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"vae": [ |
"diffusers", |
"AutoencoderKL" |
] |
} |
Community pipelines For more context about the design choices behind community pipelines, please have a look at this issue. Community pipelines allow you to get creative and build your own unique pipelines to share with the community. You can find all community pipelines in the diffusers/examples/community f... |
pipe = DiffusionPipeline.from_pretrained( |
"CompVis/stable-diffusion-v1-4", custom_pipeline="filename_in_the_community_folder", use_safetensors=True |
) If a community pipeline doesn’t work as expected, please open a GitHub issue and mention the author. You can learn more about community pipelines in the how to load community pipelines and how to contribute a community pipeline guides. Multilingual Stable Diffusion The multilingual Stable Diffusion pipeline uses a p... |
from diffusers import DiffusionPipeline |
from diffusers.utils import make_image_grid |
from transformers import ( |
pipeline, |
MBart50TokenizerFast, |
MBartForConditionalGeneration, |
) |
device = "cuda" if torch.cuda.is_available() else "cpu" |
device_dict = {"cuda": 0, "cpu": -1} |
# add language detection pipeline |
language_detection_model_ckpt = "papluca/xlm-roberta-base-language-detection" |
language_detection_pipeline = pipeline("text-classification", |
model=language_detection_model_ckpt, |
device=device_dict[device]) |
# add model for language translation |
translation_tokenizer = MBart50TokenizerFast.from_pretrained("facebook/mbart-large-50-many-to-one-mmt") |
translation_model = MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50-many-to-one-mmt").to(device) |
diffuser_pipeline = DiffusionPipeline.from_pretrained( |
"CompVis/stable-diffusion-v1-4", |
custom_pipeline="multilingual_stable_diffusion", |
detection_pipeline=language_detection_pipeline, |
translation_model=translation_model, |
translation_tokenizer=translation_tokenizer, |
torch_dtype=torch.float16, |
) |
diffuser_pipeline.enable_attention_slicing() |
diffuser_pipeline = diffuser_pipeline.to(device) |
prompt = ["a photograph of an astronaut riding a horse", |
"Una casa en la playa", |
"Ein Hund, der Orange isst", |
"Un restaurant parisien"] |
images = diffuser_pipeline(prompt).images |
make_image_grid(images, rows=2, cols=2) MagicMix MagicMix is a pipeline that can mix an image and text prompt to generate a new image that preserves the image structure. The mix_factor determines how much influence the prompt has on the layout generation, kmin controls the number of steps during the content generatio... |
from diffusers.utils import load_image, make_image_grid |
pipeline = DiffusionPipeline.from_pretrained( |
"CompVis/stable-diffusion-v1-4", |
custom_pipeline="magic_mix", |
scheduler=DDIMScheduler.from_pretrained("CompVis/stable-diffusion-v1-4", subfolder="scheduler"), |
).to('cuda') |
img = load_image("https://user-images.githubusercontent.com/59410571/209578593-141467c7-d831-4792-8b9a-b17dc5e47816.jpg") |
mix_img = pipeline(img, prompt="bed", kmin=0.3, kmax=0.5, mix_factor=0.5) |
make_image_grid([img, mix_img], rows=1, cols=2) original image image and text prompt mix |
Attention Processor An attention processor is a class for applying different types of attention mechanisms. AttnProcessor class diffusers.models.attention_processor.AttnProcessor < source > ( ) Default processor for performing attention-related computations. AttnProcessor2_0 class diffusers.models.attention_pro... |
It uses fused projection layers. For self-attention modules, all projection matrices (i.e., query, |
key, value) are fused. For cross-attention modules, key and value projection matrices are fused. This API is currently 🧪 experimental in nature and can change in future. LoRAAttnProcessor class diffusers.models.attention_processor.LoRAAttnProcessor < source > ( hidden_size: int cross_attention_dim: Optional = None ... |
The hidden size of the attention layer. cross_attention_dim (int, optional) — |
The number of channels in the encoder_hidden_states. rank (int, defaults to 4) — |
The dimension of the LoRA update matrices. network_alpha (int, optional) — |
Equivalent to alpha but it’s usage is specific to Kohya (A1111) style LoRAs. kwargs (dict) — |
Additional keyword arguments to pass to the LoRALinearLayer layers. Processor for implementing the LoRA attention mechanism. LoRAAttnProcessor2_0 class diffusers.models.attention_processor.LoRAAttnProcessor2_0 < source > ( hidden_size: int cross_attention_dim: Optional = None rank: int = 4 network_alpha: Optional... |
The hidden size of the attention layer. cross_attention_dim (int, optional) — |
The number of channels in the encoder_hidden_states. rank (int, defaults to 4) — |
The dimension of the LoRA update matrices. network_alpha (int, optional) — |
Equivalent to alpha but it’s usage is specific to Kohya (A1111) style LoRAs. kwargs (dict) — |
Additional keyword arguments to pass to the LoRALinearLayer layers. Processor for implementing the LoRA attention mechanism using PyTorch 2.0’s memory-efficient scaled dot-product |
attention. CustomDiffusionAttnProcessor class diffusers.models.attention_processor.CustomDiffusionAttnProcessor < source > ( train_kv: bool = True train_q_out: bool = True hidden_size: Optional = None cross_attention_dim: Optional = None out_bias: bool = True dropout: float = 0.0 ) Parameters train_kv (bool, de... |
Whether to newly train the key and value matrices corresponding to the text features. train_q_out (bool, defaults to True) — |
Whether to newly train query matrices corresponding to the latent image features. hidden_size (int, optional, defaults to None) — |
The hidden size of the attention layer. cross_attention_dim (int, optional, defaults to None) — |
The number of channels in the encoder_hidden_states. out_bias (bool, defaults to True) — |
Whether to include the bias parameter in train_q_out. dropout (float, optional, defaults to 0.0) — |
The dropout probability to use. Processor for implementing attention for the Custom Diffusion method. CustomDiffusionAttnProcessor2_0 class diffusers.models.attention_processor.CustomDiffusionAttnProcessor2_0 < source > ( train_kv: bool = True train_q_out: bool = True hidden_size: Optional = None cross_attention_... |
Whether to newly train the key and value matrices corresponding to the text features. train_q_out (bool, defaults to True) — |
Whether to newly train query matrices corresponding to the latent image features. hidden_size (int, optional, defaults to None) — |
The hidden size of the attention layer. cross_attention_dim (int, optional, defaults to None) — |
The number of channels in the encoder_hidden_states. out_bias (bool, defaults to True) — |
Whether to include the bias parameter in train_q_out. dropout (float, optional, defaults to 0.0) — |
The dropout probability to use. Processor for implementing attention for the Custom Diffusion method using PyTorch 2.0’s memory-efficient scaled |
dot-product attention. AttnAddedKVProcessor class diffusers.models.attention_processor.AttnAddedKVProcessor < source > ( ) Processor for performing attention-related computations with extra learnable key and value matrices for the text |
encoder. AttnAddedKVProcessor2_0 class diffusers.models.attention_processor.AttnAddedKVProcessor2_0 < source > ( ) Processor for performing scaled dot-product attention (enabled by default if you’re using PyTorch 2.0), with extra |
learnable key and value matrices for the text encoder. LoRAAttnAddedKVProcessor class diffusers.models.attention_processor.LoRAAttnAddedKVProcessor < source > ( hidden_size: int cross_attention_dim: Optional = None rank: int = 4 network_alpha: Optional = None ) Parameters hidden_size (int, optional) — |
The hidden size of the attention layer. cross_attention_dim (int, optional, defaults to None) — |
The number of channels in the encoder_hidden_states. rank (int, defaults to 4) — |
The dimension of the LoRA update matrices. network_alpha (int, optional) — |
Equivalent to alpha but it’s usage is specific to Kohya (A1111) style LoRAs. kwargs (dict) — |
Additional keyword arguments to pass to the LoRALinearLayer layers. Processor for implementing the LoRA attention mechanism with extra learnable key and value matrices for the text |
encoder. XFormersAttnProcessor class diffusers.models.attention_processor.XFormersAttnProcessor < source > ( attention_op: Optional = None ) Parameters attention_op (Callable, optional, defaults to None) — |
The base |
operator to |
use as the attention operator. It is recommended to set to None, and allow xFormers to choose the best |
operator. Processor for implementing memory efficient attention using xFormers. LoRAXFormersAttnProcessor class diffusers.models.attention_processor.LoRAXFormersAttnProcessor < source > ( hidden_size: int cross_attention_dim: int rank: int = 4 attention_op: Optional = None network_alpha: Optional = None **kwargs ... |
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