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afe9f02 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 | """Convert the original Idol checkpoints (speridlabs/idol) into the diffusers weights layout used by these blocks:
`transformer/`, `octree_decoder/` and `gaussian_decoder/`, each with a `config.json` and safetensors.
It imports the model classes next to it, so download it together with them:
hf download OzzyGT/idol_custom_blocks convert_idol_to_diffusers.py transformer_idol.py octree_decoder_idol.py \
gaussian_decoder_idol.py --local-dir idol_convert
cd idol_convert
python convert_idol_to_diffusers.py --output_dir ./idol_diffusers
python convert_idol_to_diffusers.py --checkpoint /path/to/speridlabs-idol --output_dir ./idol_diffusers
Then point the pipeline at the result with
`pipe.load_components(pretrained_model_name_or_path={"transformer": out, "octree_decoder": out, "gaussian_decoder": out})`
or upload the folder and change `IDOL_WEIGHTS_REPO` in `components.py`.
"""
import argparse
import json
import os
import re
import torch
from gaussian_decoder_idol import IdolGaussianDecoderModel
from octree_decoder_idol import IdolOctreeDecoderModel
from safetensors.torch import load_file
from transformer_idol import IdolTransformerModel
ORIGINAL_FILES = ["config.json", "denoiser.safetensors", "decoder.safetensors", "decoder_gs.safetensors"]
def split_fused(state_dict, old_prefix, new_prefixes):
"""Split a fused linear `old_prefix` along its output rows into the linears `new_prefixes`."""
for suffix in ("weight", "bias"):
tensor = state_dict.pop(f"{old_prefix}.{suffix}")
for prefix, chunk in zip(new_prefixes, tensor.chunk(len(new_prefixes), dim=0)):
state_dict[f"{prefix}.{suffix}"] = chunk.contiguous()
def rename(state_dict, rules):
converted = {}
for key, value in state_dict.items():
for pattern, replacement in rules:
key = re.sub(pattern, replacement, key)
converted[key] = value
return converted
def convert_attention(state_dict, old, new, fused_qkv):
if fused_qkv:
split_fused(state_dict, f"{old}.to_qkv", [f"{new}.to_q", f"{new}.to_k", f"{new}.to_v"])
else:
state_dict[f"{new}.to_q.weight"] = state_dict.pop(f"{old}.to_q.weight")
state_dict[f"{new}.to_q.bias"] = state_dict.pop(f"{old}.to_q.bias")
split_fused(state_dict, f"{old}.to_kv", [f"{new}.to_k", f"{new}.to_v"])
state_dict[f"{new}.to_out.0.weight"] = state_dict.pop(f"{old}.to_out.weight")
state_dict[f"{new}.to_out.0.bias"] = state_dict.pop(f"{old}.to_out.bias")
state_dict[f"{new}.norm_q.weight"] = state_dict.pop(f"{old}.q_rms_norm.gamma")
state_dict[f"{new}.norm_k.weight"] = state_dict.pop(f"{old}.k_rms_norm.gamma")
def num_blocks(state_dict):
return len({key.split(".")[1] for key in state_dict if key.startswith("blocks.")})
def convert_transformer_state_dict(state_dict):
state_dict = rename(
state_dict,
[
(r"^t_embedder\.mlp\.0\.", "t_embedder.linear_1."),
(r"^t_embedder\.mlp\.2\.", "t_embedder.linear_2."),
(r"^cam_refiner\.mlp\.", "cam_refiner.net."),
(r"\.mlp\.mlp\.", ".ff.net."),
(r"\.attn\.out\.", ".attn.to_out.0."),
(r"\.attn\.q_norm\.gamma$", ".attn.norm_q.weight"),
(r"\.attn\.k_norm\.gamma$", ".attn.norm_k.weight"),
(r"shift_table$", "scale_shift_table"),
],
)
for key in [k for k in state_dict if k.endswith(".attn.qkv.weight")]:
prefix = key[: -len(".qkv.weight")]
split_fused(state_dict, f"{prefix}.qkv", [f"{prefix}.to_q", f"{prefix}.to_k", f"{prefix}.to_v"])
return state_dict
def convert_octree_decoder_state_dict(state_dict):
state_dict = rename(
state_dict,
[
(r"^l_embedder\.mlp\.0\.", "l_embedder.linear_1."),
(r"^l_embedder\.mlp\.2\.", "l_embedder.linear_2."),
(r"\.mlp\.mlp\.", ".ff.net."),
],
)
for i in range(num_blocks(state_dict)):
convert_attention(state_dict, f"blocks.{i}.cross_attn", f"blocks.{i}.cross_attn", fused_qkv=False)
return state_dict
def convert_gaussian_decoder_state_dict(state_dict):
state_dict = rename(state_dict, [(r"\.mlp\.mlp\.", ".ff.net.")])
for i in range(num_blocks(state_dict)):
convert_attention(state_dict, f"blocks.{i}.self_attn", f"blocks.{i}.attn1", fused_qkv=True)
convert_attention(state_dict, f"blocks.{i}.cross_attn", f"blocks.{i}.attn2", fused_qkv=False)
return state_dict
def transformer_config(original):
args = original["denoiser"]["args"]
return {
"num_latent_tokens": args["q_token_length"],
"in_channels": args["in_channels"],
"camera_channels": args["cam_channels"],
"hidden_size": args["model_channels"],
"image_embed_dim": args["cond_channels"],
"image_vae_embed_dim": args["cond2_channels"],
"num_layers": args["num_blocks"],
"num_refiner_layers": args["num_refiner_blocks"],
"num_attention_heads": args["num_heads"],
"attention_head_dim": args.get("num_head_channels", 64),
"mlp_ratio": float(args["mlp_ratio"]),
}
def octree_decoder_config(original):
args = original["decoder"]["args"]
return {
"hidden_size": args["model_channels"],
"latent_channels": args["cond_channels"],
"num_layers": args["num_blocks"],
"num_attention_heads": args["num_heads"],
"mlp_ratio": float(args["mlp_ratio"]),
"max_level": original["max_voxel_level"],
}
def gaussian_decoder_config(original):
args = original["decoder_gs"]["args"]
representation = args["representation_config"]
return {
"in_channels": args["in_channels"],
"hidden_size": args["model_channels"],
"latent_channels": args["cond_channels"],
"num_layers": args["num_blocks"],
"num_attention_heads": args["num_heads"],
"mlp_ratio": float(args["mlp_ratio"]),
"gaussians_per_point": representation["num_gaussians"],
"xyz_lr": representation["lr"]["_xyz"],
"features_dc_lr": representation["lr"]["_features_dc"],
"opacity_lr": representation["lr"]["_opacity"],
"scaling_lr": representation["lr"]["_scaling"],
"rotation_lr": representation["lr"]["_rotation"],
"perturb_size": representation["perturbe_size"],
"filter_kernel_size": representation["3d_filter_kernel_size"],
"scaling_bias": representation["scaling_bias"],
"opacity_bias": representation["opacity_bias"],
}
# subfolder -> (model class, original checkpoint name, state dict converter, config builder)
COMPONENTS = {
"transformer": (IdolTransformerModel, "denoiser", convert_transformer_state_dict, transformer_config),
"octree_decoder": (IdolOctreeDecoderModel, "decoder", convert_octree_decoder_state_dict, octree_decoder_config),
"gaussian_decoder": (
IdolGaussianDecoderModel,
"decoder_gs",
convert_gaussian_decoder_state_dict,
gaussian_decoder_config,
),
}
def resolve_checkpoint(checkpoint):
if os.path.isdir(checkpoint):
return checkpoint
from huggingface_hub import snapshot_download
return snapshot_download(checkpoint, allow_patterns=ORIGINAL_FILES)
def convert_component(subfolder, checkpoint_dir):
"""Build the diffusers model for `subfolder` from the original checkpoint (float32, on CPU)."""
model_cls, original_name, convert_state_dict, make_config = COMPONENTS[subfolder]
with open(os.path.join(checkpoint_dir, "config.json")) as f:
original_config = json.load(f)
model = model_cls(**make_config(original_config))
original_state_dict = load_file(os.path.join(checkpoint_dir, f"{original_name}.safetensors"))
model.load_state_dict(convert_state_dict(original_state_dict), strict=True)
return model.float().eval()
def main():
parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
parser.add_argument("--checkpoint", default="speridlabs/idol", help="Hub repo or local folder of the original")
parser.add_argument("--output_dir", required=True)
args = parser.parse_args()
checkpoint_dir = resolve_checkpoint(args.checkpoint)
for subfolder, (model_cls, *_) in COMPONENTS.items():
model = convert_component(subfolder, checkpoint_dir)
out_dir = os.path.join(args.output_dir, subfolder)
model.save_pretrained(out_dir)
reloaded = model_cls.from_pretrained(out_dir)
reference = model.state_dict()
for key, value in reloaded.state_dict().items():
assert torch.equal(value, reference[key]), f"{subfolder}: {key} changed after save/load"
num_params = sum(p.numel() for p in model.parameters()) / 1e6
print(f"{subfolder}: {num_params:.1f}M parameters -> {out_dir}")
if __name__ == "__main__":
main()
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