LTX Video 0.9.7 (#11516)

* add upsampling pipeline * ltx upsample pipeline conversion; pipeline fixes * make fix-copies * remove print * add vae convenience methods * update * add tests * support denoising strength for upscaling & video-to-video * update docs * update doc checkpoints * update docs * fix --------- Co-authored-by: Linoy Tsaban <57615435+linoytsaban@users.noreply.github.com>

LTX Video 0.9.7 (#11516)
* add upsampling pipeline * ltx upsample pipeline conversion; pipeline fixes * make fix-copies * remove print * add vae convenience methods * update * add tests * support denoising strength for upscaling & video-to-video * update docs * update doc checkpoints * update docs * fix --------- Co-authored-by: Linoy Tsaban <57615435+linoytsaban@users.noreply.github.com>
06fee551 · Aryan · GitHub · 8b99f7e1 · 06fee551 · 06fee551
Unverified Commit 06fee551 authored May 13, 2025 by Aryan Committed by GitHub May 13, 2025
10 changed files
--- a/docs/source/en/api/pipelines/ltx_video.md
+++ b/docs/source/en/api/pipelines/ltx_video.md
@@ -31,12 +31,103 @@ Available models:
 |  Model name   | Recommended dtype |
 |:-------------:|:-----------------:|
-| [`LTX Video 0.9.0`](https://huggingface.co/Lightricks/LTX-Video/blob/main/ltx-video-2b-v0.9.safetensors) | `torch.bfloat16` |
+| [`LTX Video 2B 0.9.0`](https://huggingface.co/Lightricks/LTX-Video/blob/main/ltx-video-2b-v0.9.safetensors) | `torch.bfloat16` |
-| [`LTX Video 0.9.1`](https://huggingface.co/Lightricks/LTX-Video/blob/main/ltx-video-2b-v0.9.1.safetensors) | `torch.bfloat16` |
+| [`LTX Video 2B 0.9.1`](https://huggingface.co/Lightricks/LTX-Video/blob/main/ltx-video-2b-v0.9.1.safetensors) | `torch.bfloat16` |
-| [`LTX Video 0.9.5`](https://huggingface.co/Lightricks/LTX-Video/blob/main/ltx-video-2b-v0.9.5.safetensors) | `torch.bfloat16` |
+| [`LTX Video 2B 0.9.5`](https://huggingface.co/Lightricks/LTX-Video/blob/main/ltx-video-2b-v0.9.5.safetensors) | `torch.bfloat16` |
+| [`LTX Video 13B 0.9.7`](https://huggingface.co/Lightricks/LTX-Video/blob/main/ltxv-13b-0.9.7-dev.safetensors) | `torch.bfloat16` |
+| [`LTX Video Spatial Upscaler 0.9.7`](https://huggingface.co/Lightricks/LTX-Video/blob/main/ltxv-spatial-upscaler-0.9.7.safetensors) | `torch.bfloat16` |
 Note: The recommended dtype is for the transformer component. The VAE and text encoders can be either `torch.float32`, `torch.bfloat16` or `torch.float16` but the recommended dtype is `torch.bfloat16` as used in the original repository.
+## Recommended settings for generation
+For the best results, it is recommended to follow the guidelines mentioned in the official LTX Video [repository](https://github.com/Lightricks/LTX-Video).
+- Some variants of LTX Video are guidance-distilled. For guidance-distilled models, `guidance_scale` must be set to `1.0`. For any other models, `guidance_scale` should be set higher (e.g., `5.0`) for good generation quality.
+- For variants with a timestep-aware VAE (LTXV 0.9.1 and above), it is recommended to set `decode_timestep` to `0.05` and `image_cond_noise_scale` to `0.025`.
+- For variants that support interpolation between multiple conditioning images and videos (LTXV 0.9.5 and above), it is recommended to use similar looking images/videos for the best results. High divergence between the conditionings may lead to abrupt transitions in the generated video.
+## Using LTX Video 13B 0.9.7
+LTX Video 0.9.7 comes with a spatial latent upscaler and a 13B parameter transformer. The inference involves generating a low resolution video first, which is very fast, followed by upscaling and refining the generated video.
+<!-- TODO(aryan): modify when official checkpoints are available -->
+```python
+import torch
+from diffusers import LTXConditionPipeline, LTXLatentUpsamplePipeline
+from diffusers.pipelines.ltx.pipeline_ltx_condition import LTXVideoCondition
+from diffusers.utils import export_to_video, load_video
+pipe = LTXConditionPipeline.from_pretrained("a-r-r-o-w/LTX-Video-0.9.7-diffusers", torch_dtype=torch.bfloat16)
+pipe_upsample = LTXLatentUpsamplePipeline.from_pretrained("a-r-r-o-w/LTX-Video-0.9.7-Latent-Spatial-Upsampler-diffusers", vae=pipe.vae, torch_dtype=torch.bfloat16)
+pipe.to("cuda")
+pipe_upsample.to("cuda")
+pipe.vae.enable_tiling()
+def round_to_nearest_resolution_acceptable_by_vae(height, width):
+    height = height - (height % pipe.vae_temporal_compression_ratio)
+    width = width - (width % pipe.vae_temporal_compression_ratio)
+    return height, width
+video = load_video(
+    "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cosmos/cosmos-video2world-input-vid.mp4"
+)[:21]  # Use only the first 21 frames as conditioning
+condition1 = LTXVideoCondition(video=video, frame_index=0)
+prompt = "The video depicts a winding mountain road covered in snow, with a single vehicle traveling along it. The road is flanked by steep, rocky cliffs and sparse vegetation. The landscape is characterized by rugged terrain and a river visible in the distance. The scene captures the solitude and beauty of a winter drive through a mountainous region."
+negative_prompt = "worst quality, inconsistent motion, blurry, jittery, distorted"
+expected_height, expected_width = 768, 1152
+downscale_factor = 2 / 3
+num_frames = 161
+# Part 1. Generate video at smaller resolution
+# Text-only conditioning is also supported without the need to pass `conditions`
+downscaled_height, downscaled_width = int(expected_height * downscale_factor), int(expected_width * downscale_factor)
+downscaled_height, downscaled_width = round_to_nearest_resolution_acceptable_by_vae(downscaled_height, downscaled_width)
+latents = pipe(
+    conditions=[condition1],
+    prompt=prompt,
+    negative_prompt=negative_prompt,
+    width=downscaled_width,
+    height=downscaled_height,
+    num_frames=num_frames,
+    num_inference_steps=30,
+    generator=torch.Generator().manual_seed(0),
+    output_type="latent",
+).frames
+# Part 2. Upscale generated video using latent upsampler with fewer inference steps
+# The available latent upsampler upscales the height/width by 2x
+upscaled_height, upscaled_width = downscaled_height * 2, downscaled_width * 2
+upscaled_latents = pipe_upsample(
+    latents=latents,
+    output_type="latent"
+).frames
+# Part 3. Denoise the upscaled video with few steps to improve texture (optional, but recommended)
+video = pipe(
+    conditions=[condition1],
+    prompt=prompt,
+    negative_prompt=negative_prompt,
+    width=upscaled_width,
+    height=upscaled_height,
+    num_frames=num_frames,
+    denoise_strength=0.4,  # Effectively, 4 inference steps out of 10
+    num_inference_steps=10,
+    latents=upscaled_latents,
+    decode_timestep=0.05,
+    image_cond_noise_scale=0.025,
+    generator=torch.Generator().manual_seed(0),
+    output_type="pil",
+).frames[0]
+# Part 4. Downscale the video to the expected resolution
+video = [frame.resize((expected_width, expected_height)) for frame in video]
+export_to_video(video, "output.mp4", fps=24)
+```
 ## Loading Single Files
 Loading the original LTX Video checkpoints is also possible with [`~ModelMixin.from_single_file`]. We recommend using `from_single_file` for the Lightricks series of models, as they plan to release multiple models in the future in the single file format.
@@ -204,6 +295,12 @@ export_to_video(video, "ship.mp4", fps=24)
  - all
  - __call__
+## LTXLatentUpsamplePipeline
+[[autodoc]] LTXLatentUpsamplePipeline
+  - all
+  - __call__
 ## LTXPipelineOutput
 [[autodoc]] pipelines.ltx.pipeline_output.LTXPipelineOutput
--- a/scripts/convert_ltx_to_diffusers.py
+++ b/scripts/convert_ltx_to_diffusers.py
@@ -7,7 +7,15 @@ from accelerate import init_empty_weights
 from safetensors.torch import load_file
 from transformers import T5EncoderModel, T5Tokenizer
-from diffusers import AutoencoderKLLTXVideo, FlowMatchEulerDiscreteScheduler, LTXPipeline, LTXVideoTransformer3DModel
+from diffusers import (
+    AutoencoderKLLTXVideo,
+    FlowMatchEulerDiscreteScheduler,
+    LTXConditionPipeline,
+    LTXLatentUpsamplePipeline,
+    LTXPipeline,
+    LTXVideoTransformer3DModel,
+)
+from diffusers.pipelines.ltx.modeling_latent_upsampler import LTXLatentUpsamplerModel
 def remove_keys_(key: str, state_dict: Dict[str, Any]):
@@ -123,17 +131,10 @@ def update_state_dict_inplace(state_dict: Dict[str, Any], old_key: str, new_key:
    state_dict[new_key] = state_dict.pop(old_key)
-def convert_transformer(
+def convert_transformer(ckpt_path: str, config, dtype: torch.dtype):
-    ckpt_path: str,
-    dtype: torch.dtype,
-    version: str = "0.9.0",
-):
    PREFIX_KEY = "model.diffusion_model."
    original_state_dict = get_state_dict(load_file(ckpt_path))
-    config = {}
-    if version == "0.9.5":
-        config["_use_causal_rope_fix"] = True
    with init_empty_weights():
        transformer = LTXVideoTransformer3DModel(**config)
@@ -180,8 +181,59 @@ def convert_vae(ckpt_path: str, config, dtype: torch.dtype):
    return vae
+def convert_spatial_latent_upsampler(ckpt_path: str, config, dtype: torch.dtype):
+    original_state_dict = get_state_dict(load_file(ckpt_path))
+    with init_empty_weights():
+        latent_upsampler = LTXLatentUpsamplerModel(**config)
+    latent_upsampler.load_state_dict(original_state_dict, strict=True, assign=True)
+    latent_upsampler.to(dtype)
+    return latent_upsampler
+def get_transformer_config(version: str) -> Dict[str, Any]:
+    if version == "0.9.7":
+        config = {
+            "in_channels": 128,
+            "out_channels": 128,
+            "patch_size": 1,
+            "patch_size_t": 1,
+            "num_attention_heads": 32,
+            "attention_head_dim": 128,
+            "cross_attention_dim": 4096,
+            "num_layers": 48,
+            "activation_fn": "gelu-approximate",
+            "qk_norm": "rms_norm_across_heads",
+            "norm_elementwise_affine": False,
+            "norm_eps": 1e-6,
+            "caption_channels": 4096,
+            "attention_bias": True,
+            "attention_out_bias": True,
+        }
+    else:
+        config = {
+            "in_channels": 128,
+            "out_channels": 128,
+            "patch_size": 1,
+            "patch_size_t": 1,
+            "num_attention_heads": 32,
+            "attention_head_dim": 64,
+            "cross_attention_dim": 2048,
+            "num_layers": 28,
+            "activation_fn": "gelu-approximate",
+            "qk_norm": "rms_norm_across_heads",
+            "norm_elementwise_affine": False,
+            "norm_eps": 1e-6,
+            "caption_channels": 4096,
+            "attention_bias": True,
+            "attention_out_bias": True,
+        }
+    return config
 def get_vae_config(version: str) -> Dict[str, Any]:
-    if version == "0.9.0":
+    if version in ["0.9.0"]:
        config = {
            "in_channels": 3,
            "out_channels": 3,
@@ -210,7 +262,7 @@ def get_vae_config(version: str) -> Dict[str, Any]:
            "decoder_causal": False,
            "timestep_conditioning": False,
        }
-    elif version == "0.9.1":
+    elif version in ["0.9.1"]:
        config = {
            "in_channels": 3,
            "out_channels": 3,
@@ -240,7 +292,39 @@ def get_vae_config(version: str) -> Dict[str, Any]:
            "decoder_causal": False,
        }
        VAE_KEYS_RENAME_DICT.update(VAE_091_RENAME_DICT)
-    elif version == "0.9.5":
+    elif version in ["0.9.5"]:
+        config = {
+            "in_channels": 3,
+            "out_channels": 3,
+            "latent_channels": 128,
+            "block_out_channels": (128, 256, 512, 1024, 2048),
+            "down_block_types": (
+                "LTXVideo095DownBlock3D",
+                "LTXVideo095DownBlock3D",
+                "LTXVideo095DownBlock3D",
+                "LTXVideo095DownBlock3D",
+            ),
+            "decoder_block_out_channels": (256, 512, 1024),
+            "layers_per_block": (4, 6, 6, 2, 2),
+            "decoder_layers_per_block": (5, 5, 5, 5),
+            "spatio_temporal_scaling": (True, True, True, True),
+            "decoder_spatio_temporal_scaling": (True, True, True),
+            "decoder_inject_noise": (False, False, False, False),
+            "downsample_type": ("spatial", "temporal", "spatiotemporal", "spatiotemporal"),
+            "upsample_residual": (True, True, True),
+            "upsample_factor": (2, 2, 2),
+            "timestep_conditioning": True,
+            "patch_size": 4,
+            "patch_size_t": 1,
+            "resnet_norm_eps": 1e-6,
+            "scaling_factor": 1.0,
+            "encoder_causal": True,
+            "decoder_causal": False,
+            "spatial_compression_ratio": 32,
+            "temporal_compression_ratio": 8,
+        }
+        VAE_KEYS_RENAME_DICT.update(VAE_095_RENAME_DICT)
+    elif version in ["0.9.7"]:
        config = {
            "in_channels": 3,
            "out_channels": 3,
@@ -275,12 +359,33 @@ def get_vae_config(version: str) -> Dict[str, Any]:
    return config
+def get_spatial_latent_upsampler_config(version: str) -> Dict[str, Any]:
+    if version == "0.9.7":
+        config = {
+            "in_channels": 128,
+            "mid_channels": 512,
+            "num_blocks_per_stage": 4,
+            "dims": 3,
+            "spatial_upsample": True,
+            "temporal_upsample": False,
+        }
+    else:
+        raise ValueError(f"Unsupported version: {version}")
+    return config
 def get_args():
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--transformer_ckpt_path", type=str, default=None, help="Path to original transformer checkpoint"
    )
    parser.add_argument("--vae_ckpt_path", type=str, default=None, help="Path to original vae checkpoint")
+    parser.add_argument(
+        "--spatial_latent_upsampler_path",
+        type=str,
+        default=None,
+        help="Path to original spatial latent upsampler checkpoint",
+    )
    parser.add_argument(
        "--text_encoder_cache_dir", type=str, default=None, help="Path to text encoder cache directory"
    )
@@ -294,7 +399,11 @@ def get_args():
    parser.add_argument("--output_path", type=str, required=True, help="Path where converted model should be saved")
    parser.add_argument("--dtype", default="fp32", help="Torch dtype to save the model in.")
    parser.add_argument(
-        "--version", type=str, default="0.9.0", choices=["0.9.0", "0.9.1", "0.9.5"], help="Version of the LTX model"
+        "--version",
+        type=str,
+        default="0.9.0",
+        choices=["0.9.0", "0.9.1", "0.9.5", "0.9.7"],
+        help="Version of the LTX model",
    )
    return parser.parse_args()
@@ -320,11 +429,9 @@ if __name__ == "__main__":
    variant = VARIANT_MAPPING[args.dtype]
    output_path = Path(args.output_path)
-    if args.save_pipeline:
-        assert args.transformer_ckpt_path is not None and args.vae_ckpt_path is not None
    if args.transformer_ckpt_path is not None:
-        transformer: LTXVideoTransformer3DModel = convert_transformer(args.transformer_ckpt_path, dtype)
+        config = get_transformer_config(args.version)
+        transformer: LTXVideoTransformer3DModel = convert_transformer(args.transformer_ckpt_path, config, dtype)
        if not args.save_pipeline:
            transformer.save_pretrained(
                output_path / "transformer", safe_serialization=True, max_shard_size="5GB", variant=variant
@@ -336,6 +443,16 @@ if __name__ == "__main__":
        if not args.save_pipeline:
            vae.save_pretrained(output_path / "vae", safe_serialization=True, max_shard_size="5GB", variant=variant)
+    if args.spatial_latent_upsampler_path is not None:
+        config = get_spatial_latent_upsampler_config(args.version)
+        latent_upsampler: LTXLatentUpsamplerModel = convert_spatial_latent_upsampler(
+            args.spatial_latent_upsampler_path, config, dtype
+        )
+        if not args.save_pipeline:
+            latent_upsampler.save_pretrained(
+                output_path / "latent_upsampler", safe_serialization=True, max_shard_size="5GB", variant=variant
+            )
    if args.save_pipeline:
        text_encoder_id = "google/t5-v1_1-xxl"
        tokenizer = T5Tokenizer.from_pretrained(text_encoder_id, model_max_length=TOKENIZER_MAX_LENGTH)
@@ -348,7 +465,7 @@ if __name__ == "__main__":
        for param in text_encoder.parameters():
            param.data = param.data.contiguous()
-        if args.version == "0.9.5":
+        if args.version in ["0.9.5", "0.9.7"]:
            scheduler = FlowMatchEulerDiscreteScheduler(use_dynamic_shifting=False)
        else:
            scheduler = FlowMatchEulerDiscreteScheduler(
@@ -360,12 +477,40 @@ if __name__ == "__main__":
                shift_terminal=0.1,
            )
-        pipe = LTXPipeline(
+        if args.version in ["0.9.0", "0.9.1", "0.9.5"]:
-            scheduler=scheduler,
+            pipe = LTXPipeline(
-            vae=vae,
+                scheduler=scheduler,
-            text_encoder=text_encoder,
+                vae=vae,
-            tokenizer=tokenizer,
+                text_encoder=text_encoder,
-            transformer=transformer,
+                tokenizer=tokenizer,
-        )
+                transformer=transformer,
+            )
-        pipe.save_pretrained(args.output_path, safe_serialization=True, variant=variant, max_shard_size="5GB")
+            pipe.save_pretrained(
+                output_path.as_posix(), safe_serialization=True, variant=variant, max_shard_size="5GB"
+            )
+        elif args.version in ["0.9.7"]:
+            pipe = LTXConditionPipeline(
+                scheduler=scheduler,
+                vae=vae,
+                text_encoder=text_encoder,
+                tokenizer=tokenizer,
+                transformer=transformer,
+            )
+            pipe_upsample = LTXLatentUpsamplePipeline(
+                vae=vae,
+                latent_upsampler=latent_upsampler,
+            )
+            pipe.save_pretrained(
+                (output_path / "ltx_pipeline").as_posix(),
+                safe_serialization=True,
+                variant=variant,
+                max_shard_size="5GB",
+            )
+            pipe_upsample.save_pretrained(
+                (output_path / "ltx_upsample_pipeline").as_posix(),
+                safe_serialization=True,
+                variant=variant,
+                max_shard_size="5GB",
+            )
+        else:
+            raise ValueError(f"Unsupported version: {args.version}")
--- a/src/diffusers/__init__.py
+++ b/src/diffusers/__init__.py
@@ -420,6 +420,7 @@ else:
            "LEditsPPPipelineStableDiffusionXL",
            "LTXConditionPipeline",
            "LTXImageToVideoPipeline",
+            "LTXLatentUpsamplePipeline",
            "LTXPipeline",
            "Lumina2Pipeline",
            "Lumina2Text2ImgPipeline",
@@ -1003,6 +1004,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
            LEditsPPPipelineStableDiffusionXL,
            LTXConditionPipeline,
            LTXImageToVideoPipeline,
+            LTXLatentUpsamplePipeline,
            LTXPipeline,
            Lumina2Pipeline,
            Lumina2Text2ImgPipeline,

--- a/src/diffusers/pipelines/__init__.py
+++ b/src/diffusers/pipelines/__init__.py
@@ -268,7 +268,12 @@ else:
        ]
    )
    _import_structure["latte"] = ["LattePipeline"]
-    _import_structure["ltx"] = ["LTXPipeline", "LTXImageToVideoPipeline", "LTXConditionPipeline"]
+    _import_structure["ltx"] = [
+        "LTXPipeline",
+        "LTXImageToVideoPipeline",
+        "LTXConditionPipeline",
+        "LTXLatentUpsamplePipeline",
+    ]
    _import_structure["lumina"] = ["LuminaPipeline", "LuminaText2ImgPipeline"]
    _import_structure["lumina2"] = ["Lumina2Pipeline", "Lumina2Text2ImgPipeline"]
    _import_structure["marigold"].extend(
@@ -637,7 +642,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
            LEditsPPPipelineStableDiffusion,
            LEditsPPPipelineStableDiffusionXL,
        )
-        from .ltx import LTXConditionPipeline, LTXImageToVideoPipeline, LTXPipeline
+        from .ltx import LTXConditionPipeline, LTXImageToVideoPipeline, LTXLatentUpsamplePipeline, LTXPipeline
        from .lumina import LuminaPipeline, LuminaText2ImgPipeline
        from .lumina2 import Lumina2Pipeline, Lumina2Text2ImgPipeline
        from .marigold import (

--- a/src/diffusers/pipelines/ltx/__init__.py
+++ b/src/diffusers/pipelines/ltx/__init__.py
@@ -22,9 +22,11 @@ except OptionalDependencyNotAvailable:
    _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))
 else:
+    _import_structure["modeling_latent_upsampler"] = ["LTXLatentUpsamplerModel"]
    _import_structure["pipeline_ltx"] = ["LTXPipeline"]
    _import_structure["pipeline_ltx_condition"] = ["LTXConditionPipeline"]
    _import_structure["pipeline_ltx_image2video"] = ["LTXImageToVideoPipeline"]
+    _import_structure["pipeline_ltx_latent_upsample"] = ["LTXLatentUpsamplePipeline"]
 if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
    try:
@@ -34,9 +36,11 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
    except OptionalDependencyNotAvailable:
        from ...utils.dummy_torch_and_transformers_objects import *
    else:
+        from .modeling_latent_upsampler import LTXLatentUpsamplerModel
        from .pipeline_ltx import LTXPipeline
        from .pipeline_ltx_condition import LTXConditionPipeline
        from .pipeline_ltx_image2video import LTXImageToVideoPipeline
+        from .pipeline_ltx_latent_upsample import LTXLatentUpsamplePipeline
 else:
    import sys

--- a/src/diffusers/pipelines/ltx/modeling_latent_upsampler.py
+++ b/src/diffusers/pipelines/ltx/modeling_latent_upsampler.py
+# Copyright 2025 Lightricks and The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import Optional
+import torch
+from ...configuration_utils import ConfigMixin, register_to_config
+from ...models.modeling_utils import ModelMixin
+class ResBlock(torch.nn.Module):
+    def __init__(self, channels: int, mid_channels: Optional[int] = None, dims: int = 3):
+        super().__init__()
+        if mid_channels is None:
+            mid_channels = channels
+        Conv = torch.nn.Conv2d if dims == 2 else torch.nn.Conv3d
+        self.conv1 = Conv(channels, mid_channels, kernel_size=3, padding=1)
+        self.norm1 = torch.nn.GroupNorm(32, mid_channels)
+        self.conv2 = Conv(mid_channels, channels, kernel_size=3, padding=1)
+        self.norm2 = torch.nn.GroupNorm(32, channels)
+        self.activation = torch.nn.SiLU()
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        residual = hidden_states
+        hidden_states = self.conv1(hidden_states)
+        hidden_states = self.norm1(hidden_states)
+        hidden_states = self.activation(hidden_states)
+        hidden_states = self.conv2(hidden_states)
+        hidden_states = self.norm2(hidden_states)
+        hidden_states = self.activation(hidden_states + residual)
+        return hidden_states
+class PixelShuffleND(torch.nn.Module):
+    def __init__(self, dims, upscale_factors=(2, 2, 2)):
+        super().__init__()
+        self.dims = dims
+        self.upscale_factors = upscale_factors
+        if dims not in [1, 2, 3]:
+            raise ValueError("dims must be 1, 2, or 3")
+    def forward(self, x):
+        if self.dims == 3:
+            # spatiotemporal: b (c p1 p2 p3) d h w -> b c (d p1) (h p2) (w p3)
+            return (
+                x.unflatten(1, (-1, *self.upscale_factors[:3]))
+                .permute(0, 1, 5, 2, 6, 3, 7, 4)
+                .flatten(6, 7)
+                .flatten(4, 5)
+                .flatten(2, 3)
+            )
+        elif self.dims == 2:
+            # spatial: b (c p1 p2) h w -> b c (h p1) (w p2)
+            return (
+                x.unflatten(1, (-1, *self.upscale_factors[:2])).permute(0, 1, 4, 2, 5, 3).flatten(4, 5).flatten(2, 3)
+            )
+        elif self.dims == 1:
+            # temporal: b (c p1) f h w -> b c (f p1) h w
+            return x.unflatten(1, (-1, *self.upscale_factors[:1])).permute(0, 1, 3, 2, 4, 5).flatten(2, 3)
+class LTXLatentUpsamplerModel(ModelMixin, ConfigMixin):
+    """
+    Model to spatially upsample VAE latents.
+    Args:
+        in_channels (`int`, defaults to `128`):
+            Number of channels in the input latent
+        mid_channels (`int`, defaults to `512`):
+            Number of channels in the middle layers
+        num_blocks_per_stage (`int`, defaults to `4`):
+            Number of ResBlocks to use in each stage (pre/post upsampling)
+        dims (`int`, defaults to `3`):
+            Number of dimensions for convolutions (2 or 3)
+        spatial_upsample (`bool`, defaults to `True`):
+            Whether to spatially upsample the latent
+        temporal_upsample (`bool`, defaults to `False`):
+            Whether to temporally upsample the latent
+    """
+    @register_to_config
+    def __init__(
+        self,
+        in_channels: int = 128,
+        mid_channels: int = 512,
+        num_blocks_per_stage: int = 4,
+        dims: int = 3,
+        spatial_upsample: bool = True,
+        temporal_upsample: bool = False,
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        self.mid_channels = mid_channels
+        self.num_blocks_per_stage = num_blocks_per_stage
+        self.dims = dims
+        self.spatial_upsample = spatial_upsample
+        self.temporal_upsample = temporal_upsample
+        ConvNd = torch.nn.Conv2d if dims == 2 else torch.nn.Conv3d
+        self.initial_conv = ConvNd(in_channels, mid_channels, kernel_size=3, padding=1)
+        self.initial_norm = torch.nn.GroupNorm(32, mid_channels)
+        self.initial_activation = torch.nn.SiLU()
+        self.res_blocks = torch.nn.ModuleList([ResBlock(mid_channels, dims=dims) for _ in range(num_blocks_per_stage)])
+        if spatial_upsample and temporal_upsample:
+            self.upsampler = torch.nn.Sequential(
+                torch.nn.Conv3d(mid_channels, 8 * mid_channels, kernel_size=3, padding=1),
+                PixelShuffleND(3),
+            )
+        elif spatial_upsample:
+            self.upsampler = torch.nn.Sequential(
+                torch.nn.Conv2d(mid_channels, 4 * mid_channels, kernel_size=3, padding=1),
+                PixelShuffleND(2),
+            )
+        elif temporal_upsample:
+            self.upsampler = torch.nn.Sequential(
+                torch.nn.Conv3d(mid_channels, 2 * mid_channels, kernel_size=3, padding=1),
+                PixelShuffleND(1),
+            )
+        else:
+            raise ValueError("Either spatial_upsample or temporal_upsample must be True")
+        self.post_upsample_res_blocks = torch.nn.ModuleList(
+            [ResBlock(mid_channels, dims=dims) for _ in range(num_blocks_per_stage)]
+        )
+        self.final_conv = ConvNd(mid_channels, in_channels, kernel_size=3, padding=1)
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        batch_size, num_channels, num_frames, height, width = hidden_states.shape
+        if self.dims == 2:
+            hidden_states = hidden_states.permute(0, 2, 1, 3, 4).flatten(0, 1)
+            hidden_states = self.initial_conv(hidden_states)
+            hidden_states = self.initial_norm(hidden_states)
+            hidden_states = self.initial_activation(hidden_states)
+            for block in self.res_blocks:
+                hidden_states = block(hidden_states)
+            hidden_states = self.upsampler(hidden_states)
+            for block in self.post_upsample_res_blocks:
+                hidden_states = block(hidden_states)
+            hidden_states = self.final_conv(hidden_states)
+            hidden_states = hidden_states.unflatten(0, (batch_size, -1)).permute(0, 2, 1, 3, 4)
+        else:
+            hidden_states = self.initial_conv(hidden_states)
+            hidden_states = self.initial_norm(hidden_states)
+            hidden_states = self.initial_activation(hidden_states)
+            for block in self.res_blocks:
+                hidden_states = block(hidden_states)
+            if self.temporal_upsample:
+                hidden_states = self.upsampler(hidden_states)
+                hidden_states = hidden_states[:, :, 1:, :, :]
+            else:
+                hidden_states = hidden_states.permute(0, 2, 1, 3, 4).flatten(0, 1)
+                hidden_states = self.upsampler(hidden_states)
+                hidden_states = hidden_states.unflatten(0, (batch_size, -1)).permute(0, 2, 1, 3, 4)
+            for block in self.post_upsample_res_blocks:
+                hidden_states = block(hidden_states)
+            hidden_states = self.final_conv(hidden_states)
+        return hidden_states
--- a/src/diffusers/pipelines/ltx/pipeline_ltx_condition.py
+++ b/src/diffusers/pipelines/ltx/pipeline_ltx_condition.py
@@ -430,6 +430,7 @@ class LTXConditionPipeline(DiffusionPipeline, FromSingleFileMixin, LTXVideoLoraL
        video,
        frame_index,
        strength,
+        denoise_strength,
        height,
        width,
        callback_on_step_end_tensor_inputs=None,
@@ -497,6 +498,9 @@ class LTXConditionPipeline(DiffusionPipeline, FromSingleFileMixin, LTXVideoLoraL
            elif isinstance(video, list) and isinstance(strength, list) and len(video) != len(strength):
                raise ValueError("If `conditions` is not provided, `video` and `strength` must be of the same length.")
+        if denoise_strength < 0 or denoise_strength > 1:
+            raise ValueError(f"The value of strength should in [0.0, 1.0] but is {denoise_strength}")
    @staticmethod
    def _prepare_video_ids(
        batch_size: int,
@@ -649,6 +653,8 @@ class LTXConditionPipeline(DiffusionPipeline, FromSingleFileMixin, LTXVideoLoraL
        width: int = 704,
        num_frames: int = 161,
        num_prefix_latent_frames: int = 2,
+        sigma: Optional[torch.Tensor] = None,
+        latents: Optional[torch.Tensor] = None,
        generator: Optional[torch.Generator] = None,
        device: Optional[torch.device] = None,
        dtype: Optional[torch.dtype] = None,
@@ -658,7 +664,18 @@ class LTXConditionPipeline(DiffusionPipeline, FromSingleFileMixin, LTXVideoLoraL
        latent_width = width // self.vae_spatial_compression_ratio
        shape = (batch_size, num_channels_latents, num_latent_frames, latent_height, latent_width)
-        latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+        noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+        if latents is not None and sigma is not None:
+            if latents.shape != shape:
+                raise ValueError(
+                    f"Latents shape {latents.shape} does not match expected shape {shape}. Please check the input."
+                )
+            latents = latents.to(device=device, dtype=dtype)
+            sigma = sigma.to(device=device, dtype=dtype)
+            latents = sigma * noise + (1 - sigma) * latents
+        else:
+            latents = noise
        if len(conditions) > 0:
            condition_latent_frames_mask = torch.zeros(
@@ -766,6 +783,13 @@ class LTXConditionPipeline(DiffusionPipeline, FromSingleFileMixin, LTXVideoLoraL
        return latents, conditioning_mask, video_ids, extra_conditioning_num_latents
+    def get_timesteps(self, sigmas, timesteps, num_inference_steps, strength):
+        num_steps = min(int(num_inference_steps * strength), num_inference_steps)
+        start_index = max(num_inference_steps - num_steps, 0)
+        sigmas = sigmas[start_index:]
+        timesteps = timesteps[start_index:]
+        return sigmas, timesteps, num_inference_steps - start_index
    @property
    def guidance_scale(self):
        return self._guidance_scale
@@ -799,6 +823,7 @@ class LTXConditionPipeline(DiffusionPipeline, FromSingleFileMixin, LTXVideoLoraL
        video: List[PipelineImageInput] = None,
        frame_index: Union[int, List[int]] = 0,
        strength: Union[float, List[float]] = 1.0,
+        denoise_strength: float = 1.0,
        prompt: Union[str, List[str]] = None,
        negative_prompt: Optional[Union[str, List[str]]] = None,
        height: int = 512,
@@ -842,6 +867,10 @@ class LTXConditionPipeline(DiffusionPipeline, FromSingleFileMixin, LTXVideoLoraL
                generation. If not provided, one has to pass `conditions`.
            strength (`float` or `List[float]`, *optional*):
                The strength or strengths of the conditioning effect. If not provided, one has to pass `conditions`.
+            denoise_strength (`float`, defaults to `1.0`):
+                The strength of the noise added to the latents for editing. Higher strength leads to more noise added
+                to the latents, therefore leading to more differences between original video and generated video. This
+                is useful for video-to-video editing.
            prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
                instead.
@@ -918,8 +947,6 @@ class LTXConditionPipeline(DiffusionPipeline, FromSingleFileMixin, LTXVideoLoraL
        if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):
            callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs
-        if latents is not None:
-            raise ValueError("Passing latents is not yet supported.")
        # 1. Check inputs. Raise error if not correct
        self.check_inputs(
@@ -929,6 +956,7 @@ class LTXConditionPipeline(DiffusionPipeline, FromSingleFileMixin, LTXVideoLoraL
            video=video,
            frame_index=frame_index,
            strength=strength,
+            denoise_strength=denoise_strength,
            height=height,
            width=width,
            callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
@@ -977,8 +1005,9 @@ class LTXConditionPipeline(DiffusionPipeline, FromSingleFileMixin, LTXVideoLoraL
                strength = [strength] * num_conditions
        device = self._execution_device
+        vae_dtype = self.vae.dtype
-        # 3. Prepare text embeddings
+        # 3. Prepare text embeddings & conditioning image/video
        (
            prompt_embeds,
            prompt_attention_mask,
@@ -1000,8 +1029,6 @@ class LTXConditionPipeline(DiffusionPipeline, FromSingleFileMixin, LTXVideoLoraL
            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
            prompt_attention_mask = torch.cat([negative_prompt_attention_mask, prompt_attention_mask], dim=0)
-        vae_dtype = self.vae.dtype
        conditioning_tensors = []
        is_conditioning_image_or_video = image is not None or video is not None
        if is_conditioning_image_or_video:
@@ -1032,7 +1059,25 @@ class LTXConditionPipeline(DiffusionPipeline, FromSingleFileMixin, LTXVideoLoraL
                    )
                conditioning_tensors.append(condition_tensor)
-        # 4. Prepare latent variables
+        # 4. Prepare timesteps
+        latent_num_frames = (num_frames - 1) // self.vae_temporal_compression_ratio + 1
+        latent_height = height // self.vae_spatial_compression_ratio
+        latent_width = width // self.vae_spatial_compression_ratio
+        sigmas = linear_quadratic_schedule(num_inference_steps)
+        timesteps = sigmas * 1000
+        timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps)
+        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
+        latent_sigma = None
+        if denoise_strength < 1:
+            sigmas, timesteps, num_inference_steps = self.get_timesteps(
+                sigmas, timesteps, num_inference_steps, denoise_strength
+            )
+            latent_sigma = sigmas[:1].repeat(batch_size * num_videos_per_prompt)
+        self._num_timesteps = len(timesteps)
+        # 5. Prepare latent variables
        num_channels_latents = self.transformer.config.in_channels
        latents, conditioning_mask, video_coords, extra_conditioning_num_latents = self.prepare_latents(
            conditioning_tensors,
@@ -1043,6 +1088,8 @@ class LTXConditionPipeline(DiffusionPipeline, FromSingleFileMixin, LTXVideoLoraL
            height=height,
            width=width,
            num_frames=num_frames,
+            sigma=latent_sigma,
+            latents=latents,
            generator=generator,
            device=device,
            dtype=torch.float32,
@@ -1056,21 +1103,6 @@ class LTXConditionPipeline(DiffusionPipeline, FromSingleFileMixin, LTXVideoLoraL
        if self.do_classifier_free_guidance:
            video_coords = torch.cat([video_coords, video_coords], dim=0)
-        # 5. Prepare timesteps
-        latent_num_frames = (num_frames - 1) // self.vae_temporal_compression_ratio + 1
-        latent_height = height // self.vae_spatial_compression_ratio
-        latent_width = width // self.vae_spatial_compression_ratio
-        sigmas = linear_quadratic_schedule(num_inference_steps)
-        timesteps = sigmas * 1000
-        timesteps, num_inference_steps = retrieve_timesteps(
-            self.scheduler,
-            num_inference_steps,
-            device,
-            timesteps=timesteps,
-        )
-        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
-        self._num_timesteps = len(timesteps)
        # 6. Denoising loop
        with self.progress_bar(total=num_inference_steps) as progress_bar:
            for i, t in enumerate(timesteps):
@@ -1168,7 +1200,7 @@ class LTXConditionPipeline(DiffusionPipeline, FromSingleFileMixin, LTXVideoLoraL
            if not self.vae.config.timestep_conditioning:
                timestep = None
            else:
-                noise = torch.randn(latents.shape, generator=generator, device=device, dtype=latents.dtype)
+                noise = randn_tensor(latents.shape, generator=generator, device=device, dtype=latents.dtype)
                if not isinstance(decode_timestep, list):
                    decode_timestep = [decode_timestep] * batch_size
                if decode_noise_scale is None:

--- a/src/diffusers/pipelines/ltx/pipeline_ltx_latent_upsample.py
+++ b/src/diffusers/pipelines/ltx/pipeline_ltx_latent_upsample.py
+# Copyright 2025 Lightricks and The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import List, Optional, Union
+import torch
+from ...image_processor import PipelineImageInput
+from ...models import AutoencoderKLLTXVideo
+from ...utils import get_logger
+from ...utils.torch_utils import randn_tensor
+from ...video_processor import VideoProcessor
+from ..pipeline_utils import DiffusionPipeline
+from .modeling_latent_upsampler import LTXLatentUpsamplerModel
+from .pipeline_output import LTXPipelineOutput
+logger = get_logger(__name__)  # pylint: disable=invalid-name
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents
+def retrieve_latents(
+    encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = "sample"
+):
+    if hasattr(encoder_output, "latent_dist") and sample_mode == "sample":
+        return encoder_output.latent_dist.sample(generator)
+    elif hasattr(encoder_output, "latent_dist") and sample_mode == "argmax":
+        return encoder_output.latent_dist.mode()
+    elif hasattr(encoder_output, "latents"):
+        return encoder_output.latents
+    else:
+        raise AttributeError("Could not access latents of provided encoder_output")
+class LTXLatentUpsamplePipeline(DiffusionPipeline):
+    def __init__(
+        self,
+        vae: AutoencoderKLLTXVideo,
+        latent_upsampler: LTXLatentUpsamplerModel,
+    ) -> None:
+        super().__init__()
+        self.register_modules(vae=vae, latent_upsampler=latent_upsampler)
+        self.vae_spatial_compression_ratio = (
+            self.vae.spatial_compression_ratio if getattr(self, "vae", None) is not None else 32
+        )
+        self.vae_temporal_compression_ratio = (
+            self.vae.temporal_compression_ratio if getattr(self, "vae", None) is not None else 8
+        )
+        self.video_processor = VideoProcessor(vae_scale_factor=self.vae_spatial_compression_ratio)
+    def prepare_latents(
+        self,
+        video: Optional[torch.Tensor] = None,
+        batch_size: int = 1,
+        dtype: Optional[torch.dtype] = None,
+        device: Optional[torch.device] = None,
+        generator: Optional[torch.Generator] = None,
+        latents: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        if latents is not None:
+            return latents.to(device=device, dtype=dtype)
+        video = video.to(device=device, dtype=self.vae.dtype)
+        if isinstance(generator, list):
+            if len(generator) != batch_size:
+                raise ValueError(
+                    f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+                    f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+                )
+            init_latents = [
+                retrieve_latents(self.vae.encode(video[i].unsqueeze(0)), generator[i]) for i in range(batch_size)
+            ]
+        else:
+            init_latents = [retrieve_latents(self.vae.encode(vid.unsqueeze(0)), generator) for vid in video]
+        init_latents = torch.cat(init_latents, dim=0).to(dtype)
+        init_latents = self._normalize_latents(init_latents, self.vae.latents_mean, self.vae.latents_std)
+        return init_latents
+    @staticmethod
+    # Copied from diffusers.pipelines.ltx.pipeline_ltx.LTXPipeline._normalize_latents
+    def _normalize_latents(
+        latents: torch.Tensor, latents_mean: torch.Tensor, latents_std: torch.Tensor, scaling_factor: float = 1.0
+    ) -> torch.Tensor:
+        # Normalize latents across the channel dimension [B, C, F, H, W]
+        latents_mean = latents_mean.view(1, -1, 1, 1, 1).to(latents.device, latents.dtype)
+        latents_std = latents_std.view(1, -1, 1, 1, 1).to(latents.device, latents.dtype)
+        latents = (latents - latents_mean) * scaling_factor / latents_std
+        return latents
+    @staticmethod
+    # Copied from diffusers.pipelines.ltx.pipeline_ltx.LTXPipeline._denormalize_latents
+    def _denormalize_latents(
+        latents: torch.Tensor, latents_mean: torch.Tensor, latents_std: torch.Tensor, scaling_factor: float = 1.0
+    ) -> torch.Tensor:
+        # Denormalize latents across the channel dimension [B, C, F, H, W]
+        latents_mean = latents_mean.view(1, -1, 1, 1, 1).to(latents.device, latents.dtype)
+        latents_std = latents_std.view(1, -1, 1, 1, 1).to(latents.device, latents.dtype)
+        latents = latents * latents_std / scaling_factor + latents_mean
+        return latents
+    def enable_vae_slicing(self):
+        r"""
+        Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
+        compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
+        """
+        self.vae.enable_slicing()
+    def disable_vae_slicing(self):
+        r"""
+        Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
+        computing decoding in one step.
+        """
+        self.vae.disable_slicing()
+    def enable_vae_tiling(self):
+        r"""
+        Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
+        compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
+        processing larger images.
+        """
+        self.vae.enable_tiling()
+    def disable_vae_tiling(self):
+        r"""
+        Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to
+        computing decoding in one step.
+        """
+        self.vae.disable_tiling()
+    def check_inputs(self, video, height, width, latents):
+        if height % self.vae_spatial_compression_ratio != 0 or width % self.vae_spatial_compression_ratio != 0:
+            raise ValueError(f"`height` and `width` have to be divisible by 32 but are {height} and {width}.")
+        if video is not None and latents is not None:
+            raise ValueError("Only one of `video` or `latents` can be provided.")
+        if video is None and latents is None:
+            raise ValueError("One of `video` or `latents` has to be provided.")
+    @torch.no_grad()
+    def __call__(
+        self,
+        video: Optional[List[PipelineImageInput]] = None,
+        height: int = 512,
+        width: int = 704,
+        latents: Optional[torch.Tensor] = None,
+        decode_timestep: Union[float, List[float]] = 0.0,
+        decode_noise_scale: Optional[Union[float, List[float]]] = None,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+    ):
+        self.check_inputs(
+            video=video,
+            height=height,
+            width=width,
+            latents=latents,
+        )
+        if video is not None:
+            # Batched video input is not yet tested/supported. TODO: take a look later
+            batch_size = 1
+        else:
+            batch_size = latents.shape[0]
+        device = self._execution_device
+        if video is not None:
+            num_frames = len(video)
+            if num_frames % self.vae_temporal_compression_ratio != 1:
+                num_frames = (
+                    num_frames // self.vae_temporal_compression_ratio * self.vae_temporal_compression_ratio + 1
+                )
+                video = video[:num_frames]
+                logger.warning(
+                    f"Video length expected to be of the form `k * {self.vae_temporal_compression_ratio} + 1` but is {len(video)}. Truncating to {num_frames} frames."
+                )
+            video = self.video_processor.preprocess_video(video, height=height, width=width)
+            video = video.to(device=device, dtype=torch.float32)
+        latents = self.prepare_latents(
+            video=video,
+            batch_size=batch_size,
+            dtype=torch.float32,
+            device=device,
+            generator=generator,
+            latents=latents,
+        )
+        latents = self._denormalize_latents(
+            latents, self.vae.latents_mean, self.vae.latents_std, self.vae.config.scaling_factor
+        )
+        latents = latents.to(self.latent_upsampler.dtype)
+        latents = self.latent_upsampler(latents)
+        if output_type == "latent":
+            latents = self._normalize_latents(
+                latents, self.vae.latents_mean, self.vae.latents_std, self.vae.config.scaling_factor
+            )
+            video = latents
+        else:
+            if not self.vae.config.timestep_conditioning:
+                timestep = None
+            else:
+                noise = randn_tensor(latents.shape, generator=generator, device=device, dtype=latents.dtype)
+                if not isinstance(decode_timestep, list):
+                    decode_timestep = [decode_timestep] * batch_size
+                if decode_noise_scale is None:
+                    decode_noise_scale = decode_timestep
+                elif not isinstance(decode_noise_scale, list):
+                    decode_noise_scale = [decode_noise_scale] * batch_size
+                timestep = torch.tensor(decode_timestep, device=device, dtype=latents.dtype)
+                decode_noise_scale = torch.tensor(decode_noise_scale, device=device, dtype=latents.dtype)[
+                    :, None, None, None, None
+                ]
+                latents = (1 - decode_noise_scale) * latents + decode_noise_scale * noise
+            video = self.vae.decode(latents, timestep, return_dict=False)[0]
+            video = self.video_processor.postprocess_video(video, output_type=output_type)
+        # Offload all models
+        self.maybe_free_model_hooks()
+        if not return_dict:
+            return (video,)
+        return LTXPipelineOutput(frames=video)
--- a/src/diffusers/utils/dummy_torch_and_transformers_objects.py
+++ b/src/diffusers/utils/dummy_torch_and_transformers_objects.py
@@ -1307,6 +1307,21 @@ class LTXImageToVideoPipeline(metaclass=DummyObject):
        requires_backends(cls, ["torch", "transformers"])
+class LTXLatentUpsamplePipeline(metaclass=DummyObject):
+    _backends = ["torch", "transformers"]
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch", "transformers"])
+    @classmethod
+    def from_config(cls, *args, **kwargs):
+        requires_backends(cls, ["torch", "transformers"])
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["torch", "transformers"])
 class LTXPipeline(metaclass=DummyObject):
    _backends = ["torch", "transformers"]

--- a/tests/pipelines/ltx/test_ltx_latent_upsample.py
+++ b/tests/pipelines/ltx/test_ltx_latent_upsample.py
+# Copyright 2025 The HuggingFace Team.
+#
+# 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 unittest
+import numpy as np
+import torch
+from diffusers import AutoencoderKLLTXVideo, LTXLatentUpsamplePipeline
+from diffusers.pipelines.ltx.modeling_latent_upsampler import LTXLatentUpsamplerModel
+from diffusers.utils.testing_utils import enable_full_determinism
+from ..test_pipelines_common import PipelineTesterMixin, to_np
+enable_full_determinism()
+class LTXLatentUpsamplePipelineFastTests(PipelineTesterMixin, unittest.TestCase):
+    pipeline_class = LTXLatentUpsamplePipeline
+    params = {"video", "generator"}
+    batch_params = {"video", "generator"}
+    required_optional_params = frozenset(["generator", "latents", "return_dict"])
+    test_xformers_attention = False
+    supports_dduf = False
+    def get_dummy_components(self):
+        torch.manual_seed(0)
+        vae = AutoencoderKLLTXVideo(
+            in_channels=3,
+            out_channels=3,
+            latent_channels=8,
+            block_out_channels=(8, 8, 8, 8),
+            decoder_block_out_channels=(8, 8, 8, 8),
+            layers_per_block=(1, 1, 1, 1, 1),
+            decoder_layers_per_block=(1, 1, 1, 1, 1),
+            spatio_temporal_scaling=(True, True, False, False),
+            decoder_spatio_temporal_scaling=(True, True, False, False),
+            decoder_inject_noise=(False, False, False, False, False),
+            upsample_residual=(False, False, False, False),
+            upsample_factor=(1, 1, 1, 1),
+            timestep_conditioning=False,
+            patch_size=1,
+            patch_size_t=1,
+            encoder_causal=True,
+            decoder_causal=False,
+        )
+        vae.use_framewise_encoding = False
+        vae.use_framewise_decoding = False
+        torch.manual_seed(0)
+        latent_upsampler = LTXLatentUpsamplerModel(
+            in_channels=8,
+            mid_channels=32,
+            num_blocks_per_stage=1,
+            dims=3,
+            spatial_upsample=True,
+            temporal_upsample=False,
+        )
+        components = {
+            "vae": vae,
+            "latent_upsampler": latent_upsampler,
+        }
+        return components
+    def get_dummy_inputs(self, device, seed=0):
+        if str(device).startswith("mps"):
+            generator = torch.manual_seed(seed)
+        else:
+            generator = torch.Generator(device=device).manual_seed(seed)
+        video = torch.randn((5, 3, 32, 32), generator=generator, device=device)
+        inputs = {
+            "video": video,
+            "generator": generator,
+            "height": 16,
+            "width": 16,
+            "output_type": "pt",
+        }
+        return inputs
+    def test_inference(self):
+        device = "cpu"
+        components = self.get_dummy_components()
+        pipe = self.pipeline_class(**components)
+        pipe.to(device)
+        pipe.set_progress_bar_config(disable=None)
+        inputs = self.get_dummy_inputs(device)
+        video = pipe(**inputs).frames
+        generated_video = video[0]
+        self.assertEqual(generated_video.shape, (5, 3, 32, 32))
+        expected_video = torch.randn(5, 3, 32, 32)
+        max_diff = np.abs(generated_video - expected_video).max()
+        self.assertLessEqual(max_diff, 1e10)
+    def test_vae_tiling(self, expected_diff_max: float = 0.25):
+        generator_device = "cpu"
+        components = self.get_dummy_components()
+        pipe = self.pipeline_class(**components)
+        pipe.to("cpu")
+        pipe.set_progress_bar_config(disable=None)
+        # Without tiling
+        inputs = self.get_dummy_inputs(generator_device)
+        inputs["height"] = inputs["width"] = 128
+        output_without_tiling = pipe(**inputs)[0]
+        # With tiling
+        pipe.vae.enable_tiling(
+            tile_sample_min_height=96,
+            tile_sample_min_width=96,
+            tile_sample_stride_height=64,
+            tile_sample_stride_width=64,
+        )
+        inputs = self.get_dummy_inputs(generator_device)
+        inputs["height"] = inputs["width"] = 128
+        output_with_tiling = pipe(**inputs)[0]
+        self.assertLess(
+            (to_np(output_without_tiling) - to_np(output_with_tiling)).max(),
+            expected_diff_max,
+            "VAE tiling should not affect the inference results",
+        )
+    @unittest.skip("Test is not applicable.")
+    def test_callback_inputs(self):
+        pass
+    @unittest.skip("Test is not applicable.")
+    def test_attention_slicing_forward_pass(
+        self, test_max_difference=True, test_mean_pixel_difference=True, expected_max_diff=1e-3
+    ):
+        pass
+    @unittest.skip("Test is not applicable.")
+    def test_inference_batch_consistent(self):
+        pass
+    @unittest.skip("Test is not applicable.")
+    def test_inference_batch_single_identical(self):
+        pass