diff --git a/torchmultimodal/models/dalle2/__init__.py b/tests/diffusion_labs/__init__.py
similarity index 100%
rename from torchmultimodal/models/dalle2/__init__.py
rename to tests/diffusion_labs/__init__.py
diff --git a/tests/modules/diffusion/test_cfguidance.py b/tests/diffusion_labs/test_adapter_cfguidance.py
similarity index 96%
rename from tests/modules/diffusion/test_cfguidance.py
rename to tests/diffusion_labs/test_adapter_cfguidance.py
index dcb402b9..d0dace3d 100644
--- a/tests/modules/diffusion/test_cfguidance.py
+++ b/tests/diffusion_labs/test_adapter_cfguidance.py
@@ -9,8 +9,8 @@
 import torch
 from tests.test_utils import assert_expected, set_rng_seed
 from torch import nn
-from torchmultimodal.modules.diffusion.cfguidance import CFGuidance
-from torchmultimodal.utils.diffusion_utils import DiffusionOutput
+from torchmultimodal.diffusion_labs.modules.adapters.cfguidance import CFGuidance
+from torchmultimodal.diffusion_labs.utils.common import DiffusionOutput
 
 
 @pytest.fixture(autouse=True)
diff --git a/tests/models/dalle2/test_adm.py b/tests/diffusion_labs/test_adm.py
similarity index 94%
rename from tests/models/dalle2/test_adm.py
rename to tests/diffusion_labs/test_adm.py
index 0429e5c0..4a063c24 100644
--- a/tests/models/dalle2/test_adm.py
+++ b/tests/diffusion_labs/test_adm.py
@@ -10,9 +10,11 @@
 import torch
 from tests.test_utils import assert_expected, set_rng_seed
 from torch import nn
-from torchmultimodal.models.dalle2.adm.adm import ADM, ADMStack, ADMUNet
-from torchmultimodal.models.dalle2.adm.attention_block import ADMAttentionBlock
-from torchmultimodal.models.dalle2.adm.res_block import ADMResBlock
+from torchmultimodal.diffusion_labs.models.adm_unet.adm import ADM, ADMStack, ADMUNet
+from torchmultimodal.diffusion_labs.models.adm_unet.attention_block import (
+    ADMAttentionBlock,
+)
+from torchmultimodal.diffusion_labs.models.adm_unet.res_block import ADMResBlock
 
 
 @pytest.fixture(autouse=True)
@@ -116,7 +118,7 @@ def test_predict_variance_value_incorrect_channel_dim_error(
 
 
 # All expected values come after first testing the ADMUNet has the exact output
-# as the corresponding UNet class in d2go, then simply forward passing
+# as the corresponding author UNet implementation, then simply forward passing
 # ADMUNet with params, random seed, and initialization order in this file.
 class TestADMUNet:
     @pytest.fixture
diff --git a/tests/models/dalle2/test_adm_blocks.py b/tests/diffusion_labs/test_adm_blocks.py
similarity index 94%
rename from tests/models/dalle2/test_adm_blocks.py
rename to tests/diffusion_labs/test_adm_blocks.py
index 9bca0680..2b878955 100644
--- a/tests/models/dalle2/test_adm_blocks.py
+++ b/tests/diffusion_labs/test_adm_blocks.py
@@ -10,8 +10,10 @@
 
 import torch
 from tests.test_utils import assert_expected, set_rng_seed
-from torchmultimodal.models.dalle2.adm.attention_block import ADMAttentionBlock
-from torchmultimodal.models.dalle2.adm.res_block import (
+from torchmultimodal.diffusion_labs.models.adm_unet.attention_block import (
+    ADMAttentionBlock,
+)
+from torchmultimodal.diffusion_labs.models.adm_unet.res_block import (
     adm_res_block,
     adm_res_downsample_block,
     adm_res_upsample_block,
@@ -47,7 +49,7 @@ def t(params):
 
 
 # All expected values come after first testing the ADMResBlock has the exact output
-# as the corresponding residual block class in d2go, then simply forward passing
+# as the corresponding residual block class from ADM authors, then simply forward passing
 # ADMResBlock with params, random seed, and initialization order in this file.
 class TestADMResBlock:
     @pytest.fixture
diff --git a/tests/models/dalle2/test_adm_crossattention.py b/tests/diffusion_labs/test_adm_crossattention.py
similarity index 92%
rename from tests/models/dalle2/test_adm_crossattention.py
rename to tests/diffusion_labs/test_adm_crossattention.py
index 5328b881..fd61c778 100644
--- a/tests/models/dalle2/test_adm_crossattention.py
+++ b/tests/diffusion_labs/test_adm_crossattention.py
@@ -8,7 +8,7 @@
 
 import torch
 from tests.test_utils import assert_expected, set_rng_seed
-from torchmultimodal.models.dalle2.adm.attention_block import (
+from torchmultimodal.diffusion_labs.models.adm_unet.attention_block import (
     adm_attention,
     ADMCrossAttention,
 )
@@ -44,7 +44,7 @@ def c(params):
 
 
 # All expected values come after first testing that ADMCrossAttention has
-# the exact output as the corresponding QKVAttention class in d2go, then simply forward passing
+# the exact output as the corresponding QKVAttention class in ADM, then simply forward passing
 # ADMCrossAttention with params, random seed, and initialization order in this file.
 class TestADMCrossAttention:
     @pytest.fixture
diff --git a/tests/utils/test_diffusion_utils.py b/tests/diffusion_labs/test_common_util.py
similarity index 87%
rename from tests/utils/test_diffusion_utils.py
rename to tests/diffusion_labs/test_common_util.py
index f0466d74..406196df 100644
--- a/tests/utils/test_diffusion_utils.py
+++ b/tests/diffusion_labs/test_common_util.py
@@ -6,7 +6,7 @@
 # LICENSE file in the root directory of this source tree.
 
 from PIL import Image
-from torchmultimodal.utils.diffusion_utils import cascaded_resize
+from torchmultimodal.diffusion_labs.utils.common import cascaded_resize
 
 
 def test_cascaded_resize():
diff --git a/tests/diffusion_labs/test_dalle2.py b/tests/diffusion_labs/test_dalle2.py
new file mode 100644
index 00000000..75cba0cc
--- /dev/null
+++ b/tests/diffusion_labs/test_dalle2.py
@@ -0,0 +1,52 @@
+#!/usr/bin/env fbpython
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+from PIL import Image
+from tests.test_utils import assert_expected, set_rng_seed
+from torchmultimodal.diffusion_labs.models.dalle2.dalle2_decoder import dalle2_decoder
+from torchmultimodal.diffusion_labs.models.dalle2.transforms import Dalle2ImageTransform
+
+
+def test_dalle2_model():
+    set_rng_seed(4)
+    model = dalle2_decoder(
+        timesteps=1,
+        time_embed_dim=1,
+        cond_embed_dim=1,
+        clip_embed_dim=1,
+        clip_embed_name="clip_image",
+        predict_variance_value=True,
+        image_channels=1,
+        depth=32,
+        num_resize=1,
+        num_res_per_layer=1,
+        use_cf_guidance=True,
+        clip_image_guidance_dropout=0.1,
+        guidance_strength=7.0,
+        learn_null_emb=True,
+    )
+    model.eval()
+    x = torch.randn(1, 1, 4, 4)
+    c = torch.ones((1, 1))
+    with torch.no_grad():
+        actual = model(x, conditional_inputs={"clip_image": c}).mean()
+    expected = torch.as_tensor(0.12768)
+    assert_expected(actual, expected, rtol=0, atol=1e-4)
+
+
+def test_dalle2_image_transform():
+    img_size = 5
+    transform = Dalle2ImageTransform(image_size=img_size, image_min=-1, image_max=1)
+    image = Image.new("RGB", size=(20, 20), color=(128, 0, 0))
+    actual = transform(image).sum()
+    normalized128 = 128 / 255 * 2 - 1
+    normalized0 = -1
+    expected = torch.tensor(
+        normalized128 * img_size**2 + 2 * normalized0 * img_size**2
+    )
+    assert_expected(actual, expected, rtol=0, atol=1e-4)
diff --git a/tests/modules/losses/test_diffusion_loss.py b/tests/diffusion_labs/test_diffusion_losses.py
similarity index 85%
rename from tests/modules/losses/test_diffusion_loss.py
rename to tests/diffusion_labs/test_diffusion_losses.py
index 980968b7..a0e1e12f 100644
--- a/tests/modules/losses/test_diffusion_loss.py
+++ b/tests/diffusion_labs/test_diffusion_losses.py
@@ -9,12 +9,15 @@
 import torch
 import torch.nn as nn
 from tests.test_utils import assert_expected, set_rng_seed
-from torchmultimodal.modules.diffusion.schedules import (
-    DiffusionSchedule,
+from torchmultimodal.diffusion_labs.modules.losses.diffusion_hybrid_loss import (
+    DiffusionHybridLoss,
+)
+from torchmultimodal.diffusion_labs.modules.losses.vlb_loss import VLBLoss
+from torchmultimodal.diffusion_labs.schedules.discrete_gaussian_schedule import (
+    DiscreteGaussianSchedule,
     linear_beta_schedule,
 )
-from torchmultimodal.modules.losses.diffusion import DiffusionHybridLoss, VLBLoss
-from torchmultimodal.utils.diffusion_utils import DiffusionOutput
+from torchmultimodal.diffusion_labs.utils.common import DiffusionOutput
 
 
 @pytest.fixture(autouse=True)
@@ -24,7 +27,7 @@ def set_seed():
 
 @pytest.fixture
 def schedule():
-    return DiffusionSchedule(linear_beta_schedule(1000))
+    return DiscreteGaussianSchedule(linear_beta_schedule(1000))
 
 
 @pytest.fixture
@@ -44,7 +47,7 @@ def target():
 
 
 # All expected values come after first testing the HybridLoss has the exact output
-# as the corresponding p_losses in D2Go Guassian Diffusion
+# as the corresponding p_losses in Guassian Diffusion
 class TestDiffusionHybridLoss:
     @pytest.fixture
     def loss(self, schedule):
diff --git a/tests/transforms/test_diffusion_transforms.py b/tests/diffusion_labs/test_diffusion_transform.py
similarity index 52%
rename from tests/transforms/test_diffusion_transforms.py
rename to tests/diffusion_labs/test_diffusion_transform.py
index 43a42a2f..55bbbb66 100644
--- a/tests/transforms/test_diffusion_transforms.py
+++ b/tests/diffusion_labs/test_diffusion_transform.py
@@ -6,10 +6,7 @@
 # LICENSE file in the root directory of this source tree.
 
 import torch
-from PIL import Image
-from tests.test_utils import assert_expected
-from torchmultimodal.transforms.diffusion_transforms import (
-    Dalle2ImageTransform,
+from torchmultimodal.diffusion_labs.transforms.diffusion_transform import (
     RandomDiffusionSteps,
 )
 
@@ -30,16 +27,3 @@ def test_random_diffusion_steps():
     actual = len(transform(torch.ones(1)))
     expected = 4
     assert actual == expected, "Transform not returning correct keys"
-
-
-def test_dalle_image_transform():
-    img_size = 5
-    transform = Dalle2ImageTransform(image_size=img_size, image_min=-1, image_max=1)
-    image = Image.new("RGB", size=(20, 20), color=(128, 0, 0))
-    actual = transform(image).sum()
-    normalized128 = 128 / 255 * 2 - 1
-    normalized0 = -1
-    expected = torch.tensor(
-        normalized128 * img_size**2 + 2 * normalized0 * img_size**2
-    )
-    assert_expected(actual, expected, rtol=0, atol=1e-4)
diff --git a/tests/modules/diffusion/test_schedule.py b/tests/diffusion_labs/test_discrete_schedule.py
similarity index 93%
rename from tests/modules/diffusion/test_schedule.py
rename to tests/diffusion_labs/test_discrete_schedule.py
index 6f5f49b0..3a4312d3 100644
--- a/tests/modules/diffusion/test_schedule.py
+++ b/tests/diffusion_labs/test_discrete_schedule.py
@@ -8,9 +8,9 @@
 import pytest
 import torch
 from tests.test_utils import assert_expected, set_rng_seed
-from torchmultimodal.modules.diffusion.schedules import (
+from torchmultimodal.diffusion_labs.schedules.discrete_gaussian_schedule import (
     cosine_beta_schedule,
-    DiffusionSchedule,
+    DiscreteGaussianSchedule,
     linear_beta_schedule,
     quadratic_beta_schedule,
     sigmoid_beta_schedule,
@@ -23,11 +23,11 @@ def set_seed():
 
 
 # All expected values come after first testing the Schedule has the exact output
-# as the corresponding q methods from GaussianDiffusion in D2Go
+# as the corresponding q methods from GaussianDiffusion
 class TestDiffusionSchedule:
     @pytest.fixture
     def module(self):
-        schedule = DiffusionSchedule(linear_beta_schedule(1000))
+        schedule = DiscreteGaussianSchedule(linear_beta_schedule(1000))
         return schedule
 
     @pytest.fixture
diff --git a/tests/modules/diffusion/test_predictors.py b/tests/diffusion_labs/test_predictors.py
similarity index 77%
rename from tests/modules/diffusion/test_predictors.py
rename to tests/diffusion_labs/test_predictors.py
index 4ffc5129..001078f0 100644
--- a/tests/modules/diffusion/test_predictors.py
+++ b/tests/diffusion_labs/test_predictors.py
@@ -8,9 +8,10 @@
 import pytest
 import torch
 from tests.test_utils import assert_expected, set_rng_seed
-from torchmultimodal.modules.diffusion.predictors import NoisePredictor, TargetPredictor
-from torchmultimodal.modules.diffusion.schedules import (
-    DiffusionSchedule,
+from torchmultimodal.diffusion_labs.predictors.noise_predictor import NoisePredictor
+from torchmultimodal.diffusion_labs.predictors.target_predictor import TargetPredictor
+from torchmultimodal.diffusion_labs.schedules.discrete_gaussian_schedule import (
+    DiscreteGaussianSchedule,
     linear_beta_schedule,
 )
 
@@ -30,11 +31,11 @@ def input():
 
 
 # All expected values come after first testing the Schedule has the exact output
-# as the corresponding q methods from GaussianDiffusion in D2Go
+# as the corresponding q methods from GaussianDiffusion
 class TestNoisePredictor:
     @pytest.fixture
     def module(self):
-        schedule = DiffusionSchedule(linear_beta_schedule(1000))
+        schedule = DiscreteGaussianSchedule(linear_beta_schedule(1000))
         predictor = NoisePredictor(schedule, None)
         return predictor
 
@@ -52,7 +53,7 @@ def test_predict_noise(self, module, input):
 class TestTargetPredictor:
     @pytest.fixture
     def module(self):
-        schedule = DiffusionSchedule(linear_beta_schedule(1000))
+        schedule = DiscreteGaussianSchedule(linear_beta_schedule(1000))
         predictor = TargetPredictor(schedule, None)
         return predictor
 
diff --git a/tests/modules/diffusion/test_ddim.py b/tests/diffusion_labs/test_sampler_ddim.py
similarity index 83%
rename from tests/modules/diffusion/test_ddim.py
rename to tests/diffusion_labs/test_sampler_ddim.py
index 097298a5..c0bda474 100644
--- a/tests/modules/diffusion/test_ddim.py
+++ b/tests/diffusion_labs/test_sampler_ddim.py
@@ -9,13 +9,13 @@
 import torch
 import torch.nn as nn
 from tests.test_utils import assert_expected, set_rng_seed
-from torchmultimodal.modules.diffusion.ddim import DDIModule
-from torchmultimodal.modules.diffusion.predictors import NoisePredictor
-from torchmultimodal.modules.diffusion.schedules import (
-    DiffusionSchedule,
+from torchmultimodal.diffusion_labs.predictors.noise_predictor import NoisePredictor
+from torchmultimodal.diffusion_labs.samplers.ddim import DDIModule
+from torchmultimodal.diffusion_labs.schedules.discrete_gaussian_schedule import (
+    DiscreteGaussianSchedule,
     linear_beta_schedule,
 )
-from torchmultimodal.utils.diffusion_utils import DiffusionOutput
+from torchmultimodal.diffusion_labs.utils.common import DiffusionOutput
 
 
 class DummyUNet(nn.Module):
@@ -38,7 +38,7 @@ class TestDDIModule:
     @pytest.fixture
     def module(self):
         model = DummyUNet(True)
-        schedule = DiffusionSchedule(linear_beta_schedule(1000))
+        schedule = DiscreteGaussianSchedule(linear_beta_schedule(1000))
         predictor = NoisePredictor(schedule)
         model = DDIModule(model, schedule, predictor)
         return model
diff --git a/tests/modules/diffusion/test_ddpm.py b/tests/diffusion_labs/test_sampler_ddpm.py
similarity index 88%
rename from tests/modules/diffusion/test_ddpm.py
rename to tests/diffusion_labs/test_sampler_ddpm.py
index c94e2c88..f4a2098a 100644
--- a/tests/modules/diffusion/test_ddpm.py
+++ b/tests/diffusion_labs/test_sampler_ddpm.py
@@ -10,13 +10,13 @@
 import torch.nn as nn
 import torch.nn.functional as F
 from tests.test_utils import assert_expected, set_rng_seed
-from torchmultimodal.modules.diffusion.ddpm import DDPModule
-from torchmultimodal.modules.diffusion.predictors import NoisePredictor
-from torchmultimodal.modules.diffusion.schedules import (
-    DiffusionSchedule,
+from torchmultimodal.diffusion_labs.predictors.noise_predictor import NoisePredictor
+from torchmultimodal.diffusion_labs.samplers.ddpm import DDPModule
+from torchmultimodal.diffusion_labs.schedules.discrete_gaussian_schedule import (
+    DiscreteGaussianSchedule,
     linear_beta_schedule,
 )
-from torchmultimodal.utils.diffusion_utils import DiffusionOutput
+from torchmultimodal.diffusion_labs.utils.common import DiffusionOutput
 
 
 class DummyUNet(nn.Module):
@@ -36,13 +36,13 @@ def forward(self, x, t, c):
 
 
 # All expected values come after first testing the Schedule has the exact output
-# as the corresponding p methods from GaussianDiffusion in D2Go
+# as the corresponding p methods from GaussianDiffusion
 class TestDDPModule:
     @pytest.fixture
     def module(self):
         set_rng_seed(4)
         model = DummyUNet(True)
-        schedule = DiffusionSchedule(linear_beta_schedule(1000))
+        schedule = DiscreteGaussianSchedule(linear_beta_schedule(1000))
         predictor = NoisePredictor(schedule)
         eval_steps = torch.arange(0, 1000, 50)
         model = DDPModule(model, schedule, predictor, eval_steps)
diff --git a/torchmultimodal/models/dalle2/adm/__init__.py b/torchmultimodal/diffusion_labs/__init__.py
similarity index 100%
rename from torchmultimodal/models/dalle2/adm/__init__.py
rename to torchmultimodal/diffusion_labs/__init__.py
diff --git a/torchmultimodal/modules/diffusion/__init__.py b/torchmultimodal/diffusion_labs/models/__init__.py
similarity index 100%
rename from torchmultimodal/modules/diffusion/__init__.py
rename to torchmultimodal/diffusion_labs/models/__init__.py
diff --git a/torchmultimodal/diffusion_labs/models/adm_unet/__init__.py b/torchmultimodal/diffusion_labs/models/adm_unet/__init__.py
new file mode 100644
index 00000000..2e41cd71
--- /dev/null
+++ b/torchmultimodal/diffusion_labs/models/adm_unet/__init__.py
@@ -0,0 +1,5 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
diff --git a/torchmultimodal/models/dalle2/adm/adm.py b/torchmultimodal/diffusion_labs/models/adm_unet/adm.py
similarity index 98%
rename from torchmultimodal/models/dalle2/adm/adm.py
rename to torchmultimodal/diffusion_labs/models/adm_unet/adm.py
index dbcfb6aa..b681c10e 100644
--- a/torchmultimodal/models/dalle2/adm/adm.py
+++ b/torchmultimodal/diffusion_labs/models/adm_unet/adm.py
@@ -8,21 +8,21 @@
 
 import torch
 from torch import nn, Tensor
-from torchmultimodal.models.dalle2.adm.attention_block import (
+from torchmultimodal.diffusion_labs.models.adm_unet.attention_block import (
     adm_attn_block,
     ADMAttentionBlock,
 )
-from torchmultimodal.models.dalle2.adm.res_block import (
+from torchmultimodal.diffusion_labs.models.adm_unet.res_block import (
     adm_res_block,
     adm_res_downsample_block,
     adm_res_upsample_block,
     ADMResBlock,
 )
+from torchmultimodal.diffusion_labs.utils.common import DiffusionOutput
 from torchmultimodal.modules.layers.normalizations import Fp32GroupNorm
 from torchmultimodal.modules.layers.position_embedding import (
     SinusoidalPositionEmbeddings,
 )
-from torchmultimodal.utils.diffusion_utils import DiffusionOutput
 
 
 class ADM(nn.Module):
@@ -35,7 +35,8 @@ class ADM(nn.Module):
 
     The UNet can be swapped with any module. The UNet used in the paper is constructed in the ADMUNet class.
 
-    Code ref: https://fburl.com/code/b6lk39ym
+    Code ref:
+        https://github.com/lucidrains/DALLE2-pytorch/blob/c6c3882dc165914413ca97176b3a0103af1d7048/dalle2_pytorch/dalle2_pytorch.py#L1856
 
     Attributes:
         unet (nn.Module): model that will process three inputs: the input noised image, embedded timestep, and
@@ -383,8 +384,6 @@ def forward(
 class ADMStack(nn.ModuleList):
     """A container that acts as a ModuleList of ADM blocks and handles passing conditional inputs
     correctly to the children ADMResBlocks and ADMAttentionBlocks.
-
-    Code ref: https://fburl.com/code/x9nuqaov
     """
 
     def forward(
@@ -448,7 +447,7 @@ def adm_stack_res_down(num_channels: int, dim_cond: int) -> nn.ModuleList:
     )
 
 
-def dalle2_adm(
+def adm_unet(
     *,
     # ADM args
     time_embed_dim: int = 512,
@@ -483,9 +482,7 @@ def dalle2_adm(
         num_resize (int): number of times resolution will be scaled
         num_res_per_layer (int): number of residual blocks per resolution
     """
-    #
     # Construct UNet
-    #
     in_channels = image_channels
 
     # If predicting variance, double the channel dim of UNet output and use those values as variance
@@ -506,9 +503,7 @@ def dalle2_adm(
         out_channels=out_channels,
     )
 
-    #
     # Construct ADM (UNet + timestep/conditional projections)
-    #
     time_embed = nn.Sequential(
         SinusoidalPositionEmbeddings(embed_dim=time_embed_dim),
         nn.Linear(time_embed_dim, cond_embed_dim),
diff --git a/torchmultimodal/models/dalle2/adm/attention_block.py b/torchmultimodal/diffusion_labs/models/adm_unet/attention_block.py
similarity index 99%
rename from torchmultimodal/models/dalle2/adm/attention_block.py
rename to torchmultimodal/diffusion_labs/models/adm_unet/attention_block.py
index 0c2c2fa9..1a7ea4b9 100644
--- a/torchmultimodal/models/dalle2/adm/attention_block.py
+++ b/torchmultimodal/diffusion_labs/models/adm_unet/attention_block.py
@@ -22,8 +22,6 @@ class ADMAttentionBlock(nn.Module):
     Code ref:
     https://github.com/openai/guided-diffusion/blob/22e0df8183507e13a7813f8d38d51b072ca1e67c/guided_diffusion/unet.py#L259
 
-    Defaults taken from https://fburl.com/code/tiryi2f9.
-
     Attributes:
         num_channels (int): channel dim expected in input, determines embedding dim of q, k, v in attention module.
             Needs to be divisible by norm_groups.
diff --git a/torchmultimodal/models/dalle2/adm/res_block.py b/torchmultimodal/diffusion_labs/models/adm_unet/res_block.py
similarity index 98%
rename from torchmultimodal/models/dalle2/adm/res_block.py
rename to torchmultimodal/diffusion_labs/models/adm_unet/res_block.py
index dc611dc2..f97426ac 100644
--- a/torchmultimodal/models/dalle2/adm/res_block.py
+++ b/torchmultimodal/diffusion_labs/models/adm_unet/res_block.py
@@ -21,8 +21,6 @@ class ADMResBlock(nn.Module):
     Code ref:
     https://github.com/openai/guided-diffusion/blob/22e0df8183507e13a7813f8d38d51b072ca1e67c/guided_diffusion/unet.py#L143
 
-    Defaults taken from https://fburl.com/code/tiryi2f9.
-
     Attributes:
         in_channels (int): num channels expected in input. Needs to be divisible by norm_groups.
         out_channels (int): num channels desired in output. Needs to be divisible by norm_groups.
@@ -43,8 +41,8 @@ class ADMResBlock(nn.Module):
         norm_groups (int): number of groups used in GroupNorm layer. Defaults to 32.
 
     Args:
-        x (Tensor): input Tensor of shape [B x C x H x W]
-        conditional_embedding (Tensor): conditioning embedding vector of shape [B x C].
+        x (Tensor): input Tensor of shape [b, c, h, w]
+        conditional_embedding (Tensor): conditioning embedding vector of shape [b, c].
 
     Raises:
         TypeError: When skip_conv is not defined and in_channels != out_channels.
diff --git a/torchmultimodal/diffusion_labs/models/dalle2/__init__.py b/torchmultimodal/diffusion_labs/models/dalle2/__init__.py
new file mode 100644
index 00000000..2e41cd71
--- /dev/null
+++ b/torchmultimodal/diffusion_labs/models/dalle2/__init__.py
@@ -0,0 +1,5 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
diff --git a/torchmultimodal/models/dalle2/dalle2_decoder.py b/torchmultimodal/diffusion_labs/models/dalle2/dalle2_decoder.py
similarity index 87%
rename from torchmultimodal/models/dalle2/dalle2_decoder.py
rename to torchmultimodal/diffusion_labs/models/dalle2/dalle2_decoder.py
index 899b47da..6f4bc073 100644
--- a/torchmultimodal/models/dalle2/dalle2_decoder.py
+++ b/torchmultimodal/diffusion_labs/models/dalle2/dalle2_decoder.py
@@ -5,13 +5,14 @@
 # LICENSE file in the root directory of this source tree.
 
 import torch
-from torchmultimodal.models.dalle2.adm.adm import dalle2_adm
-from torchmultimodal.modules.diffusion.cfguidance import CFGuidance
-from torchmultimodal.modules.diffusion.ddpm import DDPModule
-from torchmultimodal.modules.diffusion.predictors import NoisePredictor
-from torchmultimodal.modules.diffusion.schedules import (
+from torchmultimodal.diffusion_labs.models.adm_unet.adm import adm_unet
+from torchmultimodal.diffusion_labs.modules.adapters.cfguidance import CFGuidance
+from torchmultimodal.diffusion_labs.predictors.noise_predictor import NoisePredictor
+from torchmultimodal.diffusion_labs.samplers.ddpm import DDPModule
+from torchmultimodal.diffusion_labs.samplers.sampler import Sampler
+from torchmultimodal.diffusion_labs.schedules.discrete_gaussian_schedule import (
     cosine_beta_schedule,
-    DiffusionSchedule,
+    DiscreteGaussianSchedule,
 )
 
 
@@ -34,7 +35,7 @@ def dalle2_decoder(
     clip_image_guidance_dropout: float = 0.1,
     guidance_strength: float = 7.0,
     learn_null_emb: bool = True,
-) -> DDPModule:
+) -> Sampler:
     """Constructs primary DALLE-2 diffusion decoder without upsampling.
 
     Consists of an ADM UNet diffusion model conditioned on CLIP image embeddings. Uses DDPM to generate
@@ -71,10 +72,8 @@ def dalle2_decoder(
         learn_null_emb (bool): If False, then unconditional embeddings are set to zero and are not trainable
             If True, then unconditional embeddings are set to random and are trainable. Defaults to True.
     """
-    #
     # Construct UNet
-    #
-    diffusion_model = dalle2_adm(
+    diffusion_model = adm_unet(
         time_embed_dim=time_embed_dim,
         cond_embed_dim=cond_embed_dim,
         clip_embed_dim=clip_embed_dim,
@@ -86,9 +85,7 @@ def dalle2_decoder(
         num_res_per_layer=num_res_per_layer,
     )
 
-    #
     # Construct CFGuidance wrapper around ADM model
-    #
     if use_cf_guidance:
         diffusion_model = CFGuidance(
             model=diffusion_model,
@@ -98,11 +95,9 @@ def dalle2_decoder(
             learn_null_emb=learn_null_emb,
         )
 
-    #
     # Construct DDPM decoder
-    #
     eval_steps = torch.linspace(0, timesteps - 1, timesteps // 4, dtype=torch.int)
-    schedule = DiffusionSchedule(cosine_beta_schedule(timesteps))
+    schedule = DiscreteGaussianSchedule(cosine_beta_schedule(timesteps))
     predictor = NoisePredictor(schedule, lambda x: x.clamp(-1, 1))
     model = DDPModule(
         model=diffusion_model,
diff --git a/torchmultimodal/transforms/diffusion_transforms.py b/torchmultimodal/diffusion_labs/models/dalle2/transforms.py
similarity index 51%
rename from torchmultimodal/transforms/diffusion_transforms.py
rename to torchmultimodal/diffusion_labs/models/dalle2/transforms.py
index adac5025..1f731b4c 100644
--- a/torchmultimodal/transforms/diffusion_transforms.py
+++ b/torchmultimodal/diffusion_labs/models/dalle2/transforms.py
@@ -5,20 +5,14 @@
 # LICENSE file in the root directory of this source tree.
 
 from functools import partial
-from typing import List, Tuple, Union
+from typing import List, Union
 
 import torch
 import torchvision.transforms as tv
 from PIL.Image import Image
 
 from torch import nn, Tensor
-from torchmultimodal.modules.diffusion.schedules import DiffusionSchedule
-from torchmultimodal.utils.diffusion_utils import cascaded_resize
-
-
-def normalize(x: Tensor, image_min: int, image_max: int) -> Tensor:
-    # Normalize image values between min and max
-    return (image_max - image_min) * x + image_min
+from torchmultimodal.diffusion_labs.utils.common import cascaded_resize, normalize
 
 
 class Dalle2ImageTransform(nn.Module):
@@ -57,33 +51,3 @@ def forward(self, image: Union[List[Image], Image]) -> Tensor:
         # pyre-ignore
         image_result = torch.stack([self.image_transform(x) for x in image])
         return image_result
-
-
-class RandomDiffusionSteps(nn.Module):
-    """Data Transform to randomly sample noised data from the diffusion schedule.
-    During diffusion training, random diffusion steps are sampled per model update.
-    This transform samples steps and returns the steps (t), seed noise, and transformed
-    data at time t (xt).
-
-    Attributes:
-        schedule (DiffusionSchedule): defines diffusion of noise through time
-        batched (bool): if True, transform expects a batched input
-
-    Args:
-        x (Tensor): data representing x0, artifact being learned. The 0 represents zero diffusion steps.
-    """
-
-    def __init__(self, schedule: DiffusionSchedule, batched: bool = True):
-        super().__init__()
-        self.schedule = schedule
-        self.batched = batched
-
-    def __call__(self, x: Tensor) -> Tuple[Tensor, Tensor, Tensor, Tensor]:
-        if not self.batched:
-            t = self.schedule.sample_steps(x.unsqueeze(0))
-            t = t.squeeze(0)
-        else:
-            t = self.schedule.sample_steps(x)
-        noise = self.schedule.sample_noise(x)
-        xt = self.schedule.q_sample(x, noise, t)
-        return x, xt, noise, t
diff --git a/torchmultimodal/diffusion_labs/modules/__init__.py b/torchmultimodal/diffusion_labs/modules/__init__.py
new file mode 100644
index 00000000..2e41cd71
--- /dev/null
+++ b/torchmultimodal/diffusion_labs/modules/__init__.py
@@ -0,0 +1,5 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
diff --git a/torchmultimodal/diffusion_labs/modules/adapters/__init__.py b/torchmultimodal/diffusion_labs/modules/adapters/__init__.py
new file mode 100644
index 00000000..2e41cd71
--- /dev/null
+++ b/torchmultimodal/diffusion_labs/modules/adapters/__init__.py
@@ -0,0 +1,5 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
diff --git a/torchmultimodal/diffusion_labs/modules/adapters/adapter.py b/torchmultimodal/diffusion_labs/modules/adapters/adapter.py
new file mode 100644
index 00000000..3635cd30
--- /dev/null
+++ b/torchmultimodal/diffusion_labs/modules/adapters/adapter.py
@@ -0,0 +1,43 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+from abc import abstractmethod
+from typing import Dict, Optional, Protocol, runtime_checkable
+
+from torch import Tensor
+
+from torchmultimodal.diffusion_labs.utils.common import DiffusionOutput
+
+
+@runtime_checkable
+class Adapter(Protocol):
+    """Adapter modules act as wrappers on the underlying denoising model. These are flexible
+    and allow the base model to be augmented to perform common diffusion tasks. Since Adapters
+    share the same signature as the underlying model and the Sampler class, multiple adapters
+    can be stacked together.
+
+    Example:
+        denoising_model = Unet(...)
+        augmented_model = Adapter2(Adapter1(denoising_model))
+        model = DDIM(augmented_model, ...)
+
+    """
+
+    @abstractmethod
+    def forward(
+        self,
+        x: Tensor,
+        timestep: Tensor,
+        conditional_inputs: Optional[Dict[str, Tensor]] = None,
+    ) -> DiffusionOutput:
+        """Model forward pass
+
+        Args:
+            x (Tensor): input Tensor of shape [b, in_channels, ...]
+            timestep (Tensor): diffusion step
+            conditional_inputs (Dict[str, Tensor]): conditional embedding as a dictionary.
+                Conditional embeddings must have at least 2 dimensions.
+        """
diff --git a/torchmultimodal/modules/diffusion/cfguidance.py b/torchmultimodal/diffusion_labs/modules/adapters/cfguidance.py
similarity index 97%
rename from torchmultimodal/modules/diffusion/cfguidance.py
rename to torchmultimodal/diffusion_labs/modules/adapters/cfguidance.py
index 837d4d0c..f1f46401 100644
--- a/torchmultimodal/modules/diffusion/cfguidance.py
+++ b/torchmultimodal/diffusion_labs/modules/adapters/cfguidance.py
@@ -9,10 +9,11 @@
 import torch
 from torch import nn, Tensor
 
-from torchmultimodal.utils.diffusion_utils import DiffusionOutput
+from torchmultimodal.diffusion_labs.modules.adapters.adapter import Adapter
+from torchmultimodal.diffusion_labs.utils.common import DiffusionOutput
 
 
-class CFGuidance(nn.Module):
+class CFGuidance(nn.Module, Adapter):
     """
     Classifier free guidance gives diffusion models the ability to sample from a conditional
     distribution, while maintaining a healthy ratio between exploitation (i.e. correlation
diff --git a/torchmultimodal/diffusion_labs/modules/losses/__init__.py b/torchmultimodal/diffusion_labs/modules/losses/__init__.py
new file mode 100644
index 00000000..2e41cd71
--- /dev/null
+++ b/torchmultimodal/diffusion_labs/modules/losses/__init__.py
@@ -0,0 +1,5 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
diff --git a/torchmultimodal/diffusion_labs/modules/losses/diffusion_hybrid_loss.py b/torchmultimodal/diffusion_labs/modules/losses/diffusion_hybrid_loss.py
new file mode 100644
index 00000000..56e28ec3
--- /dev/null
+++ b/torchmultimodal/diffusion_labs/modules/losses/diffusion_hybrid_loss.py
@@ -0,0 +1,63 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+from torch import nn, Tensor
+from torchmultimodal.diffusion_labs.modules.losses.vlb_loss import VLBLoss
+from torchmultimodal.diffusion_labs.schedules.discrete_gaussian_schedule import (
+    DiscreteGaussianSchedule,
+)
+
+
+class DiffusionHybridLoss(nn.Module):
+    """
+    Combines both simple loss (typically MSE) and VLB loss weighted by lambda, as described in Eq. 16 of
+    "Improved Denoising Diffusion Probabilistic Models" (https://arxiv.org/abs/2102.09672).
+    VLB loss is only used to train the model learned variance.
+
+    Attributes:
+        schedule (DiffusionSchedule): defines diffusion of noise through time
+        simple_loss (nn.Module): loss function computed on prediction of diffusion model and desired target
+            (typically noise). Default is nn.MSELoss.
+        lmbda (float): lambda weight for vlb loss. Default is 0.001.
+
+    Args:
+        input (Tensor): prediction of diffusion model of shape [b, c, ...]
+        target (Tensor): desired target of shape [b, c, ...]
+        mean (Tensor): predicted mean of posterior/xt of shape [b, c, ...]
+        log_variance (Tensor): predicted log variance of posterior/xt of shape [b, c, ...]
+        x0 (Tensor): data sample of shape [b, c,...]
+        xt (Tensor): noised data sample from diffusion process of shape [b, c, ...]
+        t (Tensor): diffusion timesteps of shape [b, ]
+
+    """
+
+    def __init__(
+        self,
+        schedule: DiscreteGaussianSchedule,
+        simple_loss: nn.Module = nn.MSELoss(),
+        lmbda: float = 0.001,
+    ):
+        super().__init__()
+        self.simple_loss = simple_loss
+        self.vlb_loss = VLBLoss(schedule)
+        self.lmbda = lmbda
+
+    def forward(
+        self,
+        input: Tensor,
+        target: Tensor,
+        mean: Tensor,
+        log_variance: Tensor,
+        x0: Tensor,
+        xt: Tensor,
+        t: Tensor,
+    ) -> Tensor:
+        # Detach mean as stop gradient for vlb loss
+        # Weight the vlb loss smaller, for stability, when training in a hybrid setting using
+        # another criterion to train the predictor as in the paper (recommended 0.001)
+        return self.simple_loss(input, target) + self.lmbda * self.vlb_loss(
+            mean.detach(), log_variance, x0, xt, t
+        )
diff --git a/torchmultimodal/modules/losses/diffusion.py b/torchmultimodal/diffusion_labs/modules/losses/vlb_loss.py
similarity index 64%
rename from torchmultimodal/modules/losses/diffusion.py
rename to torchmultimodal/diffusion_labs/modules/losses/vlb_loss.py
index 6ea93fb6..c411d45f 100644
--- a/torchmultimodal/modules/losses/diffusion.py
+++ b/torchmultimodal/diffusion_labs/modules/losses/vlb_loss.py
@@ -8,59 +8,9 @@
 
 import torch
 from torch import nn, Tensor
-from torchmultimodal.modules.diffusion.schedules import DiffusionSchedule
-
-
-class DiffusionHybridLoss(nn.Module):
-    """
-    Combines both simple loss (typically MSE) and VLB loss weighted by lambda, as described in Eq. 16 of
-    "Improved Denoising Diffusion Probabilistic Models" (https://arxiv.org/abs/2102.09672).
-    VLB loss is only used to train the model learned variance.
-
-    Attributes:
-        schedule (DiffusionSchedule): defines diffusion of noise through time
-        simple_loss (nn.Module): loss function computed on prediction of diffusion model and desired target
-            (typically noise). Default is nn.MSELoss.
-        lmbda (float): lambda weight for vlb loss. Default is 0.001.
-
-    Args:
-        input (Tensor): prediction of diffusion model of shape [b, c, ...]
-        target (Tensor): desired target of shape [b, c, ...]
-        mean (Tensor): predicted mean of posterior/xt of shape [b, c, ...]
-        log_variance (Tensor): predicted log variance of posterior/xt of shape [b, c, ...]
-        x0 (Tensor): data sample of shape [b, c,...]
-        xt (Tensor): noised data sample from diffusion process of shape [b, c, ...]
-        t (Tensor): diffusion timesteps of shape [b, ]
-
-    """
-
-    def __init__(
-        self,
-        schedule: DiffusionSchedule,
-        simple_loss: nn.Module = nn.MSELoss(),
-        lmbda: float = 0.001,
-    ):
-        super().__init__()
-        self.simple_loss = simple_loss
-        self.vlb_loss = VLBLoss(schedule)
-        self.lmbda = lmbda
-
-    def forward(
-        self,
-        input: Tensor,
-        target: Tensor,
-        mean: Tensor,
-        log_variance: Tensor,
-        x0: Tensor,
-        xt: Tensor,
-        t: Tensor,
-    ) -> Tensor:
-        # Detach mean as stop gradient for vlb loss
-        # Weight the vlb loss smaller, for stability, when training in a hybrid setting using
-        # another criterion to train the predictor as in the paper (recommended 0.001)
-        return self.simple_loss(input, target) + self.lmbda * self.vlb_loss(
-            mean.detach(), log_variance, x0, xt, t
-        )
+from torchmultimodal.diffusion_labs.schedules.discrete_gaussian_schedule import (
+    DiscreteGaussianSchedule,
+)
 
 
 class VLBLoss(nn.Module):
@@ -88,7 +38,7 @@ class VLBLoss(nn.Module):
 
     """
 
-    def __init__(self, schedule: DiffusionSchedule):
+    def __init__(self, schedule: DiscreteGaussianSchedule):
         super().__init__()
         self.schedule = schedule
 
diff --git a/torchmultimodal/diffusion_labs/predictors/__init__.py b/torchmultimodal/diffusion_labs/predictors/__init__.py
new file mode 100644
index 00000000..2e41cd71
--- /dev/null
+++ b/torchmultimodal/diffusion_labs/predictors/__init__.py
@@ -0,0 +1,5 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
diff --git a/torchmultimodal/diffusion_labs/predictors/noise_predictor.py b/torchmultimodal/diffusion_labs/predictors/noise_predictor.py
new file mode 100644
index 00000000..1be3494f
--- /dev/null
+++ b/torchmultimodal/diffusion_labs/predictors/noise_predictor.py
@@ -0,0 +1,55 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+from typing import Callable, Optional
+
+from torch import Tensor
+from torchmultimodal.diffusion_labs.predictors.predictor import Predictor
+from torchmultimodal.diffusion_labs.schedules.discrete_gaussian_schedule import (
+    DiscreteGaussianSchedule,
+)
+
+
+class NoisePredictor(Predictor):
+    """Given a model that's trained to predict diffusion noise and corresponding schedule,
+        this class computes the predicted noise and x0 at step t.
+
+    Attributes:
+        schedule (DiffusionSchedule): defines diffusion of noise through time
+        clamp_func (Callable): function to clamp prediction values
+    """
+
+    def __init__(
+        self, schedule: DiscreteGaussianSchedule, clamp_func: Optional[Callable] = None
+    ):
+        self.clamp_func = clamp_func
+        schedule.add_property("sqrt_recip_alphas_cumprod", _sqrt_recip_alphas_cumprod)
+        schedule.add_property(
+            "sqrt_recipm1_alphas_cumprod", _sqrt_recipm1_alphas_cumprod
+        )
+        self.schedule = schedule
+
+    def predict_x0(self, prediction: Tensor, xt: Tensor, t: Tensor) -> Tensor:
+        shape, dtype = xt.shape, xt.dtype
+        x_coef = self.schedule("sqrt_recip_alphas_cumprod", t, shape)
+        e_coef = self.schedule("sqrt_recipm1_alphas_cumprod", t, shape)
+        x0 = x_coef * xt - e_coef * prediction
+        if self.clamp_func is not None:
+            x0 = self.clamp_func(x0)
+        return x0.to(dtype)
+
+    def predict_noise(self, prediction: Tensor, xt: Tensor, t: Tensor) -> Tensor:
+        return prediction
+
+
+def _sqrt_recip_alphas_cumprod(schedule: DiscreteGaussianSchedule) -> Tensor:
+    # pyre-ignore
+    return (1.0 / schedule.alphas_cumprod).sqrt()
+
+
+def _sqrt_recipm1_alphas_cumprod(schedule: DiscreteGaussianSchedule) -> Tensor:
+    # pyre-ignore
+    return (1.0 / schedule.alphas_cumprod - 1).sqrt()
diff --git a/torchmultimodal/diffusion_labs/predictors/predictor.py b/torchmultimodal/diffusion_labs/predictors/predictor.py
new file mode 100644
index 00000000..6f95d6d2
--- /dev/null
+++ b/torchmultimodal/diffusion_labs/predictors/predictor.py
@@ -0,0 +1,44 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+from abc import abstractmethod
+from typing import Callable, Optional, Protocol, runtime_checkable
+
+from torch import Tensor
+from torchmultimodal.diffusion_labs.schedules.discrete_gaussian_schedule import (
+    DiscreteGaussianSchedule,
+)
+
+
+@runtime_checkable
+class Predictor(Protocol):
+    """Helper class to help predict various parts of the diffusion process. Different
+    implementations of each method are needed depending on what the model itself was
+    trained to predict.
+    """
+
+    schedule: DiscreteGaussianSchedule
+    clamp_func: Optional[Callable]
+
+    @abstractmethod
+    def predict_x0(self, prediction: Tensor, xt: Tensor, t: Tensor) -> Tensor:
+        """Predict x0
+
+        Args:
+            prediction (Tensor): model prediction
+            xt (Tensor): noised data to step t
+            t (Tensor): int diffusion step for xt
+        """
+
+    @abstractmethod
+    def predict_noise(self, prediction: Tensor, xt: Tensor, t: Tensor) -> Tensor:
+        """Predict noise
+
+        Args:
+            prediction (Tensor): model prediction
+            xt (Tensor): noised data to step t
+            t (Tensor): int diffusion step for xt
+        """
diff --git a/torchmultimodal/diffusion_labs/predictors/target_predictor.py b/torchmultimodal/diffusion_labs/predictors/target_predictor.py
new file mode 100644
index 00000000..40c96c8d
--- /dev/null
+++ b/torchmultimodal/diffusion_labs/predictors/target_predictor.py
@@ -0,0 +1,42 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+from typing import Callable, Optional
+
+from torch import Tensor
+from torchmultimodal.diffusion_labs.predictors.predictor import Predictor
+from torchmultimodal.diffusion_labs.schedules.discrete_gaussian_schedule import (
+    DiscreteGaussianSchedule,
+)
+
+
+class TargetPredictor(Predictor):
+    """Given a model that's trained to predict x0 and corresponding schedule,
+        this class computes the predicted noise and x0 at step t.
+
+    Attributes:
+        schedule (DiffusionSchedule): defines diffusion of noise through time
+        clamp_func (Callable): function to clamp prediction values
+    """
+
+    def __init__(
+        self, schedule: DiscreteGaussianSchedule, clamp_func: Optional[Callable] = None
+    ):
+        self.clamp_func = clamp_func
+        self.schedule = schedule
+
+    def predict_x0(self, prediction: Tensor, xt: Tensor, t: Tensor) -> Tensor:
+        if self.clamp_func is not None:
+            prediction = self.clamp_func(prediction)
+        return prediction
+
+    def predict_noise(self, prediction: Tensor, xt: Tensor, t: Tensor) -> Tensor:
+        shape, dtype = xt.shape, xt.dtype
+        x_coef = self.schedule("sqrt_recip_alphas_cumprod", t, shape)
+        e_coef = self.schedule("sqrt_recip_alphas_cumprod_minus_one", t, shape)
+        x0 = self.predict_x0(prediction, xt, t)
+        e = (x_coef * xt - x0) / e_coef
+        return e.to(dtype)
diff --git a/torchmultimodal/diffusion_labs/samplers/__init__.py b/torchmultimodal/diffusion_labs/samplers/__init__.py
new file mode 100644
index 00000000..2e41cd71
--- /dev/null
+++ b/torchmultimodal/diffusion_labs/samplers/__init__.py
@@ -0,0 +1,5 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
diff --git a/torchmultimodal/modules/diffusion/ddim.py b/torchmultimodal/diffusion_labs/samplers/ddim.py
similarity index 83%
rename from torchmultimodal/modules/diffusion/ddim.py
rename to torchmultimodal/diffusion_labs/samplers/ddim.py
index 94e024d1..ff1d2da7 100644
--- a/torchmultimodal/modules/diffusion/ddim.py
+++ b/torchmultimodal/diffusion_labs/samplers/ddim.py
@@ -8,13 +8,15 @@
 
 import torch
 from torch import nn, Tensor
-from torchmultimodal.modules.diffusion.predictors import Predictor
+from torchmultimodal.diffusion_labs.predictors.predictor import Predictor
+from torchmultimodal.diffusion_labs.samplers.sampler import Sampler
+from torchmultimodal.diffusion_labs.schedules.discrete_gaussian_schedule import (
+    DiscreteGaussianSchedule,
+)
+from torchmultimodal.diffusion_labs.utils.common import DiffusionOutput
 
-from torchmultimodal.modules.diffusion.schedules import DiffusionSchedule
-from torchmultimodal.utils.diffusion_utils import DiffusionOutput
 
-
-class DDIModule(nn.Module):
+class DDIModule(nn.Module, Sampler):
     """
     DDIModule implements "Denoising Diffusion Implicit Models" presented by Song et. al
     (https://arxiv.org/abs/2010.02502).
@@ -38,13 +40,13 @@ class DDIModule(nn.Module):
 
     Attributes:
     model (nn.Module):
-    schedule (DiffusionSchedule): defines noise diffusion throughout time
+    schedule (DiscreteGaussianSchedule): defines noise diffusion throughout time
     predictor (Predictor): used to help predict x0
     eval_steps (Tensor): a subset of steps to sample at inference time
     eta (float): scaling factor used in Equation 12 of Song et. al
                     (https://arxiv.org/abs/2010.02502)
     Args:
-        xt (Tensor): corrupted data at time t (when t = schedule.steps, xt is fully noise)
+        x (Tensor): corrupted data at time t (when t = schedule.steps, x is fully noise)
                      of shape  [b, c, ...]
         timestep (Tensor): diffusion step
         conditional_inputs (Dict): dictionary of context embeddings
@@ -54,7 +56,7 @@ class DDIModule(nn.Module):
     def __init__(
         self,
         model: nn.Module,
-        schedule: DiffusionSchedule,
+        schedule: DiscreteGaussianSchedule,
         predictor: Predictor,
         eval_steps: Optional[Tensor] = None,
         progress_bar: bool = True,
@@ -131,31 +133,31 @@ def remove_noise(
 
     def generator(
         self,
-        xt: Tensor,
+        x: Tensor,
         c: Optional[Dict[str, Tensor]] = None,
     ) -> Generator[Tensor, None, None]:
         """Generate xt for each t in self.eval_steps"""
         steps = self.eval_steps.flip(0)
         for step, next_step in zip(steps[:-1], steps[1:]):
             # Convert steps to batched tensors
-            t = step * torch.ones(xt.size(0), device=xt.device, dtype=torch.long)
-            t1 = next_step * torch.ones(xt.size(0), device=xt.device, dtype=torch.long)
+            t = step * torch.ones(x.size(0), device=x.device, dtype=torch.long)
+            t1 = next_step * torch.ones(x.size(0), device=x.device, dtype=torch.long)
             # Remove noise between step t and t+1
-            xt = self.remove_noise(xt, c, t, t1)
-            yield xt
+            x = self.remove_noise(x, c, t, t1)
+            yield x
 
     def forward(
         self,
-        xt: Tensor,
+        x: Tensor,
         timestep: Optional[Tensor] = None,
         conditional_inputs: Optional[Dict[str, Tensor]] = None,
     ) -> Union[DiffusionOutput, Tensor]:
         if self.training:
             if timestep is None:
                 raise ValueError("Must provide a timestep value during training")
-            return self.model(xt, timestep, conditional_inputs)
+            return self.model(x, timestep, conditional_inputs)
         else:
-            gen: Iterable = self.generator(xt, conditional_inputs)
+            gen: Iterable = self.generator(x, conditional_inputs)
             if self.progress_bar:
                 # Lazy import so that we don't depend on tqdm.
                 from tqdm.auto import tqdm
diff --git a/torchmultimodal/modules/diffusion/ddpm.py b/torchmultimodal/diffusion_labs/samplers/ddpm.py
similarity index 81%
rename from torchmultimodal/modules/diffusion/ddpm.py
rename to torchmultimodal/diffusion_labs/samplers/ddpm.py
index 62794595..cd2cd246 100644
--- a/torchmultimodal/modules/diffusion/ddpm.py
+++ b/torchmultimodal/diffusion_labs/samplers/ddpm.py
@@ -10,13 +10,15 @@
 import torch
 import torch.nn.functional as F
 from torch import nn, Tensor
-from torchmultimodal.modules.diffusion.predictors import Predictor
+from torchmultimodal.diffusion_labs.predictors.predictor import Predictor
+from torchmultimodal.diffusion_labs.samplers.sampler import Sampler
+from torchmultimodal.diffusion_labs.schedules.discrete_gaussian_schedule import (
+    DiscreteGaussianSchedule,
+)
+from torchmultimodal.diffusion_labs.utils.common import DiffusionOutput
 
-from torchmultimodal.modules.diffusion.schedules import DiffusionSchedule
-from torchmultimodal.utils.diffusion_utils import DiffusionOutput
 
-
-class DDPModule(nn.Module):
+class DDPModule(nn.Module, Sampler):
     """DDPModule acts as a wrapper module around an inner neural network. During training it uses the
     inner neural network to predict a single denoising step. When set to eval, calling forward will
     sample the entire diffusion schedule. This module follows the denoising diffusion process as
@@ -36,12 +38,12 @@ class DDPModule(nn.Module):
 
     Attributes:
         model (nn.Module): prediction neural network
-        schedule (DiffusionSchedule): defines diffusion of noise through time
+        schedule (DiscreteGaussianSchedule): defines diffusion of noise through time
         predictor (Predictor): predictor class to handle predictions depending on the model input
         eval_steps (Tensor): subset of steps to sample at inference
 
     Args:
-        xt (Tensor): corrupted data at time t (when t = schedule.steps, xt is equivalent to noise)
+        x (Tensor): corrupted data at time t (when t = schedule.steps, x is equivalent to noise)
         timestep (Tensor): diffusion step
         conditional_inputs (Dict): dictionary of context embeddings
 
@@ -50,7 +52,7 @@ class DDPModule(nn.Module):
     def __init__(
         self,
         model: nn.Module,
-        schedule: DiffusionSchedule,
+        schedule: DiscreteGaussianSchedule,
         predictor: Predictor,
         eval_steps: Optional[Tensor] = None,
         progress_bar: bool = True,
@@ -58,12 +60,12 @@ def __init__(
         super().__init__()
 
         self.model = model
-        self.train_schedule = schedule
-        self.train_predictor = predictor
+        self.schedule = schedule
+        self.predictor = predictor
         self.progress_bar = progress_bar
 
         if eval_steps is None:
-            eval_steps = torch.arange(self.train_schedule.steps)
+            eval_steps = torch.arange(self.schedule.steps)
             eval_steps_map = eval_steps
             self.eval_schedule = schedule
             self.eval_predictor = predictor
@@ -73,7 +75,7 @@ def __init__(
             eval_steps, _ = eval_steps.sort()
             # eval_map maps from timestep in full schedule, to timestep in truncated eval scheule
             # e.g. if train has 1000 steps, and eval has 3, then t = 500 would map to eval timestep = 1
-            eval_steps_map = torch.zeros(self.train_schedule.steps, dtype=torch.long)
+            eval_steps_map = torch.zeros(self.schedule.steps, dtype=torch.long)
             eval_steps_map[eval_steps] = torch.arange(len(eval_steps))
 
             # Compute cumulative product of only the alphas in the eval steps
@@ -103,8 +105,8 @@ def predict_parameters(
             t (Tensor): int diffusion steps
         """
         pred, value = input.prediction, input.variance_value
-        schedule = self.train_schedule if self.training else self.eval_schedule
-        predictor = self.train_predictor if self.training else self.eval_predictor
+        schedule = self.schedule if self.training else self.eval_schedule
+        predictor = self.predictor if self.training else self.eval_predictor
         timestep = t if self.training else self.eval_steps_map[t]
 
         x0 = predictor.predict_x0(pred, xt, timestep)
@@ -128,38 +130,38 @@ def remove_noise(
 
         # Predict x_{t-1}
         dtype = xt.dtype
-        noise = self.train_schedule.sample_noise(xt)
+        noise = self.schedule.sample_noise(xt)
         # Mask noise when t = 0; shape (b, 1, ..., 1) with same dims as xt
         nonzero_mask = (t != 0).to(dtype).view(-1, *([1] * (xt.dim() - 1)))
         # pyre-ignore
         return mean + nonzero_mask * (0.5 * log_variance).exp() * noise
 
     def generator(
-        self, xt: Tensor, c: Optional[Dict[str, Tensor]] = None
+        self, x: Tensor, c: Optional[Dict[str, Tensor]] = None
     ) -> Generator[Tensor, None, None]:
         """Generate xt for each t in sample_steps"""
         for step in self.eval_steps.flip(0):
-            t = step * torch.ones(xt.size(0), device=xt.device, dtype=torch.long)
-            xt = self.remove_noise(xt, t, c)
-            yield xt
+            t = step * torch.ones(x.size(0), device=x.device, dtype=torch.long)
+            x = self.remove_noise(x, t, c)
+            yield x
 
     def forward(
         self,
-        xt: Tensor,
+        x: Tensor,
         timestep: Optional[Tensor] = None,
         conditional_inputs: Optional[Dict[str, Tensor]] = None,
     ) -> Union[DiffusionOutput, Tensor]:
         if self.training:
             if timestep is None:
                 raise ValueError("Must provide a t value during training")
-            out = self.model(xt, timestep, conditional_inputs)
+            out = self.model(x, timestep, conditional_inputs)
             if not isinstance(out, DiffusionOutput):
                 raise TypeError("Model is expected to output a DiffusionOutput class")
             if out.variance_value is not None:
-                out.mean, out.log_variance = self.predict_parameters(out, xt, timestep)
+                out.mean, out.log_variance = self.predict_parameters(out, x, timestep)
             return out
         else:
-            gen: Iterable = self.generator(xt, conditional_inputs)
+            gen: Iterable = self.generator(x, conditional_inputs)
             if self.progress_bar:
                 # Lazy import so that we don't depend on tqdm.
                 from tqdm.auto import tqdm
diff --git a/torchmultimodal/diffusion_labs/samplers/sampler.py b/torchmultimodal/diffusion_labs/samplers/sampler.py
new file mode 100644
index 00000000..e5fd9c4d
--- /dev/null
+++ b/torchmultimodal/diffusion_labs/samplers/sampler.py
@@ -0,0 +1,78 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+from abc import abstractmethod
+from typing import Dict, Generator, Optional, Protocol, runtime_checkable, Union
+
+from torch import nn, Tensor
+from torchmultimodal.diffusion_labs.predictors.predictor import Predictor
+
+from torchmultimodal.diffusion_labs.schedules.schedule import DiffusionSchedule
+from torchmultimodal.diffusion_labs.utils.common import DiffusionOutput
+
+
+@runtime_checkable
+class Sampler(Protocol):
+    """Sampler class for applying the learned denoising function given the diffusion schedule.
+    During training this class passes through the model outputs but during eval, the model loops
+    the model for all the eval steps. This class implements the same forward signature as the
+    Adapter class. To access individual generative steps at eval, the Sampler.generator() method
+    will return a python generator that steps through each denoising step.
+
+    Example:
+        model = Sampler(...)
+        x = torch.randn(...)
+
+        # Generate with forward
+        model.eval()
+        with torch.no_grad():
+            img = model(x)
+
+        # Generator with generator
+        model.eval()
+        gen = model.generator(x)
+        images = []
+        with torch.no_grad():
+            for i in gen:
+                images.append(i)
+
+        img == images[-1]
+    """
+
+    model: nn.Module
+    schedule: DiffusionSchedule
+    predictor: Predictor
+    eval_steps: Tensor
+
+    @abstractmethod
+    def generator(
+        self,
+        x: Tensor,
+        c: Optional[Dict[str, Tensor]] = None,
+    ) -> Generator[Tensor, None, None]:
+        """Generator for each t in self.eval_steps
+
+        Args:
+            x (Tensor): corrupted data at time t (when t = schedule.steps, x is fully noise)
+                     of shape  [b, c, ...]
+            c (Dict): dictionary of model conditional inputs
+        """
+
+    @abstractmethod
+    def forward(
+        self,
+        x: Tensor,
+        timestep: Optional[Tensor] = None,
+        conditional_inputs: Optional[Dict[str, Tensor]] = None,
+    ) -> Union[DiffusionOutput, Tensor]:
+        """nn Module forward method
+
+        Args:
+            x (Tensor): corrupted data at time t (when t = schedule.steps, x is fully noise)
+                     of shape  [b, c, ...]
+            timestep (Optional[Tensor]): diffusion step
+            conditional_inputs (Dict): dictionary of model conditional inputs
+        """
diff --git a/torchmultimodal/diffusion_labs/schedules/__init__.py b/torchmultimodal/diffusion_labs/schedules/__init__.py
new file mode 100644
index 00000000..2e41cd71
--- /dev/null
+++ b/torchmultimodal/diffusion_labs/schedules/__init__.py
@@ -0,0 +1,5 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
diff --git a/torchmultimodal/modules/diffusion/schedules.py b/torchmultimodal/diffusion_labs/schedules/discrete_gaussian_schedule.py
similarity index 92%
rename from torchmultimodal/modules/diffusion/schedules.py
rename to torchmultimodal/diffusion_labs/schedules/discrete_gaussian_schedule.py
index 27ea6bc4..7f835149 100644
--- a/torchmultimodal/modules/diffusion/schedules.py
+++ b/torchmultimodal/diffusion_labs/schedules/discrete_gaussian_schedule.py
@@ -11,15 +11,17 @@
 import torch.nn.functional as F
 from torch import Tensor
 
+from torchmultimodal.diffusion_labs.schedules.schedule import DiffusionSchedule
 
-class DiffusionSchedule:
+
+class DiscreteGaussianSchedule(DiffusionSchedule):
     """Diffusion is a thermondynamic process of two substances intermingling, likewise diffusion probabilistic models represent
     the transformation of one distribution into another as a gradual stochastic process. Specifically, in Denoising Diffusion, we
     model the transformation from the Gaussian distribution to some data distribution over a number of time steps. DiffusionSchedule
     is a parameterized helper class to model the changing distribution from Gaussian (noise) to data (x). This is an implementation
     of a diffusion schedule with discrete time steps.
 
-    DiffusionSchedule manages all timestep properties that are a function of the variance schedule (betas). For example, to
+    DiscreteGaussianSchedule manages all timestep properties that are a function of the variance schedule (betas). For example, to
     calculate the compliment of betas (called alphas in the paper), you define alphas
 
     def alphas(schedule):
@@ -186,33 +188,33 @@ def __getattr__(self, name: str) -> Any:
             setattr(self, name, value)
             return value
         raise AttributeError(
-            f"'{self.__class__.__name__}' object has no attribute '{name}'"
+            f"{self.__class__.__name__!r} object has no attribute {name!r}"
         )
 
 
 # **************************************** Scheduler Functions ****************************************
-def _alphas(schedule: DiffusionSchedule) -> Tensor:
+def _alphas(schedule: DiscreteGaussianSchedule) -> Tensor:
     return 1.0 - schedule.betas
 
 
-def _alphas_cumprod(schedule: DiffusionSchedule) -> Tensor:
+def _alphas_cumprod(schedule: DiscreteGaussianSchedule) -> Tensor:
     return schedule.alphas.cumprod(axis=0)
 
 
-def _alphas_cumprod_prev(schedule: DiffusionSchedule) -> Tensor:
+def _alphas_cumprod_prev(schedule: DiscreteGaussianSchedule) -> Tensor:
     return F.pad(schedule.alphas_cumprod[:-1], (1, 0), value=1.0)
 
 
-def _sqrt_alphas_cumprod(schedule: DiffusionSchedule) -> Tensor:
+def _sqrt_alphas_cumprod(schedule: DiscreteGaussianSchedule) -> Tensor:
     return schedule.alphas_cumprod.sqrt()
 
 
-def _sqrt_compliment_alphas_cumprod(schedule: DiffusionSchedule) -> Tensor:
+def _sqrt_compliment_alphas_cumprod(schedule: DiscreteGaussianSchedule) -> Tensor:
     # pyre-ignore
     return (1.0 - schedule.alphas_cumprod).sqrt()
 
 
-def _lower_posterior_log_variance(schedule: DiffusionSchedule) -> Tensor:
+def _lower_posterior_log_variance(schedule: DiscreteGaussianSchedule) -> Tensor:
     # First element is 0 which has an infinite log (EQ 15 from Improving DDPMs)
     compliment_alphas_bar = 1.0 - schedule.alphas_cumprod
     compliment_alphas_bar_prev = 1.0 - schedule.alphas_cumprod_prev
@@ -222,17 +224,17 @@ def _lower_posterior_log_variance(schedule: DiffusionSchedule) -> Tensor:
     return lpv.log()
 
 
-def _upper_posterior_log_variance(schedule: DiffusionSchedule) -> Tensor:
+def _upper_posterior_log_variance(schedule: DiscreteGaussianSchedule) -> Tensor:
     return schedule.betas.log()
 
 
-def _posterior_mean_x0_coef(schedule: DiffusionSchedule) -> Tensor:
+def _posterior_mean_x0_coef(schedule: DiscreteGaussianSchedule) -> Tensor:
     alphas_cumprod_prev_sqrt = schedule.alphas_cumprod_prev.sqrt()
     compliment_alphas_cumprod = 1.0 - schedule.alphas_cumprod
     return schedule.betas * alphas_cumprod_prev_sqrt / compliment_alphas_cumprod
 
 
-def _posterior_mean_xt_coef(schedule: DiffusionSchedule) -> Tensor:
+def _posterior_mean_xt_coef(schedule: DiscreteGaussianSchedule) -> Tensor:
     compliment_alphas_cumprod_prev = 1.0 - schedule.alphas_cumprod_prev
     alphas_sqrt = schedule.alphas.sqrt()
     compliment_alphas_cumprod = 1.0 - schedule.alphas_cumprod
diff --git a/torchmultimodal/diffusion_labs/schedules/schedule.py b/torchmultimodal/diffusion_labs/schedules/schedule.py
new file mode 100644
index 00000000..0713512b
--- /dev/null
+++ b/torchmultimodal/diffusion_labs/schedules/schedule.py
@@ -0,0 +1,48 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+from abc import abstractmethod, abstractproperty
+from typing import Protocol, runtime_checkable
+
+from torch import Tensor
+
+
+@runtime_checkable
+class DiffusionSchedule(Protocol):
+    """Class that defines the entire diffusion process and provides helper functions
+    for computing various transformations given the diffusion process
+    """
+
+    @abstractmethod
+    def sample_noise(self, x_like: Tensor) -> Tensor:
+        """Sample from diffusion distribution
+
+        Args:
+           x_like (Tensor): example tensor to get meta properties for noise tensor
+        """
+
+    @abstractmethod
+    def sample_steps(self, x_like: Tensor) -> Tensor:
+        """Sample diffusion steps
+
+        Args:
+           x_like (Tensor): example tensor to get meta properties for noise tensor
+        """
+
+    @abstractmethod
+    def q_sample(self, x0: Tensor, noise: Tensor, t: Tensor) -> Tensor:
+        """Given data (x at step 0) and noise, compute xt for the given
+        diffusion t.
+
+        Args:
+            x0 (Tensor): uncorrupted data at step 0
+            noise (Tensor): sample noise, same size as x0
+            t (Tensor): int diffusion steps
+        """
+
+    @abstractproperty
+    def steps(self) -> int:
+        """Number of diffusion steps"""
diff --git a/torchmultimodal/diffusion_labs/transforms/__init__.py b/torchmultimodal/diffusion_labs/transforms/__init__.py
new file mode 100644
index 00000000..2e41cd71
--- /dev/null
+++ b/torchmultimodal/diffusion_labs/transforms/__init__.py
@@ -0,0 +1,5 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
diff --git a/torchmultimodal/diffusion_labs/transforms/diffusion_transform.py b/torchmultimodal/diffusion_labs/transforms/diffusion_transform.py
new file mode 100644
index 00000000..568f6113
--- /dev/null
+++ b/torchmultimodal/diffusion_labs/transforms/diffusion_transform.py
@@ -0,0 +1,40 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+from typing import Tuple
+
+from torch import nn, Tensor
+from torchmultimodal.diffusion_labs.schedules.schedule import DiffusionSchedule
+
+
+class RandomDiffusionSteps(nn.Module):
+    """Data Transform to randomly sample noised data from the diffusion schedule.
+    During diffusion training, random diffusion steps are sampled per model update.
+    This transform samples steps and returns the steps (t), seed noise, and transformed
+    data at time t (xt).
+
+    Attributes:
+        schedule (DiffusionSchedule): defines diffusion of noise through time
+        batched (bool): if True, transform expects a batched input
+
+    Args:
+        x (Tensor): data representing x0, artifact being learned. The 0 represents zero diffusion steps.
+    """
+
+    def __init__(self, schedule: DiffusionSchedule, batched: bool = True):
+        super().__init__()
+        self.schedule = schedule
+        self.batched = batched
+
+    def __call__(self, x: Tensor) -> Tuple[Tensor, Tensor, Tensor, Tensor]:
+        if not self.batched:
+            t = self.schedule.sample_steps(x.unsqueeze(0))
+            t = t.squeeze(0)
+        else:
+            t = self.schedule.sample_steps(x)
+        noise = self.schedule.sample_noise(x)
+        xt = self.schedule.q_sample(x, noise, t)
+        return x, xt, noise, t
diff --git a/torchmultimodal/diffusion_labs/utils/__init__.py b/torchmultimodal/diffusion_labs/utils/__init__.py
new file mode 100644
index 00000000..2e41cd71
--- /dev/null
+++ b/torchmultimodal/diffusion_labs/utils/__init__.py
@@ -0,0 +1,5 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
diff --git a/torchmultimodal/utils/diffusion_utils.py b/torchmultimodal/diffusion_labs/utils/common.py
similarity index 89%
rename from torchmultimodal/utils/diffusion_utils.py
rename to torchmultimodal/diffusion_labs/utils/common.py
index 11e74a57..22e330c1 100644
--- a/torchmultimodal/utils/diffusion_utils.py
+++ b/torchmultimodal/diffusion_labs/utils/common.py
@@ -43,6 +43,11 @@ def cascaded_resize(pil_image: Image, resolution: int) -> Image:
     return pil_image
 
 
+def normalize(x: Tensor, image_min: int, image_max: int) -> Tensor:
+    # Normalize image values between min and max
+    return (image_max - image_min) * x + image_min
+
+
 def denormalize_to_0_1(images: Tensor) -> Tensor:
     """Denormalize tensors from range [-1, 1] to [0, 1]"""
     denormed_images = torch.clamp((images + 1) / 2, 0, 1)
diff --git a/torchmultimodal/modules/diffusion/predictors.py b/torchmultimodal/modules/diffusion/predictors.py
deleted file mode 100644
index 8f561eeb..00000000
--- a/torchmultimodal/modules/diffusion/predictors.py
+++ /dev/null
@@ -1,113 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the BSD-style license found in the
-# LICENSE file in the root directory of this source tree.
-
-from abc import ABC, abstractmethod
-from typing import Callable, Optional
-
-from torch import Tensor
-from torchmultimodal.modules.diffusion.schedules import DiffusionSchedule
-
-
-class Predictor(ABC):
-    """Helper class to help predict various parts of the diffusion process. Different
-    implementations of each method are needed depending on what the model itself was
-    trained to predict.
-    """
-
-    schedule: DiffusionSchedule
-    clamp_func: Optional[Callable]
-
-    @abstractmethod
-    def predict_x0(self, prediction: Tensor, xt: Tensor, t: Tensor) -> Tensor:
-        """Predict x0
-
-        Args:
-            prediction (Tensor): model prediction
-            xt (Tensor): noised data to step t
-            t (Tensor): int diffusion step for xt
-        """
-        pass
-
-    @abstractmethod
-    def predict_noise(self, prediction: Tensor, xt: Tensor, t: Tensor) -> Tensor:
-        """Predict noise
-
-        Args:
-            prediction (Tensor): model prediction
-            xt (Tensor): noised data to step t
-            t (Tensor): int diffusion step for xt
-        """
-        pass
-
-
-class NoisePredictor(Predictor):
-    """Given a model that's trained to predict diffusion noise and corresponding schedule,
-        this class computes the predicted noise and x0 at step t.
-
-    Attributes:
-        schedule (DiffusionSchedule): defines diffusion of noise through time
-        clamp_func (Callable): function to clamp prediction values
-    """
-
-    def __init__(
-        self, schedule: DiffusionSchedule, clamp_func: Optional[Callable] = None
-    ):
-        self.clamp_func = clamp_func
-        schedule.add_property("sqrt_recip_alphas_cumprod", _sqrt_recip_alphas_cumprod)
-        schedule.add_property(
-            "sqrt_recipm1_alphas_cumprod", _sqrt_recipm1_alphas_cumprod
-        )
-        self.schedule = schedule
-
-    def predict_x0(self, prediction: Tensor, xt: Tensor, t: Tensor) -> Tensor:
-        shape, dtype = xt.shape, xt.dtype
-        x_coef = self.schedule("sqrt_recip_alphas_cumprod", t, shape)
-        e_coef = self.schedule("sqrt_recipm1_alphas_cumprod", t, shape)
-        x0 = x_coef * xt - e_coef * prediction
-        if self.clamp_func is not None:
-            x0 = self.clamp_func(x0)
-        return x0.to(dtype)
-
-    def predict_noise(self, prediction: Tensor, xt: Tensor, t: Tensor) -> Tensor:
-        return prediction
-
-
-class TargetPredictor(Predictor):
-    """Given a model that's trained to predict x0 and corresponding schedule,
-        this class computes the predicted noise and x0 at step t.
-
-    Attributes:
-        schedule (DiffusionSchedule): defines diffusion of noise through time
-        clamp_func (Callable): function to clamp prediction values
-    """
-
-    def __init__(
-        self, schedule: DiffusionSchedule, clamp_func: Optional[Callable] = None
-    ):
-        self.clamp_func = clamp_func
-        self.schedule = schedule
-
-    def predict_x0(self, prediction: Tensor, xt: Tensor, t: Tensor) -> Tensor:
-        if self.clamp_func is not None:
-            prediction = self.clamp_func(prediction)
-        return prediction
-
-    def predict_noise(self, prediction: Tensor, xt: Tensor, t: Tensor) -> Tensor:
-        # TODO: For DDIM add predict_noise
-        pass
-
-
-# TODO: Add VPredictor from https://arxiv.org/abs/2202.00512
-
-
-def _sqrt_recip_alphas_cumprod(schedule: DiffusionSchedule) -> Tensor:
-    # pyre-ignore
-    return (1.0 / schedule.alphas_cumprod).sqrt()
-
-
-def _sqrt_recipm1_alphas_cumprod(schedule: DiffusionSchedule) -> Tensor:
-    # pyre-ignore
-    return (1.0 / schedule.alphas_cumprod - 1).sqrt()