Cannot use diffusers pipeline for flux inference with full finetuning model

Using `python flux_train.py \
--mixed_precision bf16 \
--pretrained_model_name_or_path /data/flux1/flux1-dev.safetensors \
--clip_l /data/openai/clip-vit-large-patch14/clip_l.safetensors \
--t5xxl /data/google/t5-v1_1-xxl/t5xxl_fp16.safetensors \
--ae /data/flux1/ae.safetensors \
--save_model_as safetensors \
--save_every_n_epochs 5 \
--sdpa \
--persistent_data_loader_workers \
--max_data_loader_n_workers 2 \
--seed 42 \
--gradient_checkpointing \
--mixed_precision bf16 \
--save_precision bf16 \
--dataset_config ./dataset_config/0715.toml \
--output_dir /data/flux_model_hand_finetuned/0715 \
--output_name sample \
--learning_rate 5e-5 \
--max_train_epochs 20 \
--sdpa \
--highvram \
--sample_prompts /data/hand_finetuning_dataset/sample_prompts.txt \
--sample_every_n_epoch 1 \
--cache_text_encoder_outputs_to_disk \
--cache_latents_to_disk \
--optimizer_type adafactor \
--optimizer_args "relative_step=False" "scale_parameter=False" "warmup_init=False" \
--lr_scheduler constant_with_warmup \
--max_grad_norm 0.0 \
--timestep_sampling shift \
--discrete_flow_shift 3.1582 \
--model_prediction_type raw \
--guidance_scale 1.0 \
--fused_backward_pass \
--blocks_to_swap 8 \
--full_bf16 `

I got the sample.safetensor, but it fails to be loaded by diffusers pipeline, because of mismatching of some keys:

`from diffusers import FluxPipeline, FluxTransformer2DModel`
`from safetensors.torch import load_file`
`finetuned_transformer = FluxTransformer2DModel(
patch_size=1,
in_channels=64,
out_channels=64,
num_layers=19,
num_single_layers=38,
attention_head_dim=128,
num_attention_heads=24,
joint_attention_dim=4096,
pooled_projection_dim=768,
guidance_embeds=True
)`
`state_dict = load_file("/data/flux_model_hand_finetuned/0715/sample/sample.safetensors")`

I got the error information:

`Traceback (most recent call last):
File "/home/zyf/repos/sd-scripts-sd3/inference_test_hands.py", line 27, in
finetuned_transformer.load_state_dict(state_dict)
File "/home/zyf/miniconda3/envs/flux/lib/python3.10/site-packages/torch/nn/modules/module.py", line 2584, in load_state_dict
raise RuntimeError(
RuntimeError: Error(s) in loading state_dict for FluxTransformer2DModel:
Missing key(s) in state_dict: "time_text_embed.timestep_embedder.linear_1.weight", "time_text_embed.timestep_embedder.linear_1.bias", "time_text_embed.timestep_embedder.linear_2.weight", "time_text_embed.timestep_embedder.linear_2.bias", "time_text_embed.guidance_embedder.linear_1.weight", "time_text_embed.guidance_embedder.linear_1.bias", "time_text_embed.guidance_embedder.linear_2.weight", "time_text_embed.guidance_embedder.linear_2.bias", "time_text_embed.text_embedder.linear_1.weight", "time_text_embed.text_embedder.linear_1.bias", ......
Unexpected key(s) in state_dict: "double_blocks.0.img_attn.norm.key_norm.scale", "double_blocks.0.img_attn.norm.query_norm.scale", "double_blocks.0.img_attn.proj.bias", "double_blocks.0.img_attn.proj.weight", "double_blocks.0.img_attn.qkv.bias", "double_blocks.0.img_attn.qkv.weight", "double_blocks.0.img_mlp.0.bias",......`

Start with the training entry point in flux_train.py and reproduce the export using the command in the report. Compare sample.safetensors from the output directory with the keys expected by FluxTransformer2DModel in inference_test_hands.py, then verify the corrected artifact loads without missing or unexpected keys.

Written by the indexing model from the issue text.