update

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

@@ -106,6 +106,8 @@ video, audio = pipe(
     output_type="np",
     return_dict=False,
 )
+video = (video * 255).round().astype("uint8")
+video = torch.from_numpy(video)
 
 encode_video(
     video[0],
@@ -183,6 +185,8 @@ video, audio = pipe(
     output_type="np",
     return_dict=False,
 )
+video = (video * 255).round().astype("uint8")
+video = torch.from_numpy(video)
 
 encode_video(
     video[0],

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

@@ -29,8 +29,31 @@ text_encoder = AutoModel.from_pretrained(
 )
 ```
 
+## Custom models
+
 [`AutoModel`] also loads models from the [Hub](https://huggingface.co/models) that aren't included in Diffusers. Set `trust_remote_code=True` in [`AutoModel.from_pretrained`] to load custom models.
 
+A custom model repository needs a Python module with the model class, and a `config.json` with an `auto_map` entry that maps `"AutoModel"` to `"module_file.ClassName"`.
+
+```
+custom/custom-transformer-model/
+├── config.json
+├── my_model.py
+└── diffusion_pytorch_model.safetensors
+```
+
+The `config.json` includes the `auto_map` field pointing to the custom class.
+
+```json
+{
+  "auto_map": {
+    "AutoModel": "my_model.MyCustomModel"
+  }
+}
+```
+
+Then load it with `trust_remote_code=True`.
+
 ```py
 import torch
 from diffusers import AutoModel
@@ -40,7 +63,39 @@ transformer = AutoModel.from_pretrained(
 )
 ```
 
+For a real-world example, [Overworld/Waypoint-1-Small](https://huggingface.co/Overworld/Waypoint-1-Small/tree/main/transformer) hosts a custom `WorldModel` class across several modules in its `transformer` subfolder.
+
+```
+transformer/
+├── config.json          # auto_map: "model.WorldModel"
+├── model.py
+├── attn.py
+├── nn.py
+├── cache.py
+├── quantize.py
+├── __init__.py
+└── diffusion_pytorch_model.safetensors
+```
+
+```py
+import torch
+from diffusers import AutoModel
+
+transformer = AutoModel.from_pretrained(
+    "Overworld/Waypoint-1-Small", subfolder="transformer", trust_remote_code=True, torch_dtype=torch.bfloat16, device_map="cuda"
+)
+```
+
 If the custom model inherits from the [`ModelMixin`] class, it gets access to the same features as Diffusers model classes, like [regional compilation](../optimization/fp16#regional-compilation) and [group offloading](../optimization/memory#group-offloading).
 
+> [!WARNING]
+> As a precaution with `trust_remote_code=True`, pass a commit hash to the `revision` argument in [`AutoModel.from_pretrained`] to make sure the code hasn't been updated with new malicious code (unless you fully trust the model owners).
+>
+> ```py
+> transformer = AutoModel.from_pretrained(
+>     "Overworld/Waypoint-1-Small", subfolder="transformer", trust_remote_code=True, revision="a3d8cb2"
+> )
+> ```
+
 > [!NOTE]
 > Learn more about implementing custom models in the [Community components](../using-diffusers/custom_pipeline_overview#community-components) guide.

examples/dreambooth/README_z_image.md

@@ -1,347 +0,0 @@
-# DreamBooth training example for Z-Image
-
-[DreamBooth](https://huggingface.co/papers/2208.12242) is a method to personalize image generation models given just a few (3~5) images of a subject/concept.
-[LoRA](https://huggingface.co/docs/peft/conceptual_guides/adapter#low-rank-adaptation-lora) is a popular parameter-efficient fine-tuning technique that allows you to achieve full-finetuning like performance but with a fraction of learnable parameters.
-
-The `train_dreambooth_lora_z_image.py` script shows how to implement the training procedure for [LoRAs](https://huggingface.co/blog/lora) and adapt it for [Z-Image](https://huggingface.co/Tongyi-MAI/Z-Image).
-
-> [!NOTE]
-> **About Z-Image**
->
-> Z-Image is a high-quality text-to-image generation model from Alibaba's Tongyi Lab. It uses a DiT (Diffusion Transformer) architecture with Qwen3 as the text encoder. The model excels at generating images with accurate text rendering, especially for Chinese characters.
-
-> [!NOTE]
-> **Memory consumption**
->
-> Z-Image is relatively memory efficient compared to other large-scale diffusion models. Below we provide some tips and tricks to further reduce memory consumption during training.
-
-## Running locally with PyTorch
-
-### Installing the dependencies
-
-Before running the scripts, make sure to install the library's training dependencies:
-
-**Important**
-
-To make sure you can successfully run the latest versions of the example scripts, we highly recommend **installing from source** and keeping the install up to date as we update the example scripts frequently and install some example-specific requirements. To do this, execute the following steps in a new virtual environment:
-
-```bash
-git clone https://github.com/huggingface/diffusers
-cd diffusers
-pip install -e .
-```
-
-Then cd in the `examples/dreambooth` folder and run
-```bash
-pip install -r requirements_z_image.txt
-```
-
-And initialize an [🤗Accelerate](https://github.com/huggingface/accelerate/) environment with:
-
-```bash
-accelerate config
-```
-
-Or for a default accelerate configuration without answering questions about your environment
-
-```bash
-accelerate config default
-```
-
-Or if your environment doesn't support an interactive shell (e.g., a notebook)
-
-```python
-from accelerate.utils import write_basic_config
-write_basic_config()
-```
-
-When running `accelerate config`, if we specify torch compile mode to True there can be dramatic speedups.
-Note also that we use PEFT library as backend for LoRA training, make sure to have `peft>=0.6.0` installed in your environment.
-
-
-### Dog toy example
-
-Now let's get our dataset. For this example we will use some dog images: https://huggingface.co/datasets/diffusers/dog-example.
-
-Let's first download it locally:
-
-```python
-from huggingface_hub import snapshot_download
-
-local_dir = "./dog"
-snapshot_download(
-    "diffusers/dog-example",
-    local_dir=local_dir, repo_type="dataset",
-    ignore_patterns=".gitattributes",
-)
-```
-
-This will also allow us to push the trained LoRA parameters to the Hugging Face Hub platform.
-
-## Memory Optimizations
-
-> [!NOTE] 
-> Many of these techniques complement each other and can be used together to further reduce memory consumption. However some techniques may be mutually exclusive so be sure to check before launching a training run.
-
-### CPU Offloading 
-To offload parts of the model to CPU memory, you can use `--offload` flag. This will offload the VAE and text encoder to CPU memory and only move them to GPU when needed.
-
-### Latent Caching 
-Pre-encode the training images with the VAE, and then delete it to free up some memory. To enable `latent_caching` simply pass `--cache_latents`.
-
-### QLoRA: Low Precision Training with Quantization
-Perform low precision training using 8-bit or 4-bit quantization to reduce memory usage. You can use the following flags:
-
-- **FP8 training** with `torchao`: 
-Enable FP8 training by passing `--do_fp8_training`. 
-> [!IMPORTANT] 
-> Since we are utilizing FP8 tensor cores we need CUDA GPUs with compute capability at least 8.9 or greater. If you're looking for memory-efficient training on relatively older cards, we encourage you to check out other trainers.
-
-- **NF4 training** with `bitsandbytes`: 
-Alternatively, you can use 8-bit or 4-bit quantization with `bitsandbytes` by passing `--bnb_quantization_config_path` to enable 4-bit NF4 quantization.
-
-### Gradient Checkpointing and Accumulation
-* `--gradient_accumulation` refers to the number of updates steps to accumulate before performing a backward/update pass. By passing a value > 1 you can reduce the amount of backward/update passes and hence also memory requirements.
-* With `--gradient_checkpointing` we can save memory by not storing all intermediate activations during the forward pass. Instead, only a subset of these activations (the checkpoints) are stored and the rest is recomputed as needed during the backward pass. Note that this comes at the expense of a slower backward pass.
-
-### 8-bit-Adam Optimizer
-When training with `AdamW` (doesn't apply to `prodigy`) you can pass `--use_8bit_adam` to reduce the memory requirements of training. Make sure to install `bitsandbytes` if you want to do so.
-
-### Image Resolution
-An easy way to mitigate some of the memory requirements is through `--resolution`. `--resolution` refers to the resolution for input images, all the images in the train/validation dataset are resized to this.
-Note that by default, images are resized to resolution of 1024, but it's good to keep in mind in case you're training on higher resolutions.
-
-### Precision of saved LoRA layers
-By default, trained transformer layers are saved in the precision dtype in which training was performed. E.g. when training in mixed precision is enabled with `--mixed_precision="bf16"`, final finetuned layers will be saved in `torch.bfloat16` as well. 
-This reduces memory requirements significantly without a significant quality loss. Note that if you do wish to save the final layers in float32 at the expense of more memory usage, you can do so by passing `--upcast_before_saving`.
-
-## Training Examples
-
-### Z-Image Training
-
-To perform DreamBooth with LoRA on Z-Image, run:
-
-```bash
-export MODEL_NAME="Tongyi-MAI/Z-Image"
-export INSTANCE_DIR="dog"
-export OUTPUT_DIR="trained-z-image-lora"
-
-accelerate launch train_dreambooth_lora_z_image.py \
-  --pretrained_model_name_or_path=$MODEL_NAME  \
-  --instance_data_dir=$INSTANCE_DIR \
-  --output_dir=$OUTPUT_DIR \
-  --mixed_precision="bf16" \
-  --gradient_checkpointing \
-  --cache_latents \
-  --instance_prompt="a photo of sks dog" \
-  --resolution=1024 \
-  --train_batch_size=1 \
-  --guidance_scale=5.0 \
-  --use_8bit_adam \
-  --gradient_accumulation_steps=4 \
-  --optimizer="adamW" \
-  --learning_rate=1e-4 \
-  --report_to="wandb" \
-  --lr_scheduler="constant" \
-  --lr_warmup_steps=100 \
-  --max_train_steps=500 \
-  --validation_prompt="A photo of sks dog in a bucket" \
-  --validation_epochs=25 \
-  --seed="0" \
-  --push_to_hub
-```
-
-To better track our training experiments, we're using the following flags in the command above:
-
-* `report_to="wandb"` will ensure the training runs are tracked on [Weights and Biases](https://wandb.ai/site). To use it, be sure to install `wandb` with `pip install wandb`. Don't forget to call `wandb login <your_api_key>` before training if you haven't done it before.
-* `validation_prompt` and `validation_epochs` to allow the script to do a few validation inference runs. This allows us to qualitatively check if the training is progressing as expected.
-
-> [!NOTE]
-> If you want to train using long prompts, you can use `--max_sequence_length` to set the token limit. The default is 512. Note that this will use more resources and may slow down the training in some cases.
-
-### Training with FP8 Quantization
-
-For reduced memory usage with FP8 training:
-
-```bash
-export MODEL_NAME="Tongyi-MAI/Z-Image"
-export INSTANCE_DIR="dog"
-export OUTPUT_DIR="trained-z-image-lora-fp8"
-
-accelerate launch train_dreambooth_lora_z_image.py \
-  --pretrained_model_name_or_path=$MODEL_NAME  \
-  --instance_data_dir=$INSTANCE_DIR \
-  --output_dir=$OUTPUT_DIR \
-  --do_fp8_training \
-  --gradient_checkpointing \
-  --cache_latents \
-  --instance_prompt="a photo of sks dog" \
-  --resolution=1024 \
-  --train_batch_size=1 \
-  --guidance_scale=5.0 \
-  --use_8bit_adam \
-  --gradient_accumulation_steps=4 \
-  --optimizer="adamW" \
-  --learning_rate=1e-4 \
-  --report_to="wandb" \
-  --lr_scheduler="constant" \
-  --lr_warmup_steps=100 \
-  --max_train_steps=500 \
-  --validation_prompt="A photo of sks dog in a bucket" \
-  --validation_epochs=25 \
-  --seed="0" \
-  --push_to_hub
-```
-
-### FSDP on the transformer
-
-By setting the accelerate configuration with FSDP, the transformer block will be wrapped automatically. E.g. set the configuration to:
-
-```yaml
-distributed_type: FSDP
-fsdp_config:
-  fsdp_version: 2
-  fsdp_offload_params: false
-  fsdp_sharding_strategy: HYBRID_SHARD
-  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
-  fsdp_transformer_layer_cls_to_wrap: ZImageTransformerBlock
-  fsdp_forward_prefetch: true
-  fsdp_sync_module_states: false
-  fsdp_state_dict_type: FULL_STATE_DICT
-  fsdp_use_orig_params: false
-  fsdp_activation_checkpointing: true
-  fsdp_reshard_after_forward: true
-  fsdp_cpu_ram_efficient_loading: false
-```
-
-### Prodigy Optimizer
-
-Prodigy is an adaptive optimizer that dynamically adjusts the learning rate learned parameters based on past gradients, allowing for more efficient convergence. 
-By using prodigy we can "eliminate" the need for manual learning rate tuning. Read more [here](https://huggingface.co/blog/sdxl_lora_advanced_script#adaptive-optimizers).
-
-To use prodigy, first make sure to install the prodigyopt library: `pip install prodigyopt`, and then specify:
-```bash
---optimizer="prodigy"
-```
-
-> [!TIP]
-> When using prodigy it's generally good practice to set `--learning_rate=1.0`
-
-```bash
-export MODEL_NAME="Tongyi-MAI/Z-Image"
-export INSTANCE_DIR="dog"
-export OUTPUT_DIR="trained-z-image-lora-prodigy"
-
-accelerate launch train_dreambooth_lora_z_image.py \
-  --pretrained_model_name_or_path=$MODEL_NAME  \
-  --instance_data_dir=$INSTANCE_DIR \
-  --output_dir=$OUTPUT_DIR \
-  --mixed_precision="bf16" \
-  --gradient_checkpointing \
-  --cache_latents \
-  --instance_prompt="a photo of sks dog" \
-  --resolution=1024 \
-  --train_batch_size=1 \
-  --guidance_scale=5.0 \
-  --gradient_accumulation_steps=4 \
-  --optimizer="prodigy" \
-  --learning_rate=1.0 \
-  --report_to="wandb" \
-  --lr_scheduler="constant_with_warmup" \
-  --lr_warmup_steps=100 \
-  --max_train_steps=500 \
-  --validation_prompt="A photo of sks dog in a bucket" \
-  --validation_epochs=25 \
-  --seed="0" \
-  --push_to_hub
-```
-
-### LoRA Rank and Alpha
-
-Two key LoRA hyperparameters are LoRA rank and LoRA alpha:
-
-- `--rank`: Defines the dimension of the trainable LoRA matrices. A higher rank means more expressiveness and capacity to learn (and more parameters).
-- `--lora_alpha`: A scaling factor for the LoRA's output. The LoRA update is scaled by `lora_alpha / lora_rank`.
-
-**lora_alpha vs. rank:**
-
-This ratio dictates the LoRA's effective strength:
-- `lora_alpha == rank`: Scaling factor is 1. The LoRA is applied with its learned strength. (e.g., alpha=16, rank=16)
-- `lora_alpha < rank`: Scaling factor < 1. Reduces the LoRA's impact. Useful for subtle changes or to prevent overpowering the base model. (e.g., alpha=8, rank=16)
-- `lora_alpha > rank`: Scaling factor > 1. Amplifies the LoRA's impact. Allows a lower rank LoRA to have a stronger effect. (e.g., alpha=32, rank=16)
-
-> [!TIP]
-> A common starting point is to set `lora_alpha` equal to `rank`. 
-> Some also set `lora_alpha` to be twice the `rank` (e.g., lora_alpha=32 for lora_rank=16) 
-> to give the LoRA updates more influence without increasing parameter count. 
-> If you find your LoRA is "overcooking" or learning too aggressively, consider setting `lora_alpha` to half of `rank` 
-> (e.g., lora_alpha=8 for rank=16). Experimentation is often key to finding the optimal balance for your use case.
-
-### Target Modules
-
-When LoRA was first adapted from language models to diffusion models, it was applied to the cross-attention layers in the UNet that relate the image representations with the prompts that describe them. 
-More recently, SOTA text-to-image diffusion models replaced the UNet with a diffusion Transformer (DiT). With this change, we may also want to explore applying LoRA training onto different types of layers and blocks.
-
-To allow more flexibility and control over the targeted modules we added `--lora_layers`, in which you can specify in a comma separated string the exact modules for LoRA training. Here are some examples of target modules you can provide:
-
-- For attention only layers: `--lora_layers="to_k,to_q,to_v,to_out.0"`
-- For attention and feed-forward layers: `--lora_layers="to_k,to_q,to_v,to_out.0,ff.net.0.proj,ff.net.2"`
-
-> [!NOTE]
-> `--lora_layers` can also be used to specify which **blocks** to apply LoRA training to. To do so, simply add a block prefix to each layer in the comma separated string.
-
-> [!NOTE]
-> Keep in mind that while training more layers can improve quality and expressiveness, it also increases the size of the output LoRA weights.
-
-### Aspect Ratio Bucketing
-
-We've added aspect ratio bucketing support which allows training on images with different aspect ratios without cropping them to a single square resolution. This technique helps preserve the original composition of training images and can improve training efficiency.
-
-To enable aspect ratio bucketing, pass `--aspect_ratio_buckets` argument with a semicolon-separated list of height,width pairs, such as:
-
-```bash
---aspect_ratio_buckets="672,1568;688,1504;720,1456;752,1392;800,1328;832,1248;880,1184;944,1104;1024,1024;1104,944;1184,880;1248,832;1328,800;1392,752;1456,720;1504,688;1568,672"
-```
-
-### Bilingual Prompts
-
-Z-Image has strong support for both Chinese and English prompts. When training with Chinese prompts, ensure your dataset captions are properly encoded in UTF-8:
-
-```bash
---instance_prompt="一只sks狗的照片"
---validation_prompt="一只sks狗在桶里的照片"
-```
-
-> [!TIP]
-> Z-Image excels at text rendering in generated images, especially for Chinese characters. If your use case involves generating images with text, consider including text-related examples in your training data.
-
-## Inference
-
-Once you have trained a LoRA, you can load it for inference:
-
-```python
-import torch
-from diffusers import ZImagePipeline
-
-pipe = ZImagePipeline.from_pretrained("Tongyi-MAI/Z-Image", torch_dtype=torch.bfloat16)
-pipe.to("cuda")
-
-# Load your trained LoRA
-pipe.load_lora_weights("path/to/your/trained-z-image-lora")
-
-# Generate an image
-image = pipe(
-    prompt="A photo of sks dog in a bucket",
-    height=1024,
-    width=1024,
-    num_inference_steps=50,
-    guidance_scale=5.0,
-    generator=torch.Generator("cuda").manual_seed(42),
-).images[0]
-
-image.save("output.png")
-```
-
----
-
-Since Z-Image finetuning is still in an experimental phase, we encourage you to explore different settings and share your insights! 🤗

src/diffusers/modular_pipelines/modular_pipeline.py

@@ -1598,11 +1598,11 @@ class ModularPipeline(ConfigMixin, PushToHubMixin):
             else:
                 logger.warning(f"`blocks` is `None`, no default blocks class found for {self.__class__.__name__}")
 
-        self._blocks = blocks
+        self.blocks = blocks
         self._components_manager = components_manager
         self._collection = collection
-        self._component_specs = {spec.name: deepcopy(spec) for spec in self._blocks.expected_components}
-        self._config_specs = {spec.name: deepcopy(spec) for spec in self._blocks.expected_configs}
+        self._component_specs = {spec.name: deepcopy(spec) for spec in self.blocks.expected_components}
+        self._config_specs = {spec.name: deepcopy(spec) for spec in self.blocks.expected_configs}
 
         # update component_specs and config_specs based on modular_model_index.json
         if modular_config_dict is not None:
@@ -1649,9 +1649,7 @@ class ModularPipeline(ConfigMixin, PushToHubMixin):
         for name, config_spec in self._config_specs.items():
             default_configs[name] = config_spec.default
         self.register_to_config(**default_configs)
-        self.register_to_config(
-            _blocks_class_name=self._blocks.__class__.__name__ if self._blocks is not None else None
-        )
+        self.register_to_config(_blocks_class_name=self.blocks.__class__.__name__ if self.blocks is not None else None)
 
     @property
     def default_call_parameters(self) -> Dict[str, Any]:
@@ -1660,7 +1658,7 @@ class ModularPipeline(ConfigMixin, PushToHubMixin):
             - Dictionary mapping input names to their default values
         """
         params = {}
-        for input_param in self._blocks.inputs:
+        for input_param in self.blocks.inputs:
             params[input_param.name] = input_param.default
         return params
 
@@ -1831,15 +1829,7 @@ class ModularPipeline(ConfigMixin, PushToHubMixin):
         Returns:
             - The docstring of the pipeline blocks
         """
-        return self._blocks.doc
-
-    @property
-    def blocks(self) -> ModularPipelineBlocks:
-        """
-        Returns:
-            - A copy of the pipeline blocks
-        """
-        return deepcopy(self._blocks)
+        return self.blocks.doc
 
     def register_components(self, **kwargs):
         """
@@ -2575,7 +2565,7 @@ class ModularPipeline(ConfigMixin, PushToHubMixin):
         )
 
     def set_progress_bar_config(self, **kwargs):
-        for sub_block_name, sub_block in self._blocks.sub_blocks.items():
+        for sub_block_name, sub_block in self.blocks.sub_blocks.items():
             if hasattr(sub_block, "set_progress_bar_config"):
                 sub_block.set_progress_bar_config(**kwargs)
 
@@ -2629,7 +2619,7 @@ class ModularPipeline(ConfigMixin, PushToHubMixin):
 
         # Add inputs to state, using defaults if not provided in the kwargs or the state
         # if same input already in the state, will override it if provided in the kwargs
-        for expected_input_param in self._blocks.inputs:
+        for expected_input_param in self.blocks.inputs:
             name = expected_input_param.name
             default = expected_input_param.default
             kwargs_type = expected_input_param.kwargs_type
@@ -2648,9 +2638,9 @@ class ModularPipeline(ConfigMixin, PushToHubMixin):
         # Run the pipeline
         with torch.no_grad():
             try:
-                _, state = self._blocks(self, state)
+                _, state = self.blocks(self, state)
             except Exception:
-                error_msg = f"Error in block: ({self._blocks.__class__.__name__}):\n"
+                error_msg = f"Error in block: ({self.blocks.__class__.__name__}):\n"
                 logger.error(error_msg)
                 raise
 

src/diffusers/pipelines/ltx2/export_utils.py

@@ -13,20 +13,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from collections.abc import Iterator
 from fractions import Fraction
-from itertools import chain
-from typing import List, Optional, Union
+from typing import Optional
 
-import numpy as np
-import PIL.Image
 import torch
-from tqdm import tqdm
 
-from ...utils import get_logger, is_av_available
-
-
-logger = get_logger(__name__)  # pylint: disable=invalid-name
+from ...utils import is_av_available
 
 
 _CAN_USE_AV = is_av_available()
@@ -109,59 +101,11 @@ def _write_audio(
 
 
 def encode_video(
-    video: Union[List[PIL.Image.Image], np.ndarray, torch.Tensor, Iterator[torch.Tensor]],
-    fps: int,
-    audio: Optional[torch.Tensor],
-    audio_sample_rate: Optional[int],
-    output_path: str,
-    video_chunks_number: int = 1,
+    video: torch.Tensor, fps: int, audio: Optional[torch.Tensor], audio_sample_rate: Optional[int], output_path: str
 ) -> None:
-    """
-    Encodes a video with audio using the PyAV library. Based on code from the original LTX-2 repo:
-    https://github.com/Lightricks/LTX-2/blob/4f410820b198e05074a1e92de793e3b59e9ab5a0/packages/ltx-pipelines/src/ltx_pipelines/utils/media_io.py#L182
+    video_np = video.cpu().numpy()
 
-    Args:
-        video (`List[PIL.Image.Image]` or `np.ndarray` or `torch.Tensor`):
-            A video tensor of shape [frames, height, width, channels] with integer pixel values in [0, 255]. If the
-            input is a `np.ndarray`, it is expected to be a float array with values in [0, 1] (which is what pipelines
-            usually return with `output_type="np"`).
-        fps (`int`)
-            The frames per second (FPS) of the encoded video.
-        audio (`torch.Tensor`, *optional*):
-            An audio waveform of shape [audio_channels, samples].
-        audio_sample_rate: (`int`, *optional*):
-            The sampling rate of the audio waveform. For LTX 2, this is typically 24000 (24 kHz).
-        output_path (`str`):
-            The path to save the encoded video to.
-        video_chunks_number (`int`, *optional*, defaults to `1`):
-            The number of chunks to split the video into for encoding. Each chunk will be encoded separately. The
-            number of chunks to use often depends on the tiling config for the video VAE.
-    """
-    if isinstance(video, list) and isinstance(video[0], PIL.Image.Image):
-        # Pipeline output_type="pil"; assumes each image is in "RGB" mode
-        video_frames = [np.array(frame) for frame in video]
-        video = np.stack(video_frames, axis=0)
-        video = torch.from_numpy(video)
-    elif isinstance(video, np.ndarray):
-        # Pipeline output_type="np"
-        is_denormalized = np.logical_and(np.zeros_like(video) <= video, video <= np.ones_like(video))
-        if np.all(is_denormalized):
-            video = (video * 255).round().astype("uint8")
-        else:
-            logger.warning(
-                "Supplied `numpy.ndarray` does not have values in [0, 1]. The values will be assumed to be pixel "
-                "values in [0, ..., 255] and will be used as is."
-            )
-        video = torch.from_numpy(video)
-
-    if isinstance(video, torch.Tensor):
-        # Split into video_chunks_number along the frame dimension
-        video = torch.tensor_split(video, video_chunks_number, dim=0)
-        video = iter(video)
-
-    first_chunk = next(video)
-
-    _, height, width, _ = first_chunk.shape
+    _, height, width, _ = video_np.shape
 
     container = av.open(output_path, mode="w")
     stream = container.add_stream("libx264", rate=int(fps))
@@ -175,12 +119,10 @@ def encode_video(
 
         audio_stream = _prepare_audio_stream(container, audio_sample_rate)
 
-    for video_chunk in tqdm(chain([first_chunk], video), total=video_chunks_number, desc="Encoding video chunks"):
-        video_chunk_cpu = video_chunk.to("cpu").numpy()
-        for frame_array in video_chunk_cpu:
-            frame = av.VideoFrame.from_ndarray(frame_array, format="rgb24")
-            for packet in stream.encode(frame):
-                container.mux(packet)
+    for frame_array in video_np:
+        frame = av.VideoFrame.from_ndarray(frame_array, format="rgb24")
+        for packet in stream.encode(frame):
+            container.mux(packet)
 
     # Flush encoder
     for packet in stream.encode():

src/diffusers/pipelines/ltx2/pipeline_ltx2.py

@@ -69,6 +69,8 @@ EXAMPLE_DOC_STRING = """
         ...     output_type="np",
         ...     return_dict=False,
         ... )
+        >>> video = (video * 255).round().astype("uint8")
+        >>> video = torch.from_numpy(video)
 
         >>> encode_video(
         ...     video[0],

src/diffusers/pipelines/ltx2/pipeline_ltx2_image2video.py

@@ -75,6 +75,8 @@ EXAMPLE_DOC_STRING = """
         ...     output_type="np",
         ...     return_dict=False,
         ... )
+        >>> video = (video * 255).round().astype("uint8")
+        >>> video = torch.from_numpy(video)
 
         >>> encode_video(
         ...     video[0],

src/diffusers/pipelines/ltx2/pipeline_ltx2_latent_upsample.py

@@ -76,6 +76,8 @@ EXAMPLE_DOC_STRING = """
         ...     output_type="np",
         ...     return_dict=False,
         ... )[0]
+        >>> video = (video * 255).round().astype("uint8")
+        >>> video = torch.from_numpy(video)
 
         >>> encode_video(
         ...     video[0],

tests/modular_pipelines/test_modular_pipelines_common.py

@@ -37,9 +37,6 @@ class ModularPipelineTesterMixin:
     optional_params = frozenset(["num_inference_steps", "num_images_per_prompt", "latents", "output_type"])
     # this is modular specific: generator needs to be a intermediate input because it's mutable
     intermediate_params = frozenset(["generator"])
-    # Output type for the pipeline (e.g., "images" for image pipelines, "videos" for video pipelines)
-    # Subclasses can override this to change the expected output type
-    output_name = "images"
 
     def get_generator(self, seed=0):
         generator = torch.Generator("cpu").manual_seed(seed)
@@ -166,7 +163,7 @@ class ModularPipelineTesterMixin:
 
         logger.setLevel(level=diffusers.logging.WARNING)
         for batch_size, batched_input in zip(batch_sizes, batched_inputs):
-            output = pipe(**batched_input, output=self.output_name)
+            output = pipe(**batched_input, output="images")
             assert len(output) == batch_size, "Output is different from expected batch size"
 
     def test_inference_batch_single_identical(
@@ -200,16 +197,12 @@ class ModularPipelineTesterMixin:
         if "batch_size" in inputs:
             batched_inputs["batch_size"] = batch_size
 
-        output = pipe(**inputs, output=self.output_name)
-        output_batch = pipe(**batched_inputs, output=self.output_name)
+        output = pipe(**inputs, output="images")
+        output_batch = pipe(**batched_inputs, output="images")
 
         assert output_batch.shape[0] == batch_size
 
-        # For batch comparison, we only need to compare the first item
-        if output_batch.shape[0] == batch_size and output.shape[0] == 1:
-            output_batch = output_batch[0:1]
-
-        max_diff = torch.abs(output_batch - output).max()
+        max_diff = torch.abs(output_batch[0] - output[0]).max()
         assert max_diff < expected_max_diff, "Batch inference results different from single inference results"
 
     @require_accelerator
@@ -224,32 +217,19 @@ class ModularPipelineTesterMixin:
         # Reset generator in case it is used inside dummy inputs
         if "generator" in inputs:
             inputs["generator"] = self.get_generator(0)
-
-        output = pipe(**inputs, output=self.output_name)
+        output = pipe(**inputs, output="images")
 
         fp16_inputs = self.get_dummy_inputs()
         # Reset generator in case it is used inside dummy inputs
         if "generator" in fp16_inputs:
             fp16_inputs["generator"] = self.get_generator(0)
+        output_fp16 = pipe_fp16(**fp16_inputs, output="images")
 
-        output_fp16 = pipe_fp16(**fp16_inputs, output=self.output_name)
+        output = output.cpu()
+        output_fp16 = output_fp16.cpu()
 
-        output_tensor = output.float().cpu()
-        output_fp16_tensor = output_fp16.float().cpu()
-
-        # Check for NaNs in outputs (can happen with tiny models in FP16)
-        if torch.isnan(output_tensor).any() or torch.isnan(output_fp16_tensor).any():
-            pytest.skip("FP16 inference produces NaN values - this is a known issue with tiny models")
-
-        max_diff = numpy_cosine_similarity_distance(
-            output_tensor.flatten().numpy(), output_fp16_tensor.flatten().numpy()
-        )
-
-        # Check if cosine similarity is NaN (which can happen if vectors are zero or very small)
-        if torch.isnan(torch.tensor(max_diff)):
-            pytest.skip("Cosine similarity is NaN - outputs may be too small for reliable comparison")
-
-        assert max_diff < expected_max_diff, f"FP16 inference is different from FP32 inference (max_diff: {max_diff})"
+        max_diff = numpy_cosine_similarity_distance(output.flatten(), output_fp16.flatten())
+        assert max_diff < expected_max_diff, "FP16 inference is different from FP32 inference"
 
     @require_accelerator
     def test_to_device(self):
@@ -271,16 +251,14 @@ class ModularPipelineTesterMixin:
     def test_inference_is_not_nan_cpu(self):
         pipe = self.get_pipeline().to("cpu")
 
-        inputs = self.get_dummy_inputs()
-        output = pipe(**inputs, output=self.output_name)
+        output = pipe(**self.get_dummy_inputs(), output="images")
         assert torch.isnan(output).sum() == 0, "CPU Inference returns NaN"
 
     @require_accelerator
     def test_inference_is_not_nan(self):
         pipe = self.get_pipeline().to(torch_device)
 
-        inputs = self.get_dummy_inputs()
-        output = pipe(**inputs, output=self.output_name)
+        output = pipe(**self.get_dummy_inputs(), output="images")
         assert torch.isnan(output).sum() == 0, "Accelerator Inference returns NaN"
 
     def test_num_images_per_prompt(self):
@@ -300,7 +278,7 @@ class ModularPipelineTesterMixin:
                     if key in self.batch_params:
                         inputs[key] = batch_size * [inputs[key]]
 
-                images = pipe(**inputs, num_images_per_prompt=num_images_per_prompt, output=self.output_name)
+                images = pipe(**inputs, num_images_per_prompt=num_images_per_prompt, output="images")
 
                 assert images.shape[0] == batch_size * num_images_per_prompt
 
@@ -315,7 +293,8 @@ class ModularPipelineTesterMixin:
         image_slices = []
         for pipe in [base_pipe, offload_pipe]:
             inputs = self.get_dummy_inputs()
-            image = pipe(**inputs, output=self.output_name)
+            image = pipe(**inputs, output="images")
+
             image_slices.append(image[0, -3:, -3:, -1].flatten())
 
         assert torch.abs(image_slices[0] - image_slices[1]).max() < 1e-3
@@ -336,7 +315,8 @@ class ModularPipelineTesterMixin:
         image_slices = []
         for pipe in pipes:
             inputs = self.get_dummy_inputs()
-            image = pipe(**inputs, output=self.output_name)
+            image = pipe(**inputs, output="images")
+
             image_slices.append(image[0, -3:, -3:, -1].flatten())
 
         assert torch.abs(image_slices[0] - image_slices[1]).max() < 1e-3
@@ -351,13 +331,13 @@ class ModularGuiderTesterMixin:
         pipe.update_components(guider=guider)
 
         inputs = self.get_dummy_inputs()
-        out_no_cfg = pipe(**inputs, output=self.output_name)
+        out_no_cfg = pipe(**inputs, output="images")
 
         # forward pass with CFG applied
         guider = ClassifierFreeGuidance(guidance_scale=7.5)
         pipe.update_components(guider=guider)
         inputs = self.get_dummy_inputs()
-        out_cfg = pipe(**inputs, output=self.output_name)
+        out_cfg = pipe(**inputs, output="images")
 
         assert out_cfg.shape == out_no_cfg.shape
         max_diff = torch.abs(out_cfg - out_no_cfg).max()

tests/modular_pipelines/wan/test_modular_pipeline_wan.py

@@ -1,49 +0,0 @@
-# coding=utf-8
-# Copyright 2025 HuggingFace Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import pytest
-
-from diffusers.modular_pipelines import WanBlocks, WanModularPipeline
-
-from ..test_modular_pipelines_common import ModularPipelineTesterMixin
-
-
-class TestWanModularPipelineFast(ModularPipelineTesterMixin):
-    pipeline_class = WanModularPipeline
-    pipeline_blocks_class = WanBlocks
-    pretrained_model_name_or_path = "hf-internal-testing/tiny-wan-modular-pipe"
-
-    params = frozenset(["prompt", "height", "width", "num_frames"])
-    batch_params = frozenset(["prompt"])
-    optional_params = frozenset(["num_inference_steps", "num_videos_per_prompt", "latents"])
-    output_name = "videos"
-
-    def get_dummy_inputs(self, seed=0):
-        generator = self.get_generator(seed)
-        inputs = {
-            "prompt": "A painting of a squirrel eating a burger",
-            "generator": generator,
-            "num_inference_steps": 2,
-            "height": 16,
-            "width": 16,
-            "num_frames": 9,
-            "max_sequence_length": 16,
-            "output_type": "pt",
-        }
-        return inputs
-
-    @pytest.mark.skip(reason="num_videos_per_prompt")
-    def test_num_images_per_prompt(self):
-        pass

tests/modular_pipelines/z_image/test_modular_pipeline_z_image.py

@@ -1,44 +0,0 @@
-# coding=utf-8
-# Copyright 2025 HuggingFace Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-from diffusers.modular_pipelines import ZImageAutoBlocks, ZImageModularPipeline
-
-from ..test_modular_pipelines_common import ModularPipelineTesterMixin
-
-
-class TestZImageModularPipelineFast(ModularPipelineTesterMixin):
-    pipeline_class = ZImageModularPipeline
-    pipeline_blocks_class = ZImageAutoBlocks
-    pretrained_model_name_or_path = "hf-internal-testing/tiny-zimage-modular-pipe"
-
-    params = frozenset(["prompt", "height", "width"])
-    batch_params = frozenset(["prompt"])
-
-    def get_dummy_inputs(self, seed=0):
-        generator = self.get_generator(seed)
-        inputs = {
-            "prompt": "A painting of a squirrel eating a burger",
-            "generator": generator,
-            "num_inference_steps": 2,
-            "height": 32,
-            "width": 32,
-            "max_sequence_length": 16,
-            "output_type": "pt",
-        }
-        return inputs
-
-    def test_inference_batch_single_identical(self):
-        super().test_inference_batch_single_identical(expected_max_diff=5e-3)