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216 lines (179 loc) 路 9.07 KB
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#!/usr/bin/env python
from __future__ import annotations
import argparse
from contextlib import nullcontext
import torch
from accelerate import init_empty_weights
from diffusers import (
SanaControlNetModel,
)
from diffusers.models.model_loading_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
def main(args):
file_path = args.orig_ckpt_path
all_state_dict = torch.load(file_path, weights_only=True)
state_dict = all_state_dict.pop("state_dict")
converted_state_dict = {}
# Patch embeddings.
converted_state_dict["patch_embed.proj.weight"] = state_dict.pop("x_embedder.proj.weight")
converted_state_dict["patch_embed.proj.bias"] = state_dict.pop("x_embedder.proj.bias")
# Caption projection.
converted_state_dict["caption_projection.linear_1.weight"] = state_dict.pop("y_embedder.y_proj.fc1.weight")
converted_state_dict["caption_projection.linear_1.bias"] = state_dict.pop("y_embedder.y_proj.fc1.bias")
converted_state_dict["caption_projection.linear_2.weight"] = state_dict.pop("y_embedder.y_proj.fc2.weight")
converted_state_dict["caption_projection.linear_2.bias"] = state_dict.pop("y_embedder.y_proj.fc2.bias")
# AdaLN-single LN
converted_state_dict["time_embed.emb.timestep_embedder.linear_1.weight"] = state_dict.pop(
"t_embedder.mlp.0.weight"
)
converted_state_dict["time_embed.emb.timestep_embedder.linear_1.bias"] = state_dict.pop("t_embedder.mlp.0.bias")
converted_state_dict["time_embed.emb.timestep_embedder.linear_2.weight"] = state_dict.pop(
"t_embedder.mlp.2.weight"
)
converted_state_dict["time_embed.emb.timestep_embedder.linear_2.bias"] = state_dict.pop("t_embedder.mlp.2.bias")
# Shared norm.
converted_state_dict["time_embed.linear.weight"] = state_dict.pop("t_block.1.weight")
converted_state_dict["time_embed.linear.bias"] = state_dict.pop("t_block.1.bias")
# y norm
converted_state_dict["caption_norm.weight"] = state_dict.pop("attention_y_norm.weight")
# Positional embedding interpolation scale.
interpolation_scale = {512: None, 1024: None, 2048: 1.0, 4096: 2.0}
# ControlNet Input Projection.
converted_state_dict["input_block.weight"] = state_dict.pop("controlnet.0.before_proj.weight")
converted_state_dict["input_block.bias"] = state_dict.pop("controlnet.0.before_proj.bias")
for depth in range(7):
# Transformer blocks.
converted_state_dict[f"transformer_blocks.{depth}.scale_shift_table"] = state_dict.pop(
f"controlnet.{depth}.copied_block.scale_shift_table"
)
# Linear Attention is all you need 馃
# Self attention.
q, k, v = torch.chunk(state_dict.pop(f"controlnet.{depth}.copied_block.attn.qkv.weight"), 3, dim=0)
converted_state_dict[f"transformer_blocks.{depth}.attn1.to_q.weight"] = q
converted_state_dict[f"transformer_blocks.{depth}.attn1.to_k.weight"] = k
converted_state_dict[f"transformer_blocks.{depth}.attn1.to_v.weight"] = v
# Projection.
converted_state_dict[f"transformer_blocks.{depth}.attn1.to_out.0.weight"] = state_dict.pop(
f"controlnet.{depth}.copied_block.attn.proj.weight"
)
converted_state_dict[f"transformer_blocks.{depth}.attn1.to_out.0.bias"] = state_dict.pop(
f"controlnet.{depth}.copied_block.attn.proj.bias"
)
# Feed-forward.
converted_state_dict[f"transformer_blocks.{depth}.ff.conv_inverted.weight"] = state_dict.pop(
f"controlnet.{depth}.copied_block.mlp.inverted_conv.conv.weight"
)
converted_state_dict[f"transformer_blocks.{depth}.ff.conv_inverted.bias"] = state_dict.pop(
f"controlnet.{depth}.copied_block.mlp.inverted_conv.conv.bias"
)
converted_state_dict[f"transformer_blocks.{depth}.ff.conv_depth.weight"] = state_dict.pop(
f"controlnet.{depth}.copied_block.mlp.depth_conv.conv.weight"
)
converted_state_dict[f"transformer_blocks.{depth}.ff.conv_depth.bias"] = state_dict.pop(
f"controlnet.{depth}.copied_block.mlp.depth_conv.conv.bias"
)
converted_state_dict[f"transformer_blocks.{depth}.ff.conv_point.weight"] = state_dict.pop(
f"controlnet.{depth}.copied_block.mlp.point_conv.conv.weight"
)
# Cross-attention.
q = state_dict.pop(f"controlnet.{depth}.copied_block.cross_attn.q_linear.weight")
q_bias = state_dict.pop(f"controlnet.{depth}.copied_block.cross_attn.q_linear.bias")
k, v = torch.chunk(state_dict.pop(f"controlnet.{depth}.copied_block.cross_attn.kv_linear.weight"), 2, dim=0)
k_bias, v_bias = torch.chunk(
state_dict.pop(f"controlnet.{depth}.copied_block.cross_attn.kv_linear.bias"), 2, dim=0
)
converted_state_dict[f"transformer_blocks.{depth}.attn2.to_q.weight"] = q
converted_state_dict[f"transformer_blocks.{depth}.attn2.to_q.bias"] = q_bias
converted_state_dict[f"transformer_blocks.{depth}.attn2.to_k.weight"] = k
converted_state_dict[f"transformer_blocks.{depth}.attn2.to_k.bias"] = k_bias
converted_state_dict[f"transformer_blocks.{depth}.attn2.to_v.weight"] = v
converted_state_dict[f"transformer_blocks.{depth}.attn2.to_v.bias"] = v_bias
converted_state_dict[f"transformer_blocks.{depth}.attn2.to_out.0.weight"] = state_dict.pop(
f"controlnet.{depth}.copied_block.cross_attn.proj.weight"
)
converted_state_dict[f"transformer_blocks.{depth}.attn2.to_out.0.bias"] = state_dict.pop(
f"controlnet.{depth}.copied_block.cross_attn.proj.bias"
)
# ControlNet After Projection
converted_state_dict[f"controlnet_blocks.{depth}.weight"] = state_dict.pop(
f"controlnet.{depth}.after_proj.weight"
)
converted_state_dict[f"controlnet_blocks.{depth}.bias"] = state_dict.pop(f"controlnet.{depth}.after_proj.bias")
# ControlNet
with CTX():
controlnet = SanaControlNetModel(
num_attention_heads=model_kwargs[args.model_type]["num_attention_heads"],
attention_head_dim=model_kwargs[args.model_type]["attention_head_dim"],
num_layers=model_kwargs[args.model_type]["num_layers"],
num_cross_attention_heads=model_kwargs[args.model_type]["num_cross_attention_heads"],
cross_attention_head_dim=model_kwargs[args.model_type]["cross_attention_head_dim"],
cross_attention_dim=model_kwargs[args.model_type]["cross_attention_dim"],
caption_channels=2304,
sample_size=args.image_size // 32,
interpolation_scale=interpolation_scale[args.image_size],
)
if is_accelerate_available():
load_model_dict_into_meta(controlnet, converted_state_dict)
else:
controlnet.load_state_dict(converted_state_dict, strict=True, assign=True)
num_model_params = sum(p.numel() for p in controlnet.parameters())
print(f"Total number of controlnet parameters: {num_model_params}")
controlnet = controlnet.to(weight_dtype)
controlnet.load_state_dict(converted_state_dict, strict=True)
print(f"Saving Sana ControlNet in Diffusers format in {args.dump_path}.")
controlnet.save_pretrained(args.dump_path)
DTYPE_MAPPING = {
"fp32": torch.float32,
"fp16": torch.float16,
"bf16": torch.bfloat16,
}
VARIANT_MAPPING = {
"fp32": None,
"fp16": "fp16",
"bf16": "bf16",
}
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--orig_ckpt_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
)
parser.add_argument(
"--image_size",
default=1024,
type=int,
choices=[512, 1024, 2048, 4096],
required=False,
help="Image size of pretrained model, 512, 1024, 2048 or 4096.",
)
parser.add_argument(
"--model_type",
default="SanaMS_1600M_P1_ControlNet_D7",
type=str,
choices=["SanaMS_1600M_P1_ControlNet_D7", "SanaMS_600M_P1_ControlNet_D7"],
)
parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output pipeline.")
parser.add_argument("--dtype", default="fp16", type=str, choices=["fp32", "fp16", "bf16"], help="Weight dtype.")
args = parser.parse_args()
model_kwargs = {
"SanaMS_1600M_P1_ControlNet_D7": {
"num_attention_heads": 70,
"attention_head_dim": 32,
"num_cross_attention_heads": 20,
"cross_attention_head_dim": 112,
"cross_attention_dim": 2240,
"num_layers": 7,
},
"SanaMS_600M_P1_ControlNet_D7": {
"num_attention_heads": 36,
"attention_head_dim": 32,
"num_cross_attention_heads": 16,
"cross_attention_head_dim": 72,
"cross_attention_dim": 1152,
"num_layers": 7,
},
}
device = "cuda" if torch.cuda.is_available() else "cpu"
weight_dtype = DTYPE_MAPPING[args.dtype]
variant = VARIANT_MAPPING[args.dtype]
main(args)