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""" |
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This file is part of ComfyUI. |
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Copyright (C) 2024 Comfy |
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This program is free software: you can redistribute it and/or modify |
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it under the terms of the GNU General Public License as published by |
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the Free Software Foundation, either version 3 of the License, or |
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(at your option) any later version. |
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This program is distributed in the hope that it will be useful, |
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but WITHOUT ANY WARRANTY; without even the implied warranty of |
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MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the |
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GNU General Public License for more details. |
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You should have received a copy of the GNU General Public License |
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along with this program. If not, see <https://www.gnu.org/licenses/>. |
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""" |
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import torch |
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UNET_MAP_ATTENTIONS = { |
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"proj_in.weight", |
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"proj_in.bias", |
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"proj_out.weight", |
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"proj_out.bias", |
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"norm.weight", |
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"norm.bias", |
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} |
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TRANSFORMER_BLOCKS = { |
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"norm1.weight", |
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"norm1.bias", |
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"norm2.weight", |
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"norm2.bias", |
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"norm3.weight", |
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"norm3.bias", |
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"attn1.to_q.weight", |
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"attn1.to_k.weight", |
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"attn1.to_v.weight", |
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"attn1.to_out.0.weight", |
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"attn1.to_out.0.bias", |
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"attn2.to_q.weight", |
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"attn2.to_k.weight", |
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"attn2.to_v.weight", |
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"attn2.to_out.0.weight", |
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"attn2.to_out.0.bias", |
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"ff.net.0.proj.weight", |
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"ff.net.0.proj.bias", |
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"ff.net.2.weight", |
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"ff.net.2.bias", |
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} |
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UNET_MAP_RESNET = { |
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"in_layers.2.weight": "conv1.weight", |
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"in_layers.2.bias": "conv1.bias", |
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"emb_layers.1.weight": "time_emb_proj.weight", |
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"emb_layers.1.bias": "time_emb_proj.bias", |
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"out_layers.3.weight": "conv2.weight", |
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"out_layers.3.bias": "conv2.bias", |
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"skip_connection.weight": "conv_shortcut.weight", |
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"skip_connection.bias": "conv_shortcut.bias", |
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"in_layers.0.weight": "norm1.weight", |
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"in_layers.0.bias": "norm1.bias", |
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"out_layers.0.weight": "norm2.weight", |
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"out_layers.0.bias": "norm2.bias", |
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} |
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UNET_MAP_BASIC = { |
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("label_emb.0.0.weight", "class_embedding.linear_1.weight"), |
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("label_emb.0.0.bias", "class_embedding.linear_1.bias"), |
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("label_emb.0.2.weight", "class_embedding.linear_2.weight"), |
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("label_emb.0.2.bias", "class_embedding.linear_2.bias"), |
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("label_emb.0.0.weight", "add_embedding.linear_1.weight"), |
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("label_emb.0.0.bias", "add_embedding.linear_1.bias"), |
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("label_emb.0.2.weight", "add_embedding.linear_2.weight"), |
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("label_emb.0.2.bias", "add_embedding.linear_2.bias"), |
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("input_blocks.0.0.weight", "conv_in.weight"), |
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("input_blocks.0.0.bias", "conv_in.bias"), |
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("out.0.weight", "conv_norm_out.weight"), |
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("out.0.bias", "conv_norm_out.bias"), |
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("out.2.weight", "conv_out.weight"), |
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("out.2.bias", "conv_out.bias"), |
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("time_embed.0.weight", "time_embedding.linear_1.weight"), |
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("time_embed.0.bias", "time_embedding.linear_1.bias"), |
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("time_embed.2.weight", "time_embedding.linear_2.weight"), |
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("time_embed.2.bias", "time_embedding.linear_2.bias"), |
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} |
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def unet_to_diffusers(unet_config): |
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if "num_res_blocks" not in unet_config: |
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return {} |
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num_res_blocks = unet_config["num_res_blocks"] |
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channel_mult = unet_config["channel_mult"] |
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transformer_depth = unet_config["transformer_depth"][:] |
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transformer_depth_output = unet_config["transformer_depth_output"][:] |
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num_blocks = len(channel_mult) |
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transformers_mid = unet_config.get("transformer_depth_middle", None) |
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diffusers_unet_map = {} |
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for x in range(num_blocks): |
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n = 1 + (num_res_blocks[x] + 1) * x |
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for i in range(num_res_blocks[x]): |
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for b in UNET_MAP_RESNET: |
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diffusers_unet_map["down_blocks.{}.resnets.{}.{}".format(x, i, UNET_MAP_RESNET[b])] = "input_blocks.{}.0.{}".format(n, b) |
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num_transformers = transformer_depth.pop(0) |
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if num_transformers > 0: |
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for b in UNET_MAP_ATTENTIONS: |
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diffusers_unet_map["down_blocks.{}.attentions.{}.{}".format(x, i, b)] = "input_blocks.{}.1.{}".format(n, b) |
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for t in range(num_transformers): |
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for b in TRANSFORMER_BLOCKS: |
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diffusers_unet_map["down_blocks.{}.attentions.{}.transformer_blocks.{}.{}".format(x, i, t, b)] = "input_blocks.{}.1.transformer_blocks.{}.{}".format(n, t, b) |
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n += 1 |
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for k in ["weight", "bias"]: |
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diffusers_unet_map["down_blocks.{}.downsamplers.0.conv.{}".format(x, k)] = "input_blocks.{}.0.op.{}".format(n, k) |
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i = 0 |
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for b in UNET_MAP_ATTENTIONS: |
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diffusers_unet_map["mid_block.attentions.{}.{}".format(i, b)] = "middle_block.1.{}".format(b) |
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for t in range(transformers_mid): |
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for b in TRANSFORMER_BLOCKS: |
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diffusers_unet_map["mid_block.attentions.{}.transformer_blocks.{}.{}".format(i, t, b)] = "middle_block.1.transformer_blocks.{}.{}".format(t, b) |
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for i, n in enumerate([0, 2]): |
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for b in UNET_MAP_RESNET: |
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diffusers_unet_map["mid_block.resnets.{}.{}".format(i, UNET_MAP_RESNET[b])] = "middle_block.{}.{}".format(n, b) |
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num_res_blocks = list(reversed(num_res_blocks)) |
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for x in range(num_blocks): |
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n = (num_res_blocks[x] + 1) * x |
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l = num_res_blocks[x] + 1 |
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for i in range(l): |
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c = 0 |
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for b in UNET_MAP_RESNET: |
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diffusers_unet_map["up_blocks.{}.resnets.{}.{}".format(x, i, UNET_MAP_RESNET[b])] = "output_blocks.{}.0.{}".format(n, b) |
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c += 1 |
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num_transformers = transformer_depth_output.pop() |
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if num_transformers > 0: |
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c += 1 |
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for b in UNET_MAP_ATTENTIONS: |
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diffusers_unet_map["up_blocks.{}.attentions.{}.{}".format(x, i, b)] = "output_blocks.{}.1.{}".format(n, b) |
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for t in range(num_transformers): |
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for b in TRANSFORMER_BLOCKS: |
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diffusers_unet_map["up_blocks.{}.attentions.{}.transformer_blocks.{}.{}".format(x, i, t, b)] = "output_blocks.{}.1.transformer_blocks.{}.{}".format(n, t, b) |
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if i == l - 1: |
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for k in ["weight", "bias"]: |
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diffusers_unet_map["up_blocks.{}.upsamplers.0.conv.{}".format(x, k)] = "output_blocks.{}.{}.conv.{}".format(n, c, k) |
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n += 1 |
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for k in UNET_MAP_BASIC: |
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diffusers_unet_map[k[1]] = k[0] |
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return diffusers_unet_map |
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def swap_scale_shift(weight): |
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shift, scale = weight.chunk(2, dim=0) |
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new_weight = torch.cat([scale, shift], dim=0) |
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return new_weight |
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def flux_to_diffusers(mmdit_config, output_prefix=""): |
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n_double_layers = mmdit_config.get("depth", 0) |
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n_single_layers = mmdit_config.get("depth_single_blocks", 0) |
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hidden_size = mmdit_config.get("hidden_size", 0) |
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key_map = {} |
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for index in range(n_double_layers): |
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prefix_from = "transformer_blocks.{}".format(index) |
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prefix_to = "{}double_blocks.{}".format(output_prefix, index) |
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for end in ("weight", "bias"): |
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k = "{}.attn.".format(prefix_from) |
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qkv = "{}.img_attn.qkv.{}".format(prefix_to, end) |
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key_map["{}to_q.{}".format(k, end)] = (qkv, (0, 0, hidden_size)) |
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key_map["{}to_k.{}".format(k, end)] = (qkv, (0, hidden_size, hidden_size)) |
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key_map["{}to_v.{}".format(k, end)] = (qkv, (0, hidden_size * 2, hidden_size)) |
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k = "{}.attn.".format(prefix_from) |
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qkv = "{}.txt_attn.qkv.{}".format(prefix_to, end) |
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key_map["{}add_q_proj.{}".format(k, end)] = (qkv, (0, 0, hidden_size)) |
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key_map["{}add_k_proj.{}".format(k, end)] = (qkv, (0, hidden_size, hidden_size)) |
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key_map["{}add_v_proj.{}".format(k, end)] = (qkv, (0, hidden_size * 2, hidden_size)) |
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block_map = { |
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"attn.to_out.0.weight": "img_attn.proj.weight", |
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"attn.to_out.0.bias": "img_attn.proj.bias", |
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"norm1.linear.weight": "img_mod.lin.weight", |
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"norm1.linear.bias": "img_mod.lin.bias", |
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"norm1_context.linear.weight": "txt_mod.lin.weight", |
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"norm1_context.linear.bias": "txt_mod.lin.bias", |
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"attn.to_add_out.weight": "txt_attn.proj.weight", |
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"attn.to_add_out.bias": "txt_attn.proj.bias", |
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"ff.net.0.proj.weight": "img_mlp.0.weight", |
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"ff.net.0.proj.bias": "img_mlp.0.bias", |
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"ff.net.2.weight": "img_mlp.2.weight", |
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"ff.net.2.bias": "img_mlp.2.bias", |
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"ff_context.net.0.proj.weight": "txt_mlp.0.weight", |
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"ff_context.net.0.proj.bias": "txt_mlp.0.bias", |
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"ff_context.net.2.weight": "txt_mlp.2.weight", |
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"ff_context.net.2.bias": "txt_mlp.2.bias", |
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"attn.norm_q.weight": "img_attn.norm.query_norm.scale", |
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"attn.norm_k.weight": "img_attn.norm.key_norm.scale", |
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"attn.norm_added_q.weight": "txt_attn.norm.query_norm.scale", |
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"attn.norm_added_k.weight": "txt_attn.norm.key_norm.scale", |
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} |
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for k in block_map: |
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key_map["{}.{}".format(prefix_from, k)] = "{}.{}".format(prefix_to, block_map[k]) |
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for index in range(n_single_layers): |
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prefix_from = "single_transformer_blocks.{}".format(index) |
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prefix_to = "{}single_blocks.{}".format(output_prefix, index) |
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for end in ("weight", "bias"): |
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k = "{}.attn.".format(prefix_from) |
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qkv = "{}.linear1.{}".format(prefix_to, end) |
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key_map["{}to_q.{}".format(k, end)] = (qkv, (0, 0, hidden_size)) |
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key_map["{}to_k.{}".format(k, end)] = (qkv, (0, hidden_size, hidden_size)) |
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key_map["{}to_v.{}".format(k, end)] = (qkv, (0, hidden_size * 2, hidden_size)) |
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key_map["{}.proj_mlp.{}".format(prefix_from, end)] = (qkv, (0, hidden_size * 3, hidden_size * 4)) |
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block_map = { |
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"norm.linear.weight": "modulation.lin.weight", |
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"norm.linear.bias": "modulation.lin.bias", |
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"proj_out.weight": "linear2.weight", |
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"proj_out.bias": "linear2.bias", |
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"attn.norm_q.weight": "norm.query_norm.scale", |
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"attn.norm_k.weight": "norm.key_norm.scale", |
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} |
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for k in block_map: |
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key_map["{}.{}".format(prefix_from, k)] = "{}.{}".format(prefix_to, block_map[k]) |
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MAP_BASIC = { |
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("final_layer.linear.bias", "proj_out.bias"), |
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("final_layer.linear.weight", "proj_out.weight"), |
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("img_in.bias", "x_embedder.bias"), |
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("img_in.weight", "x_embedder.weight"), |
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("time_in.in_layer.bias", "time_text_embed.timestep_embedder.linear_1.bias"), |
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("time_in.in_layer.weight", "time_text_embed.timestep_embedder.linear_1.weight"), |
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("time_in.out_layer.bias", "time_text_embed.timestep_embedder.linear_2.bias"), |
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("time_in.out_layer.weight", "time_text_embed.timestep_embedder.linear_2.weight"), |
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("txt_in.bias", "context_embedder.bias"), |
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("txt_in.weight", "context_embedder.weight"), |
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("vector_in.in_layer.bias", "time_text_embed.text_embedder.linear_1.bias"), |
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("vector_in.in_layer.weight", "time_text_embed.text_embedder.linear_1.weight"), |
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("vector_in.out_layer.bias", "time_text_embed.text_embedder.linear_2.bias"), |
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("vector_in.out_layer.weight", "time_text_embed.text_embedder.linear_2.weight"), |
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("guidance_in.in_layer.bias", "time_text_embed.guidance_embedder.linear_1.bias"), |
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("guidance_in.in_layer.weight", "time_text_embed.guidance_embedder.linear_1.weight"), |
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("guidance_in.out_layer.bias", "time_text_embed.guidance_embedder.linear_2.bias"), |
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("guidance_in.out_layer.weight", "time_text_embed.guidance_embedder.linear_2.weight"), |
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("final_layer.adaLN_modulation.1.bias", "norm_out.linear.bias", swap_scale_shift), |
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("final_layer.adaLN_modulation.1.weight", "norm_out.linear.weight", swap_scale_shift), |
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("pos_embed_input.bias", "controlnet_x_embedder.bias"), |
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("pos_embed_input.weight", "controlnet_x_embedder.weight"), |
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} |
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for k in MAP_BASIC: |
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if len(k) > 2: |
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key_map[k[1]] = ("{}{}".format(output_prefix, k[0]), None, k[2]) |
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else: |
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key_map[k[1]] = "{}{}".format(output_prefix, k[0]) |
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return key_map |
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def z_image_to_diffusers(mmdit_config, output_prefix=""): |
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n_layers = mmdit_config.get("n_layers", 0) |
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hidden_size = mmdit_config.get("dim", 0) |
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n_context_refiner = mmdit_config.get("n_refiner_layers", 2) |
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n_noise_refiner = mmdit_config.get("n_refiner_layers", 2) |
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key_map = {} |
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def add_block_keys(prefix_from, prefix_to, has_adaln=True): |
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for end in ("weight", "bias"): |
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k = "{}.attention.".format(prefix_from) |
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qkv = "{}.attention.qkv.{}".format(prefix_to, end) |
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key_map["{}to_q.{}".format(k, end)] = (qkv, (0, 0, hidden_size)) |
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key_map["{}to_k.{}".format(k, end)] = (qkv, (0, hidden_size, hidden_size)) |
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key_map["{}to_v.{}".format(k, end)] = (qkv, (0, hidden_size * 2, hidden_size)) |
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block_map = { |
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"attention.norm_q.weight": "attention.q_norm.weight", |
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"attention.norm_k.weight": "attention.k_norm.weight", |
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"attention.to_out.0.weight": "attention.out.weight", |
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"attention.to_out.0.bias": "attention.out.bias", |
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"attention_norm1.weight": "attention_norm1.weight", |
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"attention_norm2.weight": "attention_norm2.weight", |
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"feed_forward.w1.weight": "feed_forward.w1.weight", |
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"feed_forward.w2.weight": "feed_forward.w2.weight", |
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"feed_forward.w3.weight": "feed_forward.w3.weight", |
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"ffn_norm1.weight": "ffn_norm1.weight", |
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"ffn_norm2.weight": "ffn_norm2.weight", |
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} |
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if has_adaln: |
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block_map["adaLN_modulation.0.weight"] = "adaLN_modulation.0.weight" |
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block_map["adaLN_modulation.0.bias"] = "adaLN_modulation.0.bias" |
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for k, v in block_map.items(): |
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key_map["{}.{}".format(prefix_from, k)] = "{}.{}".format(prefix_to, v) |
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for i in range(n_layers): |
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add_block_keys("layers.{}".format(i), "{}layers.{}".format(output_prefix, i)) |
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for i in range(n_context_refiner): |
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add_block_keys("context_refiner.{}".format(i), "{}context_refiner.{}".format(output_prefix, i)) |
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for i in range(n_noise_refiner): |
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add_block_keys("noise_refiner.{}".format(i), "{}noise_refiner.{}".format(output_prefix, i)) |
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MAP_BASIC = [ |
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("final_layer.linear.weight", "all_final_layer.2-1.linear.weight"), |
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("final_layer.linear.bias", "all_final_layer.2-1.linear.bias"), |
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("final_layer.adaLN_modulation.1.weight", "all_final_layer.2-1.adaLN_modulation.1.weight"), |
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("final_layer.adaLN_modulation.1.bias", "all_final_layer.2-1.adaLN_modulation.1.bias"), |
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("x_embedder.weight", "all_x_embedder.2-1.weight"), |
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("x_embedder.bias", "all_x_embedder.2-1.bias"), |
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("x_pad_token", "x_pad_token"), |
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("cap_embedder.0.weight", "cap_embedder.0.weight"), |
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("cap_embedder.1.weight", "cap_embedder.1.weight"), |
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("cap_embedder.1.bias", "cap_embedder.1.bias"), |
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("cap_pad_token", "cap_pad_token"), |
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("t_embedder.mlp.0.weight", "t_embedder.mlp.0.weight"), |
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("t_embedder.mlp.0.bias", "t_embedder.mlp.0.bias"), |
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("t_embedder.mlp.2.weight", "t_embedder.mlp.2.weight"), |
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("t_embedder.mlp.2.bias", "t_embedder.mlp.2.bias"), |
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] |
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for c, diffusers in MAP_BASIC: |
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key_map[diffusers] = "{}{}".format(output_prefix, c) |
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return key_map |
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