allow for image pretraining on video tokenizer

Update Discord channel link in README to use permanent link

.github/workflows/test.yml

@@ -5,11 +5,11 @@ jobs:
   build:
 
     runs-on: ubuntu-latest
-    timeout-minutes: 20
+    timeout-minutes: 60
     strategy:
       fail-fast: false
       matrix:
-        group: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
+        group: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]
 
     steps:
     - uses: actions/checkout@v4
@@ -24,4 +24,4 @@ jobs:
         python -m uv pip install -e .[test]
     - name: Test with pytest
       run: |
-        python -m pytest --num-shards 10 --shard-id ${{ matrix.group }} tests/
+        python -m pytest --num-shards 20 --shard-id ${{ matrix.group }} tests/

README.md

@@ -1,10 +1,99 @@
 <img src="./dreamer4-fig2.png" width="400px"></img>
 
-## Dreamer 4 (wip)
+## Dreamer 4
 
 Implementation of Danijar's [latest iteration](https://arxiv.org/abs/2509.24527v1) for his [Dreamer](https://danijar.com/project/dreamer4/) line of work
 
-[Temporary Discord](https://discord.gg/MkACrrkrYR)
+[Discord channel](https://discord.gg/PmGR7KRwxq) for collaborating with other researchers interested in this work
+
+## Appreciation
+
+- [@dirkmcpherson](https://github.com/dirkmcpherson) for fixes to typo errors and unpassed arguments!
+
+## Install
+
+```bash
+$ pip install dreamer4
+```
+
+## Usage
+
+```python
+import torch
+from dreamer4 import VideoTokenizer, DynamicsWorldModel
+
+# video tokenizer, learned through MAE + lpips
+
+tokenizer = VideoTokenizer(
+    dim = 512,
+    dim_latent = 32,
+    patch_size = 32,
+    image_height = 256,
+    image_width = 256
+)
+
+video = torch.randn(2, 3, 10, 256, 256)
+
+# learn the tokenizer
+
+loss = tokenizer(video)
+loss.backward()
+
+# dynamics world model
+
+world_model = DynamicsWorldModel(
+    dim = 512,
+    dim_latent = 32,
+    video_tokenizer = tokenizer,
+    num_discrete_actions = 4,
+    num_residual_streams = 1
+)
+
+# state, action, rewards
+
+video = torch.randn(2, 3, 10, 256, 256)
+discrete_actions = torch.randint(0, 4, (2, 10, 1))
+rewards = torch.randn(2, 10)
+
+# learn dynamics / behavior cloned model
+
+loss = world_model(
+    video = video,
+    rewards = rewards,
+    discrete_actions = discrete_actions
+)
+
+loss.backward()
+
+# do the above with much data
+
+# then generate dreams
+
+dreams = world_model.generate(
+    10,
+    batch_size = 2,
+    return_decoded_video = True,
+    return_for_policy_optimization = True
+)
+
+# learn from the dreams
+
+actor_loss, critic_loss = world_model.learn_from_experience(dreams)
+
+(actor_loss + critic_loss).backward()
+
+# learn from environment
+
+from dreamer4.mocks import MockEnv
+
+mock_env = MockEnv((256, 256), vectorized = True, num_envs = 4)
+
+experience = world_model.interact_with_env(mock_env, max_timesteps = 8, env_is_vectorized = True)
+
+actor_loss, critic_loss = world_model.learn_from_experience(experience)
+
+(actor_loss + critic_loss).backward()
+```
 
 ## Citation
 
@@ -19,3 +108,5 @@ Implementation of Danijar's [latest iteration](https://arxiv.org/abs/2509.24527v
     url     = {https://arxiv.org/abs/2509.24527}, 
 }
 ```
+
+*the conquest of nature is to be achieved through number and measure - angels to Descartes in a dream*

dreamer4/__init__.py

@@ -1,6 +1,7 @@
 from dreamer4.dreamer4 import (
     VideoTokenizer,
-    DynamicsWorldModel
+    DynamicsWorldModel,
+    AxialSpaceTimeTransformer
 )
 
 

dreamer4/mocks.py

@@ -7,6 +7,11 @@ from torch.nn import Module
 
 from einops import repeat
 
+# helpers
+
+def exists(v):
+    return v is not None
+
 # mock env
 
 class MockEnv(Module):
@@ -15,7 +20,11 @@ class MockEnv(Module):
         image_shape,
         reward_range = (-100, 100),
         num_envs = 1,
-        vectorized = False
+        vectorized = False,
+        terminate_after_step = None,
+        rand_terminate_prob = 0.05,
+        can_truncate = False,
+        rand_truncate_prob = 0.05,
     ):
         super().__init__()
         self.image_shape = image_shape
@@ -23,6 +32,17 @@ class MockEnv(Module):
 
         self.num_envs = num_envs
         self.vectorized = vectorized
+        assert not (vectorized and num_envs == 1)
+
+        # mocking termination and truncation
+
+        self.can_terminate = exists(terminate_after_step)
+        self.terminate_after_step = terminate_after_step
+        self.rand_terminate_prob = rand_terminate_prob
+
+        self.can_truncate = can_truncate
+        self.rand_truncate_prob = rand_truncate_prob
+
         self.register_buffer('_step', tensor(0))
 
     def get_random_state(self):
@@ -33,7 +53,12 @@ class MockEnv(Module):
         seed = None
     ):
         self._step.zero_()
-        return self.get_random_state()
+        state = self.get_random_state()
+
+        if self.vectorized:
+            state = repeat(state, '... -> b ...', b = self.num_envs)
+
+        return state
 
     def step(
         self,
@@ -43,12 +68,30 @@ class MockEnv(Module):
 
         reward = empty(()).uniform_(*self.reward_range)
 
-        if not self.vectorized:
-            return state, reward
+        if self.vectorized:
+            discrete, continuous = actions
+            assert discrete.shape[0] == self.num_envs, f'expected batch of actions for {self.num_envs} environments'
 
-        assert actions.shape[0] == self.num_envs
+            state = repeat(state, '... -> b ...', b = self.num_envs)
+            reward = repeat(reward, ' -> b', b = self.num_envs)
 
-        state = repeat(state, '... -> b ...', b = self.num_envs)
-        reward = repeat(reward, ' -> b', b = self.num_envs)
+        out = (state, reward)
 
-        return state, rewards
+
+        if self.can_terminate:
+            shape = (self.num_envs,) if self.vectorized else (1,)
+            valid_step = self._step > self.terminate_after_step
+
+            terminate = (torch.rand(shape) < self.rand_terminate_prob) & valid_step
+
+            out = (*out, terminate)
+
+            # maybe truncation
+
+            if self.can_truncate:
+                truncate = (torch.rand(shape) < self.rand_truncate_prob) & valid_step & ~terminate
+                out = (*out, truncate)
+
+        self._step.add_(1)
+
+        return out

dreamer4/trainers.py

@@ -4,7 +4,7 @@ import torch
 from torch import is_tensor
 from torch.nn import Module
 from torch.optim import AdamW
-from torch.utils.data import Dataset, DataLoader
+from torch.utils.data import Dataset, TensorDataset, DataLoader
 
 from accelerate import Accelerator
 
@@ -12,7 +12,9 @@ from adam_atan2_pytorch import MuonAdamAtan2
 
 from dreamer4.dreamer4 import (
     VideoTokenizer,
-    DynamicsWorldModel
+    DynamicsWorldModel,
+    Experience,
+    combine_experiences
 )
 
 from ema_pytorch import EMA
@@ -285,7 +287,7 @@ class DreamTrainer(Module):
         for _ in range(self.num_train_steps):
 
             dreams = self.unwrapped_model.generate(
-                self.generate_timesteps,
+                self.generate_timesteps + 1, # plus one for bootstrap value
                 batch_size = self.batch_size,
                 return_rewards_per_frame = True,
                 return_agent_actions = True,
@@ -317,3 +319,221 @@ class DreamTrainer(Module):
             self.value_head_optim.zero_grad()
 
         self.print('training complete')
+
+# training from sim
+
+class SimTrainer(Module):
+    def __init__(
+        self,
+        model: DynamicsWorldModel,
+        optim_klass = AdamW,
+        batch_size = 16,
+        generate_timesteps = 16,
+        learning_rate = 3e-4,
+        max_grad_norm = None,
+        epochs = 2,
+        weight_decay = 0.,
+        accelerate_kwargs: dict = dict(),
+        optim_kwargs: dict = dict(),
+        cpu = False,
+    ):
+        super().__init__()
+
+        self.accelerator = Accelerator(
+            cpu = cpu,
+            **accelerate_kwargs
+        )
+
+        self.model = model
+
+        optim_kwargs = dict(
+            lr = learning_rate,
+            weight_decay = weight_decay
+        )
+
+        self.policy_head_optim = AdamW(model.policy_head_parameters(), **optim_kwargs)
+        self.value_head_optim = AdamW(model.value_head_parameters(), **optim_kwargs)
+
+        self.max_grad_norm = max_grad_norm
+
+        self.epochs = epochs
+        self.batch_size = batch_size
+
+        self.generate_timesteps = generate_timesteps
+
+        self.unwrapped_model = self.model
+
+        (
+            self.model,
+            self.policy_head_optim,
+            self.value_head_optim,
+        ) = self.accelerator.prepare(
+            self.model,
+            self.policy_head_optim,
+            self.value_head_optim
+        )
+
+    @property
+    def device(self):
+        return self.accelerator.device
+
+    @property
+    def unwrapped_model(self):
+        return self.accelerator.unwrap_model(self.model)
+
+    def print(self, *args, **kwargs):
+        return self.accelerator.print(*args, **kwargs)
+
+    def learn(
+        self,
+        experience: Experience
+    ):
+
+        step_size = experience.step_size
+        agent_index = experience.agent_index
+
+        latents = experience.latents
+        old_values = experience.values
+        rewards = experience.rewards
+
+        has_agent_embed = exists(experience.agent_embed)
+        agent_embed = experience.agent_embed
+
+        discrete_actions, continuous_actions = experience.actions
+        discrete_log_probs, continuous_log_probs = experience.log_probs
+
+        discrete_old_action_unembeds, continuous_old_action_unembeds = default(experience.old_action_unembeds, (None, None))
+
+        # handle empties
+
+        empty_tensor = torch.empty_like(rewards)
+
+        agent_embed = default(agent_embed, empty_tensor)
+
+        has_discrete = exists(discrete_actions)
+        has_continuous = exists(continuous_actions)
+
+        discrete_actions = default(discrete_actions, empty_tensor)
+        continuous_actions = default(continuous_actions, empty_tensor)
+
+        discrete_log_probs = default(discrete_log_probs, empty_tensor)
+        continuous_log_probs = default(continuous_log_probs, empty_tensor)
+
+        discrete_old_action_unembeds = default(discrete_old_action_unembeds, empty_tensor)
+        continuous_old_action_unembeds = default(discrete_old_action_unembeds, empty_tensor)
+
+        # create the dataset and dataloader
+
+        dataset = TensorDataset(
+            latents,
+            discrete_actions,
+            continuous_actions,
+            discrete_log_probs,
+            continuous_log_probs,
+            agent_embed,
+            discrete_old_action_unembeds,
+            continuous_old_action_unembeds,
+            old_values,
+            rewards
+        )
+
+        dataloader = DataLoader(dataset, batch_size = self.batch_size, shuffle = True)
+
+        for epoch in range(self.epochs):
+
+            for (
+                latents,
+                discrete_actions,
+                continuous_actions,
+                discrete_log_probs,
+                continuous_log_probs,
+                agent_embed,
+                discrete_old_action_unembeds,
+                continuous_old_action_unembeds,
+                old_values,
+                rewards
+            ) in dataloader:
+
+                actions = (
+                    discrete_actions if has_discrete else None,
+                    continuous_actions if has_continuous else None
+                )
+
+                log_probs = (
+                    discrete_log_probs if has_discrete else None,
+                    continuous_log_probs if has_continuous else None
+                )
+
+                old_action_unembeds = (
+                    discrete_old_action_unembeds if has_discrete else None,
+                    continuous_old_action_unembeds if has_continuous else None
+                )
+
+                batch_experience = Experience(
+                    latents = latents,
+                    actions = actions,
+                    log_probs = log_probs,
+                    agent_embed = agent_embed if has_agent_embed else None,
+                    old_action_unembeds = old_action_unembeds,
+                    values = old_values,
+                    rewards = rewards,
+                    step_size = step_size,
+                    agent_index = agent_index
+                )
+
+                policy_head_loss, value_head_loss = self.model.learn_from_experience(batch_experience)
+
+                self.print(f'policy head loss: {policy_head_loss.item():.3f} | value head loss: {value_head_loss.item():.3f}')
+
+                # update policy head
+
+                self.accelerator.backward(policy_head_loss)
+
+                if exists(self.max_grad_norm):
+                    self.accelerator.clip_grad_norm_(self.model.policy_head_parameters()(), self.max_grad_norm)
+
+                self.policy_head_optim.step()
+                self.policy_head_optim.zero_grad()
+
+                # update value head
+
+                self.accelerator.backward(value_head_loss)
+
+                if exists(self.max_grad_norm):
+                    self.accelerator.clip_grad_norm_(self.model.value_head_parameters(), self.max_grad_norm)
+
+                self.value_head_optim.step()
+                self.value_head_optim.zero_grad()
+
+        self.print('training complete')
+
+    def forward(
+        self,
+        env,
+        num_episodes = 50000,
+        max_experiences_before_learn = 8,
+        env_is_vectorized = False
+    ):
+
+        for _ in range(num_episodes):
+
+            total_experience = 0
+            experiences = []
+
+            while total_experience < max_experiences_before_learn:
+
+                experience = self.unwrapped_model.interact_with_env(env, env_is_vectorized = env_is_vectorized)
+
+                num_experience = experience.video.shape[0]
+
+                total_experience += num_experience
+
+                experiences.append(experience.cpu())
+
+            combined_experiences = combine_experiences(experiences)
+
+            self.learn(combined_experiences)
+
+            experiences.clear()
+
+        self.print('training complete')

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "dreamer4"
-version = "0.0.62"
+version = "0.1.24"
 description = "Dreamer 4"
 authors = [
     { name = "Phil Wang", email = "lucidrains@gmail.com" }
@@ -36,7 +36,8 @@ dependencies = [
     "hyper-connections>=0.2.1",
     "torch>=2.4",
     "torchvision",
-    "x-mlps-pytorch>=0.0.29"
+    "x-mlps-pytorch>=0.0.29",
+    "vit-pytorch>=1.15.3"
 ]
 
 [project.urls]

tests/test_dreamer.py

@@ -2,6 +2,9 @@ import pytest
 param = pytest.mark.parametrize
 import torch
 
+def exists(v):
+    return v is not None
+
 @param('pred_orig_latent', (False, True))
 @param('grouped_query_attn', (False, True))
 @param('dynamics_with_video_input', (False, True))
@@ -12,7 +15,9 @@ import torch
 @param('condition_on_actions', (False, True))
 @param('num_residual_streams', (1, 4))
 @param('add_reward_embed_to_agent_token', (False, True))
-@param('use_time_kv_cache', (False, True))
+@param('add_state_pred_head', (False, True))
+@param('use_time_cache', (False, True))
+@param('var_len', (False, True))
 def test_e2e(
     pred_orig_latent,
     grouped_query_attn,
@@ -24,7 +29,9 @@ def test_e2e(
     condition_on_actions,
     num_residual_streams,
     add_reward_embed_to_agent_token,
-    use_time_kv_cache
+    add_state_pred_head,
+    use_time_cache,
+    var_len
 ):
     from dreamer4.dreamer4 import VideoTokenizer, DynamicsWorldModel
 
@@ -36,7 +43,9 @@ def test_e2e(
         patch_size = 32,
         attn_dim_head = 16,
         num_latent_tokens = 4,
-        num_residual_streams = num_residual_streams
+        num_residual_streams = num_residual_streams,
+        encoder_add_decor_aux_loss = True,
+        decorr_sample_frac = 1.
     )
 
     video = torch.randn(2, 3, 4, 256, 256)
@@ -64,12 +73,13 @@ def test_e2e(
         pred_orig_latent = pred_orig_latent,
         num_discrete_actions = 4,
         attn_dim_head = 16,
+        attn_heads = heads,
         attn_kwargs = dict(
-            heads = heads,
             query_heads = query_heads,
         ),
         prob_no_shortcut_train = prob_no_shortcut_train,
         add_reward_embed_to_agent_token = add_reward_embed_to_agent_token,
+        add_state_pred_head = add_state_pred_head,
         num_residual_streams = num_residual_streams
     )
 
@@ -92,8 +102,13 @@ def test_e2e(
     if condition_on_actions:
         actions = torch.randint(0, 4, (2, 3, 1))
 
+    lens = None
+    if var_len:
+        lens = torch.randint(1, 4, (2,))
+
     flow_loss = dynamics(
         **dynamics_input,
+        lens = lens,
         tasks = tasks,
         signal_levels = signal_levels,
         step_sizes_log2 = step_sizes_log2,
@@ -111,7 +126,7 @@ def test_e2e(
         image_width = 128,
         batch_size = 2,
         return_rewards_per_frame = True,
-        use_time_kv_cache = use_time_kv_cache
+        use_time_cache = use_time_cache
     )
 
     assert generations.video.shape == (2, 3, 10, 128, 128)
@@ -336,6 +351,15 @@ def test_action_embedder():
     assert discrete_logits.shape == (2, 3, 8)
     assert continuous_mean_log_var.shape == (2, 3, 2, 2)
 
+    # test kl div
+
+    discrete_logits_tgt, continuous_mean_log_var_tgt = embedder.unembed(action_embed)
+
+    discrete_kl_div, continuous_kl_div = embedder.kl_div((discrete_logits, continuous_mean_log_var), (discrete_logits_tgt, continuous_mean_log_var_tgt))
+
+    assert discrete_kl_div.shape == (2, 3)
+    assert continuous_kl_div.shape == (2, 3)
+
     # return discrete split by number of actions
 
     discrete_logits, continuous_mean_log_var = embedder.unembed(action_embed, return_split_discrete = True)
@@ -397,14 +421,14 @@ def test_mtp():
 
     reward_targets, mask = create_multi_token_prediction_targets(rewards, 3) # say three token lookahead
 
-    assert reward_targets.shape == (3, 15, 3)
-    assert mask.shape == (3, 15, 3)
+    assert reward_targets.shape == (3, 16, 3)
+    assert mask.shape == (3, 16, 3)
 
     actions = torch.randint(0, 10, (3, 16, 2))
     action_targets, mask = create_multi_token_prediction_targets(actions, 3)
 
-    assert action_targets.shape == (3, 15, 3, 2)
-    assert mask.shape == (3, 15, 3)
+    assert action_targets.shape == (3, 16, 3, 2)
+    assert mask.shape == (3, 16, 3)
 
     from dreamer4.dreamer4 import ActionEmbedder
 
@@ -596,11 +620,22 @@ def test_cache_generate():
         num_residual_streams = 1
     )
 
-    generated, time_kv_cache = dynamics.generate(1, return_time_kv_cache = True)
-    generated, time_kv_cache = dynamics.generate(1, time_kv_cache = time_kv_cache, return_time_kv_cache = True)
-    generated, time_kv_cache = dynamics.generate(1, time_kv_cache = time_kv_cache, return_time_kv_cache = True)
+    generated, time_cache = dynamics.generate(1, return_time_cache = True)
+    generated, time_cache = dynamics.generate(1, time_cache = time_cache, return_time_cache = True)
+    generated, time_cache = dynamics.generate(1, time_cache = time_cache, return_time_cache = True)
 
-def test_online_rl():
+@param('vectorized', (False, True))
+@param('use_pmpo', (False, True))
+@param('env_can_terminate', (False, True))
+@param('env_can_truncate', (False, True))
+@param('store_agent_embed', (False, True))
+def test_online_rl(
+    vectorized,
+    use_pmpo,
+    env_can_terminate,
+    env_can_truncate,
+    store_agent_embed
+):
     from dreamer4.dreamer4 import DynamicsWorldModel, VideoTokenizer
 
     tokenizer = VideoTokenizer(
@@ -611,7 +646,9 @@ def test_online_rl():
         dim_latent = 16,
         patch_size = 32,
         attn_dim_head = 16,
-        num_latent_tokens = 1
+        num_latent_tokens = 1,
+        image_height = 256,
+        image_width = 256,
     )
 
     world_model_and_policy = DynamicsWorldModel(
@@ -632,11 +669,163 @@ def test_online_rl():
     )
 
     from dreamer4.mocks import MockEnv
-    mock_env = MockEnv((256, 256), vectorized = False, num_envs = 4)
+    from dreamer4.dreamer4 import combine_experiences
 
-    one_experience = world_model_and_policy.interact_with_env(mock_env, max_timesteps = 16)
+    mock_env = MockEnv(
+        (256, 256),
+        vectorized = vectorized,
+        num_envs = 4,
+        terminate_after_step = 2 if env_can_terminate else None,
+        can_truncate = env_can_truncate,
+        rand_terminate_prob = 0.1
+    )
 
-    actor_loss, critic_loss = world_model_and_policy.learn_from_experience(one_experience)
+    # manually
+
+    dream_experience = world_model_and_policy.generate(10, batch_size = 1, store_agent_embed = store_agent_embed, return_for_policy_optimization = True)
+
+    one_experience = world_model_and_policy.interact_with_env(mock_env, max_timesteps = 8, env_is_vectorized = vectorized, store_agent_embed = store_agent_embed)
+    another_experience = world_model_and_policy.interact_with_env(mock_env, max_timesteps = 16, env_is_vectorized = vectorized, store_agent_embed = store_agent_embed)
+
+    combined_experience = combine_experiences([dream_experience, one_experience, another_experience])
+
+    # quick test moving the experience to different devices
+
+    if torch.cuda.is_available():
+        combined_experience = combined_experience.to(torch.device('cuda'))
+        combined_experience = combined_experience.to(world_model_and_policy.device)
+
+    if store_agent_embed:
+        assert exists(combined_experience.agent_embed)
+
+    actor_loss, critic_loss = world_model_and_policy.learn_from_experience(combined_experience, use_pmpo = use_pmpo)
 
     actor_loss.backward()
     critic_loss.backward()
+
+    # with trainer
+
+    from dreamer4.trainers import SimTrainer
+
+    trainer = SimTrainer(
+        world_model_and_policy,
+        batch_size = 4,
+        cpu = True
+    )
+
+    trainer(mock_env, num_episodes = 2, env_is_vectorized = vectorized)
+
+@param('num_video_views', (1, 2))
+def test_proprioception(
+    num_video_views
+):
+    from dreamer4.dreamer4 import VideoTokenizer, DynamicsWorldModel
+
+    tokenizer = VideoTokenizer(
+        512,
+        dim_latent = 32,
+        patch_size = 32,
+        encoder_depth = 2,
+        decoder_depth = 2,
+        time_block_every = 2,
+        attn_heads = 8,
+        image_height = 256,
+        image_width = 256,
+        attn_kwargs = dict(
+            query_heads = 16
+        )
+    )
+
+    dynamics = DynamicsWorldModel(
+        512,
+        num_agents = 1,
+        video_tokenizer = tokenizer,
+        dim_latent = 32,
+        dim_proprio = 21,
+        num_tasks = 4,
+        num_video_views = num_video_views,
+        num_discrete_actions = 4,
+        num_residual_streams = 1
+    )
+
+    if num_video_views > 1:
+        video_shape = (2, num_video_views, 3, 10, 256, 256)
+    else:
+        video_shape = (2, 3, 10, 256, 256)
+
+    video = torch.randn(*video_shape)
+    rewards = torch.randn(2, 10)
+    proprio = torch.randn(2, 10, 21)
+    discrete_actions = torch.randint(0, 4, (2, 10, 1))
+    tasks = torch.randint(0, 4, (2,))
+
+    loss = dynamics(
+        video = video,
+        rewards = rewards,
+        tasks = tasks,
+        proprio = proprio,
+        discrete_actions = discrete_actions
+    )
+
+    loss.backward()
+
+    generations = dynamics.generate(
+        10,
+        batch_size = 2,
+        return_decoded_video = True
+    )
+
+    assert exists(generations.proprio)
+    assert generations.video.shape == video_shape
+
+def test_epo():
+    from dreamer4.dreamer4 import VideoTokenizer, DynamicsWorldModel
+
+    tokenizer = VideoTokenizer(
+        512,
+        dim_latent = 32,
+        patch_size = 32,
+        encoder_depth = 2,
+        decoder_depth = 2,
+        time_block_every = 2,
+        attn_heads = 8,
+        image_height = 256,
+        image_width = 256,
+        attn_kwargs = dict(
+            query_heads = 16
+        )
+    )
+
+    dynamics = DynamicsWorldModel(
+        512,
+        num_agents = 1,
+        video_tokenizer = tokenizer,
+        dim_latent = 32,
+        dim_proprio = 21,
+        num_tasks = 4,
+        num_latent_genes = 16,
+        num_discrete_actions = 4,
+        num_residual_streams = 1
+    )
+
+    fitness = torch.randn(16,)
+    dynamics.evolve_(fitness)
+
+def test_images_to_video_tokenizer():
+    import torch
+    from dreamer4 import VideoTokenizer, DynamicsWorldModel, AxialSpaceTimeTransformer
+
+    tokenizer = VideoTokenizer(
+        dim = 512,
+        dim_latent = 32,
+        patch_size = 32,
+        image_height = 256,
+        image_width = 256,
+        encoder_add_decor_aux_loss = True
+    )
+
+    images = torch.randn(2, 3, 256, 256)
+    loss, (losses, recon_images) = tokenizer(images, return_intermediates = True)
+    loss.backward()
+
+    assert images.shape == recon_images.shape