Finalize 2nd order schedulers (#1503)

* up

* up

* finish

* finish

* up

* up

* finish

docs/source/api/schedulers.mdx

@@ -76,6 +76,33 @@ Original paper can be found [here](https://arxiv.org/abs/2206.00927) and the [im
 
 [[autodoc]] DPMSolverMultistepScheduler
 
+#### Heun scheduler inspired by Karras et. al paper
+
+Algorithm 1 of [Karras et. al](https://arxiv.org/abs/2206.00364).
+Scheduler ported from @crowsonkb's https://github.com/crowsonkb/k-diffusion library:
+
+All credit for making this scheduler work goes to [Katherine Crowson](https://github.com/crowsonkb/)
+
+[[autodoc]] HeunDiscreteScheduler
+
+#### DPM Discrete Scheduler inspired by Karras et. al paper
+
+Inspired by [Karras et. al](https://arxiv.org/abs/2206.00364).
+Scheduler ported from @crowsonkb's https://github.com/crowsonkb/k-diffusion library:
+
+All credit for making this scheduler work goes to [Katherine Crowson](https://github.com/crowsonkb/)
+
+[[autodoc]] KDPM2DiscreteScheduler
+
+#### DPM Discrete Scheduler with ancestral sampling inspired by Karras et. al paper
+
+Inspired by [Karras et. al](https://arxiv.org/abs/2206.00364).
+Scheduler ported from @crowsonkb's https://github.com/crowsonkb/k-diffusion library:
+
+All credit for making this scheduler work goes to [Katherine Crowson](https://github.com/crowsonkb/)
+
+[[autodoc]] KDPM2AncestralDiscreteScheduler
+
 #### Variance exploding, stochastic sampling from Karras et. al
 
 Original paper can be found [here](https://arxiv.org/abs/2006.11239).
@@ -86,7 +113,6 @@ Original paper can be found [here](https://arxiv.org/abs/2006.11239).
 
 Original implementation can be found [here](https://arxiv.org/abs/2206.00364).
 
-
 [[autodoc]] LMSDiscreteScheduler
 
 #### Pseudo numerical methods for diffusion models (PNDM)

examples/community/sd_text2img_k_diffusion.py

@@ -21,7 +21,7 @@ from diffusers import LMSDiscreteScheduler
 from diffusers.pipeline_utils import DiffusionPipeline
 from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
 from diffusers.utils import is_accelerate_available, logging
-from k_diffusion.external import CompVisDenoiser
+from k_diffusion.external import CompVisDenoiser, CompVisVDenoiser
 
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
@@ -33,7 +33,12 @@ class ModelWrapper:
         self.alphas_cumprod = alphas_cumprod
 
     def apply_model(self, *args, **kwargs):
-        return self.model(*args, **kwargs).sample
+        if len(args) == 3:
+            encoder_hidden_states = args[-1]
+            args = args[:2]
+        if kwargs.get("cond", None) is not None:
+            encoder_hidden_states = kwargs.pop("cond")
+        return self.model(*args, encoder_hidden_states=encoder_hidden_states, **kwargs).sample
 
 
 class StableDiffusionPipeline(DiffusionPipeline):
@@ -63,6 +68,7 @@ class StableDiffusionPipeline(DiffusionPipeline):
         feature_extractor ([`CLIPFeatureExtractor`]):
             Model that extracts features from generated images to be used as inputs for the `safety_checker`.
     """
+    _optional_components = ["safety_checker", "feature_extractor"]
 
     def __init__(
         self,
@@ -99,6 +105,9 @@ class StableDiffusionPipeline(DiffusionPipeline):
         )
 
         model = ModelWrapper(unet, scheduler.alphas_cumprod)
+        if scheduler.prediction_type == "v_prediction":
+            self.k_diffusion_model = CompVisVDenoiser(model)
+        else:
             self.k_diffusion_model = CompVisDenoiser(model)
 
     def set_sampler(self, scheduler_type: str):
@@ -417,6 +426,7 @@ class StableDiffusionPipeline(DiffusionPipeline):
         # 4. Prepare timesteps
         self.scheduler.set_timesteps(num_inference_steps, device=text_embeddings.device)
         sigmas = self.scheduler.sigmas
+        sigmas = sigmas.to(text_embeddings.dtype)
 
         # 5. Prepare latent variables
         num_channels_latents = self.unet.in_channels
@@ -437,7 +447,7 @@ class StableDiffusionPipeline(DiffusionPipeline):
         def model_fn(x, t):
             latent_model_input = torch.cat([x] * 2)
 
-            noise_pred = self.k_diffusion_model(latent_model_input, t, encoder_hidden_states=text_embeddings)
+            noise_pred = self.k_diffusion_model(latent_model_input, t, cond=text_embeddings)
 
             noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
             noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

src/diffusers/__init__.py

@@ -49,6 +49,8 @@ if is_torch_available():
         HeunDiscreteScheduler,
         IPNDMScheduler,
         KarrasVeScheduler,
+        KDPM2AncestralDiscreteScheduler,
+        KDPM2DiscreteScheduler,
         PNDMScheduler,
         RePaintScheduler,
         SchedulerMixin,

src/diffusers/pipelines/alt_diffusion/pipeline_alt_diffusion.py

@@ -558,7 +558,7 @@ class AltDiffusionPipeline(DiffusionPipeline):
                     latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
 
                 # call the callback, if provided
-                if (i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0:
+                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                     progress_bar.update()
                     if callback is not None and i % callback_steps == 0:
                         callback(i, t, latents)

src/diffusers/pipelines/alt_diffusion/pipeline_alt_diffusion_img2img.py

@@ -580,7 +580,7 @@ class AltDiffusionImg2ImgPipeline(DiffusionPipeline):
                 latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
 
                 # call the callback, if provided
-                if (i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0:
+                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                     progress_bar.update()
                     if callback is not None and i % callback_steps == 0:
                         callback(i, t, latents)

src/diffusers/pipelines/stable_diffusion/pipeline_cycle_diffusion.py

@@ -666,7 +666,7 @@ class CycleDiffusionPipeline(DiffusionPipeline):
                 ).prev_sample
 
                 # call the callback, if provided
-                if (i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0:
+                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                     progress_bar.update()
                     if callback is not None and i % callback_steps == 0:
                         callback(i, t, latents)

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py

@@ -557,7 +557,7 @@ class StableDiffusionPipeline(DiffusionPipeline):
                     latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
 
                 # call the callback, if provided
-                if (i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0:
+                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                     progress_bar.update()
                     if callback is not None and i % callback_steps == 0:
                         callback(i, t, latents)

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_image_variation.py

@@ -440,7 +440,7 @@ class StableDiffusionImageVariationPipeline(DiffusionPipeline):
                 latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
 
                 # call the callback, if provided
-                if (i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0:
+                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                     progress_bar.update()
                     if callback is not None and i % callback_steps == 0:
                         callback(i, t, latents)

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py

@@ -587,7 +587,7 @@ class StableDiffusionImg2ImgPipeline(DiffusionPipeline):
                 latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
 
                 # call the callback, if provided
-                if (i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0:
+                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                     progress_bar.update()
                     if callback is not None and i % callback_steps == 0:
                         callback(i, t, latents)

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py

@@ -701,7 +701,7 @@ class StableDiffusionInpaintPipeline(DiffusionPipeline):
                 latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
 
                 # call the callback, if provided
-                if (i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0:
+                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                     progress_bar.update()
                     if callback is not None and i % callback_steps == 0:
                         callback(i, t, latents)

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint_legacy.py

@@ -602,7 +602,7 @@ class StableDiffusionInpaintPipelineLegacy(DiffusionPipeline):
                 latents = (init_latents_proper * mask) + (latents * (1 - mask))
 
                 # call the callback, if provided
-                if (i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0:
+                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                     progress_bar.update()
                     if callback is not None and i % callback_steps == 0:
                         callback(i, t, latents)

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_upscale.py

@@ -515,7 +515,7 @@ class StableDiffusionUpscalePipeline(DiffusionPipeline):
                 latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
 
                 # call the callback, if provided
-                if (i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0:
+                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                     progress_bar.update()
                     if callback is not None and i % callback_steps == 0:
                         callback(i, t, latents)

src/diffusers/pipelines/stable_diffusion_safe/pipeline_stable_diffusion_safe.py

@@ -711,7 +711,7 @@ class StableDiffusionPipelineSafe(DiffusionPipeline):
                 latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
 
                 # call the callback, if provided
-                if (i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0:
+                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                     progress_bar.update()
                     if callback is not None and i % callback_steps == 0:
                         callback(i, t, latents)

src/diffusers/schedulers/__init__.py

@@ -22,8 +22,10 @@ if is_torch_available():
     from .scheduling_dpmsolver_multistep import DPMSolverMultistepScheduler
     from .scheduling_euler_ancestral_discrete import EulerAncestralDiscreteScheduler
     from .scheduling_euler_discrete import EulerDiscreteScheduler
-    from .scheduling_heun import HeunDiscreteScheduler
+    from .scheduling_heun_discrete import HeunDiscreteScheduler
     from .scheduling_ipndm import IPNDMScheduler
+    from .scheduling_k_dpm_2_ancestral_discrete import KDPM2AncestralDiscreteScheduler
+    from .scheduling_k_dpm_2_discrete import KDPM2DiscreteScheduler
     from .scheduling_karras_ve import KarrasVeScheduler
     from .scheduling_pndm import PNDMScheduler
     from .scheduling_repaint import RePaintScheduler

src/diffusers/schedulers/scheduling_ddim.py

@@ -106,10 +106,10 @@ class DDIMScheduler(SchedulerMixin, ConfigMixin):
             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, as done in
             stable diffusion.
-        prediction_type (`str`, default `epsilon`):
-            indicates whether the model predicts the noise (epsilon), or the samples. One of `epsilon`, `sample`.
-            `v-prediction` is not supported for this scheduler.
-
+        prediction_type (`str`, default `epsilon`, optional):
+            prediction type of the scheduler function, one of `epsilon` (predicting the noise of the diffusion
+            process), `sample` (directly predicting the noisy sample`) or `v_prediction` (see section 2.4
+            https://imagen.research.google/video/paper.pdf)
     """
 
     _compatibles = _COMPATIBLE_STABLE_DIFFUSION_SCHEDULERS.copy()

src/diffusers/schedulers/scheduling_ddpm.py

@@ -99,9 +99,10 @@ class DDPMScheduler(SchedulerMixin, ConfigMixin):
             `fixed_small_log`, `fixed_large`, `fixed_large_log`, `learned` or `learned_range`.
         clip_sample (`bool`, default `True`):
             option to clip predicted sample between -1 and 1 for numerical stability.
-        prediction_type (`str`, default `epsilon`):
-            indicates whether the model predicts the noise (epsilon), or the samples. One of `epsilon`, `sample`.
-            `v-prediction` is not supported for this scheduler.
+        prediction_type (`str`, default `epsilon`, optional):
+            prediction type of the scheduler function, one of `epsilon` (predicting the noise of the diffusion
+            process), `sample` (directly predicting the noisy sample`) or `v_prediction` (see section 2.4
+            https://imagen.research.google/video/paper.pdf)
     """
 
     _compatibles = _COMPATIBLE_STABLE_DIFFUSION_SCHEDULERS.copy()

src/diffusers/schedulers/scheduling_dpmsolver_multistep.py

@@ -87,9 +87,10 @@ class DPMSolverMultistepScheduler(SchedulerMixin, ConfigMixin):
         solver_order (`int`, default `2`):
             the order of DPM-Solver; can be `1` or `2` or `3`. We recommend to use `solver_order=2` for guided
             sampling, and `solver_order=3` for unconditional sampling.
-        prediction_type (`str`, default `epsilon`):
-            indicates whether the model predicts the noise (epsilon), or the data / `x0`. One of `epsilon`, `sample`,
-            or `v-prediction`.
+        prediction_type (`str`, default `epsilon`, optional):
+            prediction type of the scheduler function, one of `epsilon` (predicting the noise of the diffusion
+            process), `sample` (directly predicting the noisy sample`) or `v_prediction` (see section 2.4
+            https://imagen.research.google/video/paper.pdf)
         thresholding (`bool`, default `False`):
             whether to use the "dynamic thresholding" method (introduced by Imagen, https://arxiv.org/abs/2205.11487).
             For pixel-space diffusion models, you can set both `algorithm_type=dpmsolver++` and `thresholding=True` to

src/diffusers/schedulers/scheduling_euler_ancestral_discrete.py

@@ -64,6 +64,10 @@ class EulerAncestralDiscreteScheduler(SchedulerMixin, ConfigMixin):
             `linear` or `scaled_linear`.
         trained_betas (`np.ndarray`, optional):
             option to pass an array of betas directly to the constructor to bypass `beta_start`, `beta_end` etc.
+        prediction_type (`str`, default `epsilon`, optional):
+            prediction type of the scheduler function, one of `epsilon` (predicting the noise of the diffusion
+            process), `sample` (directly predicting the noisy sample`) or `v_prediction` (see section 2.4
+            https://imagen.research.google/video/paper.pdf)
 
     """
 

src/diffusers/schedulers/scheduling_euler_discrete.py

@@ -65,6 +65,10 @@ class EulerDiscreteScheduler(SchedulerMixin, ConfigMixin):
             `linear` or `scaled_linear`.
         trained_betas (`np.ndarray`, optional):
             option to pass an array of betas directly to the constructor to bypass `beta_start`, `beta_end` etc.
+        prediction_type (`str`, default `epsilon`, optional):
+            prediction type of the scheduler function, one of `epsilon` (predicting the noise of the diffusion
+            process), `sample` (directly predicting the noisy sample`) or `v_prediction` (see section 2.4
+            https://imagen.research.google/video/paper.pdf)
 
     """
 

src/diffusers/schedulers/scheduling_heun.py → src/diffusers/schedulers/scheduling_heun_discrete.py

@@ -24,14 +24,16 @@ from .scheduling_utils import SchedulerMixin, SchedulerOutput
 
 class HeunDiscreteScheduler(SchedulerMixin, ConfigMixin):
     """
-    Args:
     Implements Algorithm 2 (Heun steps) from Karras et al. (2022). for discrete beta schedules. Based on the original
     k-diffusion implementation by Katherine Crowson:
     https://github.com/crowsonkb/k-diffusion/blob/481677d114f6ea445aa009cf5bd7a9cdee909e47/k_diffusion/sampling.py#L90
+
     [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__`
     function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`.
-    [`~ConfigMixin`] also provides general loading and saving functionality via the [`~ConfigMixin.save_config`] and
-    [`~ConfigMixin.from_config`] functions.
+    [`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and
+    [`~SchedulerMixin.from_pretrained`] functions.
+
+    Args:
         num_train_timesteps (`int`): number of diffusion steps used to train the model. beta_start (`float`): the
         starting `beta` value of inference. beta_end (`float`): the final `beta` value. beta_schedule (`str`):
             the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from
@@ -40,7 +42,10 @@ class HeunDiscreteScheduler(SchedulerMixin, ConfigMixin):
             option to pass an array of betas directly to the constructor to bypass `beta_start`, `beta_end` etc.
             options to clip the variance used when adding noise to the denoised sample. Choose from `fixed_small`,
             `fixed_small_log`, `fixed_large`, `fixed_large_log`, `learned` or `learned_range`.
-        tensor_format (`str`): whether the scheduler expects pytorch or numpy arrays.
+        prediction_type (`str`, default `epsilon`, optional):
+            prediction type of the scheduler function, one of `epsilon` (predicting the noise of the diffusion
+            process), `sample` (directly predicting the noisy sample`) or `v_prediction` (see section 2.4
+            https://imagen.research.google/video/paper.pdf)
     """
 
     _compatibles = _COMPATIBLE_STABLE_DIFFUSION_SCHEDULERS.copy()
@@ -77,7 +82,7 @@ class HeunDiscreteScheduler(SchedulerMixin, ConfigMixin):
     def index_for_timestep(self, timestep):
         indices = (self.timesteps == timestep).nonzero()
         if self.state_in_first_order:
-            pos = 0 if indices.shape[0] < 2 else 1
+            pos = -1
         else:
             pos = 0
         return indices[pos].item()
@@ -132,7 +137,7 @@ class HeunDiscreteScheduler(SchedulerMixin, ConfigMixin):
         self.init_noise_sigma = self.sigmas.max()
 
         timesteps = torch.from_numpy(timesteps)
-        timesteps = torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2), timesteps[-1:]])
+        timesteps = torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2)])
 
         if str(device).startswith("mps"):
             # mps does not support float64
@@ -199,9 +204,9 @@ class HeunDiscreteScheduler(SchedulerMixin, ConfigMixin):
             )
 
         if self.state_in_first_order:
-            # 2. Convert to an ODE derivative
+            # 2. Convert to an ODE derivative for 1st order
             derivative = (sample - pred_original_sample) / sigma_hat
-            # 3. 1st order derivative
+            # 3. delta timestep
             dt = sigma_next - sigma_hat
 
             # store for 2nd order step
@@ -213,7 +218,7 @@ class HeunDiscreteScheduler(SchedulerMixin, ConfigMixin):
             derivative = (sample - pred_original_sample) / sigma_next
             derivative = (self.prev_derivative + derivative) / 2
 
-            # 3. Retrieve 1st order derivative
+            # 3. take prev timestep & sample
             dt = self.dt
             sample = self.sample