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images = images.block_until_ready() |
# CPU times: user 1min 15s, sys: 18.2 s, total: 1min 34s |
# Wall time: 1min 15s Check your image dimensions to see if they’re correct: Copied images.shape |
# (8, 1, 512, 512, 3) |
DDPMScheduler Denoising Diffusion Probabilistic Models (DDPM) by Jonathan Ho, Ajay Jain and Pieter Abbeel proposes a diffusion based model of the same name. In the context of the 🤗 Diffusers library, DDPM refers to the discrete denoising scheduler from the paper as well as the pipeline. The abstract from the paper is:... |
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, or squaredcos_cap_v2. variance_type (str, defaults to "fixed_small") — |
Clip the variance when adding noise to the denoised sample. Choose from fixed_small, fixed_small_log, |
fixed_large, fixed_large_log, learned or learned_range. clip_sample (bool, defaults to True) — |
Clip the predicted sample for numerical stability. clip_sample_range (float, defaults to 1.0) — |
The maximum magnitude for sample clipping. Valid only when clip_sample=True. prediction_type (str, defaults to epsilon, optional) — |
Prediction type of the scheduler function; can be epsilon (predicts the noise of the diffusion process), |
sample (directly predicts the noisy sample) or v_prediction` (see section 2.4 of Imagen |
Video paper). thresholding (bool, defaults to False) — |
Whether to use the “dynamic thresholding” method. This is unsuitable for latent-space diffusion models such |
as Stable Diffusion. dynamic_thresholding_ratio (float, defaults to 0.995) — |
The ratio for the dynamic thresholding method. Valid only when thresholding=True. sample_max_value (float, defaults to 1.0) — |
The threshold value for dynamic thresholding. Valid only when thresholding=True. timestep_spacing (str, defaults to "leading") — |
The way the timesteps should be scaled. Refer to Table 2 of the Common Diffusion Noise Schedules and |
Sample Steps are Flawed for more information. steps_offset (int, defaults to 0) — |
An offset added to the inference steps. You can use a combination of offset=1 and |
set_alpha_to_one=False to make the last step use step 0 for the previous alpha product like in Stable |
Diffusion. 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. DDPMScheduler explores the connections between denoising score matching and Langevin dynamics sampling. 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: Optional = None device: Union = None timesteps: Optional = 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. device (str or torch.device, optional) — |
The device to which the timesteps should be moved to. If None, the timesteps are not moved. 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 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 = None return_dict: bool = True ) → DDPMSchedulerOutput or tuple Parameters model_output (torch.FloatT... |
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 DDPMSchedulerOutput or tuple. Returns |
DDPMSchedulerOutput or tuple |
If return_dict is True, DDPMSchedulerOutput 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). DDPMSchedulerOutput class diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput < source > ( prev_sample: FloatTensor pred_original_sample: Optional = None ) Parameters prev_sample (torch.FloatTensor of shape (batch_size, num_channels, ... |
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. |
ONNX Runtime 🤗 Optimum provides a Stable Diffusion pipeline compatible with ONNX Runtime. You’ll need to install 🤗 Optimum with the following command for ONNX Runtime support: Copied pip install -q optimum["onnxruntime"] This guide will show you how to use the Stable Diffusion and Stable Diffusion XL (SDXL) pipelin... |
model_id = "runwayml/stable-diffusion-v1-5" |
pipeline = ORTStableDiffusionPipeline.from_pretrained(model_id, export=True) |
prompt = "sailing ship in storm by Leonardo da Vinci" |
image = pipeline(prompt).images[0] |
pipeline.save_pretrained("./onnx-stable-diffusion-v1-5") Generating multiple prompts in a batch seems to take too much memory. While we look into it, you may need to iterate instead of batching. To export the pipeline in the ONNX format offline and use it later for inference, |
use the optimum-cli export command: Copied optimum-cli export onnx --model runwayml/stable-diffusion-v1-5 sd_v15_onnx/ Then to perform inference (you don’t have to specify export=True again): Copied from optimum.onnxruntime import ORTStableDiffusionPipeline |
model_id = "sd_v15_onnx" |
pipeline = ORTStableDiffusionPipeline.from_pretrained(model_id) |
prompt = "sailing ship in storm by Leonardo da Vinci" |
image = pipeline(prompt).images[0] You can find more examples in 🤗 Optimum documentation, and Stable Diffusion is supported for text-to-image, image-to-image, and inpainting. Stable Diffusion XL To load and run inference with SDXL, use the ORTStableDiffusionXLPipeline: Copied from optimum.onnxruntime import ORTSta... |
model_id = "stabilityai/stable-diffusion-xl-base-1.0" |
pipeline = ORTStableDiffusionXLPipeline.from_pretrained(model_id) |
prompt = "sailing ship in storm by Leonardo da Vinci" |
image = pipeline(prompt).images[0] To export the pipeline in the ONNX format and use it later for inference, use the optimum-cli export command: Copied optimum-cli export onnx --model stabilityai/stable-diffusion-xl-base-1.0 --task stable-diffusion-xl sd_xl_onnx/ SDXL in the ONNX format is supported for text-to-image... |
Self-Attention Guidance Improving Sample Quality of Diffusion Models Using Self-Attention Guidance is by Susung Hong et al. The abstract from the paper is: Denoising diffusion models (DDMs) have attracted attention for their exceptional generation quality and diversity. This success is largely attributed to the use of ... |
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations. text_encoder (CLIPTextModel) — |
Frozen text-encoder (clip-vit-large-patch14). tokenizer (CLIPTokenizer) — |
A CLIPTokenizer to tokenize text. unet (UNet2DConditionModel) — |
A UNet2DConditionModel to denoise the encoded image latents. scheduler (SchedulerMixin) — |
A scheduler to be used in combination with unet to denoise the encoded image latents. Can be one of |
DDIMScheduler, LMSDiscreteScheduler, or PNDMScheduler. safety_checker (StableDiffusionSafetyChecker) — |
Classification module that estimates whether generated images could be considered offensive or harmful. |
Please refer to the model card for more details |
about a model’s potential harms. feature_extractor (CLIPImageProcessor) — |
A CLIPImageProcessor to extract features from generated images; used as inputs to the safety_checker. Pipeline for text-to-image generation using Stable Diffusion. 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.). The pipeline also inherits the following loading methods: load_textual_inversion() for loading textual inversion embeddings load_ip_adapter() for loading IP Adapters __call__ < source > ( prompt: Union = None height: Optional =... |
The prompt or prompts to guide image generation. If not defined, you need to pass prompt_embeds. height (int, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor) — |
The height in pixels of the generated image. width (int, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor) — |
The width in pixels of the generated image. 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. guidance_scale (float, optional, defaults to 7.5) — |
A higher guidance scale value encourages the model to generate images closely linked to the text |
prompt at the expense of lower image quality. Guidance scale is enabled when guidance_scale > 1. sag_scale (float, optional, defaults to 0.75) — |
Chosen between [0, 1.0] for better quality. negative_prompt (str or List[str], optional) — |
The prompt or prompts to guide what to not include in image generation. If not defined, you need to |
pass negative_prompt_embeds instead. Ignored when not using guidance (guidance_scale < 1). num_images_per_prompt (int, optional, defaults to 1) — |
The number of images to generate per prompt. eta (float, optional, defaults to 0.0) — |
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