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feat: init project

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Signed-off-by: storyicon <storyicon@foxmail.com>
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storyicon
2024-05-21 08:01:08 +00:00
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diffusers/scripts/convert_dit_to_diffusers.py
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import argparse
import os
import torch
from torchvision.datasets.utils import download_url
from MuseVdiffusers 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)