Resolve merge conflict

.github/workflows/pr_test_peft_backend.yml

@@ -92,12 +92,14 @@ jobs:
       run: |
         python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
         python -m uv pip install -e [quality,test]
+        # TODO (sayakpaul, DN6): revisit `--no-deps`
         if [ "${{ matrix.lib-versions }}" == "main" ]; then
-            python -m pip install -U peft@git+https://github.com/huggingface/peft.git
-            python -m uv pip install -U transformers@git+https://github.com/huggingface/transformers.git
-            pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
+            python -m pip install -U peft@git+https://github.com/huggingface/peft.git --no-deps
+            python -m uv pip install -U transformers@git+https://github.com/huggingface/transformers.git --no-deps
+            pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git --no-deps
         else
-            python -m uv pip install -U peft transformers accelerate
+            python -m uv pip install -U peft --no-deps
+            python -m uv pip install -U transformers accelerate --no-deps
         fi
 
     - name: Environment

docker/diffusers-onnxruntime-cuda/Dockerfile

@@ -28,7 +28,7 @@ ENV PATH="/opt/venv/bin:$PATH"
 # pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
 RUN python3.10 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
     python3.10 -m uv pip install --no-cache-dir \
-        torch \
+        "torch<2.5.0" \
         torchvision \
         torchaudio \
         "onnxruntime-gpu>=1.13.1" \

docker/diffusers-pytorch-compile-cuda/Dockerfile

@@ -29,7 +29,7 @@ ENV PATH="/opt/venv/bin:$PATH"
 # pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
 RUN python3.10 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
     python3.10 -m uv pip install --no-cache-dir \
-    torch \
+    "torch<2.5.0" \
     torchvision \
     torchaudio \
     invisible_watermark && \

docker/diffusers-pytorch-cpu/Dockerfile

@@ -29,7 +29,7 @@ ENV PATH="/opt/venv/bin:$PATH"
 # pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
 RUN python3.10 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
     python3.10 -m uv pip install --no-cache-dir \
-        torch \
+        "torch<2.5.0" \
         torchvision \
         torchaudio \
         invisible_watermark \

docker/diffusers-pytorch-cuda/Dockerfile

@@ -29,7 +29,7 @@ ENV PATH="/opt/venv/bin:$PATH"
 # pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
 RUN python3.10 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
     python3.10 -m uv pip install --no-cache-dir \
-    torch \
+    "torch<2.5.0" \
     torchvision \
     torchaudio \
     invisible_watermark && \

docker/diffusers-pytorch-xformers-cuda/Dockerfile

@@ -29,7 +29,7 @@ ENV PATH="/opt/venv/bin:$PATH"
 # pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
 RUN python3.10 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
     python3.10 -m pip install --no-cache-dir \
-        torch \
+        "torch<2.5.0" \
         torchvision \
         torchaudio \
         invisible_watermark && \

docs/source/en/_toctree.yml

@@ -150,6 +150,12 @@
       title: Reinforcement learning training with DDPO
     title: Methods
   title: Training
+- sections:
+  - local: quantization/overview
+    title: Getting Started
+  - local: quantization/bitsandbytes
+    title: bitsandbytes
+  title: Quantization Methods
 - sections:
   - local: optimization/fp16
     title: Speed up inference
@@ -209,6 +215,8 @@
       title: Logging
     - local: api/outputs
       title: Outputs
+    - local: api/quantization
+      title: Quantization
     title: Main Classes
   - isExpanded: false
     sections:

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

@@ -36,6 +36,10 @@ There are two models available that can be used with the text-to-video and video
 There is one model available that can be used with the image-to-video CogVideoX pipeline:
 - [`THUDM/CogVideoX-5b-I2V`](https://huggingface.co/THUDM/CogVideoX-5b-I2V): The recommended dtype for running this model is `bf16`.
 
+There are two models that support pose controllable generation (by the [Alibaba-PAI](https://huggingface.co/alibaba-pai) team):
+- [`alibaba-pai/CogVideoX-Fun-V1.1-2b-Pose`](https://huggingface.co/alibaba-pai/CogVideoX-Fun-V1.1-2b-Pose): The recommended dtype for running this model is `bf16`.
+- [`alibaba-pai/CogVideoX-Fun-V1.1-5b-Pose`](https://huggingface.co/alibaba-pai/CogVideoX-Fun-V1.1-5b-Pose): The recommended dtype for running this model is `bf16`.
+
 ## Inference
 
 Use [`torch.compile`](https://huggingface.co/docs/diffusers/main/en/tutorials/fast_diffusion#torchcompile) to reduce the inference latency.
@@ -118,6 +122,12 @@ It is also worth noting that torchao quantization is fully compatible with [torc
   - all
   - __call__
 
+## CogVideoXFunControlPipeline
+
+[[autodoc]] CogVideoXFunControlPipeline
+  - all
+  - __call__
+
 ## CogVideoXPipelineOutput
 
 [[autodoc]] pipelines.cogvideo.pipeline_output.CogVideoXPipelineOutput

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

@@ -1,4 +1,4 @@
-<!--Copyright 2024 The HuggingFace Team and The InstantX Team. All rights reserved.
+<!--Copyright 2024 The HuggingFace Team, The InstantX Team, and the XLabs 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
@@ -31,6 +31,14 @@ This controlnet code is implemented by [The InstantX Team](https://huggingface.c
 | Depth | [The InstantX Team](https://huggingface.co/InstantX) | [Link](https://huggingface.co/Shakker-Labs/FLUX.1-dev-ControlNet-Depth) |
 | Union | [The InstantX Team](https://huggingface.co/InstantX) | [Link](https://huggingface.co/InstantX/FLUX.1-dev-Controlnet-Union) |
 
+XLabs ControlNets are also supported, which was contributed by the [XLabs team](https://huggingface.co/XLabs-AI).
+
+| ControlNet type | Developer | Link |
+| -------- | ---------- | ---- |
+| Canny | [The XLabs Team](https://huggingface.co/XLabs-AI) | [Link](https://huggingface.co/XLabs-AI/flux-controlnet-canny-diffusers) |
+| Depth | [The XLabs Team](https://huggingface.co/XLabs-AI) | [Link](https://huggingface.co/XLabs-AI/flux-controlnet-depth-diffusers) |
+| HED | [The XLabs Team](https://huggingface.co/XLabs-AI) | [Link](https://huggingface.co/XLabs-AI/flux-controlnet-hed-diffusers) |
+
 
 <Tip>
 

docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_3.md

@@ -54,6 +54,11 @@ image = pipe(
 image.save("sd3_hello_world.png")
 ```
 
+**Note:** Stable Diffusion 3.5 can also be run using the SD3 pipeline, and all mentioned optimizations and techniques apply to it as well. In total there are three official models in the SD3 family:
+- [`stabilityai/stable-diffusion-3-medium-diffusers`](https://huggingface.co/stabilityai/stable-diffusion-3-medium-diffusers)
+- [`stabilityai/stable-diffusion-3.5-large`](https://huggingface.co/stabilityai/stable-diffusion-3-5-large)
+- [`stabilityai/stable-diffusion-3.5-large-turbo`](https://huggingface.co/stabilityai/stable-diffusion-3-5-large-turbo)
+
 ## Memory Optimisations for SD3
 
 SD3 uses three text encoders, one if which is the very large T5-XXL model. This makes it challenging to run the model on GPUs with less than 24GB of VRAM, even when using `fp16` precision. The following section outlines a few memory optimizations in Diffusers that make it easier to run SD3 on low resource hardware.
@@ -308,6 +313,26 @@ image = pipe("a picture of a cat holding a sign that says hello world").images[0
 image.save('sd3-single-file-t5-fp8.png')
 ```
 
+### Loading the single file checkpoint for the Stable Diffusion 3.5 Transformer Model
+
+```python
+import torch
+from diffusers import SD3Transformer2DModel, StableDiffusion3Pipeline
+
+transformer = SD3Transformer2DModel.from_single_file(
+    "https://huggingface.co/stabilityai/stable-diffusion-3.5-large-turbo/blob/main/sd3.5_large.safetensors",
+    torch_dtype=torch.bfloat16,
+)
+pipe = StableDiffusion3Pipeline.from_pretrained(
+    "stabilityai/stable-diffusion-3.5-large",
+    transformer=transformer,
+    torch_dtype=torch.bfloat16,
+)
+pipe.enable_model_cpu_offload()
+image = pipe("a cat holding a sign that says hello world").images[0]
+image.save("sd35.png")
+```
+
 ## StableDiffusion3Pipeline
 
 [[autodoc]] StableDiffusion3Pipeline

docs/source/en/api/quantization.md

@@ -0,0 +1,33 @@
+<!--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.
+
+-->
+
+# Quantization
+
+Quantization techniques reduce memory and computational costs by representing weights and activations with lower-precision data types like 8-bit integers (int8). This enables loading larger models you normally wouldn't be able to fit into memory, and speeding up inference. Diffusers supports 8-bit and 4-bit quantization with [bitsandbytes](https://huggingface.co/docs/bitsandbytes/en/index).
+
+Quantization techniques that aren't supported in Transformers can be added with the [`DiffusersQuantizer`] class.
+
+<Tip>
+
+Learn how to quantize models in the [Quantization](../quantization/overview) guide.
+
+</Tip>
+
+
+## BitsAndBytesConfig
+
+[[autodoc]] BitsAndBytesConfig
+
+## DiffusersQuantizer
+
+[[autodoc]] quantizers.base.DiffusersQuantizer