FluxPipeline produces noise when .enable_vae_slicing is used, and FluxImage2ImagePipeline does not support .enable_vae_slicing.

### Describe the bug

When using the flux pipeline, if vae slicing is enabled, it produces noise instead of images, and in the image2image pipeline it is not usable at all.

### Reproduction

```python
from diffusers import FlowMatchEulerDiscreteScheduler, AutoencoderKL, FluxTransformer2DModel, FluxPipeline, utils, FluxImg2ImgPipeline
from transformers import CLIPTextModel, CLIPTokenizer,T5EncoderModel, T5TokenizerFast
from optimum.quanto import freeze, qfloat8, quantize
import torch
import gc

async def generate_flux(prompt,
width,
height,
steps,
batch_size,
image=None,
strength=None,
model_name=None,
lora_name=None):
width = width if width is not None else 1024
height = height if height is not None else 1024
steps = steps if steps is not None else 30
batch_size = batch_size if batch_size is not None else 4
strength = strength if strength is not None else 0.7
if model_name is None:
model_name = "black-forest-labs/FLUX.1-dev"
revision = "refs/pr/3"
dtype = torch.bfloat16
scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(model_name, subfolder="scheduler", revision=revision)
text_encoder = CLIPTextModel.from_pretrained("openai/clip-vit-large-patch14", torch_dtype=dtype)
tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14", torch_dtype=dtype)
text_encoder_2 = T5EncoderModel.from_pretrained(model_name, subfolder="text_encoder_2", torch_dtype=dtype,
revision=revision)
tokenizer_2 = T5TokenizerFast.from_pretrained(model_name, subfolder="tokenizer_2", torch_dtype=dtype,
revision=revision)
vae = AutoencoderKL.from_pretrained(model_name, subfolder="vae", torch_dtype=dtype, revision=revision)
transformer = FluxTransformer2DModel.from_pretrained(model_name, subfolder="transformer", torch_dtype=dtype, revision=revision)

if image is not None:
generator = FluxImg2ImgPipeline(scheduler=scheduler,
text_encoder=text_encoder,
tokenizer=tokenizer,
text_encoder_2=text_encoder_2,
tokenizer_2=tokenizer_2,
vae=vae,
transformer=transformer)
generator.enable_vae_slicing()
else:
generator = FluxPipeline(scheduler=scheduler,
text_encoder=text_encoder,
tokenizer=tokenizer,
text_encoder_2=text_encoder_2,
tokenizer_2=tokenizer_2,
vae=vae,
transformer=transformer)
generator.enable_vae_slicing()
if lora_name is not None:
try:
generator.load_lora_weights(f"loras/flux/{lora_name}", weight_name=lora_name)
except Exception as e:
print(f"FLUX LORA ERROR: {e}")
quantize(transformer, weights=qfloat8)
freeze(transformer)
quantize(text_encoder_2, weights=qfloat8)
freeze(text_encoder_2)
generator.to("cuda")
generator.set_progress_bar_config(disable=True)
if image is not None:
images = generator(prompt=prompt,
image=image,
width=width, height=height,
num_inference_steps=steps,
strength=strength,
num_images_per_prompt=batch_size).images
else:
images = generator(prompt=prompt,
width=width, height=height,
num_inference_steps=steps,
num_images_per_prompt=batch_size).images
generator.to("cpu")

del generator, scheduler, text_encoder, text_encoder_2, tokenizer, tokenizer_2, vae, transformer
torch.cuda.empty_cache()
gc.collect()
return images
```

### Logs

```shell
The fluxpipeline one produces no errors, the fluximage2imagepipeline says it cannot find the .enable_vae_slicing
```

### System Info

accelerate==1.4.0
aiofiles==24.1.0
aiohappyeyeballs==2.6.1
aiohttp==3.11.18
aiosignal==1.3.2
annotated-types==0.7.0
anyio==4.8.0
attrs==25.3.0
backoff==2.2.1
beautifulsoup4==4.13.4
bitsandbytes==0.45.3
certifi==2025.1.31
cffi==1.17.1
chardet==5.2.0
charset-normalizer==3.4.1
click==8.1.8
contourpy==1.3.2
cryptography==44.0.2
cycler==0.12.1
dataclasses-json==0.6.7
diffusers==0.33.1
emoji==2.14.1
eval_type_backport==0.2.2
faiss-cpu==1.10.0
fastapi==0.115.11
filelock==3.17.0
filetype==1.2.0
fonttools==4.58.0
frozenlist==1.6.0
fsspec==2025.2.0
ftfy==6.3.1
greenlet==3.2.1
h11==0.14.0
html5lib==1.1
httpcore==1.0.7
httpx==0.28.1
httpx-sse==0.4.0
huggingface-hub==0.29.2
idna==3.10
imageio==2.37.0
imageio-ffmpeg==0.6.0
importlib_metadata==8.6.1
Jinja2==3.1.6
joblib==1.4.2
jsonpatch==1.33
jsonpointer==3.0.0
kiwisolver==1.4.8
langchain-core==0.3.56
langchain-text-splitters==0.3.8
langdetect==1.0.9
langsmith==0.3.34
loguru==0.7.3
lxml==5.4.0
MarkupSafe==3.0.2
marshmallow==3.26.1
matplotlib==3.10.3
mpmath==1.3.0
multidict==6.4.3
mypy_extensions==1.1.0
nest-asyncio==1.6.0
networkx==3.4.2
ninja==1.11.1.3
nltk==3.9.1
numpy==2.2.3
nvidia-cublas-cu12==12.4.5.8
nvidia-cuda-cupti-cu12==12.4.127
nvidia-cuda-nvrtc-cu12==12.4.127
nvidia-cuda-runtime-cu12==12.4.127
nvidia-cudnn-cu12==9.1.0.70
nvidia-cufft-cu12==11.2.1.3
nvidia-curand-cu12==10.3.5.147
nvidia-cusolver-cu12==11.6.1.9
nvidia-cusparse-cu12==12.3.1.170
nvidia-cusparselt-cu12==0.6.2
nvidia-nccl-cu12==2.21.5
nvidia-nvjitlink-cu12==12.4.127
nvidia-nvtx-cu12==12.4.127
olefile==0.47
opencv-python==4.11.0.86
optimum==1.24.0
optimum-quanto==0.2.7
orjson==3.10.16
packaging==24.2
peft==0.14.0
pillow==11.1.0
propcache==0.3.1
protobuf==6.30.0
psutil==7.0.0
pycparser==2.22
pydantic==2.11.3
pydantic-settings==2.9.1
pydantic_core==2.33.1
pyparsing==3.2.3
pypdf==5.4.0
python-dateutil==2.9.0.post0
python-dotenv==1.1.0
python-iso639==2025.2.18
python-magic==0.4.27
python-oxmsg==0.0.2
PyYAML==6.0.2
RapidFuzz==3.13.0
regex==2024.11.6
requests==2.32.3
requests-toolbelt==1.0.0
safetensors==0.5.3
scikit-learn==1.6.1
scipy==1.15.2
sentence-transformers==4.1.0
sentencepiece==0.2.0
setuptools==75.8.2
six==1.17.0
sniffio==1.3.1
soupsieve==2.7
SQLAlchemy==2.0.40
starlette==0.46.0
sympy==1.13.1
tenacity==9.1.2
threadpoolctl==3.6.0
timm==1.0.15
tokenizers==0.21.0
torch==2.6.0
torchaudio==2.6.0
torchvision==0.21.0
tqdm==4.67.1
transformers==4.50.2
triton==3.2.0
typing-inspect==0.9.0
typing-inspection==0.4.0
typing_extensions==4.12.2
unstructured==0.17.2
unstructured-client==0.34.0
urllib3==2.3.0
uvicorn==0.34.0
wcwidth==0.2.13
webencodings==0.5.1
wrapt==1.17.2
yarl==1.20.0
zipp==3.21.0
zstandard==0.23.0

### Who can help?

_No response_

Start by comparing the FluxPipeline and FluxImg2ImgPipeline entry points used in the reproduction, focusing on enable_vae_slicing and the VAE encode/decode paths. Reproduce the issue with the supplied example and verify that slicing no longer produces noise and is available for image-to-image generation.

Scritto dal modello di indicizzazione a partire dal testo della issue.