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import argparse
import huggingface_hub
import k_diffusion as K
import torch
from diffusers import UNet2DConditionModel
UPSCALER_REPO = "pcuenq/k-upscaler"
def resnet_to_diffusers_checkpoint(resnet, checkpoint, *, diffusers_resnet_prefix, resnet_prefix):
rv = {
# norm1
f"{diffusers_resnet_prefix}.norm1.linear.weight": checkpoint[f"{resnet_prefix}.main.0.mapper.weight"],
f"{diffusers_resnet_prefix}.norm1.linear.bias": checkpoint[f"{resnet_prefix}.main.0.mapper.bias"],
# conv1
f"{diffusers_resnet_prefix}.conv1.weight": checkpoint[f"{resnet_prefix}.main.2.weight"],
f"{diffusers_resnet_prefix}.conv1.bias": checkpoint[f"{resnet_prefix}.main.2.bias"],
# norm2
f"{diffusers_resnet_prefix}.norm2.linear.weight": checkpoint[f"{resnet_prefix}.main.4.mapper.weight"],
f"{diffusers_resnet_prefix}.norm2.linear.bias": checkpoint[f"{resnet_prefix}.main.4.mapper.bias"],
# conv2
f"{diffusers_resnet_prefix}.conv2.weight": checkpoint[f"{resnet_prefix}.main.6.weight"],
f"{diffusers_resnet_prefix}.conv2.bias": checkpoint[f"{resnet_prefix}.main.6.bias"],
}
if resnet.conv_shortcut is not None:
rv.update(
{
f"{diffusers_resnet_prefix}.conv_shortcut.weight": checkpoint[f"{resnet_prefix}.skip.weight"],
}
)
return rv
def self_attn_to_diffusers_checkpoint(checkpoint, *, diffusers_attention_prefix, attention_prefix):
weight_q, weight_k, weight_v = checkpoint[f"{attention_prefix}.qkv_proj.weight"].chunk(3, dim=0)
bias_q, bias_k, bias_v = checkpoint[f"{attention_prefix}.qkv_proj.bias"].chunk(3, dim=0)
rv = {
# norm
f"{diffusers_attention_prefix}.norm1.linear.weight": checkpoint[f"{attention_prefix}.norm_in.mapper.weight"],
f"{diffusers_attention_prefix}.norm1.linear.bias": checkpoint[f"{attention_prefix}.norm_in.mapper.bias"],
# to_q
f"{diffusers_attention_prefix}.attn1.to_q.weight": weight_q.squeeze(-1).squeeze(-1),
f"{diffusers_attention_prefix}.attn1.to_q.bias": bias_q,
# to_k
f"{diffusers_attention_prefix}.attn1.to_k.weight": weight_k.squeeze(-1).squeeze(-1),
f"{diffusers_attention_prefix}.attn1.to_k.bias": bias_k,
# to_v
f"{diffusers_attention_prefix}.attn1.to_v.weight": weight_v.squeeze(-1).squeeze(-1),
f"{diffusers_attention_prefix}.attn1.to_v.bias": bias_v,
# to_out
f"{diffusers_attention_prefix}.attn1.to_out.0.weight": checkpoint[f"{attention_prefix}.out_proj.weight"]
.squeeze(-1)
.squeeze(-1),
f"{diffusers_attention_prefix}.attn1.to_out.0.bias": checkpoint[f"{attention_prefix}.out_proj.bias"],
}
return rv
def cross_attn_to_diffusers_checkpoint(
checkpoint, *, diffusers_attention_prefix, diffusers_attention_index, attention_prefix
):
weight_k, weight_v = checkpoint[f"{attention_prefix}.kv_proj.weight"].chunk(2, dim=0)
bias_k, bias_v = checkpoint[f"{attention_prefix}.kv_proj.bias"].chunk(2, dim=0)
rv = {
# norm2 (ada groupnorm)
f"{diffusers_attention_prefix}.norm{diffusers_attention_index}.linear.weight": checkpoint[
f"{attention_prefix}.norm_dec.mapper.weight"
],
f"{diffusers_attention_prefix}.norm{diffusers_attention_index}.linear.bias": checkpoint[
f"{attention_prefix}.norm_dec.mapper.bias"
],
# layernorm on encoder_hidden_state
f"{diffusers_attention_prefix}.attn{diffusers_attention_index}.norm_cross.weight": checkpoint[
f"{attention_prefix}.norm_enc.weight"
],
f"{diffusers_attention_prefix}.attn{diffusers_attention_index}.norm_cross.bias": checkpoint[
f"{attention_prefix}.norm_enc.bias"
],
# to_q
f"{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_q.weight": checkpoint[
f"{attention_prefix}.q_proj.weight"
]
.squeeze(-1)
.squeeze(-1),
f"{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_q.bias": checkpoint[
f"{attention_prefix}.q_proj.bias"
],
# to_k
f"{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_k.weight": weight_k.squeeze(-1).squeeze(-1),
f"{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_k.bias": bias_k,
# to_v
f"{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_v.weight": weight_v.squeeze(-1).squeeze(-1),
f"{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_v.bias": bias_v,
# to_out
f"{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_out.0.weight": checkpoint[
f"{attention_prefix}.out_proj.weight"
]
.squeeze(-1)
.squeeze(-1),
f"{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_out.0.bias": checkpoint[
f"{attention_prefix}.out_proj.bias"
],
}
return rv
def block_to_diffusers_checkpoint(block, checkpoint, block_idx, block_type):
block_prefix = "inner_model.u_net.u_blocks" if block_type == "up" else "inner_model.u_net.d_blocks"
block_prefix = f"{block_prefix}.{block_idx}"
diffusers_checkpoint = {}
if not hasattr(block, "attentions"):
n = 1 # resnet only
elif not block.attentions[0].add_self_attention:
n = 2 # resnet -> cross-attention
else:
n = 3 # resnet -> self-attention -> cross-attention)
for resnet_idx, resnet in enumerate(block.resnets):
# diffusers_resnet_prefix = f"{diffusers_up_block_prefix}.resnets.{resnet_idx}"
diffusers_resnet_prefix = f"{block_type}_blocks.{block_idx}.resnets.{resnet_idx}"
idx = n * resnet_idx if block_type == "up" else n * resnet_idx + 1
resnet_prefix = f"{block_prefix}.{idx}" if block_type == "up" else f"{block_prefix}.{idx}"
diffusers_checkpoint.update(
resnet_to_diffusers_checkpoint(
resnet, checkpoint, diffusers_resnet_prefix=diffusers_resnet_prefix, resnet_prefix=resnet_prefix
)
)
if hasattr(block, "attentions"):
for attention_idx, attention in enumerate(block.attentions):
diffusers_attention_prefix = f"{block_type}_blocks.{block_idx}.attentions.{attention_idx}"
idx = n * attention_idx + 1 if block_type == "up" else n * attention_idx + 2
self_attention_prefix = f"{block_prefix}.{idx}"
cross_attention_prefix = f"{block_prefix}.{idx }"
cross_attention_index = 1 if not attention.add_self_attention else 2
idx = (
n * attention_idx + cross_attention_index
if block_type == "up"
else n * attention_idx + cross_attention_index + 1
)
cross_attention_prefix = f"{block_prefix}.{idx }"
diffusers_checkpoint.update(
cross_attn_to_diffusers_checkpoint(
checkpoint,
diffusers_attention_prefix=diffusers_attention_prefix,
diffusers_attention_index=2,
attention_prefix=cross_attention_prefix,
)
)
if attention.add_self_attention is True:
diffusers_checkpoint.update(
self_attn_to_diffusers_checkpoint(
checkpoint,
diffusers_attention_prefix=diffusers_attention_prefix,
attention_prefix=self_attention_prefix,
)
)
return diffusers_checkpoint
def unet_to_diffusers_checkpoint(model, checkpoint):
diffusers_checkpoint = {}
# pre-processing
diffusers_checkpoint.update(
{
"conv_in.weight": checkpoint["inner_model.proj_in.weight"],
"conv_in.bias": checkpoint["inner_model.proj_in.bias"],
}
)
# timestep and class embedding
diffusers_checkpoint.update(
{
"time_proj.weight": checkpoint["inner_model.timestep_embed.weight"].squeeze(-1),
"time_embedding.linear_1.weight": checkpoint["inner_model.mapping.0.weight"],
"time_embedding.linear_1.bias": checkpoint["inner_model.mapping.0.bias"],
"time_embedding.linear_2.weight": checkpoint["inner_model.mapping.2.weight"],
"time_embedding.linear_2.bias": checkpoint["inner_model.mapping.2.bias"],
"time_embedding.cond_proj.weight": checkpoint["inner_model.mapping_cond.weight"],
}
)
# down_blocks
for down_block_idx, down_block in enumerate(model.down_blocks):
diffusers_checkpoint.update(block_to_diffusers_checkpoint(down_block, checkpoint, down_block_idx, "down"))
# up_blocks
for up_block_idx, up_block in enumerate(model.up_blocks):
diffusers_checkpoint.update(block_to_diffusers_checkpoint(up_block, checkpoint, up_block_idx, "up"))
# post-processing
diffusers_checkpoint.update(
{
"conv_out.weight": checkpoint["inner_model.proj_out.weight"],
"conv_out.bias": checkpoint["inner_model.proj_out.bias"],
}
)
return diffusers_checkpoint
def unet_model_from_original_config(original_config):
in_channels = original_config["input_channels"] + original_config["unet_cond_dim"]
out_channels = original_config["input_channels"] + (1 if original_config["has_variance"] else 0)
block_out_channels = original_config["channels"]
assert (
len(set(original_config["depths"])) == 1
), "UNet2DConditionModel currently do not support blocks with different number of layers"
layers_per_block = original_config["depths"][0]
class_labels_dim = original_config["mapping_cond_dim"]
cross_attention_dim = original_config["cross_cond_dim"]
attn1_types = []
attn2_types = []
for s, c in zip(original_config["self_attn_depths"], original_config["cross_attn_depths"]):
if s:
a1 = "self"
a2 = "cross" if c else None
elif c:
a1 = "cross"
a2 = None
else:
a1 = None
a2 = None
attn1_types.append(a1)
attn2_types.append(a2)
unet = UNet2DConditionModel(
in_channels=in_channels,
out_channels=out_channels,
down_block_types=("KDownBlock2D", "KCrossAttnDownBlock2D", "KCrossAttnDownBlock2D", "KCrossAttnDownBlock2D"),
mid_block_type=None,
up_block_types=("KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KUpBlock2D"),
block_out_channels=block_out_channels,
layers_per_block=layers_per_block,
act_fn="gelu",
norm_num_groups=None,
cross_attention_dim=cross_attention_dim,
attention_head_dim=64,
time_cond_proj_dim=class_labels_dim,
resnet_time_scale_shift="scale_shift",
time_embedding_type="fourier",
timestep_post_act="gelu",
conv_in_kernel=1,
conv_out_kernel=1,
)
return unet
def main(args):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
orig_config_path = huggingface_hub.hf_hub_download(UPSCALER_REPO, "config_laion_text_cond_latent_upscaler_2.json")
orig_weights_path = huggingface_hub.hf_hub_download(
UPSCALER_REPO, "laion_text_cond_latent_upscaler_2_1_00470000_slim.pth"
)
print(f"loading original model configuration from {orig_config_path}")
print(f"loading original model checkpoint from {orig_weights_path}")
print("converting to diffusers unet")
orig_config = K.config.load_config(open(orig_config_path))["model"]
model = unet_model_from_original_config(orig_config)
orig_checkpoint = torch.load(orig_weights_path, map_location=device)["model_ema"]
converted_checkpoint = unet_to_diffusers_checkpoint(model, orig_checkpoint)
model.load_state_dict(converted_checkpoint, strict=True)
model.save_pretrained(args.dump_path)
print(f"saving converted unet model in {args.dump_path}")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
args = parser.parse_args()
main(args)
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