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... reverse_editing_direction=[ |
... False, |
... False, |
... False, |
... False, |
... ], # Direction of guidance i.e. increase all concepts |
... edit_warmup_steps=[10, 10, 10, 10], # Warmup period for each concept |
... edit_guidance_scale=[4, 5, 5, 5.4], # Guidance scale for each concept |
... edit_threshold=[ |
... 0.99, |
... 0.975, |
... 0.925, |
... 0.96, |
... ], # Threshold for each concept. Threshold equals the percentile of the latent space that will be discarded. I.e. threshold=0.99 uses 1% of the latent dimensions |
... edit_momentum_scale=0.3, # Momentum scale that will be added to the latent guidance |
... edit_mom_beta=0.6, # Momentum beta |
... edit_weights=[1, 1, 1, 1, 1], # Weights of the individual concepts against each other |
... ) |
>>> image = out.images[0] StableDiffusionSafePipelineOutput class diffusers.pipelines.semantic_stable_diffusion.SemanticStableDiffusionPipelineOutput < source > ( images: Union nsfw_content_detected: Optional ) Parameters images (List[PIL.Image.Image] or np.ndarray) — |
List of denoised PIL images of length batch_size or NumPy array of shape (batch_size, height, width, num_channels). nsfw_content_detected (List[bool]) — |
List indicating whether the corresponding generated image contains “not-safe-for-work” (nsfw) content or |
None if safety checking could not be performed. Output class for Stable Diffusion pipelines. |
Speed up inference There are several ways to optimize 🤗 Diffusers for inference speed. As a general rule of thumb, we recommend using either xFormers or torch.nn.functional.scaled_dot_product_attention in PyTorch 2.0 for their memory-efficient attention. In many cases, optimizing for speed or memory leads to improved ... |
torch.backends.cuda.matmul.allow_tf32 = True You can learn more about TF32 in the Mixed precision training guide. Half-precision weights To save GPU memory and get more speed, try loading and running the model weights directly in half-precision or float16: Copied import torch |
from diffusers import DiffusionPipeline |
pipe = DiffusionPipeline.from_pretrained( |
"runwayml/stable-diffusion-v1-5", |
torch_dtype=torch.float16, |
use_safetensors=True, |
) |
pipe = pipe.to("cuda") |
prompt = "a photo of an astronaut riding a horse on mars" |
image = pipe(prompt).images[0] Don’t use torch.autocast in any of the pipelines as it can lead to black images and is always slower than pure float16 precision. Distilled model You could also use a distilled Stable Diffusion model and autoencoder to speed up inference. During distillation, many of the UNet’s residual ... |
Paint by Example Paint by Example: Exemplar-based Image Editing with Diffusion Models is by Binxin Yang, Shuyang Gu, Bo Zhang, Ting Zhang, Xuejin Chen, Xiaoyan Sun, Dong Chen, Fang Wen. The abstract from the paper is: Language-guided image editing has achieved great success recently. In this paper, for the first time, ... |
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations. image_encoder (PaintByExampleImageEncoder) — |
Encodes the example input image. The unet is conditioned on the example image instead of a text prompt. 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. 🧪 This is an experimental feature! Pipeline for image-guided image inpainting 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.). __call__ < source > ( example_image: Union image: Union mask_image: Union height: Optional = None width: Optional = None num_inference_steps: int = 50 guidance_scale: float = 5.0 negative_prompt: Union = None num_images_per_pro... |
An example image to guide image generation. image (torch.FloatTensor or PIL.Image.Image or List[PIL.Image.Image]) — |
Image or tensor representing an image batch to be inpainted (parts of the image are masked out with |
mask_image and repainted according to prompt). mask_image (torch.FloatTensor or PIL.Image.Image or List[PIL.Image.Image]) — |
Image or tensor representing an image batch to mask image. White pixels in the mask are repainted, |
while black pixels are preserved. If mask_image is a PIL image, it is converted to a single channel |
(luminance) before use. If it’s a tensor, it should contain one color channel (L) instead of 3, so the |
expected shape would be (B, H, W, 1). 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. 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) — |
Corresponds to parameter eta (η) from the DDIM paper. Only applies |
to the DDIMScheduler, and is ignored in other schedulers. generator (torch.Generator or List[torch.Generator], optional) — |
A torch.Generator to make |
generation deterministic. latents (torch.FloatTensor, optional) — |
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image |
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents |
tensor is generated by sampling using the supplied random generator. output_type (str, optional, defaults to "pil") — |
The output format of the generated image. Choose between PIL.Image or np.array. return_dict (bool, optional, defaults to True) — |
Whether or not to return a StableDiffusionPipelineOutput instead of a |
plain tuple. callback (Callable, optional) — |
A function that calls every callback_steps steps during inference. The function is called with the |
following arguments: callback(step: int, timestep: int, latents: torch.FloatTensor). callback_steps (int, optional, defaults to 1) — |
The frequency at which the callback function is called. If not specified, the callback is called at |
every step. Returns |
StableDiffusionPipelineOutput or tuple |
If return_dict is True, StableDiffusionPipelineOutput is returned, |
otherwise a tuple is returned where the first element is a list with the generated images and the |
second element is a list of bools indicating whether the corresponding generated image contains |
“not-safe-for-work” (nsfw) content. |
The call function to the pipeline for generation. Example: Copied >>> import PIL |
>>> import requests |
>>> import torch |
>>> from io import BytesIO |
>>> from diffusers import PaintByExamplePipeline |
>>> def download_image(url): |
... response = requests.get(url) |
... return PIL.Image.open(BytesIO(response.content)).convert("RGB") |
>>> img_url = ( |
... "https://raw.githubusercontent.com/Fantasy-Studio/Paint-by-Example/main/examples/image/example_1.png" |
... ) |
>>> mask_url = ( |
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