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import torch |
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from .sd_unet import Timesteps, ResnetBlock, AttentionBlock, PushBlock, PopBlock, DownSampler, UpSampler |
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class SDXLUNet(torch.nn.Module): |
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def __init__(self): |
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super().__init__() |
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self.time_proj = Timesteps(320) |
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self.time_embedding = torch.nn.Sequential( |
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torch.nn.Linear(320, 1280), |
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torch.nn.SiLU(), |
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torch.nn.Linear(1280, 1280) |
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) |
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self.add_time_proj = Timesteps(256) |
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self.add_time_embedding = torch.nn.Sequential( |
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torch.nn.Linear(2816, 1280), |
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torch.nn.SiLU(), |
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torch.nn.Linear(1280, 1280) |
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) |
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self.conv_in = torch.nn.Conv2d(4, 320, kernel_size=3, padding=1) |
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self.blocks = torch.nn.ModuleList([ |
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ResnetBlock(320, 320, 1280), |
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PushBlock(), |
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ResnetBlock(320, 320, 1280), |
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PushBlock(), |
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DownSampler(320), |
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PushBlock(), |
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ResnetBlock(320, 640, 1280), |
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AttentionBlock(10, 64, 640, 2, 2048), |
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PushBlock(), |
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ResnetBlock(640, 640, 1280), |
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AttentionBlock(10, 64, 640, 2, 2048), |
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PushBlock(), |
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DownSampler(640), |
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PushBlock(), |
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ResnetBlock(640, 1280, 1280), |
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AttentionBlock(20, 64, 1280, 10, 2048), |
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PushBlock(), |
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ResnetBlock(1280, 1280, 1280), |
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AttentionBlock(20, 64, 1280, 10, 2048), |
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PushBlock(), |
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ResnetBlock(1280, 1280, 1280), |
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AttentionBlock(20, 64, 1280, 10, 2048), |
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ResnetBlock(1280, 1280, 1280), |
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PopBlock(), |
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ResnetBlock(2560, 1280, 1280), |
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AttentionBlock(20, 64, 1280, 10, 2048), |
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PopBlock(), |
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ResnetBlock(2560, 1280, 1280), |
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AttentionBlock(20, 64, 1280, 10, 2048), |
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PopBlock(), |
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ResnetBlock(1920, 1280, 1280), |
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AttentionBlock(20, 64, 1280, 10, 2048), |
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UpSampler(1280), |
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PopBlock(), |
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ResnetBlock(1920, 640, 1280), |
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AttentionBlock(10, 64, 640, 2, 2048), |
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PopBlock(), |
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ResnetBlock(1280, 640, 1280), |
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AttentionBlock(10, 64, 640, 2, 2048), |
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PopBlock(), |
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ResnetBlock(960, 640, 1280), |
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AttentionBlock(10, 64, 640, 2, 2048), |
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UpSampler(640), |
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PopBlock(), |
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ResnetBlock(960, 320, 1280), |
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PopBlock(), |
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ResnetBlock(640, 320, 1280), |
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PopBlock(), |
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ResnetBlock(640, 320, 1280) |
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]) |
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self.conv_norm_out = torch.nn.GroupNorm(num_channels=320, num_groups=32, eps=1e-5) |
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self.conv_act = torch.nn.SiLU() |
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self.conv_out = torch.nn.Conv2d(320, 4, kernel_size=3, padding=1) |
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def forward( |
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self, |
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sample, timestep, encoder_hidden_states, add_time_id, add_text_embeds, |
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tiled=False, tile_size=64, tile_stride=8, **kwargs |
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): |
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t_emb = self.time_proj(timestep[None]).to(sample.dtype) |
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t_emb = self.time_embedding(t_emb) |
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time_embeds = self.add_time_proj(add_time_id) |
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time_embeds = time_embeds.reshape((add_text_embeds.shape[0], -1)) |
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add_embeds = torch.concat([add_text_embeds, time_embeds], dim=-1) |
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add_embeds = add_embeds.to(sample.dtype) |
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add_embeds = self.add_time_embedding(add_embeds) |
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time_emb = t_emb + add_embeds |
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height, width = sample.shape[2], sample.shape[3] |
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hidden_states = self.conv_in(sample) |
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text_emb = encoder_hidden_states |
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res_stack = [hidden_states] |
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for i, block in enumerate(self.blocks): |
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hidden_states, time_emb, text_emb, res_stack = block( |
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hidden_states, time_emb, text_emb, res_stack, |
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tiled=tiled, tile_size=tile_size, tile_stride=tile_stride |
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) |
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hidden_states = self.conv_norm_out(hidden_states) |
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hidden_states = self.conv_act(hidden_states) |
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hidden_states = self.conv_out(hidden_states) |
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return hidden_states |
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def state_dict_converter(self): |
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return SDXLUNetStateDictConverter() |
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class SDXLUNetStateDictConverter: |
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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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block_types = [ |
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'ResnetBlock', 'PushBlock', 'ResnetBlock', 'PushBlock', 'DownSampler', 'PushBlock', |
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'ResnetBlock', 'AttentionBlock', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'DownSampler', 'PushBlock', |
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'ResnetBlock', 'AttentionBlock', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'PushBlock', |
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'ResnetBlock', 'AttentionBlock', 'ResnetBlock', |
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'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'UpSampler', |
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'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'UpSampler', |
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'PopBlock', 'ResnetBlock', 'PopBlock', 'ResnetBlock', 'PopBlock', 'ResnetBlock' |
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] |
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name_list = sorted([name for name in state_dict]) |
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rename_dict = {} |
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block_id = {"ResnetBlock": -1, "AttentionBlock": -1, "DownSampler": -1, "UpSampler": -1} |
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last_block_type_with_id = {"ResnetBlock": "", "AttentionBlock": "", "DownSampler": "", "UpSampler": ""} |
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for name in name_list: |
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names = name.split(".") |
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if names[0] in ["conv_in", "conv_norm_out", "conv_out"]: |
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pass |
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elif names[0] in ["time_embedding", "add_embedding"]: |
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if names[0] == "add_embedding": |
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names[0] = "add_time_embedding" |
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names[1] = {"linear_1": "0", "linear_2": "2"}[names[1]] |
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elif names[0] in ["down_blocks", "mid_block", "up_blocks"]: |
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if names[0] == "mid_block": |
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names.insert(1, "0") |
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block_type = {"resnets": "ResnetBlock", "attentions": "AttentionBlock", "downsamplers": "DownSampler", "upsamplers": "UpSampler"}[names[2]] |
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block_type_with_id = ".".join(names[:4]) |
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if block_type_with_id != last_block_type_with_id[block_type]: |
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block_id[block_type] += 1 |
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last_block_type_with_id[block_type] = block_type_with_id |
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while block_id[block_type] < len(block_types) and block_types[block_id[block_type]] != block_type: |
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block_id[block_type] += 1 |
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block_type_with_id = ".".join(names[:4]) |
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names = ["blocks", str(block_id[block_type])] + names[4:] |
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if "ff" in names: |
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ff_index = names.index("ff") |
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component = ".".join(names[ff_index:ff_index+3]) |
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component = {"ff.net.0": "act_fn", "ff.net.2": "ff"}[component] |
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names = names[:ff_index] + [component] + names[ff_index+3:] |
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if "to_out" in names: |
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names.pop(names.index("to_out") + 1) |
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else: |
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raise ValueError(f"Unknown parameters: {name}") |
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rename_dict[name] = ".".join(names) |
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state_dict_ = {} |
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for name, param in state_dict.items(): |
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if ".proj_in." in name or ".proj_out." in name: |
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param = param.squeeze() |
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state_dict_[rename_dict[name]] = param |
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return state_dict_ |
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def from_civitai(self, state_dict): |
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rename_dict = { |
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"model.diffusion_model.input_blocks.0.0.bias": "conv_in.bias", |
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"model.diffusion_model.input_blocks.0.0.weight": "conv_in.weight", |
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"model.diffusion_model.input_blocks.1.0.emb_layers.1.bias": "blocks.0.time_emb_proj.bias", |
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"model.diffusion_model.input_blocks.1.0.emb_layers.1.weight": "blocks.0.time_emb_proj.weight", |
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"model.diffusion_model.input_blocks.1.0.in_layers.0.bias": "blocks.0.norm1.bias", |
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"model.diffusion_model.input_blocks.1.0.in_layers.0.weight": "blocks.0.norm1.weight", |
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"model.diffusion_model.input_blocks.1.0.in_layers.2.bias": "blocks.0.conv1.bias", |
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"model.diffusion_model.input_blocks.1.0.in_layers.2.weight": "blocks.0.conv1.weight", |
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"model.diffusion_model.input_blocks.1.0.out_layers.0.bias": "blocks.0.norm2.bias", |
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"model.diffusion_model.input_blocks.1.0.out_layers.0.weight": "blocks.0.norm2.weight", |
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"model.diffusion_model.input_blocks.1.0.out_layers.3.bias": "blocks.0.conv2.bias", |
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"model.diffusion_model.input_blocks.1.0.out_layers.3.weight": "blocks.0.conv2.weight", |
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"model.diffusion_model.input_blocks.2.0.emb_layers.1.bias": "blocks.2.time_emb_proj.bias", |
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"model.diffusion_model.input_blocks.2.0.emb_layers.1.weight": "blocks.2.time_emb_proj.weight", |
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"model.diffusion_model.input_blocks.2.0.in_layers.0.bias": "blocks.2.norm1.bias", |
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"model.diffusion_model.input_blocks.2.0.in_layers.0.weight": "blocks.2.norm1.weight", |
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"model.diffusion_model.input_blocks.2.0.in_layers.2.bias": "blocks.2.conv1.bias", |
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"model.diffusion_model.input_blocks.2.0.in_layers.2.weight": "blocks.2.conv1.weight", |
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"model.diffusion_model.input_blocks.2.0.out_layers.0.bias": "blocks.2.norm2.bias", |
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"model.diffusion_model.input_blocks.2.0.out_layers.0.weight": "blocks.2.norm2.weight", |
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"model.diffusion_model.input_blocks.2.0.out_layers.3.bias": "blocks.2.conv2.bias", |
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"model.diffusion_model.input_blocks.2.0.out_layers.3.weight": "blocks.2.conv2.weight", |
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"model.diffusion_model.input_blocks.3.0.op.bias": "blocks.4.conv.bias", |
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"model.diffusion_model.input_blocks.3.0.op.weight": "blocks.4.conv.weight", |
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"model.diffusion_model.input_blocks.4.0.emb_layers.1.bias": "blocks.6.time_emb_proj.bias", |
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"model.diffusion_model.input_blocks.4.0.emb_layers.1.weight": "blocks.6.time_emb_proj.weight", |
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"model.diffusion_model.input_blocks.4.0.in_layers.0.bias": "blocks.6.norm1.bias", |
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"model.diffusion_model.input_blocks.4.0.in_layers.0.weight": "blocks.6.norm1.weight", |
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"model.diffusion_model.input_blocks.4.0.in_layers.2.bias": "blocks.6.conv1.bias", |
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"model.diffusion_model.input_blocks.4.0.in_layers.2.weight": "blocks.6.conv1.weight", |
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"model.diffusion_model.input_blocks.4.0.out_layers.0.bias": "blocks.6.norm2.bias", |
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"model.diffusion_model.input_blocks.4.0.out_layers.0.weight": "blocks.6.norm2.weight", |
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"model.diffusion_model.input_blocks.4.0.out_layers.3.bias": "blocks.6.conv2.bias", |
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"model.diffusion_model.input_blocks.4.0.out_layers.3.weight": "blocks.6.conv2.weight", |
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"model.diffusion_model.input_blocks.4.0.skip_connection.bias": "blocks.6.conv_shortcut.bias", |
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"model.diffusion_model.input_blocks.4.0.skip_connection.weight": "blocks.6.conv_shortcut.weight", |
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"model.diffusion_model.input_blocks.4.1.norm.bias": "blocks.7.norm.bias", |
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"model.diffusion_model.input_blocks.4.1.norm.weight": "blocks.7.norm.weight", |
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"model.diffusion_model.input_blocks.4.1.proj_in.bias": "blocks.7.proj_in.bias", |
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"model.diffusion_model.input_blocks.4.1.proj_in.weight": "blocks.7.proj_in.weight", |
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"model.diffusion_model.input_blocks.4.1.proj_out.bias": "blocks.7.proj_out.bias", |
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"model.diffusion_model.input_blocks.4.1.proj_out.weight": "blocks.7.proj_out.weight", |
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"model.diffusion_model.input_blocks.4.1.transformer_blocks.0.attn1.to_k.weight": "blocks.7.transformer_blocks.0.attn1.to_k.weight", |
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"model.diffusion_model.input_blocks.4.1.transformer_blocks.0.attn1.to_out.0.bias": "blocks.7.transformer_blocks.0.attn1.to_out.bias", |
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"model.diffusion_model.input_blocks.4.1.transformer_blocks.0.attn1.to_out.0.weight": "blocks.7.transformer_blocks.0.attn1.to_out.weight", |
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"model.diffusion_model.input_blocks.4.1.transformer_blocks.0.attn1.to_q.weight": "blocks.7.transformer_blocks.0.attn1.to_q.weight", |
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"model.diffusion_model.input_blocks.4.1.transformer_blocks.0.attn1.to_v.weight": "blocks.7.transformer_blocks.0.attn1.to_v.weight", |
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"model.diffusion_model.input_blocks.4.1.transformer_blocks.0.attn2.to_k.weight": "blocks.7.transformer_blocks.0.attn2.to_k.weight", |
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"model.diffusion_model.input_blocks.4.1.transformer_blocks.0.attn2.to_out.0.bias": "blocks.7.transformer_blocks.0.attn2.to_out.bias", |
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"model.diffusion_model.input_blocks.4.1.transformer_blocks.0.attn2.to_out.0.weight": "blocks.7.transformer_blocks.0.attn2.to_out.weight", |
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"model.diffusion_model.input_blocks.4.1.transformer_blocks.0.attn2.to_q.weight": "blocks.7.transformer_blocks.0.attn2.to_q.weight", |
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"model.diffusion_model.input_blocks.4.1.transformer_blocks.0.attn2.to_v.weight": "blocks.7.transformer_blocks.0.attn2.to_v.weight", |
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"model.diffusion_model.input_blocks.4.1.transformer_blocks.0.ff.net.0.proj.bias": "blocks.7.transformer_blocks.0.act_fn.proj.bias", |
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"model.diffusion_model.input_blocks.4.1.transformer_blocks.0.ff.net.0.proj.weight": "blocks.7.transformer_blocks.0.act_fn.proj.weight", |
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"model.diffusion_model.input_blocks.4.1.transformer_blocks.0.ff.net.2.bias": "blocks.7.transformer_blocks.0.ff.bias", |
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"model.diffusion_model.input_blocks.4.1.transformer_blocks.0.ff.net.2.weight": "blocks.7.transformer_blocks.0.ff.weight", |
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"model.diffusion_model.input_blocks.4.1.transformer_blocks.0.norm1.bias": "blocks.7.transformer_blocks.0.norm1.bias", |
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"model.diffusion_model.input_blocks.4.1.transformer_blocks.0.norm1.weight": "blocks.7.transformer_blocks.0.norm1.weight", |
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"model.diffusion_model.input_blocks.4.1.transformer_blocks.0.norm2.bias": "blocks.7.transformer_blocks.0.norm2.bias", |
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"model.diffusion_model.input_blocks.4.1.transformer_blocks.0.norm2.weight": "blocks.7.transformer_blocks.0.norm2.weight", |
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"model.diffusion_model.input_blocks.4.1.transformer_blocks.0.norm3.bias": "blocks.7.transformer_blocks.0.norm3.bias", |
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"model.diffusion_model.input_blocks.4.1.transformer_blocks.0.norm3.weight": "blocks.7.transformer_blocks.0.norm3.weight", |
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"model.diffusion_model.input_blocks.4.1.transformer_blocks.1.attn1.to_k.weight": "blocks.7.transformer_blocks.1.attn1.to_k.weight", |
|
"model.diffusion_model.input_blocks.4.1.transformer_blocks.1.attn1.to_out.0.bias": "blocks.7.transformer_blocks.1.attn1.to_out.bias", |
|
"model.diffusion_model.input_blocks.4.1.transformer_blocks.1.attn1.to_out.0.weight": "blocks.7.transformer_blocks.1.attn1.to_out.weight", |
|
"model.diffusion_model.input_blocks.4.1.transformer_blocks.1.attn1.to_q.weight": "blocks.7.transformer_blocks.1.attn1.to_q.weight", |
|
"model.diffusion_model.input_blocks.4.1.transformer_blocks.1.attn1.to_v.weight": "blocks.7.transformer_blocks.1.attn1.to_v.weight", |
|
"model.diffusion_model.input_blocks.4.1.transformer_blocks.1.attn2.to_k.weight": "blocks.7.transformer_blocks.1.attn2.to_k.weight", |
|
"model.diffusion_model.input_blocks.4.1.transformer_blocks.1.attn2.to_out.0.bias": "blocks.7.transformer_blocks.1.attn2.to_out.bias", |
|
"model.diffusion_model.input_blocks.4.1.transformer_blocks.1.attn2.to_out.0.weight": "blocks.7.transformer_blocks.1.attn2.to_out.weight", |
|
"model.diffusion_model.input_blocks.4.1.transformer_blocks.1.attn2.to_q.weight": "blocks.7.transformer_blocks.1.attn2.to_q.weight", |
|
"model.diffusion_model.input_blocks.4.1.transformer_blocks.1.attn2.to_v.weight": "blocks.7.transformer_blocks.1.attn2.to_v.weight", |
|
"model.diffusion_model.input_blocks.4.1.transformer_blocks.1.ff.net.0.proj.bias": "blocks.7.transformer_blocks.1.act_fn.proj.bias", |
|
"model.diffusion_model.input_blocks.4.1.transformer_blocks.1.ff.net.0.proj.weight": "blocks.7.transformer_blocks.1.act_fn.proj.weight", |
|
"model.diffusion_model.input_blocks.4.1.transformer_blocks.1.ff.net.2.bias": "blocks.7.transformer_blocks.1.ff.bias", |
|
"model.diffusion_model.input_blocks.4.1.transformer_blocks.1.ff.net.2.weight": "blocks.7.transformer_blocks.1.ff.weight", |
|
"model.diffusion_model.input_blocks.4.1.transformer_blocks.1.norm1.bias": "blocks.7.transformer_blocks.1.norm1.bias", |
|
"model.diffusion_model.input_blocks.4.1.transformer_blocks.1.norm1.weight": "blocks.7.transformer_blocks.1.norm1.weight", |
|
"model.diffusion_model.input_blocks.4.1.transformer_blocks.1.norm2.bias": "blocks.7.transformer_blocks.1.norm2.bias", |
|
"model.diffusion_model.input_blocks.4.1.transformer_blocks.1.norm2.weight": "blocks.7.transformer_blocks.1.norm2.weight", |
|
"model.diffusion_model.input_blocks.4.1.transformer_blocks.1.norm3.bias": "blocks.7.transformer_blocks.1.norm3.bias", |
|
"model.diffusion_model.input_blocks.4.1.transformer_blocks.1.norm3.weight": "blocks.7.transformer_blocks.1.norm3.weight", |
|
"model.diffusion_model.input_blocks.5.0.emb_layers.1.bias": "blocks.9.time_emb_proj.bias", |
|
"model.diffusion_model.input_blocks.5.0.emb_layers.1.weight": "blocks.9.time_emb_proj.weight", |
|
"model.diffusion_model.input_blocks.5.0.in_layers.0.bias": "blocks.9.norm1.bias", |
|
"model.diffusion_model.input_blocks.5.0.in_layers.0.weight": "blocks.9.norm1.weight", |
|
"model.diffusion_model.input_blocks.5.0.in_layers.2.bias": "blocks.9.conv1.bias", |
|
"model.diffusion_model.input_blocks.5.0.in_layers.2.weight": "blocks.9.conv1.weight", |
|
"model.diffusion_model.input_blocks.5.0.out_layers.0.bias": "blocks.9.norm2.bias", |
|
"model.diffusion_model.input_blocks.5.0.out_layers.0.weight": "blocks.9.norm2.weight", |
|
"model.diffusion_model.input_blocks.5.0.out_layers.3.bias": "blocks.9.conv2.bias", |
|
"model.diffusion_model.input_blocks.5.0.out_layers.3.weight": "blocks.9.conv2.weight", |
|
"model.diffusion_model.input_blocks.5.1.norm.bias": "blocks.10.norm.bias", |
|
"model.diffusion_model.input_blocks.5.1.norm.weight": "blocks.10.norm.weight", |
|
"model.diffusion_model.input_blocks.5.1.proj_in.bias": "blocks.10.proj_in.bias", |
|
"model.diffusion_model.input_blocks.5.1.proj_in.weight": "blocks.10.proj_in.weight", |
|
"model.diffusion_model.input_blocks.5.1.proj_out.bias": "blocks.10.proj_out.bias", |
|
"model.diffusion_model.input_blocks.5.1.proj_out.weight": "blocks.10.proj_out.weight", |
|
"model.diffusion_model.input_blocks.5.1.transformer_blocks.0.attn1.to_k.weight": "blocks.10.transformer_blocks.0.attn1.to_k.weight", |
|
"model.diffusion_model.input_blocks.5.1.transformer_blocks.0.attn1.to_out.0.bias": "blocks.10.transformer_blocks.0.attn1.to_out.bias", |
|
"model.diffusion_model.input_blocks.5.1.transformer_blocks.0.attn1.to_out.0.weight": "blocks.10.transformer_blocks.0.attn1.to_out.weight", |
|
"model.diffusion_model.input_blocks.5.1.transformer_blocks.0.attn1.to_q.weight": "blocks.10.transformer_blocks.0.attn1.to_q.weight", |
|
"model.diffusion_model.input_blocks.5.1.transformer_blocks.0.attn1.to_v.weight": "blocks.10.transformer_blocks.0.attn1.to_v.weight", |
|
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|
"model.diffusion_model.time_embed.0.bias": "time_embedding.0.bias", |
|
"model.diffusion_model.time_embed.0.weight": "time_embedding.0.weight", |
|
"model.diffusion_model.time_embed.2.bias": "time_embedding.2.bias", |
|
"model.diffusion_model.time_embed.2.weight": "time_embedding.2.weight", |
|
} |
|
state_dict_ = {} |
|
for name in state_dict: |
|
if name in rename_dict: |
|
param = state_dict[name] |
|
if ".proj_in." in name or ".proj_out." in name: |
|
param = param.squeeze() |
|
state_dict_[rename_dict[name]] = param |
|
return state_dict_ |
|
|
