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The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier |
allowed by Git. custom_revision (str, optional, defaults to "main") — |
The specific model version to use. It can be a branch name, a tag name, or a commit id similar to |
revision when loading a custom pipeline from the Hub. It can be a 🤗 Diffusers version when loading a |
custom pipeline from GitHub, otherwise it defaults to "main" when loading from the Hub. mirror (str, optional) — |
Mirror source to resolve accessibility issues if you’re 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. device_map (str or Dict[str, Union[int, str, torch.device]], optional) — |
A map that specifies where each submodule should go. It doesn’t need to be defined for each |
parameter/buffer name; once a given module name is inside, every submodule of it will be sent to the |
same device. |
Set device_map="auto" to have 🤗 Accelerate automatically compute the most optimized device_map. For |
more information about each option see designing a device |
map. max_memory (Dict, optional) — |
A dictionary device identifier for the maximum memory. Will default to the maximum memory available for |
each GPU and the available CPU RAM if unset. offload_folder (str or os.PathLike, optional) — |
The path to offload weights if device_map contains the value "disk". offload_state_dict (bool, optional) — |
If True, temporarily offloads the CPU state dict to the hard drive to avoid running out of CPU RAM if |
the weight of the CPU state dict + the biggest shard of the checkpoint does not fit. Defaults to True |
when there is some disk offload. low_cpu_mem_usage (bool, optional, defaults to True if torch version >= 1.9.0 else False) — |
Speed up model loading only loading the pretrained weights and not initializing the weights. This also |
tries to not use more than 1x model size in CPU memory (including peak memory) while loading the model. |
Only supported for PyTorch >= 1.9.0. If you are using an older version of PyTorch, setting this |
argument to True will raise an error. use_safetensors (bool, optional, defaults to None) — |
If set to None, the safetensors weights are downloaded if they’re available and if the |
safetensors library is installed. If set to True, the model is forcibly loaded from safetensors |
weights. If set to False, safetensors weights are not loaded. kwargs (remaining dictionary of keyword arguments, optional) — |
Can be used to overwrite load and saveable variables (the pipeline components of the specific pipeline |
class). The overwritten components are passed directly to the pipelines __init__ method. See example |
below for more information. variant (str, optional) — |
Load weights from a specified variant filename such as "fp16" or "ema". This is ignored when |
loading from_flax. Instantiates a image-to-image Pytorch diffusion pipeline from pretrained pipeline weight. The from_pretrained() method takes care of returning the correct pipeline class instance by: Detect the pipeline class of the pretrained_model_or_path based on the _class_name property of its |
config object Find the image-to-image pipeline linked to the pipeline class using pattern matching on pipeline class |
name. If a controlnet argument is passed, it will instantiate a StableDiffusionControlNetImg2ImgPipeline |
object. The pipeline is set in evaluation mode (model.eval()) by default. If you get the error message below, you need to finetune the weights for your downstream task: Copied Some weights of UNet2DConditionModel were not initialized from the model checkpoint at runwayml/stable-diffusion-v1-5 and are newly initialized because the shapes did not match: |
- conv_in.weight: found shape torch.Size([320, 4, 3, 3]) in the checkpoint and torch.Size([320, 9, 3, 3]) in the model instantiated |
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. To use private or gated models, log-in with |
huggingface-cli login. Examples: Copied >>> from diffusers import AutoPipelineForImage2Image |
>>> pipeline = AutoPipelineForImage2Image.from_pretrained("runwayml/stable-diffusion-v1-5") |
>>> image = pipeline(prompt, image).images[0] from_pipe < source > ( pipeline **kwargs ) Parameters pipeline (DiffusionPipeline) — |
an instantiated DiffusionPipeline object Instantiates a image-to-image Pytorch diffusion pipeline from another instantiated diffusion pipeline class. The from_pipe() method takes care of returning the correct pipeline class instance by finding the |
image-to-image pipeline linked to the pipeline class using pattern matching on pipeline class name. All the modules the pipeline contains will be used to initialize the new pipeline without reallocating |
additional memory. The pipeline is set in evaluation mode (model.eval()) by default. Examples: Copied >>> from diffusers import AutoPipelineForText2Image, AutoPipelineForImage2Image |
>>> pipe_t2i = AutoPipelineForText2Image.from_pretrained( |
... "runwayml/stable-diffusion-v1-5", requires_safety_checker=False |
... ) |
>>> pipe_i2i = AutoPipelineForImage2Image.from_pipe(pipe_t2i) |
>>> image = pipe_i2i(prompt, image).images[0] AutoPipelineForInpainting class diffusers.AutoPipelineForInpainting < source > ( *args **kwargs ) AutoPipelineForInpainting is a generic pipeline class that instantiates an inpainting pipeline class. The |
specific underlying pipeline class is automatically selected from either the |
from_pretrained() or from_pipe() methods. This class cannot be instantiated using __init__() (throws an error). Class attributes: config_name (str) — The configuration filename that stores the class and module names of all the |
diffusion pipeline’s components. from_pretrained < source > ( pretrained_model_or_path **kwargs ) Parameters pretrained_model_name_or_path (str or os.PathLike, optional) — |
Can be either: |
A string, the repo id (for example CompVis/ldm-text2im-large-256) of a pretrained pipeline |
hosted on the Hub. |
A path to a directory (for example ./my_pipeline_directory/) containing pipeline weights |
saved using |
save_pretrained(). |
torch_dtype (str or torch.dtype, optional) — |
Override the default torch.dtype and load the model with another dtype. If “auto” is passed, the |
dtype is automatically derived from the model’s weights. force_download (bool, optional, defaults to False) — |
Whether or not to force the (re-)download of the model weights and configuration files, overriding the |
cached versions if they exist. cache_dir (Union[str, os.PathLike], optional) — |
Path to a directory where a downloaded pretrained model configuration is cached if the standard cache |
is not used. resume_download (bool, optional, defaults to False) — |
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) — |
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. output_loading_info(bool, optional, defaults to False) — |
Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages. local_files_only (bool, optional, defaults to False) — |
Whether to only load local model weights and configuration files or not. If set to True, the model |
won’t be downloaded from the Hub. token (str or bool, optional) — |
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") — |
The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier |
allowed by Git. custom_revision (str, optional, defaults to "main") — |
The specific model version to use. It can be a branch name, a tag name, or a commit id similar to |
revision when loading a custom pipeline from the Hub. It can be a 🤗 Diffusers version when loading a |
custom pipeline from GitHub, otherwise it defaults to "main" when loading from the Hub. mirror (str, optional) — |
Mirror source to resolve accessibility issues if you’re 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. device_map (str or Dict[str, Union[int, str, torch.device]], optional) — |
A map that specifies where each submodule should go. It doesn’t need to be defined for each |
parameter/buffer name; once a given module name is inside, every submodule of it will be sent to the |
same device. |
Set device_map="auto" to have 🤗 Accelerate automatically compute the most optimized device_map. For |
more information about each option see designing a device |
map. max_memory (Dict, optional) — |
A dictionary device identifier for the maximum memory. Will default to the maximum memory available for |
each GPU and the available CPU RAM if unset. offload_folder (str or os.PathLike, optional) — |
The path to offload weights if device_map contains the value "disk". offload_state_dict (bool, optional) — |
If True, temporarily offloads the CPU state dict to the hard drive to avoid running out of CPU RAM if |
the weight of the CPU state dict + the biggest shard of the checkpoint does not fit. Defaults to True |
when there is some disk offload. low_cpu_mem_usage (bool, optional, defaults to True if torch version >= 1.9.0 else False) — |
Speed up model loading only loading the pretrained weights and not initializing the weights. This also |
tries to not use more than 1x model size in CPU memory (including peak memory) while loading the model. |
Only supported for PyTorch >= 1.9.0. If you are using an older version of PyTorch, setting this |
argument to True will raise an error. use_safetensors (bool, optional, defaults to None) — |
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