"""
This script modified from
https://github.com/huggingface/diffusers/blob/bc691231360a4cbc7d19a58742ebb8ed0f05e027/scripts/convert_original_stable_diffusion_to_diffusers.py
Convert original Zero1to3 checkpoint to diffusers checkpoint.
# run the convert script
$ python convert_zero123_to_diffusers.py \
--checkpoint_path /path/zero123/105000.ckpt \
--dump_path ./zero1to3 \
--original_config_file /path/zero123/configs/sd-objaverse-finetune-c_concat-256.yaml
```
"""
import argparse
import torch
from accelerate import init_empty_weights
from accelerate.utils import set_module_tensor_to_device
from pipeline_zero1to3 import CCProjection, Zero1to3StableDiffusionPipeline
from transformers import (
CLIPImageProcessor,
CLIPVisionModelWithProjection,
)
from diffusers.models import (
AutoencoderKL,
UNet2DConditionModel,
)
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import logging
logger = logging.get_logger(__name__)
def create_unet_diffusers_config(original_config, image_size: int, controlnet=False):
"""
Creates a config for the diffusers based on the config of the LDM model.
"""
if controlnet:
unet_params = original_config.model.params.control_stage_config.params
else:
if "unet_config" in original_config.model.params and original_config.model.params.unet_config is not None:
unet_params = original_config.model.params.unet_config.params
else:
unet_params = original_config.model.params.network_config.params
vae_params = original_config.model.params.first_stage_config.params.ddconfig
block_out_channels = [unet_params.model_channels * mult for mult in unet_params.channel_mult]
down_block_types = []
resolution = 1
for i in range(len(block_out_channels)):
block_type = "CrossAttnDownBlock2D" if resolution in unet_params.attention_resolutions else "DownBlock2D"
down_block_types.append(block_type)
if i != len(block_out_channels) - 1:
resolution *= 2
up_block_types = []
for i in range(len(block_out_channels)):
block_type = "CrossAttnUpBlock2D" if resolution in unet_params.attention_resolutions else "UpBlock2D"
up_block_types.append(block_type)
resolution //= 2
if unet_params.transformer_depth is not None:
transformer_layers_per_block = (
unet_params.transformer_depth
if isinstance(unet_params.transformer_depth, int)
else list(unet_params.transformer_depth)
)
else:
transformer_layers_per_block = 1
vae_scale_factor = 2 ** (len(vae_params.ch_mult) - 1)
head_dim = unet_params.num_heads if "num_heads" in unet_params else None
use_linear_projection = (
unet_params.use_linear_in_transformer if "use_linear_in_transformer" in unet_params else False
)
if use_linear_projection:
# stable diffusion 2-base-512 and 2-768
if head_dim is None:
head_dim_mult = unet_params.model_channels // unet_params.num_head_channels
head_dim = [head_dim_mult * c for c in list(unet_params.channel_mult)]
class_embed_type = None
addition_embed_type = None
addition_time_embed_dim = None
projection_class_embeddings_input_dim = None
context_dim = None
if unet_params.context_dim is not None:
context_dim = (
unet_params.context_dim if isinstance(unet_params.context_dim, int) else unet_params.context_dim[0]
)
if "num_classes" in unet_params:
if unet_params.num_classes == "sequential":
if context_dim in [2048, 1280]:
# SDXL
addition_embed_type = "text_time"
addition_time_embed_dim = 256
else:
class_embed_type = "projection"
assert "adm_in_channels" in unet_params
projection_class_embeddings_input_dim = unet_params.adm_in_channels
else:
raise NotImplementedError(f"Unknown conditional unet num_classes config: {unet_params.num_classes}")
config = {
"sample_size": image_size // vae_scale_factor,
"in_channels": unet_params.in_channels,
"down_block_types": tuple(down_block_types),
"block_out_channels": tuple(block_out_channels),
"layers_per_block": unet_params.num_res_blocks,
"cross_attention_dim": context_dim,
"attention_head_dim": head_dim,
"use_linear_projection": use_linear_projection,
"class_embed_type": class_embed_type,
"addition_embed_type": addition_embed_type,
"addition_time_embed_dim": addition_time_embed_dim,
"projection_class_embeddings_input_dim": projection_class_embeddings_input_dim,
"transformer_layers_per_block": transformer_layers_per_block,
}
if controlnet:
config["conditioning_channels"] = unet_params.hint_channels
else:
config["out_channels"] = unet_params.out_channels
config["up_block_types"] = tuple(up_block_types)
return config
def assign_to_checkpoint(
paths, checkpoint, old_checkpoint, attention_paths_to_split=None, additional_replacements=None, config=None
):
"""
This does the final conversion step: take locally converted weights and apply a global renaming to them. It splits
attention layers, and takes into account additional replacements that may arise.
Assigns the weights to the new checkpoint.
"""
assert isinstance(paths, list), "Paths should be a list of dicts containing 'old' and 'new' keys."
# Splits the attention layers into three variables.
if attention_paths_to_split is not None:
for path, path_map in attention_paths_to_split.items():
old_tensor = old_checkpoint[path]
channels = old_tensor.shape[0] // 3
target_shape = (-1, channels) if len(old_tensor.shape) == 3 else (-1)
num_heads = old_tensor.shape[0] // config["num_head_channels"] // 3
old_tensor = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:])
query, key, value = old_tensor.split(channels // num_heads, dim=1)
checkpoint[path_map["query"]] = query.reshape(target_shape)
checkpoint[path_map["key"]] = key.reshape(target_shape)
checkpoint[path_map["value"]] = value.reshape(target_shape)
for path in paths:
new_path = path["new"]
# These have already been assigned
if attention_paths_to_split is not None and new_path in attention_paths_to_split:
continue
# Global renaming happens here
new_path = new_path.replace("middle_block.0", "mid_block.resnets.0")
new_path = new_path.replace("middle_block.1", "mid_block.attentions.0")
new_path = new_path.replace("middle_block.2", "mid_block.resnets.1")
if additional_replacements is not None:
for replacement in additional_replacements:
new_path = new_path.replace(replacement["old"], replacement["new"])
# proj_attn.weight has to be converted from conv 1D to linear
is_attn_weight = "proj_attn.weight" in new_path or ("attentions" in new_path and "to_" in new_path)
shape = old_checkpoint[path["old"]].shape
if is_attn_weight and len(shape) == 3:
checkpoint[new_path] = old_checkpoint[path["old"]][:, :, 0]
elif is_attn_weight and len(shape) == 4:
checkpoint[new_path] = old_checkpoint[path["old"]][:, :, 0, 0]
else:
checkpoint[new_path] = old_checkpoint[path["old"]]
def shave_segments(path, n_shave_prefix_segments=1):
"""
Removes segments. Positive values shave the first segments, negative shave the last segments.
"""
if n_shave_prefix_segments >= 0:
return ".".join(path.split(".")[n_shave_prefix_segments:])
else:
return ".".join(path.split(".")[:n_shave_prefix_segments])
def renew_resnet_paths(old_list, n_shave_prefix_segments=0):
"""
Updates paths inside resnets to the new naming scheme (local renaming)
"""
mapping = []
for old_item in old_list:
new_item = old_item.replace("in_layers.0", "norm1")
new_item = new_item.replace("in_layers.2", "conv1")
new_item = new_item.replace("out_layers.0", "norm2")
new_item = new_item.replace("out_layers.3", "conv2")
new_item = new_item.replace("emb_layers.1", "time_emb_proj")
new_item = new_item.replace("skip_connection", "conv_shortcut")
new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
mapping.append({"old": old_item, "new": new_item})
return mapping
def renew_attention_paths(old_list, n_shave_prefix_segments=0):
"""
Updates paths inside attentions to the new naming scheme (local renaming)
"""
mapping = []
for old_item in old_list:
new_item = old_item
# new_item = new_item.replace('norm.weight', 'group_norm.weight')
# new_item = new_item.replace('norm.bias', 'group_norm.bias')
# new_item = new_item.replace('proj_out.weight', 'proj_attn.weight')
# new_item = new_item.replace('proj_out.bias', 'proj_attn.bias')
# new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
mapping.append({"old": old_item, "new": new_item})
return mapping
def convert_ldm_unet_checkpoint(
checkpoint, config, path=None, extract_ema=False, controlnet=False, skip_extract_state_dict=False
):
"""
Takes a state dict and a config, and returns a converted checkpoint.
"""
if skip_extract_state_dict:
unet_state_dict = checkpoint
else:
# extract state_dict for UNet
unet_state_dict = {}
keys = list(checkpoint.keys())
if controlnet:
unet_key = "control_model."
else:
unet_key = "model.diffusion_model."
# at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA
if sum(k.startswith("model_ema") for k in keys) > 100 and extract_ema:
logger.warning(f"Checkpoint {path} has both EMA and non-EMA weights.")
logger.warning(
"In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA"
" weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag."
)
for key in keys:
if key.startswith("model.diffusion_model"):
flat_ema_key = "model_ema." + "".join(key.split(".")[1:])
unet_state_dict[key.replace(unet_key, "")] = checkpoint[flat_ema_key]
else:
if sum(k.startswith("model_ema") for k in keys) > 100:
logger.warning(
"In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA"
" weights (usually better for inference), please make sure to add the `--extract_ema` flag."
)
for key in keys:
if key.startswith(unet_key):
unet_state_dict[key.replace(unet_key, "")] = checkpoint[key]
new_checkpoint = {}
new_checkpoint["time_embedding.linear_1.weight"] = unet_state_dict["time_embed.0.weight"]
new_checkpoint["time_embedding.linear_1.bias"] = unet_state_dict["time_embed.0.bias"]
new_checkpoint["time_embedding.linear_2.weight"] = unet_state_dict["time_embed.2.weight"]
new_checkpoint["time_embedding.linear_2.bias"] = unet_state_dict["time_embed.2.bias"]
if config["class_embed_type"] is None:
# No parameters to port
...
elif config["class_embed_type"] == "timestep" or config["class_embed_type"] == "projection":
new_checkpoint["class_embedding.linear_1.weight"] = unet_state_dict["label_emb.0.0.weight"]
new_checkpoint["class_embedding.linear_1.bias"] = unet_state_dict["label_emb.0.0.bias"]
new_checkpoint["class_embedding.linear_2.weight"] = unet_state_dict["label_emb.0.2.weight"]
new_checkpoint["class_embedding.linear_2.bias"] = unet_state_dict["label_emb.0.2.bias"]
else:
raise NotImplementedError(f"Not implemented `class_embed_type`: {config['class_embed_type']}")
if config["addition_embed_type"] == "text_time":
new_checkpoint["add_embedding.linear_1.weight"] = unet_state_dict["label_emb.0.0.weight"]
new_checkpoint["add_embedding.linear_1.bias"] = unet_state_dict["label_emb.0.0.bias"]
new_checkpoint["add_embedding.linear_2.weight"] = unet_state_dict["label_emb.0.2.weight"]
new_checkpoint["add_embedding.linear_2.bias"] = unet_state_dict["label_emb.0.2.bias"]
new_checkpoint["conv_in.weight"] = unet_state_dict["input_blocks.0.0.weight"]
new_checkpoint["conv_in.bias"] = unet_state_dict["input_blocks.0.0.bias"]
if not controlnet:
new_checkpoint["conv_norm_out.weight"] = unet_state_dict["out.0.weight"]
new_checkpoint["conv_norm_out.bias"] = unet_state_dict["out.0.bias"]
new_checkpoint["conv_out.weight"] = unet_state_dict["out.2.weight"]
new_checkpoint["conv_out.bias"] = unet_state_dict["out.2.bias"]
# Retrieves the keys for the input blocks only
num_input_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "input_blocks" in layer})
input_blocks = {
layer_id: [key for key in unet_state_dict if f"input_blocks.{layer_id}" in key]
for layer_id in range(num_input_blocks)
}
# Retrieves the keys for the middle blocks only
num_middle_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "middle_block" in layer})
middle_blocks = {
layer_id: [key for key in unet_state_dict if f"middle_block.{layer_id}" in key]
for layer_id in range(num_middle_blocks)
}
# Retrieves the keys for the output blocks only
num_output_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "output_blocks" in layer})
output_blocks = {
layer_id: [key for key in unet_state_dict if f"output_blocks.{layer_id}" in key]
for layer_id in range(num_output_blocks)
}
for i in range(1, num_input_blocks):
block_id = (i - 1) // (config["layers_per_block"] + 1)
layer_in_block_id = (i - 1) % (config["layers_per_block"] + 1)
resnets = [
key for key in input_blocks[i] if f"input_blocks.{i}.0" in key and f"input_blocks.{i}.0.op" not in key
]
attentions = [key for key in input_blocks[i] if f"input_blocks.{i}.1" in key]
if f"input_blocks.{i}.0.op.weight" in unet_state_dict:
new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.weight"] = unet_state_dict.pop(
f"input_blocks.{i}.0.op.weight"
)
new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.bias"] = unet_state_dict.pop(
f"input_blocks.{i}.0.op.bias"
)
paths = renew_resnet_paths(resnets)
meta_path = {"old": f"input_blocks.{i}.0", "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}"}
assign_to_checkpoint(
paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
)
if len(attentions):
paths = renew_attention_paths(attentions)
meta_path = {"old": f"input_blocks.{i}.1", "new": f"down_blocks.{block_id}.attentions.{layer_in_block_id}"}
assign_to_checkpoint(
paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
)
resnet_0 = middle_blocks[0]
attentions = middle_blocks[1]
resnet_1 = middle_blocks[2]
resnet_0_paths = renew_resnet_paths(resnet_0)
assign_to_checkpoint(resnet_0_paths, new_checkpoint, unet_state_dict, config=config)
resnet_1_paths = renew_resnet_paths(resnet_1)
assign_to_checkpoint(resnet_1_paths, new_checkpoint, unet_state_dict, config=config)
attentions_paths = renew_attention_paths(attentions)
meta_path = {"old": "middle_block.1", "new": "mid_block.attentions.0"}
assign_to_checkpoint(
attentions_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
)
for i in range(num_output_blocks):
block_id = i // (config["layers_per_block"] + 1)
layer_in_block_id = i % (config["layers_per_block"] + 1)
output_block_layers = [shave_segments(name, 2) for name in output_blocks[i]]
output_block_list = {}
for layer in output_block_layers:
layer_id, layer_name = layer.split(".")[0], shave_segments(layer, 1)
if layer_id in output_block_list:
output_block_list[layer_id].append(layer_name)
else:
output_block_list[layer_id] = [layer_name]
if len(output_block_list) > 1:
resnets = [key for key in output_blocks[i] if f"output_blocks.{i}.0" in key]
attentions = [key for key in output_blocks[i] if f"output_blocks.{i}.1" in key]
resnet_0_paths = renew_resnet_paths(resnets)
paths = renew_resnet_paths(resnets)
meta_path = {"old": f"output_blocks.{i}.0", "new": f"up_blocks.{block_id}.resnets.{layer_in_block_id}"}
assign_to_checkpoint(
paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
)
output_block_list = {k: sorted(v) for k, v in output_block_list.items()}
if ["conv.bias", "conv.weight"] in output_block_list.values():
index = list(output_block_list.values()).index(["conv.bias", "conv.weight"])
new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.weight"] = unet_state_dict[
f"output_blocks.{i}.{index}.conv.weight"
]
new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.bias"] = unet_state_dict[
f"output_blocks.{i}.{index}.conv.bias"
]
# Clear attentions as they have been attributed above.
if len(attentions) == 2:
attentions = []
if len(attentions):
paths = renew_attention_paths(attentions)
meta_path = {
"old": f"output_blocks.{i}.1",
"new": f"up_blocks.{block_id}.attentions.{layer_in_block_id}",
}
assign_to_checkpoint(
paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
)
else:
resnet_0_paths = renew_resnet_paths(output_block_layers, n_shave_prefix_segments=1)
for path in resnet_0_paths:
old_path = ".".join(["output_blocks", str(i), path["old"]])
new_path = ".".join(["up_blocks", str(block_id), "resnets", str(layer_in_block_id), path["new"]])
new_checkpoint[new_path] = unet_state_dict[old_path]
if controlnet:
# conditioning embedding
orig_index = 0
new_checkpoint["controlnet_cond_embedding.conv_in.weight"] = unet_state_dict.pop(
f"input_hint_block.{orig_index}.weight"
)
new_checkpoint["controlnet_cond_embedding.conv_in.bias"] = unet_state_dict.pop(
f"input_hint_block.{orig_index}.bias"
)
orig_index += 2
diffusers_index = 0
while diffusers_index < 6:
new_checkpoint[f"controlnet_cond_embedding.blocks.{diffusers_index}.weight"] = unet_state_dict.pop(
f"input_hint_block.{orig_index}.weight"
)
new_checkpoint[f"controlnet_cond_embedding.blocks.{diffusers_index}.bias"] = unet_state_dict.pop(
f"input_hint_block.{orig_index}.bias"
)
diffusers_index += 1
orig_index += 2
new_checkpoint["controlnet_cond_embedding.conv_out.weight"] = unet_state_dict.pop(
f"input_hint_block.{orig_index}.weight"
)
new_checkpoint["controlnet_cond_embedding.conv_out.bias"] = unet_state_dict.pop(
f"input_hint_block.{orig_index}.bias"
)
# down blocks
for i in range(num_input_blocks):
new_checkpoint[f"controlnet_down_blocks.{i}.weight"] = unet_state_dict.pop(f"zero_convs.{i}.0.weight")
new_checkpoint[f"controlnet_down_blocks.{i}.bias"] = unet_state_dict.pop(f"zero_convs.{i}.0.bias")
# mid block
new_checkpoint["controlnet_mid_block.weight"] = unet_state_dict.pop("middle_block_out.0.weight")
new_checkpoint["controlnet_mid_block.bias"] = unet_state_dict.pop("middle_block_out.0.bias")
return new_checkpoint
def create_vae_diffusers_config(original_config, image_size: int):
"""
Creates a config for the diffusers based on the config of the LDM model.
"""
vae_params = original_config.model.params.first_stage_config.params.ddconfig
_ = original_config.model.params.first_stage_config.params.embed_dim
block_out_channels = [vae_params.ch * mult for mult in vae_params.ch_mult]
down_block_types = ["DownEncoderBlock2D"] * len(block_out_channels)
up_block_types = ["UpDecoderBlock2D"] * len(block_out_channels)
config = {
"sample_size": image_size,
"in_channels": vae_params.in_channels,
"out_channels": vae_params.out_ch,
"down_block_types": tuple(down_block_types),
"up_block_types": tuple(up_block_types),
"block_out_channels": tuple(block_out_channels),
"latent_channels": vae_params.z_channels,
"layers_per_block": vae_params.num_res_blocks,
}
return config
def convert_ldm_vae_checkpoint(checkpoint, config):
# extract state dict for VAE
vae_state_dict = {}
vae_key = "first_stage_model."
keys = list(checkpoint.keys())
for key in keys:
if key.startswith(vae_key):
vae_state_dict[key.replace(vae_key, "")] = checkpoint.get(key)
new_checkpoint = {}
new_checkpoint["encoder.conv_in.weight"] = vae_state_dict["encoder.conv_in.weight"]
new_checkpoint["encoder.conv_in.bias"] = vae_state_dict["encoder.conv_in.bias"]
new_checkpoint["encoder.conv_out.weight"] = vae_state_dict["encoder.conv_out.weight"]
new_checkpoint["encoder.conv_out.bias"] = vae_state_dict["encoder.conv_out.bias"]
new_checkpoint["encoder.conv_norm_out.weight"] = vae_state_dict["encoder.norm_out.weight"]
new_checkpoint["encoder.conv_norm_out.bias"] = vae_state_dict["encoder.norm_out.bias"]
new_checkpoint["decoder.conv_in.weight"] = vae_state_dict["decoder.conv_in.weight"]
new_checkpoint["decoder.conv_in.bias"] = vae_state_dict["decoder.conv_in.bias"]
new_checkpoint["decoder.conv_out.weight"] = vae_state_dict["decoder.conv_out.weight"]
new_checkpoint["decoder.conv_out.bias"] = vae_state_dict["decoder.conv_out.bias"]
new_checkpoint["decoder.conv_norm_out.weight"] = vae_state_dict["decoder.norm_out.weight"]
new_checkpoint["decoder.conv_norm_out.bias"] = vae_state_dict["decoder.norm_out.bias"]
new_checkpoint["quant_conv.weight"] = vae_state_dict["quant_conv.weight"]
new_checkpoint["quant_conv.bias"] = vae_state_dict["quant_conv.bias"]
new_checkpoint["post_quant_conv.weight"] = vae_state_dict["post_quant_conv.weight"]
new_checkpoint["post_quant_conv.bias"] = vae_state_dict["post_quant_conv.bias"]
# Retrieves the keys for the encoder down blocks only
num_down_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "encoder.down" in layer})
down_blocks = {
layer_id: [key for key in vae_state_dict if f"down.{layer_id}" in key] for layer_id in range(num_down_blocks)
}
# Retrieves the keys for the decoder up blocks only
num_up_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "decoder.up" in layer})
up_blocks = {
layer_id: [key for key in vae_state_dict if f"up.{layer_id}" in key] for layer_id in range(num_up_blocks)
}
for i in range(num_down_blocks):
resnets = [key for key in down_blocks[i] if f"down.{i}" in key and f"down.{i}.downsample" not in key]
if f"encoder.down.{i}.downsample.conv.weight" in vae_state_dict:
new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.weight"] = vae_state_dict.pop(
f"encoder.down.{i}.downsample.conv.weight"
)
new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.bias"] = vae_state_dict.pop(
f"encoder.down.{i}.downsample.conv.bias"
)
paths = renew_vae_resnet_paths(resnets)
meta_path = {"old": f"down.{i}.block", "new": f"down_blocks.{i}.resnets"}
assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
mid_resnets = [key for key in vae_state_dict if "encoder.mid.block" in key]
num_mid_res_blocks = 2
for i in range(1, num_mid_res_blocks + 1):
resnets = [key for key in mid_resnets if f"encoder.mid.block_{i}" in key]
paths = renew_vae_resnet_paths(resnets)
meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"}
assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
mid_attentions = [key for key in vae_state_dict if "encoder.mid.attn" in key]
paths = renew_vae_attention_paths(mid_attentions)
meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
conv_attn_to_linear(new_checkpoint)
for i in range(num_up_blocks):
block_id = num_up_blocks - 1 - i
resnets = [
key for key in up_blocks[block_id] if f"up.{block_id}" in key and f"up.{block_id}.upsample" not in key
]
if f"decoder.up.{block_id}.upsample.conv.weight" in vae_state_dict:
new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.weight"] = vae_state_dict[
f"decoder.up.{block_id}.upsample.conv.weight"
]
new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.bias"] = vae_state_dict[
f"decoder.up.{block_id}.upsample.conv.bias"
]
paths = renew_vae_resnet_paths(resnets)
meta_path = {"old": f"up.{block_id}.block", "new": f"up_blocks.{i}.resnets"}
assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
mid_resnets = [key for key in vae_state_dict if "decoder.mid.block" in key]
num_mid_res_blocks = 2
for i in range(1, num_mid_res_blocks + 1):
resnets = [key for key in mid_resnets if f"decoder.mid.block_{i}" in key]
paths = renew_vae_resnet_paths(resnets)
meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"}
assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
mid_attentions = [key for key in vae_state_dict if "decoder.mid.attn" in key]
paths = renew_vae_attention_paths(mid_attentions)
meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
conv_attn_to_linear(new_checkpoint)
return new_checkpoint
def renew_vae_resnet_paths(old_list, n_shave_prefix_segments=0):
"""
Updates paths inside resnets to the new naming scheme (local renaming)
"""
mapping = []
for old_item in old_list:
new_item = old_item
new_item = new_item.replace("nin_shortcut", "conv_shortcut")
new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
mapping.append({"old": old_item, "new": new_item})
return mapping
def renew_vae_attention_paths(old_list, n_shave_prefix_segments=0):
"""
Updates paths inside attentions to the new naming scheme (local renaming)
"""
mapping = []
for old_item in old_list:
new_item = old_item
new_item = new_item.replace("norm.weight", "group_norm.weight")
new_item = new_item.replace("norm.bias", "group_norm.bias")
new_item = new_item.replace("q.weight", "to_q.weight")
new_item = new_item.replace("q.bias", "to_q.bias")
new_item = new_item.replace("k.weight", "to_k.weight")
new_item = new_item.replace("k.bias", "to_k.bias")
new_item = new_item.replace("v.weight", "to_v.weight")
new_item = new_item.replace("v.bias", "to_v.bias")
new_item = new_item.replace("proj_out.weight", "to_out.0.weight")
new_item = new_item.replace("proj_out.bias", "to_out.0.bias")
new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
mapping.append({"old": old_item, "new": new_item})
return mapping
def conv_attn_to_linear(checkpoint):
keys = list(checkpoint.keys())
attn_keys = ["query.weight", "key.weight", "value.weight"]
for key in keys:
if ".".join(key.split(".")[-2:]) in attn_keys:
if checkpoint[key].ndim > 2:
checkpoint[key] = checkpoint[key][:, :, 0, 0]
elif "proj_attn.weight" in key:
if checkpoint[key].ndim > 2:
checkpoint[key] = checkpoint[key][:, :, 0]
def convert_from_original_zero123_ckpt(checkpoint_path, original_config_file, extract_ema, device):
ckpt = torch.load(checkpoint_path, map_location=device)
ckpt["global_step"]
checkpoint = ckpt["state_dict"]
del ckpt
torch.cuda.empty_cache()
from omegaconf import OmegaConf
original_config = OmegaConf.load(original_config_file)
original_config.model.params.cond_stage_config.target.split(".")[-1]
num_in_channels = 8
original_config["model"]["params"]["unet_config"]["params"]["in_channels"] = num_in_channels
prediction_type = "epsilon"
image_size = 256
num_train_timesteps = getattr(original_config.model.params, "timesteps", None) or 1000
beta_start = getattr(original_config.model.params, "linear_start", None) or 0.02
beta_end = getattr(original_config.model.params, "linear_end", None) or 0.085
scheduler = DDIMScheduler(
beta_end=beta_end,
beta_schedule="scaled_linear",
beta_start=beta_start,
num_train_timesteps=num_train_timesteps,
steps_offset=1,
clip_sample=False,
set_alpha_to_one=False,
prediction_type=prediction_type,
)
scheduler.register_to_config(clip_sample=False)
# Convert the UNet2DConditionModel model.
upcast_attention = None
unet_config = create_unet_diffusers_config(original_config, image_size=image_size)
unet_config["upcast_attention"] = upcast_attention
with init_empty_weights():
unet = UNet2DConditionModel(**unet_config)
converted_unet_checkpoint = convert_ldm_unet_checkpoint(
checkpoint, unet_config, path=None, extract_ema=extract_ema
)
for param_name, param in converted_unet_checkpoint.items():
set_module_tensor_to_device(unet, param_name, "cpu", value=param)
# Convert the VAE model.
vae_config = create_vae_diffusers_config(original_config, image_size=image_size)
converted_vae_checkpoint = convert_ldm_vae_checkpoint(checkpoint, vae_config)
if (
"model" in original_config
and "params" in original_config.model
and "scale_factor" in original_config.model.params
):
vae_scaling_factor = original_config.model.params.scale_factor
else:
vae_scaling_factor = 0.18215 # default SD scaling factor
vae_config["scaling_factor"] = vae_scaling_factor
with init_empty_weights():
vae = AutoencoderKL(**vae_config)
for param_name, param in converted_vae_checkpoint.items():
set_module_tensor_to_device(vae, param_name, "cpu", value=param)
feature_extractor = CLIPImageProcessor.from_pretrained(
"lambdalabs/sd-image-variations-diffusers", subfolder="feature_extractor"
)
image_encoder = CLIPVisionModelWithProjection.from_pretrained(
"lambdalabs/sd-image-variations-diffusers", subfolder="image_encoder"
)
cc_projection = CCProjection()
cc_projection.load_state_dict(
{
"projection.weight": checkpoint["cc_projection.weight"].cpu(),
"projection.bias": checkpoint["cc_projection.bias"].cpu(),
}
)
pipe = Zero1to3StableDiffusionPipeline(
vae, image_encoder, unet, scheduler, None, feature_extractor, cc_projection, requires_safety_checker=False
)
return pipe
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
)
parser.add_argument(
"--original_config_file",
default=None,
type=str,
help="The YAML config file corresponding to the original architecture.",
)
parser.add_argument(
"--extract_ema",
action="store_true",
help=(
"Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights"
" or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield"
" higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning."
),
)
parser.add_argument(
"--to_safetensors",
action="store_true",
help="Whether to store pipeline in safetensors format or not.",
)
parser.add_argument("--half", action="store_true", help="Save weights in half precision.")
parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
parser.add_argument("--device", type=str, help="Device to use (e.g. cpu, cuda:0, cuda:1, etc.)")
args = parser.parse_args()
pipe = convert_from_original_zero123_ckpt(
checkpoint_path=args.checkpoint_path,
original_config_file=args.original_config_file,
extract_ema=args.extract_ema,
device=args.device,
)
if args.half:
pipe.to(torch_dtype=torch.float16)
pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)