Merge branch 'main' into support-lighttx2v-lora

Author: sayakpaul

src/diffusers/models/autoencoders/autoencoder_kl_wan.py

@@ -913,38 +913,21 @@ def patchify(x, patch_size):
     if patch_size == 1:
         return x
 
-    if x.dim() == 4:
-        # x shape: [batch_size, channels, height, width]
-        batch_size, channels, height, width = x.shape
-
-        # Ensure height and width are divisible by patch_size
-        if height % patch_size != 0 or width % patch_size != 0:
-            raise ValueError(f"Height ({height}) and width ({width}) must be divisible by patch_size ({patch_size})")
-
-        # Reshape to [batch_size, channels, height//patch_size, patch_size, width//patch_size, patch_size]
-        x = x.view(batch_size, channels, height // patch_size, patch_size, width // patch_size, patch_size)
-
-        # Rearrange to [batch_size, channels * patch_size * patch_size, height//patch_size, width//patch_size]
-        x = x.permute(0, 1, 3, 5, 2, 4).contiguous()
-        x = x.view(batch_size, channels * patch_size * patch_size, height // patch_size, width // patch_size)
-
-    elif x.dim() == 5:
-        # x shape: [batch_size, channels, frames, height, width]
-        batch_size, channels, frames, height, width = x.shape
-
-        # Ensure height and width are divisible by patch_size
-        if height % patch_size != 0 or width % patch_size != 0:
-            raise ValueError(f"Height ({height}) and width ({width}) must be divisible by patch_size ({patch_size})")
+    if x.dim() != 5:
+        raise ValueError(f"Invalid input shape: {x.shape}")
+    # x shape: [batch_size, channels, frames, height, width]
+    batch_size, channels, frames, height, width = x.shape
 
-        # Reshape to [batch_size, channels, frames, height//patch_size, patch_size, width//patch_size, patch_size]
-        x = x.view(batch_size, channels, frames, height // patch_size, patch_size, width // patch_size, patch_size)
+    # Ensure height and width are divisible by patch_size
+    if height % patch_size != 0 or width % patch_size != 0:
+        raise ValueError(f"Height ({height}) and width ({width}) must be divisible by patch_size ({patch_size})")
 
-        # Rearrange to [batch_size, channels * patch_size * patch_size, frames, height//patch_size, width//patch_size]
-        x = x.permute(0, 1, 4, 6, 2, 3, 5).contiguous()
-        x = x.view(batch_size, channels * patch_size * patch_size, frames, height // patch_size, width // patch_size)
+    # Reshape to [batch_size, channels, frames, height//patch_size, patch_size, width//patch_size, patch_size]
+    x = x.view(batch_size, channels, frames, height // patch_size, patch_size, width // patch_size, patch_size)
 
-    else:
-        raise ValueError(f"Invalid input shape: {x.shape}")
+    # Rearrange to [batch_size, channels * patch_size * patch_size, frames, height//patch_size, width//patch_size]
+    x = x.permute(0, 1, 6, 4, 2, 3, 5).contiguous()
+    x = x.view(batch_size, channels * patch_size * patch_size, frames, height // patch_size, width // patch_size)
 
     return x
 
@@ -953,29 +936,18 @@ def unpatchify(x, patch_size):
     if patch_size == 1:
         return x
 
-    if x.dim() == 4:
-        # x shape: [b, (c * patch_size * patch_size), h, w]
-        batch_size, c_patches, height, width = x.shape
-        channels = c_patches // (patch_size * patch_size)
-
-        # Reshape to [b, c, patch_size, patch_size, h, w]
-        x = x.view(batch_size, channels, patch_size, patch_size, height, width)
-
-        # Rearrange to [b, c, h * patch_size, w * patch_size]
-        x = x.permute(0, 1, 4, 2, 5, 3).contiguous()
-        x = x.view(batch_size, channels, height * patch_size, width * patch_size)
-
-    elif x.dim() == 5:
-        # x shape: [batch_size, (channels * patch_size * patch_size), frame, height, width]
-        batch_size, c_patches, frames, height, width = x.shape
-        channels = c_patches // (patch_size * patch_size)
+    if x.dim() != 5:
+        raise ValueError(f"Invalid input shape: {x.shape}")
+    # x shape: [batch_size, (channels * patch_size * patch_size), frame, height, width]
+    batch_size, c_patches, frames, height, width = x.shape
+    channels = c_patches // (patch_size * patch_size)
 
-        # Reshape to [b, c, patch_size, patch_size, f, h, w]
-        x = x.view(batch_size, channels, patch_size, patch_size, frames, height, width)
+    # Reshape to [b, c, patch_size, patch_size, f, h, w]
+    x = x.view(batch_size, channels, patch_size, patch_size, frames, height, width)
 
-        # Rearrange to [b, c, f, h * patch_size, w * patch_size]
-        x = x.permute(0, 1, 4, 5, 2, 6, 3).contiguous()
-        x = x.view(batch_size, channels, frames, height * patch_size, width * patch_size)
+    # Rearrange to [b, c, f, h * patch_size, w * patch_size]
+    x = x.permute(0, 1, 4, 5, 3, 6, 2).contiguous()
+    x = x.view(batch_size, channels, frames, height * patch_size, width * patch_size)
 
     return x
 
@@ -1044,7 +1016,6 @@ def __init__(
         patch_size: Optional[int] = None,
         scale_factor_temporal: Optional[int] = 4,
         scale_factor_spatial: Optional[int] = 8,
-        clip_output: bool = True,
     ) -> None:
         super().__init__()
 
@@ -1244,10 +1215,11 @@ def _decode(self, z: torch.Tensor, return_dict: bool = True):
                 out_ = self.decoder(x[:, :, i : i + 1, :, :], feat_cache=self._feat_map, feat_idx=self._conv_idx)
                 out = torch.cat([out, out_], 2)
 
-        if self.config.clip_output:
-            out = torch.clamp(out, min=-1.0, max=1.0)
         if self.config.patch_size is not None:
             out = unpatchify(out, patch_size=self.config.patch_size)
+
+        out = torch.clamp(out, min=-1.0, max=1.0)
+
         self.clear_cache()
         if not return_dict:
             return (out,)

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py

@@ -1034,7 +1034,8 @@ def __call__(
 
                 # expand the latents if we are doing classifier free guidance
                 latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
-                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+                if hasattr(self.scheduler, "scale_model_input"):
+                    latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
 
                 # predict the noise residual
                 noise_pred = self.unet(