timestep_spacing for FlaxDPMSolverMultistepScheduler (#5189)

* timestep_spacing for FlaxDPMSolverMultistepScheduler

* Style

src/diffusers/schedulers/scheduling_dpmsolver_multistep_flax.py

@@ -135,6 +135,9 @@ class FlaxDPMSolverMultistepScheduler(FlaxSchedulerMixin, ConfigMixin):
         lower_order_final (`bool`, default `True`):
             whether to use lower-order solvers in the final steps. Only valid for < 15 inference steps. We empirically
             find this trick can stabilize the sampling of DPM-Solver for steps < 15, especially for steps <= 10.
+        timestep_spacing (`str`, defaults to `"linspace"`):
+            The way the timesteps should be scaled. Refer to Table 2 of the [Common Diffusion Noise Schedules and
+            Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) for more information.
         dtype (`jnp.dtype`, *optional*, defaults to `jnp.float32`):
             the `dtype` used for params and computation.
     """
@@ -163,6 +166,7 @@ class FlaxDPMSolverMultistepScheduler(FlaxSchedulerMixin, ConfigMixin):
         algorithm_type: str = "dpmsolver++",
         solver_type: str = "midpoint",
         lower_order_final: bool = True,
+        timestep_spacing: str = "linspace",
         dtype: jnp.dtype = jnp.float32,
     ):
         self.dtype = dtype
@@ -210,12 +214,29 @@ class FlaxDPMSolverMultistepScheduler(FlaxSchedulerMixin, ConfigMixin):
             shape (`Tuple`):
                 the shape of the samples to be generated.
         """
-
-        timesteps = (
-            jnp.linspace(0, self.config.num_train_timesteps - 1, num_inference_steps + 1)
-            .round()[::-1][:-1]
-            .astype(jnp.int32)
-        )
+        last_timestep = self.config.num_train_timesteps
+        if self.config.timestep_spacing == "linspace":
+            timesteps = (
+                jnp.linspace(0, last_timestep - 1, num_inference_steps + 1).round()[::-1][:-1].astype(jnp.int32)
+            )
+        elif self.config.timestep_spacing == "leading":
+            step_ratio = last_timestep // (num_inference_steps + 1)
+            # creates integer timesteps by multiplying by ratio
+            # casting to int to avoid issues when num_inference_step is power of 3
+            timesteps = (
+                (jnp.arange(0, num_inference_steps + 1) * step_ratio).round()[::-1][:-1].copy().astype(jnp.int32)
+            )
+            timesteps += self.config.steps_offset
+        elif self.config.timestep_spacing == "trailing":
+            step_ratio = self.config.num_train_timesteps / num_inference_steps
+            # creates integer timesteps by multiplying by ratio
+            # casting to int to avoid issues when num_inference_step is power of 3
+            timesteps = jnp.arange(last_timestep, 0, -step_ratio).round().copy().astype(jnp.int32)
+            timesteps -= 1
+        else:
+            raise ValueError(
+                f"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."
+            )
 
         # initial running values