text stringlengths 0 5.54k |
|---|
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 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 hi... |
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 b... |
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 ... |
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 AutoPipelineForText2Im... |
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... |
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 imag... |
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 guidan... |
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... |
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 s... |
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 promp... |
import torch |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.