Custom sampler support for Stable Cascade Decoder

src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade.py

@@ -281,6 +281,16 @@ def do_classifier_free_guidance(self):
     def num_timesteps(self):
         return self._num_timesteps
 
+    def get_timestep_ratio_conditioning(self, t, alphas_cumprod):
+        s = torch.tensor([0.008])
+        clamp_range = [0, 1]
+        min_var = torch.cos(s / (1 + s) * torch.pi * 0.5) ** 2
+        var = alphas_cumprod[t]
+        var = var.clamp(*clamp_range)
+        s, min_var = s.to(var.device), min_var.to(var.device)
+        ratio = (((var * min_var) ** 0.5).acos() / (torch.pi * 0.5)) * (1 + s) - s
+        return ratio
+
     @torch.no_grad()
     @replace_example_docstring(EXAMPLE_DOC_STRING)
     def __call__(
@@ -434,10 +444,30 @@ def __call__(
             batch_size, image_embeddings, num_images_per_prompt, dtype, device, generator, latents, self.scheduler
         )
 
+        if isinstance(self.scheduler, DDPMWuerstchenScheduler):
+            timesteps = timesteps[:-1]
+        else:
+            if hasattr(self.scheduler.config, "clip_sample") and self.scheduler.config.clip_sample:
+                self.scheduler.config.clip_sample = False  # disample sample clipping
+                logger.warning(" set `clip_sample` to be False")
+
         # 6. Run denoising loop
-        self._num_timesteps = len(timesteps[:-1])
-        for i, t in enumerate(self.progress_bar(timesteps[:-1])):
-            timestep_ratio = t.expand(latents.size(0)).to(dtype)
+        if hasattr(self.scheduler, "betas"):
+            alphas = 1.0 - self.scheduler.betas
+            alphas_cumprod = torch.cumprod(alphas, dim=0)
+        else:
+            alphas_cumprod = []
+
+        self._num_timesteps = len(timesteps)
+        for i, t in enumerate(self.progress_bar(timesteps)):
+            if not isinstance(self.scheduler, DDPMWuerstchenScheduler):
+                if len(alphas_cumprod) > 0:
+                    timestep_ratio = self.get_timestep_ratio_conditioning(t.long().cpu(), alphas_cumprod)
+                    timestep_ratio = timestep_ratio.expand(latents.size(0)).to(dtype).to(device)
+                else:
+                    timestep_ratio = t.float().div(self.scheduler.timesteps[-1]).expand(latents.size(0)).to(dtype)
+            else:
+                timestep_ratio = t.expand(latents.size(0)).to(dtype)
 
             # 7. Denoise latents
             predicted_latents = self.decoder(
@@ -454,6 +484,8 @@ def __call__(
                 predicted_latents = torch.lerp(predicted_latents_uncond, predicted_latents_text, self.guidance_scale)
 
             # 9. Renoise latents to next timestep
+            if not isinstance(self.scheduler, DDPMWuerstchenScheduler):
+                timestep_ratio = t
             latents = self.scheduler.step(
                 model_output=predicted_latents,
                 timestep=timestep_ratio,

src/diffusers/pipelines/stable_cascade/pipeline_stable_cascade_prior.py

@@ -353,7 +353,7 @@ def num_timesteps(self):
         return self._num_timesteps
 
     def get_timestep_ratio_conditioning(self, t, alphas_cumprod):
-        s = torch.tensor([0.003])
+        s = torch.tensor([0.008])
         clamp_range = [0, 1]
         min_var = torch.cos(s / (1 + s) * torch.pi * 0.5) ** 2
         var = alphas_cumprod[t]
@@ -557,7 +557,7 @@ def __call__(
         if isinstance(self.scheduler, DDPMWuerstchenScheduler):
             timesteps = timesteps[:-1]
         else:
-            if self.scheduler.config.clip_sample:
+            if hasattr(self.scheduler.config, "clip_sample") and self.scheduler.config.clip_sample:
                 self.scheduler.config.clip_sample = False  # disample sample clipping
                 logger.warning(" set `clip_sample` to be False")
         # 6. Run denoising loop