tem erro isto: /content/VistaDream
/usr/local/lib/python3.11/dist-packages/natten/functional.py:48: FutureWarning: `torch.cuda.amp.custom_fwd(args...)` is deprecated. Please use `torch.amp.custom_fwd(args..., device_type='cuda')` instead.
  @custom_fwd(cast_inputs=torch.float16)
/usr/local/lib/python3.11/dist-packages/natten/functional.py:60: FutureWarning: `torch.cuda.amp.custom_bwd(args...)` is deprecated. Please use `torch.amp.custom_bwd(args..., device_type='cuda')` instead.
  @custom_bwd
/usr/local/lib/python3.11/dist-packages/natten/functional.py:82: FutureWarning: `torch.cuda.amp.custom_fwd(args...)` is deprecated. Please use `torch.amp.custom_fwd(args..., device_type='cuda')` instead.
  @custom_fwd(cast_inputs=torch.float16)
/usr/local/lib/python3.11/dist-packages/natten/functional.py:93: FutureWarning: `torch.cuda.amp.custom_bwd(args...)` is deprecated. Please use `torch.amp.custom_bwd(args..., device_type='cuda')` instead.
  @custom_bwd
/usr/local/lib/python3.11/dist-packages/natten/functional.py:115: FutureWarning: `torch.cuda.amp.custom_fwd(args...)` is deprecated. Please use `torch.amp.custom_fwd(args..., device_type='cuda')` instead.
  @custom_fwd(cast_inputs=torch.float16)
/usr/local/lib/python3.11/dist-packages/natten/functional.py:127: FutureWarning: `torch.cuda.amp.custom_bwd(args...)` is deprecated. Please use `torch.amp.custom_bwd(args..., device_type='cuda')` instead.
  @custom_bwd
/usr/local/lib/python3.11/dist-packages/natten/functional.py:149: FutureWarning: `torch.cuda.amp.custom_fwd(args...)` is deprecated. Please use `torch.amp.custom_fwd(args..., device_type='cuda')` instead.
  @custom_fwd(cast_inputs=torch.float16)
/usr/local/lib/python3.11/dist-packages/natten/functional.py:160: FutureWarning: `torch.cuda.amp.custom_bwd(args...)` is deprecated. Please use `torch.amp.custom_bwd(args..., device_type='cuda')` instead.
  @custom_bwd
/content/VistaDream/tools/OneFormer/oneformer/modeling/pixel_decoder/msdeformattn.py:318: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
  @autocast(enabled=False)
2025-02-13 19:00:00.386431: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:477] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
E0000 00:00:1739473200.682484   16660 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1739473200.764153   16660 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
2025-02-13 19:00:01.335600: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
Loading config tools/OneFormer/configs/ade20k/dinat/coco_pretrain_oneformer_dinat_large_bs16_160k_1280x1280.yaml with yaml.unsafe_load. Your machine may be at risk if the file contains malicious content.
Loading config tools/OneFormer/configs/ade20k/dinat/../Base-ADE20K-UnifiedSegmentation.yaml with yaml.unsafe_load. Your machine may be at risk if the file contains malicious content.
/usr/local/lib/python3.11/dist-packages/fvcore/common/checkpoint.py:252: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
  return torch.load(f, map_location=torch.device("cpu"))
The checkpoint state_dict contains keys that are not used by the model:
  text_encoder.positional_embedding
  text_encoder.transformer.resblocks.0.attn.{in_proj_bias, in_proj_weight}
  text_encoder.transformer.resblocks.0.attn.out_proj.{bias, weight}
  text_encoder.transformer.resblocks.0.ln_1.{bias, weight}
  text_encoder.transformer.resblocks.0.mlp.c_fc.{bias, weight}
  text_encoder.transformer.resblocks.0.mlp.c_proj.{bias, weight}
  text_encoder.transformer.resblocks.0.ln_2.{bias, weight}
  text_encoder.transformer.resblocks.1.attn.{in_proj_bias, in_proj_weight}
  text_encoder.transformer.resblocks.1.attn.out_proj.{bias, weight}
  text_encoder.transformer.resblocks.1.ln_1.{bias, weight}
  text_encoder.transformer.resblocks.1.mlp.c_fc.{bias, weight}
  text_encoder.transformer.resblocks.1.mlp.c_proj.{bias, weight}
  text_encoder.transformer.resblocks.1.ln_2.{bias, weight}
  text_encoder.transformer.resblocks.2.attn.{in_proj_bias, in_proj_weight}
  text_encoder.transformer.resblocks.2.attn.out_proj.{bias, weight}
  text_encoder.transformer.resblocks.2.ln_1.{bias, weight}
  text_encoder.transformer.resblocks.2.mlp.c_fc.{bias, weight}
  text_encoder.transformer.resblocks.2.mlp.c_proj.{bias, weight}
  text_encoder.transformer.resblocks.2.ln_2.{bias, weight}
  text_encoder.transformer.resblocks.3.attn.{in_proj_bias, in_proj_weight}
  text_encoder.transformer.resblocks.3.attn.out_proj.{bias, weight}
  text_encoder.transformer.resblocks.3.ln_1.{bias, weight}
  text_encoder.transformer.resblocks.3.mlp.c_fc.{bias, weight}
  text_encoder.transformer.resblocks.3.mlp.c_proj.{bias, weight}
  text_encoder.transformer.resblocks.3.ln_2.{bias, weight}
  text_encoder.transformer.resblocks.4.attn.{in_proj_bias, in_proj_weight}
  text_encoder.transformer.resblocks.4.attn.out_proj.{bias, weight}
  text_encoder.transformer.resblocks.4.ln_1.{bias, weight}
  text_encoder.transformer.resblocks.4.mlp.c_fc.{bias, weight}
  text_encoder.transformer.resblocks.4.mlp.c_proj.{bias, weight}
  text_encoder.transformer.resblocks.4.ln_2.{bias, weight}
  text_encoder.transformer.resblocks.5.attn.{in_proj_bias, in_proj_weight}
  text_encoder.transformer.resblocks.5.attn.out_proj.{bias, weight}
  text_encoder.transformer.resblocks.5.ln_1.{bias, weight}
  text_encoder.transformer.resblocks.5.mlp.c_fc.{bias, weight}
  text_encoder.transformer.resblocks.5.mlp.c_proj.{bias, weight}
  text_encoder.transformer.resblocks.5.ln_2.{bias, weight}
  text_encoder.ln_final.{bias, weight}
  text_encoder.token_embedding.weight
  text_projector.layers.0.{bias, weight}
  text_projector.layers.1.{bias, weight}
  prompt_ctx.weight
Load default preset failed.
[Errno 2] No such file or directory: '/content/VistaDream/presets/default.json'
No presets found.
No presets found.
Total VRAM 15095 MB, total RAM 12979 MB
Set vram state to: NORMAL_VRAM
Always offload VRAM
Device: cuda:0 Tesla T4 : native
VAE dtype: torch.float32
Using pytorch cross attention
Refiner unloaded.
model_type EPS
UNet ADM Dimension 2816
Using pytorch attention in VAE
Working with z of shape (1, 4, 32, 32) = 4096 dimensions.
Using pytorch attention in VAE
extra {'cond_stage_model.clip_l.logit_scale', 'cond_stage_model.clip_l.text_projection'}
left over keys: dict_keys(['cond_stage_model.clip_l.transformer.text_model.embeddings.position_ids'])
Base model loaded: /content/VistaDream/tools/Fooocus/models/checkpoints/juggernautXL_v8Rundiffusion.safetensors
VAE loaded: None
Request to load LoRAs [] for model [/content/VistaDream/tools/Fooocus/models/checkpoints/juggernautXL_v8Rundiffusion.safetensors].
Fooocus V2 Expansion: Vocab with 642 words.
Fooocus Expansion engine loaded for cuda:0, use_fp16 = True.
Requested to load SDXLClipModel
Requested to load GPT2LMHeadModel
Loading 2 new models
[Fooocus Model Management] Moving model(s) has taken 0.58 seconds
Started worker with PID 16660
Loading checkpoint shards: 100% 4/4 [00:09<00:00,  2.50s/it]
/content/VistaDream/tools/Fooocus/modules/patch.py:465: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
  result = original_loader(*args, **kwargs)
^C