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import
argparse
import
tempfile
import
torch
from
accelerate
import
load_checkpoint_and_dispatch
from
transformers
import
CLIPTextModelWithProjection
,
CLIPTokenizer
from
diffusers
import
UnCLIPPipeline
,
UNet2DConditionModel
,
UNet2DModel
from
diffusers
.
models
.
transformers
.
prior_transformer
import
PriorTransformer
from
diffusers
.
pipelines
.
unclip
.
text_proj
import
UnCLIPTextProjModel
from
diffusers
.
schedulers
.
scheduling_unclip
import
UnCLIPScheduler
r"""
Example - From the diffusers root directory:
Download weights:
```sh
$ wget https://arena.kakaocdn.net/brainrepo/models/karlo-public/v1.0.0.alpha/efdf6206d8ed593961593dc029a8affa/decoder-ckpt-step%3D01000000-of-01000000.ckpt
$ wget https://arena.kakaocdn.net/brainrepo/models/karlo-public/v1.0.0.alpha/4226b831ae0279020d134281f3c31590/improved-sr-ckpt-step%3D1.2M.ckpt
$ wget https://arena.kakaocdn.net/brainrepo/models/karlo-public/v1.0.0.alpha/85626483eaca9f581e2a78d31ff905ca/prior-ckpt-step%3D01000000-of-01000000.ckpt
$ wget https://arena.kakaocdn.net/brainrepo/models/karlo-public/v1.0.0.alpha/0b62380a75e56f073e2844ab5199153d/ViT-L-14_stats.th
```
Convert the model:
```sh
$ python scripts/convert_kakao_brain_unclip_to_diffusers.py \
--decoder_checkpoint_path ./decoder-ckpt-step\=01000000-of-01000000.ckpt \
--super_res_unet_checkpoint_path ./improved-sr-ckpt-step\=1.2M.ckpt \
--prior_checkpoint_path ./prior-ckpt-step\=01000000-of-01000000.ckpt \
--clip_stat_path ./ViT-L-14_stats.th \
--dump_path <path where to save model>
```
"""
# prior
PRIOR_ORIGINAL_PREFIX
=
"model"
# Uses default arguments
PRIOR_CONFIG
=
{}
def
prior_model_from_original_config
():
model
=
PriorTransformer
(
**
PRIOR_CONFIG
)
return
model
def
prior_original_checkpoint_to_diffusers_checkpoint
(
model
,
checkpoint
,
clip_stats_checkpoint
):
diffusers_checkpoint
=
{}
# <original>.time_embed.0 -> <diffusers>.time_embedding.linear_1
diffusers_checkpoint
.
update
(
{
"time_embedding.linear_1.weight"
:
checkpoint
[
f"
{
PRIOR_ORIGINAL_PREFIX
}
.time_embed.0.weight"
],
"time_embedding.linear_1.bias"
:
checkpoint
[
f"
{
PRIOR_ORIGINAL_PREFIX
}
.time_embed.0.bias"
],
}
)
# <original>.clip_img_proj -> <diffusers>.proj_in
diffusers_checkpoint
.
update
(
{
"proj_in.weight"
:
checkpoint
[
f"
{
PRIOR_ORIGINAL_PREFIX
}
.clip_img_proj.weight"
],
"proj_in.bias"
:
checkpoint
[
f"
{
PRIOR_ORIGINAL_PREFIX
}
.clip_img_proj.bias"
],
}
)
# <original>.text_emb_proj -> <diffusers>.embedding_proj
diffusers_checkpoint
.
update
(
{
"embedding_proj.weight"
:
checkpoint
[
f"
{
PRIOR_ORIGINAL_PREFIX
}
.text_emb_proj.weight"
],
"embedding_proj.bias"
:
checkpoint
[
f"
{
PRIOR_ORIGINAL_PREFIX
}
.text_emb_proj.bias"
],
}
)
# <original>.text_enc_proj -> <diffusers>.encoder_hidden_states_proj
diffusers_checkpoint
.
update
(
{
"encoder_hidden_states_proj.weight"
:
checkpoint
[
f"
{
PRIOR_ORIGINAL_PREFIX
}
.text_enc_proj.weight"
],
"encoder_hidden_states_proj.bias"
:
checkpoint
[
f"
{
PRIOR_ORIGINAL_PREFIX
}
.text_enc_proj.bias"
],
}
)
# <original>.positional_embedding -> <diffusers>.positional_embedding
diffusers_checkpoint
.
update
({
"positional_embedding"
:
checkpoint
[
f"
{
PRIOR_ORIGINAL_PREFIX
}
.positional_embedding"
]})
# <original>.prd_emb -> <diffusers>.prd_embedding
diffusers_checkpoint
.
update
({
"prd_embedding"
:
checkpoint
[
f"
{
PRIOR_ORIGINAL_PREFIX
}
.prd_emb"
]})
# <original>.time_embed.2 -> <diffusers>.time_embedding.linear_2
diffusers_checkpoint
.
update
(
{
"time_embedding.linear_2.weight"
:
checkpoint
[
f"
{
PRIOR_ORIGINAL_PREFIX
}
.time_embed.2.weight"
],
"time_embedding.linear_2.bias"
:
checkpoint
[
f"
{
PRIOR_ORIGINAL_PREFIX
}
.time_embed.2.bias"
],
}
)
# <original>.resblocks.<x> -> <diffusers>.transformer_blocks.<x>
for
idx
in
range
(
len
(
model
.
transformer_blocks
)):
diffusers_transformer_prefix
=
f"transformer_blocks.
{
idx
}
"
original_transformer_prefix
=
f"
{
PRIOR_ORIGINAL_PREFIX
}
.transformer.resblocks.
{
idx
}
"
# <original>.attn -> <diffusers>.attn1
diffusers_attention_prefix
=
f"
{
diffusers_transformer_prefix
}
.attn1"
original_attention_prefix
=
f"
{
original_transformer_prefix
}
.attn"
diffusers_checkpoint
.
update
(
prior_attention_to_diffusers
(
checkpoint
,
diffusers_attention_prefix
=
diffusers_attention_prefix
,
original_attention_prefix
=
original_attention_prefix
,
attention_head_dim
=
model
.
attention_head_dim
,
)
)
# <original>.mlp -> <diffusers>.ff
diffusers_ff_prefix
=
f"
{
diffusers_transformer_prefix
}
.ff"
original_ff_prefix
=
f"
{
original_transformer_prefix
}
.mlp"
diffusers_checkpoint
.
update
(
prior_ff_to_diffusers
(
checkpoint
,
diffusers_ff_prefix
=
diffusers_ff_prefix
,
original_ff_prefix
=
original_ff_prefix
)
)
# <original>.ln_1 -> <diffusers>.norm1
diffusers_checkpoint
.
update
(
{
f"
{
diffusers_transformer_prefix
}
.norm1.weight"
:
checkpoint
[
f"
{
original_transformer_prefix
}
.ln_1.weight"
],
f"
{
diffusers_transformer_prefix
}
.norm1.bias"
:
checkpoint
[
f"
{
original_transformer_prefix
}
.ln_1.bias"
],
}
)
# <original>.ln_2 -> <diffusers>.norm3
diffusers_checkpoint
.
update
(
{
f"
{
diffusers_transformer_prefix
}
.norm3.weight"
:
checkpoint
[
f"
{
original_transformer_prefix
}
.ln_2.weight"
],
f"
{
diffusers_transformer_prefix
}
.norm3.bias"
:
checkpoint
[
f"
{
original_transformer_prefix
}
.ln_2.bias"
],
}
)
# <original>.final_ln -> <diffusers>.norm_out
diffusers_checkpoint
.
update
(
{
"norm_out.weight"
:
checkpoint
[
f"
{
PRIOR_ORIGINAL_PREFIX
}
.final_ln.weight"
],
"norm_out.bias"
:
checkpoint
[
f"
{
PRIOR_ORIGINAL_PREFIX
}
.final_ln.bias"
],
}
)
# <original>.out_proj -> <diffusers>.proj_to_clip_embeddings
diffusers_checkpoint
.
update
(
{
"proj_to_clip_embeddings.weight"
:
checkpoint
[
f"
{
PRIOR_ORIGINAL_PREFIX
}
.out_proj.weight"
],
"proj_to_clip_embeddings.bias"
:
checkpoint
[
f"
{
PRIOR_ORIGINAL_PREFIX
}
.out_proj.bias"
],
}
)
# clip stats
clip_mean
,
clip_std
=
clip_stats_checkpoint
clip_mean
=
clip_mean
[
None
, :]
clip_std
=
clip_std
[
None
, :]
diffusers_checkpoint
.
update
({
"clip_mean"
:
clip_mean
,
"clip_std"
:
clip_std
})
return
diffusers_checkpoint
def
prior_attention_to_diffusers
(
checkpoint
,
*
,
diffusers_attention_prefix
,
original_attention_prefix
,
attention_head_dim
):
diffusers_checkpoint
=
{}
# <original>.c_qkv -> <diffusers>.{to_q, to_k, to_v}
[
q_weight
,
k_weight
,
v_weight
], [
q_bias
,
k_bias
,
v_bias
]
=
split_attentions
(
weight
=
checkpoint
[
f"
{
original_attention_prefix
}
.c_qkv.weight"
],
bias
=
checkpoint
[
f"
{
original_attention_prefix
}
.c_qkv.bias"
],
split
=
3
,
chunk_size
=
attention_head_dim
,
)
diffusers_checkpoint
.
update
(
{
f"
{
diffusers_attention_prefix
}
.to_q.weight"
:
q_weight
,
f"
{
diffusers_attention_prefix
}
.to_q.bias"
:
q_bias
,
f"
{
diffusers_attention_prefix
}
.to_k.weight"
:
k_weight
,
f"
{
diffusers_attention_prefix
}
.to_k.bias"
:
k_bias
,
f"
{
diffusers_attention_prefix
}
.to_v.weight"
:
v_weight
,
f"
{
diffusers_attention_prefix
}
.to_v.bias"
:
v_bias
,
}
)
# <original>.c_proj -> <diffusers>.to_out.0
diffusers_checkpoint
.
update
(
{
f"
{
diffusers_attention_prefix
}
.to_out.0.weight"
:
checkpoint
[
f"
{
original_attention_prefix
}
.c_proj.weight"
],
f"
{
diffusers_attention_prefix
}
.to_out.0.bias"
:
checkpoint
[
f"
{
original_attention_prefix
}
.c_proj.bias"
],
}
)
return
diffusers_checkpoint
def
prior_ff_to_diffusers
(
checkpoint
,
*
,
diffusers_ff_prefix
,
original_ff_prefix
):
diffusers_checkpoint
=
{
# <original>.c_fc -> <diffusers>.net.0.proj
f"
{
diffusers_ff_prefix
}
.net.
{
0
}
.proj.weight"
:
checkpoint
[
f"
{
original_ff_prefix
}
.c_fc.weight"
],
f"
{
diffusers_ff_prefix
}
.net.
{
0
}
.proj.bias"
:
checkpoint
[
f"
{
original_ff_prefix
}
.c_fc.bias"
],
# <original>.c_proj -> <diffusers>.net.2
f"
{
diffusers_ff_prefix
}
.net.
{
2
}
.weight"
:
checkpoint
[
f"
{
original_ff_prefix
}
.c_proj.weight"
],
f"
{
diffusers_ff_prefix
}
.net.
{
2
}
.bias"
:
checkpoint
[
f"
{
original_ff_prefix
}
.c_proj.bias"
],
}
return
diffusers_checkpoint
# done prior
# decoder
DECODER_ORIGINAL_PREFIX
=
"model"
# We are hardcoding the model configuration for now. If we need to generalize to more model configurations, we can
# update then.
DECODER_CONFIG
=
{
"sample_size"
:
64
,
"layers_per_block"
:
3
,
"down_block_types"
: (
"ResnetDownsampleBlock2D"
,
"SimpleCrossAttnDownBlock2D"
,
"SimpleCrossAttnDownBlock2D"
,
"SimpleCrossAttnDownBlock2D"
,
),
"up_block_types"
: (
"SimpleCrossAttnUpBlock2D"
,
"SimpleCrossAttnUpBlock2D"
,
"SimpleCrossAttnUpBlock2D"
,
"ResnetUpsampleBlock2D"
,
),
"mid_block_type"
:
"UNetMidBlock2DSimpleCrossAttn"
,
"block_out_channels"
: (
320
,
640
,
960
,
1280
),
"in_channels"
:
3
,
"out_channels"
:
6
,
"cross_attention_dim"
:
1536
,
"class_embed_type"
:
"identity"
,
"attention_head_dim"
:
64
,
"resnet_time_scale_shift"
:
"scale_shift"
,
}
def
decoder_model_from_original_config
():
model
=
UNet2DConditionModel
(
**
DECODER_CONFIG
)
return
model
def
decoder_original_checkpoint_to_diffusers_checkpoint
(
model
,
checkpoint
):
diffusers_checkpoint
=
{}
original_unet_prefix
=
DECODER_ORIGINAL_PREFIX
num_head_channels
=
DECODER_CONFIG
[
"attention_head_dim"
]
diffusers_checkpoint
.
update
(
unet_time_embeddings
(
checkpoint
,
original_unet_prefix
))
diffusers_checkpoint
.
update
(
unet_conv_in
(
checkpoint
,
original_unet_prefix
))
# <original>.input_blocks -> <diffusers>.down_blocks
original_down_block_idx
=
1
for
diffusers_down_block_idx
in
range
(
len
(
model
.
down_blocks
)):
checkpoint_update
,
num_original_down_blocks
=
unet_downblock_to_diffusers_checkpoint
(
model
,
checkpoint
,
diffusers_down_block_idx
=
diffusers_down_block_idx
,
original_down_block_idx
=
original_down_block_idx
,
original_unet_prefix
=
original_unet_prefix
,
num_head_channels
=
num_head_channels
,
)
original_down_block_idx
+=
num_original_down_blocks
diffusers_checkpoint
.
update
(
checkpoint_update
)
# done <original>.input_blocks -> <diffusers>.down_blocks
diffusers_checkpoint
.
update
(
unet_midblock_to_diffusers_checkpoint
(
model
,
checkpoint
,
original_unet_prefix
=
original_unet_prefix
,
num_head_channels
=
num_head_channels
,
)
)
# <original>.output_blocks -> <diffusers>.up_blocks
original_up_block_idx
=
0
for
diffusers_up_block_idx
in
range
(
len
(
model
.
up_blocks
)):
checkpoint_update
,
num_original_up_blocks
=
unet_upblock_to_diffusers_checkpoint
(
model
,
checkpoint
,
diffusers_up_block_idx
=
diffusers_up_block_idx
,
original_up_block_idx
=
original_up_block_idx
,
original_unet_prefix
=
original_unet_prefix
,
num_head_channels
=
num_head_channels
,
)
original_up_block_idx
+=
num_original_up_blocks
diffusers_checkpoint
.
update
(
checkpoint_update
)
# done <original>.output_blocks -> <diffusers>.up_blocks
diffusers_checkpoint
.
update
(
unet_conv_norm_out
(
checkpoint
,
original_unet_prefix
))
diffusers_checkpoint
.
update
(
unet_conv_out
(
checkpoint
,
original_unet_prefix
))
return
diffusers_checkpoint
# done decoder
# text proj
def
text_proj_from_original_config
():
# From the conditional unet constructor where the dimension of the projected time embeddings is
# constructed
time_embed_dim
=
DECODER_CONFIG
[
"block_out_channels"
][
0
]
*
4
cross_attention_dim
=
DECODER_CONFIG
[
"cross_attention_dim"
]
model
=
UnCLIPTextProjModel
(
time_embed_dim
=
time_embed_dim
,
cross_attention_dim
=
cross_attention_dim
)
return
model
# Note that the input checkpoint is the original decoder checkpoint
def
text_proj_original_checkpoint_to_diffusers_checkpoint
(
checkpoint
):
diffusers_checkpoint
=
{
# <original>.text_seq_proj.0 -> <diffusers>.encoder_hidden_states_proj
"encoder_hidden_states_proj.weight"
:
checkpoint
[
f"
{
DECODER_ORIGINAL_PREFIX
}
.text_seq_proj.0.weight"
],
"encoder_hidden_states_proj.bias"
:
checkpoint
[
f"
{
DECODER_ORIGINAL_PREFIX
}
.text_seq_proj.0.bias"
],
# <original>.text_seq_proj.1 -> <diffusers>.text_encoder_hidden_states_norm
"text_encoder_hidden_states_norm.weight"
:
checkpoint
[
f"
{
DECODER_ORIGINAL_PREFIX
}
.text_seq_proj.1.weight"
],
"text_encoder_hidden_states_norm.bias"
:
checkpoint
[
f"
{
DECODER_ORIGINAL_PREFIX
}
.text_seq_proj.1.bias"
],
# <original>.clip_tok_proj -> <diffusers>.clip_extra_context_tokens_proj
"clip_extra_context_tokens_proj.weight"
:
checkpoint
[
f"
{
DECODER_ORIGINAL_PREFIX
}
.clip_tok_proj.weight"
],
"clip_extra_context_tokens_proj.bias"
:
checkpoint
[
f"
{
DECODER_ORIGINAL_PREFIX
}
.clip_tok_proj.bias"
],
# <original>.text_feat_proj -> <diffusers>.embedding_proj
"embedding_proj.weight"
:
checkpoint
[
f"
{
DECODER_ORIGINAL_PREFIX
}
.text_feat_proj.weight"
],
"embedding_proj.bias"
:
checkpoint
[
f"
{
DECODER_ORIGINAL_PREFIX
}
.text_feat_proj.bias"
],
# <original>.cf_param -> <diffusers>.learned_classifier_free_guidance_embeddings
"learned_classifier_free_guidance_embeddings"
:
checkpoint
[
f"
{
DECODER_ORIGINAL_PREFIX
}
.cf_param"
],
# <original>.clip_emb -> <diffusers>.clip_image_embeddings_project_to_time_embeddings
"clip_image_embeddings_project_to_time_embeddings.weight"
:
checkpoint
[
f"
{
DECODER_ORIGINAL_PREFIX
}
.clip_emb.weight"
],
"clip_image_embeddings_project_to_time_embeddings.bias"
:
checkpoint
[
f"
{
DECODER_ORIGINAL_PREFIX
}
.clip_emb.bias"
],
}
return
diffusers_checkpoint
# done text proj
# super res unet first steps
SUPER_RES_UNET_FIRST_STEPS_PREFIX
=
"model_first_steps"
SUPER_RES_UNET_FIRST_STEPS_CONFIG
=
{
"sample_size"
:
256
,
"layers_per_block"
:
3
,
"down_block_types"
: (
"ResnetDownsampleBlock2D"
,
"ResnetDownsampleBlock2D"
,
"ResnetDownsampleBlock2D"
,
"ResnetDownsampleBlock2D"
,
),
"up_block_types"
: (
"ResnetUpsampleBlock2D"
,
"ResnetUpsampleBlock2D"
,
"ResnetUpsampleBlock2D"
,
"ResnetUpsampleBlock2D"
,
),
"block_out_channels"
: (
320
,
640
,
960
,
1280
),
"in_channels"
:
6
,
"out_channels"
:
3
,
"add_attention"
:
False
,
}
def
super_res_unet_first_steps_model_from_original_config
():
model
=
UNet2DModel
(
**
SUPER_RES_UNET_FIRST_STEPS_CONFIG
)
return
model
def
super_res_unet_first_steps_original_checkpoint_to_diffusers_checkpoint
(
model
,
checkpoint
):
diffusers_checkpoint
=
{}
original_unet_prefix
=
SUPER_RES_UNET_FIRST_STEPS_PREFIX
diffusers_checkpoint
.
update
(
unet_time_embeddings
(
checkpoint
,
original_unet_prefix
))
diffusers_checkpoint
.
update
(
unet_conv_in
(
checkpoint
,
original_unet_prefix
))
# <original>.input_blocks -> <diffusers>.down_blocks
original_down_block_idx
=
1
for
diffusers_down_block_idx
in
range
(
len
(
model
.
down_blocks
)):
checkpoint_update
,
num_original_down_blocks
=
unet_downblock_to_diffusers_checkpoint
(
model
,
checkpoint
,
diffusers_down_block_idx
=
diffusers_down_block_idx
,
original_down_block_idx
=
original_down_block_idx
,
original_unet_prefix
=
original_unet_prefix
,
num_head_channels
=
None
,
)
original_down_block_idx
+=
num_original_down_blocks
diffusers_checkpoint
.
update
(
checkpoint_update
)
diffusers_checkpoint
.
update
(
unet_midblock_to_diffusers_checkpoint
(
model
,
checkpoint
,
original_unet_prefix
=
original_unet_prefix
,
num_head_channels
=
None
,
)
)
# <original>.output_blocks -> <diffusers>.up_blocks
original_up_block_idx
=
0
for
diffusers_up_block_idx
in
range
(
len
(
model
.
up_blocks
)):
checkpoint_update
,
num_original_up_blocks
=
unet_upblock_to_diffusers_checkpoint
(
model
,
checkpoint
,
diffusers_up_block_idx
=
diffusers_up_block_idx
,
original_up_block_idx
=
original_up_block_idx
,
original_unet_prefix
=
original_unet_prefix
,
num_head_channels
=
None
,
)
original_up_block_idx
+=
num_original_up_blocks
diffusers_checkpoint
.
update
(
checkpoint_update
)
# done <original>.output_blocks -> <diffusers>.up_blocks
diffusers_checkpoint
.
update
(
unet_conv_norm_out
(
checkpoint
,
original_unet_prefix
))
diffusers_checkpoint
.
update
(
unet_conv_out
(
checkpoint
,
original_unet_prefix
))
return
diffusers_checkpoint
# done super res unet first steps
# super res unet last step
SUPER_RES_UNET_LAST_STEP_PREFIX
=
"model_last_step"
SUPER_RES_UNET_LAST_STEP_CONFIG
=
{
"sample_size"
:
256
,
"layers_per_block"
:
3
,
"down_block_types"
: (
"ResnetDownsampleBlock2D"
,
"ResnetDownsampleBlock2D"
,
"ResnetDownsampleBlock2D"
,
"ResnetDownsampleBlock2D"
,
),
"up_block_types"
: (
"ResnetUpsampleBlock2D"
,
"ResnetUpsampleBlock2D"
,
"ResnetUpsampleBlock2D"
,
"ResnetUpsampleBlock2D"
,
),
"block_out_channels"
: (
320
,
640
,
960
,
1280
),
"in_channels"
:
6
,
"out_channels"
:
3
,
"add_attention"
:
False
,
}
def
super_res_unet_last_step_model_from_original_config
():
model
=
UNet2DModel
(
**
SUPER_RES_UNET_LAST_STEP_CONFIG
)
return
model
def
super_res_unet_last_step_original_checkpoint_to_diffusers_checkpoint
(
model
,
checkpoint
):
diffusers_checkpoint
=
{}
original_unet_prefix
=
SUPER_RES_UNET_LAST_STEP_PREFIX
diffusers_checkpoint
.
update
(
unet_time_embeddings
(
checkpoint
,
original_unet_prefix
))
diffusers_checkpoint
.
update
(
unet_conv_in
(
checkpoint
,
original_unet_prefix
))
# <original>.input_blocks -> <diffusers>.down_blocks
original_down_block_idx
=
1
for
diffusers_down_block_idx
in
range
(
len
(
model
.
down_blocks
)):
checkpoint_update
,
num_original_down_blocks
=
unet_downblock_to_diffusers_checkpoint
(
model
,
checkpoint
,
diffusers_down_block_idx
=
diffusers_down_block_idx
,
original_down_block_idx
=
original_down_block_idx
,
original_unet_prefix
=
original_unet_prefix
,
num_head_channels
=
None
,
)
original_down_block_idx
+=
num_original_down_blocks
diffusers_checkpoint
.
update
(
checkpoint_update
)
diffusers_checkpoint
.
update
(
unet_midblock_to_diffusers_checkpoint
(
model
,
checkpoint
,
original_unet_prefix
=
original_unet_prefix
,
num_head_channels
=
None
,
)
)
# <original>.output_blocks -> <diffusers>.up_blocks
original_up_block_idx
=
0
for
diffusers_up_block_idx
in
range
(
len
(
model
.
up_blocks
)):
checkpoint_update
,
num_original_up_blocks
=
unet_upblock_to_diffusers_checkpoint
(
model
,
checkpoint
,
diffusers_up_block_idx
=
diffusers_up_block_idx
,
original_up_block_idx
=
original_up_block_idx
,
original_unet_prefix
=
original_unet_prefix
,
num_head_channels
=
None
,
)
original_up_block_idx
+=
num_original_up_blocks
diffusers_checkpoint
.
update
(
checkpoint_update
)
# done <original>.output_blocks -> <diffusers>.up_blocks
diffusers_checkpoint
.
update
(
unet_conv_norm_out
(
checkpoint
,
original_unet_prefix
))
diffusers_checkpoint
.
update
(
unet_conv_out
(
checkpoint
,
original_unet_prefix
))
return
diffusers_checkpoint
# done super res unet last step
# unet utils
# <original>.time_embed -> <diffusers>.time_embedding
def
unet_time_embeddings
(
checkpoint
,
original_unet_prefix
):
diffusers_checkpoint
=
{}
diffusers_checkpoint
.
update
(
{
"time_embedding.linear_1.weight"
:
checkpoint
[
f"
{
original_unet_prefix
}
.time_embed.0.weight"
],
"time_embedding.linear_1.bias"
:
checkpoint
[
f"
{
original_unet_prefix
}
.time_embed.0.bias"
],
"time_embedding.linear_2.weight"
:
checkpoint
[
f"
{
original_unet_prefix
}
.time_embed.2.weight"
],
"time_embedding.linear_2.bias"
:
checkpoint
[
f"
{
original_unet_prefix
}
.time_embed.2.bias"
],
}
)
return
diffusers_checkpoint
# <original>.input_blocks.0 -> <diffusers>.conv_in
def
unet_conv_in
(
checkpoint
,
original_unet_prefix
):
diffusers_checkpoint
=
{}
diffusers_checkpoint
.
update
(
{
"conv_in.weight"
:
checkpoint
[
f"
{
original_unet_prefix
}
.input_blocks.0.0.weight"
],
"conv_in.bias"
:
checkpoint
[
f"
{
original_unet_prefix
}
.input_blocks.0.0.bias"
],
}
)
return
diffusers_checkpoint
# <original>.out.0 -> <diffusers>.conv_norm_out
def
unet_conv_norm_out
(
checkpoint
,
original_unet_prefix
):
diffusers_checkpoint
=
{}
diffusers_checkpoint
.
update
(
{
"conv_norm_out.weight"
:
checkpoint
[
f"
{
original_unet_prefix
}
.out.0.weight"
],
"conv_norm_out.bias"
:
checkpoint
[
f"
{
original_unet_prefix
}
.out.0.bias"
],
}
)
return
diffusers_checkpoint
# <original>.out.2 -> <diffusers>.conv_out
def
unet_conv_out
(
checkpoint
,
original_unet_prefix
):
diffusers_checkpoint
=
{}
diffusers_checkpoint
.
update
(
{
"conv_out.weight"
:
checkpoint
[
f"
{
original_unet_prefix
}
.out.2.weight"
],
"conv_out.bias"
:
checkpoint
[
f"
{
original_unet_prefix
}
.out.2.bias"
],
}
)
return
diffusers_checkpoint
# <original>.input_blocks -> <diffusers>.down_blocks
def
unet_downblock_to_diffusers_checkpoint
(
model
,
checkpoint
,
*
,
diffusers_down_block_idx
,
original_down_block_idx
,
original_unet_prefix
,
num_head_channels
):
diffusers_checkpoint
=
{}
diffusers_resnet_prefix
=
f"down_blocks.
{
diffusers_down_block_idx
}
.resnets"
original_down_block_prefix
=
f"
{
original_unet_prefix
}
.input_blocks"
down_block
=
model
.
down_blocks
[
diffusers_down_block_idx
]
num_resnets
=
len
(
down_block
.
resnets
)
if
down_block
.
downsamplers
is
None
:
downsampler
=
False
else
:
assert
len
(
down_block
.
downsamplers
)
==
1
downsampler
=
True
# The downsample block is also a resnet
num_resnets
+=
1
for
resnet_idx_inc
in
range
(
num_resnets
):
full_resnet_prefix
=
f"
{
original_down_block_prefix
}
.
{
original_down_block_idx
+
resnet_idx_inc
}
.0"
if
downsampler
and
resnet_idx_inc
==
num_resnets
-
1
:
# this is a downsample block
full_diffusers_resnet_prefix
=
f"down_blocks.
{
diffusers_down_block_idx
}
.downsamplers.0"
else
:
# this is a regular resnet block
full_diffusers_resnet_prefix
=
f"
{
diffusers_resnet_prefix
}
.
{
resnet_idx_inc
}
"
diffusers_checkpoint
.
update
(
resnet_to_diffusers_checkpoint
(
checkpoint
,
resnet_prefix
=
full_resnet_prefix
,
diffusers_resnet_prefix
=
full_diffusers_resnet_prefix
)
)
if
hasattr
(
down_block
,
"attentions"
):
num_attentions
=
len
(
down_block
.
attentions
)
diffusers_attention_prefix
=
f"down_blocks.
{
diffusers_down_block_idx
}
.attentions"
for
attention_idx_inc
in
range
(
num_attentions
):
full_attention_prefix
=
f"
{
original_down_block_prefix
}
.
{
original_down_block_idx
+
attention_idx_inc
}
.1"
full_diffusers_attention_prefix
=
f"
{
diffusers_attention_prefix
}
.
{
attention_idx_inc
}
"
diffusers_checkpoint
.
update
(
attention_to_diffusers_checkpoint
(
checkpoint
,
attention_prefix
=
full_attention_prefix
,
diffusers_attention_prefix
=
full_diffusers_attention_prefix
,
num_head_channels
=
num_head_channels
,
)
)
num_original_down_blocks
=
num_resnets
return
diffusers_checkpoint
,
num_original_down_blocks
# <original>.middle_block -> <diffusers>.mid_block
def
unet_midblock_to_diffusers_checkpoint
(
model
,
checkpoint
,
*
,
original_unet_prefix
,
num_head_channels
):
diffusers_checkpoint
=
{}
# block 0
original_block_idx
=
0
diffusers_checkpoint
.
update
(
resnet_to_diffusers_checkpoint
(
checkpoint
,
diffusers_resnet_prefix
=
"mid_block.resnets.0"
,
resnet_prefix
=
f"
{
original_unet_prefix
}
.middle_block.
{
original_block_idx
}
"
,
)
)
original_block_idx
+=
1
# optional block 1
if
hasattr
(
model
.
mid_block
,
"attentions"
)
and
model
.
mid_block
.
attentions
[
0
]
is
not
None
:
diffusers_checkpoint
.
update
(
attention_to_diffusers_checkpoint
(
checkpoint
,
diffusers_attention_prefix
=
"mid_block.attentions.0"
,
attention_prefix
=
f"
{
original_unet_prefix
}
.middle_block.
{
original_block_idx
}
"
,
num_head_channels
=
num_head_channels
,
)
)
original_block_idx
+=
1
# block 1 or block 2
diffusers_checkpoint
.
update
(
resnet_to_diffusers_checkpoint
(
checkpoint
,
diffusers_resnet_prefix
=
"mid_block.resnets.1"
,
resnet_prefix
=
f"
{
original_unet_prefix
}
.middle_block.
{
original_block_idx
}
"
,
)
)
return
diffusers_checkpoint
# <original>.output_blocks -> <diffusers>.up_blocks
def
unet_upblock_to_diffusers_checkpoint
(
model
,
checkpoint
,
*
,
diffusers_up_block_idx
,
original_up_block_idx
,
original_unet_prefix
,
num_head_channels
):
diffusers_checkpoint
=
{}
diffusers_resnet_prefix
=
f"up_blocks.
{
diffusers_up_block_idx
}
.resnets"
original_up_block_prefix
=
f"
{
original_unet_prefix
}
.output_blocks"
up_block
=
model
.
up_blocks
[
diffusers_up_block_idx
]
num_resnets
=
len
(
up_block
.
resnets
)
if
up_block
.
upsamplers
is
None
:
upsampler
=
False
else
:
assert
len
(
up_block
.
upsamplers
)
==
1
upsampler
=
True
# The upsample block is also a resnet
num_resnets
+=
1
has_attentions
=
hasattr
(
up_block
,
"attentions"
)
for
resnet_idx_inc
in
range
(
num_resnets
):
if
upsampler
and
resnet_idx_inc
==
num_resnets
-
1
:
# this is an upsample block
if
has_attentions
:
# There is a middle attention block that we skip
original_resnet_block_idx
=
2
else
:
original_resnet_block_idx
=
1
# we add the `minus 1` because the last two resnets are stuck together in the same output block
full_resnet_prefix
=
(
f"
{
original_up_block_prefix
}
.
{
original_up_block_idx
+
resnet_idx_inc
-
1
}
.
{
original_resnet_block_idx
}
"
)
full_diffusers_resnet_prefix
=
f"up_blocks.
{
diffusers_up_block_idx
}
.upsamplers.0"
else
:
# this is a regular resnet block
full_resnet_prefix
=
f"
{
original_up_block_prefix
}
.
{
original_up_block_idx
+
resnet_idx_inc
}
.0"
full_diffusers_resnet_prefix
=
f"
{
diffusers_resnet_prefix
}
.
{
resnet_idx_inc
}
"
diffusers_checkpoint
.
update
(
resnet_to_diffusers_checkpoint
(
checkpoint
,
resnet_prefix
=
full_resnet_prefix
,
diffusers_resnet_prefix
=
full_diffusers_resnet_prefix
)
)
if
has_attentions
:
num_attentions
=
len
(
up_block
.
attentions
)
diffusers_attention_prefix
=
f"up_blocks.
{
diffusers_up_block_idx
}
.attentions"
for
attention_idx_inc
in
range
(
num_attentions
):
full_attention_prefix
=
f"
{
original_up_block_prefix
}
.
{
original_up_block_idx
+
attention_idx_inc
}
.1"
full_diffusers_attention_prefix
=
f"
{
diffusers_attention_prefix
}
.
{
attention_idx_inc
}
"
diffusers_checkpoint
.
update
(
attention_to_diffusers_checkpoint
(
checkpoint
,
attention_prefix
=
full_attention_prefix
,
diffusers_attention_prefix
=
full_diffusers_attention_prefix
,
num_head_channels
=
num_head_channels
,
)
)
num_original_down_blocks
=
num_resnets
-
1
if
upsampler
else
num_resnets
return
diffusers_checkpoint
,
num_original_down_blocks
def
resnet_to_diffusers_checkpoint
(
checkpoint
,
*
,
diffusers_resnet_prefix
,
resnet_prefix
):
diffusers_checkpoint
=
{
f"
{
diffusers_resnet_prefix
}
.norm1.weight"
:
checkpoint
[
f"
{
resnet_prefix
}
.in_layers.0.weight"
],
f"
{
diffusers_resnet_prefix
}
.norm1.bias"
:
checkpoint
[
f"
{
resnet_prefix
}
.in_layers.0.bias"
],
f"
{
diffusers_resnet_prefix
}
.conv1.weight"
:
checkpoint
[
f"
{
resnet_prefix
}
.in_layers.2.weight"
],
f"
{
diffusers_resnet_prefix
}
.conv1.bias"
:
checkpoint
[
f"
{
resnet_prefix
}
.in_layers.2.bias"
],
f"
{
diffusers_resnet_prefix
}
.time_emb_proj.weight"
:
checkpoint
[
f"
{
resnet_prefix
}
.emb_layers.1.weight"
],
f"
{
diffusers_resnet_prefix
}
.time_emb_proj.bias"
:
checkpoint
[
f"
{
resnet_prefix
}
.emb_layers.1.bias"
],
f"
{
diffusers_resnet_prefix
}
.norm2.weight"
:
checkpoint
[
f"
{
resnet_prefix
}
.out_layers.0.weight"
],
f"
{
diffusers_resnet_prefix
}
.norm2.bias"
:
checkpoint
[
f"
{
resnet_prefix
}
.out_layers.0.bias"
],
f"
{
diffusers_resnet_prefix
}
.conv2.weight"
:
checkpoint
[
f"
{
resnet_prefix
}
.out_layers.3.weight"
],
f"
{
diffusers_resnet_prefix
}
.conv2.bias"
:
checkpoint
[
f"
{
resnet_prefix
}
.out_layers.3.bias"
],
}
skip_connection_prefix
=
f"
{
resnet_prefix
}
.skip_connection"
if
f"
{
skip_connection_prefix
}
.weight"
in
checkpoint
:
diffusers_checkpoint
.
update
(
{
f"
{
diffusers_resnet_prefix
}
.conv_shortcut.weight"
:
checkpoint
[
f"
{
skip_connection_prefix
}
.weight"
],
f"
{
diffusers_resnet_prefix
}
.conv_shortcut.bias"
:
checkpoint
[
f"
{
skip_connection_prefix
}
.bias"
],
}
)
return
diffusers_checkpoint
def
attention_to_diffusers_checkpoint
(
checkpoint
,
*
,
diffusers_attention_prefix
,
attention_prefix
,
num_head_channels
):
diffusers_checkpoint
=
{}
# <original>.norm -> <diffusers>.group_norm
diffusers_checkpoint
.
update
(
{
f"
{
diffusers_attention_prefix
}
.group_norm.weight"
:
checkpoint
[
f"
{
attention_prefix
}
.norm.weight"
],
f"
{
diffusers_attention_prefix
}
.group_norm.bias"
:
checkpoint
[
f"
{
attention_prefix
}
.norm.bias"
],
}
)
# <original>.qkv -> <diffusers>.{query, key, value}
[
q_weight
,
k_weight
,
v_weight
], [
q_bias
,
k_bias
,
v_bias
]
=
split_attentions
(
weight
=
checkpoint
[
f"
{
attention_prefix
}
.qkv.weight"
][:, :,
0
],
bias
=
checkpoint
[
f"
{
attention_prefix
}
.qkv.bias"
],
split
=
3
,
chunk_size
=
num_head_channels
,
)
diffusers_checkpoint
.
update
(
{
f"
{
diffusers_attention_prefix
}
.to_q.weight"
:
q_weight
,
f"
{
diffusers_attention_prefix
}
.to_q.bias"
:
q_bias
,
f"
{
diffusers_attention_prefix
}
.to_k.weight"
:
k_weight
,
f"
{
diffusers_attention_prefix
}
.to_k.bias"
:
k_bias
,
f"
{
diffusers_attention_prefix
}
.to_v.weight"
:
v_weight
,
f"
{
diffusers_attention_prefix
}
.to_v.bias"
:
v_bias
,
}
)
# <original>.encoder_kv -> <diffusers>.{context_key, context_value}
[
encoder_k_weight
,
encoder_v_weight
], [
encoder_k_bias
,
encoder_v_bias
]
=
split_attentions
(
weight
=
checkpoint
[
f"
{
attention_prefix
}
.encoder_kv.weight"
][:, :,
0
],
bias
=
checkpoint
[
f"
{
attention_prefix
}
.encoder_kv.bias"
],
split
=
2
,
chunk_size
=
num_head_channels
,
)
diffusers_checkpoint
.
update
(
{
f"
{
diffusers_attention_prefix
}
.add_k_proj.weight"
:
encoder_k_weight
,
f"
{
diffusers_attention_prefix
}
.add_k_proj.bias"
:
encoder_k_bias
,
f"
{
diffusers_attention_prefix
}
.add_v_proj.weight"
:
encoder_v_weight
,
f"
{
diffusers_attention_prefix
}
.add_v_proj.bias"
:
encoder_v_bias
,
}
)
# <original>.proj_out (1d conv) -> <diffusers>.proj_attn (linear)
diffusers_checkpoint
.
update
(
{
f"
{
diffusers_attention_prefix
}
.to_out.0.weight"
:
checkpoint
[
f"
{
attention_prefix
}
.proj_out.weight"
][
:, :,
0
],
f"
{
diffusers_attention_prefix
}
.to_out.0.bias"
:
checkpoint
[
f"
{
attention_prefix
}
.proj_out.bias"
],
}
)
return
diffusers_checkpoint
# TODO maybe document and/or can do more efficiently (build indices in for loop and extract once for each split?)
def
split_attentions
(
*
,
weight
,
bias
,
split
,
chunk_size
):
weights
=
[
None
]
*
split
biases
=
[
None
]
*
split
weights_biases_idx
=
0
for
starting_row_index
in
range
(
0
,
weight
.
shape
[
0
],
chunk_size
):
row_indices
=
torch
.
arange
(
starting_row_index
,
starting_row_index
+
chunk_size
)
weight_rows
=
weight
[
row_indices
, :]
bias_rows
=
bias
[
row_indices
]
if
weights
[
weights_biases_idx
]
is
None
:
assert
weights
[
weights_biases_idx
]
is
None
weights
[
weights_biases_idx
]
=
weight_rows
biases
[
weights_biases_idx
]
=
bias_rows
else
:
assert
weights
[
weights_biases_idx
]
is
not
None
weights
[
weights_biases_idx
]
=
torch
.
concat
([
weights
[
weights_biases_idx
],
weight_rows
])
biases
[
weights_biases_idx
]
=
torch
.
concat
([
biases
[
weights_biases_idx
],
bias_rows
])
weights_biases_idx
=
(
weights_biases_idx
+
1
)
%
split
return
weights
,
biases
# done unet utils
# Driver functions
def
text_encoder
():
print
(
"loading CLIP text encoder"
)
clip_name
=
"openai/clip-vit-large-patch14"
# sets pad_value to 0
pad_token
=
"!"
tokenizer_model
=
CLIPTokenizer
.
from_pretrained
(
clip_name
,
pad_token
=
pad_token
,
device_map
=
"auto"
)
assert
tokenizer_model
.
convert_tokens_to_ids
(
pad_token
)
==
0
text_encoder_model
=
CLIPTextModelWithProjection
.
from_pretrained
(
clip_name
,
# `CLIPTextModel` does not support device_map="auto"
# device_map="auto"
)
print
(
"done loading CLIP text encoder"
)
return
text_encoder_model
,
tokenizer_model
def
prior
(
*
,
args
,
checkpoint_map_location
):
print
(
"loading prior"
)
prior_checkpoint
=
torch
.
load
(
args
.
prior_checkpoint_path
,
map_location
=
checkpoint_map_location
)
prior_checkpoint
=
prior_checkpoint
[
"state_dict"
]
clip_stats_checkpoint
=
torch
.
load
(
args
.
clip_stat_path
,
map_location
=
checkpoint_map_location
)
prior_model
=
prior_model_from_original_config
()
prior_diffusers_checkpoint
=
prior_original_checkpoint_to_diffusers_checkpoint
(
prior_model
,
prior_checkpoint
,
clip_stats_checkpoint
)
del
prior_checkpoint
del
clip_stats_checkpoint
load_checkpoint_to_model
(
prior_diffusers_checkpoint
,
prior_model
,
strict
=
True
)
print
(
"done loading prior"
)
return
prior_model
def
decoder
(
*
,
args
,
checkpoint_map_location
):
print
(
"loading decoder"
)
decoder_checkpoint
=
torch
.
load
(
args
.
decoder_checkpoint_path
,
map_location
=
checkpoint_map_location
)
decoder_checkpoint
=
decoder_checkpoint
[
"state_dict"
]
decoder_model
=
decoder_model_from_original_config
()
decoder_diffusers_checkpoint
=
decoder_original_checkpoint_to_diffusers_checkpoint
(
decoder_model
,
decoder_checkpoint
)
# text proj interlude
# The original decoder implementation includes a set of parameters that are used
# for creating the `encoder_hidden_states` which are what the U-net is conditioned
# on. The diffusers conditional unet directly takes the encoder_hidden_states. We pull
# the parameters into the UnCLIPTextProjModel class
text_proj_model
=
text_proj_from_original_config
()
text_proj_checkpoint
=
text_proj_original_checkpoint_to_diffusers_checkpoint
(
decoder_checkpoint
)
load_checkpoint_to_model
(
text_proj_checkpoint
,
text_proj_model
,
strict
=
True
)
# done text proj interlude
del
decoder_checkpoint
load_checkpoint_to_model
(
decoder_diffusers_checkpoint
,
decoder_model
,
strict
=
True
)
print
(
"done loading decoder"
)
return
decoder_model
,
text_proj_model
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