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import argparse

import os

import torch

from huggingface_hub import snapshot_download

from safetensors.torch import load_file

from transformers import AutoTokenizer

from diffusers import AutoencoderKL, FlowMatchEulerDiscreteScheduler, OmniGenPipeline, OmniGenTransformer2DModel

def main(args):

# checkpoint from https://huggingface.co/Shitao/OmniGen-v1

if not os.path.exists(args.origin_ckpt_path):

print("Model not found, downloading...")

cache_folder = os.getenv("HF_HUB_CACHE")

args.origin_ckpt_path = snapshot_download(

repo_id=args.origin_ckpt_path,

cache_dir=cache_folder,

ignore_patterns=["flax_model.msgpack", "rust_model.ot", "tf_model.h5", "model.pt"],

)

print(f"Downloaded model to {args.origin_ckpt_path}")

ckpt = os.path.join(args.origin_ckpt_path, "model.safetensors")

ckpt = load_file(ckpt, device="cpu")

mapping_dict = {

"pos_embed": "patch_embedding.pos_embed",

"x_embedder.proj.weight": "patch_embedding.output_image_proj.weight",

"x_embedder.proj.bias": "patch_embedding.output_image_proj.bias",

"input_x_embedder.proj.weight": "patch_embedding.input_image_proj.weight",

"input_x_embedder.proj.bias": "patch_embedding.input_image_proj.bias",

"final_layer.adaLN_modulation.1.weight": "norm_out.linear.weight",

"final_layer.adaLN_modulation.1.bias": "norm_out.linear.bias",

"final_layer.linear.weight": "proj_out.weight",

"final_layer.linear.bias": "proj_out.bias",

"time_token.mlp.0.weight": "time_token.linear_1.weight",

"time_token.mlp.0.bias": "time_token.linear_1.bias",

"time_token.mlp.2.weight": "time_token.linear_2.weight",

"time_token.mlp.2.bias": "time_token.linear_2.bias",

"t_embedder.mlp.0.weight": "t_embedder.linear_1.weight",

"t_embedder.mlp.0.bias": "t_embedder.linear_1.bias",

"t_embedder.mlp.2.weight": "t_embedder.linear_2.weight",

"t_embedder.mlp.2.bias": "t_embedder.linear_2.bias",

"llm.embed_tokens.weight": "embed_tokens.weight",

}

converted_state_dict = {}

for k, v in ckpt.items():

if k in mapping_dict:

converted_state_dict[mapping_dict[k]] = v

elif "qkv" in k:

to_q, to_k, to_v = v.chunk(3)

converted_state_dict[f"layers.{k.split('.')[2]}.self_attn.to_q.weight"] = to_q

converted_state_dict[f"layers.{k.split('.')[2]}.self_attn.to_k.weight"] = to_k

converted_state_dict[f"layers.{k.split('.')[2]}.self_attn.to_v.weight"] = to_v

elif "o_proj" in k:

converted_state_dict[f"layers.{k.split('.')[2]}.self_attn.to_out.0.weight"] = v

else:

converted_state_dict[k[4:]] = v

transformer = OmniGenTransformer2DModel(

rope_scaling={

"long_factor": [

1.0299999713897705,

1.0499999523162842,

1.0499999523162842,

1.0799999237060547,

1.2299998998641968,

1.2299998998641968,

1.2999999523162842,

1.4499999284744263,

1.5999999046325684,

1.6499998569488525,

1.8999998569488525,

2.859999895095825,

3.68999981880188,

5.419999599456787,

5.489999771118164,

5.489999771118164,

9.09000015258789,

11.579999923706055,

15.65999984741211,

15.769999504089355,

15.789999961853027,

18.360000610351562,

21.989999771118164,

23.079999923706055,

30.009998321533203,

32.35000228881836,

32.590003967285156,

35.56000518798828,

39.95000457763672,

53.840003967285156,

56.20000457763672,

57.95000457763672,

59.29000473022461,

59.77000427246094,

59.920005798339844,

61.190006256103516,

61.96000671386719,

62.50000762939453,

63.3700065612793,

63.48000717163086,

63.48000717163086,

63.66000747680664,

63.850006103515625,

64.08000946044922,

64.760009765625,

64.80001068115234,

64.81001281738281,

64.81001281738281,

],

"short_factor": [

1.05,

1.05,

1.05,

1.1,

1.1,

1.1,

1.2500000000000002,

1.2500000000000002,

1.4000000000000004,

1.4500000000000004,

1.5500000000000005,

1.8500000000000008,

1.9000000000000008,

2.000000000000001,

2.000000000000001,

2.000000000000001,

2.000000000000001,

2.000000000000001,

2.000000000000001,

2.000000000000001,

2.000000000000001,

2.000000000000001,

2.000000000000001,

2.000000000000001,

2.000000000000001,

2.000000000000001,

2.000000000000001,

2.000000000000001,

2.000000000000001,

2.000000000000001,

2.000000000000001,

2.000000000000001,

2.1000000000000005,

2.1000000000000005,

2.2,

2.3499999999999996,

2.3499999999999996,

2.3499999999999996,

2.3499999999999996,

2.3999999999999995,

2.3999999999999995,

2.6499999999999986,

2.6999999999999984,

2.8999999999999977,

2.9499999999999975,

3.049999999999997,

3.049999999999997,

3.049999999999997,

],

"type": "su",

},

patch_size=2,

in_channels=4,

pos_embed_max_size=192,

)

transformer.load_state_dict(converted_state_dict, strict=True)

transformer.to(torch.bfloat16)

num_model_params = sum(p.numel() for p in transformer.parameters())

print(f"Total number of transformer parameters: {num_model_params}")

scheduler = FlowMatchEulerDiscreteScheduler(invert_sigmas=True, num_train_timesteps=1)

vae = AutoencoderKL.from_pretrained(os.path.join(args.origin_ckpt_path, "vae"), torch_dtype=torch.float32)

tokenizer = AutoTokenizer.from_pretrained(args.origin_ckpt_path)

pipeline = OmniGenPipeline(tokenizer=tokenizer, 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="Shitao/OmniGen-v1",

type=str,

required=False,

help="Path to the checkpoint to convert.",

)

parser.add_argument(

"--dump_path", default="OmniGen-v1-diffusers", type=str, required=False, help="Path to the output pipeline."

)

args = parser.parse_args()

main(args)