small nits.

Co-authored-by: dg845 <58458699+dg845@users.noreply.github.com>

docs/source/en/api/models/helios_transformer3d.md

@@ -11,7 +11,7 @@ specific language governing permissions and limitations under the License. -->
 
 # HeliosTransformer3DModel
 
-A 14B Real-Time Autogressive Diffusion Transformer model (support T2V, I2V and V2V) for 3D video-like data from [Helios](https://github.com/PKU-YuanGroup/Helios) was introduced in [Helios: Real Real-Time Long Video Generation Model](https://huggingface.co/papers/2603.04379) by Peking University & ByteDance & etc.
+A 14B Real-Time Autogressive Diffusion Transformer model (support T2V, I2V and V2V) for 3D video-like data from [Helios](https://github.com/PKU-YuanGroup/Helios) was introduced in [Helios: Real Real-Time Long Video Generation Model](https://huggingface.co/papers/) by Peking University & ByteDance & etc.
 
 The model can be loaded with the following code snippet.
 

docs/source/en/api/pipelines/helios.md

@@ -22,7 +22,7 @@
 
 # Helios
 
-[Helios: Real Real-Time Long Video Generation Model](https://huggingface.co/papers/2603.04379) from Peking University & ByteDance & etc, by Shenghai Yuan, Yuanyang Yin, Zongjian Li, Xinwei Huang, Xiao Yang, Li Yuan.
+[Helios: Real Real-Time Long Video Generation Model](https://huggingface.co/papers/) from Peking University & ByteDance & etc, by Shenghai Yuan, Yuanyang Yin, Zongjian Li, Xinwei Huang, Xiao Yang, Li Yuan.
 
 *  <u>We introduce Helios, the first 14B video generation model that runs at 17 FPS on a single NVIDIA H100 GPU and supports minute-scale generation while matching a strong baseline in quality.</u> We make breakthroughs along three key dimensions: (1) robustness to long-video drifting without commonly used anti-drift heuristics such as self-forcing, error banks, or keyframe sampling; (2) real-time generation without standard acceleration techniques such as KV-cache, causal masking, or sparse attention; and (3) training without parallelism or sharding frameworks, enabling image-diffusion-scale batch sizes while fitting up to four 14B models within 80 GB of GPU memory. Specifically, Helios is a 14B autoregressive diffusion model with a unified input representation that natively supports T2V, I2V, and V2V tasks. To mitigate drifting in long-video generation, we characterize its typical failure modes and propose simple yet effective training strategies that explicitly simulate drifting during training, while eliminating repetitive motion at its source. For efficiency, we heavily compress the historical and noisy context and reduce the number of sampling steps, yielding computational costs comparable to—or lower than—those of 1.3B video generative models. Moreover, we introduce infrastructure-level optimizations that accelerate both inference and training while reducing memory consumption. Extensive experiments demonstrate that Helios consistently outperforms prior methods on both short- and long-video generation. All the code and models are available at [this https URL](https://pku-yuangroup.github.io/Helios-Page).
 

docs/source/en/api/pipelines/ltx2.md

@@ -193,179 +193,6 @@ encode_video(
 )
 ```
 
-## Condition Pipeline Generation
-
-You can use `LTX2ConditionPipeline` to specify image and/or video conditions at arbitrary latent indices. For example, we can specify both a first-frame and last-frame condition to perform first-last-frame-to-video (FLF2V) generation:
-
-```py
-import torch
-from diffusers import LTX2ConditionPipeline, LTX2LatentUpsamplePipeline
-from diffusers.pipelines.ltx2.latent_upsampler import LTX2LatentUpsamplerModel
-from diffusers.pipelines.ltx2.pipeline_ltx2_condition import LTX2VideoCondition
-from diffusers.pipelines.ltx2.utils import DISTILLED_SIGMA_VALUES, STAGE_2_DISTILLED_SIGMA_VALUES
-from diffusers.pipelines.ltx2.export_utils import encode_video
-from diffusers.utils import load_image
-
-device = "cuda"
-width = 768
-height = 512
-random_seed = 42
-generator = torch.Generator(device).manual_seed(random_seed)
-model_path = "rootonchair/LTX-2-19b-distilled"
-
-pipe = LTX2ConditionPipeline.from_pretrained(model_path, torch_dtype=torch.bfloat16)
-pipe.enable_sequential_cpu_offload(device=device)
-pipe.vae.enable_tiling()
-
-prompt = (
-    "CG animation style, a small blue bird takes off from the ground, flapping its wings. The bird's feathers are "
-    "delicate, with a unique pattern on its chest. The background shows a blue sky with white clouds under bright "
-    "sunshine. The camera follows the bird upward, capturing its flight and the vastness of the sky from a close-up, "
-    "low-angle perspective."
-)
-
-first_image = load_image(
-    "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/flf2v_input_first_frame.png",
-)
-last_image = load_image(
-    "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/flf2v_input_last_frame.png",
-)
-first_cond = LTX2VideoCondition(frames=first_image, index=0, strength=1.0)
-last_cond = LTX2VideoCondition(frames=last_image, index=-1, strength=1.0)
-conditions = [first_cond, last_cond]
-
-frame_rate = 24.0
-video_latent, audio_latent = pipe(
-    conditions=conditions,
-    prompt=prompt,
-    width=width,
-    height=height,
-    num_frames=121,
-    frame_rate=frame_rate,
-    num_inference_steps=8,
-    sigmas=DISTILLED_SIGMA_VALUES,
-    guidance_scale=1.0,
-    generator=generator,
-    output_type="latent",
-    return_dict=False,
-)
-
-latent_upsampler = LTX2LatentUpsamplerModel.from_pretrained(
-    model_path,
-    subfolder="latent_upsampler",
-    torch_dtype=torch.bfloat16,
-)
-upsample_pipe = LTX2LatentUpsamplePipeline(vae=pipe.vae, latent_upsampler=latent_upsampler)
-upsample_pipe.enable_model_cpu_offload(device=device)
-upscaled_video_latent = upsample_pipe(
-    latents=video_latent,
-    output_type="latent",
-    return_dict=False,
-)[0]
-
-video, audio = pipe(
-    latents=upscaled_video_latent,
-    audio_latents=audio_latent,
-    prompt=prompt,
-    width=width * 2,
-    height=height * 2,
-    num_inference_steps=3,
-    sigmas=STAGE_2_DISTILLED_SIGMA_VALUES,
-    generator=generator,
-    guidance_scale=1.0,
-    output_type="np",
-    return_dict=False,
-)
-
-encode_video(
-    video[0],
-    fps=frame_rate,
-    audio=audio[0].float().cpu(),
-    audio_sample_rate=pipe.vocoder.config.output_sampling_rate,
-    output_path="ltx2_distilled_flf2v.mp4",
-)
-```
-
-You can use both image and video conditions:
-
-```py
-import torch
-from diffusers import LTX2ConditionPipeline
-from diffusers.pipelines.ltx2.pipeline_ltx2_condition import LTX2VideoCondition
-from diffusers.pipelines.ltx2.export_utils import encode_video
-from diffusers.utils import load_image, load_video
-
-device = "cuda"
-width = 768
-height = 512
-random_seed = 42
-generator = torch.Generator(device).manual_seed(random_seed)
-model_path = "rootonchair/LTX-2-19b-distilled"
-
-pipe = LTX2ConditionPipeline.from_pretrained(model_path, torch_dtype=torch.bfloat16)
-pipe.enable_sequential_cpu_offload(device=device)
-pipe.vae.enable_tiling()
-
-prompt = (
-    "The video depicts a long, straight highway stretching into the distance, flanked by metal guardrails. The road is "
-    "divided into multiple lanes, with a few vehicles visible in the far distance. The surrounding landscape features "
-    "dry, grassy fields on one side and rolling hills on the other. The sky is mostly clear with a few scattered "
-    "clouds, suggesting a bright, sunny day. And then the camera switch to a winding mountain road covered in snow, "
-    "with a single vehicle traveling along it. The road is flanked by steep, rocky cliffs and sparse vegetation. The "
-    "landscape is characterized by rugged terrain and a river visible in the distance. The scene captures the "
-    "solitude and beauty of a winter drive through a mountainous region."
-)
-negative_prompt = (
-    "blurry, out of focus, overexposed, underexposed, low contrast, washed out colors, excessive noise, "
-    "grainy texture, poor lighting, flickering, motion blur, distorted proportions, unnatural skin tones, "
-    "deformed facial features, asymmetrical face, missing facial features, extra limbs, disfigured hands, "
-    "wrong hand count, artifacts around text, inconsistent perspective, camera shake, incorrect depth of "
-    "field, background too sharp, background clutter, distracting reflections, harsh shadows, inconsistent "
-    "lighting direction, color banding, cartoonish rendering, 3D CGI look, unrealistic materials, uncanny "
-    "valley effect, incorrect ethnicity, wrong gender, exaggerated expressions, wrong gaze direction, "
-    "mismatched lip sync, silent or muted audio, distorted voice, robotic voice, echo, background noise, "
-    "off-sync audio, incorrect dialogue, added dialogue, repetitive speech, jittery movement, awkward "
-    "pauses, incorrect timing, unnatural transitions, inconsistent framing, tilted camera, flat lighting, "
-    "inconsistent tone, cinematic oversaturation, stylized filters, or AI artifacts."
-)
-
-cond_video = load_video(
-    "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cosmos/cosmos-video2world-input-vid.mp4"
-)
-cond_image = load_image(
-    "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cosmos/cosmos-video2world-input.jpg"
-)
-video_cond = LTX2VideoCondition(frames=cond_video, index=0, strength=1.0)
-image_cond = LTX2VideoCondition(frames=cond_image, index=8, strength=1.0)
-conditions = [video_cond, image_cond]
-
-frame_rate = 24.0
-video, audio = pipe(
-    conditions=conditions,
-    prompt=prompt,
-    negative_prompt=negative_prompt,
-    width=width,
-    height=height,
-    num_frames=121,
-    frame_rate=frame_rate,
-    num_inference_steps=40,
-    guidance_scale=4.0,
-    generator=generator,
-    output_type="np",
-    return_dict=False,
-)
-
-encode_video(
-    video[0],
-    fps=frame_rate,
-    audio=audio[0].float().cpu(),
-    audio_sample_rate=pipe.vocoder.config.output_sampling_rate,
-    output_path="ltx2_cond_video.mp4",
-)
-```
-
-Because the conditioning is done via latent frames, the 8 data space frames corresponding to the specified latent frame for an image condition will tend to be static.
-
 ## LTX2Pipeline
 
 [[autodoc]] LTX2Pipeline
@@ -378,12 +205,6 @@ Because the conditioning is done via latent frames, the 8 data space frames corr
   - all
   - __call__
 
-## LTX2ConditionPipeline
-
-[[autodoc]] LTX2ConditionPipeline
-  - all
-  - __call__
-
 ## LTX2LatentUpsamplePipeline
 
 [[autodoc]] LTX2LatentUpsamplePipeline

docs/source/en/using-diffusers/helios.md

@@ -130,4 +130,4 @@ pipe.to("cuda")
 
 Learn more about Helios with the following resources.
 - Watch [video1](https://www.youtube.com/watch?v=vd_AgHtOUFQ) and [video2](https://www.youtube.com/watch?v=1GeIU2Dn7UY) for a demonstration of Helios's key features.
-- The research paper, [Helios: Real Real-Time Long Video Generation Model](https://huggingface.co/papers/2603.04379) for more details.
+- The research paper, [Helios: Real Real-Time Long Video Generation Model](https://huggingface.co/papers/) for more details.

docs/source/zh/using-diffusers/helios.md

@@ -131,4 +131,4 @@ pipe.to("cuda")
 通过以下资源了解有关 Helios 的更多信息：
 
 - [视频1](https://www.youtube.com/watch?v=vd_AgHtOUFQ)和[视频2](https://www.youtube.com/watch?v=1GeIU2Dn7UY)演示了 Helios 的主要功能;
-- 有关更多详细信息，请参阅研究论文 [Helios: Real Real-Time Long Video Generation Model](https://huggingface.co/papers/2603.04379)。
+- 有关更多详细信息，请参阅研究论文 [Helios: Real Real-Time Long Video Generation Model](https://huggingface.co/papers/)。

examples/dreambooth/train_dreambooth_lora_flux2_klein.py

@@ -1715,7 +1715,7 @@ def main(args):
                 packed_noisy_model_input = Flux2KleinPipeline._pack_latents(noisy_model_input)
 
                 # handle guidance
-                if unwrap_model(transformer).config.guidance_embeds:
+                if transformer.config.guidance_embeds:
                     guidance = torch.full([1], args.guidance_scale, device=accelerator.device)
                     guidance = guidance.expand(model_input.shape[0])
                 else:

examples/dreambooth/train_dreambooth_lora_flux2_klein_img2img.py

@@ -1682,7 +1682,7 @@ def main(args):
                 model_input_ids = torch.cat([model_input_ids, cond_model_input_ids], dim=1)
 
                 # handle guidance
-                if unwrap_model(transformer).config.guidance_embeds:
+                if transformer.config.guidance_embeds:
                     guidance = torch.full([1], args.guidance_scale, device=accelerator.device)
                     guidance = guidance.expand(model_input.shape[0])
                 else:

scripts/convert_rae_to_diffusers.py

@@ -335,11 +335,8 @@ def convert(args: argparse.Namespace) -> None:
     model_keys |= {name for name, _ in model.named_buffers()}
     loaded_keys = set(full_state_dict.keys())
     missing = model_keys - loaded_keys
-    # decoder_pos_embed is initialized in-model. trainable_cls_token is only
-    # allowed to be missing if it was absent in the source decoder checkpoint.
-    allowed_missing = {"decoder.decoder_pos_embed"}
-    if "trainable_cls_token" not in decoder_state_dict:
-        allowed_missing.add("decoder.trainable_cls_token")
+    # trainable_cls_token and decoder_pos_embed are initialized, not loaded from original checkpoint
+    allowed_missing = {"decoder.trainable_cls_token", "decoder.decoder_pos_embed"}
     if missing - allowed_missing:
         print(f"Warning: missing keys after conversion: {sorted(missing - allowed_missing)}")
 

src/diffusers/__init__.py

@@ -572,7 +572,6 @@ else:
             "LEditsPPPipelineStableDiffusionXL",
             "LongCatImageEditPipeline",
             "LongCatImagePipeline",
-            "LTX2ConditionPipeline",
             "LTX2ImageToVideoPipeline",
             "LTX2LatentUpsamplePipeline",
             "LTX2Pipeline",
@@ -1321,7 +1320,6 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             LEditsPPPipelineStableDiffusionXL,
             LongCatImageEditPipeline,
             LongCatImagePipeline,
-            LTX2ConditionPipeline,
             LTX2ImageToVideoPipeline,
             LTX2LatentUpsamplePipeline,
             LTX2Pipeline,

src/diffusers/hooks/layer_skip.py

@@ -108,17 +108,8 @@ class AttentionScoreSkipFunctionMode(torch.overrides.TorchFunctionMode):
 
 
 class AttentionProcessorSkipHook(ModelHook):
-    def __init__(
-        self,
-        skip_processor_output_fn: Callable,
-        skip_attn_scores_fn: Callable | None = None,
-        skip_attention_scores: bool = False,
-        dropout: float = 1.0,
-    ):
-        super().__init__()
+    def __init__(self, skip_processor_output_fn: Callable, skip_attention_scores: bool = False, dropout: float = 1.0):
         self.skip_processor_output_fn = skip_processor_output_fn
-        # STG default: return the values as attention output
-        self.skip_attn_scores_fn = skip_attn_scores_fn or (lambda attn, q, k, v: v)
         self.skip_attention_scores = skip_attention_scores
         self.dropout = dropout
 
@@ -128,22 +119,8 @@ class AttentionProcessorSkipHook(ModelHook):
                 raise ValueError(
                     "Cannot set `skip_attention_scores` to True when `dropout` is not 1.0. Please set `dropout` to 1.0."
                 )
-            processor_supports_skip_fn = hasattr(module.processor, "_skip_attn_scores")
-            if processor_supports_skip_fn:
-                module.processor._skip_attn_scores = True
-                module.processor._skip_attn_scores_fn = self.skip_attn_scores_fn
-            # Use try block in case attn processor raises an exception
-            try:
-                if processor_supports_skip_fn:
-                    output = self.fn_ref.original_forward(*args, **kwargs)
-                else:
-                    # Fallback to torch native SDPA intercept approach
-                    with AttentionScoreSkipFunctionMode():
-                        output = self.fn_ref.original_forward(*args, **kwargs)
-            finally:
-                if processor_supports_skip_fn:
-                    module.processor._skip_attn_scores = False
-                    module.processor._skip_attn_scores_fn = None
+            with AttentionScoreSkipFunctionMode():
+                output = self.fn_ref.original_forward(*args, **kwargs)
         else:
             if math.isclose(self.dropout, 1.0):
                 output = self.skip_processor_output_fn(module, *args, **kwargs)

src/diffusers/models/attention_dispatch.py

@@ -38,7 +38,6 @@ from ..utils import (
     is_flash_attn_available,
     is_flash_attn_version,
     is_kernels_available,
-    is_kernels_version,
     is_sageattention_available,
     is_sageattention_version,
     is_torch_npu_available,
@@ -319,7 +318,6 @@ class _HubKernelConfig:
     repo_id: str
     function_attr: str
     revision: str | None = None
-    version: int | None = None
     kernel_fn: Callable | None = None
     wrapped_forward_attr: str | None = None
     wrapped_backward_attr: str | None = None
@@ -329,34 +327,31 @@ class _HubKernelConfig:
 
 # Registry for hub-based attention kernels
 _HUB_KERNELS_REGISTRY: dict["AttentionBackendName", _HubKernelConfig] = {
+    # TODO: temporary revision for now. Remove when merged upstream into `main`.
     AttentionBackendName._FLASH_3_HUB: _HubKernelConfig(
         repo_id="kernels-community/flash-attn3",
         function_attr="flash_attn_func",
+        revision="fake-ops-return-probs",
         wrapped_forward_attr="flash_attn_interface._flash_attn_forward",
         wrapped_backward_attr="flash_attn_interface._flash_attn_backward",
-        version=1,
     ),
     AttentionBackendName._FLASH_3_VARLEN_HUB: _HubKernelConfig(
         repo_id="kernels-community/flash-attn3",
         function_attr="flash_attn_varlen_func",
-        version=1,
+        # revision="fake-ops-return-probs",
     ),
     AttentionBackendName.FLASH_HUB: _HubKernelConfig(
         repo_id="kernels-community/flash-attn2",
         function_attr="flash_attn_func",
+        revision=None,
         wrapped_forward_attr="flash_attn_interface._wrapped_flash_attn_forward",
         wrapped_backward_attr="flash_attn_interface._wrapped_flash_attn_backward",
-        version=1,
     ),
     AttentionBackendName.FLASH_VARLEN_HUB: _HubKernelConfig(
-        repo_id="kernels-community/flash-attn2",
-        function_attr="flash_attn_varlen_func",
-        version=1,
+        repo_id="kernels-community/flash-attn2", function_attr="flash_attn_varlen_func", revision=None
     ),
     AttentionBackendName.SAGE_HUB: _HubKernelConfig(
-        repo_id="kernels-community/sage-attention",
-        function_attr="sageattn",
-        version=1,
+        repo_id="kernels-community/sage_attention", function_attr="sageattn", revision=None
     ),
 }
 
@@ -526,10 +521,6 @@ def _check_attention_backend_requirements(backend: AttentionBackendName) -> None
             raise RuntimeError(
                 f"Backend '{backend.value}' is not usable because the `kernels` package isn't available. Please install it with `pip install kernels`."
             )
-        if not is_kernels_version(">=", "0.12"):
-            raise RuntimeError(
-                f"Backend '{backend.value}' needs to be used with a `kernels` version of at least 0.12. Please update with `pip install -U kernels`."
-            )
 
     elif backend == AttentionBackendName.AITER:
         if not _CAN_USE_AITER_ATTN:
@@ -703,7 +694,7 @@ def _maybe_download_kernel_for_backend(backend: AttentionBackendName) -> None:
     try:
         from kernels import get_kernel
 
-        kernel_module = get_kernel(config.repo_id, revision=config.revision, version=config.version)
+        kernel_module = get_kernel(config.repo_id, revision=config.revision)
         if needs_kernel:
             config.kernel_fn = _resolve_kernel_attr(kernel_module, config.function_attr)
 

src/diffusers/models/autoencoders/autoencoder_rae.py

@@ -226,17 +226,7 @@ class ViTMAELayer(nn.Module):
 
 
 class RAEDecoder(nn.Module):
-    """
-    Decoder implementation ported from RAE-main to keep checkpoint compatibility.
-
-    Key attributes (must match checkpoint keys):
-    - decoder_embed
-    - decoder_pos_embed
-    - decoder_layers
-    - decoder_norm
-    - decoder_pred
-    - trainable_cls_token
-    """
+    """Lightweight RAE decoder."""
 
     def __init__(
         self,
@@ -263,11 +253,7 @@ class RAEDecoder(nn.Module):
         self.num_patches = num_patches
 
         self.decoder_embed = nn.Linear(hidden_size, decoder_hidden_size, bias=True)
-        grid_size = int(num_patches**0.5)
-        pos_embed = get_2d_sincos_pos_embed(
-            decoder_hidden_size, grid_size, cls_token=True, extra_tokens=1, output_type="pt"
-        )
-        self.register_buffer("decoder_pos_embed", pos_embed.unsqueeze(0).float(), persistent=False)
+        self.register_buffer("decoder_pos_embed", torch.zeros(1, num_patches + 1, decoder_hidden_size))
 
         self.decoder_layers = nn.ModuleList(
             [
@@ -289,8 +275,27 @@ class RAEDecoder(nn.Module):
         self.decoder_pred = nn.Linear(decoder_hidden_size, patch_size**2 * num_channels, bias=True)
         self.gradient_checkpointing = False
 
+        self._initialize_weights(num_patches)
         self.trainable_cls_token = nn.Parameter(torch.zeros(1, 1, decoder_hidden_size))
 
+    def _initialize_weights(self, num_patches: int):
+        # Skip initialization when parameters are on meta device (e.g. during
+        # accelerate.init_empty_weights() used by low_cpu_mem_usage loading).
+        # The weights are initialized.
+        if self.decoder_pos_embed.device.type == "meta":
+            return
+
+        grid_size = int(num_patches**0.5)
+        pos_embed = get_2d_sincos_pos_embed(
+            self.decoder_pos_embed.shape[-1],
+            grid_size,
+            cls_token=True,
+            extra_tokens=1,
+            output_type="pt",
+            device=self.decoder_pos_embed.device,
+        )
+        self.decoder_pos_embed.data.copy_(pos_embed.unsqueeze(0).to(dtype=self.decoder_pos_embed.dtype))
+
     def interpolate_pos_encoding(self, embeddings: torch.Tensor) -> torch.Tensor:
         embeddings_positions = embeddings.shape[1] - 1
         num_positions = self.decoder_pos_embed.shape[1] - 1
@@ -440,7 +445,6 @@ class AutoencoderRAE(ModelMixin, AttentionMixin, AutoencoderMixin, ConfigMixin):
     # NOTE: gradient checkpointing is not wired up for this model yet.
     _supports_gradient_checkpointing = False
     _no_split_modules = ["ViTMAELayer"]
-    _keys_to_ignore_on_load_unexpected = ["decoder.decoder_pos_embed"]
 
     @register_to_config
     def __init__(
@@ -473,6 +477,31 @@ class AutoencoderRAE(ModelMixin, AttentionMixin, AutoencoderMixin, ConfigMixin):
                 f"Unknown encoder_type='{encoder_type}'. Available: {sorted(_ENCODER_FORWARD_FNS.keys())}"
             )
 
+        if encoder_input_size % encoder_patch_size != 0:
+            raise ValueError(
+                f"encoder_input_size={encoder_input_size} must be divisible by encoder_patch_size={encoder_patch_size}."
+            )
+
+        decoder_patch_size = patch_size
+        if decoder_patch_size <= 0:
+            raise ValueError("patch_size must be a positive integer (this is decoder_patch_size).")
+
+        num_patches = (encoder_input_size // encoder_patch_size) ** 2
+        grid = int(sqrt(num_patches))
+        if grid * grid != num_patches:
+            raise ValueError(f"Computed num_patches={num_patches} must be a perfect square.")
+
+        derived_image_size = decoder_patch_size * grid
+        if image_size is None:
+            image_size = derived_image_size
+        else:
+            image_size = int(image_size)
+            if image_size != derived_image_size:
+                raise ValueError(
+                    f"image_size={image_size} must equal decoder_patch_size*sqrt(num_patches)={derived_image_size} "
+                    f"for patch_size={decoder_patch_size} and computed num_patches={num_patches}."
+                )
+
         def _to_config_compatible(value: Any) -> Any:
             if isinstance(value, torch.Tensor):
                 return value.detach().cpu().tolist()
@@ -497,21 +526,6 @@ class AutoencoderRAE(ModelMixin, AttentionMixin, AutoencoderMixin, ConfigMixin):
             latents_std=_to_config_compatible(latents_std),
         )
 
-        self.encoder_input_size = encoder_input_size
-        self.noise_tau = float(noise_tau)
-        self.reshape_to_2d = bool(reshape_to_2d)
-        self.use_encoder_loss = bool(use_encoder_loss)
-
-        # Validate early, before building the (potentially large) encoder/decoder.
-        encoder_patch_size = int(encoder_patch_size)
-        if self.encoder_input_size % encoder_patch_size != 0:
-            raise ValueError(
-                f"encoder_input_size={self.encoder_input_size} must be divisible by encoder_patch_size={encoder_patch_size}."
-            )
-        decoder_patch_size = int(patch_size)
-        if decoder_patch_size <= 0:
-            raise ValueError("patch_size must be a positive integer (this is decoder_patch_size).")
-
         # Frozen representation encoder (built from config, no downloads)
         self.encoder: nn.Module = _build_encoder(
             encoder_type=encoder_type,
@@ -520,22 +534,7 @@ class AutoencoderRAE(ModelMixin, AttentionMixin, AutoencoderMixin, ConfigMixin):
             num_hidden_layers=encoder_num_hidden_layers,
         )
         self._encoder_forward_fn = _ENCODER_FORWARD_FNS[encoder_type]
-        num_patches = (self.encoder_input_size // encoder_patch_size) ** 2
-
-        grid = int(sqrt(num_patches))
-        if grid * grid != num_patches:
-            raise ValueError(f"Computed num_patches={num_patches} must be a perfect square.")
-
-        derived_image_size = decoder_patch_size * grid
-        if image_size is None:
-            image_size = derived_image_size
-        else:
-            image_size = int(image_size)
-            if image_size != derived_image_size:
-                raise ValueError(
-                    f"image_size={image_size} must equal decoder_patch_size*sqrt(num_patches)={derived_image_size} "
-                    f"for patch_size={decoder_patch_size} and computed num_patches={num_patches}."
-                )
+        num_patches = (encoder_input_size // encoder_patch_size) ** 2
 
         # Encoder input normalization stats (ImageNet defaults)
         if encoder_norm_mean is None:
@@ -570,6 +569,7 @@ class AutoencoderRAE(ModelMixin, AttentionMixin, AutoencoderMixin, ConfigMixin):
             num_channels=int(num_channels),
             image_size=int(image_size),
         )
+
         self.num_patches = int(num_patches)
         self.decoder_patch_size = int(decoder_patch_size)
         self.decoder_image_size = int(image_size)
@@ -579,16 +579,19 @@ class AutoencoderRAE(ModelMixin, AttentionMixin, AutoencoderMixin, ConfigMixin):
 
     def _noising(self, x: torch.Tensor, generator: torch.Generator | None = None) -> torch.Tensor:
         # Per-sample random sigma in [0, noise_tau]
-        noise_sigma = self.noise_tau * torch.rand(
+        noise_sigma = self.config.noise_tau * torch.rand(
             (x.size(0),) + (1,) * (x.ndim - 1), device=x.device, dtype=x.dtype, generator=generator
         )
         return x + noise_sigma * randn_tensor(x.shape, generator=generator, device=x.device, dtype=x.dtype)
 
     def _resize_and_normalize(self, x: torch.Tensor) -> torch.Tensor:
         _, _, h, w = x.shape
-        if h != self.encoder_input_size or w != self.encoder_input_size:
+        if h != self.config.encoder_input_size or w != self.config.encoder_input_size:
             x = F.interpolate(
-                x, size=(self.encoder_input_size, self.encoder_input_size), mode="bicubic", align_corners=False
+                x,
+                size=(self.config.encoder_input_size, self.config.encoder_input_size),
+                mode="bicubic",
+                align_corners=False,
             )
         mean = self.encoder_mean.to(device=x.device, dtype=x.dtype)
         std = self.encoder_std.to(device=x.device, dtype=x.dtype)
@@ -617,10 +620,10 @@ class AutoencoderRAE(ModelMixin, AttentionMixin, AutoencoderMixin, ConfigMixin):
         else:
             tokens = self._encoder_forward_fn(self.encoder, x)  # (B, N, C)
 
-        if self.training and self.noise_tau > 0:
+        if self.training and self.config.noise_tau > 0:
             tokens = self._noising(tokens, generator=generator)
 
-        if self.reshape_to_2d:
+        if self.config.reshape_to_2d:
             b, n, c = tokens.shape
             side = int(sqrt(n))
             if side * side != n:
@@ -657,7 +660,7 @@ class AutoencoderRAE(ModelMixin, AttentionMixin, AutoencoderMixin, ConfigMixin):
 
         z = self._denormalize_latents(z)
 
-        if self.reshape_to_2d:
+        if self.config.reshape_to_2d:
             b, c, h, w = z.shape
             tokens = z.view(b, c, h * w).transpose(1, 2).contiguous()  # (B, N, C)
         else:
@@ -666,7 +669,7 @@ class AutoencoderRAE(ModelMixin, AttentionMixin, AutoencoderMixin, ConfigMixin):
         logits = self.decoder(tokens, return_dict=True).logits
         x_rec = self.decoder.unpatchify(logits)
         x_rec = self._denormalize_image(x_rec)
-        return x_rec.to(device=z.device)
+        return x_rec
 
     @apply_forward_hook
     def decode(self, z: torch.Tensor, return_dict: bool = True) -> DecoderOutput | tuple[torch.Tensor]:

src/diffusers/pipelines/__init__.py

@@ -292,12 +292,7 @@ else:
         "LTXLatentUpsamplePipeline",
         "LTXI2VLongMultiPromptPipeline",
     ]
-    _import_structure["ltx2"] = [
-        "LTX2Pipeline",
-        "LTX2ConditionPipeline",
-        "LTX2ImageToVideoPipeline",
-        "LTX2LatentUpsamplePipeline",
-    ]
+    _import_structure["ltx2"] = ["LTX2Pipeline", "LTX2ImageToVideoPipeline", "LTX2LatentUpsamplePipeline"]
     _import_structure["lumina"] = ["LuminaPipeline", "LuminaText2ImgPipeline"]
     _import_structure["lumina2"] = ["Lumina2Pipeline", "Lumina2Text2ImgPipeline"]
     _import_structure["lucy"] = ["LucyEditPipeline"]
@@ -736,7 +731,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             LTXLatentUpsamplePipeline,
             LTXPipeline,
         )
-        from .ltx2 import LTX2ConditionPipeline, LTX2ImageToVideoPipeline, LTX2LatentUpsamplePipeline, LTX2Pipeline
+        from .ltx2 import LTX2ImageToVideoPipeline, LTX2LatentUpsamplePipeline, LTX2Pipeline
         from .lucy import LucyEditPipeline
         from .lumina import LuminaPipeline, LuminaText2ImgPipeline
         from .lumina2 import Lumina2Pipeline, Lumina2Text2ImgPipeline

src/diffusers/pipelines/helios/pipeline_helios_pyramid.py

@@ -76,8 +76,6 @@ EXAMPLE_DOC_STRING = """
 
 
 def optimized_scale(positive_flat, negative_flat):
-    positive_flat = positive_flat.float()
-    negative_flat = negative_flat.float()
     # Calculate dot production
     dot_product = torch.sum(positive_flat * negative_flat, dim=1, keepdim=True)
     # Squared norm of uncondition

src/diffusers/pipelines/ltx2/__init__.py

@@ -25,7 +25,6 @@ else:
     _import_structure["connectors"] = ["LTX2TextConnectors"]
     _import_structure["latent_upsampler"] = ["LTX2LatentUpsamplerModel"]
     _import_structure["pipeline_ltx2"] = ["LTX2Pipeline"]
-    _import_structure["pipeline_ltx2_condition"] = ["LTX2ConditionPipeline"]
     _import_structure["pipeline_ltx2_image2video"] = ["LTX2ImageToVideoPipeline"]
     _import_structure["pipeline_ltx2_latent_upsample"] = ["LTX2LatentUpsamplePipeline"]
     _import_structure["vocoder"] = ["LTX2Vocoder"]
@@ -41,7 +40,6 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
         from .connectors import LTX2TextConnectors
         from .latent_upsampler import LTX2LatentUpsamplerModel
         from .pipeline_ltx2 import LTX2Pipeline
-        from .pipeline_ltx2_condition import LTX2ConditionPipeline
         from .pipeline_ltx2_image2video import LTX2ImageToVideoPipeline
         from .pipeline_ltx2_latent_upsample import LTX2LatentUpsamplePipeline
         from .vocoder import LTX2Vocoder

src/diffusers/utils/__init__.py

@@ -86,7 +86,6 @@ from .import_utils import (
     is_inflect_available,
     is_invisible_watermark_available,
     is_kernels_available,
-    is_kernels_version,
     is_kornia_available,
     is_librosa_available,
     is_matplotlib_available,

src/diffusers/utils/dummy_torch_and_transformers_objects.py

@@ -2147,21 +2147,6 @@ class LongCatImagePipeline(metaclass=DummyObject):
         requires_backends(cls, ["torch", "transformers"])
 
 
-class LTX2ConditionPipeline(metaclass=DummyObject):
-    _backends = ["torch", "transformers"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["torch", "transformers"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["torch", "transformers"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["torch", "transformers"])
-
-
 class LTX2ImageToVideoPipeline(metaclass=DummyObject):
     _backends = ["torch", "transformers"]
 

src/diffusers/utils/import_utils.py

@@ -724,22 +724,6 @@ def is_transformers_version(operation: str, version: str):
     return compare_versions(parse(_transformers_version), operation, version)
 
 
-@cache
-def is_kernels_version(operation: str, version: str):
-    """
-    Compares the current Kernels version to a given reference with an operation.
-
-    Args:
-        operation (`str`):
-            A string representation of an operator, such as `">"` or `"<="`
-        version (`str`):
-            A version string
-    """
-    if not _kernels_available:
-        return False
-    return compare_versions(parse(_kernels_version), operation, version)
-
-
 @cache
 def is_hf_hub_version(operation: str, version: str):
     """

src/diffusers/video_processor.py

@@ -25,9 +25,9 @@ from .image_processor import VaeImageProcessor, is_valid_image, is_valid_image_i
 class VideoProcessor(VaeImageProcessor):
     r"""Simple video processor."""
 
-    def preprocess_video(self, video, height: int | None = None, width: int | None = None, **kwargs) -> torch.Tensor:
+    def preprocess_video(self, video, height: int | None = None, width: int | None = None) -> torch.Tensor:
         r"""
-        Preprocesses input video(s). Keyword arguments will be forwarded to `VaeImageProcessor.preprocess`.
+        Preprocesses input video(s).
 
         Args:
             video (`list[PIL.Image]`, `list[list[PIL.Image]]`, `torch.Tensor`, `np.array`, `list[torch.Tensor]`, `list[np.array]`):
@@ -49,10 +49,6 @@ class VideoProcessor(VaeImageProcessor):
             width (`int`, *optional*`, defaults to `None`):
                 The width in preprocessed frames of the video. If `None`, will use get_default_height_width()` to get
                 the default width.
-
-        Returns:
-            `torch.Tensor` of shape `(batch_size, num_channels, num_frames, height, width)`:
-                A 5D tensor holding the batched channels-first video(s).
         """
         if isinstance(video, list) and isinstance(video[0], np.ndarray) and video[0].ndim == 5:
             warnings.warn(
@@ -83,7 +79,7 @@ class VideoProcessor(VaeImageProcessor):
                 "Input is in incorrect format. Currently, we only support numpy.ndarray, torch.Tensor, PIL.Image.Image"
             )
 
-        video = torch.stack([self.preprocess(img, height=height, width=width, **kwargs) for img in video], dim=0)
+        video = torch.stack([self.preprocess(img, height=height, width=width) for img in video], dim=0)
 
         # move the number of channels before the number of frames.
         video = video.permute(0, 2, 1, 3, 4)
@@ -91,11 +87,10 @@ class VideoProcessor(VaeImageProcessor):
         return video
 
     def postprocess_video(
-        self, video: torch.Tensor, output_type: str = "np", **kwargs
+        self, video: torch.Tensor, output_type: str = "np"
     ) -> np.ndarray | torch.Tensor | list[PIL.Image.Image]:
         r"""
-        Converts a video tensor to a list of frames for export. Keyword arguments will be forwarded to
-        `VaeImageProcessor.postprocess`.
+        Converts a video tensor to a list of frames for export.
 
         Args:
             video (`torch.Tensor`): The video as a tensor.
@@ -105,7 +100,7 @@ class VideoProcessor(VaeImageProcessor):
         outputs = []
         for batch_idx in range(batch_size):
             batch_vid = video[batch_idx].permute(1, 0, 2, 3)
-            batch_output = self.postprocess(batch_vid, output_type, **kwargs)
+            batch_output = self.postprocess(batch_vid, output_type)
             outputs.append(batch_output)
 
         if output_type == "np":