sayakpaul/diffusers-qa-chatbot-artifacts · Datasets at Hugging Face

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webpage of your fork on GitHub. Click on ‘Pull request’ to send your changes
to the project maintainers for review. It’s OK if maintainers ask you for changes. It happens to core contributors
too! So everyone can see the changes in the Pull request, work in your local
branch and push the changes to your fork. They will automatically appear in
the pull request. Tests An extensive test suite is included to test the library behavior and several examples. Library tests can be found in
the tests folder. We like pytest and pytest-xdist because it’s faster. From the root of the
repository, here’s how to run tests with pytest for the library: Copied $ python -m pytest -n auto --dist=loadfile -s -v ./tests/ In fact, that’s how make test is implemented! You can specify a smaller set of tests in order to test only the feature
you’re working on. By default, slow tests are skipped. Set the RUN_SLOW environment variable to
yes to run them. This will download many gigabytes of models — make sure you
have enough disk space and a good Internet connection, or a lot of patience! Copied $ RUN_SLOW=yes python -m pytest -n auto --dist=loadfile -s -v ./tests/ unittest is fully supported, here’s how to run tests with it: Copied $ python -m unittest discover -s tests -t . -v
$ python -m unittest discover -s examples -t examples -v Syncing forked main with upstream (HuggingFace) main To avoid pinging the upstream repository which adds reference notes to each upstream PR and sends unnecessary notifications to the developers involved in these PRs,
when syncing the main branch of a forked repository, please, follow these steps: When possible, avoid syncing with the upstream using a branch and PR on the forked repository. Instead, merge directly into the forked main. If a PR is absolutely necessary, use the following steps after checking out your branch: Copied $ git checkout -b your-branch-for-syncing
$ git pull --squash --no-commit upstream main
$ git commit -m '<your message without GitHub references>'
$ git push --set-upstream origin your-branch-for-syncing Style guide For documentation strings, 🧨 Diffusers follows the Google style.
Text-to-image When you think of diffusion models, text-to-image is usually one of the first things that come to mind. Text-to-image generates an image from a text description (for example, “Astronaut in a jungle, cold color palette, muted colors, detailed, 8k”) which is also known as a prompt. From a very high level, a diffusion model takes a prompt and some random initial noise, and iteratively removes the noise to construct an image. The denoising process is guided by the prompt, and once the denoising process ends after a predetermined number of time steps, the image representation is decoded into an image. Read the How does Stable Diffusion work? blog post to learn more about how a latent diffusion model works. You can generate images from a prompt in 🤗 Diffusers in two steps: Load a checkpoint into the AutoPipelineForText2Image class, which automatically detects the appropriate pipeline class to use based on the checkpoint: Copied from diffusers import AutoPipelineForText2Image
import torch

pipeline = AutoPipelineForText2Image.from_pretrained(
"runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, variant="fp16"
).to("cuda") Pass a prompt to the pipeline to generate an image: Copied image = pipeline(
"stained glass of darth vader, backlight, centered composition, masterpiece, photorealistic, 8k"
).images[0]
image Popular models The most common text-to-image models are Stable Diffusion v1.5, Stable Diffusion XL (SDXL), and Kandinsky 2.2. There are also ControlNet models or adapters that can be used with text-to-image 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. Stable Diffusion v1.5 Stable Diffusion v1.5 is a latent diffusion model initialized from Stable Diffusion v1-4, and finetuned for 595K steps on 512x512 images from the LAION-Aesthetics V2 dataset. You can use this model like: Copied from diffusers import AutoPipelineForText2Image
import torch

pipeline = AutoPipelineForText2Image.from_pretrained(
"runwayml/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 Stable Diffusion XL 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 guide to learn more about how to use it. In general, you can use SDXL like: Copied from diffusers import AutoPipelineForText2Image
import torch

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 Kandinsky 2.2 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: Copied from diffusers import AutoPipelineForText2Image
import torch

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 ControlNet ControlNet models are auxiliary models or adapters that are finetuned on top of text-to-image models, such as 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 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 guide to learn more about other conditioning inputs and how to use them. In this example, let’s condition the ControlNet with a human pose estimation image. Load the ControlNet model pretrained on human pose estimations: Copied from diffusers import ControlNetModel, AutoPipelineForText2Image
from diffusers.utils import load_image
import torch

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: Copied pipeline = AutoPipelineForText2Image.from_pretrained(
"runwayml/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 Stable Diffusion v1.5 Stable Diffusion XL Kandinsky 2.2 ControlNet (pose conditioning) Configure pipeline parameters 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. Height and width 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: Copied from diffusers import AutoPipelineForText2Image
import torch

pipeline = AutoPipelineForText2Image.from_pretrained(
"runwayml/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 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! Guidance scale 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. Copied from diffusers import AutoPipelineForText2Image
import torch

pipeline = AutoPipelineForText2Image.from_pretrained(
"runwayml/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 guidance_scale = 2.5 guidance_scale = 7.5 guidance_scale = 10.5 Negative prompt 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. Copied from diffusers import AutoPipelineForText2Image
import torch

pipeline = AutoPipelineForText2Image.from_pretrained(
"runwayml/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 negative_prompt = "ugly, deformed, disfigured, poor details, bad anatomy" negative_prompt = "astronaut" Generator A torch.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 guide. 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. Copied from diffusers import AutoPipelineForText2Image
import torch

pipeline = AutoPipelineForText2Image.from_pretrained(
"runwayml/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 Control image generation 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. Prompt weighting 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 library to help you generate the weighted prompt embeddings. Learn how to create the prompt embeddings in the Prompt weighting guide. This example focuses on how to use the prompt embeddings in the pipeline. 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. Copied from diffusers import AutoPipelineForText2Image
import torch

webpage of your fork on GitHub. Click on ‘Pull request’ to send your changes

to the project maintainers for review. It’s OK if maintainers ask you for changes. It happens to core contributors

too! So everyone can see the changes in the Pull request, work in your local

branch and push the changes to your fork. They will automatically appear in

the pull request. Tests An extensive test suite is included to test the library behavior and several examples. Library tests can be found in

the tests folder. We like pytest and pytest-xdist because it’s faster. From the root of the

repository, here’s how to run tests with pytest for the library: Copied $ python -m pytest -n auto --dist=loadfile -s -v ./tests/ In fact, that’s how make test is implemented! You can specify a smaller set of tests in order to test only the feature

you’re working on. By default, slow tests are skipped. Set the RUN_SLOW environment variable to

yes to run them. This will download many gigabytes of models — make sure you

have enough disk space and a good Internet connection, or a lot of patience! Copied $ RUN_SLOW=yes python -m pytest -n auto --dist=loadfile -s -v ./tests/ unittest is fully supported, here’s how to run tests with it: Copied $ python -m unittest discover -s tests -t . -v

$ python -m unittest discover -s examples -t examples -v Syncing forked main with upstream (HuggingFace) main To avoid pinging the upstream repository which adds reference notes to each upstream PR and sends unnecessary notifications to the developers involved in these PRs,

when syncing the main branch of a forked repository, please, follow these steps: When possible, avoid syncing with the upstream using a branch and PR on the forked repository. Instead, merge directly into the forked main. If a PR is absolutely necessary, use the following steps after checking out your branch: Copied $ git checkout -b your-branch-for-syncing

$ git pull --squash --no-commit upstream main

$ git commit -m '<your message without GitHub references>'

$ git push --set-upstream origin your-branch-for-syncing Style guide For documentation strings, 🧨 Diffusers follows the Google style.

Text-to-image When you think of diffusion models, text-to-image is usually one of the first things that come to mind. Text-to-image generates an image from a text description (for example, “Astronaut in a jungle, cold color palette, muted colors, detailed, 8k”) which is also known as a prompt. From a very high level, a diffusion model takes a prompt and some random initial noise, and iteratively removes the noise to construct an image. The denoising process is guided by the prompt, and once the denoising process ends after a predetermined number of time steps, the image representation is decoded into an image. Read the How does Stable Diffusion work? blog post to learn more about how a latent diffusion model works. You can generate images from a prompt in 🤗 Diffusers in two steps: Load a checkpoint into the AutoPipelineForText2Image class, which automatically detects the appropriate pipeline class to use based on the checkpoint: Copied from diffusers import AutoPipelineForText2Image

import torch

pipeline = AutoPipelineForText2Image.from_pretrained(

"runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, variant="fp16"

).to("cuda") Pass a prompt to the pipeline to generate an image: Copied image = pipeline(

"stained glass of darth vader, backlight, centered composition, masterpiece, photorealistic, 8k"

).images[0]

image Popular models The most common text-to-image models are Stable Diffusion v1.5, Stable Diffusion XL (SDXL), and Kandinsky 2.2. There are also ControlNet models or adapters that can be used with text-to-image 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. Stable Diffusion v1.5 Stable Diffusion v1.5 is a latent diffusion model initialized from Stable Diffusion v1-4, and finetuned for 595K steps on 512x512 images from the LAION-Aesthetics V2 dataset. You can use this model like: Copied from diffusers import AutoPipelineForText2Image

import torch

pipeline = AutoPipelineForText2Image.from_pretrained(

"runwayml/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 Stable Diffusion XL 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 guide to learn more about how to use it. In general, you can use SDXL like: Copied from diffusers import AutoPipelineForText2Image

import torch

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 Kandinsky 2.2 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: Copied from diffusers import AutoPipelineForText2Image

import torch

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 ControlNet ControlNet models are auxiliary models or adapters that are finetuned on top of text-to-image models, such as 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 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 guide to learn more about other conditioning inputs and how to use them. In this example, let’s condition the ControlNet with a human pose estimation image. Load the ControlNet model pretrained on human pose estimations: Copied from diffusers import ControlNetModel, AutoPipelineForText2Image

from diffusers.utils import load_image

import torch

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: Copied pipeline = AutoPipelineForText2Image.from_pretrained(

"runwayml/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 Stable Diffusion v1.5 Stable Diffusion XL Kandinsky 2.2 ControlNet (pose conditioning) Configure pipeline parameters 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. Height and width 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: Copied from diffusers import AutoPipelineForText2Image

import torch

pipeline = AutoPipelineForText2Image.from_pretrained(

"runwayml/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 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! Guidance scale 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. Copied from diffusers import AutoPipelineForText2Image

import torch

pipeline = AutoPipelineForText2Image.from_pretrained(

"runwayml/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 guidance_scale = 2.5 guidance_scale = 7.5 guidance_scale = 10.5 Negative prompt 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. Copied from diffusers import AutoPipelineForText2Image

import torch

pipeline = AutoPipelineForText2Image.from_pretrained(

"runwayml/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 negative_prompt = "ugly, deformed, disfigured, poor details, bad anatomy" negative_prompt = "astronaut" Generator A torch.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 guide. 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. Copied from diffusers import AutoPipelineForText2Image

import torch

pipeline = AutoPipelineForText2Image.from_pretrained(

"runwayml/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 Control image generation 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. Prompt weighting 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 library to help you generate the weighted prompt embeddings. Learn how to create the prompt embeddings in the Prompt weighting guide. This example focuses on how to use the prompt embeddings in the pipeline. 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. Copied from diffusers import AutoPipelineForText2Image

import torch