diff options
author | Volpeon <git@volpeon.ink> | 2022-10-03 11:26:43 +0200 |
---|---|---|
committer | Volpeon <git@volpeon.ink> | 2022-10-03 11:26:43 +0200 |
commit | c2c7316959195b62f8bd931ea47b4fd3f9bbffa1 (patch) | |
tree | b219c33e23a52e7c08f5b441dfe05e06bc379309 /scripts | |
parent | Use euler_a for samples in learning scripts; backported improvement from Drea... (diff) | |
download | textual-inversion-diff-c2c7316959195b62f8bd931ea47b4fd3f9bbffa1.tar.gz textual-inversion-diff-c2c7316959195b62f8bd931ea47b4fd3f9bbffa1.tar.bz2 textual-inversion-diff-c2c7316959195b62f8bd931ea47b4fd3f9bbffa1.zip |
Added script to convert Differs -> SD
Diffstat (limited to 'scripts')
-rw-r--r-- | scripts/convert_diffusers_to_original_stable_diffusion.py | 234 |
1 files changed, 234 insertions, 0 deletions
diff --git a/scripts/convert_diffusers_to_original_stable_diffusion.py b/scripts/convert_diffusers_to_original_stable_diffusion.py new file mode 100644 index 0000000..9888f62 --- /dev/null +++ b/scripts/convert_diffusers_to_original_stable_diffusion.py | |||
@@ -0,0 +1,234 @@ | |||
1 | # Script for converting a HF Diffusers saved pipeline to a Stable Diffusion checkpoint. | ||
2 | # *Only* converts the UNet, VAE, and Text Encoder. | ||
3 | # Does not convert optimizer state or any other thing. | ||
4 | |||
5 | import argparse | ||
6 | import os.path as osp | ||
7 | |||
8 | import torch | ||
9 | |||
10 | |||
11 | # =================# | ||
12 | # UNet Conversion # | ||
13 | # =================# | ||
14 | |||
15 | unet_conversion_map = [ | ||
16 | # (stable-diffusion, HF Diffusers) | ||
17 | ("time_embed.0.weight", "time_embedding.linear_1.weight"), | ||
18 | ("time_embed.0.bias", "time_embedding.linear_1.bias"), | ||
19 | ("time_embed.2.weight", "time_embedding.linear_2.weight"), | ||
20 | ("time_embed.2.bias", "time_embedding.linear_2.bias"), | ||
21 | ("input_blocks.0.0.weight", "conv_in.weight"), | ||
22 | ("input_blocks.0.0.bias", "conv_in.bias"), | ||
23 | ("out.0.weight", "conv_norm_out.weight"), | ||
24 | ("out.0.bias", "conv_norm_out.bias"), | ||
25 | ("out.2.weight", "conv_out.weight"), | ||
26 | ("out.2.bias", "conv_out.bias"), | ||
27 | ] | ||
28 | |||
29 | unet_conversion_map_resnet = [ | ||
30 | # (stable-diffusion, HF Diffusers) | ||
31 | ("in_layers.0", "norm1"), | ||
32 | ("in_layers.2", "conv1"), | ||
33 | ("out_layers.0", "norm2"), | ||
34 | ("out_layers.3", "conv2"), | ||
35 | ("emb_layers.1", "time_emb_proj"), | ||
36 | ("skip_connection", "conv_shortcut"), | ||
37 | ] | ||
38 | |||
39 | unet_conversion_map_layer = [] | ||
40 | # hardcoded number of downblocks and resnets/attentions... | ||
41 | # would need smarter logic for other networks. | ||
42 | for i in range(4): | ||
43 | # loop over downblocks/upblocks | ||
44 | |||
45 | for j in range(2): | ||
46 | # loop over resnets/attentions for downblocks | ||
47 | hf_down_res_prefix = f"down_blocks.{i}.resnets.{j}." | ||
48 | sd_down_res_prefix = f"input_blocks.{3*i + j + 1}.0." | ||
49 | unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix)) | ||
50 | |||
51 | if i < 3: | ||
52 | # no attention layers in down_blocks.3 | ||
53 | hf_down_atn_prefix = f"down_blocks.{i}.attentions.{j}." | ||
54 | sd_down_atn_prefix = f"input_blocks.{3*i + j + 1}.1." | ||
55 | unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix)) | ||
56 | |||
57 | for j in range(3): | ||
58 | # loop over resnets/attentions for upblocks | ||
59 | hf_up_res_prefix = f"up_blocks.{i}.resnets.{j}." | ||
60 | sd_up_res_prefix = f"output_blocks.{3*i + j}.0." | ||
61 | unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix)) | ||
62 | |||
63 | if i > 0: | ||
64 | # no attention layers in up_blocks.0 | ||
65 | hf_up_atn_prefix = f"up_blocks.{i}.attentions.{j}." | ||
66 | sd_up_atn_prefix = f"output_blocks.{3*i + j}.1." | ||
67 | unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix)) | ||
68 | |||
69 | if i < 3: | ||
70 | # no downsample in down_blocks.3 | ||
71 | hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0.conv." | ||
72 | sd_downsample_prefix = f"input_blocks.{3*(i+1)}.0.op." | ||
73 | unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix)) | ||
74 | |||
75 | # no upsample in up_blocks.3 | ||
76 | hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0." | ||
77 | sd_upsample_prefix = f"output_blocks.{3*i + 2}.{1 if i == 0 else 2}." | ||
78 | unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix)) | ||
79 | |||
80 | hf_mid_atn_prefix = "mid_block.attentions.0." | ||
81 | sd_mid_atn_prefix = "middle_block.1." | ||
82 | unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix)) | ||
83 | |||
84 | for j in range(2): | ||
85 | hf_mid_res_prefix = f"mid_block.resnets.{j}." | ||
86 | sd_mid_res_prefix = f"middle_block.{2*j}." | ||
87 | unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix)) | ||
88 | |||
89 | |||
90 | def convert_unet_state_dict(unet_state_dict): | ||
91 | # buyer beware: this is a *brittle* function, | ||
92 | # and correct output requires that all of these pieces interact in | ||
93 | # the exact order in which I have arranged them. | ||
94 | mapping = {k: k for k in unet_state_dict.keys()} | ||
95 | for sd_name, hf_name in unet_conversion_map: | ||
96 | mapping[hf_name] = sd_name | ||
97 | for k, v in mapping.items(): | ||
98 | if "resnets" in k: | ||
99 | for sd_part, hf_part in unet_conversion_map_resnet: | ||
100 | v = v.replace(hf_part, sd_part) | ||
101 | mapping[k] = v | ||
102 | for k, v in mapping.items(): | ||
103 | for sd_part, hf_part in unet_conversion_map_layer: | ||
104 | v = v.replace(hf_part, sd_part) | ||
105 | mapping[k] = v | ||
106 | new_state_dict = {v: unet_state_dict[k] for k, v in mapping.items()} | ||
107 | return new_state_dict | ||
108 | |||
109 | |||
110 | # ================# | ||
111 | # VAE Conversion # | ||
112 | # ================# | ||
113 | |||
114 | vae_conversion_map = [ | ||
115 | # (stable-diffusion, HF Diffusers) | ||
116 | ("nin_shortcut", "conv_shortcut"), | ||
117 | ("norm_out", "conv_norm_out"), | ||
118 | ("mid.attn_1.", "mid_block.attentions.0."), | ||
119 | ] | ||
120 | |||
121 | for i in range(4): | ||
122 | # down_blocks have two resnets | ||
123 | for j in range(2): | ||
124 | hf_down_prefix = f"encoder.down_blocks.{i}.resnets.{j}." | ||
125 | sd_down_prefix = f"encoder.down.{i}.block.{j}." | ||
126 | vae_conversion_map.append((sd_down_prefix, hf_down_prefix)) | ||
127 | |||
128 | if i < 3: | ||
129 | hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0." | ||
130 | sd_downsample_prefix = f"down.{i}.downsample." | ||
131 | vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix)) | ||
132 | |||
133 | hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0." | ||
134 | sd_upsample_prefix = f"up.{3-i}.upsample." | ||
135 | vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix)) | ||
136 | |||
137 | # up_blocks have three resnets | ||
138 | # also, up blocks in hf are numbered in reverse from sd | ||
139 | for j in range(3): | ||
140 | hf_up_prefix = f"decoder.up_blocks.{i}.resnets.{j}." | ||
141 | sd_up_prefix = f"decoder.up.{3-i}.block.{j}." | ||
142 | vae_conversion_map.append((sd_up_prefix, hf_up_prefix)) | ||
143 | |||
144 | # this part accounts for mid blocks in both the encoder and the decoder | ||
145 | for i in range(2): | ||
146 | hf_mid_res_prefix = f"mid_block.resnets.{i}." | ||
147 | sd_mid_res_prefix = f"mid.block_{i+1}." | ||
148 | vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix)) | ||
149 | |||
150 | |||
151 | vae_conversion_map_attn = [ | ||
152 | # (stable-diffusion, HF Diffusers) | ||
153 | ("norm.", "group_norm."), | ||
154 | ("q.", "query."), | ||
155 | ("k.", "key."), | ||
156 | ("v.", "value."), | ||
157 | ("proj_out.", "proj_attn."), | ||
158 | ] | ||
159 | |||
160 | |||
161 | def reshape_weight_for_sd(w): | ||
162 | # convert HF linear weights to SD conv2d weights | ||
163 | return w.reshape(*w.shape, 1, 1) | ||
164 | |||
165 | |||
166 | def convert_vae_state_dict(vae_state_dict): | ||
167 | mapping = {k: k for k in vae_state_dict.keys()} | ||
168 | for k, v in mapping.items(): | ||
169 | for sd_part, hf_part in vae_conversion_map: | ||
170 | v = v.replace(hf_part, sd_part) | ||
171 | mapping[k] = v | ||
172 | for k, v in mapping.items(): | ||
173 | if "attentions" in k: | ||
174 | for sd_part, hf_part in vae_conversion_map_attn: | ||
175 | v = v.replace(hf_part, sd_part) | ||
176 | mapping[k] = v | ||
177 | new_state_dict = {v: vae_state_dict[k] for k, v in mapping.items()} | ||
178 | weights_to_convert = ["q", "k", "v", "proj_out"] | ||
179 | for k, v in new_state_dict.items(): | ||
180 | for weight_name in weights_to_convert: | ||
181 | if f"mid.attn_1.{weight_name}.weight" in k: | ||
182 | print(f"Reshaping {k} for SD format") | ||
183 | new_state_dict[k] = reshape_weight_for_sd(v) | ||
184 | return new_state_dict | ||
185 | |||
186 | |||
187 | # =========================# | ||
188 | # Text Encoder Conversion # | ||
189 | # =========================# | ||
190 | # pretty much a no-op | ||
191 | |||
192 | |||
193 | def convert_text_enc_state_dict(text_enc_dict): | ||
194 | return text_enc_dict | ||
195 | |||
196 | |||
197 | if __name__ == "__main__": | ||
198 | parser = argparse.ArgumentParser() | ||
199 | |||
200 | parser.add_argument("--model_path", default=None, type=str, required=True, help="Path to the model to convert.") | ||
201 | parser.add_argument("--checkpoint_path", default=None, type=str, required=True, help="Path to the output model.") | ||
202 | parser.add_argument("--half", action="store_true", help="Save weights in half precision.") | ||
203 | |||
204 | args = parser.parse_args() | ||
205 | |||
206 | assert args.model_path is not None, "Must provide a model path!" | ||
207 | |||
208 | assert args.checkpoint_path is not None, "Must provide a checkpoint path!" | ||
209 | |||
210 | unet_path = osp.join(args.model_path, "unet", "diffusion_pytorch_model.bin") | ||
211 | vae_path = osp.join(args.model_path, "vae", "diffusion_pytorch_model.bin") | ||
212 | text_enc_path = osp.join(args.model_path, "text_encoder", "pytorch_model.bin") | ||
213 | |||
214 | # Convert the UNet model | ||
215 | unet_state_dict = torch.load(unet_path, map_location="cpu") | ||
216 | unet_state_dict = convert_unet_state_dict(unet_state_dict) | ||
217 | unet_state_dict = {"model.diffusion_model." + k: v for k, v in unet_state_dict.items()} | ||
218 | |||
219 | # Convert the VAE model | ||
220 | vae_state_dict = torch.load(vae_path, map_location="cpu") | ||
221 | vae_state_dict = convert_vae_state_dict(vae_state_dict) | ||
222 | vae_state_dict = {"first_stage_model." + k: v for k, v in vae_state_dict.items()} | ||
223 | |||
224 | # Convert the text encoder model | ||
225 | text_enc_dict = torch.load(text_enc_path, map_location="cpu") | ||
226 | text_enc_dict = convert_text_enc_state_dict(text_enc_dict) | ||
227 | text_enc_dict = {"cond_stage_model.transformer." + k: v for k, v in text_enc_dict.items()} | ||
228 | |||
229 | # Put together new checkpoint | ||
230 | state_dict = {**unet_state_dict, **vae_state_dict, **text_enc_dict} | ||
231 | if args.half: | ||
232 | state_dict = {k: v.half() for k, v in state_dict.items()} | ||
233 | state_dict = {"state_dict": state_dict} | ||
234 | torch.save(state_dict, args.checkpoint_path) | ||