Merge branch 'main' into ltx-rename

Author: a-r-r-o-w

docs/source/en/_toctree.yml

@@ -238,6 +238,8 @@
       title: Textual Inversion
     - local: api/loaders/unet
       title: UNet
+    - local: api/loaders/transformer_sd3
+      title: SD3Transformer2D
     - local: api/loaders/peft
       title: PEFT
     title: Loaders
@@ -400,6 +402,8 @@
       title: DiT
     - local: api/pipelines/flux
       title: Flux
+    - local: api/pipelines/control_flux_inpaint
+      title: FluxControlInpaint
     - local: api/pipelines/hunyuandit
       title: Hunyuan-DiT
     - local: api/pipelines/hunyuan_video

docs/source/en/api/attnprocessor.md

@@ -86,6 +86,8 @@ An attention processor is a class for applying different types of attention mech
 
 [[autodoc]] models.attention_processor.IPAdapterAttnProcessor2_0
 
+[[autodoc]] models.attention_processor.SD3IPAdapterJointAttnProcessor2_0
+
 ## JointAttnProcessor2_0
 
 [[autodoc]] models.attention_processor.JointAttnProcessor2_0

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

@@ -24,6 +24,12 @@ Learn how to load an IP-Adapter checkpoint and image in the IP-Adapter [loading]
 
 [[autodoc]] loaders.ip_adapter.IPAdapterMixin
 
+## SD3IPAdapterMixin
+
+[[autodoc]] loaders.ip_adapter.SD3IPAdapterMixin
+    - all
+    - is_ip_adapter_active
+
 ## IPAdapterMaskProcessor
 
 [[autodoc]] image_processor.IPAdapterMaskProcessor

docs/source/en/api/loaders/transformer_sd3.md

@@ -0,0 +1,29 @@
+<!--Copyright 2024 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.
+-->
+
+# SD3Transformer2D
+
+This class is useful when *only* loading weights into a [`SD3Transformer2DModel`]. If you need to load weights into the text encoder or a text encoder and SD3Transformer2DModel, check [`SD3LoraLoaderMixin`](lora#diffusers.loaders.SD3LoraLoaderMixin) class instead.
+
+The [`SD3Transformer2DLoadersMixin`] class currently only loads IP-Adapter weights, but will be used in the future to save weights and load LoRAs.
+
+<Tip>
+
+To learn more about how to load LoRA weights, see the [LoRA](../../using-diffusers/loading_adapters#lora) loading guide.
+
+</Tip>
+
+## SD3Transformer2DLoadersMixin
+
+[[autodoc]] loaders.transformer_sd3.SD3Transformer2DLoadersMixin
+    - all
+    - _load_ip_adapter_weights

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

@@ -0,0 +1,89 @@
+<!--Copyright 2024 The HuggingFace Team, The Black Forest 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.
+-->
+
+# FluxControlInpaint
+
+FluxControlInpaintPipeline is an implementation of Inpainting for Flux.1 Depth/Canny models. It is a pipeline that allows you to inpaint images using the Flux.1 Depth/Canny models. The pipeline takes an image and a mask as input and returns the inpainted image.
+
+FLUX.1 Depth and Canny [dev] is a 12 billion parameter rectified flow transformer capable of generating an image based on a text description while following the structure of a given input image. **This is not a ControlNet model**.
+
+| Control type | Developer | Link |
+| -------- | ---------- | ---- |
+| Depth | [Black Forest Labs](https://huggingface.co/black-forest-labs) | [Link](https://huggingface.co/black-forest-labs/FLUX.1-Depth-dev) |
+| Canny | [Black Forest Labs](https://huggingface.co/black-forest-labs) | [Link](https://huggingface.co/black-forest-labs/FLUX.1-Canny-dev) |
+
+
+<Tip>
+
+Flux can be quite expensive to run on consumer hardware devices. However, you can perform a suite of optimizations to run it faster and in a more memory-friendly manner. Check out [this section](https://huggingface.co/blog/sd3#memory-optimizations-for-sd3) for more details. Additionally, Flux can benefit from quantization for memory efficiency with a trade-off in inference latency. Refer to [this blog post](https://huggingface.co/blog/quanto-diffusers) to learn more. For an exhaustive list of resources, check out [this gist](https://gist.github.com/sayakpaul/b664605caf0aa3bf8585ab109dd5ac9c).
+
+</Tip>
+
+```python
+import torch
+from diffusers import FluxControlInpaintPipeline
+from diffusers.models.transformers import FluxTransformer2DModel
+from transformers import T5EncoderModel
+from diffusers.utils import load_image, make_image_grid
+from image_gen_aux import DepthPreprocessor # https://github.com/huggingface/image_gen_aux
+from PIL import Image
+import numpy as np
+
+pipe = FluxControlInpaintPipeline.from_pretrained(
+    "black-forest-labs/FLUX.1-Depth-dev",
+    torch_dtype=torch.bfloat16,
+)
+# use following lines if you have GPU constraints
+# ---------------------------------------------------------------
+transformer = FluxTransformer2DModel.from_pretrained(
+    "sayakpaul/FLUX.1-Depth-dev-nf4", subfolder="transformer", torch_dtype=torch.bfloat16
+)
+text_encoder_2 = T5EncoderModel.from_pretrained(
+    "sayakpaul/FLUX.1-Depth-dev-nf4", subfolder="text_encoder_2", torch_dtype=torch.bfloat16
+)
+pipe.transformer = transformer
+pipe.text_encoder_2 = text_encoder_2
+pipe.enable_model_cpu_offload()
+# ---------------------------------------------------------------
+pipe.to("cuda")
+
+prompt = "a blue robot singing opera with human-like expressions"
+image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/robot.png")
+
+head_mask = np.zeros_like(image)
+head_mask[65:580,300:642] = 255
+mask_image = Image.fromarray(head_mask)
+
+processor = DepthPreprocessor.from_pretrained("LiheYoung/depth-anything-large-hf")
+control_image = processor(image)[0].convert("RGB")
+
+output = pipe(
+    prompt=prompt,
+    image=image,
+    control_image=control_image,
+    mask_image=mask_image,
+    num_inference_steps=30,
+    strength=0.9,
+    guidance_scale=10.0,
+    generator=torch.Generator().manual_seed(42),
+).images[0]
+make_image_grid([image, control_image, mask_image, output.resize(image.size)], rows=1, cols=4).save("output.png")
+```
+
+## FluxControlInpaintPipeline
+[[autodoc]] FluxControlInpaintPipeline
+	- all
+	- __call__
+
+
+## FluxPipelineOutput
+[[autodoc]] pipelines.flux.pipeline_output.FluxPipelineOutput

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

@@ -268,6 +268,43 @@ images = pipe(
 images[0].save("flux-redux.png")
 ```
 
+## 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).
+
+```py
+from diffusers import FluxControlPipeline
+from image_gen_aux import DepthPreprocessor
+from diffusers.utils import load_image
+from huggingface_hub import hf_hub_download
+import torch
+
+control_pipe = FluxControlPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16)
+control_pipe.load_lora_weights("black-forest-labs/FLUX.1-Depth-dev-lora", adapter_name="depth")
+control_pipe.load_lora_weights(
+    hf_hub_download("ByteDance/Hyper-SD", "Hyper-FLUX.1-dev-8steps-lora.safetensors"), adapter_name="hyper-sd"
+)
+control_pipe.set_adapters(["depth", "hyper-sd"], adapter_weights=[0.85, 0.125])
+control_pipe.enable_model_cpu_offload()
+
+prompt = "A robot made of exotic candies and chocolates of different kinds. The background is filled with confetti and celebratory gifts."
+control_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/robot.png")
+
+processor = DepthPreprocessor.from_pretrained("LiheYoung/depth-anything-large-hf")
+control_image = processor(control_image)[0].convert("RGB")
+
+image = control_pipe(
+    prompt=prompt,
+    control_image=control_image,
+    height=1024,
+    width=1024,
+    num_inference_steps=8,
+    guidance_scale=10.0,
+    generator=torch.Generator().manual_seed(42),
+).images[0]
+image.save("output.png")
+```
+
 ## Running FP16 inference
 
 Flux can generate high-quality images with FP16 (i.e. to accelerate inference on Turing/Volta GPUs) but produces different outputs compared to FP32/BF16. The issue is that some activations in the text encoders have to be clipped when running in FP16, which affects the overall image. Forcing text encoders to run with FP32 inference thus removes this output difference. See [here](https://github.com/huggingface/diffusers/pull/9097#issuecomment-2272292516) for details.

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

@@ -61,6 +61,44 @@ pipe = LTXImageToVideoPipeline.from_single_file(
 )
 ```
 
+Loading [LTX GGUF checkpoints](https://huggingface.co/city96/LTX-Video-gguf) are also supported:
+
+```py
+import torch
+from diffusers.utils import export_to_video
+from diffusers import LTXPipeline, LTXVideoTransformer3DModel, GGUFQuantizationConfig
+
+ckpt_path = (
+    "https://huggingface.co/city96/LTX-Video-gguf/blob/main/ltx-video-2b-v0.9-Q3_K_S.gguf"
+)
+transformer = LTXVideoTransformer3DModel.from_single_file(
+    ckpt_path,
+    quantization_config=GGUFQuantizationConfig(compute_dtype=torch.bfloat16),
+    torch_dtype=torch.bfloat16,
+)
+pipe = LTXPipeline.from_pretrained(
+    "Lightricks/LTX-Video",
+    transformer=transformer,
+    torch_dtype=torch.bfloat16,
+)
+pipe.enable_model_cpu_offload()
+
+prompt = "A woman with long brown hair and light skin smiles at another woman with long blonde hair. The woman with brown hair wears a black jacket and has a small, barely noticeable mole on her right cheek. The camera angle is a close-up, focused on the woman with brown hair's face. The lighting is warm and natural, likely from the setting sun, casting a soft glow on the scene. The scene appears to be real-life footage"
+negative_prompt = "worst quality, inconsistent motion, blurry, jittery, distorted"
+
+video = pipe(
+    prompt=prompt,
+    negative_prompt=negative_prompt,
+    width=704,
+    height=480,
+    num_frames=161,
+    num_inference_steps=50,
+).frames[0]
+export_to_video(video, "output_gguf_ltx.mp4", fps=24)
+```
+
+Make sure to read the [documentation on GGUF](../../quantization/gguf) to learn more about our GGUF support.
+
 Refer to [this section](https://huggingface.co/docs/diffusers/main/en/api/pipelines/cogvideox#memory-optimization) to learn more about optimizing memory consumption.
 
 ## LTXPipeline