import argparse
from contextlib import nullcontext
import safetensors.torch
import torch
from accelerate import init_empty_weights
from diffusers import AutoencoderKL, SD3Transformer2DModel
from diffusers.loaders.single_file_utils import convert_ldm_vae_checkpoint
from diffusers.models.modeling_utils import load_model_dict_into_meta
from diffusers.utils.import_utils import is_accelerate_available
CTX = init_empty_weights if is_accelerate_available else nullcontext
parser = argparse.ArgumentParser()
parser.add_argument("--checkpoint_path", type=str)
parser.add_argument("--output_path", type=str)
parser.add_argument("--dtype", type=str)
args = parser.parse_args()
def load_original_checkpoint(ckpt_path):
original_state_dict = safetensors.torch.load_file(ckpt_path)
keys = list(original_state_dict.keys())
for k in keys:
if "model.diffusion_model." in k:
original_state_dict[k.replace("model.diffusion_model.", "")] = original_state_dict.pop(k)
return original_state_dict
# in SD3 original implementation of AdaLayerNormContinuous, it split linear projection output into shift, scale;
# while in diffusers it split into scale, shift. Here we swap the linear projection weights in order to be able to use diffusers implementation
def swap_scale_shift(weight, dim):
shift, scale = weight.chunk(2, dim=0)
new_weight = torch.cat([scale, shift], dim=0)
return new_weight
def convert_sd3_transformer_checkpoint_to_diffusers(
original_state_dict, num_layers, caption_projection_dim, dual_attention_layers, has_qk_norm
):
converted_state_dict = {}
# Positional and patch embeddings.
converted_state_dict["pos_embed.pos_embed"] = original_state_dict.pop("pos_embed")
converted_state_dict["pos_embed.proj.weight"] = original_state_dict.pop("x_embedder.proj.weight")
converted_state_dict["pos_embed.proj.bias"] = original_state_dict.pop("x_embedder.proj.bias")
# Timestep embeddings.
converted_state_dict["time_text_embed.timestep_embedder.linear_1.weight"] = original_state_dict.pop(
"t_embedder.mlp.0.weight"
)
converted_state_dict["time_text_embed.timestep_embedder.linear_1.bias"] = original_state_dict.pop(
"t_embedder.mlp.0.bias"
)
converted_state_dict["time_text_embed.timestep_embedder.linear_2.weight"] = original_state_dict.pop(
"t_embedder.mlp.2.weight"
)
converted_state_dict["time_text_embed.timestep_embedder.linear_2.bias"] = original_state_dict.pop(
"t_embedder.mlp.2.bias"
)
# Context projections.
converted_state_dict["context_embedder.weight"] = original_state_dict.pop("context_embedder.weight")
converted_state_dict["context_embedder.bias"] = original_state_dict.pop("context_embedder.bias")
# Pooled context projection.
converted_state_dict["time_text_embed.text_embedder.linear_1.weight"] = original_state_dict.pop(
"y_embedder.mlp.0.weight"
)
converted_state_dict["time_text_embed.text_embedder.linear_1.bias"] = original_state_dict.pop(
"y_embedder.mlp.0.bias"
)
converted_state_dict["time_text_embed.text_embedder.linear_2.weight"] = original_state_dict.pop(
"y_embedder.mlp.2.weight"
)
converted_state_dict["time_text_embed.text_embedder.linear_2.bias"] = original_state_dict.pop(
"y_embedder.mlp.2.bias"
)
# Transformer blocks 🎸.
for i in range(num_layers):
# Q, K, V
sample_q, sample_k, sample_v = torch.chunk(
original_state_dict.pop(f"joint_blocks.{i}.x_block.attn.qkv.weight"), 3, dim=0
)
context_q, context_k, context_v = torch.chunk(
original_state_dict.pop(f"joint_blocks.{i}.context_block.attn.qkv.weight"), 3, dim=0
)
sample_q_bias, sample_k_bias, sample_v_bias = torch.chunk(
original_state_dict.pop(f"joint_blocks.{i}.x_block.attn.qkv.bias"), 3, dim=0
)
context_q_bias, context_k_bias, context_v_bias = torch.chunk(
original_state_dict.pop(f"joint_blocks.{i}.context_block.attn.qkv.bias"), 3, dim=0
)
converted_state_dict[f"transformer_blocks.{i}.attn.to_q.weight"] = torch.cat([sample_q])
converted_state_dict[f"transformer_blocks.{i}.attn.to_q.bias"] = torch.cat([sample_q_bias])
converted_state_dict[f"transformer_blocks.{i}.attn.to_k.weight"] = torch.cat([sample_k])
converted_state_dict[f"transformer_blocks.{i}.attn.to_k.bias"] = torch.cat([sample_k_bias])
converted_state_dict[f"transformer_blocks.{i}.attn.to_v.weight"] = torch.cat([sample_v])
converted_state_dict[f"transformer_blocks.{i}.attn.to_v.bias"] = torch.cat([sample_v_bias])
converted_state_dict[f"transformer_blocks.{i}.attn.add_q_proj.weight"] = torch.cat([context_q])
converted_state_dict[f"transformer_blocks.{i}.attn.add_q_proj.bias"] = torch.cat([context_q_bias])
converted_state_dict[f"transformer_blocks.{i}.attn.add_k_proj.weight"] = torch.cat([context_k])
converted_state_dict[f"transformer_blocks.{i}.attn.add_k_proj.bias"] = torch.cat([context_k_bias])
converted_state_dict[f"transformer_blocks.{i}.attn.add_v_proj.weight"] = torch.cat([context_v])
converted_state_dict[f"transformer_blocks.{i}.attn.add_v_proj.bias"] = torch.cat([context_v_bias])
# qk norm
if has_qk_norm:
converted_state_dict[f"transformer_blocks.{i}.attn.norm_q.weight"] = original_state_dict.pop(
f"joint_blocks.{i}.x_block.attn.ln_q.weight"
)
converted_state_dict[f"transformer_blocks.{i}.attn.norm_k.weight"] = original_state_dict.pop(
f"joint_blocks.{i}.x_block.attn.ln_k.weight"
)
converted_state_dict[f"transformer_blocks.{i}.attn.norm_added_q.weight"] = original_state_dict.pop(
f"joint_blocks.{i}.context_block.attn.ln_q.weight"
)
converted_state_dict[f"transformer_blocks.{i}.attn.norm_added_k.weight"] = original_state_dict.pop(
f"joint_blocks.{i}.context_block.attn.ln_k.weight"
)
# output projections.
converted_state_dict[f"transformer_blocks.{i}.attn.to_out.0.weight"] = original_state_dict.pop(
f"joint_blocks.{i}.x_block.attn.proj.weight"
)
converted_state_dict[f"transformer_blocks.{i}.attn.to_out.0.bias"] = original_state_dict.pop(
f"joint_blocks.{i}.x_block.attn.proj.bias"
)
if not (i == num_layers - 1):
converted_state_dict[f"transformer_blocks.{i}.attn.to_add_out.weight"] = original_state_dict.pop(
f"joint_blocks.{i}.context_block.attn.proj.weight"
)
converted_state_dict[f"transformer_blocks.{i}.attn.to_add_out.bias"] = original_state_dict.pop(
f"joint_blocks.{i}.context_block.attn.proj.bias"
)
# attn2
if i in dual_attention_layers:
# Q, K, V
sample_q2, sample_k2, sample_v2 = torch.chunk(
original_state_dict.pop(f"joint_blocks.{i}.x_block.attn2.qkv.weight"), 3, dim=0
)
sample_q2_bias, sample_k2_bias, sample_v2_bias = torch.chunk(
original_state_dict.pop(f"joint_blocks.{i}.x_block.attn2.qkv.bias"), 3, dim=0
)
converted_state_dict[f"transformer_blocks.{i}.attn2.to_q.weight"] = torch.cat([sample_q2])
converted_state_dict[f"transformer_blocks.{i}.attn2.to_q.bias"] = torch.cat([sample_q2_bias])
converted_state_dict[f"transformer_blocks.{i}.attn2.to_k.weight"] = torch.cat([sample_k2])
converted_state_dict[f"transformer_blocks.{i}.attn2.to_k.bias"] = torch.cat([sample_k2_bias])
converted_state_dict[f"transformer_blocks.{i}.attn2.to_v.weight"] = torch.cat([sample_v2])
converted_state_dict[f"transformer_blocks.{i}.attn2.to_v.bias"] = torch.cat([sample_v2_bias])
# qk norm
if has_qk_norm:
converted_state_dict[f"transformer_blocks.{i}.attn2.norm_q.weight"] = original_state_dict.pop(
f"joint_blocks.{i}.x_block.attn2.ln_q.weight"
)
converted_state_dict[f"transformer_blocks.{i}.attn2.norm_k.weight"] = original_state_dict.pop(
f"joint_blocks.{i}.x_block.attn2.ln_k.weight"
)
# output projections.
converted_state_dict[f"transformer_blocks.{i}.attn2.to_out.0.weight"] = original_state_dict.pop(
f"joint_blocks.{i}.x_block.attn2.proj.weight"
)
converted_state_dict[f"transformer_blocks.{i}.attn2.to_out.0.bias"] = original_state_dict.pop(
f"joint_blocks.{i}.x_block.attn2.proj.bias"
)
# norms.
converted_state_dict[f"transformer_blocks.{i}.norm1.linear.weight"] = original_state_dict.pop(
f"joint_blocks.{i}.x_block.adaLN_modulation.1.weight"
)
converted_state_dict[f"transformer_blocks.{i}.norm1.linear.bias"] = original_state_dict.pop(
f"joint_blocks.{i}.x_block.adaLN_modulation.1.bias"
)
if not (i == num_layers - 1):
converted_state_dict[f"transformer_blocks.{i}.norm1_context.linear.weight"] = original_state_dict.pop(
f"joint_blocks.{i}.context_block.adaLN_modulation.1.weight"
)
converted_state_dict[f"transformer_blocks.{i}.norm1_context.linear.bias"] = original_state_dict.pop(
f"joint_blocks.{i}.context_block.adaLN_modulation.1.bias"
)
else:
converted_state_dict[f"transformer_blocks.{i}.norm1_context.linear.weight"] = swap_scale_shift(
original_state_dict.pop(f"joint_blocks.{i}.context_block.adaLN_modulation.1.weight"),
dim=caption_projection_dim,
)
converted_state_dict[f"transformer_blocks.{i}.norm1_context.linear.bias"] = swap_scale_shift(
original_state_dict.pop(f"joint_blocks.{i}.context_block.adaLN_modulation.1.bias"),
dim=caption_projection_dim,
)
# ffs.
converted_state_dict[f"transformer_blocks.{i}.ff.net.0.proj.weight"] = original_state_dict.pop(
f"joint_blocks.{i}.x_block.mlp.fc1.weight"
)
converted_state_dict[f"transformer_blocks.{i}.ff.net.0.proj.bias"] = original_state_dict.pop(
f"joint_blocks.{i}.x_block.mlp.fc1.bias"
)
converted_state_dict[f"transformer_blocks.{i}.ff.net.2.weight"] = original_state_dict.pop(
f"joint_blocks.{i}.x_block.mlp.fc2.weight"
)
converted_state_dict[f"transformer_blocks.{i}.ff.net.2.bias"] = original_state_dict.pop(
f"joint_blocks.{i}.x_block.mlp.fc2.bias"
)
if not (i == num_layers - 1):
converted_state_dict[f"transformer_blocks.{i}.ff_context.net.0.proj.weight"] = original_state_dict.pop(
f"joint_blocks.{i}.context_block.mlp.fc1.weight"
)
converted_state_dict[f"transformer_blocks.{i}.ff_context.net.0.proj.bias"] = original_state_dict.pop(
f"joint_blocks.{i}.context_block.mlp.fc1.bias"
)
converted_state_dict[f"transformer_blocks.{i}.ff_context.net.2.weight"] = original_state_dict.pop(
f"joint_blocks.{i}.context_block.mlp.fc2.weight"
)
converted_state_dict[f"transformer_blocks.{i}.ff_context.net.2.bias"] = original_state_dict.pop(
f"joint_blocks.{i}.context_block.mlp.fc2.bias"
)
# Final blocks.
converted_state_dict["proj_out.weight"] = original_state_dict.pop("final_layer.linear.weight")
converted_state_dict["proj_out.bias"] = original_state_dict.pop("final_layer.linear.bias")
converted_state_dict["norm_out.linear.weight"] = swap_scale_shift(
original_state_dict.pop("final_layer.adaLN_modulation.1.weight"), dim=caption_projection_dim
)
converted_state_dict["norm_out.linear.bias"] = swap_scale_shift(
original_state_dict.pop("final_layer.adaLN_modulation.1.bias"), dim=caption_projection_dim
)
return converted_state_dict
def is_vae_in_checkpoint(original_state_dict):
return ("first_stage_model.decoder.conv_in.weight" in original_state_dict) and (
"first_stage_model.encoder.conv_in.weight" in original_state_dict
)
def get_attn2_layers(state_dict):
attn2_layers = []
for key in state_dict.keys():
if "attn2." in key:
# Extract the layer number from the key
layer_num = int(key.split(".")[1])
attn2_layers.append(layer_num)
return tuple(sorted(set(attn2_layers)))
def get_pos_embed_max_size(state_dict):
num_patches = state_dict["pos_embed"].shape[1]
pos_embed_max_size = int(num_patches**0.5)
return pos_embed_max_size
def get_caption_projection_dim(state_dict):
caption_projection_dim = state_dict["context_embedder.weight"].shape[0]
return caption_projection_dim
def main(args):
original_ckpt = load_original_checkpoint(args.checkpoint_path)
original_dtype = next(iter(original_ckpt.values())).dtype
# Initialize dtype with a default value
dtype = None
if args.dtype is None:
dtype = original_dtype
elif args.dtype == "fp16":
dtype = torch.float16
elif args.dtype == "bf16":
dtype = torch.bfloat16
elif args.dtype == "fp32":
dtype = torch.float32
else:
raise ValueError(f"Unsupported dtype: {args.dtype}")
if dtype != original_dtype:
print(
f"Checkpoint dtype {original_dtype} does not match requested dtype {dtype}. This can lead to unexpected results, proceed with caution."
)
num_layers = list(set(int(k.split(".", 2)[1]) for k in original_ckpt if "joint_blocks" in k))[-1] + 1 # noqa: C401
caption_projection_dim = get_caption_projection_dim(original_ckpt)
# () for sd3.0; (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12) for sd3.5
attn2_layers = get_attn2_layers(original_ckpt)
# sd3.5 use qk norm("rms_norm")
has_qk_norm = any("ln_q" in key for key in original_ckpt.keys())
# sd3.5 2b use pox_embed_max_size=384 and sd3.0 and sd3.5 8b use 192
pos_embed_max_size = get_pos_embed_max_size(original_ckpt)
converted_transformer_state_dict = convert_sd3_transformer_checkpoint_to_diffusers(
original_ckpt, num_layers, caption_projection_dim, attn2_layers, has_qk_norm
)
with CTX():
transformer = SD3Transformer2DModel(
sample_size=128,
patch_size=2,
in_channels=16,
joint_attention_dim=4096,
num_layers=num_layers,
caption_projection_dim=caption_projection_dim,
num_attention_heads=num_layers,
pos_embed_max_size=pos_embed_max_size,
qk_norm="rms_norm" if has_qk_norm else None,
dual_attention_layers=attn2_layers,
)
if is_accelerate_available():
load_model_dict_into_meta(transformer, converted_transformer_state_dict)
else:
transformer.load_state_dict(converted_transformer_state_dict, strict=True)
print("Saving SD3 Transformer in Diffusers format.")
transformer.to(dtype).save_pretrained(f"{args.output_path}/transformer")
if is_vae_in_checkpoint(original_ckpt):
with CTX():
vae = AutoencoderKL.from_config(
"stabilityai/stable-diffusion-xl-base-1.0",
subfolder="vae",
latent_channels=16,
use_post_quant_conv=False,
use_quant_conv=False,
scaling_factor=1.5305,
shift_factor=0.0609,
)
converted_vae_state_dict = convert_ldm_vae_checkpoint(original_ckpt, vae.config)
if is_accelerate_available():
load_model_dict_into_meta(vae, converted_vae_state_dict)
else:
vae.load_state_dict(converted_vae_state_dict, strict=True)
print("Saving SD3 Autoencoder in Diffusers format.")
vae.to(dtype).save_pretrained(f"{args.output_path}/vae")
if __name__ == "__main__":
main(args)