diffusers/scripts/convert_flux_to_diffusers.py

import argparse
from contextlib import nullcontext

import safetensors.torch
import torch
from accelerate import init_empty_weights
from huggingface_hub import hf_hub_download

from diffusers import AutoencoderKL, FluxTransformer2DModel
from diffusers.loaders.single_file_utils import convert_ldm_vae_checkpoint
from diffusers.utils.import_utils import is_accelerate_available


"""
# Transformer

python scripts/convert_flux_to_diffusers.py  \
--original_state_dict_repo_id "black-forest-labs/FLUX.1-schnell" \
--filename "flux1-schnell.sft"
--output_path "flux-schnell" \
--transformer
"""

"""
# VAE

python scripts/convert_flux_to_diffusers.py  \
--original_state_dict_repo_id "black-forest-labs/FLUX.1-schnell" \
--filename "ae.sft"
--output_path "flux-schnell" \
--vae
"""

CTX = init_empty_weights if is_accelerate_available() else nullcontext

parser = argparse.ArgumentParser()
parser.add_argument("--original_state_dict_repo_id", default=None, type=str)
parser.add_argument("--filename", default="flux.safetensors", type=str)
parser.add_argument("--checkpoint_path", default=None, type=str)
parser.add_argument("--in_channels", type=int, default=64)
parser.add_argument("--out_channels", type=int, default=None)
parser.add_argument("--vae", action="store_true")
parser.add_argument("--transformer", action="store_true")
parser.add_argument("--output_path", type=str)
parser.add_argument("--dtype", type=str, default="bf16")

args = parser.parse_args()
dtype = torch.bfloat16 if args.dtype == "bf16" else torch.float32


def load_original_checkpoint(args):
    if args.original_state_dict_repo_id is not None:
        ckpt_path = hf_hub_download(repo_id=args.original_state_dict_repo_id, filename=args.filename)
    elif args.checkpoint_path is not None:
        ckpt_path = args.checkpoint_path
    else:
        raise ValueError(" please provide either `original_state_dict_repo_id` or a local `checkpoint_path`")

    original_state_dict = safetensors.torch.load_file(ckpt_path)
    return original_state_dict


# in SD3 original implementation of AdaLayerNormContinuous, it split linear projection output into shift, scale;
# while in diffusers it split into scale, shift. Here we swap the linear projection weights in order to be able to use diffusers implementation
def swap_scale_shift(weight):
    shift, scale = weight.chunk(2, dim=0)
    new_weight = torch.cat([scale, shift], dim=0)
    return new_weight


def convert_flux_transformer_checkpoint_to_diffusers(
    original_state_dict, num_layers, num_single_layers, inner_dim, mlp_ratio=4.0
):
    converted_state_dict = {}

    ## time_text_embed.timestep_embedder <-  time_in
    converted_state_dict["time_text_embed.timestep_embedder.linear_1.weight"] = original_state_dict.pop(
        "time_in.in_layer.weight"
    )
    converted_state_dict["time_text_embed.timestep_embedder.linear_1.bias"] = original_state_dict.pop(
        "time_in.in_layer.bias"
    )
    converted_state_dict["time_text_embed.timestep_embedder.linear_2.weight"] = original_state_dict.pop(
        "time_in.out_layer.weight"
    )
    converted_state_dict["time_text_embed.timestep_embedder.linear_2.bias"] = original_state_dict.pop(
        "time_in.out_layer.bias"
    )

    ## time_text_embed.text_embedder <- vector_in
    converted_state_dict["time_text_embed.text_embedder.linear_1.weight"] = original_state_dict.pop(
        "vector_in.in_layer.weight"
    )
    converted_state_dict["time_text_embed.text_embedder.linear_1.bias"] = original_state_dict.pop(
        "vector_in.in_layer.bias"
    )
    converted_state_dict["time_text_embed.text_embedder.linear_2.weight"] = original_state_dict.pop(
        "vector_in.out_layer.weight"
    )
    converted_state_dict["time_text_embed.text_embedder.linear_2.bias"] = original_state_dict.pop(
        "vector_in.out_layer.bias"
    )

    # guidance
    has_guidance = any("guidance" in k for k in original_state_dict)
    if has_guidance:
        converted_state_dict["time_text_embed.guidance_embedder.linear_1.weight"] = original_state_dict.pop(
            "guidance_in.in_layer.weight"
        )
        converted_state_dict["time_text_embed.guidance_embedder.linear_1.bias"] = original_state_dict.pop(
            "guidance_in.in_layer.bias"
        )
        converted_state_dict["time_text_embed.guidance_embedder.linear_2.weight"] = original_state_dict.pop(
            "guidance_in.out_layer.weight"
        )
        converted_state_dict["time_text_embed.guidance_embedder.linear_2.bias"] = original_state_dict.pop(
            "guidance_in.out_layer.bias"
        )

    # context_embedder
    converted_state_dict["context_embedder.weight"] = original_state_dict.pop("txt_in.weight")
    converted_state_dict["context_embedder.bias"] = original_state_dict.pop("txt_in.bias")

    # x_embedder
    converted_state_dict["x_embedder.weight"] = original_state_dict.pop("img_in.weight")
    converted_state_dict["x_embedder.bias"] = original_state_dict.pop("img_in.bias")

    # double transformer blocks
    for i in range(num_layers):
        block_prefix = f"transformer_blocks.{i}."
        # norms.
        ## norm1
        converted_state_dict[f"{block_prefix}norm1.linear.weight"] = original_state_dict.pop(
            f"double_blocks.{i}.img_mod.lin.weight"
        )
        converted_state_dict[f"{block_prefix}norm1.linear.bias"] = original_state_dict.pop(
            f"double_blocks.{i}.img_mod.lin.bias"
        )
        ## norm1_context
        converted_state_dict[f"{block_prefix}norm1_context.linear.weight"] = original_state_dict.pop(
            f"double_blocks.{i}.txt_mod.lin.weight"
        )
        converted_state_dict[f"{block_prefix}norm1_context.linear.bias"] = original_state_dict.pop(
            f"double_blocks.{i}.txt_mod.lin.bias"
        )
        # Q, K, V
        sample_q, sample_k, sample_v = torch.chunk(
            original_state_dict.pop(f"double_blocks.{i}.img_attn.qkv.weight"), 3, dim=0
        )
        context_q, context_k, context_v = torch.chunk(
            original_state_dict.pop(f"double_blocks.{i}.txt_attn.qkv.weight"), 3, dim=0
        )
        sample_q_bias, sample_k_bias, sample_v_bias = torch.chunk(
            original_state_dict.pop(f"double_blocks.{i}.img_attn.qkv.bias"), 3, dim=0
        )
        context_q_bias, context_k_bias, context_v_bias = torch.chunk(
            original_state_dict.pop(f"double_blocks.{i}.txt_attn.qkv.bias"), 3, dim=0
        )
        converted_state_dict[f"{block_prefix}attn.to_q.weight"] = torch.cat([sample_q])
        converted_state_dict[f"{block_prefix}attn.to_q.bias"] = torch.cat([sample_q_bias])
        converted_state_dict[f"{block_prefix}attn.to_k.weight"] = torch.cat([sample_k])
        converted_state_dict[f"{block_prefix}attn.to_k.bias"] = torch.cat([sample_k_bias])
        converted_state_dict[f"{block_prefix}attn.to_v.weight"] = torch.cat([sample_v])
        converted_state_dict[f"{block_prefix}attn.to_v.bias"] = torch.cat([sample_v_bias])
        converted_state_dict[f"{block_prefix}attn.add_q_proj.weight"] = torch.cat([context_q])
        converted_state_dict[f"{block_prefix}attn.add_q_proj.bias"] = torch.cat([context_q_bias])
        converted_state_dict[f"{block_prefix}attn.add_k_proj.weight"] = torch.cat([context_k])
        converted_state_dict[f"{block_prefix}attn.add_k_proj.bias"] = torch.cat([context_k_bias])
        converted_state_dict[f"{block_prefix}attn.add_v_proj.weight"] = torch.cat([context_v])
        converted_state_dict[f"{block_prefix}attn.add_v_proj.bias"] = torch.cat([context_v_bias])
        # qk_norm
        converted_state_dict[f"{block_prefix}attn.norm_q.weight"] = original_state_dict.pop(
            f"double_blocks.{i}.img_attn.norm.query_norm.scale"
        )
        converted_state_dict[f"{block_prefix}attn.norm_k.weight"] = original_state_dict.pop(
            f"double_blocks.{i}.img_attn.norm.key_norm.scale"
        )
        converted_state_dict[f"{block_prefix}attn.norm_added_q.weight"] = original_state_dict.pop(
            f"double_blocks.{i}.txt_attn.norm.query_norm.scale"
        )
        converted_state_dict[f"{block_prefix}attn.norm_added_k.weight"] = original_state_dict.pop(
            f"double_blocks.{i}.txt_attn.norm.key_norm.scale"
        )
        # ff img_mlp
        converted_state_dict[f"{block_prefix}ff.net.0.proj.weight"] = original_state_dict.pop(
            f"double_blocks.{i}.img_mlp.0.weight"
        )
        converted_state_dict[f"{block_prefix}ff.net.0.proj.bias"] = original_state_dict.pop(
            f"double_blocks.{i}.img_mlp.0.bias"
        )
        converted_state_dict[f"{block_prefix}ff.net.2.weight"] = original_state_dict.pop(
            f"double_blocks.{i}.img_mlp.2.weight"
        )
        converted_state_dict[f"{block_prefix}ff.net.2.bias"] = original_state_dict.pop(
            f"double_blocks.{i}.img_mlp.2.bias"
        )
        converted_state_dict[f"{block_prefix}ff_context.net.0.proj.weight"] = original_state_dict.pop(
            f"double_blocks.{i}.txt_mlp.0.weight"
        )
        converted_state_dict[f"{block_prefix}ff_context.net.0.proj.bias"] = original_state_dict.pop(
            f"double_blocks.{i}.txt_mlp.0.bias"
        )
        converted_state_dict[f"{block_prefix}ff_context.net.2.weight"] = original_state_dict.pop(
            f"double_blocks.{i}.txt_mlp.2.weight"
        )
        converted_state_dict[f"{block_prefix}ff_context.net.2.bias"] = original_state_dict.pop(
            f"double_blocks.{i}.txt_mlp.2.bias"
        )
        # output projections.
        converted_state_dict[f"{block_prefix}attn.to_out.0.weight"] = original_state_dict.pop(
            f"double_blocks.{i}.img_attn.proj.weight"
        )
        converted_state_dict[f"{block_prefix}attn.to_out.0.bias"] = original_state_dict.pop(
            f"double_blocks.{i}.img_attn.proj.bias"
        )
        converted_state_dict[f"{block_prefix}attn.to_add_out.weight"] = original_state_dict.pop(
            f"double_blocks.{i}.txt_attn.proj.weight"
        )
        converted_state_dict[f"{block_prefix}attn.to_add_out.bias"] = original_state_dict.pop(
            f"double_blocks.{i}.txt_attn.proj.bias"
        )

    # single transfomer blocks
    for i in range(num_single_layers):
        block_prefix = f"single_transformer_blocks.{i}."
        # norm.linear  <- single_blocks.0.modulation.lin
        converted_state_dict[f"{block_prefix}norm.linear.weight"] = original_state_dict.pop(
            f"single_blocks.{i}.modulation.lin.weight"
        )
        converted_state_dict[f"{block_prefix}norm.linear.bias"] = original_state_dict.pop(
            f"single_blocks.{i}.modulation.lin.bias"
        )
        # Q, K, V, mlp
        mlp_hidden_dim = int(inner_dim * mlp_ratio)
        split_size = (inner_dim, inner_dim, inner_dim, mlp_hidden_dim)
        q, k, v, mlp = torch.split(original_state_dict.pop(f"single_blocks.{i}.linear1.weight"), split_size, dim=0)
        q_bias, k_bias, v_bias, mlp_bias = torch.split(
            original_state_dict.pop(f"single_blocks.{i}.linear1.bias"), split_size, dim=0
        )
        converted_state_dict[f"{block_prefix}attn.to_q.weight"] = torch.cat([q])
        converted_state_dict[f"{block_prefix}attn.to_q.bias"] = torch.cat([q_bias])
        converted_state_dict[f"{block_prefix}attn.to_k.weight"] = torch.cat([k])
        converted_state_dict[f"{block_prefix}attn.to_k.bias"] = torch.cat([k_bias])
        converted_state_dict[f"{block_prefix}attn.to_v.weight"] = torch.cat([v])
        converted_state_dict[f"{block_prefix}attn.to_v.bias"] = torch.cat([v_bias])
        converted_state_dict[f"{block_prefix}proj_mlp.weight"] = torch.cat([mlp])
        converted_state_dict[f"{block_prefix}proj_mlp.bias"] = torch.cat([mlp_bias])
        # qk norm
        converted_state_dict[f"{block_prefix}attn.norm_q.weight"] = original_state_dict.pop(
            f"single_blocks.{i}.norm.query_norm.scale"
        )
        converted_state_dict[f"{block_prefix}attn.norm_k.weight"] = original_state_dict.pop(
            f"single_blocks.{i}.norm.key_norm.scale"
        )
        # output projections.
        converted_state_dict[f"{block_prefix}proj_out.weight"] = original_state_dict.pop(
            f"single_blocks.{i}.linear2.weight"
        )
        converted_state_dict[f"{block_prefix}proj_out.bias"] = original_state_dict.pop(
            f"single_blocks.{i}.linear2.bias"
        )

    converted_state_dict["proj_out.weight"] = original_state_dict.pop("final_layer.linear.weight")
    converted_state_dict["proj_out.bias"] = original_state_dict.pop("final_layer.linear.bias")
    converted_state_dict["norm_out.linear.weight"] = swap_scale_shift(
        original_state_dict.pop("final_layer.adaLN_modulation.1.weight")
    )
    converted_state_dict["norm_out.linear.bias"] = swap_scale_shift(
        original_state_dict.pop("final_layer.adaLN_modulation.1.bias")
    )

    return converted_state_dict


def main(args):
    original_ckpt = load_original_checkpoint(args)
    has_guidance = any("guidance" in k for k in original_ckpt)

    if args.transformer:
        num_layers = 19
        num_single_layers = 38
        inner_dim = 3072
        mlp_ratio = 4.0

        converted_transformer_state_dict = convert_flux_transformer_checkpoint_to_diffusers(
            original_ckpt, num_layers, num_single_layers, inner_dim, mlp_ratio=mlp_ratio
        )
        transformer = FluxTransformer2DModel(
            in_channels=args.in_channels, out_channels=args.out_channels, guidance_embeds=has_guidance
        )
        transformer.load_state_dict(converted_transformer_state_dict, strict=True)

        print(
            f"Saving Flux Transformer in Diffusers format. Variant: {'guidance-distilled' if has_guidance else 'timestep-distilled'}"
        )
        transformer.to(dtype).save_pretrained(f"{args.output_path}/transformer")

    if args.vae:
        config = AutoencoderKL.load_config("stabilityai/stable-diffusion-3-medium-diffusers", subfolder="vae")
        vae = AutoencoderKL.from_config(config, scaling_factor=0.3611, shift_factor=0.1159).to(torch.bfloat16)

        converted_vae_state_dict = convert_ldm_vae_checkpoint(original_ckpt, vae.config)
        vae.load_state_dict(converted_vae_state_dict, strict=True)
        vae.to(dtype).save_pretrained(f"{args.output_path}/vae")


if __name__ == "__main__":
    main(args)