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Let’s load a dummy pipeline from hf-internal-testing/diffusers-dummy-pipeline. |
All you need to do is pass the custom pipeline repo id with the custom_pipeline argument alongside the repo from where you wish to load the pipeline modules. |
Copied |
from diffusers import DiffusionPipeline |
pipeline = DiffusionPipeline.from_pretrained( |
"google/ddpm-cifar10-32", custom_pipeline="hf-internal-testing/diffusers-dummy-pipeline" |
) |
This will load the custom pipeline as defined in the model repository. |
By loading a custom pipeline from the Hugging Face Hub, you are trusting that the code you are loading |
is safe 🔒. Make sure to check out the code online before loading & running it automatically. |
Loading official community pipelines |
Community pipelines are summarized in the community examples folder |
Similarly, you need to pass both the repo id from where you wish to load the weights as well as the custom_pipeline argument. Here the custom_pipeline argument should consist simply of the filename of the community pipeline excluding the .py suffix, e.g. clip_guided_stable_diffusion. |
Since community pipelines are often more complex, one can mix loading weights from an official repo id |
and passing pipeline modules directly. |
Copied |
from diffusers import DiffusionPipeline |
from transformers import CLIPFeatureExtractor, CLIPModel |
clip_model_id = "laion/CLIP-ViT-B-32-laion2B-s34B-b79K" |
feature_extractor = CLIPFeatureExtractor.from_pretrained(clip_model_id) |
clip_model = CLIPModel.from_pretrained(clip_model_id) |
pipeline = DiffusionPipeline.from_pretrained( |
"runwayml/stable-diffusion-v1-5", |
custom_pipeline="clip_guided_stable_diffusion", |
clip_model=clip_model, |
feature_extractor=feature_extractor, |
) |
Adding custom pipelines to the Hub |
To add a custom pipeline to the Hub, all you need to do is to define a pipeline class that inherits |
from DiffusionPipeline in a pipeline.py file. |
Make sure that the whole pipeline is encapsulated within a single class and that the pipeline.py file |
has only one such class. |
Let’s quickly define an example pipeline. |
Copied |
import torch |
from diffusers import DiffusionPipeline |
class MyPipeline(DiffusionPipeline): |
def __init__(self, unet, scheduler): |
super().__init__() |
self.register_modules(unet=unet, scheduler=scheduler) |
@torch.no_grad() |
def __call__(self, batch_size: int = 1, num_inference_steps: int = 50): |
# Sample gaussian noise to begin loop |
image = torch.randn((batch_size, self.unet.in_channels, self.unet.sample_size, self.unet.sample_size)) |
image = image.to(self.device) |
# set step values |
self.scheduler.set_timesteps(num_inference_steps) |
for t in self.progress_bar(self.scheduler.timesteps): |
# 1. predict noise model_output |
model_output = self.unet(image, t).sample |
# 2. predict previous mean of image x_t-1 and add variance depending on eta |
# eta corresponds to η in paper and should be between [0, 1] |
# do x_t -> x_t-1 |
image = self.scheduler.step(model_output, t, image, eta).prev_sample |
image = (image / 2 + 0.5).clamp(0, 1) |
image = image.cpu().permute(0, 2, 3, 1).numpy() |
return image |
Now you can upload this short file under the name pipeline.py in your preferred model repository. For Stable Diffusion pipelines, you may also join the community organisation for shared pipelines to upload yours. |
Finally, we can load the custom pipeline by passing the model repository name, e.g. sd-diffusers-pipelines-library/my_custom_pipeline alongside the model repository from where we want to load the unet and scheduler components. |
Copied |
my_pipeline = DiffusionPipeline.from_pretrained( |
"google/ddpm-cifar10-32", custom_pipeline="patrickvonplaten/my_custom_pipeline" |
) |
DPMSolverMultistepScheduler DPMSolverMultistep is a multistep scheduler from DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps and DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models by Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and J... |
samples, and it can generate quite good samples even in 10 steps. Tips It is recommended to set solver_order to 2 for guide sampling, and solver_order=3 for unconditional sampling. Dynamic thresholding from Imagen is supported, and for pixel-space |
diffusion models, you can set both algorithm_type="dpmsolver++" and thresholding=True to use the dynamic |
thresholding. This thresholding method is unsuitable for latent-space diffusion models such as |
Stable Diffusion. The SDE variant of DPMSolver and DPM-Solver++ is also supported, but only for the first and second-order solvers. This is a fast SDE solver for the reverse diffusion SDE. It is recommended to use the second-order sde-dpmsolver++. DPMSolverMultistepScheduler class diffusers.DPMSolverMultistepSchedule... |
The number of diffusion steps to train the model. beta_start (float, defaults to 0.0001) — |
The starting beta value of inference. beta_end (float, defaults to 0.02) — |
The final beta value. beta_schedule (str, defaults to "linear") — |
The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from |
linear, scaled_linear, or squaredcos_cap_v2. trained_betas (np.ndarray, optional) — |
Pass an array of betas directly to the constructor to bypass beta_start and beta_end. solver_order (int, defaults to 2) — |
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