Merge branch 'main' into main

Author: SHYuanBest

.github/workflows/pypi_publish.yaml

@@ -68,7 +68,7 @@ jobs:
       - name: Test installing diffusers and importing
         run: |
           pip install diffusers && pip uninstall diffusers -y
-          pip install -i https://testpypi.python.org/pypi diffusers
+          pip install -i https://test.pypi.org/simple/ diffusers
           python -c "from diffusers import __version__; print(__version__)"
           python -c "from diffusers import DiffusionPipeline; pipe = DiffusionPipeline.from_pretrained('fusing/unet-ldm-dummy-update'); pipe()"
           python -c "from diffusers import DiffusionPipeline; pipe = DiffusionPipeline.from_pretrained('hf-internal-testing/tiny-stable-diffusion-pipe', safety_checker=None); pipe('ah suh du')"

docs/source/en/_toctree.yml

@@ -435,7 +435,7 @@
     - local: api/pipelines/ledits_pp
       title: LEDITS++
     - local: api/pipelines/ltx_video
-      title: LTX
+      title: LTXVideo
     - local: api/pipelines/lumina
       title: Lumina-T2X
     - local: api/pipelines/marigold

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

@@ -18,7 +18,7 @@ The model can be loaded with the following code snippet.
 ```python
 from diffusers import AutoencoderKLLTXVideo
 
-vae = AutoencoderKLLTXVideo.from_pretrained("TODO/TODO", subfolder="vae", torch_dtype=torch.float32).to("cuda")
+vae = AutoencoderKLLTXVideo.from_pretrained("Lightricks/LTX-Video", subfolder="vae", torch_dtype=torch.float32).to("cuda")
 ```
 
 ## AutoencoderKLLTXVideo

docs/source/en/api/models/ltx_video_transformer3d.md

@@ -18,7 +18,7 @@ The model can be loaded with the following code snippet.
 ```python
 from diffusers import LTXVideoTransformer3DModel
 
-transformer = LTXVideoTransformer3DModel.from_pretrained("TODO/TODO", subfolder="transformer", torch_dtype=torch.bfloat16).to("cuda")
+transformer = LTXVideoTransformer3DModel.from_pretrained("Lightricks/LTX-Video", subfolder="transformer", torch_dtype=torch.bfloat16).to("cuda")
 ```
 
 ## LTXVideoTransformer3DModel

docs/source/en/api/models/sana_transformer2d.md

@@ -22,7 +22,7 @@ The model can be loaded with the following code snippet.
 ```python
 from diffusers import SanaTransformer2DModel
 
-transformer = SanaTransformer2DModel.from_pretrained("Efficient-Large-Model/Sana_1600M_1024px_diffusers", subfolder="transformer", torch_dtype=torch.float16)
+transformer = SanaTransformer2DModel.from_pretrained("Efficient-Large-Model/Sana_1600M_1024px_BF16_diffusers", subfolder="transformer", torch_dtype=torch.bfloat16)
 ```
 
 ## SanaTransformer2DModel

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

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License. -->
 
-# LTX
+# LTX Video
 
 [LTX Video](https://huggingface.co/Lightricks/LTX-Video) is the first DiT-based video generation model capable of generating high-quality videos in real-time. It produces 24 FPS videos at a 768x512 resolution faster than they can be watched. Trained on a large-scale dataset of diverse videos, the model generates high-resolution videos with realistic and varied content. We provide a model for both text-to-video as well as image + text-to-video usecases.
 
@@ -22,14 +22,24 @@ Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers.m
 
 </Tip>
 
+Available models:
+
+|  Model name   | Recommended dtype |
+|:-------------:|:-----------------:|
+| [`LTX Video 0.9.0`](https://huggingface.co/Lightricks/LTX-Video/blob/main/ltx-video-2b-v0.9.safetensors) | `torch.bfloat16` |
+| [`LTX Video 0.9.1`](https://huggingface.co/Lightricks/LTX-Video/blob/main/ltx-video-2b-v0.9.1.safetensors) | `torch.bfloat16` |
+
+Note: The recommended dtype is for the transformer component. The VAE and text encoders can be either `torch.float32`, `torch.bfloat16` or `torch.float16` but the recommended dtype is `torch.bfloat16` as used in the original repository.
+
 ## Loading Single Files
 
-Loading the original LTX Video checkpoints is also possible with [`~ModelMixin.from_single_file`].
+Loading the original LTX Video checkpoints is also possible with [`~ModelMixin.from_single_file`]. We recommend using `from_single_file` for the Lightricks series of models, as they plan to release multiple models in the future in the single file format.
 
 ```python
 import torch
 from diffusers import AutoencoderKLLTXVideo, LTXImageToVideoPipeline, LTXVideoTransformer3DModel
 
+# `single_file_url` could also be https://huggingface.co/Lightricks/LTX-Video/ltx-video-2b-v0.9.1.safetensors
 single_file_url = "https://huggingface.co/Lightricks/LTX-Video/ltx-video-2b-v0.9.safetensors"
 transformer = LTXVideoTransformer3DModel.from_single_file(
   single_file_url, torch_dtype=torch.bfloat16
@@ -99,6 +109,34 @@ 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.
 
+<!-- TODO(aryan): Update this when official weights are supported -->
+
+Loading and running inference with [LTX Video 0.9.1](https://huggingface.co/Lightricks/LTX-Video/blob/main/ltx-video-2b-v0.9.1.safetensors) weights.
+
+```python
+import torch
+from diffusers import LTXPipeline
+from diffusers.utils import export_to_video
+
+pipe = LTXPipeline.from_pretrained("a-r-r-o-w/LTX-Video-0.9.1-diffusers", torch_dtype=torch.bfloat16)
+pipe.to("cuda")
+
+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=768,
+    height=512,
+    num_frames=161,
+    decode_timestep=0.03,
+    decode_noise_scale=0.025,
+    num_inference_steps=50,
+).frames[0]
+export_to_video(video, "output.mp4", fps=24)
+```
+
 Refer to [this section](https://huggingface.co/docs/diffusers/main/en/api/pipelines/cogvideox#memory-optimization) to learn more about optimizing memory consumption.
 
 ## LTXPipeline

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

@@ -32,9 +32,9 @@ Available models:
 
 | Model | Recommended dtype |
 |:-----:|:-----------------:|
+| [`Efficient-Large-Model/Sana_1600M_1024px_BF16_diffusers`](https://huggingface.co/Efficient-Large-Model/Sana_1600M_1024px_BF16_diffusers) | `torch.bfloat16` |
 | [`Efficient-Large-Model/Sana_1600M_1024px_diffusers`](https://huggingface.co/Efficient-Large-Model/Sana_1600M_1024px_diffusers) | `torch.float16` |
 | [`Efficient-Large-Model/Sana_1600M_1024px_MultiLing_diffusers`](https://huggingface.co/Efficient-Large-Model/Sana_1600M_1024px_MultiLing_diffusers) | `torch.float16` |
-| [`Efficient-Large-Model/Sana_1600M_1024px_BF16_diffusers`](https://huggingface.co/Efficient-Large-Model/Sana_1600M_1024px_BF16_diffusers) | `torch.bfloat16` |
 | [`Efficient-Large-Model/Sana_1600M_512px_diffusers`](https://huggingface.co/Efficient-Large-Model/Sana_1600M_512px_diffusers) | `torch.float16` |
 | [`Efficient-Large-Model/Sana_1600M_512px_MultiLing_diffusers`](https://huggingface.co/Efficient-Large-Model/Sana_1600M_512px_MultiLing_diffusers) | `torch.float16` |
 | [`Efficient-Large-Model/Sana_600M_1024px_diffusers`](https://huggingface.co/Efficient-Large-Model/Sana_600M_1024px_diffusers) | `torch.float16` |

docs/source/en/community_projects.md

@@ -79,4 +79,8 @@ Happy exploring, and thank you for being part of the Diffusers community!
     <td><a href="https://github.com/Netwrck/stable-diffusion-server"> Stable Diffusion Server </a></td>
     <td>A server configured for Inpainting/Generation/img2img with one stable diffusion model</td>
   </tr>
+  <tr style="border-top: 2px solid black">
+    <td><a href="https://github.com/suzukimain/auto_diffusers"> Model Search </a></td>
+    <td>Search models on Civitai and Hugging Face</td>
+  </tr>
 </table>

docs/source/en/quantization/torchao.md

@@ -27,7 +27,7 @@ The example below only quantizes the weights to int8.
 ```python
 from diffusers import FluxPipeline, FluxTransformer2DModel, TorchAoConfig
 
-model_id = "black-forest-labs/Flux.1-Dev"
+model_id = "black-forest-labs/FLUX.1-dev"
 dtype = torch.bfloat16
 
 quantization_config = TorchAoConfig("int8wo")
@@ -45,7 +45,9 @@ pipe = FluxPipeline.from_pretrained(
 pipe.to("cuda")
 
 prompt = "A cat holding a sign that says hello world"
-image = pipe(prompt, num_inference_steps=28, guidance_scale=0.0).images[0]
+image = pipe(
+    prompt, num_inference_steps=50, guidance_scale=4.5, max_sequence_length=512
+).images[0]
 image.save("output.png")
 ```
 

examples/advanced_diffusion_training/train_dreambooth_lora_flux_advanced.py

@@ -74,7 +74,7 @@
     import wandb
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.32.0.dev0")
+check_min_version("0.33.0.dev0")
 
 logger = get_logger(__name__)