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prompt=prompt, |
image=init_image, |
mask_image=mask_image, |
generator=generator, |
num_inference_steps=4, |
guidance_scale=4, |
).images[0] |
make_image_grid([init_image, mask_image, image], rows=1, cols=3) AnimateDiff AnimateDiff allows you to animate images using Stable Diffusion models. To get good results, we need to generate multiple frames (16-24), and doing this with standard SD models can be very slow. |
LCM-LoRA can be used to speed up the process significantly, as you just need to do 4-8 steps for each frame. Let’s look at how we can perform animation with LCM-LoRA and AnimateDiff. Copied import torch |
from diffusers import MotionAdapter, AnimateDiffPipeline, DDIMScheduler, LCMScheduler |
from diffusers.utils import export_to_gif |
adapter = MotionAdapter.from_pretrained("diffusers/animatediff-motion-adapter-v1-5") |
pipe = AnimateDiffPipeline.from_pretrained( |
"frankjoshua/toonyou_beta6", |
motion_adapter=adapter, |
).to("cuda") |
# set scheduler |
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config) |
# load LCM-LoRA |
pipe.load_lora_weights("latent-consistency/lcm-lora-sdv1-5", adapter_name="lcm") |
pipe.load_lora_weights("guoyww/animatediff-motion-lora-zoom-in", weight_name="diffusion_pytorch_model.safetensors", adapter_name="motion-lora") |
pipe.set_adapters(["lcm", "motion-lora"], adapter_weights=[0.55, 1.2]) |
prompt = "best quality, masterpiece, 1girl, looking at viewer, blurry background, upper body, contemporary, dress" |
generator = torch.manual_seed(0) |
frames = pipe( |
prompt=prompt, |
num_inference_steps=5, |
guidance_scale=1.25, |
cross_attention_kwargs={"scale": 1}, |
num_frames=24, |
generator=generator |
).frames[0] |
export_to_gif(frames, "animation.gif") |
RePaintScheduler RePaintScheduler is a DDPM-based inpainting scheduler for unsupervised inpainting with extreme masks. It is designed to be used with the RePaintPipeline, and it is based on the paper RePaint: Inpainting using Denoising Diffusion Probabilistic Models by Andreas Lugmayr et al. The abstract from the paper... |
The number of diffusion steps to train the model. beta_start (float, defaults to 0.0001) — |
The starting beta value of inference. beta_end (float, defaults to 0.02) — |
The final beta value. beta_schedule (str, defaults to "linear") — |
The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from |
linear, scaled_linear, squaredcos_cap_v2, or sigmoid. eta (float) — |
The weight of noise for added noise in diffusion step. If its value is between 0.0 and 1.0 it corresponds |
to the DDIM scheduler, and if its value is between -0.0 and 1.0 it corresponds to the DDPM scheduler. trained_betas (np.ndarray, optional) — |
Pass an array of betas directly to the constructor to bypass beta_start and beta_end. clip_sample (bool, defaults to True) — |
Clip the predicted sample between -1 and 1 for numerical stability. RePaintScheduler is a scheduler for DDPM inpainting inside a given mask. This model inherits from SchedulerMixin and ConfigMixin. Check the superclass documentation for the generic |
methods the library implements for all schedulers such as loading and saving. 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_timesteps < source > ( num_inference_steps: int jump_length: int = 10 jump_n_sample: int = 10 device: Union = None ) Parameters num_inference_steps (int) — |
The number of diffusion steps used when generating samples with a pre-trained model. If used, |
timesteps must be None. jump_length (int, defaults to 10) — |
The number of steps taken forward in time before going backward in time for a single jump (“j” in |
RePaint paper). Take a look at Figure 9 and 10 in the paper. jump_n_sample (int, defaults to 10) — |
The number of times to make a forward time jump for a given chosen time sample. Take a look at Figure 9 |
and 10 in the paper. device (str or torch.device, optional) — |
The device to which the timesteps should be moved to. If None, the timesteps are not moved. Sets the discrete timesteps used for the diffusion chain (to be run before inference). step < source > ( model_output: FloatTensor timestep: int sample: FloatTensor original_image: FloatTensor mask: FloatTensor generator: O... |
The direct output from learned diffusion model. timestep (int) — |
The current discrete timestep in the diffusion chain. sample (torch.FloatTensor) — |
A current instance of a sample created by the diffusion process. original_image (torch.FloatTensor) — |
The original image to inpaint on. mask (torch.FloatTensor) — |
The mask where a value of 0.0 indicates which part of the original image to inpaint. generator (torch.Generator, optional) — |
A random number generator. return_dict (bool, optional, defaults to True) — |
Whether or not to return a RePaintSchedulerOutput or tuple. Returns |
RePaintSchedulerOutput or tuple |
If return_dict is True, RePaintSchedulerOutput 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). RePaintSchedulerOutput class diffusers.schedulers.scheduling_repaint.RePaintSchedulerOutput < source > ( prev_sample: FloatTensor pred_original_sample: FloatTensor ) Parameters prev_sample (torch.FloatTensor of shape (batch_size, num_chann... |
Computed sample (x_{t-1}) of previous timestep. prev_sample should be used as next model input in the |
denoising loop. pred_original_sample (torch.FloatTensor of shape (batch_size, num_channels, height, width) for images) — |
The predicted denoised sample (x_{0}) based on the model output from |
the current timestep. pred_original_sample can be used to preview progress or for guidance. Output class for the scheduler’s step function output. |
ScoreSdeVeScheduler ScoreSdeVeScheduler is a variance exploding stochastic differential equation (SDE) scheduler. It was introduced in the Score-Based Generative Modeling through Stochastic Differential Equations paper by Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, Ben Poole. Th... |
The number of diffusion steps to train the model. snr (float, defaults to 0.15) — |
A coefficient weighting the step from the model_output sample (from the network) to the random noise. sigma_min (float, defaults to 0.01) — |
The initial noise scale for the sigma sequence in the sampling procedure. The minimum sigma should mirror |
the distribution of the data. sigma_max (float, defaults to 1348.0) — |
The maximum value used for the range of continuous timesteps passed into the model. sampling_eps (float, defaults to 1e-5) — |
The end value of sampling where timesteps decrease progressively from 1 to epsilon. correct_steps (int, defaults to 1) — |
The number of correction steps performed on a produced sample. ScoreSdeVeScheduler is a variance exploding stochastic differential equation (SDE) scheduler. This model inherits from SchedulerMixin and ConfigMixin. Check the superclass documentation for the generic |
methods the library implements for all schedulers such as loading and saving. 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_sigmas < source > ( num_inference_steps: int sigma_min: float = None sigma_max: float = None sampling_eps: float = None ) Parameters num_inference_steps (int) — |
The number of diffusion steps used when generating samples with a pre-trained model. sigma_min (float, optional) — |
The initial noise scale value (overrides value given during scheduler instantiation). sigma_max (float, optional) — |
The final noise scale value (overrides value given during scheduler instantiation). sampling_eps (float, optional) — |
The final timestep value (overrides value given during scheduler instantiation). Sets the noise scales used for the diffusion chain (to be run before inference). The sigmas control the weight |
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