feat(diffusers): upgrade mindone.diffusers from v0.30.3 to v0.31.0

docs/diffusers/_toctree.yml

@@ -63,6 +63,8 @@
     title: Reproducible pipelines
   title: Inference techniques
 - sections:
+  - local: using-diffusers/cogvideox
+    title: CogVideoX
   - local: using-diffusers/sdxl
     title: Stable Diffusion XL
   - local: using-diffusers/sdxl_turbo
@@ -163,64 +165,80 @@
     sections:
     - local: api/models/overview
       title: Overview
-    - local: api/models/unet
-      title: UNet1DModel
-    - local: api/models/unet2d
-      title: UNet2DModel
-    - local: api/models/unet2d-cond
-      title: UNet2DConditionModel
-    - local: api/models/unet3d-cond
-      title: UNet3DConditionModel
-    - local: api/models/unet-motion
-      title: UNetMotionModel
-    - local: api/models/uvit2d
-      title: UViT2DModel
-    - local: api/models/vq
-      title: VQModel
-    - local: api/models/autoencoderkl
-      title: AutoencoderKL
-    - local: api/models/autoencoderkl_cogvideox
-      title: AutoencoderKLCogVideoX
-    - local: api/models/asymmetricautoencoderkl
-      title: AsymmetricAutoencoderKL
-    - local: api/models/stable_cascade_unet
-      title: StableCascadeUNet
-    - local: api/models/autoencoder_tiny
-      title: Tiny AutoEncoder
-    - local: api/models/consistency_decoder_vae
-      title: ConsistencyDecoderVAE
-    - local: api/models/transformer2d
-      title: Transformer2DModel
-    - local: api/models/pixart_transformer2d
-      title: PixArtTransformer2DModel
-    - local: api/models/dit_transformer2d
-      title: DiTTransformer2DModel
-    - local: api/models/hunyuan_transformer2d
-      title: HunyuanDiT2DModel
-    - local: api/models/aura_flow_transformer2d
-      title: AuraFlowTransformer2DModel
-    - local: api/models/flux_transformer
-      title: FluxTransformer2DModel
-    - local: api/models/latte_transformer3d
-      title: LatteTransformer3DModel
-    - local: api/models/cogvideox_transformer3d
-      title: CogVideoXTransformer3DModel
-    - local: api/models/lumina_nextdit2d
-      title: LuminaNextDiT2DModel
-    - local: api/models/transformer_temporal
-      title: TransformerTemporalModel
-    - local: api/models/sd3_transformer2d
-      title: SD3Transformer2DModel
-    - local: api/models/prior_transformer
-      title: PriorTransformer
-    - local: api/models/controlnet
-      title: ControlNetModel
-    - local: api/models/controlnet_hunyuandit
-      title: HunyuanDiT2DControlNetModel
-    - local: api/models/controlnet_sd3
-      title: SD3ControlNetModel
-    - local: api/models/controlnet_sparsectrl
-      title: SparseControlNetModel
+    - sections:
+      - local: api/models/controlnet
+        title: ControlNetModel
+      - local: api/models/controlnet_flux
+        title: FluxControlNetModel
+      - local: api/models/controlnet_hunyuandit
+        title: HunyuanDiT2DControlNetModel
+      - local: api/models/controlnet_sd3
+        title: SD3ControlNetModel
+      - local: api/models/controlnet_sparsectrl
+        title: SparseControlNetModel
+      title: ControlNets
+    - sections:
+      - local: api/models/aura_flow_transformer2d
+        title: AuraFlowTransformer2DModel
+      - local: api/models/cogvideox_transformer3d
+        title: CogVideoXTransformer3DModel
+      - local: api/models/cogview3plus_transformer2d
+        title: CogView3PlusTransformer2DModel
+      - local: api/models/dit_transformer2d
+        title: DiTTransformer2DModel
+      - local: api/models/flux_transformer
+        title: FluxTransformer2DModel
+      - local: api/models/hunyuan_transformer2d
+        title: HunyuanDiT2DModel
+      - local: api/models/latte_transformer3d
+        title: LatteTransformer3DModel
+      - local: api/models/lumina_nextdit2d
+        title: LuminaNextDiT2DModel
+      - local: api/models/pixart_transformer2d
+        title: PixArtTransformer2DModel
+      - local: api/models/prior_transformer
+        title: PriorTransformer
+      - local: api/models/sd3_transformer2d
+        title: SD3Transformer2DModel
+      - local: api/models/stable_audio_transformer
+        title: StableAudioDiTModel
+      - local: api/models/transformer2d
+        title: Transformer2DModel
+      - local: api/models/transformer_temporal
+        title: TransformerTemporalModel
+      title: Transformers
+    - sections:
+      - local: api/models/stable_cascade_unet
+        title: StableCascadeUNet
+      - local: api/models/unet
+        title: UNet1DModel
+      - local: api/models/unet2d
+        title: UNet2DModel
+      - local: api/models/unet2d-cond
+        title: UNet2DConditionModel
+      - local: api/models/unet3d-cond
+        title: UNet3DConditionModel
+      - local: api/models/unet-motion
+        title: UNetMotionModel
+      - local: api/models/uvit2d
+        title: UViT2DModel
+      title: UNets
+    - sections:
+      - local: api/models/autoencoderkl
+        title: AutoencoderKL
+      - local: api/models/autoencoderkl_cogvideox
+        title: AutoencoderKLCogVideoX
+      - local: api/models/asymmetricautoencoderkl
+        title: AsymmetricAutoencoderKL
+      - local: api/models/consistency_decoder_vae
+        title: ConsistencyDecoderVAE
+      - local: api/models/autoencoder_oobleck
+        title: Oobleck AutoEncoder
+      - local: api/models/autoencoder_tiny
+        title: Tiny AutoEncoder
+      - local: api/models/vq
+        title: VQModel
+      title: VAEs
     title: Models
   - isExpanded: false
     sections:
@@ -236,10 +254,14 @@
       title: BLIP-Diffusion
     - local: api/pipelines/cogvideox
       title: CogVideoX
+    - local: api/pipelines/cogview3
+      title: CogView3
     - local: api/pipelines/consistency_models
       title: Consistency Models
     - local: api/pipelines/controlnet
       title: ControlNet
+    - local: api/pipelines/controlnet_flux
+      title: ControlNet with Flux.1
     - local: api/pipelines/controlnet_hunyuandit
       title: ControlNet with Hunyuan-DiT
     - local: api/pipelines/controlnet_sd3

docs/diffusers/api/models/cogview3plus_transformer2d.md

@@ -0,0 +1,26 @@
+<!--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. -->
+
+# CogView3PlusTransformer2DModel
+
+A Diffusion Transformer model for 2D data from [CogView3Plus](https://github.com/THUDM/CogView3) was introduced in [CogView3: Finer and Faster Text-to-Image Generation via Relay Diffusion](https://huggingface.co/papers/2403.05121) by Tsinghua University & ZhipuAI.
+
+The model can be loaded with the following code snippet.
+
+```python
+from mindone.diffusers import CogView3PlusTransformer2DModel
+
+transformer = CogView3PlusTransformer2DModel.from_pretrained("THUDM/CogView3-Plus-3B", subfolder="transformer", mindspore_dtype=mindspore.bfloat16)
+```
+
+::: mindone.diffusers.models.transformers.transformer_cogview3plus.CogView3PlusTransformer2DModel
+
+::: mindone.diffusers.models.modeling_outputs.Transformer2DModelOutput

docs/diffusers/api/models/controlnet.md

@@ -29,7 +29,7 @@ from mindone.diffusers import StableDiffusionControlNetPipeline, ControlNetModel
 url = "https://huggingface.co/lllyasviel/ControlNet-v1-1/blob/main/control_v11p_sd15_canny.pth"  # can also be a local path
 controlnet = ControlNetModel.from_single_file(url)
 
-url = "https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/v1-5-pruned.safetensors"  # can also be a local path
+url = "https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5/blob/main/v1-5-pruned.safetensors"  # can also be a local path
 pipe = StableDiffusionControlNetPipeline.from_single_file(url, controlnet=controlnet)
 ```
 

docs/diffusers/api/models/controlnet_flux.md

@@ -0,0 +1,43 @@
+<!--Copyright 2024 The HuggingFace Team and The InstantX 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.
+-->
+
+# FluxControlNetModel
+
+FluxControlNetModel is an implementation of ControlNet for Flux.1.
+
+The ControlNet model was introduced in [Adding Conditional Control to Text-to-Image Diffusion Models](https://huggingface.co/papers/2302.05543) by Lvmin Zhang, Anyi Rao, Maneesh Agrawala. It provides a greater degree of control over text-to-image generation by conditioning the model on additional inputs such as edge maps, depth maps, segmentation maps, and keypoints for pose detection.
+
+The abstract from the paper is:
+
+*We present ControlNet, a neural network architecture to add spatial conditioning controls to large, pretrained text-to-image diffusion models. ControlNet locks the production-ready large diffusion models, and reuses their deep and robust encoding layers pretrained with billions of images as a strong backbone to learn a diverse set of conditional controls. The neural architecture is connected with "zero convolutions" (zero-initialized convolution layers) that progressively grow the parameters from zero and ensure that no harmful noise could affect the finetuning. We test various conditioning controls, eg, edges, depth, segmentation, human pose, etc, with Stable Diffusion, using single or multiple conditions, with or without prompts. We show that the training of ControlNets is robust with small (<50k) and large (>1m) datasets. Extensive results show that ControlNet may facilitate wider applications to control image diffusion models.*
+
+## Loading from the original format
+
+By default the [`FluxControlNetModel`](controlnet_flux.md#mindone.diffusers.FluxControlNetModel) should be loaded with [`ModelMixin.from_pretrained`](overview.md#mindone.diffusers.ModelMixin.from_pretrained).
+
+```py
+from mindone.diffusers import FluxControlNetPipeline
+from mindone.diffusers.models import FluxControlNetModel, FluxMultiControlNetModel
+
+controlnet = FluxControlNetModel.from_pretrained("InstantX/FLUX.1-dev-Controlnet-Canny")
+pipe = FluxControlNetPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", controlnet=controlnet)
+
+controlnet = FluxControlNetModel.from_pretrained("InstantX/FLUX.1-dev-Controlnet-Canny")
+controlnet = FluxMultiControlNetModel([controlnet])
+pipe = FluxControlNetPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", controlnet=controlnet)
+```
+
+## FluxControlNetModel
+
+::: mindone.diffusers.models.controlnet_flux.FluxControlNetModel
+
+::: mindone.diffusers.models.controlnet_flux.FluxControlNetOutput

docs/diffusers/api/pipelines/animatediff.md

@@ -29,6 +29,7 @@ The abstract of the paper is the following:
 | [AnimateDiffSparseControlNetPipeline](https://github.com/mindspore-lab/mindone/blob/master/mindone/diffusers/pipelines/animatediff/pipeline_animatediff_sparsectrl.py) | *Controlled Video-to-Video Generation with AnimateDiff using SparseCtrl*  |
 | [AnimateDiffSDXLPipeline](https://github.com/mindspore-lab/mindone/blob/master/mindone/diffusers/pipelines/animatediff/pipeline_animatediff_sdxl.py)                   | *Video-to-Video Generation with AnimateDiff*                              |
 | [AnimateDiffVideoToVideoPipeline](https://github.com/mindspore-lab/mindone/blob/master/mindone/diffusers/pipelines/animatediff/pipeline_animatediff_video2video.py)    | *Video-to-Video Generation with AnimateDiff*                              |
+| [AnimateDiffVideoToVideoControlNetPipeline](https://github.com/mindspore-lab/mindone/blob/master/mindone/diffusers/pipelines/animatediff/pipeline_animatediff_video2video_controlnet.py) | *Video-to-Video Generation with AnimateDiff using ControlNet* |
 
 ## Available checkpoints
 
@@ -507,6 +508,96 @@ Here are some sample outputs:
     </tr>
 </table>
 
+### AnimateDiffVideoToVideoControlNetPipeline
+
+!!! warning
+
+    ⚠️ MindONE currently does not support the full process for condition frames generating, as MindONE does not yet support `OpenposeDetector` from controlnet_aux. Therefore, you need to prepare the `conditioning_video` in advance to continue the process.
+
+AnimateDiff can be used together with ControlNets to enhance video-to-video generation by allowing for precise control over the output. ControlNet was introduced in [Adding Conditional Control to Text-to-Image Diffusion Models](https://huggingface.co/papers/2302.05543) by Lvmin Zhang, Anyi Rao, and Maneesh Agrawala, and allows you to condition Stable Diffusion with an additional control image to ensure that the spatial information is preserved throughout the video.
+
+This pipeline allows you to condition your generation both on the original video and on a sequence of control images.
+
+```python
+import numpy as np
+import mindspore as ms
+from PIL import Image
+
+from mindone.diffusers import AnimateDiffVideoToVideoControlNetPipeline
+from mindone.diffusers.utils import export_to_gif, load_video
+from mindone.diffusers import AutoencoderKL, ControlNetModel, MotionAdapter, LCMScheduler
+
+# Load the ControlNet
+controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-openpose", mindspore_dtype=ms.float16)
+# Load the motion adapter
+motion_adapter = MotionAdapter.from_pretrained("wangfuyun/AnimateLCM")
+# Load SD 1.5 based finetuned model
+vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse", mindspore_dtype=ms.float16)
+pipe = AnimateDiffVideoToVideoControlNetPipeline.from_pretrained(
+    "SG161222/Realistic_Vision_V5.1_noVAE",
+    motion_adapter=motion_adapter,
+    controlnet=controlnet,
+    vae=vae,
+).to(dtype=ms.float16)
+
+# Enable LCM to speed up inference
+pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config, beta_schedule="linear")
+pipe.load_lora_weights("wangfuyun/AnimateLCM", weight_name="AnimateLCM_sd15_t2v_lora.safetensors", adapter_name="lcm-lora")
+pipe.set_adapters(["lcm-lora"], [0.8])
+
+video = load_video("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/dance.gif")
+video = [frame.convert("RGB") for frame in video]
+
+prompt = "astronaut in space, dancing"
+negative_prompt = "bad quality, worst quality, jpeg artifacts, ugly"
+
+# Create controlnet preprocessor
+conditioning_video = load_video("path/to/conditioning_video")
+
+# Preprocess controlnet images
+conditioning_frames = []
+for frame in conditioning_video:
+    conditioning_frames.append(frame)
+
+strength = 0.8
+video = pipe(
+    video=video,
+    prompt=prompt,
+    negative_prompt=negative_prompt,
+    num_inference_steps=10,
+    guidance_scale=2.0,
+    controlnet_conditioning_scale=0.75,
+    conditioning_frames=conditioning_frames,
+    strength=strength,
+    generator=np.random.Generator(np.random.PCG64(seed=42)),
+)[0][0]
+
+video = [frame.resize(conditioning_frames[0].size) for frame in video]
+export_to_gif(video, f"animatediff_vid2vid_controlnet.gif", fps=8)
+```
+
+Here are some sample outputs:
+
+<table align="center">
+    <tr>
+      <th align="center">Source Video</th>
+      <th align="center">Output Video</th>
+    </tr>
+    <tr>
+        <td align="center">
+          anime girl, dancing
+          <br />
+          <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/dance.gif" alt="anime girl, dancing" />
+        </td>
+        <td align="center">
+          astronaut in space, dancing
+          <br/>
+          <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/animatediff_vid2vid_controlnet.gif" alt="astronaut in space, dancing" />
+        </td>
+    </tr>
+</table>
+
+**The lights and composition were transferred from the Source Video.**
 
 ## Using Motion LoRAs
 
@@ -722,4 +813,6 @@ export_to_gif(frames, "animatelcm-motion-lora.gif")
 
 ::: mindone.diffusers.pipelines.AnimateDiffVideoToVideoPipeline
 
+::: mindone.diffusers.pipelines.AnimateDiffVideoToVideoControlNetPipeline
+
 ::: mindone.diffusers.pipelines.animatediff.AnimateDiffPipelineOutput

docs/diffusers/api/pipelines/cogvideox.md

@@ -25,7 +25,6 @@ The abstract from the paper is:
 
     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.
 
-
 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).
 
 There are two models available that can be used with the text-to-video and video-to-video CogVideoX pipelines:
@@ -35,6 +34,10 @@ 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
 
 First, load the pipeline: