Merge branch 'main' into attn-dispatcher-cp-and-training

Author: a-r-r-o-w

.github/workflows/pr_flax_dependency_test.yml

@@ -5,19 +5,19 @@
   - local: installation
     title: Installation
   - local: quicktour
-    title: Quicktour
+    title: Quickstart
   - local: stable_diffusion
     title: Basic performance
 
-- title: DiffusionPipeline
+- title: Pipelines
   isExpanded: false
   sections:
   - local: using-diffusers/loading
-    title: Load pipelines
+    title: DiffusionPipeline
   - local: tutorials/autopipeline
     title: AutoPipeline
   - local: using-diffusers/custom_pipeline_overview
-    title: Load community pipelines and components
+    title: Community pipelines and components
   - local: using-diffusers/callback
     title: Pipeline callbacks
   - local: using-diffusers/reusing_seeds
@@ -77,7 +77,7 @@
   - local: optimization/memory
     title: Reduce memory usage
   - local: optimization/speed-memory-optims
-    title: Compile and offloading quantized models
+    title: Compiling and offloading quantized models
   - title: Community optimizations
     sections:
     - local: optimization/pruna
@@ -340,6 +340,8 @@
         title: AllegroTransformer3DModel
       - local: api/models/aura_flow_transformer2d
         title: AuraFlowTransformer2DModel
+      - local: api/models/bria_transformer
+        title: BriaTransformer2DModel
       - local: api/models/chroma_transformer
         title: ChromaTransformer2DModel
       - local: api/models/cogvideox_transformer3d
@@ -468,6 +470,8 @@
       title: AutoPipeline
     - local: api/pipelines/blip_diffusion
       title: BLIP-Diffusion
+    - local: api/pipelines/bria_3_2
+      title: Bria 3.2
     - local: api/pipelines/chroma
       title: Chroma
     - local: api/pipelines/cogvideox

docker/diffusers-flax-cpu/Dockerfile

@@ -0,0 +1,19 @@
+<!--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.
+-->
+
+# BriaTransformer2DModel
+
+A modified flux Transformer model from [Bria](https://huggingface.co/briaai/BRIA-3.2)
+
+## BriaTransformer2DModel
+
+[[autodoc]] BriaTransformer2DModel

docker/diffusers-flax-tpu/Dockerfile

@@ -0,0 +1,44 @@
+<!--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.
+-->
+
+# Bria 3.2
+
+Bria 3.2 is the next-generation commercial-ready text-to-image model. With just 4 billion parameters, it provides exceptional aesthetics and text rendering, evaluated to provide on par results to leading open-source models, and outperforming other licensed models.
+In addition to being built entirely on licensed data, 3.2 provides several advantages for enterprise and commercial use:
+
+- Efficient Compute - the model is X3 smaller than the equivalent models in the market (4B parameters vs 12B parameters other open source models)
+- Architecture Consistency: Same architecture as 3.1—ideal for users looking to upgrade without disruption.
+- Fine-tuning Speedup: 2x faster fine-tuning on L40S and A100.
+
+Original model checkpoints for Bria 3.2 can be found [here](https://huggingface.co/briaai/BRIA-3.2).
+Github repo for Bria 3.2 can be found [here](https://github.com/Bria-AI/BRIA-3.2).
+
+If you want to learn more about the Bria platform, and get free traril access, please visit [bria.ai](https://bria.ai).
+
+
+## Usage
+
+_As the model is gated, before using it with diffusers you first need to go to the [Bria 3.2 Hugging Face page](https://huggingface.co/briaai/BRIA-3.2), fill in the form and accept the gate. Once you are in, you need to login so that your system knows you’ve accepted the gate._
+
+Use the command below to log in:
+
+```bash
+hf auth login
+```
+
+
+## BriaPipeline
+
+[[autodoc]] BriaPipeline
+	- all
+	- __call__
+

docs/source/en/_toctree.yml

@@ -316,6 +316,67 @@ if integrity_checker.test_image(image_):
     raise ValueError("Your image has been flagged. Choose another prompt/image or try again.")
 ```
 
+### Kontext Inpainting
+`FluxKontextInpaintPipeline` enables image modification within a fixed mask region. It currently supports both text-based conditioning and image-reference conditioning.
+<hfoptions id="kontext-inpaint">
+<hfoption id="text-only">
+
+
+```python
+import torch
+from diffusers import FluxKontextInpaintPipeline
+from diffusers.utils import load_image
+
+prompt = "Change the yellow dinosaur to green one"
+img_url = (
+    "https://github.com/ZenAI-Vietnam/Flux-Kontext-pipelines/blob/main/assets/dinosaur_input.jpeg?raw=true"
+)
+mask_url = (
+    "https://github.com/ZenAI-Vietnam/Flux-Kontext-pipelines/blob/main/assets/dinosaur_mask.png?raw=true"
+)
+
+source = load_image(img_url)
+mask = load_image(mask_url)
+
+pipe = FluxKontextInpaintPipeline.from_pretrained(
+    "black-forest-labs/FLUX.1-Kontext-dev", torch_dtype=torch.bfloat16
+)
+pipe.to("cuda")
+
+image = pipe(prompt=prompt, image=source, mask_image=mask, strength=1.0).images[0]
+image.save("kontext_inpainting_normal.png")
+```
+</hfoption>
+<hfoption id="image conditioning">
+
+```python
+import torch
+from diffusers import FluxKontextInpaintPipeline
+from diffusers.utils import load_image
+
+pipe = FluxKontextInpaintPipeline.from_pretrained(
+    "black-forest-labs/FLUX.1-Kontext-dev", torch_dtype=torch.bfloat16
+)
+pipe.to("cuda")
+
+prompt = "Replace this ball"
+img_url = "https://images.pexels.com/photos/39362/the-ball-stadion-football-the-pitch-39362.jpeg?auto=compress&cs=tinysrgb&dpr=1&w=500"
+mask_url = "https://github.com/ZenAI-Vietnam/Flux-Kontext-pipelines/blob/main/assets/ball_mask.png?raw=true"
+image_reference_url = "https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTah3x6OL_ECMBaZ5ZlJJhNsyC-OSMLWAI-xw&s"
+
+source = load_image(img_url)
+mask = load_image(mask_url)
+image_reference = load_image(image_reference_url)
+
+mask = pipe.mask_processor.blur(mask, blur_factor=12)
+image = pipe(
+    prompt=prompt, image=source, mask_image=mask, image_reference=image_reference, strength=1.0
+).images[0]
+image.save("kontext_inpainting_ref.png")
+```
+</hfoption>
+</hfoptions>
+
 ## Combining Flux Turbo LoRAs with Flux Control, Fill, and Redux
 
 We can combine Flux Turbo LoRAs with Flux Control and other pipelines like Fill and Redux to enable few-steps' inference. The example below shows how to do that for Flux Control LoRA for depth and turbo LoRA from [`ByteDance/Hyper-SD`](https://hf.co/ByteDance/Hyper-SD).
@@ -646,3 +707,15 @@ image.save("flux-fp8-dev.png")
 [[autodoc]] FluxFillPipeline
 	- all
 	- __call__
+
+## FluxKontextPipeline
+
+[[autodoc]] FluxKontextPipeline
+	- all
+	- __call__
+
+## FluxKontextInpaintPipeline
+
+[[autodoc]] FluxKontextInpaintPipeline
+	- all
+	- __call__

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

@@ -37,6 +37,7 @@ The table below lists all the pipelines currently available in 🤗 Diffusers an
 | [AudioLDM2](audioldm2) | text2audio |
 | [AuraFlow](auraflow) | text2image |
 | [BLIP Diffusion](blip_diffusion) | text2image |
+| [Bria 3.2](bria_3_2) | text2image |
 | [CogVideoX](cogvideox) | text2video |
 | [Consistency Models](consistency_models) | unconditional image generation |
 | [ControlNet](controlnet) | text2image, image2image, inpainting |
@@ -112,3 +113,17 @@ The table below lists all the pipelines currently available in 🤗 Diffusers an
 ## PushToHubMixin
 
 [[autodoc]] utils.PushToHubMixin
+
+## Callbacks
+
+[[autodoc]] callbacks.PipelineCallback
+
+[[autodoc]] callbacks.SDCFGCutoffCallback
+
+[[autodoc]] callbacks.SDXLCFGCutoffCallback
+
+[[autodoc]] callbacks.SDXLControlnetCFGCutoffCallback
+
+[[autodoc]] callbacks.IPAdapterScaleCutoffCallback
+
+[[autodoc]] callbacks.SD3CFGCutoffCallback