0.5.1 k schedulers

src/diffusers/__init__.py

@@ -37,6 +37,11 @@
         PNDMScheduler,
         SchedulerMixin,
         ScoreSdeVeScheduler,
+        EulerDiscreteScheduler,
+        EulerAncestralDiscreteScheduler,
+        DPM2DiscreteScheduler,
+        DPM2AncestralDiscreteScheduler,
+        HeunDiscreteScheduler
     )
     from .training_utils import EMAModel
 else:

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py

@@ -8,7 +8,9 @@
 from ...configuration_utils import FrozenDict
 from ...models import AutoencoderKL, UNet2DConditionModel
 from ...pipeline_utils import DiffusionPipeline
-from ...schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
+from ...schedulers import DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler, \
+DPM2DiscreteScheduler, DPM2AncestralDiscreteScheduler, EulerDiscreteScheduler, \
+EulerAncestralDiscreteScheduler, HeunDiscreteScheduler
 from ...utils import deprecate, logging
 from . import StableDiffusionPipelineOutput
 from .safety_checker import StableDiffusionSafetyChecker
@@ -51,7 +53,8 @@ def __init__(
         text_encoder: CLIPTextModel,
         tokenizer: CLIPTokenizer,
         unet: UNet2DConditionModel,
-        scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
+        scheduler: Union[DDIMScheduler, PNDMScheduler, DPM2DiscreteScheduler, DPM2AncestralDiscreteScheduler,
+        LMSDiscreteScheduler, EulerDiscreteScheduler, EulerAncestralDiscreteScheduler, HeunDiscreteScheduler],
         safety_checker: StableDiffusionSafetyChecker,
         feature_extractor: CLIPFeatureExtractor,
     ):
@@ -320,7 +323,7 @@ def __call__(
         for i, t in enumerate(self.progress_bar(timesteps_tensor)):
             # expand the latents if we are doing classifier free guidance
             latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
-            latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+            latent_model_input = self.scheduler.scale_model_input(latent_model_input, t, i)
 
             # predict the noise residual
             noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
@@ -331,7 +334,7 @@ def __call__(
                 noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
 
             # compute the previous noisy sample x_t -> x_t-1
-            latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
+            latents = self.scheduler.step(noise_pred, t, i, latents, **extra_step_kwargs).prev_sample
 
             # call the callback, if provided
             if callback is not None and i % callback_steps == 0:

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py

@@ -10,7 +10,9 @@
 from ...configuration_utils import FrozenDict
 from ...models import AutoencoderKL, UNet2DConditionModel
 from ...pipeline_utils import DiffusionPipeline
-from ...schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
+from ...schedulers import DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler, \
+DPM2DiscreteScheduler, DPM2AncestralDiscreteScheduler, EulerDiscreteScheduler, \
+EulerAncestralDiscreteScheduler, HeunDiscreteScheduler
 from ...utils import deprecate, logging
 from . import StableDiffusionPipelineOutput
 from .safety_checker import StableDiffusionSafetyChecker
@@ -63,7 +65,12 @@ def __init__(
         text_encoder: CLIPTextModel,
         tokenizer: CLIPTokenizer,
         unet: UNet2DConditionModel,
-        scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
+        scheduler: Union[
+            DDIMScheduler, PNDMScheduler, DPM2DiscreteScheduler, 
+            DPM2AncestralDiscreteScheduler, LMSDiscreteScheduler, 
+            EulerDiscreteScheduler, EulerAncestralDiscreteScheduler, 
+            HeunDiscreteScheduler
+        ],
         safety_checker: StableDiffusionSafetyChecker,
         feature_extractor: CLIPFeatureExtractor,
     ):
@@ -319,8 +326,13 @@ def __call__(
         init_timestep = int(num_inference_steps * strength) + offset
         init_timestep = min(init_timestep, num_inference_steps)
 
-        timesteps = self.scheduler.timesteps[-init_timestep]
-        timesteps = torch.tensor([timesteps] * batch_size * num_images_per_prompt, device=self.device)
+        if not isinstance(self.scheduler, DDIMScheduler) and not isinstance(self.scheduler, PNDMScheduler):
+            timesteps = torch.tensor(
+                [num_inference_steps - init_timestep] * batch_size, dtype=torch.long, device=self.device
+            )
+        else:
+            timesteps = self.scheduler.timesteps[-init_timestep]
+            timesteps = torch.tensor([timesteps] * batch_size * num_images_per_prompt, device=self.device)
 
         # add noise to latents using the timesteps
         noise = torch.randn(init_latents.shape, generator=generator, device=self.device, dtype=latents_dtype)
@@ -346,7 +358,7 @@ def __call__(
         for i, t in enumerate(self.progress_bar(timesteps)):
             # expand the latents if we are doing classifier free guidance
             latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
-            latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+            latent_model_input = self.scheduler.scale_model_input(latent_model_input, t, i)
 
             # predict the noise residual
             noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
@@ -357,7 +369,7 @@ def __call__(
                 noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
 
             # compute the previous noisy sample x_t -> x_t-1
-            latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
+            latents = self.scheduler.step(noise_pred, t, i, latents, **extra_step_kwargs).prev_sample
 
             # call the callback, if provided
             if callback is not None and i % callback_steps == 0:

src/diffusers/schedulers/__init__.py

@@ -41,5 +41,11 @@
 
 if is_scipy_available() and is_torch_available():
     from .scheduling_lms_discrete import LMSDiscreteScheduler
+    from .scheduling_dpm2_discrete import DPM2DiscreteScheduler
+    from .scheduling_dpm2_ancestral_discrete import DPM2AncestralDiscreteScheduler
+    from .scheduling_euler_discrete import EulerDiscreteScheduler
+    from .scheduling_euler_ancestral_discrete import EulerAncestralDiscreteScheduler
+    from .scheduling_heun_discrete import HeunDiscreteScheduler
+
 else:
     from ..utils.dummy_torch_and_scipy_objects import *  # noqa F403

src/diffusers/schedulers/scheduling_ddim.py

@@ -151,7 +151,9 @@ def __init__(
         self.num_inference_steps = None
         self.timesteps = torch.from_numpy(np.arange(0, num_train_timesteps)[::-1].copy())
 
-    def scale_model_input(self, sample: torch.FloatTensor, timestep: Optional[int] = None) -> torch.FloatTensor:
+    def scale_model_input(
+        self, sample: torch.FloatTensor, 
+        *args, **kwargs) -> torch.FloatTensor:
         """
         Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
         current timestep.
@@ -200,6 +202,7 @@ def step(
         self,
         model_output: torch.FloatTensor,
         timestep: int,
+        step_index:int,
         sample: torch.FloatTensor,
         eta: float = 0.0,
         use_clipped_model_output: bool = False,

src/diffusers/schedulers/scheduling_ddpm.py

@@ -145,7 +145,9 @@ def __init__(
 
         self.variance_type = variance_type
 
-    def scale_model_input(self, sample: torch.FloatTensor, timestep: Optional[int] = None) -> torch.FloatTensor:
+    def scale_model_input(
+        self, sample: torch.FloatTensor, 
+        *args, **kwargs) -> torch.FloatTensor:
         """
         Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
         current timestep.
@@ -211,6 +213,7 @@ def step(
         self,
         model_output: torch.FloatTensor,
         timestep: int,
+        step_index:int,
         sample: torch.FloatTensor,
         predict_epsilon=True,
         generator=None,