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* support PixArt-DMD --------- Co-authored-by: jschen <chenjunsong4@h-partners.com> Co-authored-by: badayvedat <badayvedat@gmail.com> Co-authored-by: Vedat Baday <54285744+badayvedat@users.noreply.github.com> Co-authored-by: Sayak Paul <spsayakpaul@gmail.com> Co-authored-by: YiYi Xu <yixu310@gmail.com> Co-authored-by: yiyixuxu <yixu310@gmail,com>
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import argparse | ||
import os | ||
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import torch | ||
from transformers import T5EncoderModel, T5Tokenizer | ||
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from diffusers import AutoencoderKL, DPMSolverMultistepScheduler, PixArtSigmaPipeline, Transformer2DModel | ||
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ckpt_id = "PixArt-alpha" | ||
# https://github.com/PixArt-alpha/PixArt-sigma/blob/dd087141864e30ec44f12cb7448dd654be065e88/scripts/inference.py#L158 | ||
interpolation_scale = {256: 0.5, 512: 1, 1024: 2, 2048: 4} | ||
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def main(args): | ||
all_state_dict = torch.load(args.orig_ckpt_path) | ||
state_dict = all_state_dict.pop("state_dict") | ||
converted_state_dict = {} | ||
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# Patch embeddings. | ||
converted_state_dict["pos_embed.proj.weight"] = state_dict.pop("x_embedder.proj.weight") | ||
converted_state_dict["pos_embed.proj.bias"] = state_dict.pop("x_embedder.proj.bias") | ||
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# Caption projection. | ||
converted_state_dict["caption_projection.linear_1.weight"] = state_dict.pop("y_embedder.y_proj.fc1.weight") | ||
converted_state_dict["caption_projection.linear_1.bias"] = state_dict.pop("y_embedder.y_proj.fc1.bias") | ||
converted_state_dict["caption_projection.linear_2.weight"] = state_dict.pop("y_embedder.y_proj.fc2.weight") | ||
converted_state_dict["caption_projection.linear_2.bias"] = state_dict.pop("y_embedder.y_proj.fc2.bias") | ||
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# AdaLN-single LN | ||
converted_state_dict["adaln_single.emb.timestep_embedder.linear_1.weight"] = state_dict.pop( | ||
"t_embedder.mlp.0.weight" | ||
) | ||
converted_state_dict["adaln_single.emb.timestep_embedder.linear_1.bias"] = state_dict.pop("t_embedder.mlp.0.bias") | ||
converted_state_dict["adaln_single.emb.timestep_embedder.linear_2.weight"] = state_dict.pop( | ||
"t_embedder.mlp.2.weight" | ||
) | ||
converted_state_dict["adaln_single.emb.timestep_embedder.linear_2.bias"] = state_dict.pop("t_embedder.mlp.2.bias") | ||
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if args.micro_condition: | ||
# Resolution. | ||
converted_state_dict["adaln_single.emb.resolution_embedder.linear_1.weight"] = state_dict.pop( | ||
"csize_embedder.mlp.0.weight" | ||
) | ||
converted_state_dict["adaln_single.emb.resolution_embedder.linear_1.bias"] = state_dict.pop( | ||
"csize_embedder.mlp.0.bias" | ||
) | ||
converted_state_dict["adaln_single.emb.resolution_embedder.linear_2.weight"] = state_dict.pop( | ||
"csize_embedder.mlp.2.weight" | ||
) | ||
converted_state_dict["adaln_single.emb.resolution_embedder.linear_2.bias"] = state_dict.pop( | ||
"csize_embedder.mlp.2.bias" | ||
) | ||
# Aspect ratio. | ||
converted_state_dict["adaln_single.emb.aspect_ratio_embedder.linear_1.weight"] = state_dict.pop( | ||
"ar_embedder.mlp.0.weight" | ||
) | ||
converted_state_dict["adaln_single.emb.aspect_ratio_embedder.linear_1.bias"] = state_dict.pop( | ||
"ar_embedder.mlp.0.bias" | ||
) | ||
converted_state_dict["adaln_single.emb.aspect_ratio_embedder.linear_2.weight"] = state_dict.pop( | ||
"ar_embedder.mlp.2.weight" | ||
) | ||
converted_state_dict["adaln_single.emb.aspect_ratio_embedder.linear_2.bias"] = state_dict.pop( | ||
"ar_embedder.mlp.2.bias" | ||
) | ||
# Shared norm. | ||
converted_state_dict["adaln_single.linear.weight"] = state_dict.pop("t_block.1.weight") | ||
converted_state_dict["adaln_single.linear.bias"] = state_dict.pop("t_block.1.bias") | ||
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for depth in range(28): | ||
# Transformer blocks. | ||
converted_state_dict[f"transformer_blocks.{depth}.scale_shift_table"] = state_dict.pop( | ||
f"blocks.{depth}.scale_shift_table" | ||
) | ||
# Attention is all you need 🤘 | ||
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# Self attention. | ||
q, k, v = torch.chunk(state_dict.pop(f"blocks.{depth}.attn.qkv.weight"), 3, dim=0) | ||
q_bias, k_bias, v_bias = torch.chunk(state_dict.pop(f"blocks.{depth}.attn.qkv.bias"), 3, dim=0) | ||
converted_state_dict[f"transformer_blocks.{depth}.attn1.to_q.weight"] = q | ||
converted_state_dict[f"transformer_blocks.{depth}.attn1.to_q.bias"] = q_bias | ||
converted_state_dict[f"transformer_blocks.{depth}.attn1.to_k.weight"] = k | ||
converted_state_dict[f"transformer_blocks.{depth}.attn1.to_k.bias"] = k_bias | ||
converted_state_dict[f"transformer_blocks.{depth}.attn1.to_v.weight"] = v | ||
converted_state_dict[f"transformer_blocks.{depth}.attn1.to_v.bias"] = v_bias | ||
# Projection. | ||
converted_state_dict[f"transformer_blocks.{depth}.attn1.to_out.0.weight"] = state_dict.pop( | ||
f"blocks.{depth}.attn.proj.weight" | ||
) | ||
converted_state_dict[f"transformer_blocks.{depth}.attn1.to_out.0.bias"] = state_dict.pop( | ||
f"blocks.{depth}.attn.proj.bias" | ||
) | ||
if args.qk_norm: | ||
converted_state_dict[f"transformer_blocks.{depth}.attn1.q_norm.weight"] = state_dict.pop( | ||
f"blocks.{depth}.attn.q_norm.weight" | ||
) | ||
converted_state_dict[f"transformer_blocks.{depth}.attn1.q_norm.bias"] = state_dict.pop( | ||
f"blocks.{depth}.attn.q_norm.bias" | ||
) | ||
converted_state_dict[f"transformer_blocks.{depth}.attn1.k_norm.weight"] = state_dict.pop( | ||
f"blocks.{depth}.attn.k_norm.weight" | ||
) | ||
converted_state_dict[f"transformer_blocks.{depth}.attn1.k_norm.bias"] = state_dict.pop( | ||
f"blocks.{depth}.attn.k_norm.bias" | ||
) | ||
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# Feed-forward. | ||
converted_state_dict[f"transformer_blocks.{depth}.ff.net.0.proj.weight"] = state_dict.pop( | ||
f"blocks.{depth}.mlp.fc1.weight" | ||
) | ||
converted_state_dict[f"transformer_blocks.{depth}.ff.net.0.proj.bias"] = state_dict.pop( | ||
f"blocks.{depth}.mlp.fc1.bias" | ||
) | ||
converted_state_dict[f"transformer_blocks.{depth}.ff.net.2.weight"] = state_dict.pop( | ||
f"blocks.{depth}.mlp.fc2.weight" | ||
) | ||
converted_state_dict[f"transformer_blocks.{depth}.ff.net.2.bias"] = state_dict.pop( | ||
f"blocks.{depth}.mlp.fc2.bias" | ||
) | ||
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# Cross-attention. | ||
q = state_dict.pop(f"blocks.{depth}.cross_attn.q_linear.weight") | ||
q_bias = state_dict.pop(f"blocks.{depth}.cross_attn.q_linear.bias") | ||
k, v = torch.chunk(state_dict.pop(f"blocks.{depth}.cross_attn.kv_linear.weight"), 2, dim=0) | ||
k_bias, v_bias = torch.chunk(state_dict.pop(f"blocks.{depth}.cross_attn.kv_linear.bias"), 2, dim=0) | ||
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converted_state_dict[f"transformer_blocks.{depth}.attn2.to_q.weight"] = q | ||
converted_state_dict[f"transformer_blocks.{depth}.attn2.to_q.bias"] = q_bias | ||
converted_state_dict[f"transformer_blocks.{depth}.attn2.to_k.weight"] = k | ||
converted_state_dict[f"transformer_blocks.{depth}.attn2.to_k.bias"] = k_bias | ||
converted_state_dict[f"transformer_blocks.{depth}.attn2.to_v.weight"] = v | ||
converted_state_dict[f"transformer_blocks.{depth}.attn2.to_v.bias"] = v_bias | ||
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converted_state_dict[f"transformer_blocks.{depth}.attn2.to_out.0.weight"] = state_dict.pop( | ||
f"blocks.{depth}.cross_attn.proj.weight" | ||
) | ||
converted_state_dict[f"transformer_blocks.{depth}.attn2.to_out.0.bias"] = state_dict.pop( | ||
f"blocks.{depth}.cross_attn.proj.bias" | ||
) | ||
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# Final block. | ||
converted_state_dict["proj_out.weight"] = state_dict.pop("final_layer.linear.weight") | ||
converted_state_dict["proj_out.bias"] = state_dict.pop("final_layer.linear.bias") | ||
converted_state_dict["scale_shift_table"] = state_dict.pop("final_layer.scale_shift_table") | ||
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# PixArt 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, | ||
cross_attention_dim=1152, | ||
activation_fn="gelu-approximate", | ||
num_embeds_ada_norm=1000, | ||
norm_type="ada_norm_single", | ||
norm_elementwise_affine=False, | ||
norm_eps=1e-6, | ||
caption_channels=4096, | ||
interpolation_scale=interpolation_scale[args.image_size], | ||
use_additional_conditions=args.micro_condition, | ||
) | ||
transformer.load_state_dict(converted_state_dict, strict=True) | ||
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assert transformer.pos_embed.pos_embed is not None | ||
try: | ||
state_dict.pop("y_embedder.y_embedding") | ||
state_dict.pop("pos_embed") | ||
except Exception as e: | ||
print(f"Skipping {str(e)}") | ||
pass | ||
assert len(state_dict) == 0, f"State dict is not empty, {state_dict.keys()}" | ||
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num_model_params = sum(p.numel() for p in transformer.parameters()) | ||
print(f"Total number of transformer parameters: {num_model_params}") | ||
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if args.only_transformer: | ||
transformer.save_pretrained(os.path.join(args.dump_path, "transformer")) | ||
else: | ||
# pixart-Sigma vae link: https://huggingface.co/PixArt-alpha/pixart_sigma_sdxlvae_T5_diffusers/tree/main/vae | ||
vae = AutoencoderKL.from_pretrained(f"{ckpt_id}/pixart_sigma_sdxlvae_T5_diffusers", subfolder="vae") | ||
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scheduler = DPMSolverMultistepScheduler() | ||
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tokenizer = T5Tokenizer.from_pretrained(f"{ckpt_id}/pixart_sigma_sdxlvae_T5_diffusers", subfolder="tokenizer") | ||
text_encoder = T5EncoderModel.from_pretrained( | ||
f"{ckpt_id}/pixart_sigma_sdxlvae_T5_diffusers", subfolder="text_encoder" | ||
) | ||
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pipeline = PixArtSigmaPipeline( | ||
tokenizer=tokenizer, text_encoder=text_encoder, transformer=transformer, vae=vae, scheduler=scheduler | ||
) | ||
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pipeline.save_pretrained(args.dump_path) | ||
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if __name__ == "__main__": | ||
parser = argparse.ArgumentParser() | ||
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parser.add_argument( | ||
"--micro_condition", action="store_true", help="If use Micro-condition in PixArtMS structure during training." | ||
) | ||
parser.add_argument("--qk_norm", action="store_true", help="If use qk norm during training.") | ||
parser.add_argument( | ||
"--orig_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, 2048], | ||
required=False, | ||
help="Image size of pretrained model, 256, 512, 1024, or 2048.", | ||
) | ||
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) | ||
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args = parser.parse_args() | ||
main(args) |
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