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Copy pathconvert_sana_to_diffusers.py
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318 lines (274 loc) · 13 KB
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#!/usr/bin/env python
from __future__ import annotations
import argparse
import os
from contextlib import nullcontext
import torch
from accelerate import init_empty_weights
from huggingface_hub import hf_hub_download, snapshot_download
from termcolor import colored
from transformers import AutoModelForCausalLM, AutoTokenizer
from diffusers import (
AutoencoderDC,
DPMSolverMultistepScheduler,
FlowMatchEulerDiscreteScheduler,
SanaPipeline,
SanaTransformer2DModel,
)
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
ckpt_ids = [
"Efficient-Large-Model/Sana_1600M_4Kpx_BF16/checkpoints/Sana_1600M_4Kpx_BF16.pth",
"Efficient-Large-Model/Sana_1600M_2Kpx_BF16/checkpoints/Sana_1600M_2Kpx_BF16.pth",
"Efficient-Large-Model/Sana_1600M_1024px_MultiLing/checkpoints/Sana_1600M_1024px_MultiLing.pth",
"Efficient-Large-Model/Sana_1600M_1024px_BF16/checkpoints/Sana_1600M_1024px_BF16.pth",
"Efficient-Large-Model/Sana_1600M_512px_MultiLing/checkpoints/Sana_1600M_512px_MultiLing.pth",
"Efficient-Large-Model/Sana_1600M_1024px/checkpoints/Sana_1600M_1024px.pth",
"Efficient-Large-Model/Sana_1600M_512px/checkpoints/Sana_1600M_512px.pth",
"Efficient-Large-Model/Sana_600M_1024px/checkpoints/Sana_600M_1024px_MultiLing.pth",
"Efficient-Large-Model/Sana_600M_512px/checkpoints/Sana_600M_512px_MultiLing.pth",
]
# https://github.com/NVlabs/Sana/blob/main/scripts/inference.py
def main(args):
cache_dir_path = os.path.expanduser("~/.cache/huggingface/hub")
if args.orig_ckpt_path is None or args.orig_ckpt_path in ckpt_ids:
ckpt_id = args.orig_ckpt_path or ckpt_ids[0]
snapshot_download(
repo_id=f"{'/'.join(ckpt_id.split('/')[:2])}",
cache_dir=cache_dir_path,
repo_type="model",
)
file_path = hf_hub_download(
repo_id=f"{'/'.join(ckpt_id.split('/')[:2])}",
filename=f"{'/'.join(ckpt_id.split('/')[2:])}",
cache_dir=cache_dir_path,
repo_type="model",
)
else:
file_path = args.orig_ckpt_path
print(colored(f"Loading checkpoint from {file_path}", "green", attrs=["bold"]))
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")
# scheduler
if args.image_size == 4096:
flow_shift = 6.0
else:
flow_shift = 3.0
# model config
if args.model_type == "SanaMS_1600M_P1_D20":
layer_num = 20
elif args.model_type == "SanaMS_600M_P1_D28":
layer_num = 28
else:
raise ValueError(f"{args.model_type} is not supported.")
# Positional embedding interpolation scale.
interpolation_scale = {512: None, 1024: None, 2048: 1.0, 4096: 2.0}
for depth in range(layer_num):
# Transformer blocks.
converted_state_dict[f"transformer_blocks.{depth}.scale_shift_table"] = state_dict.pop(
f"blocks.{depth}.scale_shift_table"
)
# Linear Attention is all you need 🤘
# Self attention.
q, k, v = torch.chunk(state_dict.pop(f"blocks.{depth}.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"blocks.{depth}.attn.proj.weight"
)
converted_state_dict[f"transformer_blocks.{depth}.attn1.to_out.0.bias"] = state_dict.pop(
f"blocks.{depth}.attn.proj.bias"
)
# Feed-forward.
converted_state_dict[f"transformer_blocks.{depth}.ff.conv_inverted.weight"] = state_dict.pop(
f"blocks.{depth}.mlp.inverted_conv.conv.weight"
)
converted_state_dict[f"transformer_blocks.{depth}.ff.conv_inverted.bias"] = state_dict.pop(
f"blocks.{depth}.mlp.inverted_conv.conv.bias"
)
converted_state_dict[f"transformer_blocks.{depth}.ff.conv_depth.weight"] = state_dict.pop(
f"blocks.{depth}.mlp.depth_conv.conv.weight"
)
converted_state_dict[f"transformer_blocks.{depth}.ff.conv_depth.bias"] = state_dict.pop(
f"blocks.{depth}.mlp.depth_conv.conv.bias"
)
converted_state_dict[f"transformer_blocks.{depth}.ff.conv_point.weight"] = state_dict.pop(
f"blocks.{depth}.mlp.point_conv.conv.weight"
)
# Cross-attention.
q = state_dict.pop(f"blocks.{depth}.cross_attn.q_linear.weight")
q_bias = state_dict.pop(f"blocks.{depth}.cross_attn.q_linear.bias")
k, v = torch.chunk(state_dict.pop(f"blocks.{depth}.cross_attn.kv_linear.weight"), 2, dim=0)
k_bias, v_bias = torch.chunk(state_dict.pop(f"blocks.{depth}.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"blocks.{depth}.cross_attn.proj.weight"
)
converted_state_dict[f"transformer_blocks.{depth}.attn2.to_out.0.bias"] = state_dict.pop(
f"blocks.{depth}.cross_attn.proj.bias"
)
# Final block.
converted_state_dict["proj_out.weight"] = state_dict.pop("final_layer.linear.weight")
converted_state_dict["proj_out.bias"] = state_dict.pop("final_layer.linear.bias")
converted_state_dict["scale_shift_table"] = state_dict.pop("final_layer.scale_shift_table")
# Transformer
with CTX():
transformer = SanaTransformer2DModel(
in_channels=32,
out_channels=32,
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,
mlp_ratio=2.5,
attention_bias=False,
sample_size=args.image_size // 32,
patch_size=1,
norm_elementwise_affine=False,
norm_eps=1e-6,
interpolation_scale=interpolation_scale[args.image_size],
)
if is_accelerate_available():
load_model_dict_into_meta(transformer, converted_state_dict)
else:
transformer.load_state_dict(converted_state_dict, strict=True, assign=True)
try:
state_dict.pop("y_embedder.y_embedding")
state_dict.pop("pos_embed")
except KeyError:
print("y_embedder.y_embedding or pos_embed not found in the state_dict")
assert len(state_dict) == 0, f"State dict is not empty, {state_dict.keys()}"
num_model_params = sum(p.numel() for p in transformer.parameters())
print(f"Total number of transformer parameters: {num_model_params}")
transformer = transformer.to(weight_dtype)
if not args.save_full_pipeline:
print(
colored(
f"Only saving transformer model of {args.model_type}. "
f"Set --save_full_pipeline to save the whole SanaPipeline",
"green",
attrs=["bold"],
)
)
transformer.save_pretrained(
os.path.join(args.dump_path, "transformer"), safe_serialization=True, max_shard_size="5GB", variant=variant
)
else:
print(colored(f"Saving the whole SanaPipeline containing {args.model_type}", "green", attrs=["bold"]))
# VAE
ae = AutoencoderDC.from_pretrained("mit-han-lab/dc-ae-f32c32-sana-1.0-diffusers", torch_dtype=torch.float32)
# Text Encoder
text_encoder_model_path = "google/gemma-2-2b-it"
tokenizer = AutoTokenizer.from_pretrained(text_encoder_model_path)
tokenizer.padding_side = "right"
text_encoder = AutoModelForCausalLM.from_pretrained(
text_encoder_model_path, torch_dtype=torch.bfloat16
).get_decoder()
# Scheduler
if args.scheduler_type == "flow-dpm_solver":
scheduler = DPMSolverMultistepScheduler(
flow_shift=flow_shift,
use_flow_sigmas=True,
prediction_type="flow_prediction",
)
elif args.scheduler_type == "flow-euler":
scheduler = FlowMatchEulerDiscreteScheduler(shift=flow_shift)
else:
raise ValueError(f"Scheduler type {args.scheduler_type} is not supported")
pipe = SanaPipeline(
tokenizer=tokenizer,
text_encoder=text_encoder,
transformer=transformer,
vae=ae,
scheduler=scheduler,
)
pipe.save_pretrained(args.dump_path, safe_serialization=True, max_shard_size="5GB", variant=variant)
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=False, 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_D20", type=str, choices=["SanaMS_1600M_P1_D20", "SanaMS_600M_P1_D28"]
)
parser.add_argument(
"--scheduler_type", default="flow-dpm_solver", type=str, choices=["flow-dpm_solver", "flow-euler"]
)
parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output pipeline.")
parser.add_argument("--save_full_pipeline", action="store_true", help="save all the pipelien elemets in one.")
parser.add_argument("--dtype", default="fp32", type=str, choices=["fp32", "fp16", "bf16"], help="Weight dtype.")
args = parser.parse_args()
model_kwargs = {
"SanaMS_1600M_P1_D20": {
"num_attention_heads": 70,
"attention_head_dim": 32,
"num_cross_attention_heads": 20,
"cross_attention_head_dim": 112,
"cross_attention_dim": 2240,
"num_layers": 20,
},
"SanaMS_600M_P1_D28": {
"num_attention_heads": 36,
"attention_head_dim": 32,
"num_cross_attention_heads": 16,
"cross_attention_head_dim": 72,
"cross_attention_dim": 1152,
"num_layers": 28,
},
}
device = "cuda" if torch.cuda.is_available() else "cpu"
weight_dtype = DTYPE_MAPPING[args.dtype]
variant = VARIANT_MAPPING[args.dtype]
main(args)
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