Merge remote-tracking branch 'origin/main'

src/diffusers/models/resnet.py

-
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
@@ -29,6 +28,7 @@ def conv_nd(dims, *args, **kwargs):
         return nn.Conv3d(*args, **kwargs)
     raise ValueError(f"unsupported dimensions: {dims}")
 
+
 def conv_transpose_nd(dims, *args, **kwargs):
     """
     Create a 1D, 2D, or 3D convolution module.
@@ -64,7 +64,7 @@ class Upsample(nn.Module):
                  upsampling occurs in the inner-two dimensions.
     """
 
-    def __init__(self, channels, use_conv, use_conv_transpose=False, dims=2, out_channels=None):
+    def __init__(self, channels, use_conv=False, use_conv_transpose=False, dims=2, out_channels=None):
         super().__init__()
         self.channels = channels
         self.out_channels = out_channels or channels
@@ -73,7 +73,7 @@ class Upsample(nn.Module):
         self.use_conv_transpose = use_conv_transpose
 
         if use_conv_transpose:
-            self.conv = conv_transpose_nd(dims, channels, out_channels, 4, 2, 1)
+            self.conv = conv_transpose_nd(dims, channels, self.out_channels, 4, 2, 1)
         elif use_conv:
             self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=1)
 
@@ -125,87 +125,7 @@ class Downsample(nn.Module):
         return self.down(x)
 
 
-class UNetUpsample(nn.Module):
-    def __init__(self, in_channels, with_conv):
-        super().__init__()
-        self.with_conv = with_conv
-        if self.with_conv:
-            self.conv = torch.nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1)
-
-    def forward(self, x):
-        x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest")
-        if self.with_conv:
-            x = self.conv(x)
-        return x
-
-class GlideUpsample(nn.Module):
-    """
-    An upsampling layer with an optional convolution.
-
-    :param channels: channels in the inputs and outputs.
-    :param use_conv: a bool determining if a convolution is applied.
-    :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
-                 upsampling occurs in the inner-two dimensions.
-    """
-
-    def __init__(self, channels, use_conv, dims=2, out_channels=None):
-        super().__init__()
-        self.channels = channels
-        self.out_channels = out_channels or channels
-        self.use_conv = use_conv
-        self.dims = dims
-        if use_conv:
-            self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=1)
-
-    def forward(self, x):
-        assert x.shape[1] == self.channels
-        if self.dims == 3:
-            x = F.interpolate(x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest")
-        else:
-            x = F.interpolate(x, scale_factor=2, mode="nearest")
-        if self.use_conv:
-            x = self.conv(x)
-        return x
-
-
-class LDMUpsample(nn.Module):
-    """
-    An upsampling layer with an optional convolution.
-    :param channels: channels in the inputs and outputs.
-    :param use_conv: a bool determining if a convolution is applied.
-    :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
-                 upsampling occurs in the inner-two dimensions.
-    """
-
-    def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1):
-        super().__init__()
-        self.channels = channels
-        self.out_channels = out_channels or channels
-        self.use_conv = use_conv
-        self.dims = dims
-        if use_conv:
-            self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=padding)
-
-    def forward(self, x):
-        assert x.shape[1] == self.channels
-        if self.dims == 3:
-            x = F.interpolate(x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest")
-        else:
-            x = F.interpolate(x, scale_factor=2, mode="nearest")
-        if self.use_conv:
-            x = self.conv(x)
-        return x
-
-
-class GradTTSUpsample(torch.nn.Module):
-    def __init__(self, dim):
-        super(Upsample, self).__init__()
-        self.conv = torch.nn.ConvTranspose2d(dim, dim, 4, 2, 1)
-
-    def forward(self, x):
-        return self.conv(x)
-
-
+# TODO (patil-suraj): needs test
 class Upsample1d(nn.Module):
     def __init__(self, dim):
         super().__init__()

src/diffusers/models/unet.py

@@ -31,6 +31,7 @@ from tqdm import tqdm
 from ..configuration_utils import ConfigMixin
 from ..modeling_utils import ModelMixin
 from .embeddings import get_timestep_embedding
+from .resnet import Upsample
 
 
 def nonlinearity(x):
@@ -42,20 +43,6 @@ def Normalize(in_channels):
     return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
 
 
-class Upsample(nn.Module):
-    def __init__(self, in_channels, with_conv):
-        super().__init__()
-        self.with_conv = with_conv
-        if self.with_conv:
-            self.conv = torch.nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1)
-
-    def forward(self, x):
-        x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest")
-        if self.with_conv:
-            x = self.conv(x)
-        return x
-
-
 class Downsample(nn.Module):
     def __init__(self, in_channels, with_conv):
         super().__init__()
@@ -259,7 +246,7 @@ class UNetModel(ModelMixin, ConfigMixin):
             up.block = block
             up.attn = attn
             if i_level != 0:
-                up.upsample = Upsample(block_in, resamp_with_conv)
+                up.upsample = Upsample(block_in, use_conv=resamp_with_conv)
                 curr_res = curr_res * 2
             self.up.insert(0, up)  # prepend to get consistent order
 

src/diffusers/models/unet_glide.py

@@ -8,6 +8,7 @@ import torch.nn.functional as F
 from ..configuration_utils import ConfigMixin
 from ..modeling_utils import ModelMixin
 from .embeddings import get_timestep_embedding
+from .resnet import Upsample
 
 
 def convert_module_to_f16(l):
@@ -125,36 +126,6 @@ class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
         return x
 
 
-class Upsample(nn.Module):
-    """
-    An upsampling layer with an optional convolution.
-
-    :param channels: channels in the inputs and outputs.
-    :param use_conv: a bool determining if a convolution is applied.
-    :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
-                 upsampling occurs in the inner-two dimensions.
-    """
-
-    def __init__(self, channels, use_conv, dims=2, out_channels=None):
-        super().__init__()
-        self.channels = channels
-        self.out_channels = out_channels or channels
-        self.use_conv = use_conv
-        self.dims = dims
-        if use_conv:
-            self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=1)
-
-    def forward(self, x):
-        assert x.shape[1] == self.channels
-        if self.dims == 3:
-            x = F.interpolate(x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest")
-        else:
-            x = F.interpolate(x, scale_factor=2, mode="nearest")
-        if self.use_conv:
-            x = self.conv(x)
-        return x
-
-
 class Downsample(nn.Module):
     """
     A downsampling layer with an optional convolution.
@@ -231,8 +202,8 @@ class ResBlock(TimestepBlock):
         self.updown = up or down
 
         if up:
-            self.h_upd = Upsample(channels, False, dims)
-            self.x_upd = Upsample(channels, False, dims)
+            self.h_upd = Upsample(channels, use_conv=False, dims=dims)
+            self.x_upd = Upsample(channels, use_conv=False, dims=dims)
         elif down:
             self.h_upd = Downsample(channels, False, dims)
             self.x_upd = Downsample(channels, False, dims)
@@ -567,7 +538,7 @@ class GlideUNetModel(ModelMixin, ConfigMixin):
                             up=True,
                         )
                         if resblock_updown
-                        else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch)
+                        else Upsample(ch, use_conv=conv_resample, dims=dims, out_channels=out_ch)
                     )
                     ds //= 2
                 self.output_blocks.append(TimestepEmbedSequential(*layers))

src/diffusers/models/unet_grad_tts.py

@@ -10,6 +10,7 @@ except:
 from ..configuration_utils import ConfigMixin
 from ..modeling_utils import ModelMixin
 from .embeddings import get_timestep_embedding
+from .resnet import Upsample
 
 
 class Mish(torch.nn.Module):
@@ -17,15 +18,6 @@ class Mish(torch.nn.Module):
         return x * torch.tanh(torch.nn.functional.softplus(x))
 
 
-class Upsample(torch.nn.Module):
-    def __init__(self, dim):
-        super(Upsample, self).__init__()
-        self.conv = torch.nn.ConvTranspose2d(dim, dim, 4, 2, 1)
-
-    def forward(self, x):
-        return self.conv(x)
-
-
 class Downsample(torch.nn.Module):
     def __init__(self, dim):
         super(Downsample, self).__init__()
@@ -166,7 +158,7 @@ class UNetGradTTSModel(ModelMixin, ConfigMixin):
                         ResnetBlock(dim_out * 2, dim_in, time_emb_dim=dim),
                         ResnetBlock(dim_in, dim_in, time_emb_dim=dim),
                         Residual(Rezero(LinearAttention(dim_in))),
-                        Upsample(dim_in),
+                        Upsample(dim_in, use_conv_transpose=True),
                     ]
                 )
             )

src/diffusers/models/unet_ldm.py

@@ -17,6 +17,7 @@ except:
 from ..configuration_utils import ConfigMixin
 from ..modeling_utils import ModelMixin
 from .embeddings import get_timestep_embedding
+from .resnet import Upsample
 
 
 def exists(val):
@@ -81,60 +82,62 @@ def Normalize(in_channels):
     return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
 
 
-class LinearAttention(nn.Module):
-    def __init__(self, dim, heads=4, dim_head=32):
-        super().__init__()
-        self.heads = heads
-        hidden_dim = dim_head * heads
-        self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias=False)
-        self.to_out = nn.Conv2d(hidden_dim, dim, 1)
-
-    def forward(self, x):
-        b, c, h, w = x.shape
-        qkv = self.to_qkv(x)
-        q, k, v = rearrange(qkv, "b (qkv heads c) h w -> qkv b heads c (h w)", heads=self.heads, qkv=3)
-        k = k.softmax(dim=-1)
-        context = torch.einsum("bhdn,bhen->bhde", k, v)
-        out = torch.einsum("bhde,bhdn->bhen", context, q)
-        out = rearrange(out, "b heads c (h w) -> b (heads c) h w", heads=self.heads, h=h, w=w)
-        return self.to_out(out)
-
-
-class SpatialSelfAttention(nn.Module):
-    def __init__(self, in_channels):
-        super().__init__()
-        self.in_channels = in_channels
-
-        self.norm = Normalize(in_channels)
-        self.q = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0)
-        self.k = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0)
-        self.v = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0)
-        self.proj_out = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0)
-
-    def forward(self, x):
-        h_ = x
-        h_ = self.norm(h_)
-        q = self.q(h_)
-        k = self.k(h_)
-        v = self.v(h_)
-
-        # compute attention
-        b, c, h, w = q.shape
-        q = rearrange(q, "b c h w -> b (h w) c")
-        k = rearrange(k, "b c h w -> b c (h w)")
-        w_ = torch.einsum("bij,bjk->bik", q, k)
-
-        w_ = w_ * (int(c) ** (-0.5))
-        w_ = torch.nn.functional.softmax(w_, dim=2)
-
-        # attend to values
-        v = rearrange(v, "b c h w -> b c (h w)")
-        w_ = rearrange(w_, "b i j -> b j i")
-        h_ = torch.einsum("bij,bjk->bik", v, w_)
-        h_ = rearrange(h_, "b c (h w) -> b c h w", h=h)
-        h_ = self.proj_out(h_)
-
-        return x + h_
+# class LinearAttention(nn.Module):
+#    def __init__(self, dim, heads=4, dim_head=32):
+#        super().__init__()
+#        self.heads = heads
+#        hidden_dim = dim_head * heads
+#        self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias=False)
+#        self.to_out = nn.Conv2d(hidden_dim, dim, 1)
+#
+#    def forward(self, x):
+#        b, c, h, w = x.shape
+#        qkv = self.to_qkv(x)
+#        q, k, v = rearrange(qkv, "b (qkv heads c) h w -> qkv b heads c (h w)", heads=self.heads, qkv=3)
+#        import ipdb; ipdb.set_trace()
+#        k = k.softmax(dim=-1)
+#        context = torch.einsum("bhdn,bhen->bhde", k, v)
+#        out = torch.einsum("bhde,bhdn->bhen", context, q)
+#        out = rearrange(out, "b heads c (h w) -> b (heads c) h w", heads=self.heads, h=h, w=w)
+#        return self.to_out(out)
+#
+
+# class SpatialSelfAttention(nn.Module):
+#    def __init__(self, in_channels):
+#        super().__init__()
+#        self.in_channels = in_channels
+#
+#        self.norm = Normalize(in_channels)
+#        self.q = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0)
+#        self.k = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0)
+#        self.v = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0)
+#        self.proj_out = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0)
+#
+#    def forward(self, x):
+#        h_ = x
+#        h_ = self.norm(h_)
+#        q = self.q(h_)
+#        k = self.k(h_)
+#        v = self.v(h_)
+#
+# compute attention
+#        b, c, h, w = q.shape
+#        q = rearrange(q, "b c h w -> b (h w) c")
+#        k = rearrange(k, "b c h w -> b c (h w)")
+#        w_ = torch.einsum("bij,bjk->bik", q, k)
+#
+#        w_ = w_ * (int(c) ** (-0.5))
+#        w_ = torch.nn.functional.softmax(w_, dim=2)
+#
+# attend to values
+#        v = rearrange(v, "b c h w -> b c (h w)")
+#        w_ = rearrange(w_, "b i j -> b j i")
+#        h_ = torch.einsum("bij,bjk->bik", v, w_)
+#        h_ = rearrange(h_, "b c (h w) -> b c h w", h=h)
+#        h_ = self.proj_out(h_)
+#
+#        return x + h_
+#
 
 
 class CrossAttention(nn.Module):
@@ -377,35 +380,6 @@ class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
         return x
 
 
-class Upsample(nn.Module):
-    """
-    An upsampling layer with an optional convolution.
-    :param channels: channels in the inputs and outputs.
-    :param use_conv: a bool determining if a convolution is applied.
-    :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
-                 upsampling occurs in the inner-two dimensions.
-    """
-
-    def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1):
-        super().__init__()
-        self.channels = channels
-        self.out_channels = out_channels or channels
-        self.use_conv = use_conv
-        self.dims = dims
-        if use_conv:
-            self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=padding)
-
-    def forward(self, x):
-        assert x.shape[1] == self.channels
-        if self.dims == 3:
-            x = F.interpolate(x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest")
-        else:
-            x = F.interpolate(x, scale_factor=2, mode="nearest")
-        if self.use_conv:
-            x = self.conv(x)
-        return x
-
-
 class Downsample(nn.Module):
     """
     A downsampling layer with an optional convolution.
@@ -480,8 +454,8 @@ class ResBlock(TimestepBlock):
         self.updown = up or down
 
         if up:
-            self.h_upd = Upsample(channels, False, dims)
-            self.x_upd = Upsample(channels, False, dims)
+            self.h_upd = Upsample(channels, use_conv=False, dims=dims)
+            self.x_upd = Upsample(channels, use_conv=False, dims=dims)
         elif down:
             self.h_upd = Downsample(channels, False, dims)
             self.x_upd = Downsample(channels, False, dims)
@@ -948,7 +922,7 @@ class UNetLDMModel(ModelMixin, ConfigMixin):
                             up=True,
                         )
                         if resblock_updown
-                        else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch)
+                        else Upsample(ch, use_conv=conv_resample, dims=dims, out_channels=out_ch)
                     )
                     ds //= 2
                 self.output_blocks.append(TimestepEmbedSequential(*layers))

tests/test_layers_utils.py

@@ -22,6 +22,7 @@ import numpy as np
 import torch
 
 from diffusers.models.embeddings import get_timestep_embedding
+from diffusers.models.resnet import Upsample
 from diffusers.testing_utils import floats_tensor, slow, torch_device
 
 
@@ -113,3 +114,53 @@ class EmbeddingsTests(unittest.TestCase):
             torch.tensor([-0.9801, -0.9464, -0.9349, -0.3952, 0.8887, -0.9709, 0.5299, -0.2853, -0.9927]),
             1e-3,
         )
+
+
+class UpsampleBlockTests(unittest.TestCase):
+    def test_upsample_default(self):
+        torch.manual_seed(0)
+        sample = torch.randn(1, 32, 32, 32)
+        upsample = Upsample(channels=32, use_conv=False)
+        with torch.no_grad():
+            upsampled = upsample(sample)
+
+        assert upsampled.shape == (1, 32, 64, 64)
+        output_slice = upsampled[0, -1, -3:, -3:]
+        expected_slice = torch.tensor([-0.2173, -1.2079, -1.2079, 0.2952, 1.1254, 1.1254, 0.2952, 1.1254, 1.1254])
+        assert torch.allclose(output_slice.flatten(), expected_slice, atol=1e-3)
+
+    def test_upsample_with_conv(self):
+        torch.manual_seed(0)
+        sample = torch.randn(1, 32, 32, 32)
+        upsample = Upsample(channels=32, use_conv=True)
+        with torch.no_grad():
+            upsampled = upsample(sample)
+
+        assert upsampled.shape == (1, 32, 64, 64)
+        output_slice = upsampled[0, -1, -3:, -3:]
+        expected_slice = torch.tensor([0.7145, 1.3773, 0.3492, 0.8448, 1.0839, -0.3341, 0.5956, 0.1250, -0.4841])
+        assert torch.allclose(output_slice.flatten(), expected_slice, atol=1e-3)
+
+    def test_upsample_with_conv_out_dim(self):
+        torch.manual_seed(0)
+        sample = torch.randn(1, 32, 32, 32)
+        upsample = Upsample(channels=32, use_conv=True, out_channels=64)
+        with torch.no_grad():
+            upsampled = upsample(sample)
+
+        assert upsampled.shape == (1, 64, 64, 64)
+        output_slice = upsampled[0, -1, -3:, -3:]
+        expected_slice = torch.tensor([0.2703, 0.1656, -0.2538, -0.0553, -0.2984, 0.1044, 0.1155, 0.2579, 0.7755])
+        assert torch.allclose(output_slice.flatten(), expected_slice, atol=1e-3)
+
+    def test_upsample_with_transpose(self):
+        torch.manual_seed(0)
+        sample = torch.randn(1, 32, 32, 32)
+        upsample = Upsample(channels=32, use_conv=False, use_conv_transpose=True)
+        with torch.no_grad():
+            upsampled = upsample(sample)
+
+        assert upsampled.shape == (1, 32, 64, 64)
+        output_slice = upsampled[0, -1, -3:, -3:]
+        expected_slice = torch.tensor([-0.3028, -0.1582, 0.0071, 0.0350, -0.4799, -0.1139, 0.1056, -0.1153, -0.1046])
+        assert torch.allclose(output_slice.flatten(), expected_slice, atol=1e-3)

tests/test_modeling_utils.py

@@ -21,7 +21,7 @@ import unittest
 import numpy as np
 import torch
 
-from diffusers import (
+from diffusers import (  # GradTTSPipeline,
     BDDMPipeline,
     DDIMPipeline,
     DDIMScheduler,
@@ -30,7 +30,6 @@ from diffusers import (
     GlidePipeline,
     GlideSuperResUNetModel,
     GlideTextToImageUNetModel,
-    GradTTSPipeline,
     GradTTSScheduler,
     LatentDiffusionPipeline,
     NCSNpp,
@@ -511,6 +510,28 @@ class UNetLDMModelTests(ModelTesterMixin, unittest.TestCase):
 
         self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3))
 
+    def test_output_pretrained_spatial_transformer(self):
+        model = UNetLDMModel.from_pretrained("fusing/unet-ldm-dummy-spatial")
+        model.eval()
+
+        torch.manual_seed(0)
+        if torch.cuda.is_available():
+            torch.cuda.manual_seed_all(0)
+
+        noise = torch.randn(1, model.config.in_channels, model.config.image_size, model.config.image_size)
+        context = torch.ones((1, 16, 64), dtype=torch.float32)
+        time_step = torch.tensor([10] * noise.shape[0])
+
+        with torch.no_grad():
+            output = model(noise, time_step, context=context)
+
+        output_slice = output[0, -1, -3:, -3:].flatten()
+        # fmt: off
+        expected_output_slice = torch.tensor([61.3445, 56.9005, 29.4339, 59.5497, 60.7375, 34.1719, 48.1951, 42.6569, 25.0890])
+        # fmt: on
+
+        self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3))
+
 
 class UNetGradTTSModelTests(ModelTesterMixin, unittest.TestCase):
     model_class = UNetGradTTSModel