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Copy pathconvert_sana_video_to_diffusers.py
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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 (
AutoencoderKLLTX2Video,
AutoencoderKLWan,
DPMSolverMultistepScheduler,
FlowMatchEulerDiscreteScheduler,
SanaVideoPipeline,
SanaVideoTransformer3DModel,
UniPCMultistepScheduler,
)
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-Video_2B_480p/checkpoints/SANA_Video_2B_480p.pth",
"Efficient-Large-Model/SANA-Video_2B_720p/checkpoints/SANA_Video_2B_720p_LTXVAE.pth",
]
# https://github.com/NVlabs/Sana/blob/main/inference_video_scripts/inference_sana_video.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_embedding.weight"] = state_dict.pop("x_embedder.proj.weight")
converted_state_dict["patch_embedding.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")
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
flow_shift = 8.0
if args.task == "i2v":
assert args.scheduler_type == "flow-euler", "Scheduler type must be flow-euler for i2v task."
# model config
layer_num = 20
# Positional embedding interpolation scale.
qk_norm = True
# sample size
if args.video_size == 480:
sample_size = 30 # Wan-VAE: 8xp2 downsample factor
patch_size = (1, 2, 2)
in_channels = 16
out_channels = 16
elif args.video_size == 720:
sample_size = 22 # DC-AE-V: 32xp1 downsample factor
patch_size = (1, 1, 1)
in_channels = 32
out_channels = 32
else:
raise ValueError(f"Video size {args.video_size} is not supported.")
if args.vae_type == "ltx2":
sample_size = 22
patch_size = (1, 1, 1)
in_channels = 128
out_channels = 128
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
if qk_norm is not None:
# Add Q/K normalization for self-attention (attn1) - needed for Sana-Sprint and Sana-1.5
converted_state_dict[f"transformer_blocks.{depth}.attn1.norm_q.weight"] = state_dict.pop(
f"blocks.{depth}.attn.q_norm.weight"
)
converted_state_dict[f"transformer_blocks.{depth}.attn1.norm_k.weight"] = state_dict.pop(
f"blocks.{depth}.attn.k_norm.weight"
)
# 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"
)
converted_state_dict[f"transformer_blocks.{depth}.ff.conv_temp.weight"] = state_dict.pop(
f"blocks.{depth}.mlp.t_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
if qk_norm is not None:
# Add Q/K normalization for cross-attention (attn2) - needed for Sana-Sprint and Sana-1.5
converted_state_dict[f"transformer_blocks.{depth}.attn2.norm_q.weight"] = state_dict.pop(
f"blocks.{depth}.cross_attn.q_norm.weight"
)
converted_state_dict[f"transformer_blocks.{depth}.attn2.norm_k.weight"] = state_dict.pop(
f"blocks.{depth}.cross_attn.k_norm.weight"
)
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_kwargs = {
"in_channels": in_channels,
"out_channels": out_channels,
"num_attention_heads": 20,
"attention_head_dim": 112,
"num_layers": 20,
"num_cross_attention_heads": 20,
"cross_attention_head_dim": 112,
"cross_attention_dim": 2240,
"caption_channels": 2304,
"mlp_ratio": 3.0,
"attention_bias": False,
"sample_size": sample_size,
"patch_size": patch_size,
"norm_elementwise_affine": False,
"norm_eps": 1e-6,
"qk_norm": "rms_norm_across_heads",
"rope_max_seq_len": 1024,
}
transformer = SanaVideoTransformer3DModel(**transformer_kwargs)
transformer.load_state_dict(converted_state_dict, strict=True, assign=True)
try:
state_dict.pop("y_embedder.y_embedding")
state_dict.pop("pos_embed")
state_dict.pop("logvar_linear.weight")
state_dict.pop("logvar_linear.bias")
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 Pipeline",
"green",
attrs=["bold"],
)
)
transformer.save_pretrained(
os.path.join(args.dump_path, "transformer"), safe_serialization=True, max_shard_size="5GB"
)
else:
print(colored(f"Saving the whole Pipeline containing {args.model_type}", "green", attrs=["bold"]))
# VAE
if args.vae_type == "ltx2":
vae_path = args.vae_path or "Lightricks/LTX-2"
vae = AutoencoderKLLTX2Video.from_pretrained(vae_path, subfolder="vae", torch_dtype=torch.float32)
else:
vae_path = args.vae_path or "Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
vae = AutoencoderKLWan.from_pretrained(vae_path, subfolder="vae", torch_dtype=torch.float32)
# Text Encoder
text_encoder_model_path = "Efficient-Large-Model/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()
# Choose the appropriate pipeline and scheduler based on model type
# Original Sana 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)
elif args.scheduler_type == "uni-pc":
scheduler = UniPCMultistepScheduler(
prediction_type="flow_prediction",
use_flow_sigmas=True,
num_train_timesteps=1000,
flow_shift=flow_shift,
)
else:
raise ValueError(f"Scheduler type {args.scheduler_type} is not supported")
pipe = SanaVideoPipeline(
tokenizer=tokenizer,
text_encoder=text_encoder,
transformer=transformer,
vae=vae,
scheduler=scheduler,
)
pipe.save_pretrained(args.dump_path, safe_serialization=True, max_shard_size="5GB")
DTYPE_MAPPING = {
"fp32": torch.float32,
"fp16": torch.float16,
"bf16": torch.bfloat16,
}
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(
"--video_size",
default=480,
type=int,
choices=[480, 720],
required=False,
help="Video size of pretrained model, 480 or 720.",
)
parser.add_argument(
"--model_type",
default="SanaVideo",
type=str,
choices=[
"SanaVideo",
],
)
parser.add_argument(
"--scheduler_type",
default="flow-dpm_solver",
type=str,
choices=["flow-dpm_solver", "flow-euler", "uni-pc"],
help="Scheduler type to use.",
)
parser.add_argument(
"--vae_type",
default="wan",
type=str,
choices=["wan", "ltx2"],
help="VAE type to use for saving full pipeline (ltx2 uses patchify 1x1x1).",
)
parser.add_argument(
"--vae_path",
default=None,
type=str,
required=False,
help="Optional VAE path or repo id. If not set, a default is used per VAE type.",
)
parser.add_argument(
"--task", default="t2v", type=str, required=True, choices=["t2v", "i2v"], help="Task to convert, t2v or i2v."
)
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 pipeline elements in one.")
parser.add_argument("--dtype", default="fp32", type=str, choices=["fp32", "fp16", "bf16"], help="Weight dtype.")
args = parser.parse_args()
device = "cuda" if torch.cuda.is_available() else "cpu"
weight_dtype = DTYPE_MAPPING[args.dtype]
main(args)
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