text: Title: WAN2.1 apply_group_offloading **ERROR** result
  Body: ### Describe the bug
  
  I am attempting to use the WAN 2.1 model from the diffusers library to complete an image-to-video task on an NVIDIA RTX 4090. To optimize memory usage, I chose the group offload method and intended to compare resource consumption across different configurations. However, during testing, I encountered two main issues:
  1. When using the group_offload_leaf_stream method:
  I received warnings that some layers were not executed during the forward pass: 
  ```
  It seems like some layers were not executed during the forward pass. This may lead to problems when applying lazy prefetching with automatic tracing and lead to device-mismatch related errors. Please make sure that all layers are executed during the forward pass. The following layers were not executed:
  unexecuted_layers=['blocks.25.attn2.norm_added_q', 'blocks.10.attn2.norm_added_q', 'blocks.13.attn2.norm_added_q', 'blocks.11.attn2.norm_added_q', 'blocks.34.attn2.norm_added_q', 'blocks.0.attn2.norm_added_q', 'blocks.35.attn2.norm_added_q', 'blocks.33.attn2.norm_added_q', 'blocks.21.attn2.norm_added_q', 'blocks.20.attn2.norm_added_q', 'blocks.3.attn2.norm_added_q', 'blocks.7.attn2.norm_added_q', 'blocks.22.attn2.norm_added_q', 'blocks.14.attn2.norm_added_q', 'blocks.29.attn2.norm_added_q', 'blocks.9.attn2.norm_added_q', 'blocks.1.attn2.norm_added_q', 'blocks.37.attn2.norm_added_q', 'blocks.18.attn2.norm_added_q', 'blocks.30.attn2.norm_added_q', 'blocks.4.attn2.norm_added_q', 'blocks.32.attn2.norm_added_q', 'blocks.36.attn2.norm_added_q', 'blocks.26.attn2.norm_added_q', 'blocks.6.attn2.norm_added_q', 'blocks.38.attn2.norm_added_q', 'blocks.17.attn2.norm_added_q', 'blocks.12.attn2.norm_added_q', 'blocks.19.attn2.norm_added_q', 'blocks.16.attn2.norm_added_q', 'blocks.15.attn2.norm_added_q', 'blocks.28.attn2.norm_added_q', 'blocks.24.attn2.norm_added_q', 'blocks.31.attn2.norm_added_q', 'blocks.8.attn2.norm_added_q', 'blocks.5.attn2.norm_added_q', 'blocks.27.attn2.norm_added_q', 'blocks.2.attn2.norm_added_q', 'blocks.39.attn2.norm_added_q', 'blocks.23.attn2.norm_added_q']
  ```
  
  ![Image](https://github.com/user-attachments/assets/dcfb2837-0fa1-47b5-a220-576662074938)
  
  This issue resulted in severe degradation of the generated output.
  这是我选择的图像：
  ![Image](https://github.com/user-attachments/assets/30232325-69bb-439b-b415-99e9f65f8123)
  我得到了错误的视频：
  https://github.com/user-attachments/assets/7a8b55a2-6a71-493a-b7ae-64566b321954
  当我使用默认pipe即不采用 group_offload_leaf_stream我得到了正确的结果：
  https://github.com/user-attachments/assets/9b54c2f2-fa93-422f-b3df-619ee96bb3c8
  
  2.When using the group_offload_block_1_stream method:
  I encountered a runtime error: "RuntimeError: Input type (torch.cuda.FloatTensor) and weight type (torch.FloatTensor) should be the same". It appears that the VAE module was not correctly assigned to the GPU device.
  ```
  Traceback (most recent call last):
    File "/maindata/data/shared/public/haobang.geng/code/video-generate/i2v-baseline/wanx-all-profile.py", line 171, in <module>
      main(args)
    File "/maindata/data/shared/public/haobang.geng/miniconda/envs/vdm/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context
      return func(*args, **kwargs)
    File "/maindata/data/shared/public/haobang.geng/code/video-generate/i2v-baseline/wanx-all-profile.py", line 143, in main
      run_inference()
    File "/maindata/data/shared/public/haobang.geng/miniconda/envs/vdm/lib/python3.10/site-packages/memory_profiler.py", line 1188, in wrapper
      val = prof(func)(*args, **kwargs)
    File "/maindata/data/shared/public/haobang.geng/miniconda/envs/vdm/lib/python3.10/site-packages/memory_profiler.py", line 761, in f
      return func(*args, **kwds)
    File "/maindata/data/shared/public/haobang.geng/code/video-generate/i2v-baseline/wanx-all-profile.py", line 130, in run_inference
      output = pipe(
    File "/maindata/data/shared/public/haobang.geng/miniconda/envs/vdm/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context
      return func(*args, **kwargs)
    File "/maindata/data/shared/public/haobang.geng/miniconda/envs/vdm/lib/python3.10/site-packages/diffusers/pipelines/wan/pipeline_wan_i2v.py", line 587, in __call__
      latents, condition = self.prepare_latents(
    File "/maindata/data/shared/public/haobang.geng/miniconda/envs/vdm/lib/python3.10/site-packages/diffusers/pipelines/wan/pipeline_wan_i2v.py", line 392, in prepare_latents
      latent_condition = retrieve_latents(self.vae.encode(video_condition), generator)
    File "/maindata/data/shared/public/haobang.geng/miniconda/envs/vdm/lib/python3.10/site-packages/diffusers/utils/accelerate_utils.py", line 46, in wrapper
      return method(self, *args, **kwargs)
    File "/maindata/data/shared/public/haobang.geng/miniconda/envs/vdm/lib/python3.10/site-packages/diffusers/models/autoencoders/autoencoder_kl_wan.py", line 795, in encode
      h = self._encode(x)
    File "/maindata/data/shared/public/haobang.geng/miniconda/envs/vdm/lib/python3.10/site-packages/diffusers/models/autoencoders/autoencoder_kl_wan.py", line 762, in _encode
      out = self.encoder(x[:, :, :1, :, :], feat_cache=self._enc_feat_map, feat_idx=self._enc_conv_idx)
    File "/maindata/data/shared/public/haobang.geng/miniconda/envs/vdm/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1739, in _wrapped_call_impl
      return self._call_impl(*args, **kwargs)
    File "/maindata/data/shared/public/haobang.geng/miniconda/envs/vdm/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1750, in _call_impl
      return forward_call(*args, **kwargs)
    File "/maindata/data/shared/public/haobang.geng/miniconda/envs/vdm/lib/python3.10/site-packages/diffusers/models/autoencoders/autoencoder_kl_wan.py", line 439, in forward
      x = self.conv_in(x, feat_cache[idx])
    File "/maindata/data/shared/public/haobang.geng/miniconda/envs/vdm/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1739, in _wrapped_call_impl
      return self._call_impl(*args, **kwargs)
    File "/maindata/data/shared/public/haobang.geng/miniconda/envs/vdm/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1750, in _call_impl
      return forward_call(*args, **kwargs)
    File "/maindata/data/shared/public/haobang.geng/miniconda/envs/vdm/lib/python3.10/site-packages/diffusers/models/autoencoders/autoencoder_kl_wan.py", line 78, in forward
      return super().forward(x)
    File "/maindata/data/shared/public/haobang.geng/miniconda/envs/vdm/lib/python3.10/site-packages/torch/nn/modules/conv.py", line 725, in forward
      return self._conv_forward(input, self.weight, self.bias)
    File "/maindata/data/shared/public/haobang.geng/miniconda/envs/vdm/lib/python3.10/site-packages/torch/nn/modules/conv.py", line 720, in _conv_forward
      return F.conv3d(
  RuntimeError: Input type (torch.cuda.FloatTensor) and weight type (torch.FloatTensor) should be the same
  ```
  Request for Help:
  
  Are there recommended approaches to ensure all layers are properly executed, especially for the group_offload_leaf_stream method?
  How can I resolve the device mismatch issue related to the VAE?
  Any suggestions or guidance would be greatly appreciated!
  
  ### Reproduction
  
  here is my code
  ```python
  import argparse
  import functools
  import json
  import os
  import pathlib
  import psutil
  import time
  
  import torch
  from diffusers import FluxPipeline
  from diffusers.hooks import apply_group_offloading
  from memory_profiler import profile
  import torch
  import numpy as np
  from diffusers import AutoencoderKLWan, WanImageToVideoPipeline
  from diffusers.utils import export_to_video, load_image
  from transformers import CLIPVisionModel
  from diffusers import FlowMatchEulerDiscreteScheduler, UniPCMultistepScheduler, WanPipeline
  
  
  def get_memory_usage():
      process = psutil.Process(os.getpid())
      mem_bytes = process.memory_info().rss
      return mem_bytes
  
  
  @profile(precision=2)
  def apply_offload(pipe: FluxPipeline, method: str) -> None:
      if method == "full_cuda":
          pipe.to("cuda")
      
      elif method == "model_offload":
          pipe.enable_model_cpu_offload()
      
      elif method == "sequential_offload":
          pipe.enable_sequential_cpu_offload()
      
      elif method == "group_offload_block_1":
          offloader_fn = functools.partial(
              apply_group_offloading,
              onload_device=torch.device("cuda"),
              offload_device=torch.device("cpu"),
              offload_type="block_level",
              num_blocks_per_group=1,
              use_stream=False,
          )
          list(map(offloader_fn, [pipe.text_encoder, pipe.transformer, pipe.vae, pipe.image_encoder]))
  
      elif method == "group_offload_leaf":
          offloader_fn = functools.partial(
              apply_group_offloading,
              onload_device=torch.device("cuda"),
              offload_device=torch.device("cpu"),
              offload_type="leaf_level",
              use_stream=False,
          )
          list(map(offloader_fn, [pipe.text_encoder, pipe.transformer, pipe.vae, pipe.image_encoder]))
  
      
      elif method == "group_offload_block_1_stream":
          offloader_fn = functools.partial(
              apply_group_offloading,
              onload_device=torch.device("cuda"),
              offload_device=torch.device("cpu"),
              offload_type="block_level",
              num_blocks_per_group=1,
              use_stream=True,
          )
          list(map(offloader_fn, [pipe.text_encoder, pipe.transformer, pipe.vae, pipe.image_encoder]))
      
      elif method == "group_offload_leaf_stream":
          offloader_fn = functools.partial(
              apply_group_offloading,
              onload_device=torch.device("cuda"),
              offload_device=torch.device("cpu"),
              offload_type="leaf_level",
              use_stream=True,
          )
          list(map(offloader_fn, [pipe.text_encoder, pipe.transformer, pipe.vae, pipe.image_encoder]))
  
  
  @profile(precision=2)
  def load_pipeline():
      model_id = "Wan2.1-I2V-14B-480P-Diffusers"
      image_encoder = CLIPVisionModel.from_pretrained(
          model_id, subfolder="image_encoder", torch_dtype=torch.float32
      )
      vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
      scheduler_b = UniPCMultistepScheduler(prediction_type="flow_prediction", use_flow_sigmas=True, flow_shift=3.0)
  
      pipe = WanImageToVideoPipeline.from_pretrained(
          model_id, vae=vae, image_encoder=image_encoder, torch_dtype=torch.bfloat16, scheduler=scheduler_b
      )
      return pipe
  
  
  @torch.no_grad()
  def main(args):
      os.makedirs(args.output_dir, exist_ok=True)
      os.makedirs(f"./results/check-wanmulti-framework/{args.method}/", exist_ok=True)
      pipe = load_pipeline()
      apply_offload(pipe, args.method)
      apply_offload_memory_usage = get_memory_usage()
  
      torch.cuda.reset_peak_memory_stats()
      cuda_model_memory = torch.cuda.max_memory_reserved()
  
      output_dir = pathlib.Path(args.output_dir)
      output_dir.mkdir(exist_ok=True, parents=True)
  
      run_inference_memory_usage_list = []
      
      def cpu_mem_callback():
          nonlocal run_inference_memory_usage_list
          run_inference_memory_usage_list.append(get_memory_usage())
  
      @profile(precision=2)
      def run_inference():
          image = load_image("./dataset/character-img/imgs3/1.jpeg")
          max_area = 480 * 832
          aspect_ratio = image.height / image.width
          mod_value = pipe.vae_scale_factor_spatial * pipe.transformer.config.patch_size[1]
          height = round(np.sqrt(max_area * aspect_ratio)) // mod_value * mod_value
          width = round(np.sqrt(max_area / aspect_ratio)) // mod_value * mod_value
          prompt = (
              "A person smile."
          )
          negative_prompt = "Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards"
          generator = torch.Generator("cuda").manual_seed(100)
          output = pipe(
              image=image,
              prompt=prompt,
              negative_prompt=negative_prompt,
              height=height,
              width=width,
              num_frames=81,
              guidance_scale=5.0,
              generator=generator,
          ).frames[0]
          export_to_video(output, f"./results/check-wanmulti-framework/{args.method}/wanx_diffusers.mp4", fps=16)
  
      t1 = time.time()
      run_inference()
      torch.cuda.synchronize()
      t2 = time.time()
      cuda_inference_memory = torch.cuda.max_memory_reserved()
      time_required = t2 - t1
      # run_inference_memory_usage = sum(run_inference_memory_usage_list) / len(run_inference_memory_usage_list)
      # print(f"Run inference memory usage list: {run_inference_memory_usage_list}")
  
      info = {
          "time": round(time_required, 2),
          "cuda_model_memory": round(cuda_model_memory / 1024**3, 2),
          "cuda_inference_memory": round(cuda_inference_memory / 1024**3, 2),
          "cpu_offload_memory": round(apply_offload_memory_usage / 1024**3, 2),
      }
      with open(output_dir / f"memory_usage_{args.method}.json", "w") as f:
          json.dump(info, f, indent=4)
  
  
  def get_args():
      parser = argparse.ArgumentParser()
      parser.add_argument("--method", type=str, default="full_cuda", choices=["full_cuda", "model_offload", "sequential_offload", "group_offload_block_1", "group_offload_leaf", "group_offload_block_1_stream", "group_offload_leaf_stream"])
      parser.add_argument("--output_dir", type=str, default="./results/offload_profiling")
      return parser.parse_args()
  
  
  if __name__ == "__main__":
      args = get_args()
      main(args)
  ```
  here is my environment
  ```
  Package                           Version
  --------------------------------- --------------------
  absl-py                           2.1.0
  accelerate                        1.4.0
  addict                            2.4.0
  aiofiles                          23.2.1
  aiohappyeyeballs                  2.4.3
  aiohttp                           3.10.10
  aiosignal                         1.3.1
  airportsdata                      20241001
  albucore                          0.0.17
  albumentations                    1.4.18
  aliyun-python-sdk-core            2.16.0
  aliyun-python-sdk-kms             2.16.5
  altair                            5.4.1
  annotated-types                   0.7.0
  antlr4-python3-runtime            4.9.3
  anyio                             4.6.2.post1
  astor                             0.8.1
  asttokens                         2.4.1
  astunparse                        1.6.3
  async-timeout                     4.0.3
  attrs                             24.2.0
  av                                13.1.0
  beautifulsoup4                    4.12.3
  blake3                            1.0.4
  blinker                           1.9.0
  boto3                             1.35.60
  botocore                          1.35.60
  braceexpand                       0.1.7
  certifi                           2024.8.30
  cffi                              1.17.1
  charset-normalizer                3.4.0
  click                             8.1.7
  clip                              0.2.0
  cloudpickle                       3.1.0
  coloredlogs                       15.0.1
  comm                              0.2.2
  compressed-tensors                0.8.0
  ConfigArgParse                    1.7
  contourpy                         1.3.0
  controlnet_aux                    0.0.7
  cpm-kernels                       1.0.11
  crcmod                            1.7
  cryptography                      44.0.1
  cupy-cuda12x                      13.3.0
  cycler                            0.12.1
  Cython                            3.0.12
  dash                              2.18.2
  dash-core-components              2.0.0
  dash-html-components              2.0.0
  dash-table                        5.0.0
  dashscope                         1.22.2
  datasets                          3.0.1
  debugpy                           1.8.10
  decorator                         4.4.2
  decord                            0.6.0
  deepspeed                         0.15.2
  depyf                             0.18.0
  diffsynth                         1.1.2
  diffusers                         0.33.0.dev0
  dill                              0.3.8
  diskcache                         5.6.3
  distro                            1.9.0
  dnspython                         2.7.0
  docker-pycreds                    0.4.0
  easydict                          1.13
  einops                            0.8.0
  email_validator                   2.2.0
  eval_type_backport                0.2.0
  exceptiongroup                    1.2.2
  executing                         2.1.0
  facexlib                          0.3.0
  fairscale                         0.4.13
  fastapi                           0.115.2
  fastjsonschema                    2.20.0
  fastrlock                         0.8.3
  ffmpy                             0.4.0
  filelock                          3.16.1
  filterpy                          1.4.5
  flash-attn                        2.6.3
  Flask                             3.0.3
  flatbuffers                       24.3.25
  fonttools                         4.54.1
  frozenlist                        1.4.1
  fsspec                            2024.6.1
  ftfy                              6.3.0
  func_timeout                      4.3.5
  future                            1.0.0
  fvcore                            0.1.5.post20221221
  gast                              0.6.0
  gguf                              0.10.0
  gitdb                             4.0.11
  GitPython                         3.1.43
  google-pasta                      0.2.0
  gradio                            5.5.0
  gradio_client                     1.4.2
  grpcio                            1.66.2
  h11                               0.14.0
  h5py                              3.12.1
  hjson                             3.1.0
  httpcore                          1.0.6
  httptools                         0.6.4
  httpx                             0.27.2
  huggingface-hub                   0.29.1
  humanfriendly                     10.0
  idna                              3.10
  imageio                           2.36.0
  imageio-ffmpeg                    0.5.1
  imgaug                            0.4.0
  importlib_metadata                8.5.0
  iniconfig                         2.0.0
  interegular                       0.3.3
  iopath                            0.1.10
  ipykernel                         6.29.5
  ipython                           8.29.0
  ipywidgets                        8.1.5
  itsdangerous                      2.2.0
  jaxtyping                         0.2.34
  jedi                              0.19.1
  Jinja2                            3.1.4
  jiter                             0.7.0
  jmespath                          0.10.0
  joblib                            1.4.2
  jsonschema                        4.23.0
  jsonschema-specifications         2024.10.1
  jupyter_client                    8.6.3
  jupyter_core                      5.7.2
  jupyterlab_widgets                3.0.13
  keras                             3.7.0
  kiwisolver                        1.4.7
  lark                              1.2.2
  lazy_loader                       0.4
  libclang                          18.1.1
  libigl                            2.5.1
  linkify-it-py                     2.0.3
  llvmlite                          0.43.0
  lm-format-enforcer                0.10.9
  lmdb                              1.6.2
  loguru                            0.7.3
  lvis                              0.5.3
  Markdown                          3.7
  markdown-it-py                    2.2.0
  MarkupSafe                        2.1.5
  matplotlib                        3.9.2
  matplotlib-inline                 0.1.7
  mdit-py-plugins                   0.3.3
  mdurl                             0.1.2
  memory-profiler                   0.61.0
  mistral_common                    1.5.1
  ml-dtypes                         0.4.1
  modelscope                        1.23.2
  moviepy                           1.0.3
  mpmath                            1.3.0
  msgpack                           1.1.0
  msgspec                           0.18.6
  multidict                         6.1.0
  multiprocess                      0.70.16
  namex                             0.0.8
  narwhals                          1.10.0
  natsort                           8.4.0
  nbformat                          5.10.4
  nest-asyncio                      1.6.0
  networkx                          3.4.1
  ninja                             1.11.1.3
  numba                             0.60.0
  numpy                             1.26.4
  nvdiffrast                        0.3.3
  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-ml-py                      12.560.30
  nvidia-nccl-cu12                  2.21.5
  nvidia-nvjitlink-cu12             12.4.127
  nvidia-nvtx-cu12                  12.4.127
  omegaconf                         2.3.0
  onnxruntime                       1.20.0
  open3d                            0.18.0
  openai                            1.54.4
  openai-clip                       1.0.1
  opencv-python                     4.10.0.84
  opencv-python-headless            4.10.0.84
  opt_einsum                        3.4.0
  optree                            0.13.1
  orjson                            3.10.7
  oss2                              2.19.1
  outlines                          0.0.46
  packaging                         24.1
  pandas                            2.2.3
  parso                             0.8.4
  partial-json-parser               0.2.1.1.post4
  peft                              0.13.2
  pexpect                           4.9.0
  pillow                            10.4.0
  pip                               24.2
  platformdirs                      4.3.6
  plotly                            5.24.1
  pluggy                            1.5.0
  pooch                             1.8.2
  portalocker                       2.10.1
  proglog                           0.1.10
  prometheus_client                 0.21.0
  prometheus-fastapi-instrumentator 7.0.0
  prompt_toolkit                    3.0.48
  propcache                         0.2.0
  protobuf                          5.28.2
  psutil                            6.0.0
  ptyprocess                        0.7.0
  pudb                              2024.1.2
  pure_eval                         0.2.3
  py-cpuinfo                        9.0.0
  pyairports                        2.1.1
  pyarrow                           17.0.0
  pybind11                          2.13.6
  pycocoevalcap                     1.2
  pycocotools                       2.0.8
  pycountry                         24.6.1
  pycparser                         2.22
  pycryptodome                      3.21.0
  pydantic                          2.9.2
  pydantic_core                     2.23.4
  pydub                             0.25.1
  Pygments                          2.18.0
  pyiqa                             0.1.10
  PyMatting                         1.1.12
  PyMCubes                          0.1.6
  pyparsing                         3.2.0
  pyquaternion                      0.9.9
  pytest                            8.3.4
  python-dateutil                   2.9.0.post0
  python-dotenv                     1.0.1
  python-multipart                  0.0.12
  pytorch3d                         0.7.8
  pytz                              2024.2
  PyYAML                            6.0.2
  pyzmq                             26.2.0
  qwen-vl-utils                     0.0.10
  ray                               2.37.0
  referencing                       0.35.1
  regex                             2024.9.11
  rembg                             2.0.59
  requests                          2.32.3
  requests-toolbelt                 1.0.0
  retrying                          1.3.4
  rich                              13.9.2
  rpds-py                           0.20.0
  ruff                              0.6.9
  s3transfer                        0.10.3
  safehttpx                         0.1.1
  safetensors                       0.4.5
  scikit-image                      0.24.0
  scikit-learn                      1.5.2
  scikit-video                      1.1.11
  scipy                             1.14.1
  semantic-version                  2.10.0
  sentencepiece                     0.2.0
  sentry-sdk                        2.18.0
  setproctitle                      1.3.3
  setuptools                        75.2.0
  shapely                           2.0.7
  shellingham                       1.5.4
  six                               1.16.0
  sk-video                          1.1.10
  smmap                             5.0.1
  sniffio                           1.3.1
  soupsieve                         2.6
  stack-data                        0.6.3
  starlette                         0.40.0
  SwissArmyTransformer              0.4.12
  sympy                             1.13.1
  tabulate                          0.9.0
  tenacity                          9.0.0
  tensorboard                       2.18.0
  tensorboard-data-server           0.7.2
  tensorboardX                      2.6.2.2
  tensorflow-io-gcs-filesystem      0.37.1
  termcolor                         2.5.0
  thop                              0.1.1.post2209072238
  threadpoolctl                     3.5.0
  tifffile                          2024.9.20
  tiktoken                          0.7.0
  timm                              1.0.11
  tokenizers                        0.20.3
  tomesd                            0.1.3
  tomli                             2.2.1
  tomlkit                           0.12.0
  torch                             2.6.0
  torchaudio                        2.6.0
  torchdiffeq                       0.2.4
  torchsde                          0.2.6
  torchvision                       0.21.0
  tornado                           6.4.2
  tqdm                              4.66.5
  traitlets                         5.14.3
  trampoline                        0.1.2
  transformers                      4.46.2
  transformers-stream-generator     0.0.4
  trimesh                           4.5.2
  triton                            3.2.0
  typeguard                         2.13.3
  typer                             0.12.5
  typing_extensions                 4.12.2
  tzdata                            2024.2
  uc-micro-py                       1.0.3
  urllib3                           2.2.3
  urwid                             2.6.16
  urwid_readline                    0.15.1
  uvicorn                           0.32.0
  uvloop                            0.21.0
  wandb                             0.18.7
  watchfiles                        0.24.0
  wcwidth                           0.2.13
  webdataset                        0.2.100
  websocket-client                  1.8.0
  websockets                        12.0
  Werkzeug                          3.0.4
  wheel                             0.44.0
  widgetsnbextension                4.0.13
  wrapt                             1.17.0
  xatlas                            0.0.9
  xxhash                            3.5.0
  yacs                              0.1.8
  yapf                              0.43.0
  yarl                              1.15.3
  zipp                              3.20.2
  ```
  
  
  
  
  
  ### Logs
  
  ```shell
  
  ```
  
  ### System Info
  
  - 🤗 Diffusers version: 0.33.0.dev0
  - Platform: Linux-3.10.0-1160.el7.x86_64-x86_64-with-glibc2.35
  - Running on Google Colab?: No
  - Python version: 3.10.15
  - PyTorch version (GPU?): 2.6.0+cu124 (True)
  - Flax version (CPU?/GPU?/TPU?): not installed (NA)
  - Jax version: not installed
  - JaxLib version: not installed
  - Huggingface_hub version: 0.29.1
  - Transformers version: 4.46.2
  - Accelerate version: 1.4.0
  - PEFT version: 0.13.2
  - Bitsandbytes version: not installed
  - Safetensors version: 0.4.5
  - xFormers version: not installed
  - Accelerator: NVIDIA A800-SXM4-80GB, 81251 MiB
  - Using GPU in script?: <fill in>
  - Using distributed or parallel set-up in script?: <fill in>
  
  ### Who can help?
  
  @DN6 @a-r-r-o-w 