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DDIM loop 内小张量分配优化，attention mask 缓存到 GPU

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This commit is contained in:
qhy
2026-02-10 16:53:00 +08:00
parent ed637c972b
commit 91a9b0febc
5 changed files with 283 additions and 32 deletions
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1
.gitignore vendored
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@@ -55,7 +55,6 @@ coverage.xml
*.pot *.pot
# Django stuff: # Django stuff:
*.log
local_settings.py local_settings.py
db.sqlite3 db.sqlite3

40
src/unifolm_wma/models/samplers/ddim.py
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@@ -67,11 +67,12 @@ class DDIMSampler(object):
ddim_timesteps=self.ddim_timesteps, ddim_timesteps=self.ddim_timesteps,
eta=ddim_eta, eta=ddim_eta,
verbose=verbose) verbose=verbose)
self.register_buffer('ddim_sigmas', ddim_sigmas) # Ensure tensors are on correct device for efficient indexing
self.register_buffer('ddim_alphas', ddim_alphas) self.register_buffer('ddim_sigmas', to_torch(torch.as_tensor(ddim_sigmas)))
self.register_buffer('ddim_alphas_prev', ddim_alphas_prev) self.register_buffer('ddim_alphas', to_torch(torch.as_tensor(ddim_alphas)))
self.register_buffer('ddim_alphas_prev', to_torch(torch.as_tensor(ddim_alphas_prev)))
self.register_buffer('ddim_sqrt_one_minus_alphas', self.register_buffer('ddim_sqrt_one_minus_alphas',
np.sqrt(1. - ddim_alphas)) to_torch(torch.as_tensor(np.sqrt(1. - ddim_alphas))))
sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt( sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt(
(1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) *
(1 - self.alphas_cumprod / self.alphas_cumprod_prev)) (1 - self.alphas_cumprod / self.alphas_cumprod_prev))
@@ -241,9 +242,10 @@ class DDIMSampler(object):
dp_ddim_scheduler_action.set_timesteps(len(timesteps)) dp_ddim_scheduler_action.set_timesteps(len(timesteps))
dp_ddim_scheduler_state.set_timesteps(len(timesteps)) dp_ddim_scheduler_state.set_timesteps(len(timesteps))
ts = torch.empty((b, ), device=device, dtype=torch.long)
for i, step in enumerate(iterator): for i, step in enumerate(iterator):
index = total_steps - i - 1 index = total_steps - i - 1
ts = torch.full((b, ), step, device=device, dtype=torch.long) ts.fill_(step)
# Use mask to blend noised original latent (img_orig) & new sampled latent (img) # Use mask to blend noised original latent (img_orig) & new sampled latent (img)
if mask is not None: if mask is not None:
@@ -325,10 +327,6 @@ class DDIMSampler(object):
guidance_rescale=0.0, guidance_rescale=0.0,
**kwargs): **kwargs):
b, *_, device = *x.shape, x.device b, *_, device = *x.shape, x.device
if x.dim() == 5:
is_video = True
else:
is_video = False
if unconditional_conditioning is None or unconditional_guidance_scale == 1.: if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
model_output, model_output_action, model_output_state = self.model.apply_model( model_output, model_output_action, model_output_state = self.model.apply_model(
@@ -377,17 +375,11 @@ class DDIMSampler(object):
sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
sigmas = self.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas sigmas = self.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
if is_video: # Use 0-d tensors directly (already on device); broadcasting handles shape
size = (b, 1, 1, 1, 1) a_t = alphas[index]
else: a_prev = alphas_prev[index]
size = (b, 1, 1, 1) sigma_t = sigmas[index]
sqrt_one_minus_at = sqrt_one_minus_alphas[index]
a_t = torch.full(size, alphas[index], device=device)
a_prev = torch.full(size, alphas_prev[index], device=device)
sigma_t = torch.full(size, sigmas[index], device=device)
sqrt_one_minus_at = torch.full(size,
sqrt_one_minus_alphas[index],
device=device)
if self.model.parameterization != "v": if self.model.parameterization != "v":
pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt() pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
@@ -395,12 +387,8 @@ class DDIMSampler(object):
pred_x0 = self.model.predict_start_from_z_and_v(x, t, model_output) pred_x0 = self.model.predict_start_from_z_and_v(x, t, model_output)
if self.model.use_dynamic_rescale: if self.model.use_dynamic_rescale:
scale_t = torch.full(size, scale_t = self.ddim_scale_arr[index]
self.ddim_scale_arr[index], prev_scale_t = self.ddim_scale_arr_prev[index]
device=device)
prev_scale_t = torch.full(size,
self.ddim_scale_arr_prev[index],
device=device)
rescale = (prev_scale_t / scale_t) rescale = (prev_scale_t / scale_t)
pred_x0 *= rescale pred_x0 *= rescale

23
src/unifolm_wma/modules/attention.py
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@@ -275,7 +275,8 @@ class CrossAttention(nn.Module):
attn_mask_aa = self._get_attn_mask_aa(x.shape[0], attn_mask_aa = self._get_attn_mask_aa(x.shape[0],
q.shape[1], q.shape[1],
k_aa.shape[1], k_aa.shape[1],
block_size=16).to(k_aa.device) block_size=16,
device=k_aa.device)
else: else:
if not spatial_self_attn: if not spatial_self_attn:
assert 1 > 2, ">>> ERROR: you should never go into here ..." assert 1 > 2, ">>> ERROR: you should never go into here ..."
@@ -386,14 +387,26 @@ class CrossAttention(nn.Module):
return self.to_out(out) return self.to_out(out)
def _get_attn_mask_aa(self, b, l1, l2, block_size=16): def _get_attn_mask_aa(self, b, l1, l2, block_size=16, device=None):
cache_key = (b, l1, l2, block_size)
if hasattr(self, '_attn_mask_aa_cache_key') and self._attn_mask_aa_cache_key == cache_key:
cached = self._attn_mask_aa_cache
if device is not None and cached.device != torch.device(device):
cached = cached.to(device)
self._attn_mask_aa_cache = cached
return cached
target_device = device if device is not None else 'cpu'
num_token = l2 // block_size num_token = l2 // block_size
start_positions = ((torch.arange(b) % block_size) + 1) * num_token start_positions = ((torch.arange(b, device=target_device) % block_size) + 1) * num_token
col_indices = torch.arange(l2) col_indices = torch.arange(l2, device=target_device)
mask_2d = col_indices.unsqueeze(0) >= start_positions.unsqueeze(1) mask_2d = col_indices.unsqueeze(0) >= start_positions.unsqueeze(1)
mask = mask_2d.unsqueeze(1).expand(b, l1, l2) mask = mask_2d.unsqueeze(1).expand(b, l1, l2)
attn_mask = torch.zeros_like(mask, dtype=torch.float) attn_mask = torch.zeros(b, l1, l2, dtype=torch.float, device=target_device)
attn_mask[mask] = float('-inf') attn_mask[mask] = float('-inf')
self._attn_mask_aa_cache_key = cache_key
self._attn_mask_aa_cache = attn_mask
return attn_mask return attn_mask

121
unitree_z1_dual_arm_cleanup_pencils/case1/output.log Normal file
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@@ -0,0 +1,121 @@
2026-02-10 15:38:28.973314: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2026-02-10 15:38:29.023024: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2026-02-10 15:38:29.023070: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2026-02-10 15:38:29.024393: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
2026-02-10 15:38:29.031901: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2026-02-10 15:38:29.955454: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
Global seed set to 123
INFO:mainlogger:LatentVisualDiffusion: Running in v-prediction mode
INFO:unifolm_wma.models.diffusion_head.conditional_unet1d:number of parameters: 5.010531e+08
INFO:unifolm_wma.models.diffusion_head.conditional_unet1d:number of parameters: 5.010531e+08
AE working on z of shape (1, 4, 32, 32) = 4096 dimensions.
INFO:root:Loaded ViT-H-14 model config.
DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): hf-mirror.com:443
DEBUG:urllib3.connectionpool:https://hf-mirror.com:443 "HEAD /laion/CLIP-ViT-H-14-laion2B-s32B-b79K/resolve/main/open_clip_pytorch_model.bin HTTP/1.1" 302 0
INFO:root:Loading pretrained ViT-H-14 weights (laion2b_s32b_b79k).
INFO:root:Loaded ViT-H-14 model config.
DEBUG:urllib3.connectionpool:https://hf-mirror.com:443 "HEAD /laion/CLIP-ViT-H-14-laion2B-s32B-b79K/resolve/main/open_clip_pytorch_model.bin HTTP/1.1" 302 0
INFO:root:Loading pretrained ViT-H-14 weights (laion2b_s32b_b79k).
>>> model checkpoint loaded.
>>> Load pre-trained model ...
INFO:root:***** Configing Data *****
>>> unitree_z1_stackbox: 1 data samples loaded.
>>> unitree_z1_stackbox: data stats loaded.
>>> unitree_z1_stackbox: normalizer initiated.
>>> unitree_z1_dual_arm_stackbox: 1 data samples loaded.
>>> unitree_z1_dual_arm_stackbox: data stats loaded.
>>> unitree_z1_dual_arm_stackbox: normalizer initiated.
>>> unitree_z1_dual_arm_stackbox_v2: 1 data samples loaded.
>>> unitree_z1_dual_arm_stackbox_v2: data stats loaded.
>>> unitree_z1_dual_arm_stackbox_v2: normalizer initiated.
>>> unitree_z1_dual_arm_cleanup_pencils: 1 data samples loaded.
>>> unitree_z1_dual_arm_cleanup_pencils: data stats loaded.
>>> unitree_z1_dual_arm_cleanup_pencils: normalizer initiated.
>>> unitree_g1_pack_camera: 1 data samples loaded.
>>> unitree_g1_pack_camera: data stats loaded.
>>> unitree_g1_pack_camera: normalizer initiated.
>>> Dataset is successfully loaded ...
>>> Generate 16 frames under each generation ...
DEBUG:h5py._conv:Creating converter from 3 to 5
DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13
DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 41 9
DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 62 4096
0%| | 0/8 [00:00<?, ?it/s]>>> Step 0: generating actions ...
>>> Step 0: interacting with world model ...
DEBUG:PIL.Image:Importing BlpImagePlugin
DEBUG:PIL.Image:Importing BmpImagePlugin
DEBUG:PIL.Image:Importing BufrStubImagePlugin
DEBUG:PIL.Image:Importing CurImagePlugin
DEBUG:PIL.Image:Importing DcxImagePlugin
DEBUG:PIL.Image:Importing DdsImagePlugin
DEBUG:PIL.Image:Importing EpsImagePlugin
DEBUG:PIL.Image:Importing FitsImagePlugin
DEBUG:PIL.Image:Importing FitsStubImagePlugin
DEBUG:PIL.Image:Importing FliImagePlugin
DEBUG:PIL.Image:Importing FpxImagePlugin
DEBUG:PIL.Image:Image: failed to import FpxImagePlugin: No module named 'olefile'
DEBUG:PIL.Image:Importing FtexImagePlugin
DEBUG:PIL.Image:Importing GbrImagePlugin
DEBUG:PIL.Image:Importing GifImagePlugin
DEBUG:PIL.Image:Importing GribStubImagePlugin
DEBUG:PIL.Image:Importing Hdf5StubImagePlugin
DEBUG:PIL.Image:Importing IcnsImagePlugin
DEBUG:PIL.Image:Importing IcoImagePlugin
DEBUG:PIL.Image:Importing ImImagePlugin
DEBUG:PIL.Image:Importing ImtImagePlugin
DEBUG:PIL.Image:Importing IptcImagePlugin
DEBUG:PIL.Image:Importing JpegImagePlugin
DEBUG:PIL.Image:Importing Jpeg2KImagePlugin
DEBUG:PIL.Image:Importing McIdasImagePlugin
DEBUG:PIL.Image:Importing MicImagePlugin
DEBUG:PIL.Image:Image: failed to import MicImagePlugin: No module named 'olefile'
DEBUG:PIL.Image:Importing MpegImagePlugin
DEBUG:PIL.Image:Importing MpoImagePlugin
DEBUG:PIL.Image:Importing MspImagePlugin
DEBUG:PIL.Image:Importing PalmImagePlugin
DEBUG:PIL.Image:Importing PcdImagePlugin
DEBUG:PIL.Image:Importing PcxImagePlugin
DEBUG:PIL.Image:Importing PdfImagePlugin
DEBUG:PIL.Image:Importing PixarImagePlugin
DEBUG:PIL.Image:Importing PngImagePlugin
DEBUG:PIL.Image:Importing PpmImagePlugin
DEBUG:PIL.Image:Importing PsdImagePlugin
DEBUG:PIL.Image:Importing QoiImagePlugin
DEBUG:PIL.Image:Importing SgiImagePlugin
DEBUG:PIL.Image:Importing SpiderImagePlugin
DEBUG:PIL.Image:Importing SunImagePlugin
DEBUG:PIL.Image:Importing TgaImagePlugin
DEBUG:PIL.Image:Importing TiffImagePlugin
DEBUG:PIL.Image:Importing WebPImagePlugin
DEBUG:PIL.Image:Importing WmfImagePlugin
DEBUG:PIL.Image:Importing XbmImagePlugin
DEBUG:PIL.Image:Importing XpmImagePlugin
DEBUG:PIL.Image:Importing XVThumbImagePlugin
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>>>>>>>>>>>>>>>>>>>>>>>>
>>> Step 1: generating actions ...
>>> Step 1: interacting with world model ...
>>>>>>>>>>>>>>>>>>>>>>>>
>>> Step 2: generating actions ...
>>> Step 2: interacting with world model ...
>>>>>>>>>>>>>>>>>>>>>>>>
>>> Step 3: generating actions ...
>>> Step 3: interacting with world model ...
>>>>>>>>>>>>>>>>>>>>>>>>
>>> Step 4: generating actions ...
>>> Step 4: interacting with world model ...
>>>>>>>>>>>>>>>>>>>>>>>>
>>> Step 5: generating actions ...
>>> Step 5: interacting with world model ...
>>>>>>>>>>>>>>>>>>>>>>>>

130
unitree_z1_dual_arm_stackbox_v2/case1/output.log Normal file
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@@ -0,0 +1,130 @@
2026-02-10 16:42:59.052755: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2026-02-10 16:42:59.102749: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2026-02-10 16:42:59.102803: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2026-02-10 16:42:59.104125: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
2026-02-10 16:42:59.111711: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2026-02-10 16:43:00.040735: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
Global seed set to 123
INFO:mainlogger:LatentVisualDiffusion: Running in v-prediction mode
INFO:unifolm_wma.models.diffusion_head.conditional_unet1d:number of parameters: 5.010531e+08
INFO:unifolm_wma.models.diffusion_head.conditional_unet1d:number of parameters: 5.010531e+08
AE working on z of shape (1, 4, 32, 32) = 4096 dimensions.
INFO:root:Loaded ViT-H-14 model config.
DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): hf-mirror.com:443
DEBUG:urllib3.connectionpool:https://hf-mirror.com:443 "HEAD /laion/CLIP-ViT-H-14-laion2B-s32B-b79K/resolve/main/open_clip_pytorch_model.bin HTTP/1.1" 302 0
INFO:root:Loading pretrained ViT-H-14 weights (laion2b_s32b_b79k).
INFO:root:Loaded ViT-H-14 model config.
DEBUG:urllib3.connectionpool:https://hf-mirror.com:443 "HEAD /laion/CLIP-ViT-H-14-laion2B-s32B-b79K/resolve/main/open_clip_pytorch_model.bin HTTP/1.1" 302 0
INFO:root:Loading pretrained ViT-H-14 weights (laion2b_s32b_b79k).
>>> model checkpoint loaded.
>>> Load pre-trained model ...
INFO:root:***** Configing Data *****
>>> unitree_z1_stackbox: 1 data samples loaded.
>>> unitree_z1_stackbox: data stats loaded.
>>> unitree_z1_stackbox: normalizer initiated.
>>> unitree_z1_dual_arm_stackbox: 1 data samples loaded.
>>> unitree_z1_dual_arm_stackbox: data stats loaded.
>>> unitree_z1_dual_arm_stackbox: normalizer initiated.
>>> unitree_z1_dual_arm_stackbox_v2: 1 data samples loaded.
>>> unitree_z1_dual_arm_stackbox_v2: data stats loaded.
>>> unitree_z1_dual_arm_stackbox_v2: normalizer initiated.
>>> unitree_z1_dual_arm_cleanup_pencils: 1 data samples loaded.
>>> unitree_z1_dual_arm_cleanup_pencils: data stats loaded.
>>> unitree_z1_dual_arm_cleanup_pencils: normalizer initiated.
>>> unitree_g1_pack_camera: 1 data samples loaded.
>>> unitree_g1_pack_camera: data stats loaded.
>>> unitree_g1_pack_camera: normalizer initiated.
>>> Dataset is successfully loaded ...
>>> Generate 16 frames under each generation ...
DEBUG:h5py._conv:Creating converter from 3 to 5
DEBUG:PIL.PngImagePlugin:STREAM b'IHDR' 16 13
DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 41 9
DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 62 4096
0%| | 0/11 [00:00<?, ?it/s]>>> Step 0: generating actions ...
>>> Step 0: interacting with world model ...
DEBUG:PIL.Image:Importing BlpImagePlugin
DEBUG:PIL.Image:Importing BmpImagePlugin
DEBUG:PIL.Image:Importing BufrStubImagePlugin
DEBUG:PIL.Image:Importing CurImagePlugin
DEBUG:PIL.Image:Importing DcxImagePlugin
DEBUG:PIL.Image:Importing DdsImagePlugin
DEBUG:PIL.Image:Importing EpsImagePlugin
DEBUG:PIL.Image:Importing FitsImagePlugin
DEBUG:PIL.Image:Importing FitsStubImagePlugin
DEBUG:PIL.Image:Importing FliImagePlugin
DEBUG:PIL.Image:Importing FpxImagePlugin
DEBUG:PIL.Image:Image: failed to import FpxImagePlugin: No module named 'olefile'
DEBUG:PIL.Image:Importing FtexImagePlugin
DEBUG:PIL.Image:Importing GbrImagePlugin
DEBUG:PIL.Image:Importing GifImagePlugin
DEBUG:PIL.Image:Importing GribStubImagePlugin
DEBUG:PIL.Image:Importing Hdf5StubImagePlugin
DEBUG:PIL.Image:Importing IcnsImagePlugin
DEBUG:PIL.Image:Importing IcoImagePlugin
DEBUG:PIL.Image:Importing ImImagePlugin
DEBUG:PIL.Image:Importing ImtImagePlugin
DEBUG:PIL.Image:Importing IptcImagePlugin
DEBUG:PIL.Image:Importing JpegImagePlugin
DEBUG:PIL.Image:Importing Jpeg2KImagePlugin
DEBUG:PIL.Image:Importing McIdasImagePlugin
DEBUG:PIL.Image:Importing MicImagePlugin
DEBUG:PIL.Image:Image: failed to import MicImagePlugin: No module named 'olefile'
DEBUG:PIL.Image:Importing MpegImagePlugin
DEBUG:PIL.Image:Importing MpoImagePlugin
DEBUG:PIL.Image:Importing MspImagePlugin
DEBUG:PIL.Image:Importing PalmImagePlugin
DEBUG:PIL.Image:Importing PcdImagePlugin
DEBUG:PIL.Image:Importing PcxImagePlugin
DEBUG:PIL.Image:Importing PdfImagePlugin
DEBUG:PIL.Image:Importing PixarImagePlugin
DEBUG:PIL.Image:Importing PngImagePlugin
DEBUG:PIL.Image:Importing PpmImagePlugin
DEBUG:PIL.Image:Importing PsdImagePlugin
DEBUG:PIL.Image:Importing QoiImagePlugin
DEBUG:PIL.Image:Importing SgiImagePlugin
DEBUG:PIL.Image:Importing SpiderImagePlugin
DEBUG:PIL.Image:Importing SunImagePlugin
DEBUG:PIL.Image:Importing TgaImagePlugin
DEBUG:PIL.Image:Importing TiffImagePlugin
DEBUG:PIL.Image:Importing WebPImagePlugin
DEBUG:PIL.Image:Importing WmfImagePlugin
DEBUG:PIL.Image:Importing XbmImagePlugin
DEBUG:PIL.Image:Importing XpmImagePlugin
DEBUG:PIL.Image:Importing XVThumbImagePlugin
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>>>>>>>>>>>>>>>>>>>>>>>>
>>> Step 1: generating actions ...
>>> Step 1: interacting with world model ...
>>>>>>>>>>>>>>>>>>>>>>>>
>>> Step 2: generating actions ...
>>> Step 2: interacting with world model ...
>>>>>>>>>>>>>>>>>>>>>>>>
>>> Step 3: generating actions ...
>>> Step 3: interacting with world model ...
>>>>>>>>>>>>>>>>>>>>>>>>
>>> Step 4: generating actions ...
>>> Step 4: interacting with world model ...
>>>>>>>>>>>>>>>>>>>>>>>>
>>> Step 5: generating actions ...
>>> Step 5: interacting with world model ...
>>>>>>>>>>>>>>>>>>>>>>>>
>>> Step 6: generating actions ...
>>> Step 6: interacting with world model ...
>>>>>>>>>>>>>>>>>>>>>>>>
>>> Step 7: generating actions ...
>>> Step 7: interacting with world model ...
>>>>>>>>>>>>>>>>>>>>>>>>