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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 diffusers import (
DCAE,
DCAE_HF,
FlowDPMSolverMultistepScheduler,
FlowMatchEulerDiscreteScheduler,
SanaPipeline,
SanaTransformer2DModel,
)
from diffusers.models.modeling_utils import load_model_dict_into_meta
from diffusers.utils.import_utils import is_accelerate_available
from termcolor import colored
from transformers import AutoModelForCausalLM, AutoTokenizer
CTX = init_empty_weights if is_accelerate_available else nullcontext
ckpt_id = "Sana"
# https://github.com/NVlabs/Sana/blob/main/scripts/inference.py
def main(args):
all_state_dict = torch.load(args.orig_ckpt_path, map_location=torch.device("cpu"))
state_dict = all_state_dict.pop("state_dict")
converted_state_dict = {}
# Patch embeddings.
converted_state_dict["pos_embed.proj.weight"] = state_dict.pop("x_embedder.proj.weight")
converted_state_dict["pos_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["adaln_single.emb.timestep_embedder.linear_1.weight"] = state_dict.pop(
"t_embedder.mlp.0.weight"
)
converted_state_dict["adaln_single.emb.timestep_embedder.linear_1.bias"] = state_dict.pop("t_embedder.mlp.0.bias")
converted_state_dict["adaln_single.emb.timestep_embedder.linear_2.weight"] = state_dict.pop(
"t_embedder.mlp.2.weight"
)
converted_state_dict["adaln_single.emb.timestep_embedder.linear_2.bias"] = state_dict.pop("t_embedder.mlp.2.bias")
# Shared norm.
converted_state_dict["adaln_single.linear.weight"] = state_dict.pop("t_block.1.weight")
converted_state_dict["adaln_single.linear.bias"] = state_dict.pop("t_block.1.bias")
# y norm
converted_state_dict["caption_norm.weight"] = state_dict.pop("attention_y_norm.weight")
if args.model_type == "SanaMS_1600M_P1_D20":
layer_num = 20
flow_shift = 3.0
elif args.model_type == "SanaMS_600M_P1_D28":
layer_num = 28
flow_shift = 4.0
else:
raise ValueError(f"{args.model_type} is not supported.")
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.inverted_conv.conv.weight"] = state_dict.pop(
f"blocks.{depth}.mlp.inverted_conv.conv.weight"
)
converted_state_dict[f"transformer_blocks.{depth}.ff.inverted_conv.conv.bias"] = state_dict.pop(
f"blocks.{depth}.mlp.inverted_conv.conv.bias"
)
converted_state_dict[f"transformer_blocks.{depth}.ff.depth_conv.conv.weight"] = state_dict.pop(
f"blocks.{depth}.mlp.depth_conv.conv.weight"
)
converted_state_dict[f"transformer_blocks.{depth}.ff.depth_conv.conv.bias"] = state_dict.pop(
f"blocks.{depth}.mlp.depth_conv.conv.bias"
)
converted_state_dict[f"transformer_blocks.{depth}.ff.point_conv.conv.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(
num_attention_heads=model_kwargs[args.model_type]["num_attention_heads"],
attention_head_dim=model_kwargs[args.model_type]["attention_head_dim"],
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"],
in_channels=32,
out_channels=32,
num_layers=model_kwargs[args.model_type]["num_layers"],
cross_attention_dim=model_kwargs[args.model_type]["cross_attention_dim"],
attention_bias=False,
sample_size=32,
patch_size=1,
activation_fn=("silu", "silu", None),
upcast_attention=False,
norm_type="ada_norm_single",
norm_elementwise_affine=False,
norm_eps=1e-6,
use_additional_conditions=False,
caption_channels=2304,
use_caption_norm=True,
caption_norm_scale_factor=0.1,
attention_type="default",
use_pe=False,
expand_ratio=2.5,
ff_bias=(True, True, False),
ff_norm=(None, None, None),
)
if is_accelerate_available():
load_model_dict_into_meta(transformer, converted_state_dict)
else:
transformer.load_state_dict(converted_state_dict, strict=True)
try:
state_dict.pop("y_embedder.y_embedding")
state_dict.pop("pos_embed")
except:
pass
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}")
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.to(weight_dtype).save_pretrained(os.path.join(args.dump_path, "transformer"))
else:
print(colored(f"Saving the whole SanaPipeline containing {args.model_type}", "green", attrs=["bold"]))
# VAE
dc_ae = DCAE_HF.from_pretrained(f"mit-han-lab/dc-ae-f32c32-sana-1.0")
dc_ae_state_dict = dc_ae.state_dict()
dc_ae = DCAE(
in_channels=3,
latent_channels=32,
encoder_width_list=[128, 256, 512, 512, 1024, 1024],
encoder_depth_list=[2, 2, 2, 3, 3, 3],
encoder_block_type=["ResBlock", "ResBlock", "ResBlock", "EViTS5_GLU", "EViTS5_GLU", "EViTS5_GLU"],
encoder_norm="rms2d",
encoder_act="silu",
downsample_block_type="Conv",
decoder_width_list=[128, 256, 512, 512, 1024, 1024],
decoder_depth_list=[3, 3, 3, 3, 3, 3],
decoder_block_type=["ResBlock", "ResBlock", "ResBlock", "EViTS5_GLU", "EViTS5_GLU", "EViTS5_GLU"],
decoder_norm="rms2d",
decoder_act="silu",
upsample_block_type="InterpolateConv",
scaling_factor=0.41407,
)
dc_ae.load_state_dict(dc_ae_state_dict, strict=True)
dc_ae.to(torch.float32).to(device)
# 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()
.to(device)
)
# Scheduler
if args.scheduler_type == "flow-dpm_solver":
scheduler = FlowDPMSolverMultistepScheduler(flow_shift=flow_shift)
elif args.scheduler_type == "flow-euler":
scheduler = FlowMatchEulerDiscreteScheduler(shift=flow_shift)
else:
raise ValueError(f"Scheduler type {args.scheduler_type} is not supported")
# transformer
transformer.to(device).to(weight_dtype)
pipe = SanaPipeline(
tokenizer=tokenizer,
text_encoder=text_encoder,
transformer=transformer,
vae=dc_ae,
scheduler=scheduler,
)
image = pipe(
"a dog",
height=1024,
width=1024,
guidance_scale=5.0,
)[0]
image[0].save("sana.png")
pipe.save_pretrained(args.dump_path)
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],
required=False,
help="Image size of pretrained model, 512 or 1024.",
)
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.")
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 = torch.float16
main(args)
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