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import argparse
import os
import torch
from safetensors.torch import load_file
from transformers import AutoModel, AutoTokenizer
from diffusers import AutoencoderKL, FlowMatchEulerDiscreteScheduler, LuminaNextDiT2DModel, LuminaPipeline
def main(args):
# checkpoint from https://huggingface.co/Alpha-VLLM/Lumina-Next-SFT or https://huggingface.co/Alpha-VLLM/Lumina-Next-T2I
all_sd = load_file(args.origin_ckpt_path, device="cpu")
converted_state_dict = {}
# pad token
converted_state_dict["pad_token"] = all_sd["pad_token"]
# patch embed
converted_state_dict["patch_embedder.weight"] = all_sd["x_embedder.weight"]
converted_state_dict["patch_embedder.bias"] = all_sd["x_embedder.bias"]
# time and caption embed
converted_state_dict["time_caption_embed.timestep_embedder.linear_1.weight"] = all_sd["t_embedder.mlp.0.weight"]
converted_state_dict["time_caption_embed.timestep_embedder.linear_1.bias"] = all_sd["t_embedder.mlp.0.bias"]
converted_state_dict["time_caption_embed.timestep_embedder.linear_2.weight"] = all_sd["t_embedder.mlp.2.weight"]
converted_state_dict["time_caption_embed.timestep_embedder.linear_2.bias"] = all_sd["t_embedder.mlp.2.bias"]
converted_state_dict["time_caption_embed.caption_embedder.0.weight"] = all_sd["cap_embedder.0.weight"]
converted_state_dict["time_caption_embed.caption_embedder.0.bias"] = all_sd["cap_embedder.0.bias"]
converted_state_dict["time_caption_embed.caption_embedder.1.weight"] = all_sd["cap_embedder.1.weight"]
converted_state_dict["time_caption_embed.caption_embedder.1.bias"] = all_sd["cap_embedder.1.bias"]
for i in range(24):
# adaln
converted_state_dict[f"layers.{i}.gate"] = all_sd[f"layers.{i}.attention.gate"]
converted_state_dict[f"layers.{i}.adaLN_modulation.1.weight"] = all_sd[f"layers.{i}.adaLN_modulation.1.weight"]
converted_state_dict[f"layers.{i}.adaLN_modulation.1.bias"] = all_sd[f"layers.{i}.adaLN_modulation.1.bias"]
# qkv
converted_state_dict[f"layers.{i}.attn1.to_q.weight"] = all_sd[f"layers.{i}.attention.wq.weight"]
converted_state_dict[f"layers.{i}.attn1.to_k.weight"] = all_sd[f"layers.{i}.attention.wk.weight"]
converted_state_dict[f"layers.{i}.attn1.to_v.weight"] = all_sd[f"layers.{i}.attention.wv.weight"]
# cap
converted_state_dict[f"layers.{i}.attn2.to_q.weight"] = all_sd[f"layers.{i}.attention.wq.weight"]
converted_state_dict[f"layers.{i}.attn2.to_k.weight"] = all_sd[f"layers.{i}.attention.wk_y.weight"]
converted_state_dict[f"layers.{i}.attn2.to_v.weight"] = all_sd[f"layers.{i}.attention.wv_y.weight"]
# output
converted_state_dict[f"layers.{i}.attn2.to_out.0.weight"] = all_sd[f"layers.{i}.attention.wo.weight"]
# attention
# qk norm
converted_state_dict[f"layers.{i}.attn1.norm_q.weight"] = all_sd[f"layers.{i}.attention.q_norm.weight"]
converted_state_dict[f"layers.{i}.attn1.norm_q.bias"] = all_sd[f"layers.{i}.attention.q_norm.bias"]
converted_state_dict[f"layers.{i}.attn1.norm_k.weight"] = all_sd[f"layers.{i}.attention.k_norm.weight"]
converted_state_dict[f"layers.{i}.attn1.norm_k.bias"] = all_sd[f"layers.{i}.attention.k_norm.bias"]
converted_state_dict[f"layers.{i}.attn2.norm_q.weight"] = all_sd[f"layers.{i}.attention.q_norm.weight"]
converted_state_dict[f"layers.{i}.attn2.norm_q.bias"] = all_sd[f"layers.{i}.attention.q_norm.bias"]
converted_state_dict[f"layers.{i}.attn2.norm_k.weight"] = all_sd[f"layers.{i}.attention.ky_norm.weight"]
converted_state_dict[f"layers.{i}.attn2.norm_k.bias"] = all_sd[f"layers.{i}.attention.ky_norm.bias"]
# attention norm
converted_state_dict[f"layers.{i}.attn_norm1.weight"] = all_sd[f"layers.{i}.attention_norm1.weight"]
converted_state_dict[f"layers.{i}.attn_norm2.weight"] = all_sd[f"layers.{i}.attention_norm2.weight"]
converted_state_dict[f"layers.{i}.norm1_context.weight"] = all_sd[f"layers.{i}.attention_y_norm.weight"]
# feed forward
converted_state_dict[f"layers.{i}.feed_forward.linear_1.weight"] = all_sd[f"layers.{i}.feed_forward.w1.weight"]
converted_state_dict[f"layers.{i}.feed_forward.linear_2.weight"] = all_sd[f"layers.{i}.feed_forward.w2.weight"]
converted_state_dict[f"layers.{i}.feed_forward.linear_3.weight"] = all_sd[f"layers.{i}.feed_forward.w3.weight"]
# feed forward norm
converted_state_dict[f"layers.{i}.ffn_norm1.weight"] = all_sd[f"layers.{i}.ffn_norm1.weight"]
converted_state_dict[f"layers.{i}.ffn_norm2.weight"] = all_sd[f"layers.{i}.ffn_norm2.weight"]
# final layer
converted_state_dict["final_layer.linear.weight"] = all_sd["final_layer.linear.weight"]
converted_state_dict["final_layer.linear.bias"] = all_sd["final_layer.linear.bias"]
converted_state_dict["final_layer.adaLN_modulation.1.weight"] = all_sd["final_layer.adaLN_modulation.1.weight"]
converted_state_dict["final_layer.adaLN_modulation.1.bias"] = all_sd["final_layer.adaLN_modulation.1.bias"]
# Lumina-Next-SFT 2B
transformer = LuminaNextDiT2DModel(
sample_size=128,
patch_size=2,
in_channels=4,
hidden_size=2304,
num_layers=24,
num_attention_heads=32,
num_kv_heads=8,
multiple_of=256,
ffn_dim_multiplier=None,
norm_eps=1e-5,
learn_sigma=True,
qk_norm=True,
cross_attention_dim=2048,
scaling_factor=1.0,
)
transformer.load_state_dict(converted_state_dict, strict=True)
num_model_params = sum(p.numel() for p in transformer.parameters())
print(f"Total number of transformer parameters: {num_model_params}")
if args.only_transformer:
transformer.save_pretrained(os.path.join(args.dump_path, "transformer"))
else:
scheduler = FlowMatchEulerDiscreteScheduler()
vae = AutoencoderKL.from_pretrained("stabilityai/sdxl-vae", torch_dtype=torch.float32)
tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b")
text_encoder = AutoModel.from_pretrained("google/gemma-2b")
pipeline = LuminaPipeline(
tokenizer=tokenizer, text_encoder=text_encoder, transformer=transformer, vae=vae, scheduler=scheduler
)
pipeline.save_pretrained(args.dump_path)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--origin_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=[256, 512, 1024],
required=False,
help="Image size of pretrained model, either 512 or 1024.",
)
parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output pipeline.")
parser.add_argument("--only_transformer", default=True, type=bool, required=True)
args = parser.parse_args()
main(args)