[PNDM in LDM pipeline] use inspect in pipeline instead of unused kwargs

src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion.py

@@ -1,3 +1,4 @@
+import inspect
 from typing import Optional, Tuple, Union
 
 import torch
@@ -59,6 +60,12 @@ def __call__(
 
         self.scheduler.set_timesteps(num_inference_steps)
 
+        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
+        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        extra_kwrags = {}
+        if not accepts_eta:
+            extra_kwrags["eta"] = eta
+
         for t in tqdm(self.scheduler.timesteps):
             if guidance_scale == 1.0:
                 # guidance_scale of 1 means no guidance
@@ -79,7 +86,7 @@ def __call__(
                 noise_pred = noise_pred_uncond + guidance_scale * (noise_prediction_text - noise_pred_uncond)
 
             # compute the previous noisy sample x_t -> x_t-1
-            latents = self.scheduler.step(noise_pred, t, latents, eta=eta)["prev_sample"]
+            latents = self.scheduler.step(noise_pred, t, latents, **extra_kwrags)["prev_sample"]
 
         # scale and decode the image latents with vae
         latents = 1 / 0.18215 * latents

src/diffusers/pipelines/latent_diffusion_uncond/pipeline_latent_diffusion_uncond.py

@@ -1,3 +1,5 @@
+import inspect
+
 import torch
 
 from tqdm.auto import tqdm
@@ -31,11 +33,17 @@ def __call__(
 
         self.scheduler.set_timesteps(num_inference_steps)
 
+        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
+        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        extra_kwrags = {}
+        if not accepts_eta:
+            extra_kwrags["eta"] = eta
+
         for t in tqdm(self.scheduler.timesteps):
             # predict the noise residual
             noise_prediction = self.unet(latents, t)["sample"]
             # compute the previous noisy sample x_t -> x_t-1
-            latents = self.scheduler.step(noise_prediction, t, latents, eta)["prev_sample"]
+            latents = self.scheduler.step(noise_prediction, t, latents, **extra_kwrags)["prev_sample"]
 
         # decode the image latents with the VAE
         image = self.vqvae.decode(latents)

src/diffusers/schedulers/scheduling_pndm.py

@@ -116,7 +116,6 @@ def step(
         model_output: Union[torch.FloatTensor, np.ndarray],
         timestep: int,
         sample: Union[torch.FloatTensor, np.ndarray],
-        **kwargs,
     ):
         if self.counter < len(self.prk_timesteps):
             return self.step_prk(model_output=model_output, timestep=timestep, sample=sample)