1 files changed, 40 insertions, 72 deletions

diff --git a/training/functional.py b/training/functional.py
index e7f02cb..68071bc 100644
--- a/training/functional.py
+++ b/training/functional.py
@@ -23,7 +23,7 @@ from models.clip.embeddings import ManagedCLIPTextEmbeddings, patch_managed_embe
 from models.clip.util import get_extended_embeddings
 from models.clip.tokenizer import MultiCLIPTokenizer
 from training.util import AverageMeter
-from util.noise import perlin_noise
+from util.slerp import slerp
 
 
 def const(result=None):
@@ -270,62 +270,16 @@ def snr_weight(noisy_latents, latents, gamma):
     )
 
 
-def slerp(v1, v2, t, DOT_THR=0.9995, to_cpu=False, zdim=-1):
-    """SLERP for pytorch tensors interpolating `v1` to `v2` with scale of `t`.
-
-    `DOT_THR` determines when the vectors are too close to parallel.
-        If they are too close, then a regular linear interpolation is used.
-
-    `to_cpu` is a flag that optionally computes SLERP on the CPU.
-        If the input tensors were on a GPU, it moves them back after the computation.  
-
-    `zdim` is the feature dimension over which to compute norms and find angles.
-        For example: if a sequence of 5 vectors is input with shape [5, 768]
-        Then `zdim = 1` or `zdim = -1` computes SLERP along the feature dim of 768.
-
-    Theory Reference:
-    https://splines.readthedocs.io/en/latest/rotation/slerp.html
-    PyTorch reference:
-    https://discuss.pytorch.org/t/help-regarding-slerp-function-for-generative-model-sampling/32475/3
-    Numpy reference: 
-    https://gist.github.com/dvschultz/3af50c40df002da3b751efab1daddf2c
-    """
-
-    # check if we need to move to the cpu
-    if to_cpu:
-        orig_device = v1.device
-        v1, v2 = v1.to('cpu'), v2.to('cpu')
-
-    # take the dot product between normalized vectors
-    v1_norm = v1 / torch.norm(v1, dim=zdim, keepdim=True)
-    v2_norm = v2 / torch.norm(v2, dim=zdim, keepdim=True)
-    dot = (v1_norm * v2_norm).sum(zdim)
-
-    for _ in range(len(dot.shape), len(v1.shape)):
-        dot = dot[..., None]
-
-    # if the vectors are too close, return a simple linear interpolation
-    if (torch.abs(dot) > DOT_THR).any():
-        res = (1 - t) * v1 + t * v2
-    else:
-        # compute the angle terms we need
-        theta = torch.acos(dot)
-        theta_t = theta * t
-        sin_theta = torch.sin(theta)
-        sin_theta_t = torch.sin(theta_t)
-
-        # compute the sine scaling terms for the vectors
-        s1 = torch.sin(theta - theta_t) / sin_theta
-        s2 = sin_theta_t / sin_theta
-
-        # interpolate the vectors
-        res = s1 * v1 + s2 * v2
-
-    # check if we need to move them back to the original device
-    if to_cpu:
-        res.to(orig_device)
-
-    return res
+def make_solid_image(color: float, shape, vae, dtype, device, generator):
+    img = torch.tensor(
+        [[[[color]]]],
+        dtype=dtype,
+        device=device
+    ).expand(1, *shape)
+    img = img * 2 - 1
+    img = vae.encode(img).latent_dist.sample(generator=generator)
+    img *= vae.config.scaling_factor
+    return img
 
 
 def loss_step(
@@ -361,20 +315,29 @@ def loss_step(
     )
 
     if offset_noise_strength != 0:
-        cache_key = f"img_white_{images.shape[2]}_{images.shape[3]}"
-
-        if cache_key not in cache:
-            img_white = torch.tensor(
-                [[[[1]]]],
-                dtype=latents.dtype,
-                device=latents.device
-            ).expand(1, images.shape[1], images.shape[2], images.shape[3])
-            img_white = img_white * 2 - 1
-            img_white = vae.encode(img_white).latent_dist.sample(generator=generator)
-            img_white *= vae.config.scaling_factor
-            cache[cache_key] = img_white
+        solid_image = partial(
+            make_solid_image,
+            shape=images.shape[1:],
+            vae=vae,
+            dtype=latents.dtype,
+            device=latents.device,
+            generator=generator
+        )
+
+        white_cache_key = f"img_white_{images.shape[2]}_{images.shape[3]}"
+        black_cache_key = f"img_black_{images.shape[2]}_{images.shape[3]}"
+
+        if white_cache_key not in cache:
+            img_white = solid_image(1)
+            cache[white_cache_key] = img_white
         else:
-            img_white = cache[cache_key]
+            img_white = cache[white_cache_key]
+
+        if black_cache_key not in cache:
+            img_black = solid_image(0)
+            cache[black_cache_key] = img_black
+        else:
+            img_black = cache[black_cache_key]
 
         offset_strength = torch.rand(
             (bsz, 1, 1, 1),
@@ -384,8 +347,13 @@ def loss_step(
             generator=generator
         )
         offset_strength = offset_noise_strength * (offset_strength * 2 - 1)
-        offset_strength = offset_strength.expand(noise.shape)
-        noise = slerp(noise, img_white.expand(noise.shape), offset_strength, zdim=(-1, -2))
+        offset_images = torch.where(
+            offset_strength >= 0,
+            img_white.expand(noise.shape),
+            img_black.expand(noise.shape)
+        )
+        offset_strength = offset_strength.abs().expand(noise.shape)
+        noise = slerp(noise, offset_images, offset_strength, zdim=(-1, -2))
 
     # Sample a random timestep for each image
     timesteps = torch.randint(