Deprecate predict_epsilon

examples/community/bit_diffusion.py

@@ -138,7 +138,7 @@ def ddpm_bit_scheduler_step(
     model_output: torch.FloatTensor,
     timestep: int,
     sample: torch.FloatTensor,
-    predict_epsilon=True,
+    prediction_type="epsilon",
     generator=None,
     return_dict: bool = True,
 ) -> Union[DDPMSchedulerOutput, Tuple]:
@@ -150,8 +150,8 @@ def ddpm_bit_scheduler_step(
         timestep (`int`): current discrete timestep in the diffusion chain.
         sample (`torch.FloatTensor`):
             current instance of sample being created by diffusion process.
-        predict_epsilon (`bool`):
-            optional flag to use when model predicts the samples directly instead of the noise, epsilon.
+        prediction_type (`str`, default `epsilon`):
+            indicates whether the model predicts the noise (epsilon), or the samples (`sample`).
         generator: random number generator.
         return_dict (`bool`): option for returning tuple rather than DDPMSchedulerOutput class
     Returns:
@@ -174,10 +174,12 @@ def ddpm_bit_scheduler_step(
 
     # 2. compute predicted original sample from predicted noise also called
     # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
-    if predict_epsilon:
+    if prediction_type == "epsilon":
         pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)
-    else:
+    elif prediction_type == "sample":
         pred_original_sample = model_output
+    else:
+        raise ValueError(f"Unsupported prediction_type {prediction_type}.")
 
     # 3. Clip "predicted x_0"
     scale = self.bit_scale

examples/unconditional_image_generation/train_unconditional.py

@@ -194,9 +194,10 @@ def parse_args():
     )
 
     parser.add_argument(
-        "--predict_epsilon",
-        action="store_true",
-        default=True,
+        "--prediction_type",
+        type=str,
+        default="epsilon",
+        choices=["epsilon", "sample"],
         help="Whether the model should predict the 'epsilon'/noise error or directly the reconstructed image 'x0'.",
     )
 
@@ -256,13 +257,13 @@ def main(args):
             "UpBlock2D",
         ),
     )
-    accepts_predict_epsilon = "predict_epsilon" in set(inspect.signature(DDPMScheduler.__init__).parameters.keys())
+    accepts_prediction_type = "prediction_type" in set(inspect.signature(DDPMScheduler.__init__).parameters.keys())
 
-    if accepts_predict_epsilon:
+    if accepts_prediction_type:
         noise_scheduler = DDPMScheduler(
             num_train_timesteps=args.ddpm_num_steps,
             beta_schedule=args.ddpm_beta_schedule,
-            predict_epsilon=args.predict_epsilon,
+            prediction_type=args.prediction_type,
         )
     else:
         noise_scheduler = DDPMScheduler(num_train_timesteps=args.ddpm_num_steps, beta_schedule=args.ddpm_beta_schedule)
@@ -365,9 +366,9 @@ def transforms(examples):
                 # Predict the noise residual
                 model_output = model(noisy_images, timesteps).sample
 
-                if args.predict_epsilon:
+                if args.prediction_type == "epsilon":
                     loss = F.mse_loss(model_output, noise)  # this could have different weights!
-                else:
+                elif args.prediction_type == "sample":
                     alpha_t = _extract_into_tensor(
                         noise_scheduler.alphas_cumprod, timesteps, (clean_images.shape[0], 1, 1, 1)
                     )
@@ -376,6 +377,8 @@ def transforms(examples):
                         model_output, clean_images, reduction="none"
                     )  # use SNR weighting from distillation paper
                     loss = loss.mean()
+                else:
+                    raise ValueError(f"Unsupported prediction type: {args.prediction_type}")
 
                 accelerator.backward(loss)
 

src/diffusers/configuration_utils.py

@@ -195,6 +195,11 @@ def from_config(cls, config: Union[FrozenDict, Dict[str, Any]] = None, return_un
         if "dtype" in unused_kwargs:
             init_dict["dtype"] = unused_kwargs.pop("dtype")
 
+        if "predict_epsilon" in unused_kwargs and "prediction_type" not in init_dict:
+            deprecate("remove this", "0.10.0", "remove")
+            predict_epsilon = unused_kwargs.pop("predict_epsilon")
+            init_dict["prediction_type"] = "epsilon" if predict_epsilon else "sample"
+
         # Return model and optionally state and/or unused_kwargs
         model = cls(**init_dict)
 

src/diffusers/experimental/rl/value_guided_sampling.py

@@ -89,6 +89,7 @@ def run_diffusion(self, x, conditions, n_guide_steps, scale):
                 x = x + scale * grad
                 x = self.reset_x0(x, conditions, self.action_dim)
             prev_x = self.unet(x.permute(0, 2, 1), timesteps).sample.permute(0, 2, 1)
+            # TODO: set prediction_type when instantiating the model
             x = self.scheduler.step(prev_x, i, x, predict_epsilon=False)["prev_sample"]
 
             # apply conditions to the trajectory

src/diffusers/pipelines/ddpm/pipeline_ddpm.py

@@ -70,14 +70,14 @@ def __call__(
             generated images.
         """
         message = (
-            "Please make sure to instantiate your scheduler with `predict_epsilon` instead. E.g. `scheduler ="
-            " DDPMScheduler.from_pretrained(<model_id>, predict_epsilon=True)`."
+            "Please make sure to instantiate your scheduler with `prediction_type` instead. E.g. `scheduler ="
+            " DDPMScheduler.from_pretrained(<model_id>, prediction_type='epsilon')`."
         )
         predict_epsilon = deprecate("predict_epsilon", "0.10.0", message, take_from=kwargs)
 
         if predict_epsilon is not None:
             new_config = dict(self.scheduler.config)
-            new_config["predict_epsilon"] = predict_epsilon
+            new_config["prediction_type"] = "epsilon" if predict_epsilon else "sample"
             self.scheduler._internal_dict = FrozenDict(new_config)
 
         if generator is not None and generator.device.type != self.device.type and self.device.type != "mps":
@@ -114,9 +114,7 @@ def __call__(
             model_output = self.unet(image, t).sample
 
             # 2. compute previous image: x_t -> x_t-1
-            image = self.scheduler.step(
-                model_output, t, image, generator=generator, predict_epsilon=predict_epsilon
-            ).prev_sample
+            image = self.scheduler.step(model_output, t, image, generator=generator).prev_sample
 
         image = (image / 2 + 0.5).clamp(0, 1)
         image = image.cpu().permute(0, 2, 3, 1).numpy()

src/diffusers/schedulers/scheduling_ddim.py

@@ -23,7 +23,7 @@
 import torch
 
 from ..configuration_utils import ConfigMixin, register_to_config
-from ..utils import _COMPATIBLE_STABLE_DIFFUSION_SCHEDULERS, BaseOutput
+from ..utils import _COMPATIBLE_STABLE_DIFFUSION_SCHEDULERS, BaseOutput, deprecate
 from .scheduling_utils import SchedulerMixin
 
 
@@ -106,6 +106,9 @@ 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.
 
     """
 
@@ -123,7 +126,16 @@ def __init__(
         set_alpha_to_one: bool = True,
         steps_offset: int = 0,
         prediction_type: str = "epsilon",
+        **kwargs,
     ):
+        message = (
+            "Please make sure to instantiate your scheduler with `prediction_type` instead. E.g. `scheduler ="
+            " DDIMScheduler.from_pretrained(<model_id>, prediction_type='epsilon')`."
+        )
+        predict_epsilon = deprecate("predict_epsilon", "0.10.0", message, take_from=kwargs)
+        if predict_epsilon is not None:
+            self.register_to_config(prediction_type="epsilon" if predict_epsilon else "sample")
+
         if trained_betas is not None:
             self.betas = torch.from_numpy(trained_betas)
         elif beta_schedule == "linear":
@@ -139,8 +151,6 @@ def __init__(
         else:
             raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}")
 
-        self.prediction_type = prediction_type
-
         self.alphas = 1.0 - self.betas
         self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)
 
@@ -261,17 +271,17 @@ def step(
 
         # 3. compute predicted original sample from predicted noise also called
         # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
-        if self.prediction_type == "epsilon":
+        if self.config.prediction_type == "epsilon":
             pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)
-        elif self.prediction_type == "sample":
+        elif self.config.prediction_type == "sample":
             pred_original_sample = model_output
-        elif self.prediction_type == "v_prediction":
+        elif self.config.prediction_type == "v_prediction":
             pred_original_sample = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
             # predict V
             model_output = (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
         else:
             raise ValueError(
-                f"prediction_type given as {self.prediction_type} must be one of `epsilon`, `sample`, or"
+                f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"
                 " `v_prediction`"
             )
 

src/diffusers/schedulers/scheduling_ddim_flax.py

@@ -23,6 +23,7 @@
 import jax.numpy as jnp
 
 from ..configuration_utils import ConfigMixin, register_to_config
+from ..utils import deprecate
 from .scheduling_utils_flax import (
     _FLAX_COMPATIBLE_STABLE_DIFFUSION_SCHEDULERS,
     FlaxSchedulerMixin,
@@ -108,6 +109,10 @@ class FlaxDDIMScheduler(FlaxSchedulerMixin, 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.
+
     """
 
     _compatibles = _FLAX_COMPATIBLE_STABLE_DIFFUSION_SCHEDULERS.copy()
@@ -125,7 +130,17 @@ def __init__(
         beta_schedule: str = "linear",
         set_alpha_to_one: bool = True,
         steps_offset: int = 0,
+        prediction_type: str = "epsilon",
+        **kwargs,
     ):
+        message = (
+            "Please make sure to instantiate your scheduler with `prediction_type` instead. E.g. `scheduler ="
+            " FlaxDDIMScheduler.from_pretrained(<model_id>, prediction_type='epsilon')`."
+        )
+        predict_epsilon = deprecate("predict_epsilon", "0.10.0", message, take_from=kwargs)
+        if predict_epsilon is not None:
+            self.register_to_config(prediction_type="epsilon" if predict_epsilon else "sample")
+
         if beta_schedule == "linear":
             self.betas = jnp.linspace(beta_start, beta_end, num_train_timesteps, dtype=jnp.float32)
         elif beta_schedule == "scaled_linear":
@@ -259,7 +274,19 @@ def step(
 
         # 3. compute predicted original sample from predicted noise also called
         # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
-        pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)
+        if self.config.prediction_type == "epsilon":
+            pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)
+        elif self.config.prediction_type == "sample":
+            pred_original_sample = model_output
+        elif self.config.prediction_type == "v_prediction":
+            pred_original_sample = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
+            # predict V
+            model_output = (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
+        else:
+            raise ValueError(
+                f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"
+                " `v_prediction`"
+            )
 
         # 4. compute variance: "sigma_t(η)" -> see formula (16)
         # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)

src/diffusers/schedulers/scheduling_ddpm.py

@@ -99,9 +99,9 @@ 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.
-        predict_epsilon (`bool`):
-            optional flag to use when the model predicts the noise (epsilon), or the samples instead of the noise.
-
+        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.
     """
 
     _compatibles = _COMPATIBLE_STABLE_DIFFUSION_SCHEDULERS.copy()
@@ -116,8 +116,17 @@ def __init__(
         trained_betas: Optional[np.ndarray] = None,
         variance_type: str = "fixed_small",
         clip_sample: bool = True,
-        predict_epsilon: bool = True,
+        prediction_type: str = "epsilon",
+        **kwargs,
     ):
+        message = (
+            "Please make sure to instantiate your scheduler with `prediction_type` instead. E.g. `scheduler ="
+            " DDPMScheduler.from_pretrained(<model_id>, prediction_type='epsilon')`."
+        )
+        predict_epsilon = deprecate("predict_epsilon", "0.10.0", message, take_from=kwargs)
+        if predict_epsilon is not None:
+            self.register_to_config(prediction_type="epsilon" if predict_epsilon else "sample")
+
         if trained_betas is not None:
             self.betas = torch.from_numpy(trained_betas)
         elif beta_schedule == "linear":
@@ -241,13 +250,13 @@ def step(
 
         """
         message = (
-            "Please make sure to instantiate your scheduler with `predict_epsilon` instead. E.g. `scheduler ="
-            " DDPMScheduler.from_pretrained(<model_id>, predict_epsilon=True)`."
+            "Please make sure to instantiate your scheduler with `prediction_type` instead. E.g. `scheduler ="
+            " DDPMScheduler.from_pretrained(<model_id>, prediction_type='epsilon')`."
         )
         predict_epsilon = deprecate("predict_epsilon", "0.10.0", message, take_from=kwargs)
-        if predict_epsilon is not None and predict_epsilon != self.config.predict_epsilon:
+        if predict_epsilon is not None:
             new_config = dict(self.config)
-            new_config["predict_epsilon"] = predict_epsilon
+            new_config["prediction_type"] = "epsilon" if predict_epsilon else "sample"
             self._internal_dict = FrozenDict(new_config)
 
         t = timestep
@@ -265,10 +274,15 @@ def step(
 
         # 2. compute predicted original sample from predicted noise also called
         # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
-        if self.config.predict_epsilon:
+        if self.config.prediction_type == "epsilon":
             pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)
-        else:
+        elif self.config.prediction_type == "sample":
             pred_original_sample = model_output
+        else:
+            raise ValueError(
+                f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` "
+                " for the DDPMScheduler."
+            )
 
         # 3. Clip "predicted x_0"
         if self.config.clip_sample: