49 lines
2.0 KiB
Plaintext
49 lines
2.0 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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# Conditional Image Generation
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The [`DiffusionPipeline`] is the easiest way to use a pre-trained diffusion system for inference
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Start by creating an instance of [`DiffusionPipeline`] and specify which pipeline checkpoint you would like to download.
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You can use the [`DiffusionPipeline`] for any [Diffusers' checkpoint](https://huggingface.co/models?library=diffusers&sort=downloads).
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In this guide though, you'll use [`DiffusionPipeline`] for text-to-image generation with [Latent Diffusion](https://huggingface.co/CompVis/ldm-text2im-large-256):
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```python
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>>> from diffusers import DiffusionPipeline
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>>> generator = DiffusionPipeline.from_pretrained("CompVis/ldm-text2im-large-256")
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```
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The [`DiffusionPipeline`] downloads and caches all modeling, tokenization, and scheduling components.
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Because the model consists of roughly 1.4 billion parameters, we strongly recommend running it on GPU.
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You can move the generator object to GPU, just like you would in PyTorch.
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```python
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>>> generator.to("cuda")
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```
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Now you can use the `generator` on your text prompt:
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```python
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>>> image = generator("An image of a squirrel in Picasso style").images[0]
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```
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The output is by default wrapped into a [PIL Image object](https://pillow.readthedocs.io/en/stable/reference/Image.html?highlight=image#the-image-class).
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You can save the image by simply calling:
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```python
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>>> image.save("image_of_squirrel_painting.png")
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```
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