "blocks.7.norm1.bias",
"encoder.down.2.block.1.norm1.weight": "blocks.7.norm1.weight",
"encoder.down.2.block.1.norm2.bias": "blocks.7.norm2.bias",
"encoder.down.2.block.1.norm2.weight": "blocks.7.norm2.weight",
"encoder.down.2.downsample.conv.bias": "blocks.8.conv.bias",
"encoder.down.2.downsample.conv.weight": "blocks.8.conv.weight",
"encoder.down.3.block.0.conv1.bias": "blocks.9.conv1.bias",
"encoder.down.3.block.0.conv1.weight": "blocks.9.conv1.weight",
"encoder.down.3.block.0.conv2.bias": "blocks.9.conv2.bias",
"encoder.down.3.block.0.conv2.weight": "blocks.9.conv2.weight",
"encoder.down.3.block.0.norm1.bias": "blocks.9.norm1.bias",
"encoder.down.3.block.0.norm1.weight": "blocks.9.norm1.weight",
"encoder.down.3.block.0.norm2.bias": "blocks.9.norm2.bias",
"encoder.down.3.block.0.norm2.weight": "blocks.9.norm2.weight",
"encoder.down.3.block.1.conv1.bias": "blocks.10.conv1.bias",
"encoder.down.3.block.1.conv1.weight": "blocks.10.conv1.weight",
"encoder.down.3.block.1.conv2.bias": "blocks.10.conv2.bias",
"encoder.down.3.block.1.conv2.weight": "blocks.10.conv2.weight",
"encoder.down.3.block.1.norm1.bias": "blocks.10.norm1.bias",
"encoder.down.3.block.1.norm1.weight": "blocks.10.norm1.weight",
"encoder.down.3.block.1.norm2.bias": "blocks.10.norm2.bias",
"encoder.down.3.block.1.norm2.weight": "blocks.10.norm2.weight",
"encoder.mid.attn_1.k.bias": "blocks.12.transformer_blocks.0.to_k.bias",
"encoder.mid.attn_1.k.weight": "blocks.12.transformer_blocks.0.to_k.weight",
"encoder.mid.attn_1.norm.bias": "blocks.12.norm.bias",
"encoder.mid.attn_1.norm.weight": "blocks.12.norm.weight",
"encoder.mid.attn_1.proj_out.bias": "blocks.12.transformer_blocks.0.to_out.bias",
"encoder.mid.attn_1.proj_out.weight": "blocks.12.transformer_blocks.0.to_out.weight",
"encoder.mid.attn_1.q.bias": "blocks.12.transformer_blocks.0.to_q.bias",
"encoder.mid.attn_1.q.weight": "blocks.12.transformer_blocks.0.to_q.weight",
"encoder.mid.attn_1.v.bias": "blocks.12.transformer_blocks.0.to_v.bias",
"encoder.mid.attn_1.v.weight": "blocks.12.transformer_blocks.0.to_v.weight",
"encoder.mid.block_1.conv1.bias": "blocks.11.conv1.bias",
"encoder.mid.block_1.conv1.weight": "blocks.11.conv1.weight",
"encoder.mid.block_1.conv2.bias": "blocks.11.conv2.bias",
"encoder.mid.block_1.conv2.weight": "blocks.11.conv2.weight",
"encoder.mid.block_1.norm1.bias": "blocks.11.norm1.bias",
"encoder.mid.block_1.norm1.weight": "blocks.11.norm1.weight",
"encoder.mid.block_1.norm2.bias": "blocks.11.norm2.bias",
"encoder.mid.block_1.norm2.weight": "blocks.11.norm2.weight",
"encoder.mid.block_2.conv1.bias": "blocks.13.conv1.bias",
"encoder.mid.block_2.conv1.weight": "blocks.13.conv1.weight",
"encoder.mid.block_2.conv2.bias": "blocks.13.conv2.bias",
"encoder.mid.block_2.conv2.weight": "blocks.13.conv2.weight",
"encoder.mid.block_2.norm1.bias": "blocks.13.norm1.bias",
"encoder.mid.block_2.norm1.weight": "blocks.13.norm1.weight",
"encoder.mid.block_2.norm2.bias": "blocks.13.norm2.bias",
"encoder.mid.block_2.norm2.weight": "blocks.13.norm2.weight",
"encoder.norm_out.bias": "conv_norm_out.bias",
"encoder.norm_out.weight": "conv_norm_out.weight",
}
state_dict_ = {}
for name in state_dict:
if name in rename_dict:
param = state_dict[name]
state_dict_[rename_dict[name]] = param
return state_dict_
def FluxVAEDecoderStateDictConverter(state_dict):
rename_dict = {
"decoder.conv_in.bias": "conv_in.bias",
"decoder.conv_in.weight": "conv_in.weight",
"decoder.conv_out.bias": "conv_out.bias",
"decoder.conv_out.weight": "conv_out.weight",
"decoder.mid.attn_1.k.bias": "blocks.1.transformer_blocks.0.to_k.bias",
"decoder.mid.attn_1.k.weight": "blocks.1.transformer_blocks.0.to_k.weight",
"decoder.mid.attn_1.norm.bias": "blocks.1.norm.bias",
"decoder.mid.attn_1.norm.weight": "blocks.1.norm.weight",
"decoder.mid.attn_1.proj_out.bias": "blocks.1.transformer_blocks.0.to_out.bias",
"decoder.mid.attn_1.proj_out.weight": "blocks.1.transformer_blocks.0.to_out.weight",
"decoder.mid.attn_1.q.bias": "blocks.1.transformer_blocks.0.to_q.bias",
"decoder.mid.attn_1.q.weight": "blocks.1.transformer_blocks.0.to_q.weight",
"decoder.mid.attn_1.v.bias": "blocks.1.transformer_blocks.0.to_v.bias",
"decoder.mid.attn_1.v.weight": "blocks.1.transformer_blocks.0.to_v.weight",
"decoder.mid.block_1.conv1.bias": "blocks.0.conv1.bias",
"decoder.mid.block_1.conv1.weight": "blocks.0.conv1.weight",
"decoder.mid.block_1.conv2.bias": "blocks.0.conv2.bias",
"decoder.mid.block_1.conv2.weight": "blocks.0.conv2.weight",
"decoder.mid.block_1.norm1.bias": "blocks.0.norm1.bias",
"decoder.mid.block_1.norm1.weight": "blocks.0.norm1.weight",
"decoder.mid.block_1.norm2.bias": "blocks.0.norm2.bias",
"decoder.mid.block_1.norm2.weight": "blocks.0.norm2.weight",
"decoder.mid.block_2.conv1.bias": "blocks.2.conv1.bias",
"decoder.mid.block_2.conv1.weight": "blocks.2.conv1.weight",
"decoder.mid.block_2.conv2.bias": "blocks.2.conv2.bias",
"decoder.mid.block_2.conv2.weight": "blocks.2.conv2.weight",
"decoder.mid.block_2.norm1.bias": "blocks.2.norm1.bias",
"decoder.mid.block_2.norm1.weight": "blocks.2.norm1.weight",
"decoder.mid.block_2.norm2.bias": "blocks.2.norm2.bias",
"decoder.mid.block_2.norm2.weight": "blocks.2.norm2.weight",
"decoder.norm_out.bias": "conv_norm_out.bias",
"decoder.norm_out.weight": "conv_norm_out.weight",
"decoder.up.0.block.0.conv1.bias": "blocks.15.conv1.bias",
"decoder.up.0.block.0.conv1.weight": "blocks.15.conv1.weight",
"decoder.up.0.block.0.conv2.bias": "blocks.15.conv2.bias",
"decoder.up.0.block.0.conv2.weight": "blocks.15.conv2.weight",
"decoder.up.0.block.0.nin_shortcut.bias": "blocks.15.conv_shortcut.bias",
"decoder.up.0.block.0.nin_shortcut.weight": "blocks.15.conv_shortcut.weight",
"decoder.up.0.block.0.norm1.bias": "blocks.15.norm1.bias",
"decoder.up.0.block.0.norm1.weight": "blocks.15.norm1.weight",
"decoder.up.0.block.0.norm2.bias": "blocks.15.norm2.bias",
"decoder.up.0.block.0.norm2.weight": "blocks.15.norm2.weight",
"decoder.up.0.block.1.conv1.bias": "blocks.16.conv1.bias",
"decoder.up.0.block.1.conv1.weight": "blocks.16.conv1.weight",
"decoder.up.0.block.1.conv2.bias": "blocks.16.conv2.bias",
"decoder.up.0.block.1.conv2.weight": "blocks.16.conv2.weight",
"decoder.up.0.block.1.norm1.bias": "blocks.16.norm1.bias",
"decoder.up.0.block.1.norm1.weight": "blocks.16.norm1.weight",
"decoder.up.0.block.1.norm2.bias": "blocks.16.norm2.bias",
"decoder.up.0.block.1.norm2.weight": "blocks.16.norm2.weight",
"decoder.up.0.block.2.conv1.bias": "blocks.17.conv1.bias",
"decoder.up.0.block.2.conv1.weight": "blocks.17.conv1.weight",
"decoder.up.0.block.2.conv2.bias": "blocks.17.conv2.bias",
"decoder.up.0.block.2.conv2.weight": "blocks.17.conv2.weight",
"decoder.up.0.block.2.norm1.bias": "blocks.17.norm1.bias",
"decoder.up.0.block.2.norm1.weight": "blocks.17.norm1.weight",
"decoder.up.0.block.2.norm2.bias": "blocks.17.norm2.bias",
"decoder.up.0.block.2.norm2.weight": "blocks.17.norm2.weight",
"decoder.up.1.block.0.conv1.bias": "blocks.11.conv1.bias",
"decoder.up.1.block.0.conv1.weight": "blocks.11.conv1.weight",
"decoder.up.1.block.0.conv2.bias": "blocks.11.conv2.bias",
"decoder.up.1.block.0.conv2.weight": "blocks.11.conv2.weight",
"decoder.up.1.block.0.nin_shortcut.bias": "blocks.11.conv_shortcut.bias",
"decoder.up.1.block.0.nin_shortcut.weight": "blocks.11.conv_shortcut.weight",
"decoder.up.1.block.0.norm1.bias": "blocks.11.norm1.bias",
"decoder.up.1.block.0.norm1.weight": "blocks.11.norm1.weight",
"decoder.up.1.block.0.norm2.bias": "blocks.11.norm2.bias",
"decoder.up.1.block.0.norm2.weight": "blocks.11.norm2.weight",
"decoder.up.1.block.1.conv1.bias": "blocks.12.conv1.bias",
"decoder.up.1.block.1.conv1.weight": "blocks.12.conv1.weight",
"decoder.up.1.block.1.conv2.bias": "blocks.12.conv2.bias",
"decoder.up.1.block.1.conv2.weight": "blocks.12.conv2.weight",
"decoder.up.1.block.1.norm1.bias": "blocks.12.norm1.bias",
"decoder.up.1.block.1.norm1.weight": "blocks.12.norm1.weight",
"decoder.up.1.block.1.norm2.bias": "blocks.12.norm2.bias",
"decoder.up.1.block.1.norm2.weight": "blocks.12.norm2.weight",
"decoder.up.1.block.2.conv1.bias": "blocks.13.conv1.bias",
"decoder.up.1.block.2.conv1.weight": "blocks.13.conv1.weight",
"decoder.up.1.block.2.conv2.bias": "blocks.13.conv2.bias",
"decoder.up.1.block.2.conv2.weight": "blocks.13.conv2.weight",
"decoder.up.1.block.2.norm1.bias": "blocks.13.norm1.bias",
"decoder.up.1.block.2.norm1.weight": "blocks.13.norm1.weight",
"decoder.up.1.block.2.norm2.bias": "blocks.13.norm2.bias",
"decoder.up.1.block.2.norm2.weight": "blocks.13.norm2.weight",
"decoder.up.1.upsample.conv.bias": "blocks.14.conv.bias",
"decoder.up.1.upsample.conv.weight": "blocks.14.conv.weight",
"decoder.up.2.block.0.conv1.bias": "blocks.7.conv1.bias",
"decoder.up.2.block.0.conv1.weight": "blocks.7.conv1.weight",
"decoder.up.2.block.0.conv2.bias": "blocks.7.conv2.bias",
"decoder.up.2.block.0.conv2.weight": "blocks.7.conv2.weight",
"decoder.up.2.block.0.norm1.bias": "blocks.7.norm1.bias",
"decoder.up.2.block.0.norm1.weight": "blocks.7.norm1.weight",
"decoder.up.2.block.0.norm2.bias": "blocks.7.norm2.bias",
"decoder.up.2.block.0.norm2.weight": "blocks.7.norm2.weight",
"decoder.up.2.block.1.conv1.bias": "blocks.8.conv1.bias",
"decoder.up.2.block.1.conv1.weight": "blocks.8.conv1.weight",
"decoder.up.2.block.1.conv2.bias": "blocks.8.conv2.bias",
"decoder.up.2.block.1.conv2.weight": "blocks.8.conv2.weight",
"decoder.up.2.block.1.norm1.bias": "blocks.8.norm1.bias",
"decoder.up.2.block.1.norm1.weight": "blocks.8.norm1.weight",
"decoder.up.2.block.1.norm2.bias": "blocks.8.norm2.bias",
"decoder.up.2.block.1.norm2.weight": "blocks.8.norm2.weight",
"decoder.up.2.block.2.conv1.bias": "blocks.9.conv1.bias",
"decoder.up.2.block.2.conv1.weight": "blocks.9.conv1.weight",
"decoder.up.2.block.2.conv2.bias": "blocks.9.conv2.bias",
"decoder.up.2.block.2.conv2.weight": "blocks.9.conv2.weight",
"decoder.up.2.block.2.norm1.bias": "blocks.9.norm1.bias",
"decoder.up.2.block.2.norm1.weight": "blocks.9.norm1.weight",
"decoder.up.2.block.2.norm2.bias": "blocks.9.norm2.bias",
"decoder.up.2.block.2.norm2.weight": "blocks.9.norm2.weight",
"decoder.up.2.upsample.conv.bias": "blocks.10.conv.bias",
"decoder.up.2.upsample.conv.weight": "blocks.10.conv.weight",
"decoder.up.3.block.0.conv1.bias": "blocks.3.conv1.bias",
"decoder.up.3.block.0.conv1.weight": "blocks.3.conv1.weight",
"decoder.up.3.block.0.conv2.bias": "blocks.3.conv2.bias",
"decoder.up.3.block.0.conv2.weight": "blocks.3.conv2.weight",
"decoder.up.3.block.0.norm1.bias": "blocks.3.norm1.bias",
"decoder.up.3.block.0.norm1.weight": "blocks.3.norm1.weight",
"decoder.up.3.block.0.norm2.bias": "blocks.3.norm2.bias",
"decoder.up.3.block.0.norm2.weight": "blocks.3.norm2.weight",
"decoder.up.3.block.1.conv1.bias": "blocks.4.conv1.bias",
"decoder.up.3.block.1.conv1.weight": "blocks.4.conv1.weight",
"decoder.up.3.block.1.conv2.bias": "blocks.4.conv2.bias",
"decoder.up.3.block.1.conv2.weight": "blocks.4.conv2.weight",
"decoder.up.3.block.1.norm1.bias": "blocks.4.norm1.bias",
"decoder.up.3.block.1.norm1.weight": "blocks.4.norm1.weight",
"decoder.up.3.block.1.norm2.bias": "blocks.4.norm2.bias",
"decoder.up.3.block.1.norm2.weight": "blocks.4.norm2.weight",
"decoder.up.3.block.2.conv1.bias": "blocks.5.conv1.bias",
"decoder.up.3.block.2.conv1.weight": "blocks.5.conv1.weight",
"decoder.up.3.block.2.conv2.bias": "blocks.5.conv2.bias",
"decoder.up.3.block.2.conv2.weight": "blocks.5.conv2.weight",
"decoder.up.3.block.2.norm1.bias": "blocks.5.norm1.bias",
"decoder.up.3.block.2.norm1.weight": "blocks.5.norm1.weight",
"decoder.up.3.block.2.norm2.bias": "blocks.5.norm2.bias",
"decoder.up.3.block.2.norm2.weight": "blocks.5.norm2.weight",
"decoder.up.3.upsample.conv.bias": "blocks.6.conv.bias",
"decoder.up.3.upsample.conv.weight": "blocks.6.conv.weight",
}
state_dict_ = {}
for name in state_dict:
if name in rename_dict:
param = state_dict[name]
state_dict_[rename_dict[name]] = param
return state_dict_
def FluxVAEEncoderStateDictConverterDiffusers(state_dict):
# architecture
block_types = [
'ResnetBlock', 'ResnetBlock', 'DownSampler',
'ResnetBlock', 'ResnetBlock', 'DownSampler',
'ResnetBlock', 'ResnetBlock', 'DownSampler',
'ResnetBlock', 'ResnetBlock',
'ResnetBlock', 'VAEAttentionBlock', 'ResnetBlock'
]
# Rename each parameter
local_rename_dict = {
"quant_conv": "quant_conv",
"encoder.conv_in": "conv_in",
"encoder.mid_block.attentions.0.group_norm": "blocks.12.norm",
"encoder.mid_block.attentions.0.to_q": "blocks.12.transformer_blocks.0.to_q",
"encoder.mid_block.attentions.0.to_k": "blocks.12.transformer_blocks.0.to_k",
"encoder.mid_block.attentions.0.to_v": "blocks.12.transformer_blocks.0.to_v",
"encoder.mid_block.attentions.0.to_out.0": "blocks.12.transformer_blocks.0.to_out",
"encoder.mid_block.resnets.0.norm1": "blocks.11.norm1",
"encoder.mid_block.resnets.0.conv1": "blocks.11.conv1",
"encoder.mid_block.resnets.0.norm2": "blocks.11.norm2",
"encoder.mid_block.resnets.0.conv2": "blocks.11.conv2",
"encoder.mid_block.resnets.1.norm1": "blocks.13.norm1",
"encoder.mid_block.resnets.1.conv1": "blocks.13.conv1",
"encoder.mid_block.resnets.1.norm2": "blocks.13.norm2",
"encoder.mid_block.resnets.1.conv2": "blocks.13.conv2",
"encoder.conv_norm_out": "conv_norm_out",
"encoder.conv_out": "conv_out",
}
name_list = sorted([name for name in state_dict])
rename_dict = {}
block_id = {"ResnetBlock": -1, "DownSampler": -1, "UpSampler": -1}
last_block_type_with_id = {"ResnetBlock": "", "DownSampler": "", "UpSampler": ""}
for name in name_list:
names = name.split(".")
name_prefix = ".".join(names[:-1])
if name_prefix in local_rename_dict:
rename_dict[name] = local_rename_dict[name_prefix] + "." + names[-1]
elif name.startswith("encoder.down_blocks"):
block_type = {"resnets": "ResnetBlock", "downsamplers": "DownSampler", "upsamplers": "UpSampler"}[names[3]]
block_type_with_id = ".".join(names[:5])
if block_type_with_id != last_block_type_with_id[block_type]:
block_id[block_type] += 1
last_block_type_with_id[block_type] = block_type_with_id
while block_id[block_type] < len(block_types) and block_types[block_id[block_type]] != block_type:
block_id[block_type] += 1
block_type_with_id = ".".join(names[:5])
names = ["blocks", str(block_id[block_type])] + names[5:]
rename_dict[name] = ".".join(names)
# Convert state_dict
state_dict_ = {}
for name in state_dict:
if name in rename_dict:
state_dict_[rename_dict[name]] = state_dict[name]
return state_dict_
def FluxVAEDecoderStateDictConverterDiffusers(state_dict):
# architecture
block_types = [
'ResnetBlock', 'VAEAttentionBlock', 'ResnetBlock',
'ResnetBlock', 'ResnetBlock', 'ResnetBlock', 'UpSampler',
'ResnetBlock', 'ResnetBlock', 'ResnetBlock', 'UpSampler',
'ResnetBlock', 'ResnetBlock', 'ResnetBlock', 'UpSampler',
'ResnetBlock', 'ResnetBlock', 'ResnetBlock'
]
# Rename each parameter
local_rename_dict = {
"post_quant_conv": "post_quant_conv",
"decoder.conv_in": "conv_in",
"decoder.mid_block.attentions.0.group_norm": "blocks.1.norm",
"decoder.mid_block.attentions.0.to_q": "blocks.1.transformer_blocks.0.to_q",
"decoder.mid_block.attentions.0.to_k": "blocks.1.transformer_blocks.0.to_k",
"decoder.mid_block.attentions.0.to_v": "blocks.1.transformer_blocks.0.to_v",
"decoder.mid_block.attentions.0.to_out.0": "blocks.1.transformer_blocks.0.to_out",
"decoder.mid_block.resnets.0.norm1": "blocks.0.norm1",
"decoder.mid_block.resnets.0.conv1": "blocks.0.conv1",
"decoder.mid_block.resnets.0.norm2": "blocks.0.norm2",
"decoder.mid_block.resnets.0.conv2": "blocks.0.conv2",
"decoder.mid_block.resnets.1.norm1": "blocks.2.norm1",
"decoder.mid_block.resnets.1.conv1": "blocks.2.conv1",
"decoder.mid_block.resnets.1.norm2": "blocks.2.norm2",
"decoder.mid_block.resnets.1.conv2": "blocks.2.conv2",
"decoder.conv_norm_out": "conv_norm_out",
"decoder.conv_out": "conv_out",
}
name_list = sorted([name for name in state_dict])
rename_dict = {}
block_id = {"ResnetBlock": 2, "DownSampler": 2, "UpSampler": 2}
last_block_type_with_id = {"ResnetBlock": "", "DownSampler": "", "UpSampler": ""}
for name in name_list:
names = name.split(".")
name_prefix = ".".join(names[:-1])
if name_prefix in local_rename_dict:
rename_dict[name] = local_rename_dict[name_prefix] + "." + names[-1]
elif name.startswith("decoder.up_blocks"):
block_type = {"resnets": "ResnetBlock", "downsamplers": "DownSampler", "upsamplers": "UpSampler"}[names[3]]
block_type_with_id = ".".join(names[:5])
if block_type_with_id != last_block_type_with_id[block_type]:
block_id[block_type] += 1
last_block_type_with_id[block_type] = block_type_with_id
while block_id[block_type] < len(block_types) and block_types[block_id[block_type]] != block_type:
block_id[block_type] += 1
block_type_with_id = ".".join(names[:5])
names = ["blocks", str(block_id[block_type])] + names[5:]
rename_dict[name] = ".".join(names)
# Convert state_dict
state_dict_ = {}
for name in state_dict:
if name in rename_dict:
state_dict_[rename_dict[name]] = state_dict[name]
return state_dict_
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