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@ -213,7 +213,7 @@ identical to the weights of the "main" checkpoint, just loaded in a different fr
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Also variants do not correspond to different model structures, *e.g.* [stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) is not a variant of [stable-diffusion-2-0](https://huggingface.co/stabilityai/stable-diffusion-2) since the model structure is different (Stable Diffusion 1-5 uses a different `CLIPTextModel` compared to Stable Diffusion 2.0).
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Pipeline checkpoints that are identical in model structure, but have been trained on different datasets, trained with vastly different training setups and thus correspond to different official releases (such as [Stable Diffusion v1-4](https://huggingface.co/CompVis/stable-diffusion-v1-4) and [Stable Diffusion v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5)) should probably be stored in individual repositories instead of as variations of eachother.
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Pipeline checkpoints that are identical in model structure, but have been trained on different datasets, trained with vastly different training setups and thus correspond to different official releases (such as [Stable Diffusion v1-4](https://huggingface.co/CompVis/stable-diffusion-v1-4) and [Stable Diffusion v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5)) should probably be stored in individual repositories instead of as variations of each other.
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#### So what are checkpoint variants then?
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@ -345,7 +345,7 @@ and
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pipe = DiffusionPipeline.from_pretrained("diffusers/stable-diffusion-variants", variant="fp16")
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```
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works.
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work.
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<Tip>
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@ -399,7 +399,7 @@ As a class method, [`DiffusionPipeline.from_pretrained`] is responsible for two
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- Download the latest version of the folder structure required to run the `repo_id` with `diffusers` and cache them. If the latest folder structure is available in the local cache, [`DiffusionPipeline.from_pretrained`] will simply reuse the cache and **not** re-download the files.
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- Load the cached weights into the _correct_ pipeline class – one of the [officially supported pipeline classes](./api/overview#diffusers-summary) - and return an instance of the class. The _correct_ pipeline class is thereby retrieved from the `model_index.json` file.
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The underlying folder structure of diffusion pipelines correspond 1-to-1 to their corresponding class instances, *e.g.* [`StableDiffusionPipeline`] for [`runwayml/stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5)
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The underlying folder structure of diffusion pipelines corresponds 1-to-1 to their corresponding class instances, *e.g.* [`StableDiffusionPipeline`] for [`runwayml/stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5).
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This can be better understood by looking at an example. Let's load a pipeline class instance `pipe` and print it:
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```python
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@ -531,7 +531,7 @@ In the case of `runwayml/stable-diffusion-v1-5` the `model_index.json` is theref
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"class"
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]
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```
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- The `"name"` field corresponds both to the name of the subfolder in which the configuration and weights are stored as well as the attribute name of the pipeline class (as can be seen [here](https://huggingface.co/runwayml/stable-diffusion-v1-5/tree/main/bert) and [here](https://github.com/huggingface/diffusers/blob/cd502b25cf0debac6f98d27a6638ef95208d1ea2/src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion.py#L42)
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- The `"name"` field corresponds both to the name of the subfolder in which the configuration and weights are stored as well as the attribute name of the pipeline class (as can be seen [here](https://huggingface.co/runwayml/stable-diffusion-v1-5/tree/main/bert) and [here](https://github.com/huggingface/diffusers/blob/cd502b25cf0debac6f98d27a6638ef95208d1ea2/src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion.py#L42))
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- The `"library"` field corresponds to the name of the library, *e.g.* `diffusers` or `transformers` from which the `"class"` should be loaded
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- The `"class"` field corresponds to the name of the class, *e.g.* [`CLIPTokenizer`](https://huggingface.co/docs/transformers/main/en/model_doc/clip#transformers.CLIPTokenizer) or [`UNet2DConditionModel`]
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@ -652,6 +652,6 @@ euler_anc = EulerAncestralDiscreteScheduler.from_pretrained(repo_id, subfolder="
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euler = EulerDiscreteScheduler.from_pretrained(repo_id, subfolder="scheduler")
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dpm = DPMSolverMultistepScheduler.from_pretrained(repo_id, subfolder="scheduler")
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# replace `dpm` with any of `ddpm`, `ddim`, `pndm`, `lms`, `euler`, `euler_anc`
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# replace `dpm` with any of `ddpm`, `ddim`, `pndm`, `lms`, `euler_anc`, `euler`
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pipeline = StableDiffusionPipeline.from_pretrained(repo_id, scheduler=dpm)
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```
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