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modules_forge/patch_precision.py

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+# Consistent with Kohya to reduce differences between model training and inference.
+
+import torch
+import math
+import einops
+import numpy as np
+
+import ldm_patched.ldm.modules.diffusionmodules.openaimodel
+import ldm_patched.modules.model_sampling
+import ldm_patched.modules.sd1_clip
+
+from ldm_patched.ldm.modules.diffusionmodules.util import make_beta_schedule
+
+
+def patched_timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False):
+    # Consistent with Kohya to reduce differences between model training and inference.
+
+    if not repeat_only:
+        half = dim // 2
+        freqs = torch.exp(
+            -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half
+        ).to(device=timesteps.device)
+        args = timesteps[:, None].float() * freqs[None]
+        embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
+        if dim % 2:
+            embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
+    else:
+        embedding = einops.repeat(timesteps, 'b -> b d', d=dim)
+    return embedding
+
+
+def patched_register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000,
+                          linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
+    # Consistent with Kohya to reduce differences between model training and inference.
+
+    if given_betas is not None:
+        betas = given_betas
+    else:
+        betas = make_beta_schedule(
+            beta_schedule,
+            timesteps,
+            linear_start=linear_start,
+            linear_end=linear_end,
+            cosine_s=cosine_s)
+
+    alphas = 1. - betas
+    alphas_cumprod = np.cumprod(alphas, axis=0)
+    timesteps, = betas.shape
+    self.num_timesteps = int(timesteps)
+    self.linear_start = linear_start
+    self.linear_end = linear_end
+    sigmas = torch.tensor(((1 - alphas_cumprod) / alphas_cumprod) ** 0.5, dtype=torch.float32)
+    self.set_sigmas(sigmas)
+    return
+
+
+def patch_all_precision():
+    ldm_patched.ldm.modules.diffusionmodules.openaimodel.timestep_embedding = patched_timestep_embedding
+    ldm_patched.modules.model_sampling.ModelSamplingDiscrete._register_schedule = patched_register_schedule
+    return