Made dummy latents smaller. Minor code cleanups

2 files changed, 8 insertions, 5 deletions

diff --git a/modules/processing.py b/modules/processing.py
index 3caac25e..539cde38 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -557,7 +557,8 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         else:

             # Dummy zero conditioning if we're not using inpainting model.

             # Still takes up a bit of memory, but no encoder call.

-            image_conditioning = torch.zeros(x.shape[0], 5, x.shape[-2], x.shape[-1], dtype=x.dtype, device=x.device)

+            # Pretty sure we can just make this a 1x1 image since its not going to be used besides its batch size.

+            image_conditioning = torch.zeros(x.shape[0], 5, 1, 1, dtype=x.dtype, device=x.device)

 

         return image_conditioning

 

@@ -759,8 +760,8 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
             self.image_conditioning = self.image_conditioning.to(shared.device).type(self.sd_model.dtype)

         else:

             self.image_conditioning = torch.zeros(

-                self.init_latent.shape[0], 5, self.init_latent.shape[-2], self.init_latent.shape[-1], 

-                dtype=self.init_latent.dtype, 

+                self.init_latent.shape[0], 5, 1, 1,

+                dtype=self.init_latent.dtype,

                 device=self.init_latent.device

             )

 

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index c21be26e..cc682593 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -138,7 +138,7 @@ class VanillaStableDiffusionSampler:
         if self.stop_at is not None and self.step > self.stop_at:

             raise InterruptedException

 

-        # Have to unwrap the inpainting conditioning here to perform pre-preocessing

+        # Have to unwrap the inpainting conditioning here to perform pre-processing

         image_conditioning = None

         if isinstance(cond, dict):

             image_conditioning = cond["c_concat"][0]

@@ -146,7 +146,7 @@ class VanillaStableDiffusionSampler:
             unconditional_conditioning = unconditional_conditioning["c_crossattn"][0]

 

         conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)

-        unconditional_conditioning = prompt_parser.reconstruct_cond_batch(unconditional_conditioning, self.step)            

+        unconditional_conditioning = prompt_parser.reconstruct_cond_batch(unconditional_conditioning, self.step)

 

         assert all([len(conds) == 1 for conds in conds_list]), 'composition via AND is not supported for DDIM/PLMS samplers'

         cond = tensor

@@ -165,6 +165,8 @@ class VanillaStableDiffusionSampler:
             img_orig = self.sampler.model.q_sample(self.init_latent, ts)

             x_dec = img_orig * self.mask + self.nmask * x_dec

 

+        # Wrap the image conditioning back up since the DDIM code can accept the dict directly.

+        # Note that they need to be lists because it just concatenates them later.

         if image_conditioning is not None:

             cond = {"c_concat": [image_conditioning], "c_crossattn": [cond]}

             unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}