89 lines
3.1 KiB
Python
89 lines
3.1 KiB
Python
import re
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import torch
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import logging
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# conversion code from https://github.com/huggingface/diffusers/blob/main/scripts/convert_diffusers_to_original_stable_diffusion.py
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# ================#
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# VAE Conversion #
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# ================#
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vae_conversion_map = [
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# (stable-diffusion, HF Diffusers)
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("nin_shortcut", "conv_shortcut"),
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("norm_out", "conv_norm_out"),
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("mid.attn_1.", "mid_block.attentions.0."),
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]
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for i in range(4):
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# down_blocks have two resnets
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for j in range(2):
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hf_down_prefix = f"encoder.down_blocks.{i}.resnets.{j}."
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sd_down_prefix = f"encoder.down.{i}.block.{j}."
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vae_conversion_map.append((sd_down_prefix, hf_down_prefix))
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if i < 3:
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hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0."
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sd_downsample_prefix = f"down.{i}.downsample."
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vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))
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hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0."
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sd_upsample_prefix = f"up.{3 - i}.upsample."
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vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))
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# up_blocks have three resnets
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# also, up blocks in hf are numbered in reverse from sd
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for j in range(3):
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hf_up_prefix = f"decoder.up_blocks.{i}.resnets.{j}."
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sd_up_prefix = f"decoder.up.{3 - i}.block.{j}."
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vae_conversion_map.append((sd_up_prefix, hf_up_prefix))
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# this part accounts for mid blocks in both the encoder and the decoder
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for i in range(2):
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hf_mid_res_prefix = f"mid_block.resnets.{i}."
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sd_mid_res_prefix = f"mid.block_{i + 1}."
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vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))
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vae_conversion_map_attn = [
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# (stable-diffusion, HF Diffusers)
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("norm.", "group_norm."),
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("q.", "query."),
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("k.", "key."),
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("v.", "value."),
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("q.", "to_q."),
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("k.", "to_k."),
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("v.", "to_v."),
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("proj_out.", "to_out.0."),
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("proj_out.", "proj_attn."),
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]
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def reshape_weight_for_sd(w):
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# convert SD conv2d weights to HF linear weights
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w.view(w.shape[0], w.shape[1])
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def convert_vae_state_dict_to_hf(vae_state_dict):
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mapping = {k: k for k in vae_state_dict.keys()}
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conv3d = False
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for k, v in mapping.items():
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for sd_part, hf_part in vae_conversion_map:
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v = v.replace(sd_part, hf_part)
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# if v.endswith(".conv.weight"):
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# if not conv3d and vae_state_dict[k].ndim == 5:
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# conv3d = True
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mapping[k] = v
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for k, v in mapping.items():
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if "attentions" in k:
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for sd_part, hf_part in vae_conversion_map_attn:
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v = v.replace(sd_part, hf_part)
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mapping[k] = v
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new_state_dict = {v: vae_state_dict[k] for k, v in mapping.items()}
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weights_to_convert = ["to_q", "to_k", "to_v", "to_out.0", "proj_attn", "value", "key", "query"]
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for k, v in new_state_dict.items():
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for weight_name in weights_to_convert:
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if f"mid_block.attentions.0.{weight_name}.weight" in k:
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logging.debug(f"Reshaping {k} for HF format")
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new_state_dict[k] = reshape_weight_for_sd(v)
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return new_state_dict
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