Merge branch 'main' into fastercache

Author: a-r-r-o-w

.github/workflows/trufflehog.yml

@@ -13,3 +13,6 @@ jobs:
         fetch-depth: 0
     - name: Secret Scanning
       uses: trufflesecurity/trufflehog@main
+      with:
+        extra_args: --results=verified,unknown
+

docs/source/en/_toctree.yml

@@ -89,6 +89,8 @@
     title: Kandinsky
   - local: using-diffusers/ip_adapter
     title: IP-Adapter
+  - local: using-diffusers/omnigen
+    title: OmniGen
   - local: using-diffusers/pag
     title: PAG
   - local: using-diffusers/controlnet
@@ -276,6 +278,8 @@
         title: ConsisIDTransformer3DModel
       - local: api/models/cogview3plus_transformer2d
         title: CogView3PlusTransformer2DModel
+      - local: api/models/cogview4_transformer2d
+        title: CogView4Transformer2DModel
       - local: api/models/dit_transformer2d
         title: DiTTransformer2DModel
       - local: api/models/flux_transformer
@@ -288,10 +292,14 @@
         title: LatteTransformer3DModel
       - local: api/models/lumina_nextdit2d
         title: LuminaNextDiT2DModel
+      - local: api/models/lumina2_transformer2d
+        title: Lumina2Transformer2DModel
       - local: api/models/ltx_video_transformer3d
         title: LTXVideoTransformer3DModel
       - local: api/models/mochi_transformer3d
         title: MochiTransformer3DModel
+      - local: api/models/omnigen_transformer
+        title: OmniGenTransformer2DModel
       - local: api/models/pixart_transformer2d
         title: PixArtTransformer2DModel
       - local: api/models/prior_transformer
@@ -376,6 +384,8 @@
       title: CogVideoX
     - local: api/pipelines/cogview3
       title: CogView3
+    - local: api/pipelines/cogview4
+      title: CogView4
     - local: api/pipelines/consisid
       title: ConsisID
     - local: api/pipelines/consistency_models
@@ -438,6 +448,8 @@
       title: LEDITS++
     - local: api/pipelines/ltx_video
       title: LTXVideo
+    - local: api/pipelines/lumina2
+      title: Lumina 2.0
     - local: api/pipelines/lumina
       title: Lumina-T2X
     - local: api/pipelines/marigold
@@ -448,6 +460,8 @@
       title: MultiDiffusion
     - local: api/pipelines/musicldm
       title: MusicLDM
+    - local: api/pipelines/omnigen
+      title: OmniGen
     - local: api/pipelines/pag
       title: PAG
     - local: api/pipelines/paint_by_example

docs/source/en/api/models/cogview4_transformer2d.md

@@ -0,0 +1,30 @@
+<!--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. -->
+
+# CogView4Transformer2DModel
+
+A Diffusion Transformer model for 2D data from [CogView4]()
+
+The model can be loaded with the following code snippet.
+
+```python
+from diffusers import CogView4Transformer2DModel
+
+transformer = CogView4Transformer2DModel.from_pretrained("THUDM/CogView4-6B", subfolder="transformer", torch_dtype=torch.bfloat16).to("cuda")
+```
+
+## CogView4Transformer2DModel
+
+[[autodoc]] CogView4Transformer2DModel
+
+## Transformer2DModelOutput
+
+[[autodoc]] models.modeling_outputs.Transformer2DModelOutput

docs/source/en/api/models/lumina2_transformer2d.md

@@ -0,0 +1,30 @@
+<!-- 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. -->
+
+# Lumina2Transformer2DModel
+
+A Diffusion Transformer model for 3D video-like data was introduced in [Lumina Image 2.0](https://huggingface.co/Alpha-VLLM/Lumina-Image-2.0) by Alpha-VLLM.
+
+The model can be loaded with the following code snippet.
+
+```python
+from diffusers import Lumina2Transformer2DModel
+
+transformer = Lumina2Transformer2DModel.from_pretrained("Alpha-VLLM/Lumina-Image-2.0", subfolder="transformer", torch_dtype=torch.bfloat16)
+```
+
+## Lumina2Transformer2DModel
+
+[[autodoc]] Lumina2Transformer2DModel
+
+## Transformer2DModelOutput
+
+[[autodoc]] models.modeling_outputs.Transformer2DModelOutput

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

@@ -0,0 +1,30 @@
+<!--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.
+-->
+
+# OmniGenTransformer2DModel
+
+A Transformer model that accepts multimodal instructions to generate images for [OmniGen](https://github.com/VectorSpaceLab/OmniGen/).
+
+The abstract from the paper is:
+
+*The emergence of Large Language Models (LLMs) has unified language  generation tasks and revolutionized human-machine interaction.  However, in the realm of image generation, a unified model capable of handling various tasks within a single framework remains largely unexplored. In this work, we introduce OmniGen, a new diffusion model for unified image generation. OmniGen is characterized by the following features: 1) Unification: OmniGen not only demonstrates text-to-image generation capabilities but also inherently supports various downstream tasks, such as image editing, subject-driven generation, and visual conditional generation. 2) Simplicity: The architecture of OmniGen is highly simplified, eliminating the need for additional plugins. Moreover, compared to existing diffusion models, it is more user-friendly and can complete complex tasks end-to-end through instructions without the need for extra intermediate steps, greatly simplifying the image generation workflow. 3) Knowledge Transfer: Benefit from learning in a unified format, OmniGen effectively transfers knowledge across different tasks, manages unseen tasks and domains, and exhibits novel capabilities. We also explore the model’s reasoning capabilities and potential applications of the chain-of-thought mechanism.  This work represents the first attempt at a general-purpose image generation model,  and we will release our resources at https://github.com/VectorSpaceLab/OmniGen to foster future advancements.*
+
+```python
+import torch
+from diffusers import OmniGenTransformer2DModel
+
+transformer = OmniGenTransformer2DModel.from_pretrained("Shitao/OmniGen-v1-diffusers", subfolder="transformer", torch_dtype=torch.bfloat16)
+```
+
+## OmniGenTransformer2DModel
+
+[[autodoc]] OmniGenTransformer2DModel

docs/source/en/api/pipelines/cogview4.md

@@ -0,0 +1,34 @@
+<!--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.
+-->
+
+# CogView4
+
+<Tip>
+
+Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
+
+</Tip>
+
+This pipeline was contributed by [zRzRzRzRzRzRzR](https://github.com/zRzRzRzRzRzRzR). The original codebase can be found [here](https://huggingface.co/THUDM). The original weights can be found under [hf.co/THUDM](https://huggingface.co/THUDM).
+
+## CogView4Pipeline
+
+[[autodoc]] CogView4Pipeline
+  - all
+  - __call__
+
+## CogView4PipelineOutput
+
+[[autodoc]] pipelines.cogview4.pipeline_output.CogView4PipelineOutput

docs/source/en/api/pipelines/lumina2.md

@@ -0,0 +1,83 @@
+<!-- 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. -->
+
+# Lumina2
+
+[Lumina Image 2.0: A Unified and Efficient Image Generative Model](https://huggingface.co/Alpha-VLLM/Lumina-Image-2.0) is a 2 billion parameter flow-based diffusion transformer capable of generating diverse images from text descriptions.
+
+The abstract from the paper is:
+
+*We introduce Lumina-Image 2.0, an advanced text-to-image model that surpasses previous state-of-the-art methods across multiple benchmarks, while also shedding light on its potential to evolve into a generalist vision intelligence model. Lumina-Image 2.0 exhibits three key properties: (1) Unification – it adopts a unified architecture that treats text and image tokens as a joint sequence, enabling natural cross-modal interactions and facilitating task expansion. Besides, since high-quality captioners can provide semantically better-aligned text-image training pairs, we introduce a unified captioning system, UniCaptioner, which generates comprehensive and precise captions for the model. This not only accelerates model convergence but also enhances prompt adherence, variable-length prompt handling, and task generalization via prompt templates. (2) Efficiency – to improve the efficiency of the unified architecture, we develop a set of optimization techniques that improve semantic learning and fine-grained texture generation during training while incorporating inference-time acceleration strategies without compromising image quality. (3) Transparency – we open-source all training details, code, and models to ensure full reproducibility, aiming to bridge the gap between well-resourced closed-source research teams and independent developers.*
+
+<Tip>
+
+Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
+
+</Tip>
+
+## Using Single File loading with Lumina Image 2.0
+
+Single file loading for Lumina Image 2.0 is available for the `Lumina2Transformer2DModel`
+
+```python
+import torch
+from diffusers import Lumina2Transformer2DModel, Lumina2Text2ImgPipeline
+
+ckpt_path = "https://huggingface.co/Alpha-VLLM/Lumina-Image-2.0/blob/main/consolidated.00-of-01.pth"
+transformer = Lumina2Transformer2DModel.from_single_file(
+    ckpt_path, torch_dtype=torch.bfloat16
+)
+
+pipe = Lumina2Text2ImgPipeline.from_pretrained(
+    "Alpha-VLLM/Lumina-Image-2.0", transformer=transformer, torch_dtype=torch.bfloat16
+)
+pipe.enable_model_cpu_offload()
+image = pipe(
+    "a cat holding a sign that says hello",
+    generator=torch.Generator("cpu").manual_seed(0),
+).images[0]
+image.save("lumina-single-file.png")
+
+```
+
+## Using GGUF Quantized Checkpoints with Lumina Image 2.0
+
+GGUF Quantized checkpoints for the `Lumina2Transformer2DModel` can be loaded via `from_single_file` with the `GGUFQuantizationConfig` 
+
+```python
+from diffusers import Lumina2Transformer2DModel, Lumina2Text2ImgPipeline, GGUFQuantizationConfig 
+
+ckpt_path = "https://huggingface.co/calcuis/lumina-gguf/blob/main/lumina2-q4_0.gguf"
+transformer = Lumina2Transformer2DModel.from_single_file(
+    ckpt_path,
+    quantization_config=GGUFQuantizationConfig(compute_dtype=torch.bfloat16),
+    torch_dtype=torch.bfloat16,
+)
+
+pipe = Lumina2Text2ImgPipeline.from_pretrained(
+    "Alpha-VLLM/Lumina-Image-2.0", transformer=transformer, torch_dtype=torch.bfloat16
+)
+pipe.enable_model_cpu_offload()
+image = pipe(
+    "a cat holding a sign that says hello",
+    generator=torch.Generator("cpu").manual_seed(0),
+).images[0]
+image.save("lumina-gguf.png")
+```
+
+## Lumina2Text2ImgPipeline
+
+[[autodoc]] Lumina2Text2ImgPipeline
+  - all
+  - __call__

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

@@ -0,0 +1,80 @@
+<!--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.
+-->
+
+# OmniGen
+
+[OmniGen: Unified Image Generation](https://arxiv.org/pdf/2409.11340) from BAAI, by Shitao Xiao, Yueze Wang, Junjie Zhou, Huaying Yuan, Xingrun Xing, Ruiran Yan, Chaofan Li, Shuting Wang, Tiejun Huang, Zheng Liu.
+
+The abstract from the paper is:
+
+*The emergence of Large Language Models (LLMs) has unified language  generation tasks and revolutionized human-machine interaction.  However, in the realm of image generation, a unified model capable of handling various tasks within a single framework remains largely unexplored. In this work, we introduce OmniGen, a new diffusion model for unified image generation. OmniGen is characterized by the following features: 1) Unification: OmniGen not only demonstrates text-to-image generation capabilities but also inherently supports various downstream tasks, such as image editing, subject-driven generation, and visual conditional generation. 2) Simplicity: The architecture of OmniGen is highly simplified, eliminating the need for additional plugins. Moreover, compared to existing diffusion models, it is more user-friendly and can complete complex tasks end-to-end through instructions without the need for extra intermediate steps, greatly simplifying the image generation workflow. 3) Knowledge Transfer: Benefit from learning in a unified format, OmniGen effectively transfers knowledge across different tasks, manages unseen tasks and domains, and exhibits novel capabilities. We also explore the model’s reasoning capabilities and potential applications of the chain-of-thought mechanism.  This work represents the first attempt at a general-purpose image generation model,  and we will release our resources at https://github.com/VectorSpaceLab/OmniGen to foster future advancements.*
+
+<Tip>
+
+Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers.md) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading.md#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
+
+</Tip>
+
+This pipeline was contributed by [staoxiao](https://github.com/staoxiao). The original codebase can be found [here](https://github.com/VectorSpaceLab/OmniGen). The original weights can be found under [hf.co/shitao](https://huggingface.co/Shitao/OmniGen-v1).
+
+## Inference
+
+First, load the pipeline:
+
+```python
+import torch
+from diffusers import OmniGenPipeline
+
+pipe = OmniGenPipeline.from_pretrained("Shitao/OmniGen-v1-diffusers", torch_dtype=torch.bfloat16)
+pipe.to("cuda")
+```
+
+For text-to-image, pass a text prompt. By default, OmniGen generates a 1024x1024 image. 
+You can try setting the `height` and `width` parameters to generate images with different size.
+
+```python
+prompt = "Realistic photo. A young woman sits on a sofa, holding a book and facing the camera. She wears delicate silver hoop earrings adorned with tiny, sparkling diamonds that catch the light, with her long chestnut hair cascading over her shoulders. Her eyes are focused and gentle, framed by long, dark lashes. She is dressed in a cozy cream sweater, which complements her warm, inviting smile. Behind her, there is a table with a cup of water in a sleek, minimalist blue mug. The background is a serene indoor setting with soft natural light filtering through a window, adorned with tasteful art and flowers, creating a cozy and peaceful ambiance. 4K, HD."
+image = pipe(
+    prompt=prompt,
+    height=1024,
+    width=1024,
+    guidance_scale=3,
+    generator=torch.Generator(device="cpu").manual_seed(111),
+).images[0]
+image.save("output.png")
+```
+
+OmniGen supports multimodal inputs. 
+When the input includes an image, you need to add a placeholder `<img><|image_1|></img>` in the text prompt to represent the image. 
+It is recommended to enable `use_input_image_size_as_output` to keep the edited image the same size as the original image.
+
+```python
+prompt="<img><|image_1|></img> Remove the woman's earrings. Replace the mug with a clear glass filled with sparkling iced cola."
+input_images=[load_image("https://raw.githubusercontent.com/VectorSpaceLab/OmniGen/main/imgs/docs_img/t2i_woman_with_book.png")]
+image = pipe(
+    prompt=prompt, 
+    input_images=input_images, 
+    guidance_scale=2, 
+    img_guidance_scale=1.6,
+    use_input_image_size_as_output=True,
+    generator=torch.Generator(device="cpu").manual_seed(222)).images[0]
+image.save("output.png")
+```
+
+## OmniGenPipeline
+
+[[autodoc]] OmniGenPipeline
+  - all
+  - __call__

docs/source/en/api/utilities.md

@@ -45,3 +45,7 @@ Utility and helper functions for working with 🤗 Diffusers.
 ## apply_layerwise_casting
 
 [[autodoc]] hooks.layerwise_casting.apply_layerwise_casting
+
+## apply_group_offloading
+
+[[autodoc]] hooks.group_offloading.apply_group_offloading