mirror of
https://github.com/huggingface/diffusers.git
synced 2026-01-27 17:22:53 +03:00
67 lines
2.9 KiB
Markdown
67 lines
2.9 KiB
Markdown
<!--Copyright 2025 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.
|
|
-->
|
|
|
|
# AutoPipeline
|
|
|
|
[AutoPipeline](../api/models/auto_model) is a *task-and-model* pipeline that automatically selects the correct pipeline subclass based on the task. It handles the complexity of loading different pipeline subclasses without needing to know the specific pipeline subclass name.
|
|
|
|
This is unlike [`DiffusionPipeline`], a *model-only* pipeline that automatically selects the pipeline subclass based on the model.
|
|
|
|
[`AutoPipelineForImage2Image`] returns a specific pipeline subclass, (for example, [`StableDiffusionXLImg2ImgPipeline`]), which can only be used for image-to-image tasks.
|
|
|
|
```py
|
|
import torch
|
|
from diffusers import AutoPipelineForImage2Image
|
|
|
|
pipeline = AutoPipelineForImage2Image.from_pretrained(
|
|
"RunDiffusion/Juggernaut-XL-v9", torch_dtype=torch.bfloat16, device_map="cuda",
|
|
)
|
|
print(pipeline)
|
|
"StableDiffusionXLImg2ImgPipeline {
|
|
"_class_name": "StableDiffusionXLImg2ImgPipeline",
|
|
...
|
|
"
|
|
```
|
|
|
|
Loading the same model with [`DiffusionPipeline`] returns the [`StableDiffusionXLPipeline`] subclass. It can be used for text-to-image, image-to-image, or inpainting tasks depending on the inputs.
|
|
|
|
```py
|
|
import torch
|
|
from diffusers import DiffusionPipeline
|
|
|
|
pipeline = DiffusionPipeline.from_pretrained(
|
|
"RunDiffusion/Juggernaut-XL-v9", torch_dtype=torch.bfloat16, device_map="cuda",
|
|
)
|
|
print(pipeline)
|
|
"StableDiffusionXLPipeline {
|
|
"_class_name": "StableDiffusionXLPipeline",
|
|
...
|
|
"
|
|
```
|
|
|
|
Check the [mappings](https://github.com/huggingface/diffusers/blob/130fd8df54f24ffb006d84787b598d8adc899f23/src/diffusers/pipelines/auto_pipeline.py#L114) to see whether a model is supported or not.
|
|
|
|
Trying to load an unsupported model returns an error.
|
|
|
|
```py
|
|
import torch
|
|
from diffusers import AutoPipelineForImage2Image
|
|
|
|
pipeline = AutoPipelineForImage2Image.from_pretrained(
|
|
"openai/shap-e-img2img", torch_dtype=torch.float16,
|
|
)
|
|
"ValueError: AutoPipeline can't find a pipeline linked to ShapEImg2ImgPipeline for None"
|
|
```
|
|
|
|
There are three types of [AutoPipeline](../api/models/auto_model) classes, [`AutoPipelineForText2Image`], [`AutoPipelineForImage2Image`] and [`AutoPipelineForInpainting`]. Each of these classes have a predefined mapping, linking a pipeline to their task-specific subclass.
|
|
|
|
When [`~AutoPipelineForText2Image.from_pretrained`] is called, it extracts the class name from the `model_index.json` file and selects the appropriate pipeline subclass for the task based on the mapping. |