text stringlengths 0 5.54k |
|---|
Check out the Spaces below to try out image inpainting yourself! |
Contribute a community pipeline 💡 Take a look at GitHub Issue #841 for more context about why we’re adding community pipelines to help everyone easily share their work without being slowed down. Community pipelines allow you to add any additional features you’d like on top of the DiffusionPipeline. The main benefit of... |
import torch |
class UnetSchedulerOneForwardPipeline(DiffusionPipeline): |
def __init__(self, unet, scheduler): |
super().__init__() To ensure your pipeline and its components (unet and scheduler) can be saved with save_pretrained(), add them to the register_modules function: Copied from diffusers import DiffusionPipeline |
import torch |
class UnetSchedulerOneForwardPipeline(DiffusionPipeline): |
def __init__(self, unet, scheduler): |
super().__init__() |
+ self.register_modules(unet=unet, scheduler=scheduler) Cool, the __init__ step is done and you can move to the forward pass now! 🔥 Define the forward pass In the forward pass, which we recommend defining as __call__, you have complete creative freedom to add whatever feature you’d like. For our amazing one-s... |
import torch |
class UnetSchedulerOneForwardPipeline(DiffusionPipeline): |
def __init__(self, unet, scheduler): |
super().__init__() |
self.register_modules(unet=unet, scheduler=scheduler) |
+ def __call__(self): |
+ image = torch.randn( |
+ (1, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size), |
+ ) |
+ timestep = 1 |
+ model_output = self.unet(image, timestep).sample |
+ scheduler_output = self.scheduler.step(model_output, timestep, image).prev_sample |
+ return scheduler_output That’s it! 🚀 You can now run this pipeline by passing a unet and scheduler to it: Copied from diffusers import DDPMScheduler, UNet2DModel |
scheduler = DDPMScheduler() |
unet = UNet2DModel() |
pipeline = UnetSchedulerOneForwardPipeline(unet=unet, scheduler=scheduler) |
output = pipeline() But what’s even better is you can load pre-existing weights into the pipeline if the pipeline structure is identical. For example, you can load the google/ddpm-cifar10-32 weights into the one-step pipeline: Copied pipeline = UnetSchedulerOneForwardPipeline.from_pretrained("google/ddpm-cifar10-32",... |
output = pipeline() Share your pipeline Open a Pull Request on the 🧨 Diffusers repository to add your awesome pipeline in one_step_unet.py to the examples/community subfolder. Once it is merged, anyone with diffusers >= 0.4.0 installed can use this pipeline magically 🪄 by specifying it in the custom_pipeline argumen... |
pipe = DiffusionPipeline.from_pretrained( |
"google/ddpm-cifar10-32", custom_pipeline="one_step_unet", use_safetensors=True |
) |
pipe() Another way to share your community pipeline is to upload the one_step_unet.py file directly to your preferred model repository on the Hub. Instead of specifying the one_step_unet.py file, pass the model repository id to the custom_pipeline argument: Copied from diffusers import DiffusionPipeline |
pipeline = DiffusionPipeline.from_pretrained( |
"google/ddpm-cifar10-32", custom_pipeline="stevhliu/one_step_unet", use_safetensors=True |
) Take a look at the following table to compare the two sharing workflows to help you decide the best option for you: GitHub community pipeline HF Hub community pipeline usage same same review process open a Pull Request on GitHub and undergo a review process from the Diffusers team before merging; may be slower uploa... |
from transformers import CLIPImageProcessor, CLIPModel |
model_id = "CompVis/stable-diffusion-v1-4" |
clip_model_id = "laion/CLIP-ViT-B-32-laion2B-s34B-b79K" |
feature_extractor = CLIPImageProcessor.from_pretrained(clip_model_id) |
clip_model = CLIPModel.from_pretrained(clip_model_id, torch_dtype=torch.float16) |
pipeline = DiffusionPipeline.from_pretrained( |
model_id, |
custom_pipeline="clip_guided_stable_diffusion", |
clip_model=clip_model, |
feature_extractor=feature_extractor, |
scheduler=scheduler, |
torch_dtype=torch.float16, |
use_safetensors=True, |
) The magic behind community pipelines is contained in the following code. It allows the community pipeline to be loaded from GitHub or the Hub, and it’ll be available to all 🧨 Diffusers packages. Copied # 2. Load the pipeline class, if using custom module then load it from the Hub |
# if we load from explicit class, let's use it |
if custom_pipeline is not None: |
pipeline_class = get_class_from_dynamic_module( |
custom_pipeline, module_file=CUSTOM_PIPELINE_FILE_NAME, cache_dir=custom_pipeline |
) |
elif cls != DiffusionPipeline: |
pipeline_class = cls |
else: |
diffusers_module = importlib.import_module(cls.__module__.split(".")[0]) |
pipeline_class = getattr(diffusers_module, config_dict["_class_name"]) |
Latent Diffusion |
Overview |
Latent Diffusion was proposed in High-Resolution Image Synthesis with Latent Diffusion Models by Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, Björn Ommer. |
The abstract of the paper is the following: |
By decomposing the image formation process into a sequential application of denoising autoencoders, diffusion models (DMs) achieve state-of-the-art synthesis results on image data and beyond. Additionally, their formulation allows for a guiding mechanism to control the image generation process without retraining. Howev... |
The original codebase can be found here. |
Tips: |
Available Pipelines: |
Pipeline |
Tasks |
Colab |
pipeline_latent_diffusion.py |
Text-to-Image Generation |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.