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The factor the timesteps will be multiplied by when calculating the consistency model boundary conditions |
c_skip and c_out. Increasing this will decrease the approximation error (although the approximation |
error at the default of 10.0 is already pretty small). rescale_betas_zero_snr (bool, defaults to False) — |
Whether to rescale the betas to have zero terminal SNR. This enables the model to generate very bright and |
dark samples instead of limiting it to samples with medium brightness. Loosely related to |
--offset_noise. LCMScheduler extends the denoising procedure introduced in denoising diffusion probabilistic models (DDPMs) with |
non-Markovian guidance. This model inherits from SchedulerMixin and ConfigMixin. ~ConfigMixin takes care of storing all config |
attributes that are passed in the scheduler’s __init__ function, such as num_train_timesteps. They can be |
accessed via scheduler.config.num_train_timesteps. SchedulerMixin provides general loading and saving |
functionality via the SchedulerMixin.save_pretrained() and from_pretrained() functions. scale_model_input < source > ( sample: FloatTensor timestep: Optional = None ) → torch.FloatTensor Parameters sample (torch.FloatTensor) — |
The input sample. timestep (int, optional) — |
The current timestep in the diffusion chain. Returns |
torch.FloatTensor |
A scaled input sample. |
Ensures interchangeability with schedulers that need to scale the denoising model input depending on the |
current timestep. set_begin_index < source > ( begin_index: int = 0 ) Parameters begin_index (int) — |
The begin index for the scheduler. Sets the begin index for the scheduler. This function should be run from pipeline before the inference. set_timesteps < source > ( num_inference_steps: Optional = None device: Union = None original_inference_steps: Optional = None timesteps: Optional = None strength: int = 1.0 )... |
The number of diffusion steps used when generating samples with a pre-trained model. If used, |
timesteps must be None. device (str or torch.device, optional) — |
The device to which the timesteps should be moved to. If None, the timesteps are not moved. original_inference_steps (int, optional) — |
The original number of inference steps, which will be used to generate a linearly-spaced timestep |
schedule (which is different from the standard diffusers implementation). We will then take |
num_inference_steps timesteps from this schedule, evenly spaced in terms of indices, and use that as |
our final timestep schedule. If not set, this will default to the original_inference_steps attribute. timesteps (List[int], optional) — |
Custom timesteps used to support arbitrary spacing between timesteps. If None, then the default |
timestep spacing strategy of equal spacing between timesteps on the training/distillation timestep |
schedule is used. If timesteps is passed, num_inference_steps must be None. Sets the discrete timesteps used for the diffusion chain (to be run before inference). step < source > ( model_output: FloatTensor timestep: int sample: FloatTensor generator: Optional = None return_dict: bool = True ) → ~schedulers.sched... |
The direct output from learned diffusion model. timestep (float) — |
The current discrete timestep in the diffusion chain. sample (torch.FloatTensor) — |
A current instance of a sample created by the diffusion process. generator (torch.Generator, optional) — |
A random number generator. return_dict (bool, optional, defaults to True) — |
Whether or not to return a LCMSchedulerOutput or tuple. Returns |
~schedulers.scheduling_utils.LCMSchedulerOutput or tuple |
If return_dict is True, LCMSchedulerOutput is returned, otherwise a |
tuple is returned where the first element is the sample tensor. |
Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion |
process from the learned model outputs (most often the predicted noise). |
Stable Diffusion 2 Stable Diffusion 2 is a text-to-image latent diffusion model built upon the work of the original Stable Diffusion, and it was led by Robin Rombach and Katherine Crowson from Stability AI and LAION. The Stable Diffusion 2.0 release includes robust text-to-image models trained using a brand new text en... |
These models are trained on an aesthetic subset of the LAION-5B dataset created by the DeepFloyd team at Stability AI, which is then further filtered to remove adult content using LAION’s NSFW filter. For more details about how Stable Diffusion 2 works and how it differs from the original Stable Diffusion, please refer... |
import torch |
repo_id = "stabilityai/stable-diffusion-2-base" |
pipe = DiffusionPipeline.from_pretrained(repo_id, torch_dtype=torch.float16, revision="fp16") |
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config) |
pipe = pipe.to("cuda") |
prompt = "High quality photo of an astronaut riding a horse in space" |
image = pipe(prompt, num_inference_steps=25).images[0] |
image Inpainting Copied import torch |
from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler |
from diffusers.utils import load_image, make_image_grid |
img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png" |
mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png" |
init_image = load_image(img_url).resize((512, 512)) |
mask_image = load_image(mask_url).resize((512, 512)) |
repo_id = "stabilityai/stable-diffusion-2-inpainting" |
pipe = DiffusionPipeline.from_pretrained(repo_id, torch_dtype=torch.float16, revision="fp16") |
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config) |
pipe = pipe.to("cuda") |
prompt = "Face of a yellow cat, high resolution, sitting on a park bench" |
image = pipe(prompt=prompt, image=init_image, mask_image=mask_image, num_inference_steps=25).images[0] |
make_image_grid([init_image, mask_image, image], rows=1, cols=3) Super-resolution Copied from diffusers import StableDiffusionUpscalePipeline |
from diffusers.utils import load_image, make_image_grid |
import torch |
# load model and scheduler |
model_id = "stabilityai/stable-diffusion-x4-upscaler" |
pipeline = StableDiffusionUpscalePipeline.from_pretrained(model_id, torch_dtype=torch.float16) |
pipeline = pipeline.to("cuda") |
# let's download an image |
url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale/low_res_cat.png" |
low_res_img = load_image(url) |
low_res_img = low_res_img.resize((128, 128)) |
prompt = "a white cat" |
upscaled_image = pipeline(prompt=prompt, image=low_res_img).images[0] |
make_image_grid([low_res_img.resize((512, 512)), upscaled_image.resize((512, 512))], rows=1, cols=2) Depth-to-image Copied import torch |
from diffusers import StableDiffusionDepth2ImgPipeline |
from diffusers.utils import load_image, make_image_grid |
pipe = StableDiffusionDepth2ImgPipeline.from_pretrained( |
"stabilityai/stable-diffusion-2-depth", |
torch_dtype=torch.float16, |
).to("cuda") |
url = "http://images.cocodataset.org/val2017/000000039769.jpg" |
init_image = load_image(url) |
prompt = "two tigers" |
negative_prompt = "bad, deformed, ugly, bad anotomy" |
image = pipe(prompt=prompt, image=init_image, negative_prompt=negative_prompt, strength=0.7).images[0] |
make_image_grid([init_image, image], rows=1, cols=2) |
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