[remote code] document trust remote code.

docs/source/en/_toctree.yml

@@ -29,7 +29,7 @@
     - local: using-diffusers/schedulers
       title: Load and compare different schedulers
     - local: using-diffusers/custom_pipeline_overview
-      title: Load community pipelines
+      title: Load community pipelines and components
     - local: using-diffusers/using_safetensors
       title: Load safetensors
     - local: using-diffusers/other-formats

docs/source/en/using-diffusers/custom_pipeline_overview.md

@@ -10,10 +10,12 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
 specific language governing permissions and limitations under the License.
 -->
 
-# Load community pipelines
+# Load community pipelines and components
 
 [[open-in-colab]]
 
+## Community pipelines
+
 Community pipelines are any [`DiffusionPipeline`] class that are different from the original implementation as specified in their paper (for example, the [`StableDiffusionControlNetPipeline`] corresponds to the [Text-to-Image Generation with ControlNet Conditioning](https://arxiv.org/abs/2302.05543) paper). They provide additional functionality or extend the original implementation of a pipeline.
 
 There are many cool community pipelines like [Speech to Image](https://github.com/huggingface/diffusers/tree/main/examples/community#speech-to-image) or [Composable Stable Diffusion](https://github.com/huggingface/diffusers/tree/main/examples/community#composable-stable-diffusion), and you can find all the official community pipelines [here](https://github.com/huggingface/diffusers/tree/main/examples/community).
@@ -54,4 +56,111 @@ pipeline = DiffusionPipeline.from_pretrained(
 )
 ```
 
-For more information about community pipelines, take a look at the [Community pipelines](custom_pipeline_examples) guide for how to use them and if you're interested in adding a community pipeline check out the [How to contribute a community pipeline](contribute_pipeline) guide!
+For more information about community pipelines, take a look at the [Community pipelines](custom_pipeline_examples) guide for how to use them and if you're interested in adding a community pipeline check out the [How to contribute a community pipeline](contribute_pipeline) guide!
+
+## Community components
+
+If your pipeline has custom components that Diffusers doesn't support already, you need to accompany the Python modules that implement them. These customized components could be VAE, UNet, scheduler, etc. For the text encoder, we rely on `transformers` anyway. So, that should be handled separately (more info here). The pipeline code itself can be customized as well.
+
+Community components allow users to build pipelines that may have customized components that are not part of Diffusers. This section shows how users should use community components to build a community pipeline.
+
+You'll use the [showlab/show-1-base](https://huggingface.co/showlab/show-1-base) pipeline checkpoint as an example here. Here, you have a custom UNet and a customized pipeline (`TextToVideoIFPipeline`). For convenience, let's call the UNet `ShowOneUNet3DConditionModel`.
+
+"showlab/show-1-base" already provides the checkpoints in the Diffusers format, which is a great starting point. So, let's start loading up the components which are already well-supported:
+
+1. **Text encoder**
+
+```python
+from transformers import T5Tokenizer, T5EncoderModel
+
+pipe_id = "showlab/show-1-base"
+tokenizer = T5Tokenizer.from_pretrained(pipe_id, subfolder="tokenizer")
+text_encoder = T5EncoderModel.from_pretrained(pipe_id, subfolder="text_encoder")
+```
+
+2. **Scheduler**
+
+```python
+from diffusers import DPMSolverMultistepScheduler
+
+scheduler = DPMSolverMultistepScheduler.from_pretrained(pipe_id, subfolder="scheduler")
+```
+
+3. **Image processor**
+
+```python
+from transformers import CLIPFeatureExtractor
+
+feature_extractor = CLIPFeatureExtractor.from_pretrained(pipe_id, subfolder="feature_extractor")
+```
+
+Now, you need to implement the custom UNet. The implementation is available [here](https://github.com/showlab/Show-1/blob/main/showone/models/unet_3d_condition.py). So, let's create a Python script called `showone_unet_3d_condition.py` and copy over the implementation, changing the `UNet3DConditionModel` classname to `ShowOneUNet3DConditionModel` to avoid any conflicts with Diffusers. This is because Diffusers already has one `UNet3DConditionModel`. We put all the components needed to implement the class in `showone_unet_3d_condition.py` only. You can find the entire file [here](https://huggingface.co/sayakpaul/show-1-base-with-code/blob/main/unet/showone_unet_3d_condition.py).
+
+Once this is done, we can initialize the UNet:
+
+```python
+from showone_unet_3d_condition import ShowOneUNet3DConditionModel
+
+unet = ShowOneUNet3DConditionModel.from_pretrained(pipe_id, subfolder="unet")
+```
+
+Then implement the custom `TextToVideoIFPipeline` in another Python script: `pipeline_t2v_base_pixel.py`. This is already available [here](https://github.com/showlab/Show-1/blob/main/showone/pipelines/pipeline_t2v_base_pixel.py). 
+
+Now that you have all the components, initialize the `TextToVideoIFPipeline`:
+
+```python
+from pipeline_t2v_base_pixel import TextToVideoIFPipeline
+import torch
+
+pipeline = TextToVideoIFPipeline(
+    unet=unet, 
+    text_encoder=text_encoder, 
+    tokenizer=tokenizer, 
+    scheduler=scheduler, 
+    feature_extractor=feature_extractor
+)
+pipeline = pipeline.to(device="cuda")
+pipeline.torch_dtype = torch.float16
+```
+
+Push to the pipeline to the Hub to share with the community:
+
+```python
+pipeline.push_to_hub("custom-t2v-pipeline")
+```
+
+After the pipeline is successfully pushed, you need a couple of changes:
+
+1. In `model_index.json` file, change the `_class_name` attribute. It should be like [so](https://huggingface.co/sayakpaul/show-1-base-with-code/blob/main/model_index.json#L2).
+2. Upload `showone_unet_3d_condition.py` to the `unet` directory ([example](https://huggingface.co/sayakpaul/show-1-base-with-code/blob/main/unet/showone_unet_3d_condition.py)).
+3. Upload `pipeline_t2v_base_pixel.py` to the pipeline base directory ([example](https://huggingface.co/sayakpaul/show-1-base-with-code/blob/main/unet/showone_unet_3d_condition.py)).
+
+To run inference, just do:
+
+```python
+from diffusers import DiffusionPipeline
+import torch
+
+pipeline = DiffusionPipeline.from_pretrained(
+    "<change-username>/<change-id>", trust_remote_code=True, torch_dtype=torch.float16
+).to("cuda")
+
+prompt = "hello"
+
+# Text embeds
+prompt_embeds, negative_embeds = pipeline.encode_prompt(prompt)
+
+# Keyframes generation (8x64x40, 2fps)
+video_frames = pipeline(
+    prompt_embeds=prompt_embeds,
+    negative_prompt_embeds=negative_embeds,
+    num_frames=8,
+    height=40,
+    width=64,
+    num_inference_steps=2,
+    guidance_scale=9.0,
+    output_type="pt"
+).frames
+```
+
+Here, notice the use of the `trust_remote_code` argument while initializing the pipeline. It is responsible for handling all the "magic" behind the scenes.