Merge branch 'main' into cache-docs-fixes

Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>

docs/source/en/api/cache.md

@@ -29,7 +29,7 @@ Cache methods speedup diffusion transformers by storing and reusing intermediate
 
 [[autodoc]] apply_faster_cache
 
-### FirstBlockCacheConfig
+## FirstBlockCacheConfig
 
 [[autodoc]] FirstBlockCacheConfig
 

docs/source/en/optimization/cache.md

@@ -66,4 +66,8 @@ config = FasterCacheConfig(
     tensor_format="BFCHW",
 )
 pipeline.transformer.enable_cache(config)
-```
+```
+
+## FirstBlockCache
+
+[FirstBlock Cache](https://huggingface.co/docs/diffusers/main/en/api/cache#diffusers.FirstBlockCacheConfig) builds on the ideas of [TeaCache](https://huggingface.co/papers/2411.19108). It is much simpler to implement generically for a wide range of models and has been integrated first for experimental purposes.

src/diffusers/models/cache_utils.py

@@ -41,9 +41,11 @@ class CacheMixin:
         Enable caching techniques on the model.
 
         Args:
-            config (`Union[PyramidAttentionBroadcastConfig]`):
+            config (`Union[PyramidAttentionBroadcastConfig, FasterCacheConfig, FirstBlockCacheConfig]`):
                 The configuration for applying the caching technique. Currently supported caching techniques are:
                     - [`~hooks.PyramidAttentionBroadcastConfig`]
+                    - [`~hooks.FasterCacheConfig`]
+                    - [`~hooks.FirstBlockCacheConfig`]
 
         Example:
 

src/diffusers/models/transformers/transformer_z_image.py

@@ -69,10 +69,7 @@ class TimestepEmbedder(nn.Module):
 
     def forward(self, t):
         t_freq = self.timestep_embedding(t, self.frequency_embedding_size)
-        weight_dtype = self.mlp[0].weight.dtype
-        if weight_dtype.is_floating_point:
-            t_freq = t_freq.to(weight_dtype)
-        t_emb = self.mlp(t_freq)
+        t_emb = self.mlp(t_freq.to(self.mlp[0].weight.dtype))
         return t_emb
 
 
@@ -129,10 +126,6 @@ class ZSingleStreamAttnProcessor:
         dtype = query.dtype
         query, key = query.to(dtype), key.to(dtype)
 
-        # From [batch, seq_len] to [batch, 1, 1, seq_len] -> broadcast to [batch, heads, seq_len, seq_len]
-        if attention_mask is not None and attention_mask.ndim == 2:
-            attention_mask = attention_mask[:, None, None, :]
-
         # Compute joint attention
         hidden_states = dispatch_attention_fn(
             query,
@@ -313,10 +306,6 @@ class RopeEmbedder:
         if self.freqs_cis is None:
             self.freqs_cis = self.precompute_freqs_cis(self.axes_dims, self.axes_lens, theta=self.theta)
             self.freqs_cis = [freqs_cis.to(device) for freqs_cis in self.freqs_cis]
-        else:
-            # Ensure freqs_cis are on the same device as ids
-            if self.freqs_cis[0].device != device:
-                self.freqs_cis = [freqs_cis.to(device) for freqs_cis in self.freqs_cis]
 
         result = []
         for i in range(len(self.axes_dims)):
@@ -328,7 +317,6 @@ class RopeEmbedder:
 class ZImageTransformer2DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, FromOriginalModelMixin):
     _supports_gradient_checkpointing = True
     _no_split_modules = ["ZImageTransformerBlock"]
-    _skip_layerwise_casting_patterns = ["t_embedder", "cap_embedder"]  # precision sensitive layers
 
     @register_to_config
     def __init__(
@@ -565,6 +553,8 @@ class ZImageTransformer2DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, FromOr
         t = t * self.t_scale
         t = self.t_embedder(t)
 
+        adaln_input = t
+
         (
             x,
             cap_feats,
@@ -582,9 +572,6 @@ class ZImageTransformer2DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, FromOr
 
         x = torch.cat(x, dim=0)
         x = self.all_x_embedder[f"{patch_size}-{f_patch_size}"](x)
-
-        # Match t_embedder output dtype to x for layerwise casting compatibility
-        adaln_input = t.type_as(x)
         x[torch.cat(x_inner_pad_mask)] = self.x_pad_token
         x = list(x.split(x_item_seqlens, dim=0))
         x_freqs_cis = list(self.rope_embedder(torch.cat(x_pos_ids, dim=0)).split(x_item_seqlens, dim=0))

src/diffusers/pipelines/flux2/pipeline_flux2.py

@@ -861,6 +861,7 @@ class Flux2Pipeline(DiffusionPipeline, Flux2LoraLoaderMixin):
         if output_type == "latent":
             image = latents
         else:
+            torch.save({"pred": latents}, "pred_d.pt")
             latents = self._unpack_latents_with_ids(latents, latent_ids)
 
             latents_bn_mean = self.vae.bn.running_mean.view(1, -1, 1, 1).to(latents.device, latents.dtype)

src/diffusers/pipelines/z_image/pipeline_z_image.py

@@ -165,16 +165,21 @@ class ZImagePipeline(DiffusionPipeline, FromSingleFileMixin):
         self,
         prompt: Union[str, List[str]],
         device: Optional[torch.device] = None,
+        dtype: Optional[torch.dtype] = None,
+        num_images_per_prompt: int = 1,
         do_classifier_free_guidance: bool = True,
         negative_prompt: Optional[Union[str, List[str]]] = None,
         prompt_embeds: Optional[List[torch.FloatTensor]] = None,
         negative_prompt_embeds: Optional[torch.FloatTensor] = None,
         max_sequence_length: int = 512,
+        lora_scale: Optional[float] = None,
     ):
         prompt = [prompt] if isinstance(prompt, str) else prompt
         prompt_embeds = self._encode_prompt(
             prompt=prompt,
             device=device,
+            dtype=dtype,
+            num_images_per_prompt=num_images_per_prompt,
             prompt_embeds=prompt_embeds,
             max_sequence_length=max_sequence_length,
         )
@@ -188,6 +193,8 @@ class ZImagePipeline(DiffusionPipeline, FromSingleFileMixin):
             negative_prompt_embeds = self._encode_prompt(
                 prompt=negative_prompt,
                 device=device,
+                dtype=dtype,
+                num_images_per_prompt=num_images_per_prompt,
                 prompt_embeds=negative_prompt_embeds,
                 max_sequence_length=max_sequence_length,
             )
@@ -199,9 +206,12 @@ class ZImagePipeline(DiffusionPipeline, FromSingleFileMixin):
         self,
         prompt: Union[str, List[str]],
         device: Optional[torch.device] = None,
+        dtype: Optional[torch.dtype] = None,
+        num_images_per_prompt: int = 1,
         prompt_embeds: Optional[List[torch.FloatTensor]] = None,
         max_sequence_length: int = 512,
     ) -> List[torch.FloatTensor]:
+        assert num_images_per_prompt == 1
         device = device or self._execution_device
 
         if prompt_embeds is not None:
@@ -407,6 +417,8 @@ class ZImagePipeline(DiffusionPipeline, FromSingleFileMixin):
                 f"Please adjust the width to a multiple of {vae_scale}."
             )
 
+        assert self.dtype == torch.bfloat16
+        dtype = self.dtype
         device = self._execution_device
 
         self._guidance_scale = guidance_scale
@@ -422,6 +434,10 @@ class ZImagePipeline(DiffusionPipeline, FromSingleFileMixin):
         else:
             batch_size = len(prompt_embeds)
 
+        lora_scale = (
+            self.joint_attention_kwargs.get("scale", None) if self.joint_attention_kwargs is not None else None
+        )
+
         # If prompt_embeds is provided and prompt is None, skip encoding
         if prompt_embeds is not None and prompt is None:
             if self.do_classifier_free_guidance and negative_prompt_embeds is None:
@@ -439,8 +455,11 @@ class ZImagePipeline(DiffusionPipeline, FromSingleFileMixin):
                 do_classifier_free_guidance=self.do_classifier_free_guidance,
                 prompt_embeds=prompt_embeds,
                 negative_prompt_embeds=negative_prompt_embeds,
+                dtype=dtype,
                 device=device,
+                num_images_per_prompt=num_images_per_prompt,
                 max_sequence_length=max_sequence_length,
+                lora_scale=lora_scale,
             )
 
         # 4. Prepare latent variables
@@ -456,14 +475,6 @@ class ZImagePipeline(DiffusionPipeline, FromSingleFileMixin):
             generator,
             latents,
         )
-
-        # Repeat prompt_embeds for num_images_per_prompt
-        if num_images_per_prompt > 1:
-            prompt_embeds = [pe for pe in prompt_embeds for _ in range(num_images_per_prompt)]
-            if self.do_classifier_free_guidance and negative_prompt_embeds:
-                negative_prompt_embeds = [npe for npe in negative_prompt_embeds for _ in range(num_images_per_prompt)]
-
-        actual_batch_size = batch_size * num_images_per_prompt
         image_seq_len = (latents.shape[2] // 2) * (latents.shape[3] // 2)
 
         # 5. Prepare timesteps
@@ -512,12 +523,12 @@ class ZImagePipeline(DiffusionPipeline, FromSingleFileMixin):
                 apply_cfg = self.do_classifier_free_guidance and current_guidance_scale > 0
 
                 if apply_cfg:
-                    latents_typed = latents.to(self.transformer.dtype)
+                    latents_typed = latents if latents.dtype == dtype else latents.to(dtype)
                     latent_model_input = latents_typed.repeat(2, 1, 1, 1)
                     prompt_embeds_model_input = prompt_embeds + negative_prompt_embeds
                     timestep_model_input = timestep.repeat(2)
                 else:
-                    latent_model_input = latents.to(self.transformer.dtype)
+                    latent_model_input = latents if latents.dtype == dtype else latents.to(dtype)
                     prompt_embeds_model_input = prompt_embeds
                     timestep_model_input = timestep
 
@@ -532,11 +543,11 @@ class ZImagePipeline(DiffusionPipeline, FromSingleFileMixin):
 
                 if apply_cfg:
                     # Perform CFG
-                    pos_out = model_out_list[:actual_batch_size]
-                    neg_out = model_out_list[actual_batch_size:]
+                    pos_out = model_out_list[:batch_size]
+                    neg_out = model_out_list[batch_size:]
 
                     noise_pred = []
-                    for j in range(actual_batch_size):
+                    for j in range(batch_size):
                         pos = pos_out[j].float()
                         neg = neg_out[j].float()
 
@@ -577,11 +588,11 @@ class ZImagePipeline(DiffusionPipeline, FromSingleFileMixin):
                 if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                     progress_bar.update()
 
+        latents = latents.to(dtype)
         if output_type == "latent":
             image = latents
 
         else:
-            latents = latents.to(self.vae.dtype)
             latents = (latents / self.vae.config.scaling_factor) + self.vae.config.shift_factor
 
             image = self.vae.decode(latents, return_dict=False)[0]

src/diffusers/schedulers/scheduling_cosine_dpmsolver_multistep.py

@@ -429,22 +429,7 @@ class CosineDPMSolverMultistepScheduler(SchedulerMixin, ConfigMixin):
         return x_t
 
     # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.index_for_timestep
-    def index_for_timestep(
-        self, timestep: Union[int, torch.Tensor], schedule_timesteps: Optional[torch.Tensor] = None
-    ) -> int:
-        """
-        Find the index for a given timestep in the schedule.
-
-        Args:
-            timestep (`int` or `torch.Tensor`):
-                The timestep for which to find the index.
-            schedule_timesteps (`torch.Tensor`, *optional*):
-                The timestep schedule to search in. If `None`, uses `self.timesteps`.
-
-        Returns:
-            `int`:
-                The index of the timestep in the schedule.
-        """
+    def index_for_timestep(self, timestep, schedule_timesteps=None):
         if schedule_timesteps is None:
             schedule_timesteps = self.timesteps
 
@@ -467,10 +452,6 @@ class CosineDPMSolverMultistepScheduler(SchedulerMixin, ConfigMixin):
     def _init_step_index(self, timestep):
         """
         Initialize the step_index counter for the scheduler.
-
-        Args:
-            timestep (`int` or `torch.Tensor`):
-                The current timestep for which to initialize the step index.
         """
 
         if self.begin_index is None:

src/diffusers/schedulers/scheduling_deis_multistep.py

@@ -401,17 +401,6 @@ class DEISMultistepScheduler(SchedulerMixin, ConfigMixin):
 
     # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler._sigma_to_alpha_sigma_t
     def _sigma_to_alpha_sigma_t(self, sigma):
-        """
-        Convert sigma values to alpha_t and sigma_t values.
-
-        Args:
-            sigma (`torch.Tensor`):
-                The sigma value(s) to convert.
-
-        Returns:
-            `Tuple[torch.Tensor, torch.Tensor]`:
-                A tuple containing (alpha_t, sigma_t) values.
-        """
         if self.config.use_flow_sigmas:
             alpha_t = 1 - sigma
             sigma_t = sigma
@@ -819,22 +808,7 @@ class DEISMultistepScheduler(SchedulerMixin, ConfigMixin):
             raise NotImplementedError("only support log-rho multistep deis now")
 
     # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.index_for_timestep
-    def index_for_timestep(
-        self, timestep: Union[int, torch.Tensor], schedule_timesteps: Optional[torch.Tensor] = None
-    ) -> int:
-        """
-        Find the index for a given timestep in the schedule.
-
-        Args:
-            timestep (`int` or `torch.Tensor`):
-                The timestep for which to find the index.
-            schedule_timesteps (`torch.Tensor`, *optional*):
-                The timestep schedule to search in. If `None`, uses `self.timesteps`.
-
-        Returns:
-            `int`:
-                The index of the timestep in the schedule.
-        """
+    def index_for_timestep(self, timestep, schedule_timesteps=None):
         if schedule_timesteps is None:
             schedule_timesteps = self.timesteps
 
@@ -857,10 +831,6 @@ class DEISMultistepScheduler(SchedulerMixin, ConfigMixin):
     def _init_step_index(self, timestep):
         """
         Initialize the step_index counter for the scheduler.
-
-        Args:
-            timestep (`int` or `torch.Tensor`):
-                The current timestep for which to initialize the step index.
         """
 
         if self.begin_index is None:
@@ -957,21 +927,6 @@ class DEISMultistepScheduler(SchedulerMixin, ConfigMixin):
         noise: torch.Tensor,
         timesteps: torch.IntTensor,
     ) -> torch.Tensor:
-        """
-        Add noise to the original samples according to the noise schedule at the specified timesteps.
-
-        Args:
-            original_samples (`torch.Tensor`):
-                The original samples without noise.
-            noise (`torch.Tensor`):
-                The noise to add to the samples.
-            timesteps (`torch.IntTensor`):
-                The timesteps at which to add noise to the samples.
-
-        Returns:
-            `torch.Tensor`:
-                The noisy samples.
-        """
         # Make sure sigmas and timesteps have the same device and dtype as original_samples
         sigmas = self.sigmas.to(device=original_samples.device, dtype=original_samples.dtype)
         if original_samples.device.type == "mps" and torch.is_floating_point(timesteps):

src/diffusers/schedulers/scheduling_dpmsolver_multistep.py

@@ -127,17 +127,18 @@ class DPMSolverMultistepScheduler(SchedulerMixin, ConfigMixin):
             The starting `beta` value of inference.
         beta_end (`float`, defaults to 0.02):
             The final `beta` value.
-        beta_schedule (`"linear"`, `"scaled_linear"`, or `"squaredcos_cap_v2"`, defaults to `"linear"`):
-            The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model.
+        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`.
         solver_order (`int`, defaults to 2):
             The DPMSolver order which can be `1` or `2` or `3`. It is recommended to use `solver_order=2` for guided
             sampling, and `solver_order=3` for unconditional sampling.
-        prediction_type (`"epsilon"`, `"sample"`, `"v_prediction"`, or `"flow_prediction"`, defaults to `"epsilon"`):
-            Prediction type of the scheduler function. `epsilon` predicts the noise of the diffusion process, `sample`
-            directly predicts the noisy sample, `v_prediction` predicts the velocity (see section 2.4 of [Imagen
-            Video](https://huggingface.co/papers/2210.02303) paper), and `flow_prediction` predicts the flow.
+        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), `v_prediction` (see section 2.4 of [Imagen
+            Video](https://imagen.research.google/video/paper.pdf) paper), or `flow_prediction`.
         thresholding (`bool`, defaults to `False`):
             Whether to use the "dynamic thresholding" method. This is unsuitable for latent-space diffusion models such
             as Stable Diffusion.
@@ -146,14 +147,15 @@ class DPMSolverMultistepScheduler(SchedulerMixin, ConfigMixin):
         sample_max_value (`float`, defaults to 1.0):
             The threshold value for dynamic thresholding. Valid only when `thresholding=True` and
             `algorithm_type="dpmsolver++"`.
-        algorithm_type (`"dpmsolver"`, `"dpmsolver++"`, `"sde-dpmsolver"`, or `"sde-dpmsolver++"`, defaults to `"dpmsolver++"`):
-            Algorithm type for the solver. The `dpmsolver` type implements the algorithms in the
-            [DPMSolver](https://huggingface.co/papers/2206.00927) paper, and the `dpmsolver++` type implements the
-            algorithms in the [DPMSolver++](https://huggingface.co/papers/2211.01095) paper. It is recommended to use
-            `dpmsolver++` or `sde-dpmsolver++` with `solver_order=2` for guided sampling like in Stable Diffusion.
-        solver_type (`"midpoint"` or `"heun"`, defaults to `"midpoint"`):
-            Solver type for the second-order solver. The solver type slightly affects the sample quality, especially
-            for a small number of steps. It is recommended to use `midpoint` solvers.
+        algorithm_type (`str`, defaults to `dpmsolver++`):
+            Algorithm type for the solver; can be `dpmsolver`, `dpmsolver++`, `sde-dpmsolver` or `sde-dpmsolver++`. The
+            `dpmsolver` type implements the algorithms in the [DPMSolver](https://huggingface.co/papers/2206.00927)
+            paper, and the `dpmsolver++` type implements the algorithms in the
+            [DPMSolver++](https://huggingface.co/papers/2211.01095) paper. It is recommended to use `dpmsolver++` or
+            `sde-dpmsolver++` with `solver_order=2` for guided sampling like in Stable Diffusion.
+        solver_type (`str`, defaults to `midpoint`):
+            Solver type for the second-order solver; can be `midpoint` or `heun`. The solver type slightly affects the
+            sample quality, especially for a small number of steps. It is recommended to use `midpoint` solvers.
         lower_order_final (`bool`, defaults to `True`):
             Whether to use lower-order solvers in the final steps. Only valid for < 15 inference steps. This can
             stabilize the sampling of DPMSolver for steps < 15, especially for steps <= 10.
@@ -177,16 +179,16 @@ class DPMSolverMultistepScheduler(SchedulerMixin, ConfigMixin):
             Whether to use flow sigmas for step sizes in the noise schedule during the sampling process.
         flow_shift (`float`, *optional*, defaults to 1.0):
             The shift value for the timestep schedule for flow matching.
-        final_sigmas_type (`"zero"` or `"sigma_min"`, *optional*, defaults to `"zero"`):
+        final_sigmas_type (`str`, defaults to `"zero"`):
             The final `sigma` value for the noise schedule during the sampling process. If `"sigma_min"`, the final
-            sigma is the same as the last sigma in the training schedule. If `"zero"`, the final sigma is set to 0.
+            sigma is the same as the last sigma in the training schedule. If `zero`, the final sigma is set to 0.
         lambda_min_clipped (`float`, defaults to `-inf`):
             Clipping threshold for the minimum value of `lambda(t)` for numerical stability. This is critical for the
             cosine (`squaredcos_cap_v2`) noise schedule.
-        variance_type (`"learned"` or `"learned_range"`, *optional*):
-            Set to `"learned"` or `"learned_range"` for diffusion models that predict variance. If set, the model's
-            output contains the predicted Gaussian variance.
-        timestep_spacing (`"linspace"`, `"leading"`, or `"trailing"`, defaults to `"linspace"`):
+        variance_type (`str`, *optional*):
+            Set to "learned" or "learned_range" for diffusion models that predict variance. If set, the model's output
+            contains the predicted Gaussian variance.
+        timestep_spacing (`str`, defaults to `"linspace"`):
             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.
         steps_offset (`int`, defaults to 0):
@@ -195,10 +197,6 @@ class DPMSolverMultistepScheduler(SchedulerMixin, ConfigMixin):
             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).
-        use_dynamic_shifting (`bool`, defaults to `False`):
-            Whether to use dynamic shifting for the timestep schedule.
-        time_shift_type (`"exponential"`, defaults to `"exponential"`):
-            The type of time shift to apply when using dynamic shifting.
     """
 
     _compatibles = [e.name for e in KarrasDiffusionSchedulers]
@@ -210,15 +208,15 @@ class DPMSolverMultistepScheduler(SchedulerMixin, ConfigMixin):
         num_train_timesteps: int = 1000,
         beta_start: float = 0.0001,
         beta_end: float = 0.02,
-        beta_schedule: Literal["linear", "scaled_linear", "squaredcos_cap_v2"] = "linear",
+        beta_schedule: str = "linear",
         trained_betas: Optional[Union[np.ndarray, List[float]]] = None,
         solver_order: int = 2,
-        prediction_type: Literal["epsilon", "sample", "v_prediction", "flow_prediction"] = "epsilon",
+        prediction_type: str = "epsilon",
         thresholding: bool = False,
         dynamic_thresholding_ratio: float = 0.995,
         sample_max_value: float = 1.0,
-        algorithm_type: Literal["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"] = "dpmsolver++",
-        solver_type: Literal["midpoint", "heun"] = "midpoint",
+        algorithm_type: str = "dpmsolver++",
+        solver_type: str = "midpoint",
         lower_order_final: bool = True,
         euler_at_final: bool = False,
         use_karras_sigmas: Optional[bool] = False,
@@ -227,14 +225,14 @@ class DPMSolverMultistepScheduler(SchedulerMixin, ConfigMixin):
         use_lu_lambdas: Optional[bool] = False,
         use_flow_sigmas: Optional[bool] = False,
         flow_shift: Optional[float] = 1.0,
-        final_sigmas_type: Optional[Literal["zero", "sigma_min"]] = "zero",
+        final_sigmas_type: Optional[str] = "zero",  # "zero", "sigma_min"
         lambda_min_clipped: float = -float("inf"),
-        variance_type: Optional[Literal["learned", "learned_range"]] = None,
-        timestep_spacing: Literal["linspace", "leading", "trailing"] = "linspace",
+        variance_type: Optional[str] = None,
+        timestep_spacing: str = "linspace",
         steps_offset: int = 0,
         rescale_betas_zero_snr: bool = False,
         use_dynamic_shifting: bool = False,
-        time_shift_type: Literal["exponential"] = "exponential",
+        time_shift_type: str = "exponential",
     ):
         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.")
@@ -333,22 +331,19 @@ class DPMSolverMultistepScheduler(SchedulerMixin, ConfigMixin):
 
     def set_timesteps(
         self,
-        num_inference_steps: Optional[int] = None,
-        device: Optional[Union[str, torch.device]] = None,
+        num_inference_steps: int = None,
+        device: Union[str, torch.device] = None,
         mu: Optional[float] = None,
         timesteps: Optional[List[int]] = None,
-    ) -> None:
+    ):
         """
         Sets the discrete timesteps used for the diffusion chain (to be run before inference).
 
         Args:
-            num_inference_steps (`int`, *optional*):
+            num_inference_steps (`int`):
                 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.
-            mu (`float`, *optional*):
-                The mu parameter for dynamic shifting. If provided, requires `use_dynamic_shifting=True` and
-                `time_shift_type="exponential"`.
             timesteps (`List[int]`, *optional*):
                 Custom timesteps used to support arbitrary timesteps schedule. If `None`, timesteps will be generated
                 based on the `timestep_spacing` attribute. If `timesteps` is passed, `num_inference_steps` and `sigmas`
@@ -508,7 +503,7 @@ class DPMSolverMultistepScheduler(SchedulerMixin, ConfigMixin):
         return sample
 
     # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._sigma_to_t
-    def _sigma_to_t(self, sigma: np.ndarray, log_sigmas: np.ndarray) -> np.ndarray:
+    def _sigma_to_t(self, sigma, log_sigmas):
         """
         Convert sigma values to corresponding timestep values through interpolation.
 
@@ -544,18 +539,7 @@ class DPMSolverMultistepScheduler(SchedulerMixin, ConfigMixin):
         t = t.reshape(sigma.shape)
         return t
 
-    def _sigma_to_alpha_sigma_t(self, sigma: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
-        """
-        Convert sigma values to alpha_t and sigma_t values.
-
-        Args:
-            sigma (`torch.Tensor`):
-                The sigma value(s) to convert.
-
-        Returns:
-            `Tuple[torch.Tensor, torch.Tensor]`:
-                A tuple containing (alpha_t, sigma_t) values.
-        """
+    def _sigma_to_alpha_sigma_t(self, sigma):
         if self.config.use_flow_sigmas:
             alpha_t = 1 - sigma
             sigma_t = sigma
@@ -604,21 +588,8 @@ class DPMSolverMultistepScheduler(SchedulerMixin, ConfigMixin):
         sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
         return sigmas
 
-    def _convert_to_lu(self, in_lambdas: torch.Tensor, num_inference_steps: int) -> torch.Tensor:
-        """
-        Construct the noise schedule as proposed in [DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model
-        Sampling in Around 10 Steps](https://huggingface.co/papers/2206.00927) by Lu et al. (2022).
-
-        Args:
-            in_lambdas (`torch.Tensor`):
-                The input lambda values to be converted.
-            num_inference_steps (`int`):
-                The number of inference steps to generate the noise schedule for.
-
-        Returns:
-            `torch.Tensor`:
-                The converted lambda values following the Lu noise schedule.
-        """
+    def _convert_to_lu(self, in_lambdas: torch.Tensor, num_inference_steps) -> torch.Tensor:
+        """Constructs the noise schedule of Lu et al. (2022)."""
 
         lambda_min: float = in_lambdas[-1].item()
         lambda_max: float = in_lambdas[0].item()
@@ -1098,22 +1069,7 @@ class DPMSolverMultistepScheduler(SchedulerMixin, ConfigMixin):
             )
         return x_t
 
-    def index_for_timestep(
-        self, timestep: Union[int, torch.Tensor], schedule_timesteps: Optional[torch.Tensor] = None
-    ) -> int:
-        """
-        Find the index for a given timestep in the schedule.
-
-        Args:
-            timestep (`int` or `torch.Tensor`):
-                The timestep for which to find the index.
-            schedule_timesteps (`torch.Tensor`, *optional*):
-                The timestep schedule to search in. If `None`, uses `self.timesteps`.
-
-        Returns:
-            `int`:
-                The index of the timestep in the schedule.
-        """
+    def index_for_timestep(self, timestep, schedule_timesteps=None):
         if schedule_timesteps is None:
             schedule_timesteps = self.timesteps
 
@@ -1132,13 +1088,9 @@ class DPMSolverMultistepScheduler(SchedulerMixin, ConfigMixin):
 
         return step_index
 
-    def _init_step_index(self, timestep: Union[int, torch.Tensor]) -> None:
+    def _init_step_index(self, timestep):
         """
         Initialize the step_index counter for the scheduler.
-
-        Args:
-            timestep (`int` or `torch.Tensor`):
-                The current timestep for which to initialize the step index.
         """
 
         if self.begin_index is None:
@@ -1153,7 +1105,7 @@ class DPMSolverMultistepScheduler(SchedulerMixin, ConfigMixin):
         model_output: torch.Tensor,
         timestep: Union[int, torch.Tensor],
         sample: torch.Tensor,
-        generator: Optional[torch.Generator] = None,
+        generator=None,
         variance_noise: Optional[torch.Tensor] = None,
         return_dict: bool = True,
     ) -> Union[SchedulerOutput, Tuple]:
@@ -1163,22 +1115,22 @@ class DPMSolverMultistepScheduler(SchedulerMixin, ConfigMixin):
 
         Args:
             model_output (`torch.Tensor`):
-                The direct output from the learned diffusion model.
-            timestep (`int` or `torch.Tensor`):
+                The direct output from learned diffusion model.
+            timestep (`int`):
                 The current discrete timestep in the diffusion chain.
             sample (`torch.Tensor`):
                 A current instance of a sample created by the diffusion process.
             generator (`torch.Generator`, *optional*):
                 A random number generator.
-            variance_noise (`torch.Tensor`, *optional*):
+            variance_noise (`torch.Tensor`):
                 Alternative to generating noise with `generator` by directly providing the noise for the variance
                 itself. Useful for methods such as [`LEdits++`].
-            return_dict (`bool`, defaults to `True`):
+            return_dict (`bool`):
                 Whether or not to return a [`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`.
 
         Returns:
             [`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`:
-                If `return_dict` is `True`, [`~schedulers.scheduling_utils.SchedulerOutput`] is returned, otherwise a
+                If return_dict is `True`, [`~schedulers.scheduling_utils.SchedulerOutput`] is returned, otherwise a
                 tuple is returned where the first element is the sample tensor.
 
         """
@@ -1258,21 +1210,6 @@ class DPMSolverMultistepScheduler(SchedulerMixin, ConfigMixin):
         noise: torch.Tensor,
         timesteps: torch.IntTensor,
     ) -> torch.Tensor:
-        """
-        Add noise to the original samples according to the noise schedule at the specified timesteps.
-
-        Args:
-            original_samples (`torch.Tensor`):
-                The original samples without noise.
-            noise (`torch.Tensor`):
-                The noise to add to the samples.
-            timesteps (`torch.IntTensor`):
-                The timesteps at which to add noise to the samples.
-
-        Returns:
-            `torch.Tensor`:
-                The noisy samples.
-        """
         # Make sure sigmas and timesteps have the same device and dtype as original_samples
         sigmas = self.sigmas.to(device=original_samples.device, dtype=original_samples.dtype)
         if original_samples.device.type == "mps" and torch.is_floating_point(timesteps):

src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py

@@ -413,17 +413,6 @@ class DPMSolverMultistepInverseScheduler(SchedulerMixin, ConfigMixin):
 
     # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler._sigma_to_alpha_sigma_t
     def _sigma_to_alpha_sigma_t(self, sigma):
-        """
-        Convert sigma values to alpha_t and sigma_t values.
-
-        Args:
-            sigma (`torch.Tensor`):
-                The sigma value(s) to convert.
-
-        Returns:
-            `Tuple[torch.Tensor, torch.Tensor]`:
-                A tuple containing (alpha_t, sigma_t) values.
-        """
         if self.config.use_flow_sigmas:
             alpha_t = 1 - sigma
             sigma_t = sigma

src/diffusers/schedulers/scheduling_dpmsolver_singlestep.py

@@ -491,17 +491,6 @@ class DPMSolverSinglestepScheduler(SchedulerMixin, ConfigMixin):
 
     # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler._sigma_to_alpha_sigma_t
     def _sigma_to_alpha_sigma_t(self, sigma):
-        """
-        Convert sigma values to alpha_t and sigma_t values.
-
-        Args:
-            sigma (`torch.Tensor`):
-                The sigma value(s) to convert.
-
-        Returns:
-            `Tuple[torch.Tensor, torch.Tensor]`:
-                A tuple containing (alpha_t, sigma_t) values.
-        """
         if self.config.use_flow_sigmas:
             alpha_t = 1 - sigma
             sigma_t = sigma
@@ -1090,22 +1079,7 @@ class DPMSolverSinglestepScheduler(SchedulerMixin, ConfigMixin):
             raise ValueError(f"Order must be 1, 2, 3, got {order}")
 
     # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.index_for_timestep
-    def index_for_timestep(
-        self, timestep: Union[int, torch.Tensor], schedule_timesteps: Optional[torch.Tensor] = None
-    ) -> int:
-        """
-        Find the index for a given timestep in the schedule.
-
-        Args:
-            timestep (`int` or `torch.Tensor`):
-                The timestep for which to find the index.
-            schedule_timesteps (`torch.Tensor`, *optional*):
-                The timestep schedule to search in. If `None`, uses `self.timesteps`.
-
-        Returns:
-            `int`:
-                The index of the timestep in the schedule.
-        """
+    def index_for_timestep(self, timestep, schedule_timesteps=None):
         if schedule_timesteps is None:
             schedule_timesteps = self.timesteps
 
@@ -1128,10 +1102,6 @@ class DPMSolverSinglestepScheduler(SchedulerMixin, ConfigMixin):
     def _init_step_index(self, timestep):
         """
         Initialize the step_index counter for the scheduler.
-
-        Args:
-            timestep (`int` or `torch.Tensor`):
-                The current timestep for which to initialize the step index.
         """
 
         if self.begin_index is None:
@@ -1234,21 +1204,6 @@ class DPMSolverSinglestepScheduler(SchedulerMixin, ConfigMixin):
         noise: torch.Tensor,
         timesteps: torch.IntTensor,
     ) -> torch.Tensor:
-        """
-        Add noise to the original samples according to the noise schedule at the specified timesteps.
-
-        Args:
-            original_samples (`torch.Tensor`):
-                The original samples without noise.
-            noise (`torch.Tensor`):
-                The noise to add to the samples.
-            timesteps (`torch.IntTensor`):
-                The timesteps at which to add noise to the samples.
-
-        Returns:
-            `torch.Tensor`:
-                The noisy samples.
-        """
         # Make sure sigmas and timesteps have the same device and dtype as original_samples
         sigmas = self.sigmas.to(device=original_samples.device, dtype=original_samples.dtype)
         if original_samples.device.type == "mps" and torch.is_floating_point(timesteps):

src/diffusers/schedulers/scheduling_edm_dpmsolver_multistep.py

@@ -578,22 +578,7 @@ class EDMDPMSolverMultistepScheduler(SchedulerMixin, ConfigMixin):
         return x_t
 
     # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.index_for_timestep
-    def index_for_timestep(
-        self, timestep: Union[int, torch.Tensor], schedule_timesteps: Optional[torch.Tensor] = None
-    ) -> int:
-        """
-        Find the index for a given timestep in the schedule.
-
-        Args:
-            timestep (`int` or `torch.Tensor`):
-                The timestep for which to find the index.
-            schedule_timesteps (`torch.Tensor`, *optional*):
-                The timestep schedule to search in. If `None`, uses `self.timesteps`.
-
-        Returns:
-            `int`:
-                The index of the timestep in the schedule.
-        """
+    def index_for_timestep(self, timestep, schedule_timesteps=None):
         if schedule_timesteps is None:
             schedule_timesteps = self.timesteps
 
@@ -616,10 +601,6 @@ class EDMDPMSolverMultistepScheduler(SchedulerMixin, ConfigMixin):
     def _init_step_index(self, timestep):
         """
         Initialize the step_index counter for the scheduler.
-
-        Args:
-            timestep (`int` or `torch.Tensor`):
-                The current timestep for which to initialize the step index.
         """
 
         if self.begin_index is None:

src/diffusers/schedulers/scheduling_sasolver.py

@@ -423,17 +423,6 @@ class SASolverScheduler(SchedulerMixin, ConfigMixin):
 
     # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler._sigma_to_alpha_sigma_t
     def _sigma_to_alpha_sigma_t(self, sigma):
-        """
-        Convert sigma values to alpha_t and sigma_t values.
-
-        Args:
-            sigma (`torch.Tensor`):
-                The sigma value(s) to convert.
-
-        Returns:
-            `Tuple[torch.Tensor, torch.Tensor]`:
-                A tuple containing (alpha_t, sigma_t) values.
-        """
         if self.config.use_flow_sigmas:
             alpha_t = 1 - sigma
             sigma_t = sigma
@@ -1114,22 +1103,7 @@ class SASolverScheduler(SchedulerMixin, ConfigMixin):
         return x_t
 
     # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.index_for_timestep
-    def index_for_timestep(
-        self, timestep: Union[int, torch.Tensor], schedule_timesteps: Optional[torch.Tensor] = None
-    ) -> int:
-        """
-        Find the index for a given timestep in the schedule.
-
-        Args:
-            timestep (`int` or `torch.Tensor`):
-                The timestep for which to find the index.
-            schedule_timesteps (`torch.Tensor`, *optional*):
-                The timestep schedule to search in. If `None`, uses `self.timesteps`.
-
-        Returns:
-            `int`:
-                The index of the timestep in the schedule.
-        """
+    def index_for_timestep(self, timestep, schedule_timesteps=None):
         if schedule_timesteps is None:
             schedule_timesteps = self.timesteps
 
@@ -1152,10 +1126,6 @@ class SASolverScheduler(SchedulerMixin, ConfigMixin):
     def _init_step_index(self, timestep):
         """
         Initialize the step_index counter for the scheduler.
-
-        Args:
-            timestep (`int` or `torch.Tensor`):
-                The current timestep for which to initialize the step index.
         """
 
         if self.begin_index is None:

src/diffusers/schedulers/scheduling_unipc_multistep.py

@@ -513,17 +513,6 @@ class UniPCMultistepScheduler(SchedulerMixin, ConfigMixin):
 
     # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler._sigma_to_alpha_sigma_t
     def _sigma_to_alpha_sigma_t(self, sigma):
-        """
-        Convert sigma values to alpha_t and sigma_t values.
-
-        Args:
-            sigma (`torch.Tensor`):
-                The sigma value(s) to convert.
-
-        Returns:
-            `Tuple[torch.Tensor, torch.Tensor]`:
-                A tuple containing (alpha_t, sigma_t) values.
-        """
         if self.config.use_flow_sigmas:
             alpha_t = 1 - sigma
             sigma_t = sigma
@@ -995,22 +984,7 @@ class UniPCMultistepScheduler(SchedulerMixin, ConfigMixin):
         return x_t
 
     # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.index_for_timestep
-    def index_for_timestep(
-        self, timestep: Union[int, torch.Tensor], schedule_timesteps: Optional[torch.Tensor] = None
-    ) -> int:
-        """
-        Find the index for a given timestep in the schedule.
-
-        Args:
-            timestep (`int` or `torch.Tensor`):
-                The timestep for which to find the index.
-            schedule_timesteps (`torch.Tensor`, *optional*):
-                The timestep schedule to search in. If `None`, uses `self.timesteps`.
-
-        Returns:
-            `int`:
-                The index of the timestep in the schedule.
-        """
+    def index_for_timestep(self, timestep, schedule_timesteps=None):
         if schedule_timesteps is None:
             schedule_timesteps = self.timesteps
 
@@ -1033,10 +1007,6 @@ class UniPCMultistepScheduler(SchedulerMixin, ConfigMixin):
     def _init_step_index(self, timestep):
         """
         Initialize the step_index counter for the scheduler.
-
-        Args:
-            timestep (`int` or `torch.Tensor`):
-                The current timestep for which to initialize the step index.
         """
 
         if self.begin_index is None:
@@ -1149,21 +1119,6 @@ class UniPCMultistepScheduler(SchedulerMixin, ConfigMixin):
         noise: torch.Tensor,
         timesteps: torch.IntTensor,
     ) -> torch.Tensor:
-        """
-        Add noise to the original samples according to the noise schedule at the specified timesteps.
-
-        Args:
-            original_samples (`torch.Tensor`):
-                The original samples without noise.
-            noise (`torch.Tensor`):
-                The noise to add to the samples.
-            timesteps (`torch.IntTensor`):
-                The timesteps at which to add noise to the samples.
-
-        Returns:
-            `torch.Tensor`:
-                The noisy samples.
-        """
         # Make sure sigmas and timesteps have the same device and dtype as original_samples
         sigmas = self.sigmas.to(device=original_samples.device, dtype=original_samples.dtype)
         if original_samples.device.type == "mps" and torch.is_floating_point(timesteps):

tests/pipelines/z_image/test_z_image.py

@@ -1,306 +0,0 @@
-# Copyright 2025 Alibaba Z-Image Team and The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import gc
-import os
-import unittest
-
-import numpy as np
-import torch
-from transformers import Qwen2Tokenizer, Qwen3Config, Qwen3Model
-
-from diffusers import (
-    AutoencoderKL,
-    FlowMatchEulerDiscreteScheduler,
-    ZImagePipeline,
-    ZImageTransformer2DModel,
-)
-
-from ...testing_utils import torch_device
-from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
-from ..test_pipelines_common import PipelineTesterMixin, to_np
-
-
-# Z-Image requires torch.use_deterministic_algorithms(False) due to complex64 RoPE operations
-# Cannot use enable_full_determinism() which sets it to True
-os.environ["CUDA_LAUNCH_BLOCKING"] = "1"
-os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":16:8"
-torch.use_deterministic_algorithms(False)
-torch.backends.cudnn.deterministic = True
-torch.backends.cudnn.benchmark = False
-if hasattr(torch.backends, "cuda"):
-    torch.backends.cuda.matmul.allow_tf32 = False
-
-# Note: Some tests (test_float16_inference, test_save_load_float16) may fail in full suite
-# due to RopeEmbedder cache state pollution between tests. They pass when run individually.
-# This is a known test isolation issue, not a functional bug.
-
-
-class ZImagePipelineFastTests(PipelineTesterMixin, unittest.TestCase):
-    pipeline_class = ZImagePipeline
-    params = TEXT_TO_IMAGE_PARAMS - {"cross_attention_kwargs"}
-    batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
-    image_params = TEXT_TO_IMAGE_IMAGE_PARAMS
-    image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS
-    required_optional_params = frozenset(
-        [
-            "num_inference_steps",
-            "generator",
-            "latents",
-            "return_dict",
-            "callback_on_step_end",
-            "callback_on_step_end_tensor_inputs",
-        ]
-    )
-    supports_dduf = False
-    test_xformers_attention = False
-    test_layerwise_casting = True
-    test_group_offloading = True
-
-    def setUp(self):
-        gc.collect()
-        if torch.cuda.is_available():
-            torch.cuda.empty_cache()
-            torch.cuda.synchronize()
-        torch.manual_seed(0)
-        if torch.cuda.is_available():
-            torch.cuda.manual_seed_all(0)
-
-    def tearDown(self):
-        super().tearDown()
-        gc.collect()
-        if torch.cuda.is_available():
-            torch.cuda.empty_cache()
-            torch.cuda.synchronize()
-        torch.manual_seed(0)
-        if torch.cuda.is_available():
-            torch.cuda.manual_seed_all(0)
-
-    def get_dummy_components(self):
-        torch.manual_seed(0)
-        transformer = ZImageTransformer2DModel(
-            all_patch_size=(2,),
-            all_f_patch_size=(1,),
-            in_channels=16,
-            dim=32,
-            n_layers=2,
-            n_refiner_layers=1,
-            n_heads=2,
-            n_kv_heads=2,
-            norm_eps=1e-5,
-            qk_norm=True,
-            cap_feat_dim=16,
-            rope_theta=256.0,
-            t_scale=1000.0,
-            axes_dims=[8, 4, 4],
-            axes_lens=[256, 32, 32],
-        )
-
-        torch.manual_seed(0)
-        vae = AutoencoderKL(
-            in_channels=3,
-            out_channels=3,
-            down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
-            up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
-            block_out_channels=[32, 64],
-            layers_per_block=1,
-            latent_channels=16,
-            norm_num_groups=32,
-            sample_size=32,
-            scaling_factor=0.3611,
-            shift_factor=0.1159,
-        )
-
-        torch.manual_seed(0)
-        scheduler = FlowMatchEulerDiscreteScheduler()
-
-        torch.manual_seed(0)
-        config = Qwen3Config(
-            hidden_size=16,
-            intermediate_size=16,
-            num_hidden_layers=2,
-            num_attention_heads=2,
-            num_key_value_heads=2,
-            vocab_size=151936,
-            max_position_embeddings=512,
-        )
-        text_encoder = Qwen3Model(config)
-        tokenizer = Qwen2Tokenizer.from_pretrained("hf-internal-testing/tiny-random-Qwen2VLForConditionalGeneration")
-
-        components = {
-            "transformer": transformer,
-            "vae": vae,
-            "scheduler": scheduler,
-            "text_encoder": text_encoder,
-            "tokenizer": tokenizer,
-        }
-        return components
-
-    def get_dummy_inputs(self, device, seed=0):
-        if str(device).startswith("mps"):
-            generator = torch.manual_seed(seed)
-        else:
-            generator = torch.Generator(device=device).manual_seed(seed)
-
-        inputs = {
-            "prompt": "dance monkey",
-            "negative_prompt": "bad quality",
-            "generator": generator,
-            "num_inference_steps": 2,
-            "guidance_scale": 3.0,
-            "cfg_normalization": False,
-            "cfg_truncation": 1.0,
-            "height": 32,
-            "width": 32,
-            "max_sequence_length": 16,
-            "output_type": "pt",
-        }
-
-        return inputs
-
-    def test_inference(self):
-        device = "cpu"
-
-        components = self.get_dummy_components()
-        pipe = self.pipeline_class(**components)
-        pipe.to(device)
-        pipe.set_progress_bar_config(disable=None)
-
-        inputs = self.get_dummy_inputs(device)
-        image = pipe(**inputs).images
-        generated_image = image[0]
-        self.assertEqual(generated_image.shape, (3, 32, 32))
-
-        # fmt: off
-        expected_slice = torch.tensor([0.4521, 0.4512, 0.4693, 0.5115, 0.5250, 0.5271, 0.4776, 0.4688, 0.2765, 0.2164, 0.5656, 0.6909, 0.3831, 0.5431, 0.5493, 0.4732])
-        # fmt: on
-
-        generated_slice = generated_image.flatten()
-        generated_slice = torch.cat([generated_slice[:8], generated_slice[-8:]])
-        self.assertTrue(torch.allclose(generated_slice, expected_slice, atol=5e-2))
-
-    def test_inference_batch_single_identical(self):
-        self._test_inference_batch_single_identical(batch_size=3, expected_max_diff=1e-1)
-
-    def test_num_images_per_prompt(self):
-        import inspect
-
-        sig = inspect.signature(self.pipeline_class.__call__)
-
-        if "num_images_per_prompt" not in sig.parameters:
-            return
-
-        components = self.get_dummy_components()
-        pipe = self.pipeline_class(**components)
-        pipe = pipe.to(torch_device)
-        pipe.set_progress_bar_config(disable=None)
-
-        batch_sizes = [1, 2]
-        num_images_per_prompts = [1, 2]
-
-        for batch_size in batch_sizes:
-            for num_images_per_prompt in num_images_per_prompts:
-                inputs = self.get_dummy_inputs(torch_device)
-
-                for key in inputs.keys():
-                    if key in self.batch_params:
-                        inputs[key] = batch_size * [inputs[key]]
-
-                images = pipe(**inputs, num_images_per_prompt=num_images_per_prompt)[0]
-
-                assert images.shape[0] == batch_size * num_images_per_prompt
-
-        del pipe
-        gc.collect()
-        if torch.cuda.is_available():
-            torch.cuda.empty_cache()
-            torch.cuda.synchronize()
-
-    def test_attention_slicing_forward_pass(
-        self, test_max_difference=True, test_mean_pixel_difference=True, expected_max_diff=1e-3
-    ):
-        if not self.test_attention_slicing:
-            return
-
-        components = self.get_dummy_components()
-        pipe = self.pipeline_class(**components)
-        for component in pipe.components.values():
-            if hasattr(component, "set_default_attn_processor"):
-                component.set_default_attn_processor()
-        pipe.to(torch_device)
-        pipe.set_progress_bar_config(disable=None)
-
-        generator_device = "cpu"
-        inputs = self.get_dummy_inputs(generator_device)
-        output_without_slicing = pipe(**inputs)[0]
-
-        pipe.enable_attention_slicing(slice_size=1)
-        inputs = self.get_dummy_inputs(generator_device)
-        output_with_slicing1 = pipe(**inputs)[0]
-
-        pipe.enable_attention_slicing(slice_size=2)
-        inputs = self.get_dummy_inputs(generator_device)
-        output_with_slicing2 = pipe(**inputs)[0]
-
-        if test_max_difference:
-            max_diff1 = np.abs(to_np(output_with_slicing1) - to_np(output_without_slicing)).max()
-            max_diff2 = np.abs(to_np(output_with_slicing2) - to_np(output_without_slicing)).max()
-            self.assertLess(
-                max(max_diff1, max_diff2),
-                expected_max_diff,
-                "Attention slicing should not affect the inference results",
-            )
-
-    def test_vae_tiling(self, expected_diff_max: float = 0.2):
-        generator_device = "cpu"
-        components = self.get_dummy_components()
-
-        pipe = self.pipeline_class(**components)
-        pipe.to("cpu")
-        pipe.set_progress_bar_config(disable=None)
-
-        # Without tiling
-        inputs = self.get_dummy_inputs(generator_device)
-        inputs["height"] = inputs["width"] = 128
-        output_without_tiling = pipe(**inputs)[0]
-
-        # With tiling (standard AutoencoderKL doesn't accept parameters)
-        pipe.vae.enable_tiling()
-        inputs = self.get_dummy_inputs(generator_device)
-        inputs["height"] = inputs["width"] = 128
-        output_with_tiling = pipe(**inputs)[0]
-
-        self.assertLess(
-            (to_np(output_without_tiling) - to_np(output_with_tiling)).max(),
-            expected_diff_max,
-            "VAE tiling should not affect the inference results",
-        )
-
-    def test_pipeline_with_accelerator_device_map(self, expected_max_difference=5e-4):
-        # Z-Image RoPE embeddings (complex64) have slightly higher numerical tolerance
-        super().test_pipeline_with_accelerator_device_map(expected_max_difference=expected_max_difference)
-
-    def test_group_offloading_inference(self):
-        # Block-level offloading conflicts with RoPE cache. Pipeline-level offloading (tested separately) works fine.
-        self.skipTest("Using test_pipeline_level_group_offloading_inference instead")
-
-    def test_save_load_float16(self, expected_max_diff=1e-2):
-        gc.collect()
-        if torch.cuda.is_available():
-            torch.cuda.empty_cache()
-            torch.cuda.synchronize()
-        torch.manual_seed(0)
-        if torch.cuda.is_available():
-            torch.cuda.manual_seed_all(0)
-        super().test_save_load_float16(expected_max_diff=expected_max_diff)