Switched to trailing timestep spacing to make timesteps for consistant across schedulers. Honed in on targeted guidance. It is finally perfect. (I think)

toolkit/sampler.py

@@ -26,8 +26,8 @@ SCHEDULER_TIMESTEPS = 1000
 SCHEDLER_SCHEDULE = "scaled_linear"
 
 sdxl_sampler_config = {
-    "_class_name": "EulerDiscreteScheduler",
-    "_diffusers_version": "0.19.0.dev0",
+    "_class_name": "EulerAncestralDiscreteScheduler",
+    "_diffusers_version": "0.24.0.dev0",
     "beta_end": 0.012,
     "beta_schedule": "scaled_linear",
     "beta_start": 0.00085,
@@ -37,11 +37,10 @@ sdxl_sampler_config = {
     "prediction_type": "epsilon",
     "sample_max_value": 1.0,
     "set_alpha_to_one": False,
-    "skip_prk_steps": True,
+    "skip_prk_steps": False,
     "steps_offset": 1,
-    "timestep_spacing": "leading",
-    "trained_betas": None,
-    "use_karras_sigmas": False
+    "timestep_spacing": "trailing",
+    "trained_betas": None
 }
 
 
@@ -86,7 +85,6 @@ def get_sampler(
 
     scheduler = scheduler_cls.from_config(config)
 
-
     return scheduler
 
 

toolkit/stable_diffusion_model.py

@@ -674,14 +674,8 @@ class StableDiffusion:
 
         for idx in range(original_samples.shape[0]):
 
-            if scheduler_class_name not in index_noise_schedulers:
-                # convert to idx
-                noise_timesteps = [(self.noise_scheduler.timesteps == t).nonzero().item() for t in timesteps_chunks[idx]]
-                noise_timesteps = torch.tensor(noise_timesteps, device=self.device_torch)
-            else:
-                noise_timesteps = timesteps_chunks[idx]
-
             # the add noise for ddpm solver is broken, do it ourselves
+            noise_timesteps = timesteps_chunks[idx]
             if scheduler_class_name == 'DPMSolverMultistepScheduler':
                 # Make sure sigmas and timesteps have the same device and dtype as original_samples
                 sigmas = self.noise_scheduler.sigmas.to(device=original_samples_chunks[idx].device, dtype=original_samples_chunks[idx].dtype)