remove unused __init__ arg for scm scheduler

scripts/convert_sana_to_diffusers.py

@@ -300,20 +300,11 @@ def main(args):
 
             # SCM Scheduler for Sana Sprint
             scheduler_config = {
-                "beta_end": 0.02,
-                "beta_schedule": "linear",
-                "beta_start": 0.0001,
-                "clip_sample": True,
-                "clip_sample_range": 1.0,
-                "dynamic_thresholding_ratio": 0.995,
                 "num_train_timesteps": 1000,
                 "prediction_type": "trigflow",
-                "rescale_betas_zero_snr": False,
-                "sample_max_value": 1.0,
-                "set_alpha_to_one": True,
-                "steps_offset": 0,
-                "thresholding": False,
-                "timestep_spacing": "leading",
+                "max_timesteps": 1.57080,
+                "intermediate_timesteps": 1.3,
+                "sigma_data": 0.5,
             }
             scheduler = SCMScheduler(**scheduler_config)
             pipe = SanaSprintPipeline(

src/diffusers/schedulers/scheduling_scm.py

@@ -16,7 +16,7 @@
 # and https://github.com/hojonathanho/diffusion
 
 from dataclasses import dataclass
-from typing import List, Optional, Tuple, Union
+from typing import Optional, Tuple, Union
 
 import numpy as np
 import torch
@@ -57,45 +57,14 @@ class SCMScheduler(SchedulerMixin, ConfigMixin):
     Args:
         num_train_timesteps (`int`, defaults to 1000):
             The number of diffusion steps to train the model.
-        beta_start (`float`, defaults to 0.0001):
-            The starting `beta` value of inference.
-        beta_end (`float`, defaults to 0.02):
-            The final `beta` value.
-        beta_schedule (`str`, defaults to `"linear"`):
-            The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from
-            `linear`, `scaled_linear`, or `squaredcos_cap_v2`.
-        trained_betas (`np.ndarray`, *optional*):
-            Pass an array of betas directly to the constructor to bypass `beta_start` and `beta_end`.
-        clip_sample (`bool`, defaults to `True`):
-            Clip the predicted sample for numerical stability.
-        clip_sample_range (`float`, defaults to 1.0):
-            The maximum magnitude for sample clipping. Valid only when `clip_sample=True`.
-        set_alpha_to_one (`bool`, defaults to `True`):
-            Each diffusion step uses the alphas product value at that step and at the previous one. For the final step
-            there is no previous alpha. When this option is `True` the previous alpha product is fixed to `1`,
-            otherwise it uses the alpha value at step 0.
-        steps_offset (`int`, defaults to 0):
-            An offset added to the inference steps. You can use a combination of `offset=1` and
-            `set_alpha_to_one=False` to make the last step use step 0 for the previous alpha product like in Stable
-            Diffusion.
-        prediction_type (`str`, defaults to `epsilon`, *optional*):
-            Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process),
-            `sample` (directly predicts the noisy sample`) or `v_prediction` (see section 2.4 of [Imagen
-            Video](https://imagen.research.google/video/paper.pdf) paper).
-        thresholding (`bool`, defaults to `False`):
-            Whether to use the "dynamic thresholding" method. This is unsuitable for latent-space diffusion models such
-            as Stable Diffusion.
-        dynamic_thresholding_ratio (`float`, defaults to 0.995):
-            The ratio for the dynamic thresholding method. Valid only when `thresholding=True`.
-        sample_max_value (`float`, defaults to 1.0):
-            The threshold value for dynamic thresholding. Valid only when `thresholding=True`.
-        timestep_spacing (`str`, defaults to `"leading"`):
-            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.
-        rescale_betas_zero_snr (`bool`, defaults to `False`):
-            Whether to rescale the betas to have zero terminal SNR. This enables the model to generate very bright and
-            dark samples instead of limiting it to samples with medium brightness. Loosely related to
-            [`--offset_noise`](https://github.com/huggingface/diffusers/blob/74fd735eb073eb1d774b1ab4154a0876eb82f055/examples/dreambooth/train_dreambooth.py#L506).
+        prediction_type (`str`, defaults to `trigflow`):
+            Prediction type of the scheduler function. Currently only supports "trigflow".
+        max_timesteps (`float`, defaults to 1.57080):
+            The maximum timestep value used in the diffusion process.
+        intermediate_timesteps (`float`, *optional*, defaults to 1.3):
+            The intermediate timestep value used when num_inference_steps=2.
+        sigma_data (`float`, defaults to 0.5):
+            The standard deviation of the noise added during multi-step inference.
     """
 
     # _compatibles = [e.name for e in KarrasDiffusionSchedulers]
@@ -105,24 +74,26 @@ class SCMScheduler(SchedulerMixin, ConfigMixin):
     def __init__(
         self,
         num_train_timesteps: int = 1000,
-        beta_start: float = 0.0001,
-        beta_end: float = 0.02,
-        beta_schedule: str = "linear",
-        trained_betas: Optional[Union[np.ndarray, List[float]]] = None,
-        clip_sample: bool = True,
-        set_alpha_to_one: bool = True,
-        steps_offset: int = 0,
         prediction_type: str = "trigflow",
-        thresholding: bool = False,
-        dynamic_thresholding_ratio: float = 0.995,
-        clip_sample_range: float = 1.0,
-        sample_max_value: float = 1.0,
-        timestep_spacing: str = "leading",
-        rescale_betas_zero_snr: bool = False,
         max_timesteps: float = 1.57080,
-        intermediate_timesteps: Optional[int] = 1.3,
+        intermediate_timesteps: Optional[float] = 1.3,
         sigma_data: float = 0.5,
     ):
+        """
+        Initialize the SCM scheduler.
+
+        Args:
+            num_train_timesteps (`int`, defaults to 1000):
+                The number of diffusion steps to train the model.
+            prediction_type (`str`, defaults to `trigflow`):
+                Prediction type of the scheduler function. Currently only supports "trigflow".
+            max_timesteps (`float`, defaults to 1.57080):
+                The maximum timestep value used in the diffusion process.
+            intermediate_timesteps (`float`, *optional*, defaults to 1.3):
+                The intermediate timestep value used when num_inference_steps=2.
+            sigma_data (`float`, defaults to 0.5):
+                The standard deviation of the noise added during multi-step inference.
+        """
         # standard deviation of the initial noise distribution
         self.init_noise_sigma = 1.0