Merge branch 'main' into feat/mag-cache

Author: AlanPonnachan

docs/source/en/_toctree.yml

@@ -359,6 +359,8 @@
         title: HunyuanDiT2DModel
       - local: api/models/hunyuanimage_transformer_2d
         title: HunyuanImageTransformer2DModel
+      - local: api/models/hunyuan_video15_transformer_3d
+        title: HunyuanVideo15Transformer3DModel
       - local: api/models/hunyuan_video_transformer_3d
         title: HunyuanVideoTransformer3DModel
       - local: api/models/latte_transformer3d
@@ -433,6 +435,8 @@
         title: AutoencoderKLHunyuanImageRefiner
       - local: api/models/autoencoder_kl_hunyuan_video
         title: AutoencoderKLHunyuanVideo
+      - local: api/models/autoencoder_kl_hunyuan_video15
+        title: AutoencoderKLHunyuanVideo15
       - local: api/models/autoencoderkl_ltx_video
         title: AutoencoderKLLTXVideo
       - local: api/models/autoencoderkl_magvit
@@ -652,6 +656,8 @@
         title: Framepack
       - local: api/pipelines/hunyuan_video
         title: HunyuanVideo
+      - local: api/pipelines/hunyuan_video15
+        title: HunyuanVideo1.5
       - local: api/pipelines/i2vgenxl
         title: I2VGen-XL
       - local: api/pipelines/kandinsky5_video

docs/source/en/api/loaders/lora.md

@@ -31,6 +31,7 @@ LoRA is a fast and lightweight training method that inserts and trains a signifi
 - [`AmusedLoraLoaderMixin`] is for the [`AmusedPipeline`].
 - [`HiDreamImageLoraLoaderMixin`] provides similar functions for [HiDream Image](https://huggingface.co/docs/diffusers/main/en/api/pipelines/hidream)
 - [`QwenImageLoraLoaderMixin`] provides similar functions for [Qwen Image](https://huggingface.co/docs/diffusers/main/en/api/pipelines/qwen).
+- [`ZImageLoraLoaderMixin`] provides similar functions for [Z-Image](https://huggingface.co/docs/diffusers/main/en/api/pipelines/zimage).
 - [`Flux2LoraLoaderMixin`] provides similar functions for [Flux2](https://huggingface.co/docs/diffusers/main/en/api/pipelines/flux2).
 - [`LoraBaseMixin`] provides a base class with several utility methods to fuse, unfuse, unload, LoRAs and more.
 
@@ -112,6 +113,10 @@ LoRA is a fast and lightweight training method that inserts and trains a signifi
 
 [[autodoc]] loaders.lora_pipeline.QwenImageLoraLoaderMixin
 
+## ZImageLoraLoaderMixin
+
+[[autodoc]] loaders.lora_pipeline.ZImageLoraLoaderMixin
+
 ## KandinskyLoraLoaderMixin
 [[autodoc]] loaders.lora_pipeline.KandinskyLoraLoaderMixin
 

docs/source/en/api/models/autoencoder_kl_hunyuan_video15.md

@@ -0,0 +1,36 @@
+<!-- Copyright 2025 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. -->
+
+# AutoencoderKLHunyuanVideo15
+
+The 3D variational autoencoder (VAE) model with KL loss used in [HunyuanVideo1.5](https://github.com/Tencent/HunyuanVideo1-1.5) by Tencent.
+
+The model can be loaded with the following code snippet.
+
+```python
+from diffusers import AutoencoderKLHunyuanVideo15
+
+vae = AutoencoderKLHunyuanVideo15.from_pretrained("hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v", subfolder="vae", torch_dtype=torch.float32)
+
+# make sure to enable tiling to avoid OOM
+vae.enable_tiling()
+```
+
+## AutoencoderKLHunyuanVideo15
+
+[[autodoc]] AutoencoderKLHunyuanVideo15
+  - decode
+  - encode
+  - all
+
+## DecoderOutput
+
+[[autodoc]] models.autoencoders.vae.DecoderOutput

docs/source/en/api/models/hunyuan_video15_transformer_3d.md

@@ -0,0 +1,30 @@
+<!-- Copyright 2025 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. -->
+
+# HunyuanVideo15Transformer3DModel
+
+A Diffusion Transformer model for 3D video-like data used in [HunyuanVideo1.5](https://github.com/Tencent/HunyuanVideo1-1.5).
+
+The model can be loaded with the following code snippet.
+
+```python
+from diffusers import HunyuanVideo15Transformer3DModel
+
+transformer = HunyuanVideo15Transformer3DModel.from_pretrained("hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v" subfolder="transformer", torch_dtype=torch.bfloat16)
+```
+
+## HunyuanVideo15Transformer3DModel
+
+[[autodoc]] HunyuanVideo15Transformer3DModel
+
+## Transformer2DModelOutput
+
+[[autodoc]] models.modeling_outputs.Transformer2DModelOutput

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

@@ -21,9 +21,10 @@ With only 8 billion parameters, FIBO provides a new level of image quality, prom
 FIBO is trained exclusively on a structured prompt and will not work with freeform text prompts.
 you can use the [FIBO-VLM-prompt-to-JSON](https://huggingface.co/briaai/FIBO-VLM-prompt-to-JSON) model or the [FIBO-gemini-prompt-to-JSON](https://huggingface.co/briaai/FIBO-gemini-prompt-to-JSON)  to convert your freeform text prompt to a structured JSON prompt.
 
-its not recommended to use freeform text prompts directly with FIBO, as it will not produce the best results.
+> [!NOTE]
+> Avoid using freeform text prompts directly with FIBO because it does not produce the best results.
 
-you can learn more about FIBO in  [Bria Fibo Hugging Face page](https://huggingface.co/briaai/FIBO).
+Refer to the Bria Fibo Hugging Face [page](https://huggingface.co/briaai/FIBO) to learn more.
 
 
 ## Usage
@@ -37,9 +38,8 @@ hf auth login
 ```
 
 
-## BriaPipeline
+## BriaFiboPipeline
 
-[[autodoc]] BriaPipeline
+[[autodoc]] BriaFiboPipeline
 	- all
-	- __call__
-
+	- __call__

docs/source/en/api/pipelines/flux2.md

@@ -26,6 +26,12 @@ Original model checkpoints for Flux can be found [here](https://huggingface.co/b
 >
 > [Caching](../../optimization/cache) may also speed up inference by storing and reusing intermediate outputs.
 
+## Caption upsampling
+
+Flux.2 can potentially generate better better outputs with better prompts. We can "upsample"
+an input prompt by setting the `caption_upsample_temperature` argument in the pipeline call arguments.
+The [official implementation](https://github.com/black-forest-labs/flux2/blob/5a5d316b1b42f6b59a8c9194b77c8256be848432/src/flux2/text_encoder.py#L140) recommends this value to be 0.15.
+
 ## Flux2Pipeline
 
 [[autodoc]] Flux2Pipeline

docs/source/en/api/pipelines/hunyuan_video15.md

@@ -0,0 +1,120 @@
+<!-- Copyright 2025 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. -->
+
+
+# HunyuanVideo-1.5
+
+HunyuanVideo-1.5 is a lightweight yet powerful video generation model that achieves state-of-the-art visual quality and motion coherence with only 8.3 billion parameters, enabling efficient inference on consumer-grade GPUs. This achievement is built upon several key components, including meticulous data curation, an advanced DiT architecture with selective and sliding tile attention (SSTA), enhanced bilingual understanding through glyph-aware text encoding, progressive pre-training and post-training, and an efficient video super-resolution network. Leveraging these designs, we developed a unified framework capable of high-quality text-to-video and image-to-video generation across multiple durations and resolutions. Extensive experiments demonstrate that this compact and proficient model establishes a new state-of-the-art among open-source models.
+
+You can find all the original HunyuanVideo checkpoints under the [Tencent](https://huggingface.co/tencent) organization.
+
+> [!TIP]
+> Click on the HunyuanVideo models in the right sidebar for more examples of video generation tasks.
+>
+> The examples below use a checkpoint from [hunyuanvideo-community](https://huggingface.co/hunyuanvideo-community) because the weights are stored in a layout compatible with Diffusers.
+
+The example below demonstrates how to generate a video optimized for memory or inference speed.
+
+<hfoptions id="usage">
+<hfoption id="memory">
+
+Refer to the [Reduce memory usage](../../optimization/memory) guide for more details about the various memory saving techniques.
+
+
+```py
+import torch
+from diffusers import AutoModel, HunyuanVideo15Pipeline
+from diffusers.utils import export_to_video
+
+
+pipeline = HunyuanVideo15Pipeline.from_pretrained(
+    "HunyuanVideo-1.5-Diffusers-480p_t2v",
+    torch_dtype=torch.bfloat16,
+)
+
+# model-offloading and tiling
+pipeline.enable_model_cpu_offload()
+pipeline.vae.enable_tiling()
+
+prompt = "A fluffy teddy bear sits on a bed of soft pillows surrounded by children's toys."
+video = pipeline(prompt=prompt, num_frames=61, num_inference_steps=30).frames[0]
+export_to_video(video, "output.mp4", fps=15)
+```
+
+## Notes
+
+- HunyuanVideo1.5 use attention masks with variable-length sequences. For best performance, we recommend using an attention backend that handles padding efficiently.
+
+    - **H100/H800:** `_flash_3_hub` or `_flash_varlen_3`
+    - **A100/A800/RTX 4090:** `flash_hub` or `flash_varlen`
+    - **Other GPUs:** `sage_hub`
+
+Refer to the [Attention backends](../../optimization/attention_backends) guide for more details about using a different backend.
+
+
+```py
+pipe.transformer.set_attention_backend("flash_hub")  # or your preferred backend
+```
+
+- [`HunyuanVideo15Pipeline`] use guider and does not take `guidance_scale` parameter at runtime. 
+
+You can check the default guider configuration using `pipe.guider`:
+
+```py
+>>> pipe.guider 
+ClassifierFreeGuidance {
+  "_class_name": "ClassifierFreeGuidance",
+  "_diffusers_version": "0.36.0.dev0",
+  "enabled": true,
+  "guidance_rescale": 0.0,
+  "guidance_scale": 6.0,
+  "start": 0.0,
+  "stop": 1.0,
+  "use_original_formulation": false
+}
+
+State:
+  step: None
+  num_inference_steps: None
+  timestep: None
+  count_prepared: 0
+  enabled: True
+  num_conditions: 2
+```
+
+To update guider configuration, you can run `pipe.guider = pipe.guider.new(...)`
+
+```py
+pipe.guider = pipe.guider.new(guidance_scale=5.0)
+```
+
+Read more on Guider [here](../../modular_diffusers/guiders).
+
+
+
+## HunyuanVideo15Pipeline
+
+[[autodoc]] HunyuanVideo15Pipeline
+  - all
+  - __call__
+
+## HunyuanVideo15ImageToVideoPipeline
+
+[[autodoc]] HunyuanVideo15ImageToVideoPipeline
+  - all
+  - __call__
+
+## HunyuanVideo15PipelineOutput
+
+[[autodoc]] pipelines.hunyuan_video1_5.pipeline_output.HunyuanVideo15PipelineOutput

pyproject.toml

@@ -1,5 +1,8 @@
 [tool.ruff]
 line-length = 119
+extend-exclude = [
+    "src/diffusers/pipelines/flux2/system_messages.py",
+]
 
 [tool.ruff.lint]
 # Never enforce `E501` (line length violations).