Upstream patches

1 files changed, 17 insertions, 5 deletions

diff --git a/pipelines/stable_diffusion/vlpn_stable_diffusion.py b/pipelines/stable_diffusion/vlpn_stable_diffusion.py
index 707b639..a43a8e4 100644
--- a/pipelines/stable_diffusion/vlpn_stable_diffusion.py
+++ b/pipelines/stable_diffusion/vlpn_stable_diffusion.py
@@ -236,12 +236,24 @@ class VlpnStableDiffusion(DiffusionPipeline):
     def prepare_latents(self, batch_size, num_images_per_prompt, num_channels_latents, height, width, dtype, device, generator, latents=None):
         shape = (batch_size * num_images_per_prompt, num_channels_latents, height // 8, width // 8)
 
+        if isinstance(generator, list) and len(generator) != batch_size:
+            raise ValueError(
+                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+            )
+
         if latents is None:
-            if device.type == "mps":
-                # randn does not work reproducibly on mps
-                latents = torch.randn(shape, generator=generator, device="cpu", dtype=dtype).to(device)
+            rand_device = "cpu" if device.type == "mps" else device
+
+            if isinstance(generator, list):
+                shape = (1,) + shape[1:]
+                latents = [
+                    torch.randn(shape, generator=generator[i], device=rand_device, dtype=dtype)
+                    for i in range(batch_size)
+                ]
+                latents = torch.cat(latents, dim=0).to(device)
             else:
-                latents = torch.randn(shape, generator=generator, device=device, dtype=dtype)
+                latents = torch.randn(shape, generator=generator, device=rand_device, dtype=dtype).to(device)
         else:
             if latents.shape != shape:
                 raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
@@ -311,7 +323,7 @@ class VlpnStableDiffusion(DiffusionPipeline):
         num_inference_steps: Optional[int] = 50,
         guidance_scale: Optional[float] = 7.5,
         eta: Optional[float] = 0.0,
-        generator: Optional[torch.Generator] = None,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
         image: Optional[Union[torch.FloatTensor, PIL.Image.Image]] = None,
         output_type: Optional[str] = "pil",
         return_dict: bool = True,