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("--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(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)