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/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/conditional_image_generation.md
|
https://huggingface.co/docs/diffusers/en/using-diffusers/conditional_image_generation/#popular-models
|
.md
|
models for more direct control in generating images. The results from each model are slightly different because of their architecture and training process, but no matter which model you choose, their usage is more or less the same. Let's use the same prompt for each model and compare their results.
|
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/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/conditional_image_generation.md
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https://huggingface.co/docs/diffusers/en/using-diffusers/conditional_image_generation/#stable-diffusion-v15
|
.md
|
[Stable Diffusion v1.5](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) is a latent diffusion model initialized from [Stable Diffusion v1-4](https://huggingface.co/CompVis/stable-diffusion-v1-4), and finetuned for 595K steps on 512x512 images from the LAION-Aesthetics V2 dataset. You can use this model like:
```py
from diffusers import AutoPipelineForText2Image
import torch
|
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/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/conditional_image_generation.md
|
https://huggingface.co/docs/diffusers/en/using-diffusers/conditional_image_generation/#stable-diffusion-v15
|
.md
|
pipeline = AutoPipelineForText2Image.from_pretrained(
"stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16, variant="fp16"
).to("cuda")
generator = torch.Generator("cuda").manual_seed(31)
image = pipeline("Astronaut in a jungle, cold color palette, muted colors, detailed, 8k", generator=generator).images[0]
image
```
|
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/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/conditional_image_generation.md
|
https://huggingface.co/docs/diffusers/en/using-diffusers/conditional_image_generation/#stable-diffusion-xl
|
.md
|
SDXL is a much larger version of the previous Stable Diffusion models, and involves a two-stage model process that adds even more details to an image. It also includes some additional *micro-conditionings* to generate high-quality images centered subjects. Take a look at the more comprehensive [SDXL](sdxl) guide to learn more about how to use it. In general, you can use SDXL like:
```py
from diffusers import AutoPipelineForText2Image
import torch
|
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/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/conditional_image_generation.md
|
https://huggingface.co/docs/diffusers/en/using-diffusers/conditional_image_generation/#stable-diffusion-xl
|
.md
|
pipeline = AutoPipelineForText2Image.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16"
).to("cuda")
generator = torch.Generator("cuda").manual_seed(31)
image = pipeline("Astronaut in a jungle, cold color palette, muted colors, detailed, 8k", generator=generator).images[0]
image
```
|
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/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/conditional_image_generation.md
|
https://huggingface.co/docs/diffusers/en/using-diffusers/conditional_image_generation/#kandinsky-22
|
.md
|
The Kandinsky model is a bit different from the Stable Diffusion models because it also uses an image prior model to create embeddings that are used to better align text and images in the diffusion model.
The easiest way to use Kandinsky 2.2 is:
```py
from diffusers import AutoPipelineForText2Image
import torch
|
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/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/conditional_image_generation.md
|
https://huggingface.co/docs/diffusers/en/using-diffusers/conditional_image_generation/#kandinsky-22
|
.md
|
pipeline = AutoPipelineForText2Image.from_pretrained(
"kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16
).to("cuda")
generator = torch.Generator("cuda").manual_seed(31)
image = pipeline("Astronaut in a jungle, cold color palette, muted colors, detailed, 8k", generator=generator).images[0]
image
```
|
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/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/conditional_image_generation.md
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https://huggingface.co/docs/diffusers/en/using-diffusers/conditional_image_generation/#controlnet
|
.md
|
ControlNet models are auxiliary models or adapters that are finetuned on top of text-to-image models, such as [Stable Diffusion v1.5](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5). Using ControlNet models in combination with text-to-image models offers diverse options for more explicit control over how to generate an image. With ControlNet, you add an additional conditioning input image to the model. For example, if you provide an image of a human pose (usually represented as multiple
|
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/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/conditional_image_generation.md
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https://huggingface.co/docs/diffusers/en/using-diffusers/conditional_image_generation/#controlnet
|
.md
|
conditioning input image to the model. For example, if you provide an image of a human pose (usually represented as multiple keypoints that are connected into a skeleton) as a conditioning input, the model generates an image that follows the pose of the image. Check out the more in-depth [ControlNet](controlnet) guide to learn more about other conditioning inputs and how to use them.
|
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/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/conditional_image_generation.md
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https://huggingface.co/docs/diffusers/en/using-diffusers/conditional_image_generation/#controlnet
|
.md
|
In this example, let's condition the ControlNet with a human pose estimation image. Load the ControlNet model pretrained on human pose estimations:
```py
from diffusers import ControlNetModel, AutoPipelineForText2Image
from diffusers.utils import load_image
import torch
|
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/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/conditional_image_generation.md
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https://huggingface.co/docs/diffusers/en/using-diffusers/conditional_image_generation/#controlnet
|
.md
|
controlnet = ControlNetModel.from_pretrained(
"lllyasviel/control_v11p_sd15_openpose", torch_dtype=torch.float16, variant="fp16"
).to("cuda")
pose_image = load_image("https://huggingface.co/lllyasviel/control_v11p_sd15_openpose/resolve/main/images/control.png")
```
Pass the `controlnet` to the [`AutoPipelineForText2Image`], and provide the prompt and pose estimation image:
```py
pipeline = AutoPipelineForText2Image.from_pretrained(
|
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/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/conditional_image_generation.md
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https://huggingface.co/docs/diffusers/en/using-diffusers/conditional_image_generation/#controlnet
|
.md
|
```py
pipeline = AutoPipelineForText2Image.from_pretrained(
"stable-diffusion-v1-5/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16, variant="fp16"
).to("cuda")
generator = torch.Generator("cuda").manual_seed(31)
image = pipeline("Astronaut in a jungle, cold color palette, muted colors, detailed, 8k", image=pose_image, generator=generator).images[0]
image
```
<div class="flex flex-row gap-4">
<div class="flex-1">
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https://huggingface.co/docs/diffusers/en/using-diffusers/conditional_image_generation/#controlnet
|
.md
|
image
```
<div class="flex flex-row gap-4">
<div class="flex-1">
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/text2img-1.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">Stable Diffusion v1.5</figcaption>
</div>
<div class="flex-1">
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sdxl-text2img.png"/>
|
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/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/conditional_image_generation.md
|
https://huggingface.co/docs/diffusers/en/using-diffusers/conditional_image_generation/#controlnet
|
.md
|
<figcaption class="mt-2 text-center text-sm text-gray-500">Stable Diffusion XL</figcaption>
</div>
<div class="flex-1">
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/text2img-2.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">Kandinsky 2.2</figcaption>
</div>
<div class="flex-1">
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/text2img-3.png"/>
|
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/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/conditional_image_generation.md
|
https://huggingface.co/docs/diffusers/en/using-diffusers/conditional_image_generation/#controlnet
|
.md
|
<figcaption class="mt-2 text-center text-sm text-gray-500">ControlNet (pose conditioning)</figcaption>
</div>
</div>
|
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/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/conditional_image_generation.md
|
https://huggingface.co/docs/diffusers/en/using-diffusers/conditional_image_generation/#configure-pipeline-parameters
|
.md
|
There are a number of parameters that can be configured in the pipeline that affect how an image is generated. You can change the image's output size, specify a negative prompt to improve image quality, and more. This section dives deeper into how to use these parameters.
|
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/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/conditional_image_generation.md
|
https://huggingface.co/docs/diffusers/en/using-diffusers/conditional_image_generation/#height-and-width
|
.md
|
The `height` and `width` parameters control the height and width (in pixels) of the generated image. By default, the Stable Diffusion v1.5 model outputs 512x512 images, but you can change this to any size that is a multiple of 8. For example, to create a rectangular image:
```py
from diffusers import AutoPipelineForText2Image
import torch
|
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/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/conditional_image_generation.md
|
https://huggingface.co/docs/diffusers/en/using-diffusers/conditional_image_generation/#height-and-width
|
.md
|
pipeline = AutoPipelineForText2Image.from_pretrained(
"stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16, variant="fp16"
).to("cuda")
image = pipeline(
"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k", height=768, width=512
).images[0]
image
```
<div class="flex justify-center">
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/text2img-hw.png"/>
</div>
<Tip warning={true}>
|
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|
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/conditional_image_generation.md
|
https://huggingface.co/docs/diffusers/en/using-diffusers/conditional_image_generation/#height-and-width
|
.md
|
</div>
<Tip warning={true}>
Other models may have different default image sizes depending on the image sizes in the training dataset. For example, SDXL's default image size is 1024x1024 and using lower `height` and `width` values may result in lower quality images. Make sure you check the model's API reference first!
</Tip>
|
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/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/conditional_image_generation.md
|
https://huggingface.co/docs/diffusers/en/using-diffusers/conditional_image_generation/#guidance-scale
|
.md
|
The `guidance_scale` parameter affects how much the prompt influences image generation. A lower value gives the model "creativity" to generate images that are more loosely related to the prompt. Higher `guidance_scale` values push the model to follow the prompt more closely, and if this value is too high, you may observe some artifacts in the generated image.
```py
from diffusers import AutoPipelineForText2Image
import torch
|
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/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/conditional_image_generation.md
|
https://huggingface.co/docs/diffusers/en/using-diffusers/conditional_image_generation/#guidance-scale
|
.md
|
pipeline = AutoPipelineForText2Image.from_pretrained(
"stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16
).to("cuda")
image = pipeline(
"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k", guidance_scale=3.5
).images[0]
image
```
<div class="flex flex-row gap-4">
<div class="flex-1">
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/text2img-guidance-scale-2.5.png"/>
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/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/conditional_image_generation.md
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https://huggingface.co/docs/diffusers/en/using-diffusers/conditional_image_generation/#guidance-scale
|
.md
|
<figcaption class="mt-2 text-center text-sm text-gray-500">guidance_scale = 2.5</figcaption>
</div>
<div class="flex-1">
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/text2img-guidance-scale-7.5.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">guidance_scale = 7.5</figcaption>
</div>
<div class="flex-1">
|
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/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/conditional_image_generation.md
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https://huggingface.co/docs/diffusers/en/using-diffusers/conditional_image_generation/#guidance-scale
|
.md
|
<figcaption class="mt-2 text-center text-sm text-gray-500">guidance_scale = 7.5</figcaption>
</div>
<div class="flex-1">
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/text2img-guidance-scale-10.5.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">guidance_scale = 10.5</figcaption>
</div>
</div>
|
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/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/conditional_image_generation.md
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https://huggingface.co/docs/diffusers/en/using-diffusers/conditional_image_generation/#negative-prompt
|
.md
|
Just like how a prompt guides generation, a *negative prompt* steers the model away from things you don't want the model to generate. This is commonly used to improve overall image quality by removing poor or bad image features such as "low resolution" or "bad details". You can also use a negative prompt to remove or modify the content and style of an image.
```py
from diffusers import AutoPipelineForText2Image
import torch
|
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/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/conditional_image_generation.md
|
https://huggingface.co/docs/diffusers/en/using-diffusers/conditional_image_generation/#negative-prompt
|
.md
|
pipeline = AutoPipelineForText2Image.from_pretrained(
"stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16
).to("cuda")
image = pipeline(
prompt="Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
negative_prompt="ugly, deformed, disfigured, poor details, bad anatomy",
).images[0]
image
```
<div class="flex flex-row gap-4">
<div class="flex-1">
|
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|
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/conditional_image_generation.md
|
https://huggingface.co/docs/diffusers/en/using-diffusers/conditional_image_generation/#negative-prompt
|
.md
|
).images[0]
image
```
<div class="flex flex-row gap-4">
<div class="flex-1">
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/text2img-neg-prompt-1.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">negative_prompt = "ugly, deformed, disfigured, poor details, bad anatomy"</figcaption>
</div>
<div class="flex-1">
|
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/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/conditional_image_generation.md
|
https://huggingface.co/docs/diffusers/en/using-diffusers/conditional_image_generation/#negative-prompt
|
.md
|
</div>
<div class="flex-1">
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/text2img-neg-prompt-2.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">negative_prompt = "astronaut"</figcaption>
</div>
</div>
|
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/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/conditional_image_generation.md
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https://huggingface.co/docs/diffusers/en/using-diffusers/conditional_image_generation/#generator
|
.md
|
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html#generator) object enables reproducibility in a pipeline by setting a manual seed. You can use a `Generator` to generate batches of images and iteratively improve on an image generated from a seed as detailed in the [Improve image quality with deterministic generation](reusing_seeds) guide.
|
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/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/conditional_image_generation.md
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https://huggingface.co/docs/diffusers/en/using-diffusers/conditional_image_generation/#generator
|
.md
|
You can set a seed and `Generator` as shown below. Creating an image with a `Generator` should return the same result each time instead of randomly generating a new image.
```py
from diffusers import AutoPipelineForText2Image
import torch
|
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/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/conditional_image_generation.md
|
https://huggingface.co/docs/diffusers/en/using-diffusers/conditional_image_generation/#generator
|
.md
|
pipeline = AutoPipelineForText2Image.from_pretrained(
"stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16
).to("cuda")
generator = torch.Generator(device="cuda").manual_seed(30)
image = pipeline(
"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
generator=generator,
).images[0]
image
```
|
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/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/conditional_image_generation.md
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https://huggingface.co/docs/diffusers/en/using-diffusers/conditional_image_generation/#control-image-generation
|
.md
|
There are several ways to exert more control over how an image is generated outside of configuring a pipeline's parameters, such as prompt weighting and ControlNet models.
|
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/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/conditional_image_generation.md
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https://huggingface.co/docs/diffusers/en/using-diffusers/conditional_image_generation/#prompt-weighting
|
.md
|
Prompt weighting is a technique for increasing or decreasing the importance of concepts in a prompt to emphasize or minimize certain features in an image. We recommend using the [Compel](https://github.com/damian0815/compel) library to help you generate the weighted prompt embeddings.
<Tip>
Learn how to create the prompt embeddings in the [Prompt weighting](weighted_prompts) guide. This example focuses on how to use the prompt embeddings in the pipeline.
</Tip>
|
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/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/conditional_image_generation.md
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https://huggingface.co/docs/diffusers/en/using-diffusers/conditional_image_generation/#prompt-weighting
|
.md
|
</Tip>
Once you've created the embeddings, you can pass them to the `prompt_embeds` (and `negative_prompt_embeds` if you're using a negative prompt) parameter in the pipeline.
```py
from diffusers import AutoPipelineForText2Image
import torch
|
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/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/conditional_image_generation.md
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https://huggingface.co/docs/diffusers/en/using-diffusers/conditional_image_generation/#prompt-weighting
|
.md
|
pipeline = AutoPipelineForText2Image.from_pretrained(
"stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16
).to("cuda")
image = pipeline(
prompt_embeds=prompt_embeds, # generated from Compel
negative_prompt_embeds=negative_prompt_embeds, # generated from Compel
).images[0]
```
|
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https://huggingface.co/docs/diffusers/en/using-diffusers/conditional_image_generation/#controlnet
|
.md
|
As you saw in the [ControlNet](#controlnet) section, these models offer a more flexible and accurate way to generate images by incorporating an additional conditioning image input. Each ControlNet model is pretrained on a particular type of conditioning image to generate new images that resemble it. For example, if you take a ControlNet model pretrained on depth maps, you can give the model a depth map as a conditioning input and it'll generate an image that preserves the spatial information in it. This is
|
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/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/conditional_image_generation.md
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https://huggingface.co/docs/diffusers/en/using-diffusers/conditional_image_generation/#controlnet
|
.md
|
the model a depth map as a conditioning input and it'll generate an image that preserves the spatial information in it. This is quicker and easier than specifying the depth information in a prompt. You can even combine multiple conditioning inputs with a [MultiControlNet](controlnet#multicontrolnet)!
|
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/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/conditional_image_generation.md
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https://huggingface.co/docs/diffusers/en/using-diffusers/conditional_image_generation/#controlnet
|
.md
|
There are many types of conditioning inputs you can use, and 🤗 Diffusers supports ControlNet for Stable Diffusion and SDXL models. Take a look at the more comprehensive [ControlNet](controlnet) guide to learn how you can use these models.
|
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https://huggingface.co/docs/diffusers/en/using-diffusers/conditional_image_generation/#optimize
|
.md
|
Diffusion models are large, and the iterative nature of denoising an image is computationally expensive and intensive. But this doesn't mean you need access to powerful - or even many - GPUs to use them. There are many optimization techniques for running diffusion models on consumer and free-tier resources. For example, you can load model weights in half-precision to save GPU memory and increase speed or offload the entire model to the GPU to save even more memory.
|
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/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/conditional_image_generation.md
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https://huggingface.co/docs/diffusers/en/using-diffusers/conditional_image_generation/#optimize
|
.md
|
PyTorch 2.0 also supports a more memory-efficient attention mechanism called [*scaled dot product attention*](../optimization/torch2.0#scaled-dot-product-attention) that is automatically enabled if you're using PyTorch 2.0. You can combine this with [`torch.compile`](https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html) to speed your code up even more:
```py
from diffusers import AutoPipelineForText2Image
import torch
|
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/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/conditional_image_generation.md
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https://huggingface.co/docs/diffusers/en/using-diffusers/conditional_image_generation/#optimize
|
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|
pipeline = AutoPipelineForText2Image.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16, variant="fp16").to("cuda")
pipeline.unet = torch.compile(pipeline.unet, mode="reduce-overhead", fullgraph=True)
```
For more tips on how to optimize your code to save memory and speed up inference, read the [Memory and speed](../optimization/fp16) and [Torch 2.0](../optimization/torch2.0) guides.
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https://huggingface.co/docs/diffusers/en/using-diffusers/sdxl/
|
.md
|
<!--Copyright 2024 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
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https://huggingface.co/docs/diffusers/en/using-diffusers/sdxl/
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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.
-->
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https://huggingface.co/docs/diffusers/en/using-diffusers/sdxl/#stable-diffusion-xl
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.md
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[[open-in-colab]]
[Stable Diffusion XL](https://huggingface.co/papers/2307.01952) (SDXL) is a powerful text-to-image generation model that iterates on the previous Stable Diffusion models in three key ways:
1. the UNet is 3x larger and SDXL combines a second text encoder (OpenCLIP ViT-bigG/14) with the original text encoder to significantly increase the number of parameters
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https://huggingface.co/docs/diffusers/en/using-diffusers/sdxl/#stable-diffusion-xl
|
.md
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2. introduces size and crop-conditioning to preserve training data from being discarded and gain more control over how a generated image should be cropped
3. introduces a two-stage model process; the *base* model (can also be run as a standalone model) generates an image as an input to the *refiner* model which adds additional high-quality details
This guide will show you how to use SDXL for text-to-image, image-to-image, and inpainting.
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https://huggingface.co/docs/diffusers/en/using-diffusers/sdxl/#stable-diffusion-xl
|
.md
|
This guide will show you how to use SDXL for text-to-image, image-to-image, and inpainting.
Before you begin, make sure you have the following libraries installed:
```py
# uncomment to install the necessary libraries in Colab
#!pip install -q diffusers transformers accelerate invisible-watermark>=0.2.0
```
<Tip warning={true}>
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#!pip install -q diffusers transformers accelerate invisible-watermark>=0.2.0
```
<Tip warning={true}>
We recommend installing the [invisible-watermark](https://pypi.org/project/invisible-watermark/) library to help identify images that are generated. If the invisible-watermark library is installed, it is used by default. To disable the watermarker:
```py
pipeline = StableDiffusionXLPipeline.from_pretrained(..., add_watermarker=False)
```
</Tip>
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https://huggingface.co/docs/diffusers/en/using-diffusers/sdxl/#load-model-checkpoints
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Model weights may be stored in separate subfolders on the Hub or locally, in which case, you should use the [`~StableDiffusionXLPipeline.from_pretrained`] method:
```py
from diffusers import StableDiffusionXLPipeline, StableDiffusionXLImg2ImgPipeline
import torch
pipeline = StableDiffusionXLPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
).to("cuda")
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https://huggingface.co/docs/diffusers/en/using-diffusers/sdxl/#load-model-checkpoints
|
.md
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refiner = StableDiffusionXLImg2ImgPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-refiner-1.0", torch_dtype=torch.float16, use_safetensors=True, variant="fp16"
).to("cuda")
```
You can also use the [`~StableDiffusionXLPipeline.from_single_file`] method to load a model checkpoint stored in a single file format (`.ckpt` or `.safetensors`) from the Hub or locally:
```py
from diffusers import StableDiffusionXLPipeline, StableDiffusionXLImg2ImgPipeline
import torch
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https://huggingface.co/docs/diffusers/en/using-diffusers/sdxl/#load-model-checkpoints
|
.md
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pipeline = StableDiffusionXLPipeline.from_single_file(
"https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/sd_xl_base_1.0.safetensors",
torch_dtype=torch.float16
).to("cuda")
refiner = StableDiffusionXLImg2ImgPipeline.from_single_file(
"https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0/blob/main/sd_xl_refiner_1.0.safetensors", torch_dtype=torch.float16
).to("cuda")
```
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https://huggingface.co/docs/diffusers/en/using-diffusers/sdxl/#text-to-image
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.md
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For text-to-image, pass a text prompt. By default, SDXL generates a 1024x1024 image for the best results. You can try setting the `height` and `width` parameters to 768x768 or 512x512, but anything below 512x512 is not likely to work.
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline_text2image = AutoPipelineForText2Image.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
).to("cuda")
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https://huggingface.co/docs/diffusers/en/using-diffusers/sdxl/#text-to-image
|
.md
|
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipeline_text2image(prompt=prompt).images[0]
image
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sdxl-text2img.png" alt="generated image of an astronaut in a jungle"/>
</div>
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https://huggingface.co/docs/diffusers/en/using-diffusers/sdxl/#image-to-image
|
.md
|
For image-to-image, SDXL works especially well with image sizes between 768x768 and 1024x1024. Pass an initial image, and a text prompt to condition the image with:
```py
from diffusers import AutoPipelineForImage2Image
from diffusers.utils import load_image, make_image_grid
# use from_pipe to avoid consuming additional memory when loading a checkpoint
pipeline = AutoPipelineForImage2Image.from_pipe(pipeline_text2image).to("cuda")
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https://huggingface.co/docs/diffusers/en/using-diffusers/sdxl/#image-to-image
|
.md
|
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sdxl-text2img.png"
init_image = load_image(url)
prompt = "a dog catching a frisbee in the jungle"
image = pipeline(prompt, image=init_image, strength=0.8, guidance_scale=10.5).images[0]
make_image_grid([init_image, image], rows=1, cols=2)
```
<div class="flex justify-center">
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make_image_grid([init_image, image], rows=1, cols=2)
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sdxl-img2img.png" alt="generated image of a dog catching a frisbee in a jungle"/>
</div>
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https://huggingface.co/docs/diffusers/en/using-diffusers/sdxl/#inpainting
|
.md
|
For inpainting, you'll need the original image and a mask of what you want to replace in the original image. Create a prompt to describe what you want to replace the masked area with.
```py
from diffusers import AutoPipelineForInpainting
from diffusers.utils import load_image, make_image_grid
# use from_pipe to avoid consuming additional memory when loading a checkpoint
pipeline = AutoPipelineForInpainting.from_pipe(pipeline_text2image).to("cuda")
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https://huggingface.co/docs/diffusers/en/using-diffusers/sdxl/#inpainting
|
.md
|
img_url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sdxl-text2img.png"
mask_url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sdxl-inpaint-mask.png"
init_image = load_image(img_url)
mask_image = load_image(mask_url)
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|
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|
init_image = load_image(img_url)
mask_image = load_image(mask_url)
prompt = "A deep sea diver floating"
image = pipeline(prompt=prompt, image=init_image, mask_image=mask_image, strength=0.85, guidance_scale=12.5).images[0]
make_image_grid([init_image, mask_image, image], rows=1, cols=3)
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sdxl-inpaint.png" alt="generated image of a deep sea diver in a jungle"/>
</div>
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https://huggingface.co/docs/diffusers/en/using-diffusers/sdxl/#refine-image-quality
|
.md
|
SDXL includes a [refiner model](https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0) specialized in denoising low-noise stage images to generate higher-quality images from the base model. There are two ways to use the refiner:
1. use the base and refiner models together to produce a refined image
2. use the base model to produce an image, and subsequently use the refiner model to add more details to the image (this is how SDXL was originally trained)
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https://huggingface.co/docs/diffusers/en/using-diffusers/sdxl/#base--refiner-model
|
.md
|
When you use the base and refiner model together to generate an image, this is known as an [*ensemble of expert denoisers*](https://research.nvidia.com/labs/dir/eDiff-I/). The ensemble of expert denoisers approach requires fewer overall denoising steps versus passing the base model's output to the refiner model, so it should be significantly faster to run. However, you won't be able to inspect the base model's output because it still contains a large amount of noise.
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https://huggingface.co/docs/diffusers/en/using-diffusers/sdxl/#base--refiner-model
|
.md
|
As an ensemble of expert denoisers, the base model serves as the expert during the high-noise diffusion stage and the refiner model serves as the expert during the low-noise diffusion stage. Load the base and refiner model:
```py
from diffusers import DiffusionPipeline
import torch
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https://huggingface.co/docs/diffusers/en/using-diffusers/sdxl/#base--refiner-model
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|
base = DiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
).to("cuda")
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https://huggingface.co/docs/diffusers/en/using-diffusers/sdxl/#base--refiner-model
|
.md
|
refiner = DiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-refiner-1.0",
text_encoder_2=base.text_encoder_2,
vae=base.vae,
torch_dtype=torch.float16,
use_safetensors=True,
variant="fp16",
).to("cuda")
```
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|
.md
|
To use this approach, you need to define the number of timesteps for each model to run through their respective stages. For the base model, this is controlled by the [`denoising_end`](https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion/stable_diffusion_xl#diffusers.StableDiffusionXLPipeline.__call__.denoising_end) parameter and for the refiner model, it is controlled by the
|
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https://huggingface.co/docs/diffusers/en/using-diffusers/sdxl/#base--refiner-model
|
.md
|
parameter and for the refiner model, it is controlled by the [`denoising_start`](https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion/stable_diffusion_xl#diffusers.StableDiffusionXLImg2ImgPipeline.__call__.denoising_start) parameter.
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|
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|
<Tip>
The `denoising_end` and `denoising_start` parameters should be a float between 0 and 1. These parameters are represented as a proportion of discrete timesteps as defined by the scheduler. If you're also using the `strength` parameter, it'll be ignored because the number of denoising steps is determined by the discrete timesteps the model is trained on and the declared fractional cutoff.
</Tip>
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|
</Tip>
Let's set `denoising_end=0.8` so the base model performs the first 80% of denoising the **high-noise** timesteps and set `denoising_start=0.8` so the refiner model performs the last 20% of denoising the **low-noise** timesteps. The base model output should be in **latent** space instead of a PIL image.
```py
prompt = "A majestic lion jumping from a big stone at night"
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|
.md
|
image = base(
prompt=prompt,
num_inference_steps=40,
denoising_end=0.8,
output_type="latent",
).images
image = refiner(
prompt=prompt,
num_inference_steps=40,
denoising_start=0.8,
image=image,
).images[0]
image
```
<div class="flex gap-4">
<div>
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/lion_base.png" alt="generated image of a lion on a rock at night" />
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|
.md
|
<figcaption class="mt-2 text-center text-sm text-gray-500">default base model</figcaption>
</div>
<div>
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/lion_refined.png" alt="generated image of a lion on a rock at night in higher quality" />
<figcaption class="mt-2 text-center text-sm text-gray-500">ensemble of expert denoisers</figcaption>
</div>
</div>
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|
.md
|
<figcaption class="mt-2 text-center text-sm text-gray-500">ensemble of expert denoisers</figcaption>
</div>
</div>
The refiner model can also be used for inpainting in the [`StableDiffusionXLInpaintPipeline`]:
```py
from diffusers import StableDiffusionXLInpaintPipeline
from diffusers.utils import load_image, make_image_grid
import torch
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https://huggingface.co/docs/diffusers/en/using-diffusers/sdxl/#base--refiner-model
|
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|
base = StableDiffusionXLInpaintPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
).to("cuda")
refiner = StableDiffusionXLInpaintPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-refiner-1.0",
text_encoder_2=base.text_encoder_2,
vae=base.vae,
torch_dtype=torch.float16,
use_safetensors=True,
variant="fp16",
).to("cuda")
|
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|
img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"
init_image = load_image(img_url)
mask_image = load_image(mask_url)
prompt = "A majestic tiger sitting on a bench"
num_inference_steps = 75
high_noise_frac = 0.7
|
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|
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|
image = base(
prompt=prompt,
image=init_image,
mask_image=mask_image,
num_inference_steps=num_inference_steps,
denoising_end=high_noise_frac,
output_type="latent",
).images
image = refiner(
prompt=prompt,
image=image,
mask_image=mask_image,
num_inference_steps=num_inference_steps,
denoising_start=high_noise_frac,
).images[0]
make_image_grid([init_image, mask_image, image.resize((512, 512))], rows=1, cols=3)
```
This ensemble of expert denoisers method works well for all available schedulers!
|
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https://huggingface.co/docs/diffusers/en/using-diffusers/sdxl/#base-to-refiner-model
|
.md
|
SDXL gets a boost in image quality by using the refiner model to add additional high-quality details to the fully-denoised image from the base model, in an image-to-image setting.
Load the base and refiner models:
```py
from diffusers import DiffusionPipeline
import torch
base = DiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
).to("cuda")
|
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|
https://huggingface.co/docs/diffusers/en/using-diffusers/sdxl/#base-to-refiner-model
|
.md
|
refiner = DiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-refiner-1.0",
text_encoder_2=base.text_encoder_2,
vae=base.vae,
torch_dtype=torch.float16,
use_safetensors=True,
variant="fp16",
).to("cuda")
```
<Tip warning={true}>
|
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https://huggingface.co/docs/diffusers/en/using-diffusers/sdxl/#base-to-refiner-model
|
.md
|
vae=base.vae,
torch_dtype=torch.float16,
use_safetensors=True,
variant="fp16",
).to("cuda")
```
<Tip warning={true}>
You can use SDXL refiner with a different base model. For example, you can use the [Hunyuan-DiT](../../api/pipelines/hunyuandit) or [PixArt-Sigma](../../api/pipelines/pixart_sigma) pipelines to generate images with better prompt adherence. Once you have generated an image, you can pass it to the SDXL refiner model to enhance final generation quality.
</Tip>
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https://huggingface.co/docs/diffusers/en/using-diffusers/sdxl/#base-to-refiner-model
|
.md
|
</Tip>
Generate an image from the base model, and set the model output to **latent** space:
```py
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
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|
.md
|
image = base(prompt=prompt, output_type="latent").images[0]
```
Pass the generated image to the refiner model:
```py
image = refiner(prompt=prompt, image=image[None, :]).images[0]
```
<div class="flex gap-4">
<div>
<img class="rounded-xl" src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/sd_xl/init_image.png" alt="generated image of an astronaut riding a green horse on Mars" />
<figcaption class="mt-2 text-center text-sm text-gray-500">base model</figcaption>
</div>
<div>
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/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/sdxl.md
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https://huggingface.co/docs/diffusers/en/using-diffusers/sdxl/#base-to-refiner-model
|
.md
|
<figcaption class="mt-2 text-center text-sm text-gray-500">base model</figcaption>
</div>
<div>
<img class="rounded-xl" src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/sd_xl/refined_image.png" alt="higher quality generated image of an astronaut riding a green horse on Mars" />
<figcaption class="mt-2 text-center text-sm text-gray-500">base model + refiner model</figcaption>
</div>
</div>
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/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/sdxl.md
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https://huggingface.co/docs/diffusers/en/using-diffusers/sdxl/#base-to-refiner-model
|
.md
|
<figcaption class="mt-2 text-center text-sm text-gray-500">base model + refiner model</figcaption>
</div>
</div>
For inpainting, load the base and the refiner model in the [`StableDiffusionXLInpaintPipeline`], remove the `denoising_end` and `denoising_start` parameters, and choose a smaller number of inference steps for the refiner.
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/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/sdxl.md
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https://huggingface.co/docs/diffusers/en/using-diffusers/sdxl/#micro-conditioning
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.md
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SDXL training involves several additional conditioning techniques, which are referred to as *micro-conditioning*. These include original image size, target image size, and cropping parameters. The micro-conditionings can be used at inference time to create high-quality, centered images.
<Tip>
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/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/sdxl.md
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https://huggingface.co/docs/diffusers/en/using-diffusers/sdxl/#micro-conditioning
|
.md
|
<Tip>
You can use both micro-conditioning and negative micro-conditioning parameters thanks to classifier-free guidance. They are available in the [`StableDiffusionXLPipeline`], [`StableDiffusionXLImg2ImgPipeline`], [`StableDiffusionXLInpaintPipeline`], and [`StableDiffusionXLControlNetPipeline`].
</Tip>
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/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/sdxl.md
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https://huggingface.co/docs/diffusers/en/using-diffusers/sdxl/#size-conditioning
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.md
|
There are two types of size conditioning:
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/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/sdxl.md
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https://huggingface.co/docs/diffusers/en/using-diffusers/sdxl/#size-conditioning
|
.md
|
- [`original_size`](https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion/stable_diffusion_xl#diffusers.StableDiffusionXLPipeline.__call__.original_size) conditioning comes from upscaled images in the training batch (because it would be wasteful to discard the smaller images which make up almost 40% of the total training data). This way, SDXL learns that upscaling artifacts are not supposed to be present in high-resolution images. During inference, you can use `original_size` to
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/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/sdxl.md
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https://huggingface.co/docs/diffusers/en/using-diffusers/sdxl/#size-conditioning
|
.md
|
upscaling artifacts are not supposed to be present in high-resolution images. During inference, you can use `original_size` to indicate the original image resolution. Using the default value of `(1024, 1024)` produces higher-quality images that resemble the 1024x1024 images in the dataset. If you choose to use a lower resolution, such as `(256, 256)`, the model still generates 1024x1024 images, but they'll look like the low resolution images (simpler patterns, blurring) in the dataset.
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/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/sdxl.md
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https://huggingface.co/docs/diffusers/en/using-diffusers/sdxl/#size-conditioning
|
.md
|
- [`target_size`](https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion/stable_diffusion_xl#diffusers.StableDiffusionXLPipeline.__call__.target_size) conditioning comes from finetuning SDXL to support different image aspect ratios. During inference, if you use the default value of `(1024, 1024)`, you'll get an image that resembles the composition of square images in the dataset. We recommend using the same value for `target_size` and `original_size`, but feel free to experiment with
|
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/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/sdxl.md
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https://huggingface.co/docs/diffusers/en/using-diffusers/sdxl/#size-conditioning
|
.md
|
in the dataset. We recommend using the same value for `target_size` and `original_size`, but feel free to experiment with other options!
|
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/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/sdxl.md
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https://huggingface.co/docs/diffusers/en/using-diffusers/sdxl/#size-conditioning
|
.md
|
🤗 Diffusers also lets you specify negative conditions about an image's size to steer generation away from certain image resolutions:
```py
from diffusers import StableDiffusionXLPipeline
import torch
|
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/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/sdxl.md
|
https://huggingface.co/docs/diffusers/en/using-diffusers/sdxl/#size-conditioning
|
.md
|
pipe = StableDiffusionXLPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
).to("cuda")
|
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/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/sdxl.md
|
https://huggingface.co/docs/diffusers/en/using-diffusers/sdxl/#size-conditioning
|
.md
|
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(
prompt=prompt,
negative_original_size=(512, 512),
negative_target_size=(1024, 1024),
).images[0]
```
<div class="flex flex-col justify-center">
<img src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/sd_xl/negative_conditions.png"/>
<figcaption class="text-center">Images negatively conditioned on image resolutions of (128, 128), (256, 256), and (512, 512).</figcaption>
</div>
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/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/sdxl.md
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https://huggingface.co/docs/diffusers/en/using-diffusers/sdxl/#crop-conditioning
|
.md
|
Images generated by previous Stable Diffusion models may sometimes appear to be cropped. This is because images are actually cropped during training so that all the images in a batch have the same size. By conditioning on crop coordinates, SDXL *learns* that no cropping - coordinates `(0, 0)` - usually correlates with centered subjects and complete faces (this is the default value in 🤗 Diffusers). You can experiment with different coordinates if you want to generate off-centered compositions!
```py
|
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/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/sdxl.md
|
https://huggingface.co/docs/diffusers/en/using-diffusers/sdxl/#crop-conditioning
|
.md
|
```py
from diffusers import StableDiffusionXLPipeline
import torch
|
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/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/sdxl.md
|
https://huggingface.co/docs/diffusers/en/using-diffusers/sdxl/#crop-conditioning
|
.md
|
pipeline = StableDiffusionXLPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
).to("cuda")
|
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/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/sdxl.md
|
https://huggingface.co/docs/diffusers/en/using-diffusers/sdxl/#crop-conditioning
|
.md
|
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipeline(prompt=prompt, crops_coords_top_left=(256, 0)).images[0]
image
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sdxl-cropped.png" alt="generated image of an astronaut in a jungle, slightly cropped"/>
</div>
You can also specify negative cropping coordinates to steer generation away from certain cropping parameters:
|
58_11_3
|
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/sdxl.md
|
https://huggingface.co/docs/diffusers/en/using-diffusers/sdxl/#crop-conditioning
|
.md
|
</div>
You can also specify negative cropping coordinates to steer generation away from certain cropping parameters:
```py
from diffusers import StableDiffusionXLPipeline
import torch
|
58_11_4
|
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/sdxl.md
|
https://huggingface.co/docs/diffusers/en/using-diffusers/sdxl/#crop-conditioning
|
.md
|
pipe = StableDiffusionXLPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
).to("cuda")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(
prompt=prompt,
negative_original_size=(512, 512),
negative_crops_coords_top_left=(0, 0),
negative_target_size=(1024, 1024),
).images[0]
image
```
|
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/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/sdxl.md
|
https://huggingface.co/docs/diffusers/en/using-diffusers/sdxl/#use-a-different-prompt-for-each-text-encoder
|
.md
|
SDXL uses two text-encoders, so it is possible to pass a different prompt to each text-encoder, which can [improve quality](https://github.com/huggingface/diffusers/issues/4004#issuecomment-1627764201). Pass your original prompt to `prompt` and the second prompt to `prompt_2` (use `negative_prompt` and `negative_prompt_2` if you're using negative prompts):
```py
from diffusers import StableDiffusionXLPipeline
import torch
|
58_12_0
|
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/sdxl.md
|
https://huggingface.co/docs/diffusers/en/using-diffusers/sdxl/#use-a-different-prompt-for-each-text-encoder
|
.md
|
pipeline = StableDiffusionXLPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
).to("cuda")
|
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/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/sdxl.md
|
https://huggingface.co/docs/diffusers/en/using-diffusers/sdxl/#use-a-different-prompt-for-each-text-encoder
|
.md
|
# prompt is passed to OAI CLIP-ViT/L-14
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
# prompt_2 is passed to OpenCLIP-ViT/bigG-14
prompt_2 = "Van Gogh painting"
image = pipeline(prompt=prompt, prompt_2=prompt_2).images[0]
image
```
<div class="flex justify-center">
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/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/sdxl.md
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https://huggingface.co/docs/diffusers/en/using-diffusers/sdxl/#use-a-different-prompt-for-each-text-encoder
|
.md
|
image = pipeline(prompt=prompt, prompt_2=prompt_2).images[0]
image
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sdxl-double-prompt.png" alt="generated image of an astronaut in a jungle in the style of a van gogh painting"/>
</div>
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|
https://huggingface.co/docs/diffusers/en/using-diffusers/sdxl/#use-a-different-prompt-for-each-text-encoder
|
.md
|
</div>
The dual text-encoders also support textual inversion embeddings that need to be loaded separately as explained in the [SDXL textual inversion](textual_inversion_inference#stable-diffusion-xl) section.
|
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|
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