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This model inherits from DiffusionPipeline. Check the superclass documentation for the generic methods the |
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) |
Pipeline for sampling actions from a diffusion model trained to predict sequences of states. |
Original implementation inspired by this repository: https://github.com/jannerm/diffuser. |
DDIM Denoising Diffusion Implicit Models (DDIM) by Jiaming Song, Chenlin Meng and Stefano Ermon. The abstract from the paper is: Denoising diffusion probabilistic models (DDPMs) have achieved high quality image generation without adversarial training, yet they require simulating a Markov chain for many steps to produce... |
A UNet2DModel to denoise the encoded image latents. scheduler (SchedulerMixin) — |
A scheduler to be used in combination with unet to denoise the encoded image. Can be one of |
DDPMScheduler, or DDIMScheduler. Pipeline for image generation. This model inherits from DiffusionPipeline. Check the superclass documentation for the generic methods |
implemented for all pipelines (downloading, saving, running on a particular device, etc.). __call__ < source > ( batch_size: int = 1 generator: Union = None eta: float = 0.0 num_inference_steps: int = 50 use_clipped_model_output: Optional = None output_type: Optional = 'pil' return_dict: bool = True ) → ImagePipelin... |
The number of images to generate. generator (torch.Generator, optional) — |
A torch.Generator to make |
generation deterministic. eta (float, optional, defaults to 0.0) — |
Corresponds to parameter eta (η) from the DDIM paper. Only applies |
to the DDIMScheduler, and is ignored in other schedulers. A value of 0 corresponds to |
DDIM and 1 corresponds to DDPM. num_inference_steps (int, optional, defaults to 50) — |
The number of denoising steps. More denoising steps usually lead to a higher quality image at the |
expense of slower inference. use_clipped_model_output (bool, optional, defaults to None) — |
If True or False, see documentation for DDIMScheduler.step(). If None, nothing is passed |
downstream to the scheduler (use None for schedulers which don’t support this argument). output_type (str, optional, defaults to "pil") — |
The output format of the generated image. Choose between PIL.Image or np.array. return_dict (bool, optional, defaults to True) — |
Whether or not to return a ImagePipelineOutput instead of a plain tuple. Returns |
ImagePipelineOutput or tuple |
If return_dict is True, ImagePipelineOutput is returned, otherwise a tuple is |
returned where the first element is a list with the generated images |
The call function to the pipeline for generation. Example: Copied >>> from diffusers import DDIMPipeline |
>>> import PIL.Image |
>>> import numpy as np |
>>> # load model and scheduler |
>>> pipe = DDIMPipeline.from_pretrained("fusing/ddim-lsun-bedroom") |
>>> # run pipeline in inference (sample random noise and denoise) |
>>> image = pipe(eta=0.0, num_inference_steps=50) |
>>> # process image to PIL |
>>> image_processed = image.cpu().permute(0, 2, 3, 1) |
>>> image_processed = (image_processed + 1.0) * 127.5 |
>>> image_processed = image_processed.numpy().astype(np.uint8) |
>>> image_pil = PIL.Image.fromarray(image_processed[0]) |
>>> # save image |
>>> image_pil.save("test.png") ImagePipelineOutput class diffusers.ImagePipelineOutput < source > ( images: Union ) Parameters images (List[PIL.Image.Image] or np.ndarray) — |
List of denoised PIL images of length batch_size or NumPy array of shape (batch_size, height, width, num_channels). Output class for image pipelines. |
How to use Stable Diffusion on Habana Gaudi |
🤗 Diffusers is compatible with Habana Gaudi through 🤗 Optimum Habana. |
Requirements |
Optimum Habana 1.5 or later, here is how to install it. |
SynapseAI 1.9. |
Inference Pipeline |
To generate images with Stable Diffusion 1 and 2 on Gaudi, you need to instantiate two instances: |
A pipeline with GaudiStableDiffusionPipeline. This pipeline supports text-to-image generation. |
A scheduler with GaudiDDIMScheduler. This scheduler has been optimized for Habana Gaudi. |
When initializing the pipeline, you have to specify use_habana=True to deploy it on HPUs. |
Furthermore, in order to get the fastest possible generations you should enable HPU graphs with use_hpu_graphs=True. |
Finally, you will need to specify a Gaudi configuration which can be downloaded from the Hugging Face Hub. |
Copied |
from optimum.habana import GaudiConfig |
from optimum.habana.diffusers import GaudiDDIMScheduler, GaudiStableDiffusionPipeline |
model_name = "stabilityai/stable-diffusion-2-base" |
scheduler = GaudiDDIMScheduler.from_pretrained(model_name, subfolder="scheduler") |
pipeline = GaudiStableDiffusionPipeline.from_pretrained( |
model_name, |
scheduler=scheduler, |
use_habana=True, |
use_hpu_graphs=True, |
gaudi_config="Habana/stable-diffusion", |
) |
You can then call the pipeline to generate images by batches from one or several prompts: |
Copied |
outputs = pipeline( |
prompt=[ |
"High quality photo of an astronaut riding a horse in space", |
"Face of a yellow cat, high resolution, sitting on a park bench", |
], |
num_images_per_prompt=10, |
batch_size=4, |
) |
For more information, check out Optimum Habana’s documentation and the example provided in the official Github repository. |
Benchmark |
Here are the latencies for Habana first-generation Gaudi and Gaudi2 with the Habana/stable-diffusion Gaudi configuration (mixed precision bf16/fp32): |
Stable Diffusion v1.5 (512x512 resolution): |
Latency (batch size = 1) |
Throughput (batch size = 8) |
first-generation Gaudi |
4.22s |
0.29 images/s |
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