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prompt = "A <cat-toy> backpack" |
image = pipe(prompt, num_inference_steps=50).images[0] |
image.save("cat-backpack.png") To load a Textual Inversion embedding vector in Automatic1111 format, make sure to download the vector first |
(for example from civitAI) and then load the vector locally: Copied from diffusers import StableDiffusionPipeline |
import torch |
model_id = "runwayml/stable-diffusion-v1-5" |
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda") |
pipe.load_textual_inversion("./charturnerv2.pt", token="charturnerv2") |
prompt = "charturnerv2, multiple views of the same character in the same outfit, a character turnaround of a woman wearing a black jacket and red shirt, best quality, intricate details." |
image = pipe(prompt, num_inference_steps=50).images[0] |
image.save("character.png") load_lora_weights < source > ( pretrained_model_name_or_path_or_dict: Union adapter_name = None **kwargs ) Parameters pretrained_model_name_or_path_or_dict (str or os.PathLike or dict) — |
See lora_state_dict(). kwargs (dict, optional) — |
See lora_state_dict(). adapter_name (str, optional) — |
Adapter name to be used for referencing the loaded adapter model. If not specified, it will use |
default_{i} where i is the total number of adapters being loaded. Load LoRA weights specified in pretrained_model_name_or_path_or_dict into self.unet and |
self.text_encoder. All kwargs are forwarded to self.lora_state_dict. See lora_state_dict() for more details on how the state dict is loaded. See load_lora_into_unet() for more details on how the state dict is loaded into |
self.unet. See load_lora_into_text_encoder() for more details on how the state dict is loaded |
into self.text_encoder. save_lora_weights < source > ( save_directory: Union unet_lora_layers: Dict = None text_encoder_lora_layers: Dict = None transformer_lora_layers: Dict = None is_main_process: bool = True weight_name: str = None save_function: Callable = None safe_serialization: bool = True ) Parameters sa... |
Directory to save LoRA parameters to. Will be created if it doesn’t exist. unet_lora_layers (Dict[str, torch.nn.Module] or Dict[str, torch.Tensor]) — |
State dict of the LoRA layers corresponding to the unet. text_encoder_lora_layers (Dict[str, torch.nn.Module] or Dict[str, torch.Tensor]) — |
State dict of the LoRA layers corresponding to the text_encoder. Must explicitly pass the text |
encoder LoRA state dict because it comes from 🤗 Transformers. is_main_process (bool, optional, defaults to True) — |
Whether the process calling this is the main process or not. Useful during distributed training and you |
need to call this function on all processes. In this case, set is_main_process=True only on the main |
process to avoid race conditions. save_function (Callable) — |
The function to use to save the state dictionary. Useful during distributed training when you need to |
replace torch.save with another method. Can be configured with the environment variable |
DIFFUSERS_SAVE_MODE. safe_serialization (bool, optional, defaults to True) — |
Whether to save the model using safetensors or the traditional PyTorch way with pickle. Save the LoRA parameters corresponding to the UNet and text encoder. disable_freeu < source > ( ) Disables the FreeU mechanism if enabled. enable_freeu < source > ( s1: float s2: float b1: float b2: float ) Parameters ... |
Scaling factor for stage 1 to attenuate the contributions of the skip features. This is done to |
mitigate “oversmoothing effect” in the enhanced denoising process. s2 (float) — |
Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to |
mitigate “oversmoothing effect” in the enhanced denoising process. b1 (float) — Scaling factor for stage 1 to amplify the contributions of backbone features. b2 (float) — Scaling factor for stage 2 to amplify the contributions of backbone features. Enables the FreeU mechanism as in https://arxiv.org/abs/2309.114... |
that are known to work well for different pipelines such as Stable Diffusion v1, v2, and Stable Diffusion XL. StableDiffusionXLInstructPix2PixPipeline class diffusers.StableDiffusionXLInstructPix2PixPipeline < source > ( vae: AutoencoderKL text_encoder: CLIPTextModel text_encoder_2: CLIPTextModelWithProjection token... |
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. text_encoder (CLIPTextModel) — |
Frozen text-encoder. Stable Diffusion XL uses the text portion of |
CLIP, specifically |
the clip-vit-large-patch14 variant. text_encoder_2 ( CLIPTextModelWithProjection) — |
Second frozen text-encoder. Stable Diffusion XL uses the text and pool portion of |
CLIP, |
specifically the |
laion/CLIP-ViT-bigG-14-laion2B-39B-b160k |
variant. tokenizer (CLIPTokenizer) — |
Tokenizer of class |
CLIPTokenizer. tokenizer_2 (CLIPTokenizer) — |
Second Tokenizer of class |
CLIPTokenizer. unet (UNet2DConditionModel) — Conditional U-Net architecture 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. requires_aesthetics_score (bool, optional, defaults to "False") — |
Whether the unet requires a aesthetic_score condition to be passed during inference. Also see the config |
of stabilityai/stable-diffusion-xl-refiner-1-0. force_zeros_for_empty_prompt (bool, optional, defaults to "True") — |
Whether the negative prompt embeddings shall be forced to always be set to 0. Also see the config of |
stabilityai/stable-diffusion-xl-base-1-0. add_watermarker (bool, optional) — |
Whether to use the invisible_watermark library to |
watermark output images. If not defined, it will default to True if the package is installed, otherwise no |
watermarker will be used. Pipeline for pixel-level image editing by following text instructions. Based on Stable Diffusion XL. This model inherits from DiffusionPipeline. Check the superclass documentation for the generic methods the |
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) The pipeline also inherits the following loading methods: load_textual_inversion() for loading textual inversion embeddings from_single_file() for loading .ckpt files load_lora_weights() for loading LoRA weigh... |
The prompt or prompts to guide the image generation. If not defined, one has to pass prompt_embeds. |
instead. prompt_2 (str or List[str], optional) — |
The prompt or prompts to be sent to the tokenizer_2 and text_encoder_2. If not defined, prompt is |
used in both text-encoders image (torch.FloatTensor or PIL.Image.Image or np.ndarray or List[torch.FloatTensor] or List[PIL.Image.Image] or List[np.ndarray]) — |
The image(s) to modify with the pipeline. 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. denoising_end (float, optional) — |
When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be |
completed before it is intentionally prematurely terminated. As a result, the returned sample will |
still retain a substantial amount of noise as determined by the discrete timesteps selected by the |
scheduler. The denoising_end parameter should ideally be utilized when this pipeline forms a part of a |
“Mixture of Denoisers” multi-pipeline setup, as elaborated in Refining the Image |
Output guidance_scale (float, optional, defaults to 5.0) — |
Guidance scale as defined in Classifier-Free Diffusion Guidance. |
guidance_scale is defined as w of equation 2. of Imagen |
Paper. Guidance scale is enabled by setting guidance_scale > 1. Higher guidance scale encourages to generate images that are closely linked to the text prompt, |
usually at the expense of lower image quality. image_guidance_scale (float, optional, defaults to 1.5) — |
Image guidance scale is to push the generated image towards the inital image image. Image guidance |
scale is enabled by setting image_guidance_scale > 1. Higher image guidance scale encourages to |
generate images that are closely linked to the source image image, usually at the expense of lower |
image quality. This pipeline requires a value of at least 1. negative_prompt (str or List[str], optional) — |
The prompt or prompts not to guide the image generation. If not defined, one has to pass |
negative_prompt_embeds instead. Ignored when not using guidance (i.e., ignored if guidance_scale is |
less than 1). negative_prompt_2 (str or List[str], optional) — |
The prompt or prompts not to guide the image generation to be sent to tokenizer_2 and |
text_encoder_2. If not defined, negative_prompt is used in both text-encoders. 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) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to |
schedulers.DDIMScheduler, will be ignored for others. generator (torch.Generator or List[torch.Generator], optional) — |
One or a list of torch generator(s) |
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 will ge generated by sampling using the supplied random generator. prompt_embeds (torch.FloatTensor, optional) — |
Pre-generated text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not |
provided, text embeddings will be generated from prompt input argument. negative_prompt_embeds (torch.FloatTensor, optional) — |
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