Flux1-Dev inference with single file ComfyUI/SD-Forge Safetensors

Is it possible to run inference with diffusers using a single-file safetensors created for ComfyUI/SD-Forge?

It looks like FluxPipeline.from_single_file() might be intended for this purpose, but I'm getting the following errors:

import torch
from diffusers import FluxPipeline

pipe = FluxPipeline.from_single_file("./flux1-dev-fp8.safetensors", torch_dtype=torch.float8_e4m3fn, use_safetensors=True)

Traceback (most recent call last):
  File "/home/user/flux/imgen.py", line 9, in <module>
    pipe = FluxPipeline.from_single_file("./flux1-dev-fp8.safetensors", torch_dtype=torch.float8_e4m3fn, use_safetensors=True)
  File "/home/user/.local/lib/python3.13/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
    return fn(*args, **kwargs)
  File "/home/user/.local/lib/python3.13/site-packages/diffusers/loaders/single_file.py", line 509, in from_single_file
    loaded_sub_model = load_single_file_sub_model(
        library_name=library_name,
    ...<11 lines>...
        **kwargs,
    )
  File "/home/user/.local/lib/python3.13/site-packages/diffusers/loaders/single_file.py", line 127, in load_single_file_sub_model
    loaded_sub_model = create_diffusers_t5_model_from_checkpoint(
        class_obj,
    ...<4 lines>...
        local_files_only=local_files_only,
    )
  File "/home/user/.local/lib/python3.13/site-packages/diffusers/loaders/single_file_utils.py", line 2156, in create_diffusers_t5_model_from_checkpoint
    model.load_state_dict(diffusers_format_checkpoint)
    ~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/user/.local/lib/python3.13/site-packages/torch/nn/modules/module.py", line 2641, in load_state_dict
    raise RuntimeError(
    ...<3 lines>...
    )
RuntimeError: Error(s) in loading state_dict for T5EncoderModel:
	Missing key(s) in state_dict: "encoder.embed_tokens.weight". 

I checked the safetensors file and the T5 encoder is present. However, it is named differently, which confuses diffusers.

Beginnen Sie mit FluxPipeline.from_single_file in diffusers/loaders/single_file.py und der T5-Konvertierungslogik in diffusers/loaders/single_file_utils.py. Vergleichen Sie die im Issue beschriebene Benennung der safetensors-T5-Schlüssel mit den von T5EncoderModel erwarteten Schlüsseln; fertig ist die Aufgabe, wenn der gemeldete Single-File-Flux1-Dev-Checkpoint ohne den Fehler missing encoder.embed_tokens.weight geladen wird.