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pass negative_prompt_embeds instead. Ignored when not using guidance (guidance_scale < 1). num_images_per_prompt (int, optional, defaults to 1) β |
The number of images to generate per prompt. eta (float, optional, defaults to 0.0) β |
Corresponds to parameter eta (Ξ·) from the DDIM paper. Only applies |
to the DDIMScheduler, and is ignored in other schedulers. generator (torch.Generator, optional) β |
A torch.Generator to make |
generation deterministic. latents (torch.FloatTensor, optional) β |
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. prompt_embeds (torch.FloatTensor, optional) β |
Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not |
provided, text embeddings are generated from the prompt input argument. negative_prompt_embeds (torch.FloatTensor, optional) β |
Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If |
not provided, negative_prompt_embeds are generated from the negative_prompt input argument. |
ip_adapter_image β (PipelineImageInput, optional): |
Optional image input to work with IP Adapters. output_type (str, optional, defaults to "pil") β |
The output format of the generated image. Choose between PIL.Image or np.array. return_dict (bool, optional, defaults to True) β |
Whether or not to return a StableDiffusionPipelineOutput instead of a |
plain tuple. callback_on_step_end (Callable, optional) β |
A function that calls at the end of each denoising steps during the inference. The function is called |
with the following arguments: callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict). callback_kwargs will include a list of all tensors as specified by |
callback_on_step_end_tensor_inputs. callback_on_step_end_tensor_inputs (List, optional) β |
The list of tensor inputs for the callback_on_step_end function. The tensors specified in the list |
will be passed as callback_kwargs argument. You will only be able to include variables listed in the |
._callback_tensor_inputs attribute of your pipeline class. 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 |
βnot-safe-for-workβ (nsfw) content. |
The call function to the pipeline for generation. Examples: Copied >>> import PIL |
>>> import requests |
>>> import torch |
>>> from io import BytesIO |
>>> from diffusers import StableDiffusionInstructPix2PixPipeline |
>>> def download_image(url): |
... response = requests.get(url) |
... return PIL.Image.open(BytesIO(response.content)).convert("RGB") |
>>> img_url = "https://huggingface.co/datasets/diffusers/diffusers-images-docs/resolve/main/mountain.png" |
>>> image = download_image(img_url).resize((512, 512)) |
>>> pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained( |
... "timbrooks/instruct-pix2pix", torch_dtype=torch.float16 |
... ) |
>>> pipe = pipe.to("cuda") |
>>> prompt = "make the mountains snowy" |
>>> image = pipe(prompt=prompt, image=image).images[0] load_textual_inversion < source > ( pretrained_model_name_or_path: Union token: Union = None tokenizer: Optional = None text_encoder: Optional = None **kwargs ) Parameters pretrained_model_name_or_path (str or os.PathLike or List[str or os.PathLike] or Dict ... |
Can be either one of the following or a list of them: |
A string, the model id (for example sd-concepts-library/low-poly-hd-logos-icons) of a |
pretrained model hosted on the Hub. |
A path to a directory (for example ./my_text_inversion_directory/) containing the textual |
inversion weights. |
A path to a file (for example ./my_text_inversions.pt) containing textual inversion weights. |
A torch state |
dict. |
token (str or List[str], optional) β |
Override the token to use for the textual inversion weights. If pretrained_model_name_or_path is a |
list, then token must also be a list of equal length. text_encoder (CLIPTextModel, optional) β |
Frozen text-encoder (clip-vit-large-patch14). |
If not specified, function will take self.tokenizer. tokenizer (CLIPTokenizer, optional) β |
A CLIPTokenizer to tokenize text. If not specified, function will take self.tokenizer. weight_name (str, optional) β |
Name of a custom weight file. This should be used when: |
The saved textual inversion file is in π€ Diffusers format, but was saved under a specific weight |
name such as text_inv.bin. |
The saved textual inversion file is in the Automatic1111 format. |
cache_dir (Union[str, os.PathLike], optional) β |
Path to a directory where a downloaded pretrained model configuration is cached if the standard cache |
is not used. force_download (bool, optional, defaults to False) β |
Whether or not to force the (re-)download of the model weights and configuration files, overriding the |
cached versions if they exist. resume_download (bool, optional, defaults to False) β |
Whether or not to resume downloading the model weights and configuration files. If set to False, any |
incompletely downloaded files are deleted. proxies (Dict[str, str], optional) β |
A dictionary of proxy servers to use by protocol or endpoint, for example, {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}. The proxies are used on each request. local_files_only (bool, optional, defaults to False) β |
Whether to only load local model weights and configuration files or not. If set to True, the model |
wonβt be downloaded from the Hub. token (str or bool, optional) β |
The token to use as HTTP bearer authorization for remote files. If True, the token generated from |
diffusers-cli login (stored in ~/.huggingface) is used. revision (str, optional, defaults to "main") β |
The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier |
allowed by Git. subfolder (str, optional, defaults to "") β |
The subfolder location of a model file within a larger model repository on the Hub or locally. mirror (str, optional) β |
Mirror source to resolve accessibility issues if youβre downloading a model in China. We do not |
guarantee the timeliness or safety of the source, and you should refer to the mirror site for more |
information. Load Textual Inversion embeddings into the text encoder of StableDiffusionPipeline (both π€ Diffusers and |
Automatic1111 formats are supported). Example: To load a Textual Inversion embedding vector in π€ Diffusers format: 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("sd-concepts-library/cat-toy") |
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