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Music_Generator / diffusers /scripts /convert_dit_to_diffusers.py

deepanway

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	import argparse
	import os

	import torch
	from torchvision.datasets.utils import download_url

	from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, Transformer2DModel


	pretrained_models = {512: "DiT-XL-2-512x512.pt", 256: "DiT-XL-2-256x256.pt"}


	def download_model(model_name):
	"""
	Downloads a pre-trained DiT model from the web.
	"""
	local_path = f"pretrained_models/{model_name}"
	if not os.path.isfile(local_path):
	os.makedirs("pretrained_models", exist_ok=True)
	web_path = f"https://dl.fbaipublicfiles.com/DiT/models/{model_name}"
	download_url(web_path, "pretrained_models")
	model = torch.load(local_path, map_location=lambda storage, loc: storage)
	return model


	def main(args):
	state_dict = download_model(pretrained_models[args.image_size])

	state_dict["pos_embed.proj.weight"] = state_dict["x_embedder.proj.weight"]
	state_dict["pos_embed.proj.bias"] = state_dict["x_embedder.proj.bias"]
	state_dict.pop("x_embedder.proj.weight")
	state_dict.pop("x_embedder.proj.bias")

	for depth in range(28):
	state_dict[f"transformer_blocks.{depth}.norm1.emb.timestep_embedder.linear_1.weight"] = state_dict[
	"t_embedder.mlp.0.weight"
	]
	state_dict[f"transformer_blocks.{depth}.norm1.emb.timestep_embedder.linear_1.bias"] = state_dict[
	"t_embedder.mlp.0.bias"
	]
	state_dict[f"transformer_blocks.{depth}.norm1.emb.timestep_embedder.linear_2.weight"] = state_dict[
	"t_embedder.mlp.2.weight"
	]
	state_dict[f"transformer_blocks.{depth}.norm1.emb.timestep_embedder.linear_2.bias"] = state_dict[
	"t_embedder.mlp.2.bias"
	]
	state_dict[f"transformer_blocks.{depth}.norm1.emb.class_embedder.embedding_table.weight"] = state_dict[
	"y_embedder.embedding_table.weight"
	]

	state_dict[f"transformer_blocks.{depth}.norm1.linear.weight"] = state_dict[
	f"blocks.{depth}.adaLN_modulation.1.weight"
	]
	state_dict[f"transformer_blocks.{depth}.norm1.linear.bias"] = state_dict[
	f"blocks.{depth}.adaLN_modulation.1.bias"
	]

	q, k, v = torch.chunk(state_dict[f"blocks.{depth}.attn.qkv.weight"], 3, dim=0)
	q_bias, k_bias, v_bias = torch.chunk(state_dict[f"blocks.{depth}.attn.qkv.bias"], 3, dim=0)

	state_dict[f"transformer_blocks.{depth}.attn1.to_q.weight"] = q
	state_dict[f"transformer_blocks.{depth}.attn1.to_q.bias"] = q_bias
	state_dict[f"transformer_blocks.{depth}.attn1.to_k.weight"] = k
	state_dict[f"transformer_blocks.{depth}.attn1.to_k.bias"] = k_bias
	state_dict[f"transformer_blocks.{depth}.attn1.to_v.weight"] = v
	state_dict[f"transformer_blocks.{depth}.attn1.to_v.bias"] = v_bias

	state_dict[f"transformer_blocks.{depth}.attn1.to_out.0.weight"] = state_dict[
	f"blocks.{depth}.attn.proj.weight"
	]
	state_dict[f"transformer_blocks.{depth}.attn1.to_out.0.bias"] = state_dict[f"blocks.{depth}.attn.proj.bias"]

	state_dict[f"transformer_blocks.{depth}.ff.net.0.proj.weight"] = state_dict[f"blocks.{depth}.mlp.fc1.weight"]
	state_dict[f"transformer_blocks.{depth}.ff.net.0.proj.bias"] = state_dict[f"blocks.{depth}.mlp.fc1.bias"]
	state_dict[f"transformer_blocks.{depth}.ff.net.2.weight"] = state_dict[f"blocks.{depth}.mlp.fc2.weight"]
	state_dict[f"transformer_blocks.{depth}.ff.net.2.bias"] = state_dict[f"blocks.{depth}.mlp.fc2.bias"]

	state_dict.pop(f"blocks.{depth}.attn.qkv.weight")
	state_dict.pop(f"blocks.{depth}.attn.qkv.bias")
	state_dict.pop(f"blocks.{depth}.attn.proj.weight")
	state_dict.pop(f"blocks.{depth}.attn.proj.bias")
	state_dict.pop(f"blocks.{depth}.mlp.fc1.weight")
	state_dict.pop(f"blocks.{depth}.mlp.fc1.bias")
	state_dict.pop(f"blocks.{depth}.mlp.fc2.weight")
	state_dict.pop(f"blocks.{depth}.mlp.fc2.bias")
	state_dict.pop(f"blocks.{depth}.adaLN_modulation.1.weight")
	state_dict.pop(f"blocks.{depth}.adaLN_modulation.1.bias")

	state_dict.pop("t_embedder.mlp.0.weight")
	state_dict.pop("t_embedder.mlp.0.bias")
	state_dict.pop("t_embedder.mlp.2.weight")
	state_dict.pop("t_embedder.mlp.2.bias")
	state_dict.pop("y_embedder.embedding_table.weight")

	state_dict["proj_out_1.weight"] = state_dict["final_layer.adaLN_modulation.1.weight"]
	state_dict["proj_out_1.bias"] = state_dict["final_layer.adaLN_modulation.1.bias"]
	state_dict["proj_out_2.weight"] = state_dict["final_layer.linear.weight"]
	state_dict["proj_out_2.bias"] = state_dict["final_layer.linear.bias"]

	state_dict.pop("final_layer.linear.weight")
	state_dict.pop("final_layer.linear.bias")
	state_dict.pop("final_layer.adaLN_modulation.1.weight")
	state_dict.pop("final_layer.adaLN_modulation.1.bias")

	# DiT XL/2
	transformer = Transformer2DModel(
	sample_size=args.image_size // 8,
	num_layers=28,
	attention_head_dim=72,
	in_channels=4,
	out_channels=8,
	patch_size=2,
	attention_bias=True,
	num_attention_heads=16,
	activation_fn="gelu-approximate",
	num_embeds_ada_norm=1000,
	norm_type="ada_norm_zero",
	norm_elementwise_affine=False,
	)
	transformer.load_state_dict(state_dict, strict=True)

	scheduler = DDIMScheduler(
	num_train_timesteps=1000,
	beta_schedule="linear",
	prediction_type="epsilon",
	clip_sample=False,
	)

	vae = AutoencoderKL.from_pretrained(args.vae_model)

	pipeline = DiTPipeline(transformer=transformer, vae=vae, scheduler=scheduler)

	if args.save:
	pipeline.save_pretrained(args.checkpoint_path)


	if __name__ == "__main__":
	parser = argparse.ArgumentParser()

	parser.add_argument(
	"--image_size",
	default=256,
	type=int,
	required=False,
	help="Image size of pretrained model, either 256 or 512.",
	)
	parser.add_argument(
	"--vae_model",
	default="stabilityai/sd-vae-ft-ema",
	type=str,
	required=False,
	help="Path to pretrained VAE model, either stabilityai/sd-vae-ft-mse or stabilityai/sd-vae-ft-ema.",
	)
	parser.add_argument(
	"--save", default=True, type=bool, required=False, help="Whether to save the converted pipeline or not."
	)
	parser.add_argument(
	"--checkpoint_path", default=None, type=str, required=True, help="Path to the output pipeline."
	)

	args = parser.parse_args()
	main(args)