122 lines
5.3 KiB
Plaintext
122 lines
5.3 KiB
Plaintext
<!--Copyright 2022 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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-->
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# Loading and Saving Custom Pipelines
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Diffusers allows you to conveniently load any custom pipeline from the Hugging Face Hub as well as any [official community pipeline](https://github.com/huggingface/diffusers/tree/main/examples/community)
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via the [`DiffusionPipeline`] class.
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## Loading custom pipelines from the Hub
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Custom pipelines can be easily loaded from any model repository on the Hub that defines a diffusion pipeline in a `pipeline.py` file.
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Let's load a dummy pipeline from [hf-internal-testing/diffusers-dummy-pipeline](https://huggingface.co/hf-internal-testing/diffusers-dummy-pipeline).
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All you need to do is pass the custom pipeline repo id with the `custom_pipeline` argument alongside the repo from where you wish to load the pipeline modules.
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```python
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from diffusers import DiffusionPipeline
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pipeline = DiffusionPipeline.from_pretrained(
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"google/ddpm-cifar10-32", custom_pipeline="hf-internal-testing/diffusers-dummy-pipeline"
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)
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```
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This will load the custom pipeline as defined in the [model repository](https://huggingface.co/hf-internal-testing/diffusers-dummy-pipeline/blob/main/pipeline.py).
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<Tip warning={true} >
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By loading a custom pipeline from the Hugging Face Hub, you are trusting that the code you are loading
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is safe 🔒. Make sure to check out the code online before loading & running it automatically.
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</Tip>
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## Loading official community pipelines
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Community pipelines are summarized in the [community examples folder](https://github.com/huggingface/diffusers/tree/main/examples/community)
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Similarly, you need to pass both the *repo id* from where you wish to load the weights as well as the `custom_pipeline` argument. Here the `custom_pipeline` argument should consist simply of the filename of the community pipeline excluding the `.py` suffix, *e.g.* `clip_guided_stable_diffusion`.
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Since community pipelines are often more complex, one can mix loading weights from an official *repo id*
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and passing pipeline modules directly.
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```python
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from diffusers import DiffusionPipeline
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from transformers import CLIPFeatureExtractor, CLIPModel
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clip_model_id = "laion/CLIP-ViT-B-32-laion2B-s34B-b79K"
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feature_extractor = CLIPFeatureExtractor.from_pretrained(clip_model_id)
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clip_model = CLIPModel.from_pretrained(clip_model_id)
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pipeline = DiffusionPipeline.from_pretrained(
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"runwayml/stable-diffusion-v1-5",
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custom_pipeline="clip_guided_stable_diffusion",
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clip_model=clip_model,
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feature_extractor=feature_extractor,
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)
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```
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## Adding custom pipelines to the Hub
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To add a custom pipeline to the Hub, all you need to do is to define a pipeline class that inherits
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from [`DiffusionPipeline`] in a `pipeline.py` file.
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Make sure that the whole pipeline is encapsulated within a single class and that the `pipeline.py` file
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has only one such class.
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Let's quickly define an example pipeline.
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```python
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import torch
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from diffusers import DiffusionPipeline
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class MyPipeline(DiffusionPipeline):
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def __init__(self, unet, scheduler):
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super().__init__()
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self.register_modules(unet=unet, scheduler=scheduler)
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@torch.no_grad()
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def __call__(self, batch_size: int = 1, num_inference_steps: int = 50):
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# Sample gaussian noise to begin loop
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image = torch.randn((batch_size, self.unet.in_channels, self.unet.sample_size, self.unet.sample_size))
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image = image.to(self.device)
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# set step values
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self.scheduler.set_timesteps(num_inference_steps)
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for t in self.progress_bar(self.scheduler.timesteps):
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# 1. predict noise model_output
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model_output = self.unet(image, t).sample
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# 2. predict previous mean of image x_t-1 and add variance depending on eta
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# eta corresponds to η in paper and should be between [0, 1]
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# do x_t -> x_t-1
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image = self.scheduler.step(model_output, t, image, eta).prev_sample
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image = (image / 2 + 0.5).clamp(0, 1)
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image = image.cpu().permute(0, 2, 3, 1).numpy()
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return image
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```
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Now you can upload this short file under the name `pipeline.py` in your preferred [model repository](https://huggingface.co/docs/hub/models-uploading). For Stable Diffusion pipelines, you may also [join the community organisation for shared pipelines](https://huggingface.co/organizations/sd-diffusers-pipelines-library/share/BUPyDUuHcciGTOKaExlqtfFcyCZsVFdrjr) to upload yours.
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Finally, we can load the custom pipeline by passing the model repository name, *e.g.* `sd-diffusers-pipelines-library/my_custom_pipeline` alongside the model repository from where we want to load the `unet` and `scheduler` components.
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```python
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my_pipeline = DiffusionPipeline.from_pretrained(
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"google/ddpm-cifar10-32", custom_pipeline="patrickvonplaten/my_custom_pipeline"
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)
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```
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