Improve docstrings and type hints in scheduling_ddpm_flax.py (#13024)

docs: improve docstring scheduling_ddpm_flax.py

docker/diffusers-pytorch-cuda/Dockerfile

@@ -2,7 +2,7 @@ FROM nvidia/cuda:12.1.0-runtime-ubuntu20.04
 LABEL maintainer="Hugging Face"
 LABEL repository="diffusers"
 
-ARG PYTHON_VERSION=3.12
+ARG PYTHON_VERSION=3.11
 ENV DEBIAN_FRONTEND=noninteractive
 
 RUN apt-get -y update \
@@ -32,10 +32,12 @@ RUN uv venv --python ${PYTHON_VERSION} --seed ${VIRTUAL_ENV}
 ENV PATH="$VIRTUAL_ENV/bin:$PATH"
 
 # pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
+# Install torch, torchvision, and torchaudio together to ensure compatibility
 RUN uv pip install --no-cache-dir \
     torch \
     torchvision \
-    torchaudio
+    torchaudio \
+    --index-url https://download.pytorch.org/whl/cu121
 
 RUN uv pip install --no-cache-dir "git+https://github.com/huggingface/diffusers.git@main#egg=diffusers[test]"
 

docker/diffusers-pytorch-xformers-cuda/Dockerfile

@@ -2,7 +2,7 @@ FROM nvidia/cuda:12.1.0-runtime-ubuntu20.04
 LABEL maintainer="Hugging Face"
 LABEL repository="diffusers"
 
-ARG PYTHON_VERSION=3.12
+ARG PYTHON_VERSION=3.11
 ENV DEBIAN_FRONTEND=noninteractive
 
 RUN apt-get -y update \
@@ -32,10 +32,12 @@ RUN uv venv --python ${PYTHON_VERSION} --seed ${VIRTUAL_ENV}
 ENV PATH="$VIRTUAL_ENV/bin:$PATH"
 
 # pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
+# Install torch, torchvision, and torchaudio together to ensure compatibility
 RUN uv pip install --no-cache-dir \
     torch \
     torchvision \
-    torchaudio
+    torchaudio \
+    --index-url https://download.pytorch.org/whl/cu121
 
 RUN uv pip install --no-cache-dir "git+https://github.com/huggingface/diffusers.git@main#egg=diffusers[test]"
 

src/diffusers/models/resnet.py

@@ -366,7 +366,12 @@ class ResnetBlock2D(nn.Module):
         hidden_states = self.conv2(hidden_states)
 
         if self.conv_shortcut is not None:
-            input_tensor = self.conv_shortcut(input_tensor.contiguous())
+            # Only use contiguous() during training to avoid DDP gradient stride mismatch warning.
+            # In inference mode (eval or no_grad), skip contiguous() for better performance, especially on CPU.
+            # Issue: https://github.com/huggingface/diffusers/issues/12975
+            if self.training:
+                input_tensor = input_tensor.contiguous()
+            input_tensor = self.conv_shortcut(input_tensor)
 
         output_tensor = (input_tensor + hidden_states) / self.output_scale_factor
 

src/diffusers/models/transformers/transformer_longcat_image.py

@@ -23,7 +23,7 @@ from ...configuration_utils import ConfigMixin, register_to_config
 from ...loaders import FromOriginalModelMixin, PeftAdapterMixin
 from ...utils import logging
 from ...utils.torch_utils import maybe_allow_in_graph
-from ..attention import AttentionModuleMixin, FeedForward
+from ..attention import AttentionMixin, AttentionModuleMixin, FeedForward
 from ..attention_dispatch import dispatch_attention_fn
 from ..cache_utils import CacheMixin
 from ..embeddings import TimestepEmbedding, Timesteps, apply_rotary_emb, get_1d_rotary_pos_embed
@@ -400,12 +400,14 @@ class LongCatImageTransformer2DModel(
     PeftAdapterMixin,
     FromOriginalModelMixin,
     CacheMixin,
+    AttentionMixin,
 ):
     """
     The Transformer model introduced in Longcat-Image.
     """
 
     _supports_gradient_checkpointing = True
+    _repeated_blocks = ["LongCatImageTransformerBlock", "LongCatImageSingleTransformerBlock"]
 
     @register_to_config
     def __init__(

src/diffusers/schedulers/scheduling_ddim_flax.py

@@ -22,6 +22,7 @@ import flax
 import jax.numpy as jnp
 
 from ..configuration_utils import ConfigMixin, register_to_config
+from ..utils import logging
 from .scheduling_utils_flax import (
     CommonSchedulerState,
     FlaxKarrasDiffusionSchedulers,
@@ -32,6 +33,9 @@ from .scheduling_utils_flax import (
 )
 
 
+logger = logging.get_logger(__name__)
+
+
 @flax.struct.dataclass
 class DDIMSchedulerState:
     common: CommonSchedulerState
@@ -125,6 +129,10 @@ class FlaxDDIMScheduler(FlaxSchedulerMixin, ConfigMixin):
         prediction_type: str = "epsilon",
         dtype: jnp.dtype = jnp.float32,
     ):
+        logger.warning(
+            "Flax classes are deprecated and will be removed in Diffusers v1.0.0. We "
+            "recommend migrating to PyTorch classes or pinning your version of Diffusers."
+        )
         self.dtype = dtype
 
     def create_state(self, common: Optional[CommonSchedulerState] = None) -> DDIMSchedulerState:
@@ -152,7 +160,10 @@ class FlaxDDIMScheduler(FlaxSchedulerMixin, ConfigMixin):
         )
 
     def scale_model_input(
-        self, state: DDIMSchedulerState, sample: jnp.ndarray, timestep: Optional[int] = None
+        self,
+        state: DDIMSchedulerState,
+        sample: jnp.ndarray,
+        timestep: Optional[int] = None,
     ) -> jnp.ndarray:
         """
         Args:
@@ -190,7 +201,9 @@ class FlaxDDIMScheduler(FlaxSchedulerMixin, ConfigMixin):
     def _get_variance(self, state: DDIMSchedulerState, timestep, prev_timestep):
         alpha_prod_t = state.common.alphas_cumprod[timestep]
         alpha_prod_t_prev = jnp.where(
-            prev_timestep >= 0, state.common.alphas_cumprod[prev_timestep], state.final_alpha_cumprod
+            prev_timestep >= 0,
+            state.common.alphas_cumprod[prev_timestep],
+            state.final_alpha_cumprod,
         )
         beta_prod_t = 1 - alpha_prod_t
         beta_prod_t_prev = 1 - alpha_prod_t_prev

src/diffusers/schedulers/scheduling_ddim_inverse.py

@@ -99,7 +99,7 @@ def betas_for_alpha_bar(
 
 
 # Copied from diffusers.schedulers.scheduling_ddim.rescale_zero_terminal_snr
-def rescale_zero_terminal_snr(betas):
+def rescale_zero_terminal_snr(betas: torch.Tensor) -> torch.Tensor:
     """
     Rescales betas to have zero terminal SNR Based on https://huggingface.co/papers/2305.08891 (Algorithm 1)
 
@@ -187,14 +187,14 @@ class DDIMInverseScheduler(SchedulerMixin, ConfigMixin):
         num_train_timesteps: int = 1000,
         beta_start: float = 0.0001,
         beta_end: float = 0.02,
-        beta_schedule: str = "linear",
+        beta_schedule: Literal["linear", "scaled_linear", "squaredcos_cap_v2"] = "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 = "epsilon",
+        prediction_type: Literal["epsilon", "sample", "v_prediction"] = "epsilon",
         clip_sample_range: float = 1.0,
-        timestep_spacing: str = "leading",
+        timestep_spacing: Literal["leading", "trailing"] = "leading",
         rescale_betas_zero_snr: bool = False,
         **kwargs,
     ):
@@ -210,7 +210,15 @@ class DDIMInverseScheduler(SchedulerMixin, ConfigMixin):
             self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)
         elif beta_schedule == "scaled_linear":
             # this schedule is very specific to the latent diffusion model.
-            self.betas = torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2
+            self.betas = (
+                torch.linspace(
+                    beta_start**0.5,
+                    beta_end**0.5,
+                    num_train_timesteps,
+                    dtype=torch.float32,
+                )
+                ** 2
+            )
         elif beta_schedule == "squaredcos_cap_v2":
             # Glide cosine schedule
             self.betas = betas_for_alpha_bar(num_train_timesteps)
@@ -256,7 +264,11 @@ class DDIMInverseScheduler(SchedulerMixin, ConfigMixin):
         """
         return sample
 
-    def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None):
+    def set_timesteps(
+        self,
+        num_inference_steps: int,
+        device: Optional[Union[str, torch.device]] = None,
+    ) -> None:
         """
         Sets the discrete timesteps used for the diffusion chain (to be run before inference).
 
@@ -308,20 +320,10 @@ class DDIMInverseScheduler(SchedulerMixin, ConfigMixin):
         Args:
             model_output (`torch.Tensor`):
                 The direct output from learned diffusion model.
-            timestep (`float`):
+            timestep (`int`):
                 The current discrete timestep in the diffusion chain.
             sample (`torch.Tensor`):
                 A current instance of a sample created by the diffusion process.
-            eta (`float`):
-                The weight of noise for added noise in diffusion step.
-            use_clipped_model_output (`bool`, defaults to `False`):
-                If `True`, computes "corrected" `model_output` from the clipped predicted original sample. Necessary
-                because predicted original sample is clipped to [-1, 1] when `self.config.clip_sample` is `True`. If no
-                clipping has happened, "corrected" `model_output` would coincide with the one provided as input and
-                `use_clipped_model_output` has no effect.
-            variance_noise (`torch.Tensor`):
-                Alternative to generating noise with `generator` by directly providing the noise for the variance
-                itself. Useful for methods such as [`CycleDiffusion`].
             return_dict (`bool`, *optional*, defaults to `True`):
                 Whether or not to return a [`~schedulers.scheduling_ddim_inverse.DDIMInverseSchedulerOutput`] or
                 `tuple`.
@@ -335,7 +337,8 @@ class DDIMInverseScheduler(SchedulerMixin, ConfigMixin):
         # 1. get previous step value (=t+1)
         prev_timestep = timestep
         timestep = min(
-            timestep - self.config.num_train_timesteps // self.num_inference_steps, self.config.num_train_timesteps - 1
+            timestep - self.config.num_train_timesteps // self.num_inference_steps,
+            self.config.num_train_timesteps - 1,
         )
 
         # 2. compute alphas, betas
@@ -378,5 +381,5 @@ class DDIMInverseScheduler(SchedulerMixin, ConfigMixin):
             return (prev_sample, pred_original_sample)
         return DDIMSchedulerOutput(prev_sample=prev_sample, pred_original_sample=pred_original_sample)
 
-    def __len__(self):
+    def __len__(self) -> int:
         return self.config.num_train_timesteps

src/diffusers/schedulers/scheduling_ddim_parallel.py

@@ -101,7 +101,7 @@ def betas_for_alpha_bar(
 
 
 # Copied from diffusers.schedulers.scheduling_ddim.rescale_zero_terminal_snr
-def rescale_zero_terminal_snr(betas):
+def rescale_zero_terminal_snr(betas: torch.Tensor) -> torch.Tensor:
     """
     Rescales betas to have zero terminal SNR Based on https://huggingface.co/papers/2305.08891 (Algorithm 1)
 
@@ -266,7 +266,7 @@ class DDIMParallelScheduler(SchedulerMixin, ConfigMixin):
         """
         return sample
 
-    def _get_variance(self, timestep, prev_timestep=None):
+    def _get_variance(self, timestep: int, prev_timestep: Optional[int] = None) -> torch.Tensor:
         if prev_timestep is None:
             prev_timestep = timestep - self.config.num_train_timesteps // self.num_inference_steps
 
@@ -279,7 +279,7 @@ class DDIMParallelScheduler(SchedulerMixin, ConfigMixin):
 
         return variance
 
-    def _batch_get_variance(self, t, prev_t):
+    def _batch_get_variance(self, t: torch.Tensor, prev_t: torch.Tensor) -> torch.Tensor:
         alpha_prod_t = self.alphas_cumprod[t]
         alpha_prod_t_prev = self.alphas_cumprod[torch.clip(prev_t, min=0)]
         alpha_prod_t_prev[prev_t < 0] = torch.tensor(1.0)
@@ -335,7 +335,7 @@ class DDIMParallelScheduler(SchedulerMixin, ConfigMixin):
         return sample
 
     # Copied from diffusers.schedulers.scheduling_ddim.DDIMScheduler.set_timesteps
-    def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None):
+    def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None) -> None:
         """
         Sets the discrete timesteps used for the diffusion chain (to be run before inference).
 
@@ -392,7 +392,7 @@ class DDIMParallelScheduler(SchedulerMixin, ConfigMixin):
         sample: torch.Tensor,
         eta: float = 0.0,
         use_clipped_model_output: bool = False,
-        generator=None,
+        generator: Optional[torch.Generator] = None,
         variance_noise: Optional[torch.Tensor] = None,
         return_dict: bool = True,
     ) -> Union[DDIMParallelSchedulerOutput, Tuple]:
@@ -406,11 +406,13 @@ class DDIMParallelScheduler(SchedulerMixin, ConfigMixin):
             sample (`torch.Tensor`):
                 current instance of sample being created by diffusion process.
             eta (`float`): weight of noise for added noise in diffusion step.
-            use_clipped_model_output (`bool`): if `True`, compute "corrected" `model_output` from the clipped
-                predicted original sample. Necessary because predicted original sample is clipped to [-1, 1] when
-                `self.config.clip_sample` is `True`. If no clipping has happened, "corrected" `model_output` would
-                coincide with the one provided as input and `use_clipped_model_output` will have not effect.
-            generator: random number generator.
+            use_clipped_model_output (`bool`, defaults to `False`):
+                If `True`, compute "corrected" `model_output` from the clipped predicted original sample. This
+                correction is necessary because the predicted original sample is clipped to [-1, 1] when
+                `self.config.clip_sample` is `True`. If no clipping occurred, the "corrected" `model_output` matches
+                the input and `use_clipped_model_output` has no effect.
+            generator (`torch.Generator`, *optional*):
+                Random number generator.
             variance_noise (`torch.Tensor`): instead of generating noise for the variance using `generator`, we
                 can directly provide the noise for the variance itself. This is useful for methods such as
                 CycleDiffusion. (https://huggingface.co/papers/2210.05559)
@@ -496,7 +498,10 @@ class DDIMParallelScheduler(SchedulerMixin, ConfigMixin):
 
             if variance_noise is None:
                 variance_noise = randn_tensor(
-                    model_output.shape, generator=generator, device=model_output.device, dtype=model_output.dtype
+                    model_output.shape,
+                    generator=generator,
+                    device=model_output.device,
+                    dtype=model_output.dtype,
                 )
             variance = std_dev_t * variance_noise
 
@@ -513,7 +518,7 @@ class DDIMParallelScheduler(SchedulerMixin, ConfigMixin):
     def batch_step_no_noise(
         self,
         model_output: torch.Tensor,
-        timesteps: List[int],
+        timesteps: torch.Tensor,
         sample: torch.Tensor,
         eta: float = 0.0,
         use_clipped_model_output: bool = False,
@@ -528,7 +533,7 @@ class DDIMParallelScheduler(SchedulerMixin, ConfigMixin):
 
         Args:
             model_output (`torch.Tensor`): direct output from learned diffusion model.
-            timesteps (`List[int]`):
+            timesteps (`torch.Tensor`):
                 current discrete timesteps in the diffusion chain. This is now a list of integers.
             sample (`torch.Tensor`):
                 current instance of sample being created by diffusion process.
@@ -696,5 +701,5 @@ class DDIMParallelScheduler(SchedulerMixin, ConfigMixin):
         velocity = sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
         return velocity
 
-    def __len__(self):
+    def __len__(self) -> int:
         return self.config.num_train_timesteps

src/diffusers/schedulers/scheduling_ddpm_flax.py

@@ -22,6 +22,7 @@ import jax
 import jax.numpy as jnp
 
 from ..configuration_utils import ConfigMixin, register_to_config
+from ..utils import logging
 from .scheduling_utils_flax import (
     CommonSchedulerState,
     FlaxKarrasDiffusionSchedulers,
@@ -32,6 +33,9 @@ from .scheduling_utils_flax import (
 )
 
 
+logger = logging.get_logger(__name__)
+
+
 @flax.struct.dataclass
 class DDPMSchedulerState:
     common: CommonSchedulerState
@@ -42,7 +46,12 @@ class DDPMSchedulerState:
     num_inference_steps: Optional[int] = None
 
     @classmethod
-    def create(cls, common: CommonSchedulerState, init_noise_sigma: jnp.ndarray, timesteps: jnp.ndarray):
+    def create(
+        cls,
+        common: CommonSchedulerState,
+        init_noise_sigma: jnp.ndarray,
+        timesteps: jnp.ndarray,
+    ):
         return cls(common=common, init_noise_sigma=init_noise_sigma, timesteps=timesteps)
 
 
@@ -105,6 +114,10 @@ class FlaxDDPMScheduler(FlaxSchedulerMixin, ConfigMixin):
         prediction_type: str = "epsilon",
         dtype: jnp.dtype = jnp.float32,
     ):
+        logger.warning(
+            "Flax classes are deprecated and will be removed in Diffusers v1.0.0. We "
+            "recommend migrating to PyTorch classes or pinning your version of Diffusers."
+        )
         self.dtype = dtype
 
     def create_state(self, common: Optional[CommonSchedulerState] = None) -> DDPMSchedulerState:
@@ -123,7 +136,10 @@ class FlaxDDPMScheduler(FlaxSchedulerMixin, ConfigMixin):
         )
 
     def scale_model_input(
-        self, state: DDPMSchedulerState, sample: jnp.ndarray, timestep: Optional[int] = None
+        self,
+        state: DDPMSchedulerState,
+        sample: jnp.ndarray,
+        timestep: Optional[int] = None,
     ) -> jnp.ndarray:
         """
         Args:

src/diffusers/schedulers/scheduling_unipc_multistep.py

@@ -226,6 +226,7 @@ class UniPCMultistepScheduler(SchedulerMixin, ConfigMixin):
         time_shift_type: Literal["exponential"] = "exponential",
         sigma_min: Optional[float] = None,
         sigma_max: Optional[float] = None,
+        shift_terminal: Optional[float] = None,
     ) -> None:
         if self.config.use_beta_sigmas and not is_scipy_available():
             raise ImportError("Make sure to install scipy if you want to use beta sigmas.")
@@ -245,6 +246,8 @@ class UniPCMultistepScheduler(SchedulerMixin, ConfigMixin):
             self.betas = betas_for_alpha_bar(num_train_timesteps)
         else:
             raise NotImplementedError(f"{beta_schedule} is not implemented for {self.__class__}")
+        if shift_terminal is not None and not use_flow_sigmas:
+            raise ValueError("`shift_terminal` is only supported when `use_flow_sigmas=True`.")
 
         if rescale_betas_zero_snr:
             self.betas = rescale_zero_terminal_snr(self.betas)
@@ -313,8 +316,12 @@ class UniPCMultistepScheduler(SchedulerMixin, ConfigMixin):
         self._begin_index = begin_index
 
     def set_timesteps(
-        self, num_inference_steps: int, device: Optional[Union[str, torch.device]] = None, mu: Optional[float] = None
-    ) -> None:
+        self,
+        num_inference_steps: Optional[int] = None,
+        device: Union[str, torch.device] = None,
+        sigmas: Optional[List[float]] = None,
+        mu: Optional[float] = None,
+    ):
         """
         Sets the discrete timesteps used for the diffusion chain (to be run before inference).
 
@@ -323,13 +330,24 @@ class UniPCMultistepScheduler(SchedulerMixin, ConfigMixin):
                 The number of diffusion steps used when generating samples with a pre-trained model.
             device (`str` or `torch.device`, *optional*):
                 The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
+            sigmas (`List[float]`, *optional*):
+                Custom values for sigmas to be used for each diffusion step. If `None`, the sigmas are computed
+                automatically.
             mu (`float`, *optional*):
                 Optional mu parameter for dynamic shifting when using exponential time shift type.
         """
+        if self.config.use_dynamic_shifting and mu is None:
+            raise ValueError("`mu` must be passed when `use_dynamic_shifting` is set to be `True`")
+
+        if sigmas is not None:
+            if not self.config.use_flow_sigmas:
+                raise ValueError(
+                    "Passing `sigmas` is only supported when `use_flow_sigmas=True`. "
+                    "Please set `use_flow_sigmas=True` during scheduler initialization."
+                )
+            num_inference_steps = len(sigmas)
+
         # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://huggingface.co/papers/2305.08891
-        if mu is not None:
-            assert self.config.use_dynamic_shifting and self.config.time_shift_type == "exponential"
-            self.config.flow_shift = np.exp(mu)
         if self.config.timestep_spacing == "linspace":
             timesteps = (
                 np.linspace(0, self.config.num_train_timesteps - 1, num_inference_steps + 1)
@@ -354,8 +372,9 @@ class UniPCMultistepScheduler(SchedulerMixin, ConfigMixin):
                 f"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."
             )
 
-        sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
         if self.config.use_karras_sigmas:
+            if sigmas is None:
+                sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
             log_sigmas = np.log(sigmas)
             sigmas = np.flip(sigmas).copy()
             sigmas = self._convert_to_karras(in_sigmas=sigmas, num_inference_steps=num_inference_steps)
@@ -375,6 +394,8 @@ class UniPCMultistepScheduler(SchedulerMixin, ConfigMixin):
                 )
             sigmas = np.concatenate([sigmas, [sigma_last]]).astype(np.float32)
         elif self.config.use_exponential_sigmas:
+            if sigmas is None:
+                sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
             log_sigmas = np.log(sigmas)
             sigmas = np.flip(sigmas).copy()
             sigmas = self._convert_to_exponential(in_sigmas=sigmas, num_inference_steps=num_inference_steps)
@@ -389,6 +410,8 @@ class UniPCMultistepScheduler(SchedulerMixin, ConfigMixin):
                 )
             sigmas = np.concatenate([sigmas, [sigma_last]]).astype(np.float32)
         elif self.config.use_beta_sigmas:
+            if sigmas is None:
+                sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
             log_sigmas = np.log(sigmas)
             sigmas = np.flip(sigmas).copy()
             sigmas = self._convert_to_beta(in_sigmas=sigmas, num_inference_steps=num_inference_steps)
@@ -403,9 +426,18 @@ class UniPCMultistepScheduler(SchedulerMixin, ConfigMixin):
                 )
             sigmas = np.concatenate([sigmas, [sigma_last]]).astype(np.float32)
         elif self.config.use_flow_sigmas:
-            alphas = np.linspace(1, 1 / self.config.num_train_timesteps, num_inference_steps + 1)
-            sigmas = 1.0 - alphas
-            sigmas = np.flip(self.config.flow_shift * sigmas / (1 + (self.config.flow_shift - 1) * sigmas))[:-1].copy()
+            if sigmas is None:
+                sigmas = np.linspace(1, 1 / self.config.num_train_timesteps, num_inference_steps + 1)[:-1]
+            if self.config.use_dynamic_shifting:
+                sigmas = self.time_shift(mu, 1.0, sigmas)
+            else:
+                sigmas = self.config.flow_shift * sigmas / (1 + (self.config.flow_shift - 1) * sigmas)
+            if self.config.shift_terminal:
+                sigmas = self.stretch_shift_to_terminal(sigmas)
+            eps = 1e-6
+            if np.fabs(sigmas[0] - 1) < eps:
+                # to avoid inf torch.log(alpha_si) in multistep_uni_p_bh_update during first/second update
+                sigmas[0] -= eps
             timesteps = (sigmas * self.config.num_train_timesteps).copy()
             if self.config.final_sigmas_type == "sigma_min":
                 sigma_last = sigmas[-1]
@@ -417,6 +449,8 @@ class UniPCMultistepScheduler(SchedulerMixin, ConfigMixin):
                 )
             sigmas = np.concatenate([sigmas, [sigma_last]]).astype(np.float32)
         else:
+            if sigmas is None:
+                sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
             sigmas = np.interp(timesteps, np.arange(0, len(sigmas)), sigmas)
             if self.config.final_sigmas_type == "sigma_min":
                 sigma_last = ((1 - self.alphas_cumprod[0]) / self.alphas_cumprod[0]) ** 0.5
@@ -446,6 +480,43 @@ class UniPCMultistepScheduler(SchedulerMixin, ConfigMixin):
         self._begin_index = None
         self.sigmas = self.sigmas.to("cpu")  # to avoid too much CPU/GPU communication
 
+    # Copied from diffusers.schedulers.scheduling_flow_match_euler_discrete.FlowMatchEulerDiscreteScheduler.time_shift
+    def time_shift(self, mu: float, sigma: float, t: torch.Tensor):
+        if self.config.time_shift_type == "exponential":
+            return self._time_shift_exponential(mu, sigma, t)
+        elif self.config.time_shift_type == "linear":
+            return self._time_shift_linear(mu, sigma, t)
+
+    # Copied from diffusers.schedulers.scheduling_flow_match_euler_discrete.FlowMatchEulerDiscreteScheduler.stretch_shift_to_terminal
+    def stretch_shift_to_terminal(self, t: torch.Tensor) -> torch.Tensor:
+        r"""
+        Stretches and shifts the timestep schedule to ensure it terminates at the configured `shift_terminal` config
+        value.
+
+        Reference:
+        https://github.com/Lightricks/LTX-Video/blob/a01a171f8fe3d99dce2728d60a73fecf4d4238ae/ltx_video/schedulers/rf.py#L51
+
+        Args:
+            t (`torch.Tensor`):
+                A tensor of timesteps to be stretched and shifted.
+
+        Returns:
+            `torch.Tensor`:
+                A tensor of adjusted timesteps such that the final value equals `self.config.shift_terminal`.
+        """
+        one_minus_z = 1 - t
+        scale_factor = one_minus_z[-1] / (1 - self.config.shift_terminal)
+        stretched_t = 1 - (one_minus_z / scale_factor)
+        return stretched_t
+
+    # Copied from diffusers.schedulers.scheduling_flow_match_euler_discrete.FlowMatchEulerDiscreteScheduler._time_shift_exponential
+    def _time_shift_exponential(self, mu, sigma, t):
+        return math.exp(mu) / (math.exp(mu) + (1 / t - 1) ** sigma)
+
+    # Copied from diffusers.schedulers.scheduling_flow_match_euler_discrete.FlowMatchEulerDiscreteScheduler._time_shift_linear
+    def _time_shift_linear(self, mu, sigma, t):
+        return mu / (mu + (1 / t - 1) ** sigma)
+
     # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler._threshold_sample
     def _threshold_sample(self, sample: torch.Tensor) -> torch.Tensor:
         """

tests/modular_pipelines/flux/test_modular_pipeline_flux.py

@@ -37,14 +37,9 @@ class TestFluxModularPipelineFast(ModularPipelineTesterMixin):
     pipeline_class = FluxModularPipeline
     pipeline_blocks_class = FluxAutoBlocks
     pretrained_model_name_or_path = "hf-internal-testing/tiny-flux-modular"
-    default_repo_id = "black-forest-labs/FLUX.1-dev"
 
     params = frozenset(["prompt", "height", "width", "guidance_scale"])
     batch_params = frozenset(["prompt"])
-    # should choose from the dict returned by `get_dummy_inputs`
-    text_encoder_block_params = frozenset(["prompt", "max_sequence_length"])
-    decode_block_params = frozenset(["output_type"])
-    vae_encoder_block_params = None  # None if vae_encoder is not supported
 
     def get_dummy_inputs(self, seed=0):
         generator = self.get_generator(seed)
@@ -68,21 +63,10 @@ class TestFluxImg2ImgModularPipelineFast(ModularPipelineTesterMixin):
     pipeline_class = FluxModularPipeline
     pipeline_blocks_class = FluxAutoBlocks
     pretrained_model_name_or_path = "hf-internal-testing/tiny-flux-modular"
-    default_repo_id = "black-forest-labs/FLUX.1-dev"
 
     params = frozenset(["prompt", "height", "width", "guidance_scale", "image"])
     batch_params = frozenset(["prompt", "image"])
 
-    # should choose from the dict returned by `get_dummy_inputs`
-    text_encoder_block_params = frozenset(
-        [
-            "prompt",
-            "max_sequence_length",
-        ]
-    )
-    decode_block_params = frozenset(["output_type"])
-    vae_encoder_block_params = frozenset(["image", "height", "width"])
-
     def get_pipeline(self, components_manager=None, torch_dtype=torch.float32):
         pipeline = super().get_pipeline(components_manager, torch_dtype)
 
@@ -145,13 +129,9 @@ class TestFluxKontextModularPipelineFast(ModularPipelineTesterMixin):
     pipeline_class = FluxKontextModularPipeline
     pipeline_blocks_class = FluxKontextAutoBlocks
     pretrained_model_name_or_path = "hf-internal-testing/tiny-flux-kontext-pipe"
-    default_repo_id = "black-forest-labs/FLUX.1-kontext-dev"
 
     params = frozenset(["prompt", "height", "width", "guidance_scale", "image"])
     batch_params = frozenset(["prompt", "image"])
-    text_encoder_block_params = frozenset(["prompt", "max_sequence_length"])
-    decode_block_params = frozenset(["latents"])
-    vae_encoder_block_params = frozenset(["image", "height", "width"])
 
     def get_dummy_inputs(self, seed=0):
         generator = self.get_generator(seed)

tests/modular_pipelines/flux2/test_modular_pipeline_flux2.py

@@ -32,14 +32,9 @@ class TestFlux2ModularPipelineFast(ModularPipelineTesterMixin):
     pipeline_class = Flux2ModularPipeline
     pipeline_blocks_class = Flux2AutoBlocks
     pretrained_model_name_or_path = "hf-internal-testing/tiny-flux2-modular"
-    default_repo_id = "black-forest-labs/FLUX.2-dev"
 
     params = frozenset(["prompt", "height", "width", "guidance_scale"])
     batch_params = frozenset(["prompt"])
-    # should choose from the dict returned by `get_dummy_inputs`
-    text_encoder_block_params = frozenset(["prompt", "max_sequence_length", "text_encoder_out_layers"])
-    decode_block_params = frozenset(["output_type"])
-    vae_encoder_block_params = None
 
     def get_dummy_inputs(self, seed=0):
         generator = self.get_generator(seed)
@@ -68,10 +63,6 @@ class TestFlux2ImageConditionedModularPipelineFast(ModularPipelineTesterMixin):
 
     params = frozenset(["prompt", "height", "width", "guidance_scale", "image"])
     batch_params = frozenset(["prompt", "image"])
-    # should choose from the dict returned by `get_dummy_inputs`
-    text_encoder_block_params = frozenset(["prompt", "max_sequence_length", "text_encoder_out_layers"])
-    decode_block_params = frozenset(["output_type"])
-    vae_encoder_block_params = frozenset(["image", "height", "width"])
 
     def get_dummy_inputs(self, seed=0):
         generator = self.get_generator(seed)

tests/modular_pipelines/qwen/test_modular_pipeline_qwenimage.py

@@ -34,16 +34,10 @@ class TestQwenImageModularPipelineFast(ModularPipelineTesterMixin, ModularGuider
     pipeline_class = QwenImageModularPipeline
     pipeline_blocks_class = QwenImageAutoBlocks
     pretrained_model_name_or_path = "hf-internal-testing/tiny-qwenimage-modular"
-    default_repo_id = "Qwen/Qwen-Image"
 
     params = frozenset(["prompt", "height", "width", "negative_prompt", "attention_kwargs", "image", "mask_image"])
     batch_params = frozenset(["prompt", "negative_prompt", "image", "mask_image"])
 
-    # should choose from the dict returned by `get_dummy_inputs`
-    text_encoder_block_params = frozenset(["prompt", "negative_prompt", "max_sequence_length"])
-    decode_block_params = frozenset(["output_type"])
-    vae_encoder_block_params = None  # None if vae_encoder is not supported
-
     def get_dummy_inputs(self):
         generator = self.get_generator()
         inputs = {
@@ -66,16 +60,10 @@ class TestQwenImageEditModularPipelineFast(ModularPipelineTesterMixin, ModularGu
     pipeline_class = QwenImageEditModularPipeline
     pipeline_blocks_class = QwenImageEditAutoBlocks
     pretrained_model_name_or_path = "hf-internal-testing/tiny-qwenimage-edit-modular"
-    default_repo_id = "Qwen/Qwen-Image-Edit"
 
     params = frozenset(["prompt", "height", "width", "negative_prompt", "attention_kwargs", "image", "mask_image"])
     batch_params = frozenset(["prompt", "negative_prompt", "image", "mask_image"])
 
-    # should choose from the dict returned by `get_dummy_inputs`
-    text_encoder_block_params = frozenset(["prompt", "negative_prompt", "max_sequence_length"])
-    decode_block_params = frozenset(["output_type"])
-    vae_encoder_block_params = frozenset(["image", "height", "width"])
-
     def get_dummy_inputs(self):
         generator = self.get_generator()
         inputs = {
@@ -98,7 +86,6 @@ class TestQwenImageEditPlusModularPipelineFast(ModularPipelineTesterMixin, Modul
     pipeline_class = QwenImageEditPlusModularPipeline
     pipeline_blocks_class = QwenImageEditPlusAutoBlocks
     pretrained_model_name_or_path = "hf-internal-testing/tiny-qwenimage-edit-plus-modular"
-    default_repo_id = "Qwen/Qwen-Image-Edit-2509"
 
     # No `mask_image` yet.
     params = frozenset(["prompt", "height", "width", "negative_prompt", "attention_kwargs", "image"])

tests/modular_pipelines/stable_diffusion_xl/test_modular_pipeline_stable_diffusion_xl.py

@@ -279,8 +279,6 @@ class TestSDXLModularPipelineFast(
     pipeline_class = StableDiffusionXLModularPipeline
     pipeline_blocks_class = StableDiffusionXLAutoBlocks
     pretrained_model_name_or_path = "hf-internal-testing/tiny-sdxl-modular"
-    default_repo_id = "stabilityai/stable-diffusion-xl-base-1.0"
-
     params = frozenset(
         [
             "prompt",
@@ -293,11 +291,6 @@ class TestSDXLModularPipelineFast(
     batch_params = frozenset(["prompt", "negative_prompt"])
     expected_image_output_shape = (1, 3, 64, 64)
 
-    # should choose from the dict returned by `get_dummy_inputs`
-    text_encoder_block_params = frozenset(["prompt"])
-    decode_block_params = frozenset(["output_type"])
-    vae_encoder_block_params = None  # None if vae_encoder is not supported
-
     def get_dummy_inputs(self, seed=0):
         generator = self.get_generator(seed)
         inputs = {
@@ -346,11 +339,6 @@ class TestSDXLImg2ImgModularPipelineFast(
     batch_params = frozenset(["prompt", "negative_prompt", "image"])
     expected_image_output_shape = (1, 3, 64, 64)
 
-    # should choose from the dict returned by `get_dummy_inputs`
-    text_encoder_block_params = frozenset(["prompt"])
-    decode_block_params = frozenset(["output_type"])
-    vae_encoder_block_params = frozenset(["image"])
-
     def get_dummy_inputs(self, seed=0):
         generator = self.get_generator(seed)
         inputs = {

tests/modular_pipelines/test_modular_pipelines_common.py

@@ -48,12 +48,6 @@ class ModularPipelineTesterMixin:
             "You need to set the attribute `pretrained_model_name_or_path` in the child test class. See existing pipeline tests for reference."
         )
 
-    @property
-    def default_repo_id(self) -> str:
-        raise NotImplementedError(
-            "You need to set the attribute `default_repo_id` in the child test class. See existing pipeline tests for reference."
-        )
-
     @property
     def pipeline_blocks_class(self) -> Union[Callable, ModularPipelineBlocks]:
         raise NotImplementedError(
@@ -96,30 +90,6 @@ class ModularPipelineTesterMixin:
             "See existing pipeline tests for reference."
         )
 
-    def text_encoder_block_params(self) -> frozenset:
-        raise NotImplementedError(
-            "You need to set the attribute `text_encoder_block_params` in the child test class. "
-            "`text_encoder_block_params` are the parameters required to be passed to the text encoder block. "
-            " if should be a subset of the parameters returned by `get_dummy_inputs`"
-            "See existing pipeline tests for reference."
-        )
-
-    def decode_block_params(self) -> frozenset:
-        raise NotImplementedError(
-            "You need to set the attribute `decode_block_params` in the child test class. "
-            "`decode_block_params` are the parameters required to be passed to the decode block. "
-            " if should be a subset of the parameters returned by `get_dummy_inputs`"
-            "See existing pipeline tests for reference."
-        )
-
-    def vae_encoder_block_params(self) -> frozenset:
-        raise NotImplementedError(
-            "You need to set the attribute `vae_encoder_block_params` in the child test class. "
-            "`vae_encoder_block_params` are the parameters required to be passed to the vae encoder block. "
-            " if should be a subset of the parameters returned by `get_dummy_inputs`"
-            "See existing pipeline tests for reference."
-        )
-
     def setup_method(self):
         # clean up the VRAM before each test
         torch.compiler.reset()
@@ -154,96 +124,6 @@ class ModularPipelineTesterMixin:
         _check_for_parameters(self.params, input_parameters, "input")
         _check_for_parameters(self.optional_params, optional_parameters, "optional")
 
-    def test_loading_from_default_repo(self):
-        if self.default_repo_id is None:
-            return
-
-        try:
-            pipe = ModularPipeline.from_pretrained(self.default_repo_id)
-            assert pipe.blocks.__class__ == self.pipeline_blocks_class
-        except Exception as e:
-            assert False, f"Failed to load pipeline from default repo: {e}"
-
-    def test_modular_inference(self):
-        # run the pipeline to get the base output for comparison
-        pipe = self.get_pipeline()
-        pipe.to(torch_device, torch.float32)
-
-        inputs = self.get_dummy_inputs()
-        standard_output = pipe(**inputs, output="images")
-
-        # create text, denoise, decoder (and optional vae encoder) nodes
-        blocks = self.pipeline_blocks_class()
-
-        assert "text_encoder" in blocks.sub_blocks, "`text_encoder` block is not present in the pipeline"
-        assert "denoise" in blocks.sub_blocks, "`denoise` block is not present in the pipeline"
-        assert "decode" in blocks.sub_blocks, "`decode` block is not present in the pipeline"
-        if self.vae_encoder_block_params is not None:
-            assert "vae_encoder" in blocks.sub_blocks, "`vae_encoder` block is not present in the pipeline"
-
-        # manually set the components in the sub_pipe
-        # a hack to workaround the fact the default pipeline properties are often incorrect for testing cases,
-        # #e.g. vae_scale_factor is ususally not 8 because vae is configured to be smaller for testing
-        def manually_set_all_components(pipe: ModularPipeline, sub_pipe: ModularPipeline):
-            for n, comp in pipe.components.items():
-                if not hasattr(sub_pipe, n):
-                    setattr(sub_pipe, n, comp)
-
-        text_node = blocks.sub_blocks["text_encoder"].init_pipeline(self.pretrained_model_name_or_path)
-        text_node.load_components(torch_dtype=torch.float32)
-        text_node.to(torch_device)
-        manually_set_all_components(pipe, text_node)
-
-        denoise_node = blocks.sub_blocks["denoise"].init_pipeline(self.pretrained_model_name_or_path)
-        denoise_node.load_components(torch_dtype=torch.float32)
-        denoise_node.to(torch_device)
-        manually_set_all_components(pipe, denoise_node)
-
-        decoder_node = blocks.sub_blocks["decode"].init_pipeline(self.pretrained_model_name_or_path)
-        decoder_node.load_components(torch_dtype=torch.float32)
-        decoder_node.to(torch_device)
-        manually_set_all_components(pipe, decoder_node)
-
-        if self.vae_encoder_block_params is not None:
-            vae_encoder_node = blocks.sub_blocks["vae_encoder"].init_pipeline(self.pretrained_model_name_or_path)
-            vae_encoder_node.load_components(torch_dtype=torch.float32)
-            vae_encoder_node.to(torch_device)
-            manually_set_all_components(pipe, vae_encoder_node)
-        else:
-            vae_encoder_node = None
-
-        # prepare inputs for each node
-        inputs = self.get_dummy_inputs()
-
-        def get_block_inputs(inputs: dict, block_params: frozenset) -> tuple[dict, dict]:
-            block_inputs = {}
-            for name in block_params:
-                if name in inputs:
-                    block_inputs[name] = inputs.pop(name)
-            return block_inputs, inputs
-
-        text_inputs, inputs = get_block_inputs(inputs, self.text_encoder_block_params)
-        decoder_inputs, inputs = get_block_inputs(inputs, self.decode_block_params)
-        if vae_encoder_node is not None:
-            vae_encoder_inputs, inputs = get_block_inputs(inputs, self.vae_encoder_block_params)
-
-        # this is also to make sure pipelines mark text outputs as denoiser_input_fields
-        text_output = text_node(**text_inputs).get_by_kwargs("denoiser_input_fields")
-        if vae_encoder_node is not None:
-            vae_encoder_output = vae_encoder_node(**vae_encoder_inputs).values
-            denoise_inputs = {**text_output, **vae_encoder_output, **inputs}
-        else:
-            denoise_inputs = {**text_output, **inputs}
-
-        # denoise node output should be "latents"
-        latents = denoise_node(**denoise_inputs).latents
-        # denoder node input should be "latents" and output should be "images"
-        modular_output = decoder_node(**decoder_inputs, latents=latents).images
-
-        assert modular_output.shape == standard_output.shape, (
-            f"Modular output should have same shape as standard output {standard_output.shape}, but got {modular_output.shape}"
-        )
-
     def test_inference_batch_consistent(self, batch_sizes=[2], batch_generator=True):
         pipe = self.get_pipeline().to(torch_device)
 

tests/pipelines/kandinsky2_2/test_kandinsky_inpaint.py

@@ -248,6 +248,9 @@ class KandinskyV22InpaintPipelineFastTests(PipelineTesterMixin, unittest.TestCas
     def test_float16_inference(self):
         super().test_float16_inference(expected_max_diff=5e-1)
 
+    def test_save_load_dduf(self):
+        super().test_save_load_dduf(atol=1e-3, rtol=1e-3)
+
     @is_flaky()
     def test_model_cpu_offload_forward_pass(self):
         super().test_inference_batch_single_identical(expected_max_diff=8e-4)

tests/pipelines/kandinsky3/test_kandinsky3_img2img.py

@@ -191,6 +191,9 @@ class Kandinsky3Img2ImgPipelineFastTests(PipelineTesterMixin, unittest.TestCase)
     def test_inference_batch_single_identical(self):
         super().test_inference_batch_single_identical(expected_max_diff=1e-2)
 
+    def test_save_load_dduf(self):
+        super().test_save_load_dduf(atol=1e-3, rtol=1e-3)
+
 
 @slow
 @require_torch_accelerator