forked from huggingface/diffusers
-
Notifications
You must be signed in to change notification settings - Fork 0
/
convert_lumina_to_diffusers.py
142 lines (113 loc) · 6.92 KB
/
convert_lumina_to_diffusers.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
import argparse
import os
import torch
from safetensors.torch import load_file
from transformers import AutoModel, AutoTokenizer
from diffusers import AutoencoderKL, FlowMatchEulerDiscreteScheduler, LuminaNextDiT2DModel, LuminaText2ImgPipeline
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 = LuminaText2ImgPipeline(
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)