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... generator=generator, |
... device=torch_device, |
... ) Denoise the image Start by scaling the input with the initial noise distribution, sigma, the noise scale value, which is required for improved schedulers like UniPCMultistepScheduler: Copied >>> latents = latents * scheduler.init_noise_sigma The last step is to create the denoising loop that’ll progressively t... |
>>> scheduler.set_timesteps(num_inference_steps) |
>>> for t in tqdm(scheduler.timesteps): |
... # expand the latents if we are doing classifier-free guidance to avoid doing two forward passes. |
... 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... |
Pipelines |
Pipelines provide a simple way to run state-of-the-art diffusion models in inference. |
Most diffusion systems consist of multiple independently-trained models and highly adaptable scheduler |
components - all of which are needed to have a functioning end-to-end diffusion system. |
As an example, Stable Diffusion has three independently trained models: |
Autoencoder |
Conditional Unet |
CLIP text encoder |
a scheduler component, scheduler, |
a CLIPImageProcessor, |
as well as a safety checker. |
All of these components are necessary to run stable diffusion in inference even though they were trained |
or created independently from each other. |
To that end, we strive to offer all open-sourced, state-of-the-art diffusion system under a unified API. |
More specifically, we strive to provide pipelines that |
can load the officially published weights and yield 1-to-1 the same outputs as the original implementation according to the corresponding paper (e.g. LDMTextToImagePipeline, uses the officially released weights of High-Resolution Image Synthesis with Latent Diffusion Models), |
have a simple user interface to run the model in inference (see the Pipelines API section), |
are easy to understand with code that is self-explanatory and can be read along-side the official paper (see Pipelines summary), |
can easily be contributed by the community (see the Contribution section). |
Note that pipelines do not (and should not) offer any training functionality. |
If you are looking for official training examples, please have a look at examples. |
🧨 Diffusers Summary |
The following table summarizes all officially supported pipelines, their corresponding paper, and if |
available a colab notebook to directly try them out. |
Pipeline |
Paper |
Tasks |
Colab |
alt_diffusion |
AltDiffusion |
Image-to-Image Text-Guided Generation |
- |
audio_diffusion |
Audio Diffusion |
Unconditional Audio Generation |
controlnet |
ControlNet with Stable Diffusion |
Image-to-Image Text-Guided Generation |
cycle_diffusion |
Cycle Diffusion |
Image-to-Image Text-Guided Generation |
dance_diffusion |
Dance Diffusion |
Unconditional Audio Generation |
ddpm |
Denoising Diffusion Probabilistic Models |
Unconditional Image Generation |
ddim |
Denoising Diffusion Implicit Models |
Unconditional Image Generation |
if |
IF |
Image Generation |
if_img2img |
IF |
Image-to-Image Generation |
if_inpainting |
IF |
Image-to-Image Generation |
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