UltraEdit/diffusers/scripts/convert_pixart_alpha_to_diffusers.py

import argparse
import os

import torch
from transformers import T5EncoderModel, T5Tokenizer

from diffusers import AutoencoderKL, DPMSolverMultistepScheduler, PixArtAlphaPipeline, Transformer2DModel


ckpt_id = "PixArt-alpha/PixArt-alpha"
# https://github.com/PixArt-alpha/PixArt-alpha/blob/0f55e922376d8b797edd44d25d0e7464b260dcab/scripts/inference.py#L125
interpolation_scale = {256: 0.5, 512: 1, 1024: 2}


def main(args):
    all_state_dict = torch.load(args.orig_ckpt_path, map_location="cpu")
    state_dict = all_state_dict.pop("state_dict")
    converted_state_dict = {}

    # 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")

    # 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")

    # 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")

    if args.image_size == 1024:
        # 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")

    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 🤘

        # 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"
        )

        # 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"
        )

        # 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)

        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

        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"
        )

    # 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")

    # 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,
        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,
    )
    transformer.load_state_dict(converted_state_dict, strict=True)

    assert transformer.pos_embed.pos_embed is not None
    state_dict.pop("pos_embed")
    state_dict.pop("y_embedder.y_embedding")
    assert len(state_dict) == 0, f"State dict is not empty, {state_dict.keys()}"

    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 = DPMSolverMultistepScheduler()

        vae = AutoencoderKL.from_pretrained(ckpt_id, subfolder="sd-vae-ft-ema")

        tokenizer = T5Tokenizer.from_pretrained(ckpt_id, subfolder="t5-v1_1-xxl")
        text_encoder = T5EncoderModel.from_pretrained(ckpt_id, subfolder="t5-v1_1-xxl")

        pipeline = PixArtAlphaPipeline(
            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(
        "--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],
        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)