🐛 [Bug] FLUX Accuracy Issue

Bug Description

FLUX fails to generate actual images. It has accuracy issue.

To Reproduce

import torch
import torch_tensorrt
from diffusers import FluxPipeline
from diffusers.models.transformers.transformer_flux import FluxTransformer2DModel

DEVICE = "cuda:0"

enabled_precisions = {torch.float16}
pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-dev",
    torch_dtype=torch.float16,
).to(torch.float16)
from diffusers.models.transformers.transformer_flux import FluxTransformer2DModel

pipe.transformer = FluxTransformer2DModel(
    num_layers=1, num_single_layers=1, guidance_embeds=True
).to(torch.float16)

backbone = pipe.transformer
pipe.to(DEVICE)
batch_size = 1

settings = {
    "strict": False,
    "allow_complex_guards_as_runtime_asserts": True,
    "enabled_precisions": enabled_precisions,
    "truncate_double": True,
    "min_block_size": 1,
    "use_python_runtime": True,
    "immutable_weights": False,
    "offload_module_to_cpu": True,
}

trt_gm = torch_tensorrt.MutableTorchTensorRTModule(backbone, **settings)
pipe.transformer = trt_gm

image = pipe(
    "Test",
    output_type="pil",
    num_inference_steps=2,
    num_images_per_prompt=batch_size,
).images

backbone.to(DEVICE)
inp, kwinp = trt_gm.arg_inputs, trt_gm.kwarg_inputs
trt_result = trt_gm(*inp, **kwinp)[0]
pytorch_result = backbone(*inp, **kwinp)[0]
assert torch.allclose(trt_result, pytorch_result)

Expected behavior

Environment

Build information about Torch-TensorRT can be found by turning on debug messages

• Torch-TensorRT Version (e.g. 1.0.0): main branch
• PyTorch Version (e.g. 1.0): nightly
• CPU Architecture:
• OS (e.g., Linux):
• How you installed PyTorch (conda, pip, libtorch, source):
• Build command you used (if compiling from source):
• Are you using local sources or building from archives:
• Python version:
• CUDA version:
• GPU models and configuration:
• Any other relevant information:

Additional context

Start with the provided FLUX example using FluxPipeline, FluxTransformer2DModel, and MutableTorchTensorRTModule. Reproduce the two-step generation and compare trt_result with pytorch_result using the shown torch.allclose assertion, then inspect the relevant Torch-TensorRT handling for this transformer. Done means FLUX generates valid images and the TensorRT and PyTorch outputs agree for the reported setup.