text stringlengths 0 5.54k |
|---|
... latent_model_input = torch.cat([latents] * 2) |
... latent_model_input = scheduler.scale_model_input(latent_model_input, timestep=t) |
... # predict the noise residual |
... with torch.no_grad(): |
... noise_pred = unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample |
... # perform guidance |
... noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) |
... noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) |
... # compute the previous noisy sample x_t -> x_t-1 |
... latents = scheduler.step(noise_pred, t, latents).prev_sample Decode the image The final step is to use the vae to decode the latent representation into an image and get the decoded output with sample: Copied # scale and decode the image latents with vae |
latents = 1 / 0.18215 * latents |
with torch.no_grad(): |
image = vae.decode(latents).sample Lastly, convert the image to a PIL.Image to see your generated image! Copied >>> image = (image / 2 + 0.5).clamp(0, 1).squeeze() |
>>> image = (image.permute(1, 2, 0) * 255).to(torch.uint8).cpu().numpy() |
>>> images = (image * 255).round().astype("uint8") |
>>> image = Image.fromarray(image) |
>>> image Next steps From basic to complex pipelines, you’ve seen that all you really need to write your own diffusion system is a denoising loop. The loop should set the scheduler’s timesteps, iterate over them, and alternate between calling the UNet model to predict the noise residual and passing it to the schedule... |
Scalable Diffusion Models with Transformers (DiT) |
Overview |
Scalable Diffusion Models with Transformers (DiT) by William Peebles and Saining Xie. |
The abstract of the paper is the following: |
We explore a new class of diffusion models based on the transformer architecture. We train latent diffusion models of images, replacing the commonly-used U-Net backbone with a transformer that operates on latent patches. We analyze the scalability of our Diffusion Transformers (DiTs) through the lens of forward pass co... |
The original codebase of this paper can be found here: facebookresearch/dit. |
Available Pipelines: |
Pipeline |
Tasks |
Colab |
pipeline_dit.py |
Conditional Image Generation |
- |
Usage example |
Copied |
from diffusers import DiTPipeline, DPMSolverMultistepScheduler |
import torch |
pipe = DiTPipeline.from_pretrained("facebook/DiT-XL-2-256", torch_dtype=torch.float16) |
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config) |
pipe = pipe.to("cuda") |
# pick words from Imagenet class labels |
pipe.labels # to print all available words |
# pick words that exist in ImageNet |
words = ["white shark", "umbrella"] |
class_ids = pipe.get_label_ids(words) |
generator = torch.manual_seed(33) |
output = pipe(class_labels=class_ids, num_inference_steps=25, generator=generator) |
image = output.images[0] # label 'white shark' |
DiTPipeline |
class diffusers.DiTPipeline |
< |
source |
> |
( |
transformer: Transformer2DModel |
vae: AutoencoderKL |
scheduler: KarrasDiffusionSchedulers |
id2label: typing.Union[typing.Dict[int, str], NoneType] = None |
) |
Parameters |
transformer (Transformer2DModel) — |
Class conditioned Transformer in Diffusion model to denoise the encoded image latents. |
vae (AutoencoderKL) — |
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. |
scheduler (DDIMScheduler) — |
A scheduler to be used in combination with dit to denoise the encoded image latents. |
This pipeline 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.) |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.