fix some precision problems

fooocus_version.py

@@ -1 +1 @@
-version = '2.1.836'
+version = '2.1.837'

modules/patch.py

@@ -1,6 +1,8 @@
 import os
 import torch
 import time
+import numpy as np
+import math
 import ldm_patched.modules.model_base
 import ldm_patched.ldm.modules.diffusionmodules.openaimodel
 import ldm_patched.modules.samplers
@@ -510,6 +512,48 @@ def build_loaded(module, loader_name):
     return
 
 
+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 = 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():
     if not hasattr(ldm_patched.modules.model_management, 'load_models_gpu_origin'):
         ldm_patched.modules.model_management.load_models_gpu_origin = ldm_patched.modules.model_management.load_models_gpu
@@ -523,6 +567,10 @@ def patch_all():
     ldm_patched.modules.samplers.KSamplerX0Inpaint.forward = patched_KSamplerX0Inpaint_forward
     ldm_patched.k_diffusion.sampling.BrownianTreeNoiseSampler = BrownianTreeNoiseSamplerPatched
 
+    # Precision fix
+    ldm_patched.ldm.modules.diffusionmodules.openaimodel.timestep_embedding = patched_timestep_embedding
+    ldm_patched.modules.model_base.ModelSamplingDiscrete._register_schedule = patched_register_schedule
+
     warnings.filterwarnings(action='ignore', module='torchsde')
 
     build_loaded(safetensors.torch, 'load_file')

update_log.md

@@ -1,3 +1,7 @@
+# 2.1.837
+
+* Fix some precision-related problems.
+
 # 2.1.836
 
 * Avoid blip tokenizer download from torch hub