update

docs/source/en/api/modular_diffusers/pipeline_blocks.md

@@ -14,8 +14,4 @@
 
 ## AutoPipelineBlocks
 
-[[autodoc]] diffusers.modular_pipelines.modular_pipeline.AutoPipelineBlocks
-
-## ConditionalPipelineBlocks
-
-[[autodoc]] diffusers.modular_pipelines.modular_pipeline.ConditionalPipelineBlocks
+[[autodoc]] diffusers.modular_pipelines.modular_pipeline.AutoPipelineBlocks

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

@@ -46,20 +46,6 @@ output = pipe(
 output.save("output.png")
 ```
 
-## Cosmos2_5_TransferPipeline
-
-[[autodoc]] Cosmos2_5_TransferPipeline
-  - all
-  - __call__
-
-
-## Cosmos2_5_PredictBasePipeline
-
-[[autodoc]] Cosmos2_5_PredictBasePipeline
-  - all
-  - __call__
-
-
 ## CosmosTextToWorldPipeline
 
 [[autodoc]] CosmosTextToWorldPipeline
@@ -84,6 +70,12 @@ output.save("output.png")
   - all
   - __call__
 
+## Cosmos2_5_PredictBasePipeline
+
+[[autodoc]] Cosmos2_5_PredictBasePipeline
+  - all
+  - __call__
+
 ## CosmosPipelineOutput
 
 [[autodoc]] pipelines.cosmos.pipeline_output.CosmosPipelineOutput

docs/source/en/modular_diffusers/auto_pipeline_blocks.md

@@ -121,7 +121,7 @@ from diffusers.modular_pipelines import AutoPipelineBlocks
 
 class AutoImageBlocks(AutoPipelineBlocks):
     # List of sub-block classes to choose from
-    block_classes = [InpaintBlock, ImageToImageBlock, TextToImageBlock]
+    block_classes = [block_inpaint_cls, block_i2i_cls, block_t2i_cls]
     # Names for each block in the same order
     block_names = ["inpaint", "img2img", "text2img"]
     # Trigger inputs that determine which block to run
@@ -129,8 +129,8 @@ class AutoImageBlocks(AutoPipelineBlocks):
     # - "image" triggers img2img workflow (but only if mask is not provided)
     # - if none of above, runs the text2img workflow (default)
     block_trigger_inputs = ["mask", "image", None]
+    # Description is extremely important for AutoPipelineBlocks
 
-    @property
     def description(self):
         return (
             "Pipeline generates images given different types of conditions!\n"
@@ -141,7 +141,7 @@ class AutoImageBlocks(AutoPipelineBlocks):
         )
 ```
 
-It is **very** important to include a `description` to avoid any confusion over how to run a block and what inputs are required. While [`~modular_pipelines.AutoPipelineBlocks`] are convenient, its conditional logic may be difficult to figure out if it isn't properly explained.
+It is **very** important to include a `description` to avoid any confusion over how to run a block and what inputs are required. While [`~modular_pipelines.AutoPipelineBlocks`] are convenient, it's conditional logic may be difficult to figure out if it isn't properly explained.
 
 Create an instance of `AutoImageBlocks`.
 
@@ -152,74 +152,5 @@ auto_blocks = AutoImageBlocks()
 For more complex compositions, such as nested [`~modular_pipelines.AutoPipelineBlocks`] blocks when they're used as sub-blocks in larger pipelines, use the [`~modular_pipelines.SequentialPipelineBlocks.get_execution_blocks`] method to extract the a block that is actually run based on your input.
 
 ```py
-auto_blocks.get_execution_blocks(mask=True)
-```
-
-## ConditionalPipelineBlocks
-
-[`~modular_pipelines.AutoPipelineBlocks`] is a special case of [`~modular_pipelines.ConditionalPipelineBlocks`]. While [`~modular_pipelines.AutoPipelineBlocks`] selects blocks based on whether a trigger input is provided or not, [`~modular_pipelines.ConditionalPipelineBlocks`] is able to select a block based on custom selection logic provided in the `select_block` method.
-
-Here is the same example written using [`~modular_pipelines.ConditionalPipelineBlocks`] directly:
-
-```py
-from diffusers.modular_pipelines import ConditionalPipelineBlocks
-
-class AutoImageBlocks(ConditionalPipelineBlocks):
-    block_classes = [InpaintBlock, ImageToImageBlock, TextToImageBlock]
-    block_names = ["inpaint", "img2img", "text2img"]
-    block_trigger_inputs = ["mask", "image"]
-    default_block_name = "text2img"
-
-    @property
-    def description(self):
-        return (
-            "Pipeline generates images given different types of conditions!\n"
-            + "This is an auto pipeline block that works for text2img, img2img and inpainting tasks.\n"
-            + " - inpaint workflow is run when `mask` is provided.\n"
-            + " - img2img workflow is run when `image` is provided (but only when `mask` is not provided).\n"
-            + " - text2img workflow is run when neither `image` nor `mask` is provided.\n"
-        )
-
-    def select_block(self, mask=None, image=None) -> str | None:
-        if mask is not None:
-            return "inpaint"
-        if image is not None:
-            return "img2img"
-        return None  # falls back to default_block_name ("text2img")
-```
-
-The inputs listed in `block_trigger_inputs` are passed as keyword arguments to `select_block()`. When `select_block` returns `None`, it falls back to `default_block_name`. If `default_block_name` is also `None`, the entire conditional block is skipped — this is useful for optional processing steps that should only run when specific inputs are provided.
-
-## Workflows
-
-Pipelines that contain conditional blocks ([`~modular_pipelines.AutoPipelineBlocks`] or [`~modular_pipelines.ConditionalPipelineBlocks]`) can support multiple workflows — for example, our SDXL modular pipeline supports a dozen workflows all in one pipeline. But this also means it can be confusing for users to know what workflows are supported and how to run them. For pipeline builders, it's useful to be able to extract only the blocks relevant to a specific workflow.
-
-We recommend defining a `_workflow_map` to give each workflow a name and explicitly list the inputs it requires.
-
-```py
-from diffusers.modular_pipelines import SequentialPipelineBlocks
-
-class MyPipelineBlocks(SequentialPipelineBlocks):
-    block_classes = [TextEncoderBlock, AutoImageBlocks, DecodeBlock]
-    block_names = ["text_encoder", "auto_image", "decode"]
-
-    _workflow_map = {
-        "text2image": {"prompt": True},
-        "image2image": {"image": True, "prompt": True},
-        "inpaint": {"mask": True, "image": True, "prompt": True},
-    }
-```
-
-All of our built-in modular pipelines come with pre-defined workflows. The `available_workflows` property lists all supported workflows:
-
-```py
-pipeline_blocks = MyPipelineBlocks()
-pipeline_blocks.available_workflows
-# ['text2image', 'image2image', 'inpaint']
-```
-
-Retrieve a specific workflow with `get_workflow` to inspect and debug a specific block that executes the workflow.
-
-```py
-pipeline_blocks.get_workflow("inpaint")
+auto_blocks.get_execution_blocks("mask")
 ```

docs/source/en/training/distributed_inference.md

@@ -111,7 +111,7 @@ if __name__ == "__main__":
 Call `torchrun` to run the inference script and use the `--nproc_per_node` argument to set the number of GPUs to use.
 
 ```bash
-torchrun --nproc_per_node=2 run_distributed.py
+torchrun run_distributed.py --nproc_per_node=2
 ```
 
 ## device_map

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

@@ -97,5 +97,32 @@ If the custom model inherits from the [`ModelMixin`] class, it gets access to th
 > )
 > ```
 
+### Saving custom models
+
+Use [`~ConfigMixin.register_for_auto_class`] to add the `auto_map` entry to `config.json` automatically when saving. This avoids having to manually edit the config file.
+
+```py
+# my_model.py
+from diffusers import ModelMixin, ConfigMixin
+
+class MyCustomModel(ModelMixin, ConfigMixin):
+    ...
+
+MyCustomModel.register_for_auto_class("AutoModel")
+
+model = MyCustomModel(...)
+model.save_pretrained("./my_model")
+```
+
+The saved `config.json` will include the `auto_map` field.
+
+```json
+{
+  "auto_map": {
+    "AutoModel": "my_model.MyCustomModel"
+  }
+}
+```
+
 > [!NOTE]
 > Learn more about implementing custom models in the [Community components](../using-diffusers/custom_pipeline_overview#community-components) guide.

scripts/convert_cosmos_to_diffusers.py

@@ -94,15 +94,9 @@ python scripts/convert_cosmos_to_diffusers.py \
     --transformer_type Cosmos-2.5-Transfer-General-2B \
     --transformer_ckpt_path $transformer_ckpt_path \
     --vae_type wan2.1 \
-    --output_path converted/transfer/2b/general/depth/pipeline \
+    --output_path converted/transfer/2b/general/depth \
     --save_pipeline
 
-python scripts/convert_cosmos_to_diffusers.py \
-    --transformer_type Cosmos-2.5-Transfer-General-2B \
-    --transformer_ckpt_path $transformer_ckpt_path \
-    --vae_type wan2.1 \
-    --output_path converted/transfer/2b/general/depth/models
-
 # edge
 transformer_ckpt_path=~/.cache/huggingface/hub/models--nvidia--Cosmos-Transfer2.5-2B/snapshots/eb5325b77d358944da58a690157dd2b8071bbf85/general/edge/61f5694b-0ad5-4ecd-8ad7-c8545627d125_ema_bf16.pt
 
@@ -126,15 +120,9 @@ python scripts/convert_cosmos_to_diffusers.py \
     --transformer_type Cosmos-2.5-Transfer-General-2B \
     --transformer_ckpt_path $transformer_ckpt_path \
     --vae_type wan2.1 \
-    --output_path converted/transfer/2b/general/blur/pipeline \
+    --output_path converted/transfer/2b/general/blur \
     --save_pipeline
 
-python scripts/convert_cosmos_to_diffusers.py \
-    --transformer_type Cosmos-2.5-Transfer-General-2B \
-    --transformer_ckpt_path $transformer_ckpt_path \
-    --vae_type wan2.1 \
-    --output_path converted/transfer/2b/general/blur/models
-
 # seg
 transformer_ckpt_path=~/.cache/huggingface/hub/models--nvidia--Cosmos-Transfer2.5-2B/snapshots/eb5325b77d358944da58a690157dd2b8071bbf85/general/seg/5136ef49-6d8d-42e8-8abf-7dac722a304a_ema_bf16.pt
 
@@ -142,14 +130,8 @@ python scripts/convert_cosmos_to_diffusers.py \
     --transformer_type Cosmos-2.5-Transfer-General-2B \
     --transformer_ckpt_path $transformer_ckpt_path \
     --vae_type wan2.1 \
-    --output_path converted/transfer/2b/general/seg/pipeline \
+    --output_path converted/transfer/2b/general/seg \
     --save_pipeline
-
-python scripts/convert_cosmos_to_diffusers.py \
-    --transformer_type Cosmos-2.5-Transfer-General-2B \
-    --transformer_ckpt_path $transformer_ckpt_path \
-    --vae_type wan2.1 \
-    --output_path converted/transfer/2b/general/seg/models
 ```
 """
 

src/diffusers/configuration_utils.py

@@ -107,6 +107,38 @@ class ConfigMixin:
     has_compatibles = False
 
     _deprecated_kwargs = []
+    _auto_class = None
+
+    @classmethod
+    def register_for_auto_class(cls, auto_class="AutoModel"):
+        """
+        Register this class with the given auto class so that it can be loaded with `AutoModel.from_pretrained(...,
+        trust_remote_code=True)`.
+
+        When the config is saved, the resulting `config.json` will include an `auto_map` entry mapping the auto class
+        to this class's module and class name.
+
+        Args:
+            auto_class (`str` or type, *optional*, defaults to `"AutoModel"`):
+                The auto class to register this class with. Can be a string (e.g. `"AutoModel"`) or the class itself.
+                Currently only `"AutoModel"` is supported.
+
+        Example:
+
+        ```python
+        from diffusers import ModelMixin, ConfigMixin
+
+
+        class MyCustomModel(ModelMixin, ConfigMixin): ...
+
+
+        MyCustomModel.register_for_auto_class("AutoModel")
+        ```
+        """
+        if auto_class != "AutoModel":
+            raise ValueError(f"Only 'AutoModel' is supported, got '{auto_class}'.")
+
+        cls._auto_class = auto_class
 
     def register_to_config(self, **kwargs):
         if self.config_name is None:
@@ -621,6 +653,12 @@ class ConfigMixin:
         # pop the `_pre_quantization_dtype` as torch.dtypes are not serializable.
         _ = config_dict.pop("_pre_quantization_dtype", None)
 
+        if getattr(self, "_auto_class", None) is not None:
+            module = self.__class__.__module__.split(".")[-1]
+            auto_map = config_dict.get("auto_map", {})
+            auto_map[self._auto_class] = f"{module}.{self.__class__.__name__}"
+            config_dict["auto_map"] = auto_map
+
         return json.dumps(config_dict, indent=2, sort_keys=True) + "\n"
 
     def to_json_file(self, json_file_path: str | os.PathLike):

src/diffusers/loaders/lora_conversion_utils.py

@@ -856,7 +856,7 @@ def _convert_kohya_flux_lora_to_diffusers(state_dict):
                 )
             state_dict = {k: v for k, v in state_dict.items() if not k.startswith("text_encoders.t5xxl.transformer.")}
 
-        has_diffb = any("diff_b" in k and k.startswith(("lora_unet_", "lora_te_", "lora_te1_")) for k in state_dict)
+        has_diffb = any("diff_b" in k and k.startswith(("lora_unet_", "lora_te_")) for k in state_dict)
         if has_diffb:
             zero_status_diff_b = state_dict_all_zero(state_dict, ".diff_b")
             if zero_status_diff_b:
@@ -895,7 +895,7 @@ def _convert_kohya_flux_lora_to_diffusers(state_dict):
         state_dict = {
             _custom_replace(k, limit_substrings): v
             for k, v in state_dict.items()
-            if k.startswith(("lora_unet_", "lora_te_", "lora_te1_"))
+            if k.startswith(("lora_unet_", "lora_te_"))
         }
 
         if any("text_projection" in k for k in state_dict):

src/diffusers/models/attention_dispatch.py

@@ -329,11 +329,7 @@ class _HubKernelConfig:
 _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",
+        repo_id="kernels-community/flash-attn3", function_attr="flash_attn_func", revision="fake-ops-return-probs"
     ),
     AttentionBackendName._FLASH_3_VARLEN_HUB: _HubKernelConfig(
         repo_id="kernels-community/flash-attn3",
@@ -733,7 +729,7 @@ def _wrapped_flash_attn_3(
 ) -> tuple[torch.Tensor, torch.Tensor]:
     # Hardcoded for now because pytorch does not support tuple/int type hints
     window_size = (-1, -1)
-    result = flash_attn_3_func(
+    out, lse, *_ = flash_attn_3_func(
         q=q,
         k=k,
         v=v,
@@ -750,9 +746,7 @@ def _wrapped_flash_attn_3(
         pack_gqa=pack_gqa,
         deterministic=deterministic,
         sm_margin=sm_margin,
-        return_attn_probs=True,
     )
-    out, lse, *_ = result
     lse = lse.permute(0, 2, 1)
     return out, lse
 
@@ -1296,62 +1290,36 @@ def _flash_attention_3_hub_forward_op(
     if enable_gqa:
         raise ValueError("`enable_gqa` is not yet supported for flash-attn 3 hub kernels.")
 
-    config = _HUB_KERNELS_REGISTRY[AttentionBackendName._FLASH_3_HUB]
-    wrapped_forward_fn = config.wrapped_forward_fn
-    if wrapped_forward_fn is None:
-        raise RuntimeError(
-            "Flash attention 3 hub kernels must expose `flash_attn_interface._flash_attn_forward` "
-            "for context parallel execution."
-        )
-
-    if scale is None:
-        scale = query.shape[-1] ** (-0.5)
-
-    out, softmax_lse, *_ = wrapped_forward_fn(
-        query,
-        key,
-        value,
-        None,
-        None,  # k_new, v_new
-        None,  # qv
-        None,  # out
-        None,
-        None,
-        None,  # cu_seqlens_q/k/k_new
-        None,
-        None,  # seqused_q/k
-        None,
-        None,  # max_seqlen_q/k
-        None,
-        None,
-        None,  # page_table, kv_batch_idx, leftpad_k
-        None,
-        None,
-        None,  # rotary_cos/sin, seqlens_rotary
-        None,
-        None,
-        None,  # q_descale, k_descale, v_descale
-        scale,
+    func = _HUB_KERNELS_REGISTRY[AttentionBackendName._FLASH_3_HUB].kernel_fn
+    out = func(
+        q=query,
+        k=key,
+        v=value,
+        softmax_scale=scale,
         causal=is_causal,
-        window_size_left=window_size[0],
-        window_size_right=window_size[1],
-        attention_chunk=0,
+        qv=None,
+        q_descale=None,
+        k_descale=None,
+        v_descale=None,
+        window_size=window_size,
         softcap=softcap,
         num_splits=num_splits,
         pack_gqa=pack_gqa,
+        deterministic=deterministic,
         sm_margin=sm_margin,
+        return_attn_probs=return_lse,
     )
 
-    lse = softmax_lse.permute(0, 2, 1).contiguous() if return_lse else None
+    lse = None
+    if return_lse:
+        out, lse = out
+        lse = lse.permute(0, 2, 1).contiguous()
 
     if _save_ctx:
-        ctx.save_for_backward(query, key, value, out, softmax_lse)
+        ctx.save_for_backward(query, key, value)
         ctx.scale = scale
         ctx.is_causal = is_causal
-        ctx.window_size = window_size
-        ctx.softcap = softcap
-        ctx.deterministic = deterministic
-        ctx.sm_margin = sm_margin
+        ctx._hub_kernel = func
 
     return (out, lse) if return_lse else out
 
@@ -1360,49 +1328,54 @@ def _flash_attention_3_hub_backward_op(
     ctx: torch.autograd.function.FunctionCtx,
     grad_out: torch.Tensor,
     *args,
-    **kwargs,
+    window_size: tuple[int, int] = (-1, -1),
+    softcap: float = 0.0,
+    num_splits: int = 1,
+    pack_gqa: bool | None = None,
+    deterministic: bool = False,
+    sm_margin: int = 0,
 ):
-    config = _HUB_KERNELS_REGISTRY[AttentionBackendName._FLASH_3_HUB]
-    wrapped_backward_fn = config.wrapped_backward_fn
-    if wrapped_backward_fn is None:
-        raise RuntimeError(
-            "Flash attention 3 hub kernels must expose `flash_attn_interface._flash_attn_backward` "
-            "for context parallel execution."
+    query, key, value = ctx.saved_tensors
+    kernel_fn = ctx._hub_kernel
+    # NOTE: Unlike the FA2 hub kernel, the FA3 hub kernel does not expose separate wrapped forward/backward
+    # primitives (no `wrapped_forward_attr`/`wrapped_backward_attr` in its `_HubKernelConfig`). We
+    # therefore rerun the forward pass under `torch.enable_grad()` and differentiate through it with
+    # `torch.autograd.grad()`. This is a second forward pass during backward; it can be avoided once
+    # the FA3 hub exposes a dedicated fused backward kernel (analogous to `_wrapped_flash_attn_backward`
+    # in the FA2 hub), at which point this can be refactored to match `_flash_attention_hub_backward_op`.
+    with torch.enable_grad():
+        query_r = query.detach().requires_grad_(True)
+        key_r = key.detach().requires_grad_(True)
+        value_r = value.detach().requires_grad_(True)
+
+        out = kernel_fn(
+            q=query_r,
+            k=key_r,
+            v=value_r,
+            softmax_scale=ctx.scale,
+            causal=ctx.is_causal,
+            qv=None,
+            q_descale=None,
+            k_descale=None,
+            v_descale=None,
+            window_size=window_size,
+            softcap=softcap,
+            num_splits=num_splits,
+            pack_gqa=pack_gqa,
+            deterministic=deterministic,
+            sm_margin=sm_margin,
+            return_attn_probs=False,
         )
+        if isinstance(out, tuple):
+            out = out[0]
 
-    query, key, value, out, softmax_lse = ctx.saved_tensors
-    grad_query = torch.empty_like(query)
-    grad_key = torch.empty_like(key)
-    grad_value = torch.empty_like(value)
-
-    wrapped_backward_fn(
-        grad_out,
-        query,
-        key,
-        value,
-        out,
-        softmax_lse,
-        None,
-        None,  # cu_seqlens_q, cu_seqlens_k
-        None,
-        None,  # seqused_q, seqused_k
-        None,
-        None,  # max_seqlen_q, max_seqlen_k
-        grad_query,
-        grad_key,
-        grad_value,
-        ctx.scale,
-        ctx.is_causal,
-        ctx.window_size[0],
-        ctx.window_size[1],
-        ctx.softcap,
-        ctx.deterministic,
-        ctx.sm_margin,
-    )
-
-    grad_query = grad_query[..., : grad_out.shape[-1]]
-    grad_key = grad_key[..., : grad_out.shape[-1]]
-    grad_value = grad_value[..., : grad_out.shape[-1]]
+        grad_query, grad_key, grad_value = torch.autograd.grad(
+            out,
+            (query_r, key_r, value_r),
+            grad_out,
+            retain_graph=False,
+            allow_unused=False,
+        )
 
     return grad_query, grad_key, grad_value
 
@@ -2703,7 +2676,7 @@ def _flash_varlen_attention_3(
     key_packed = torch.cat(key_valid, dim=0)
     value_packed = torch.cat(value_valid, dim=0)
 
-    result = flash_attn_3_varlen_func(
+    out, lse, *_ = flash_attn_3_varlen_func(
         q=query_packed,
         k=key_packed,
         v=value_packed,
@@ -2713,13 +2686,7 @@ def _flash_varlen_attention_3(
         max_seqlen_k=max_seqlen_k,
         softmax_scale=scale,
         causal=is_causal,
-        return_attn_probs=return_lse,
     )
-    if isinstance(result, tuple):
-        out, lse, *_ = result
-    else:
-        out = result
-        lse = None
     out = out.unflatten(0, (batch_size, -1))
 
     return (out, lse) if return_lse else out

src/diffusers/models/controlnets/controlnet_cosmos.py

@@ -191,12 +191,7 @@ class CosmosControlNetModel(ModelMixin, ConfigMixin, FromOriginalModelMixin):
                 dim=1,
             )
 
-        if condition_mask is not None:
-            control_hidden_states = torch.cat([control_hidden_states, condition_mask], dim=1)
-        else:
-            control_hidden_states = torch.cat(
-                [control_hidden_states, torch.zeros_like(controls_latents[:, :1])], dim=1
-            )
+        control_hidden_states = torch.cat([control_hidden_states, torch.zeros_like(controls_latents[:, :1])], dim=1)
 
         padding_mask_resized = transforms.functional.resize(
             padding_mask, list(control_hidden_states.shape[-2:]), interpolation=transforms.InterpolationMode.NEAREST

src/diffusers/modular_pipelines/modular_pipeline.py

@@ -1633,14 +1633,7 @@ class ModularPipeline(ConfigMixin, PushToHubMixin):
                 blocks_class_name = self.default_blocks_name
             if blocks_class_name is not None:
                 diffusers_module = importlib.import_module("diffusers")
-                blocks_class = getattr(diffusers_module, blocks_class_name, None)
-                # If the blocks_class is not found or is a base class (e.g. SequentialPipelineBlocks saved by from_blocks_dict) with empty block_classes
-                # fall back to default_blocks_name
-                if blocks_class is None or not blocks_class.block_classes:
-                    blocks_class_name = self.default_blocks_name
-                    blocks_class = getattr(diffusers_module, blocks_class_name)
-
-            if blocks_class is not None:
+                blocks_class = getattr(diffusers_module, blocks_class_name)
                 blocks = blocks_class()
             else:
                 logger.warning(f"`blocks` is `None`, no default blocks class found for {self.__class__.__name__}")

src/diffusers/pipelines/cosmos/pipeline_cosmos2_5_transfer.py

@@ -17,6 +17,9 @@ from typing import Callable, Dict, List, Optional, Union
 import numpy as np
 import PIL.Image
 import torch
+import torchvision
+import torchvision.transforms
+import torchvision.transforms.functional
 from transformers import AutoTokenizer, Qwen2_5_VLForConditionalGeneration
 
 from ...callbacks import MultiPipelineCallbacks, PipelineCallback
@@ -51,13 +54,11 @@ else:
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
 
-def _maybe_pad_or_trim_video(video: torch.Tensor, num_frames: int):
+def _maybe_pad_video(video: torch.Tensor, num_frames: int):
     n_pad_frames = num_frames - video.shape[2]
     if n_pad_frames > 0:
         last_frame = video[:, :, -1:, :, :]
         video = torch.cat((video, last_frame.repeat(1, 1, n_pad_frames, 1, 1)), dim=2)
-    elif num_frames < video.shape[2]:
-        video = video[:, :, :num_frames, :, :]
     return video
 
 
@@ -133,8 +134,8 @@ EXAMPLE_DOC_STRING = """
         >>> controls = [Image.fromarray(x.numpy()) for x in controls.permute(1, 2, 3, 0)]
         >>> export_to_video(controls, "edge_controlled_video_edge.mp4", fps=30)
 
-        >>> # Transfer inference with controls.
         >>> video = pipe(
+        ...     video=input_video[:num_frames],
         ...     controls=controls,
         ...     controls_conditioning_scale=1.0,
         ...     prompt=prompt,
@@ -148,7 +149,7 @@ EXAMPLE_DOC_STRING = """
 
 class Cosmos2_5_TransferPipeline(DiffusionPipeline):
     r"""
-    Pipeline for Cosmos Transfer2.5, supporting auto-regressive inference.
+    Pipeline for Cosmos Transfer2.5 base model.
 
     This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
     implemented for all pipelines (downloading, saving, running on a particular device, etc.).
@@ -165,14 +166,12 @@ class Cosmos2_5_TransferPipeline(DiffusionPipeline):
             A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
         vae ([`AutoencoderKLWan`]):
             Variational Auto-Encoder (VAE) Model to encode and decode videos to and from latent representations.
-        controlnet ([`CosmosControlNetModel`]):
-            ControlNet used to condition generation on control inputs.
     """
 
     model_cpu_offload_seq = "text_encoder->transformer->controlnet->vae"
     _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]
     # We mark safety_checker as optional here to get around some test failures, but it is not really optional
-    _optional_components = ["safety_checker"]
+    _optional_components = ["safety_checker", "controlnet"]
     _exclude_from_cpu_offload = ["safety_checker"]
 
     def __init__(
@@ -182,8 +181,8 @@ class Cosmos2_5_TransferPipeline(DiffusionPipeline):
         transformer: CosmosTransformer3DModel,
         vae: AutoencoderKLWan,
         scheduler: UniPCMultistepScheduler,
-        controlnet: CosmosControlNetModel,
-        safety_checker: Optional[CosmosSafetyChecker] = None,
+        controlnet: Optional[CosmosControlNetModel],
+        safety_checker: CosmosSafetyChecker = None,
     ):
         super().__init__()
 
@@ -385,11 +384,10 @@ class Cosmos2_5_TransferPipeline(DiffusionPipeline):
         num_frames_in: int = 93,
         num_frames_out: int = 93,
         do_classifier_free_guidance: bool = True,
-        dtype: Optional[torch.dtype] = None,
-        device: Optional[torch.device] = None,
-        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
-        latents: Optional[torch.Tensor] = None,
-        num_cond_latent_frames: int = 0,
+        dtype: torch.dtype | None = None,
+        device: torch.device | None = None,
+        generator: torch.Generator | list[torch.Generator] | None = None,
+        latents: torch.Tensor | None = None,
     ) -> torch.Tensor:
         if isinstance(generator, list) and len(generator) != batch_size:
             raise ValueError(
@@ -404,14 +402,10 @@ class Cosmos2_5_TransferPipeline(DiffusionPipeline):
         W = width // self.vae_scale_factor_spatial
         shape = (B, C, T, H, W)
 
-        if latents is not None:
-            if latents.shape[1:] != shape[1:]:
-                raise ValueError(f"Unexpected `latents` shape, got {latents.shape}, expected {shape}.")
-            latents = latents.to(device=device, dtype=dtype)
-        else:
-            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
-
         if num_frames_in == 0:
+            if latents is None:
+                latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+
             cond_mask = torch.zeros((B, 1, T, H, W), dtype=latents.dtype, device=latents.device)
             cond_indicator = torch.zeros((B, 1, T, 1, 1), dtype=latents.dtype, device=latents.device)
 
@@ -441,12 +435,16 @@ class Cosmos2_5_TransferPipeline(DiffusionPipeline):
             latents_std = self.latents_std.to(device=device, dtype=dtype)
             cond_latents = (cond_latents - latents_mean) / latents_std
 
+            if latents is None:
+                latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+            else:
+                latents = latents.to(device=device, dtype=dtype)
+
             padding_shape = (B, 1, T, H, W)
             ones_padding = latents.new_ones(padding_shape)
             zeros_padding = latents.new_zeros(padding_shape)
 
-            cond_indicator = latents.new_zeros(B, 1, latents.size(2), 1, 1)
-            cond_indicator[:, :, 0:num_cond_latent_frames, :, :] = 1.0
+            cond_indicator = latents.new_zeros(1, 1, latents.size(2), 1, 1)
             cond_mask = cond_indicator * ones_padding + (1 - cond_indicator) * zeros_padding
 
             return (
@@ -456,7 +454,34 @@ class Cosmos2_5_TransferPipeline(DiffusionPipeline):
                 cond_indicator,
             )
 
-    # Modified from diffusers.pipelines.cosmos.pipeline_cosmos_text2world.CosmosTextToWorldPipeline.check_inputs
+    def _encode_controls(
+        self,
+        controls: Optional[torch.Tensor],
+        height: int,
+        width: int,
+        num_frames: int,
+        dtype: torch.dtype,
+        device: torch.device,
+        generator: torch.Generator | list[torch.Generator] | None,
+    ) -> Optional[torch.Tensor]:
+        if controls is None:
+            return None
+
+        control_video = self.video_processor.preprocess_video(controls, height, width)
+        control_video = _maybe_pad_video(control_video, num_frames)
+
+        control_video = control_video.to(device=device, dtype=self.vae.dtype)
+        control_latents = [
+            retrieve_latents(self.vae.encode(vid.unsqueeze(0)), generator=generator) for vid in control_video
+        ]
+        control_latents = torch.cat(control_latents, dim=0).to(dtype)
+
+        latents_mean = self.latents_mean.to(device=device, dtype=dtype)
+        latents_std = self.latents_std.to(device=device, dtype=dtype)
+        control_latents = (control_latents - latents_mean) / latents_std
+        return control_latents
+
+    # Copied from diffusers.pipelines.cosmos.pipeline_cosmos_text2world.CosmosTextToWorldPipeline.check_inputs
     def check_inputs(
         self,
         prompt,
@@ -464,25 +489,9 @@ class Cosmos2_5_TransferPipeline(DiffusionPipeline):
         width,
         prompt_embeds=None,
         callback_on_step_end_tensor_inputs=None,
-        num_ar_conditional_frames=None,
-        num_ar_latent_conditional_frames=None,
-        num_frames_per_chunk=None,
-        num_frames=None,
-        conditional_frame_timestep=0.1,
     ):
-        if width <= 0 or height <= 0 or height % 16 != 0 or width % 16 != 0:
-            raise ValueError(
-                f"`height` and `width` have to be divisible by 16 (& positive) but are {height} and {width}."
-            )
-
-        if num_frames is not None and num_frames <= 0:
-            raise ValueError(f"`num_frames` has to be a positive integer when provided but is {num_frames}.")
-
-        if conditional_frame_timestep < 0 or conditional_frame_timestep > 1:
-            raise ValueError(
-                "`conditional_frame_timestep` has to be a float in the [0, 1] interval but is "
-                f"{conditional_frame_timestep}."
-            )
+        if height % 16 != 0 or width % 16 != 0:
+            raise ValueError(f"`height` and `width` have to be divisible by 16 but are {height} and {width}.")
 
         if callback_on_step_end_tensor_inputs is not None and not all(
             k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
@@ -503,46 +512,6 @@ class Cosmos2_5_TransferPipeline(DiffusionPipeline):
         elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
             raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
 
-        if num_ar_latent_conditional_frames is not None and num_ar_conditional_frames is not None:
-            raise ValueError(
-                "Provide only one of `num_ar_conditional_frames` or `num_ar_latent_conditional_frames`, not both."
-            )
-        if num_ar_latent_conditional_frames is None and num_ar_conditional_frames is None:
-            raise ValueError("Provide either `num_ar_conditional_frames` or `num_ar_latent_conditional_frames`.")
-        if num_ar_latent_conditional_frames is not None and num_ar_latent_conditional_frames < 0:
-            raise ValueError("`num_ar_latent_conditional_frames` must be >= 0.")
-        if num_ar_conditional_frames is not None and num_ar_conditional_frames < 0:
-            raise ValueError("`num_ar_conditional_frames` must be >= 0.")
-
-        if num_ar_latent_conditional_frames is not None:
-            num_ar_conditional_frames = max(
-                0, (num_ar_latent_conditional_frames - 1) * self.vae_scale_factor_temporal + 1
-            )
-
-        min_chunk_len = self.vae_scale_factor_temporal + 1
-        if num_frames_per_chunk < min_chunk_len:
-            logger.warning(f"{num_frames_per_chunk=} must be larger than {min_chunk_len=}, setting to min_chunk_len")
-            num_frames_per_chunk = min_chunk_len
-
-        max_frames_by_rope = None
-        if getattr(self.transformer.config, "max_size", None) is not None:
-            max_frames_by_rope = max(
-                size // patch
-                for size, patch in zip(self.transformer.config.max_size, self.transformer.config.patch_size)
-            )
-            if num_frames_per_chunk > max_frames_by_rope:
-                raise ValueError(
-                    f"{num_frames_per_chunk=} is too large for RoPE setting ({max_frames_by_rope=}). "
-                    "Please reduce `num_frames_per_chunk`."
-                )
-
-        if num_ar_conditional_frames >= num_frames_per_chunk:
-            raise ValueError(
-                f"{num_ar_conditional_frames=} must be smaller than {num_frames_per_chunk=} for chunked generation."
-            )
-
-        return num_frames_per_chunk
-
     @property
     def guidance_scale(self):
         return self._guidance_scale
@@ -567,22 +536,23 @@ class Cosmos2_5_TransferPipeline(DiffusionPipeline):
     @replace_example_docstring(EXAMPLE_DOC_STRING)
     def __call__(
         self,
-        controls: PipelineImageInput | List[PipelineImageInput],
-        controls_conditioning_scale: Union[float, List[float]] = 1.0,
+        image: PipelineImageInput | None = None,
+        video: List[PipelineImageInput] | None = None,
         prompt: Union[str, List[str]] | None = None,
         negative_prompt: Union[str, List[str]] = DEFAULT_NEGATIVE_PROMPT,
         height: int = 704,
-        width: Optional[int] = None,
-        num_frames: Optional[int] = None,
-        num_frames_per_chunk: int = 93,
+        width: int | None = None,
+        num_frames: int = 93,
         num_inference_steps: int = 36,
         guidance_scale: float = 3.0,
-        num_videos_per_prompt: int = 1,
-        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
-        latents: Optional[torch.Tensor] = None,
-        prompt_embeds: Optional[torch.Tensor] = None,
-        negative_prompt_embeds: Optional[torch.Tensor] = None,
-        output_type: Optional[str] = "pil",
+        num_videos_per_prompt: Optional[int] = 1,
+        generator: torch.Generator | list[torch.Generator] | None = None,
+        latents: torch.Tensor | None = None,
+        controls: Optional[PipelineImageInput | List[PipelineImageInput]] = None,
+        controls_conditioning_scale: float | list[float] = 1.0,
+        prompt_embeds: torch.Tensor | None = None,
+        negative_prompt_embeds: torch.Tensor | None = None,
+        output_type: str = "pil",
         return_dict: bool = True,
         callback_on_step_end: Optional[
             Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]
@@ -590,26 +560,24 @@ class Cosmos2_5_TransferPipeline(DiffusionPipeline):
         callback_on_step_end_tensor_inputs: List[str] = ["latents"],
         max_sequence_length: int = 512,
         conditional_frame_timestep: float = 0.1,
-        num_ar_conditional_frames: Optional[int] = 1,
-        num_ar_latent_conditional_frames: Optional[int] = None,
     ):
         r"""
-        `controls` drive the conditioning through ControlNet. Controls are assumed to be pre-processed, e.g. edge maps
-        are pre-computed.
+        The call function to the pipeline for generation. Supports three modes:
 
-        Setting `num_frames` will restrict the total number of frames output, if not provided or assigned to None
-        (default) then the number of output frames will match the input `controls`.
+        - **Text2World**: `image=None`, `video=None`, `prompt` provided. Generates a world clip.
+        - **Image2World**: `image` provided, `video=None`, `prompt` provided. Conditions on a single frame.
+        - **Video2World**: `video` provided, `image=None`, `prompt` provided. Conditions on an input clip.
 
-        Auto-regressive inference is supported and thus a sliding window of `num_frames_per_chunk` frames are used per
-        denoising loop. In addition, when auto-regressive inference is performed, the previous
-        `num_ar_latent_conditional_frames` or `num_ar_conditional_frames` are used to condition the following denoising
-        inference loops.
+        Set `num_frames=93` (default) to produce a world video, or `num_frames=1` to produce a single image frame (the
+        above in "*2Image mode").
+
+        Outputs follow `output_type` (e.g., `"pil"` returns a list of `num_frames` PIL images per prompt).
 
         Args:
-            controls (`PipelineImageInput`, `List[PipelineImageInput]`):
-                Control image or video input used by the ControlNet.
-            controls_conditioning_scale (`float` or `List[float]`, *optional*, defaults to `1.0`):
-                The scale factor(s) for the ControlNet outputs. A single float is broadcast to all control blocks.
+            image (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, *optional*):
+                Optional single image for Image2World conditioning. Must be `None` when `video` is provided.
+            video (`List[PIL.Image.Image]`, `np.ndarray`, `torch.Tensor`, *optional*):
+                Optional input video for Video2World conditioning. Must be `None` when `image` is provided.
             prompt (`str` or `List[str]`, *optional*):
                 The prompt or prompts to guide generation. Required unless `prompt_embeds` is supplied.
             height (`int`, defaults to `704`):
@@ -617,10 +585,9 @@ class Cosmos2_5_TransferPipeline(DiffusionPipeline):
             width (`int`, *optional*):
                 The width in pixels of the generated image. If not provided, this will be determined based on the
                 aspect ratio of the input and the provided height.
-            num_frames (`int`, *optional*):
-                Number of output frames. Defaults to `None` to output the same number of frames as the input
-                `controls`.
-            num_inference_steps (`int`, defaults to `36`):
+            num_frames (`int`, defaults to `93`):
+                Number of output frames. Use `93` for world (video) generation; set to `1` to return a single frame.
+            num_inference_steps (`int`, defaults to `35`):
                 The number of denoising steps. More denoising steps usually lead to a higher quality image at the
                 expense of slower inference.
             guidance_scale (`float`, defaults to `3.0`):
@@ -634,9 +601,13 @@ class Cosmos2_5_TransferPipeline(DiffusionPipeline):
                 A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
                 generation deterministic.
             latents (`torch.Tensor`, *optional*):
-                Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs. Can be used to
-                tweak the same generation with different prompts. If not provided, a latents tensor is generated by
-                sampling using the supplied random `generator`.
+                Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor is generated by sampling using the supplied random `generator`.
+            controls (`PipelineImageInput`, `List[PipelineImageInput]`, *optional*):
+                Control image or video input used by the ControlNet. If `None`, ControlNet is skipped.
+            controls_conditioning_scale (`float` or `List[float]`, *optional*, defaults to `1.0`):
+                The scale factor(s) for the ControlNet outputs. A single float is broadcast to all control blocks.
             prompt_embeds (`torch.Tensor`, *optional*):
                 Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
                 provided, text embeddings will be generated from `prompt` input argument.
@@ -659,18 +630,7 @@ class Cosmos2_5_TransferPipeline(DiffusionPipeline):
             max_sequence_length (`int`, defaults to `512`):
                 The maximum number of tokens in the prompt. If the prompt exceeds this length, it will be truncated. If
                 the prompt is shorter than this length, it will be padded.
-            num_ar_conditional_frames (`int`, *optional*, defaults to `1`):
-                Number of frames to condition on subsequent inference loops in auto-regressive inference, i.e. for the
-                second chunk and onwards. Only used if `num_ar_latent_conditional_frames` is `None`.
 
-                This is only used when auto-regressive inference is performed, i.e. when the number of frames in
-                controls is > num_frames_per_chunk
-            num_ar_latent_conditional_frames (`int`, *optional*):
-                Number of latent frames to condition on subsequent inference loops in auto-regressive inference, i.e.
-                for the second chunk and onwards. Only used if `num_ar_conditional_frames` is `None`.
-
-                This is only used when auto-regressive inference is performed, i.e. when the number of frames in
-                controls is > num_frames_per_chunk
         Examples:
 
         Returns:
@@ -690,40 +650,21 @@ class Cosmos2_5_TransferPipeline(DiffusionPipeline):
             callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs
 
         if width is None:
-            frame = controls[0] if isinstance(controls, list) else controls
-            if isinstance(frame, list):
-                frame = frame[0]
-            if isinstance(frame, (torch.Tensor, np.ndarray)):
-                if frame.ndim == 5:
-                    frame = frame[0, 0]
-                elif frame.ndim == 4:
-                    frame = frame[0]
+            frame = image or video[0] if image or video else None
+            if frame is None and controls is not None:
+                frame = controls[0] if isinstance(controls, list) else controls
+                if isinstance(frame, (torch.Tensor, np.ndarray)) and len(frame.shape) == 4:
+                    frame = controls[0]
 
-            if isinstance(frame, PIL.Image.Image):
+            if frame is None:
+                width = int((height + 16) * (1280 / 720))
+            elif isinstance(frame, PIL.Image.Image):
                 width = int((height + 16) * (frame.width / frame.height))
             else:
-                if frame.ndim != 3:
-                    raise ValueError("`controls` must contain 3D frames in CHW format.")
                 width = int((height + 16) * (frame.shape[2] / frame.shape[1]))  # NOTE: assuming C H W
 
-        num_frames_per_chunk = self.check_inputs(
-            prompt,
-            height,
-            width,
-            prompt_embeds,
-            callback_on_step_end_tensor_inputs,
-            num_ar_conditional_frames,
-            num_ar_latent_conditional_frames,
-            num_frames_per_chunk,
-            num_frames,
-            conditional_frame_timestep,
-        )
-
-        if num_ar_latent_conditional_frames is not None:
-            num_cond_latent_frames = num_ar_latent_conditional_frames
-            num_ar_conditional_frames = max(0, (num_cond_latent_frames - 1) * self.vae_scale_factor_temporal + 1)
-        else:
-            num_cond_latent_frames = max(0, (num_ar_conditional_frames - 1) // self.vae_scale_factor_temporal + 1)
+        # Check inputs. Raise error if not correct
+        self.check_inputs(prompt, height, width, prompt_embeds, callback_on_step_end_tensor_inputs)
 
         self._guidance_scale = guidance_scale
         self._current_timestep = None
@@ -768,137 +709,102 @@ class Cosmos2_5_TransferPipeline(DiffusionPipeline):
         vae_dtype = self.vae.dtype
         transformer_dtype = self.transformer.dtype
 
-        if getattr(self.transformer.config, "img_context_dim_in", None):
-            img_context = torch.zeros(
-                batch_size,
-                self.transformer.config.img_context_num_tokens,
-                self.transformer.config.img_context_dim_in,
-                device=prompt_embeds.device,
+        img_context = torch.zeros(
+            batch_size,
+            self.transformer.config.img_context_num_tokens,
+            self.transformer.config.img_context_dim_in,
+            device=prompt_embeds.device,
+            dtype=transformer_dtype,
+        )
+        encoder_hidden_states = (prompt_embeds, img_context)
+        neg_encoder_hidden_states = (negative_prompt_embeds, img_context)
+
+        num_frames_in = None
+        if image is not None:
+            if batch_size != 1:
+                raise ValueError(f"batch_size must be 1 for image input (given {batch_size})")
+
+            image = torchvision.transforms.functional.to_tensor(image).unsqueeze(0)
+            video = torch.cat([image, torch.zeros_like(image).repeat(num_frames - 1, 1, 1, 1)], dim=0)
+            video = video.unsqueeze(0)
+            num_frames_in = 1
+        elif video is None:
+            video = torch.zeros(batch_size, num_frames, 3, height, width, dtype=torch.uint8)
+            num_frames_in = 0
+        else:
+            num_frames_in = len(video)
+
+            if batch_size != 1:
+                raise ValueError(f"batch_size must be 1 for video input (given {batch_size})")
+
+        assert video is not None
+        video = self.video_processor.preprocess_video(video, height, width)
+
+        # pad with last frame (for video2world)
+        num_frames_out = num_frames
+        video = _maybe_pad_video(video, num_frames_out)
+        assert num_frames_in <= num_frames_out, f"expected ({num_frames_in=}) <= ({num_frames_out=})"
+
+        video = video.to(device=device, dtype=vae_dtype)
+
+        num_channels_latents = self.transformer.config.in_channels - 1
+        latents, cond_latent, cond_mask, cond_indicator = self.prepare_latents(
+            video=video,
+            batch_size=batch_size * num_videos_per_prompt,
+            num_channels_latents=num_channels_latents,
+            height=height,
+            width=width,
+            num_frames_in=num_frames_in,
+            num_frames_out=num_frames,
+            do_classifier_free_guidance=self.do_classifier_free_guidance,
+            dtype=torch.float32,
+            device=device,
+            generator=generator,
+            latents=latents,
+        )
+        cond_timestep = torch.ones_like(cond_indicator) * conditional_frame_timestep
+        cond_mask = cond_mask.to(transformer_dtype)
+
+        controls_latents = None
+        if controls is not None:
+            controls_latents = self._encode_controls(
+                controls,
+                height=height,
+                width=width,
+                num_frames=num_frames,
                 dtype=transformer_dtype,
+                device=device,
+                generator=generator,
             )
 
-            if num_videos_per_prompt > 1:
-                img_context = img_context.repeat_interleave(num_videos_per_prompt, dim=0)
+        padding_mask = latents.new_zeros(1, 1, height, width, dtype=transformer_dtype)
 
-            encoder_hidden_states = (prompt_embeds, img_context)
-            neg_encoder_hidden_states = (negative_prompt_embeds, img_context)
-        else:
-            encoder_hidden_states = prompt_embeds
-            neg_encoder_hidden_states = negative_prompt_embeds
+        # Denoising loop
+        self.scheduler.set_timesteps(num_inference_steps, device=device)
+        timesteps = self.scheduler.timesteps
+        self._num_timesteps = len(timesteps)
+        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
 
-        control_video = self.video_processor.preprocess_video(controls, height, width)
-        if control_video.shape[0] != batch_size:
-            if control_video.shape[0] == 1:
-                control_video = control_video.repeat(batch_size, 1, 1, 1, 1)
-            else:
-                raise ValueError(
-                    f"Expected controls batch size {batch_size} to match prompt batch size, but got {control_video.shape[0]}."
+        gt_velocity = (latents - cond_latent) * cond_mask
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i, t in enumerate(timesteps):
+                if self.interrupt:
+                    continue
+
+                self._current_timestep = t.cpu().item()
+
+                # NOTE: assumes sigma(t) \in [0, 1]
+                sigma_t = (
+                    torch.tensor(self.scheduler.sigmas[i].item())
+                    .unsqueeze(0)
+                    .to(device=device, dtype=transformer_dtype)
                 )
 
-        num_frames_out = control_video.shape[2]
-        if num_frames is not None:
-            num_frames_out = min(num_frames_out, num_frames)
-
-        control_video = _maybe_pad_or_trim_video(control_video, num_frames_out)
-
-        # chunk information
-        num_latent_frames_per_chunk = (num_frames_per_chunk - 1) // self.vae_scale_factor_temporal + 1
-        chunk_stride = num_frames_per_chunk - num_ar_conditional_frames
-        chunk_idxs = [
-            (start_idx, min(start_idx + num_frames_per_chunk, num_frames_out))
-            for start_idx in range(0, num_frames_out - num_ar_conditional_frames, chunk_stride)
-        ]
-
-        video_chunks = []
-        latents_mean = self.latents_mean.to(dtype=vae_dtype, device=device)
-        latents_std = self.latents_std.to(dtype=vae_dtype, device=device)
-
-        def decode_latents(latents):
-            latents = latents * latents_std + latents_mean
-            video = self.vae.decode(latents.to(dtype=self.vae.dtype, device=device), return_dict=False)[0]
-            return video
-
-        latents_arg = latents
-        initial_num_cond_latent_frames = 0
-        latent_chunks = []
-        num_chunks = len(chunk_idxs)
-        total_steps = num_inference_steps * num_chunks
-        with self.progress_bar(total=total_steps) as progress_bar:
-            for chunk_idx, (start_idx, end_idx) in enumerate(chunk_idxs):
-                if chunk_idx == 0:
-                    prev_output = torch.zeros((batch_size, num_frames_per_chunk, 3, height, width), dtype=vae_dtype)
-                    prev_output = self.video_processor.preprocess_video(prev_output, height, width)
-                else:
-                    prev_output = video_chunks[-1].clone()
-                    if num_ar_conditional_frames > 0:
-                        prev_output[:, :, :num_ar_conditional_frames] = prev_output[:, :, -num_ar_conditional_frames:]
-                        prev_output[:, :, num_ar_conditional_frames:] = -1  # -1 == 0 in processed video space
-                    else:
-                        prev_output.fill_(-1)
-
-                chunk_video = prev_output.to(device=device, dtype=vae_dtype)
-                chunk_video = _maybe_pad_or_trim_video(chunk_video, num_frames_per_chunk)
-                latents, cond_latent, cond_mask, cond_indicator = self.prepare_latents(
-                    video=chunk_video,
-                    batch_size=batch_size * num_videos_per_prompt,
-                    num_channels_latents=self.transformer.config.in_channels - 1,
-                    height=height,
-                    width=width,
-                    num_frames_in=chunk_video.shape[2],
-                    num_frames_out=num_frames_per_chunk,
-                    do_classifier_free_guidance=self.do_classifier_free_guidance,
-                    dtype=torch.float32,
-                    device=device,
-                    generator=generator,
-                    num_cond_latent_frames=initial_num_cond_latent_frames
-                    if chunk_idx == 0
-                    else num_cond_latent_frames,
-                    latents=latents_arg,
-                )
-                cond_mask = cond_mask.to(transformer_dtype)
-                cond_timestep = torch.ones_like(cond_indicator) * conditional_frame_timestep
-                padding_mask = latents.new_zeros(1, 1, height, width, dtype=transformer_dtype)
-
-                chunk_control_video = control_video[:, :, start_idx:end_idx, ...].to(
-                    device=device, dtype=self.vae.dtype
-                )
-                chunk_control_video = _maybe_pad_or_trim_video(chunk_control_video, num_frames_per_chunk)
-                if isinstance(generator, list):
-                    controls_latents = [
-                        retrieve_latents(self.vae.encode(chunk_control_video[i].unsqueeze(0)), generator=generator[i])
-                        for i in range(chunk_control_video.shape[0])
-                    ]
-                else:
-                    controls_latents = [
-                        retrieve_latents(self.vae.encode(vid.unsqueeze(0)), generator=generator)
-                        for vid in chunk_control_video
-                    ]
-                controls_latents = torch.cat(controls_latents, dim=0).to(transformer_dtype)
-
-                controls_latents = (controls_latents - latents_mean) / latents_std
-
-                # Denoising loop
-                self.scheduler.set_timesteps(num_inference_steps, device=device)
-                timesteps = self.scheduler.timesteps
-                self._num_timesteps = len(timesteps)
-
-                gt_velocity = (latents - cond_latent) * cond_mask
-                for i, t in enumerate(timesteps):
-                    if self.interrupt:
-                        continue
-
-                    self._current_timestep = t.cpu().item()
-
-                    # NOTE: assumes sigma(t) \in [0, 1]
-                    sigma_t = (
-                        torch.tensor(self.scheduler.sigmas[i].item())
-                        .unsqueeze(0)
-                        .to(device=device, dtype=transformer_dtype)
-                    )
-
-                    in_latents = cond_mask * cond_latent + (1 - cond_mask) * latents
-                    in_latents = in_latents.to(transformer_dtype)
-                    in_timestep = cond_indicator * cond_timestep + (1 - cond_indicator) * sigma_t
+                in_latents = cond_mask * cond_latent + (1 - cond_mask) * latents
+                in_latents = in_latents.to(transformer_dtype)
+                in_timestep = cond_indicator * cond_timestep + (1 - cond_indicator) * sigma_t
+                control_blocks = None
+                if controls_latents is not None and self.controlnet is not None:
                     control_output = self.controlnet(
                         controls_latents=controls_latents,
                         latents=in_latents,
@@ -911,18 +817,20 @@ class Cosmos2_5_TransferPipeline(DiffusionPipeline):
                     )
                     control_blocks = control_output[0]
 
-                    noise_pred = self.transformer(
-                        hidden_states=in_latents,
-                        timestep=in_timestep,
-                        encoder_hidden_states=encoder_hidden_states,
-                        block_controlnet_hidden_states=control_blocks,
-                        condition_mask=cond_mask,
-                        padding_mask=padding_mask,
-                        return_dict=False,
-                    )[0]
-                    noise_pred = gt_velocity + noise_pred * (1 - cond_mask)
+                noise_pred = self.transformer(
+                    hidden_states=in_latents,
+                    timestep=in_timestep,
+                    encoder_hidden_states=encoder_hidden_states,
+                    block_controlnet_hidden_states=control_blocks,
+                    condition_mask=cond_mask,
+                    padding_mask=padding_mask,
+                    return_dict=False,
+                )[0]
+                noise_pred = gt_velocity + noise_pred * (1 - cond_mask)
 
-                    if self.do_classifier_free_guidance:
+                if self.do_classifier_free_guidance:
+                    control_blocks = None
+                    if controls_latents is not None and self.controlnet is not None:
                         control_output = self.controlnet(
                             controls_latents=controls_latents,
                             latents=in_latents,
@@ -935,50 +843,46 @@ class Cosmos2_5_TransferPipeline(DiffusionPipeline):
                         )
                         control_blocks = control_output[0]
 
-                        noise_pred_neg = self.transformer(
-                            hidden_states=in_latents,
-                            timestep=in_timestep,
-                            encoder_hidden_states=neg_encoder_hidden_states,  # NOTE: negative prompt
-                            block_controlnet_hidden_states=control_blocks,
-                            condition_mask=cond_mask,
-                            padding_mask=padding_mask,
-                            return_dict=False,
-                        )[0]
-                        # NOTE: replace velocity (noise_pred_neg) with gt_velocity for conditioning inputs only
-                        noise_pred_neg = gt_velocity + noise_pred_neg * (1 - cond_mask)
-                        noise_pred = noise_pred + self.guidance_scale * (noise_pred - noise_pred_neg)
+                    noise_pred_neg = self.transformer(
+                        hidden_states=in_latents,
+                        timestep=in_timestep,
+                        encoder_hidden_states=neg_encoder_hidden_states,  # NOTE: negative prompt
+                        block_controlnet_hidden_states=control_blocks,
+                        condition_mask=cond_mask,
+                        padding_mask=padding_mask,
+                        return_dict=False,
+                    )[0]
+                    # NOTE: replace velocity (noise_pred_neg) with gt_velocity for conditioning inputs only
+                    noise_pred_neg = gt_velocity + noise_pred_neg * (1 - cond_mask)
+                    noise_pred = noise_pred + self.guidance_scale * (noise_pred - noise_pred_neg)
 
-                    latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
+                latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
 
-                    # call the callback, if provided
-                    if callback_on_step_end is not None:
-                        callback_kwargs = {}
-                        for k in callback_on_step_end_tensor_inputs:
-                            callback_kwargs[k] = locals()[k]
-                        callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
+                if callback_on_step_end is not None:
+                    callback_kwargs = {}
+                    for k in callback_on_step_end_tensor_inputs:
+                        callback_kwargs[k] = locals()[k]
+                    callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
 
-                        latents = callback_outputs.pop("latents", latents)
-                        prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
-                        negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
+                    latents = callback_outputs.pop("latents", latents)
+                    prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
+                    negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
 
-                    if i == total_steps - 1 or ((i + 1) % self.scheduler.order == 0):
-                        progress_bar.update()
+                # call the callback, if provided
+                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+                    progress_bar.update()
 
-                    if XLA_AVAILABLE:
-                        xm.mark_step()
-
-                video_chunks.append(decode_latents(latents).detach().cpu())
-                latent_chunks.append(latents.detach().cpu())
+                if XLA_AVAILABLE:
+                    xm.mark_step()
 
         self._current_timestep = None
 
         if not output_type == "latent":
-            video_chunks = [
-                chunk[:, :, num_ar_conditional_frames:, ...] if chunk_idx != 0 else chunk
-                for chunk_idx, chunk in enumerate(video_chunks)
-            ]
-            video = torch.cat(video_chunks, dim=2)
-            video = video[:, :, :num_frames_out, ...]
+            latents_mean = self.latents_mean.to(latents.device, latents.dtype)
+            latents_std = self.latents_std.to(latents.device, latents.dtype)
+            latents = latents * latents_std + latents_mean
+            video = self.vae.decode(latents.to(self.vae.dtype), return_dict=False)[0]
+            video = self._match_num_frames(video, num_frames)
 
             assert self.safety_checker is not None
             self.safety_checker.to(device)
@@ -995,13 +899,7 @@ class Cosmos2_5_TransferPipeline(DiffusionPipeline):
             video = torch.from_numpy(video).permute(0, 4, 1, 2, 3)
             video = self.video_processor.postprocess_video(video, output_type=output_type)
         else:
-            latent_T = (num_frames_out - 1) // self.vae_scale_factor_temporal + 1
-            latent_chunks = [
-                chunk[:, :, num_cond_latent_frames:, ...] if chunk_idx != 0 else chunk
-                for chunk_idx, chunk in enumerate(latent_chunks)
-            ]
-            video = torch.cat(latent_chunks, dim=2)
-            video = video[:, :, :latent_T, ...]
+            video = latents
 
         # Offload all models
         self.maybe_free_model_hooks()
@@ -1010,3 +908,19 @@ class Cosmos2_5_TransferPipeline(DiffusionPipeline):
             return (video,)
 
         return CosmosPipelineOutput(frames=video)
+
+    def _match_num_frames(self, video: torch.Tensor, target_num_frames: int) -> torch.Tensor:
+        if target_num_frames <= 0 or video.shape[2] == target_num_frames:
+            return video
+
+        frames_per_latent = max(self.vae_scale_factor_temporal, 1)
+        video = torch.repeat_interleave(video, repeats=frames_per_latent, dim=2)
+
+        current_frames = video.shape[2]
+        if current_frames < target_num_frames:
+            pad = video[:, :, -1:, :, :].repeat(1, 1, target_num_frames - current_frames, 1, 1)
+            video = torch.cat([video, pad], dim=2)
+        elif current_frames > target_num_frames:
+            video = video[:, :, :target_num_frames]
+
+        return video

src/diffusers/pipelines/ltx2/pipeline_ltx2_image2video.py

@@ -699,13 +699,9 @@ class LTX2ImageToVideoPipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraL
         mask_shape = (batch_size, 1, num_frames, height, width)
 
         if latents is not None:
+            conditioning_mask = latents.new_zeros(mask_shape)
+            conditioning_mask[:, :, 0] = 1.0
             if latents.ndim == 5:
-                # conditioning_mask needs to the same shape as latents in two stages generation.
-                batch_size, _, num_frames, height, width = latents.shape
-                mask_shape = (batch_size, 1, num_frames, height, width)
-                conditioning_mask = latents.new_zeros(mask_shape)
-                conditioning_mask[:, :, 0] = 1.0
-
                 latents = self._normalize_latents(
                     latents, self.vae.latents_mean, self.vae.latents_std, self.vae.config.scaling_factor
                 )
@@ -714,9 +710,6 @@ class LTX2ImageToVideoPipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraL
                 latents = self._pack_latents(
                     latents, self.transformer_spatial_patch_size, self.transformer_temporal_patch_size
                 )
-            else:
-                conditioning_mask = latents.new_zeros(mask_shape)
-                conditioning_mask[:, :, 0] = 1.0
             conditioning_mask = self._pack_latents(
                 conditioning_mask, self.transformer_spatial_patch_size, self.transformer_temporal_patch_size
             ).squeeze(-1)

src/diffusers/pipelines/z_image/pipeline_z_image.py

@@ -276,7 +276,7 @@ class ZImagePipeline(DiffusionPipeline, ZImageLoraLoaderMixin, FromSingleFileMix
 
     @property
     def do_classifier_free_guidance(self):
-        return self._guidance_scale > 0
+        return self._guidance_scale > 1
 
     @property
     def joint_attention_kwargs(self):

tests/models/controlnets/test_models_controlnet_cosmos.py

@@ -131,26 +131,6 @@ class CosmosControlNetModelTests(ModelTesterMixin, unittest.TestCase):
         self.assertIsInstance(output[0], list)
         self.assertEqual(len(output[0]), init_dict["n_controlnet_blocks"])
 
-    def test_condition_mask_changes_output(self):
-        """Test that condition mask affects control outputs."""
-        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
-        model = self.model_class(**init_dict)
-        model.to(torch_device)
-        model.eval()
-
-        inputs_no_mask = dict(inputs_dict)
-        inputs_no_mask["condition_mask"] = torch.zeros_like(inputs_dict["condition_mask"])
-
-        with torch.no_grad():
-            output_no_mask = model(**inputs_no_mask)
-            output_with_mask = model(**inputs_dict)
-
-        self.assertEqual(len(output_no_mask.control_block_samples), len(output_with_mask.control_block_samples))
-        for no_mask_tensor, with_mask_tensor in zip(
-            output_no_mask.control_block_samples, output_with_mask.control_block_samples
-        ):
-            self.assertFalse(torch.allclose(no_mask_tensor, with_mask_tensor))
-
     def test_conditioning_scale_single(self):
         """Test that a single conditioning scale is broadcast to all blocks."""
         init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()

tests/models/test_models_auto.py

@@ -1,9 +1,14 @@
+import json
+import os
+import tempfile
 import unittest
 from unittest.mock import MagicMock, patch
 
 from transformers import CLIPTextModel, LongformerModel
 
+from diffusers import ConfigMixin
 from diffusers.models import AutoModel, UNet2DConditionModel
+from diffusers.models.modeling_utils import ModelMixin
 
 
 class TestAutoModel(unittest.TestCase):
@@ -100,3 +105,51 @@ class TestAutoModelFromConfig(unittest.TestCase):
     def test_from_config_raises_on_none(self):
         with self.assertRaises(ValueError, msg="Please provide a `pretrained_model_name_or_path_or_dict`"):
             AutoModel.from_config(None)
+
+
+class TestRegisterForAutoClass(unittest.TestCase):
+    def test_register_for_auto_class_sets_attribute(self):
+        class DummyModel(ModelMixin, ConfigMixin):
+            config_name = "config.json"
+
+        DummyModel.register_for_auto_class("AutoModel")
+        self.assertEqual(DummyModel._auto_class, "AutoModel")
+
+    def test_register_for_auto_class_rejects_unsupported(self):
+        class DummyModel(ModelMixin, ConfigMixin):
+            config_name = "config.json"
+
+        with self.assertRaises(ValueError, msg="Only 'AutoModel' is supported"):
+            DummyModel.register_for_auto_class("AutoPipeline")
+
+    def test_auto_map_in_saved_config(self):
+        class DummyModel(ModelMixin, ConfigMixin):
+            config_name = "config.json"
+
+        DummyModel.register_for_auto_class("AutoModel")
+        model = DummyModel()
+
+        with tempfile.TemporaryDirectory() as tmpdir:
+            model.save_config(tmpdir)
+            config_path = os.path.join(tmpdir, "config.json")
+            with open(config_path, "r") as f:
+                config = json.load(f)
+
+        self.assertIn("auto_map", config)
+        self.assertIn("AutoModel", config["auto_map"])
+        module_name = DummyModel.__module__.split(".")[-1]
+        self.assertEqual(config["auto_map"]["AutoModel"], f"{module_name}.DummyModel")
+
+    def test_no_auto_map_without_register(self):
+        class DummyModel(ModelMixin, ConfigMixin):
+            config_name = "config.json"
+
+        model = DummyModel()
+
+        with tempfile.TemporaryDirectory() as tmpdir:
+            model.save_config(tmpdir)
+            config_path = os.path.join(tmpdir, "config.json")
+            with open(config_path, "r") as f:
+                config = json.load(f)
+
+        self.assertNotIn("auto_map", config)

tests/modular_pipelines/test_modular_pipelines_common.py

@@ -1,6 +1,4 @@
 import gc
-import json
-import os
 import tempfile
 from typing import Callable
 
@@ -351,33 +349,6 @@ class ModularPipelineTesterMixin:
 
         assert torch.abs(image_slices[0] - image_slices[1]).max() < 1e-3
 
-    def test_modular_index_consistency(self):
-        pipe = self.get_pipeline()
-        components_spec = pipe._component_specs
-        components = sorted(components_spec.keys())
-
-        with tempfile.TemporaryDirectory() as tmpdir:
-            pipe.save_pretrained(tmpdir)
-            index_file = os.path.join(tmpdir, "modular_model_index.json")
-            assert os.path.exists(index_file)
-
-            with open(index_file) as f:
-                index_contents = json.load(f)
-
-            compulsory_keys = {"_blocks_class_name", "_class_name", "_diffusers_version"}
-            for k in compulsory_keys:
-                assert k in index_contents
-
-            to_check_attrs = {"pretrained_model_name_or_path", "revision", "subfolder"}
-            for component in components:
-                spec = components_spec[component]
-                for attr in to_check_attrs:
-                    if getattr(spec, "pretrained_model_name_or_path", None) is not None:
-                        for attr in to_check_attrs:
-                            assert component in index_contents, f"{component} should be present in index but isn't."
-                            attr_value_from_index = index_contents[component][2][attr]
-                            assert getattr(spec, attr) == attr_value_from_index
-
     def test_workflow_map(self):
         blocks = self.pipeline_blocks_class()
         if blocks._workflow_map is None:
@@ -728,27 +699,3 @@ class TestLoadComponentsSkipBehavior:
 
         # Verify test_component was not loaded
         assert not hasattr(pipe, "test_component") or pipe.test_component is None
-
-
-class TestModularPipelineInitFallback:
-    """Test that ModularPipeline.__init__ falls back to default_blocks_name when
-    _blocks_class_name is a base class (e.g. SequentialPipelineBlocks saved by from_blocks_dict)."""
-
-    def test_init_fallback_when_blocks_class_name_is_base_class(self, tmp_path):
-        # 1. Load pipeline and get a workflow (returns a base SequentialPipelineBlocks)
-        pipe = ModularPipeline.from_pretrained("hf-internal-testing/tiny-stable-diffusion-xl-pipe")
-        t2i_blocks = pipe.blocks.get_workflow("text2image")
-        assert t2i_blocks.__class__.__name__ == "SequentialPipelineBlocks"
-
-        # 2. Use init_pipeline to create a new pipeline from the workflow blocks
-        t2i_pipe = t2i_blocks.init_pipeline("hf-internal-testing/tiny-stable-diffusion-xl-pipe")
-
-        # 3. Save and reload — the saved config will have _blocks_class_name="SequentialPipelineBlocks"
-        save_dir = str(tmp_path / "pipeline")
-        t2i_pipe.save_pretrained(save_dir)
-        loaded_pipe = ModularPipeline.from_pretrained(save_dir)
-
-        # 4. Verify it fell back to default_blocks_name and has correct blocks
-        assert loaded_pipe.__class__.__name__ == pipe.__class__.__name__
-        assert loaded_pipe._blocks.__class__.__name__ == pipe._blocks.__class__.__name__
-        assert len(loaded_pipe._blocks.sub_blocks) == len(pipe._blocks.sub_blocks)

tests/pipelines/cosmos/test_cosmos2_5_transfer.py

@@ -55,7 +55,7 @@ class Cosmos2_5_TransferWrapper(Cosmos2_5_TransferPipeline):
 class Cosmos2_5_TransferPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
     pipeline_class = Cosmos2_5_TransferWrapper
     params = TEXT_TO_IMAGE_PARAMS - {"cross_attention_kwargs"}
-    batch_params = TEXT_TO_IMAGE_BATCH_PARAMS.union({"controls"})
+    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(
@@ -176,19 +176,15 @@ class Cosmos2_5_TransferPipelineFastTests(PipelineTesterMixin, unittest.TestCase
         else:
             generator = torch.Generator(device=device).manual_seed(seed)
 
-        controls_generator = torch.Generator(device="cpu").manual_seed(seed)
-
         inputs = {
             "prompt": "dance monkey",
             "negative_prompt": "bad quality",
-            "controls": [torch.randn(3, 32, 32, generator=controls_generator) for _ in range(5)],
             "generator": generator,
             "num_inference_steps": 2,
             "guidance_scale": 3.0,
             "height": 32,
             "width": 32,
             "num_frames": 3,
-            "num_frames_per_chunk": 16,
             "max_sequence_length": 16,
             "output_type": "pt",
         }
@@ -216,56 +212,6 @@ class Cosmos2_5_TransferPipelineFastTests(PipelineTesterMixin, unittest.TestCase
         self.assertEqual(generated_video.shape, (3, 3, 32, 32))
         self.assertTrue(torch.isfinite(generated_video).all())
 
-    def test_inference_autoregressive_multi_chunk(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)
-        inputs["num_frames"] = 5
-        inputs["num_frames_per_chunk"] = 3
-        inputs["num_ar_conditional_frames"] = 1
-
-        video = pipe(**inputs).frames
-        generated_video = video[0]
-        self.assertEqual(generated_video.shape, (5, 3, 32, 32))
-        self.assertTrue(torch.isfinite(generated_video).all())
-
-    def test_inference_autoregressive_multi_chunk_no_condition_frames(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)
-        inputs["num_frames"] = 5
-        inputs["num_frames_per_chunk"] = 3
-        inputs["num_ar_conditional_frames"] = 0
-
-        video = pipe(**inputs).frames
-        generated_video = video[0]
-        self.assertEqual(generated_video.shape, (5, 3, 32, 32))
-        self.assertTrue(torch.isfinite(generated_video).all())
-
-    def test_num_frames_per_chunk_above_rope_raises(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)
-        inputs["num_frames_per_chunk"] = 17
-
-        with self.assertRaisesRegex(ValueError, "too large for RoPE setting"):
-            pipe(**inputs)
-
     def test_inference_with_controls(self):
         """Test inference with control inputs (ControlNet)."""
         device = "cpu"
@@ -276,13 +222,13 @@ class Cosmos2_5_TransferPipelineFastTests(PipelineTesterMixin, unittest.TestCase
         pipe.set_progress_bar_config(disable=None)
 
         inputs = self.get_dummy_inputs(device)
-        inputs["controls"] = [torch.randn(3, 32, 32) for _ in range(5)]  # list of 5 frames (C, H, W)
+        # Add control video input - should be a video tensor
+        inputs["controls"] = [torch.randn(3, 3, 32, 32)]  # num_frames, channels, height, width
         inputs["controls_conditioning_scale"] = 1.0
-        inputs["num_frames"] = None
 
         video = pipe(**inputs).frames
         generated_video = video[0]
-        self.assertEqual(generated_video.shape, (5, 3, 32, 32))
+        self.assertEqual(generated_video.shape, (3, 3, 32, 32))
         self.assertTrue(torch.isfinite(generated_video).all())
 
     def test_callback_inputs(self):

tests/pipelines/ltx2/test_ltx2_image2video.py

@@ -24,8 +24,7 @@ from diffusers import (
     LTX2ImageToVideoPipeline,
     LTX2VideoTransformer3DModel,
 )
-from diffusers.pipelines.ltx2 import LTX2LatentUpsamplePipeline, LTX2TextConnectors
-from diffusers.pipelines.ltx2.latent_upsampler import LTX2LatentUpsamplerModel
+from diffusers.pipelines.ltx2 import LTX2TextConnectors
 from diffusers.pipelines.ltx2.vocoder import LTX2Vocoder
 
 from ...testing_utils import enable_full_determinism
@@ -175,15 +174,6 @@ class LTX2ImageToVideoPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
 
         return components
 
-    def get_dummy_upsample_component(self, in_channels=4, mid_channels=32, num_blocks_per_stage=1):
-        upsampler = LTX2LatentUpsamplerModel(
-            in_channels=in_channels,
-            mid_channels=mid_channels,
-            num_blocks_per_stage=num_blocks_per_stage,
-        )
-
-        return upsampler
-
     def get_dummy_inputs(self, device, seed=0):
         if str(device).startswith("mps"):
             generator = torch.manual_seed(seed)
@@ -297,60 +287,5 @@ class LTX2ImageToVideoPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
         assert torch.allclose(expected_video_slice, generated_video_slice, atol=1e-4, rtol=1e-4)
         assert torch.allclose(expected_audio_slice, generated_audio_slice, atol=1e-4, rtol=1e-4)
 
-    def test_two_stages_inference_with_upsampler(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)
-        inputs["output_type"] = "latent"
-        first_stage_output = pipe(**inputs)
-        video_latent = first_stage_output.frames
-        audio_latent = first_stage_output.audio
-
-        self.assertEqual(video_latent.shape, (1, 4, 3, 16, 16))
-        self.assertEqual(audio_latent.shape, (1, 2, 5, 2))
-        self.assertEqual(audio_latent.shape[1], components["vocoder"].config.out_channels)
-
-        upsampler = self.get_dummy_upsample_component(in_channels=video_latent.shape[1])
-        upsample_pipe = LTX2LatentUpsamplePipeline(vae=pipe.vae, latent_upsampler=upsampler)
-        upscaled_video_latent = upsample_pipe(latents=video_latent, output_type="latent", return_dict=False)[0]
-        self.assertEqual(upscaled_video_latent.shape, (1, 4, 3, 32, 32))
-
-        inputs["latents"] = upscaled_video_latent
-        inputs["audio_latents"] = audio_latent
-        inputs["output_type"] = "pt"
-        second_stage_output = pipe(**inputs)
-        video = second_stage_output.frames
-        audio = second_stage_output.audio
-
-        self.assertEqual(video.shape, (1, 5, 3, 64, 64))
-        self.assertEqual(audio.shape[0], 1)
-        self.assertEqual(audio.shape[1], components["vocoder"].config.out_channels)
-
-        # fmt: off
-        expected_video_slice = torch.tensor(
-            [
-                0.4497, 0.6757, 0.4219, 0.7686, 0.4525, 0.6483, 0.3969, 0.7404, 0.3541, 0.3039, 0.4592, 0.3521, 0.3665, 0.2785, 0.3336, 0.3079
-            ]
-        )
-        expected_audio_slice = torch.tensor(
-            [
-                0.0271, 0.0492, 0.1249, 0.1126, 0.1661, 0.1060, 0.1717, 0.0944, 0.0672, -0.0069, 0.0688, 0.0097, 0.0808, 0.1231, 0.0986, 0.0739
-            ]
-        )
-        # fmt: on
-
-        video = video.flatten()
-        audio = audio.flatten()
-        generated_video_slice = torch.cat([video[:8], video[-8:]])
-        generated_audio_slice = torch.cat([audio[:8], audio[-8:]])
-
-        assert torch.allclose(expected_video_slice, generated_video_slice, atol=1e-4, rtol=1e-4)
-        assert torch.allclose(expected_audio_slice, generated_audio_slice, atol=1e-4, rtol=1e-4)
-
     def test_inference_batch_single_identical(self):
         self._test_inference_batch_single_identical(batch_size=2, expected_max_diff=2e-2)