text stringlengths 0 5.54k |
|---|
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. 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. Pipeline for text-to-image generation using Stable Diffusion XL and k-diffusion. 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... |
computing decoding in one step. disable_vae_tiling < source > ( ) Disable tiled VAE decoding. If enable_vae_tiling was previously enabled, this method will go back to |
computing decoding in one step. enable_freeu < source > ( s1: float s2: float b1: float b2: float ) Parameters s1 (float) — |
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. enable_vae_slicing < source > ( ) Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to |
compute decoding in several steps. This is useful to save some memory and allow larger batch sizes. enable_vae_tiling < source > ( ) Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to |
compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow |
processing larger images. encode_prompt < source > ( prompt: str prompt_2: Optional = None device: Optional = None num_images_per_prompt: int = 1 do_classifier_free_guidance: bool = True negative_prompt: Optional = None negative_prompt_2: Optional = None prompt_embeds: Optional = None negative_prompt_embeds: Optional... |
prompt to be encoded 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 |
device — (torch.device): |
torch device num_images_per_prompt (int) — |
number of images that should be generated per prompt do_classifier_free_guidance (bool) — |
whether to use classifier free guidance or not 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 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) — |
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, e.g. prompt |
weighting. If not provided, negative_prompt_embeds will be generated from negative_prompt input |
argument. pooled_prompt_embeds (torch.FloatTensor, optional) — |
Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. |
If not provided, pooled text embeddings will be generated from prompt input argument. negative_pooled_prompt_embeds (torch.FloatTensor, optional) — |
Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, e.g. prompt |
weighting. If not provided, pooled negative_prompt_embeds will be generated from negative_prompt |
input argument. lora_scale (float, optional) — |
A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded. clip_skip (int, optional) — |
Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that |
the output of the pre-final layer will be used for computing the prompt embeddings. Encodes the prompt into text encoder hidden states. fuse_qkv_projections < source > ( unet: bool = True vae: bool = True ) Parameters unet (bool, defaults to True) — To apply fusion on the UNet. vae (bool, defaults to True) ... |
key, value) are fused. For cross-attention modules, key and value projection matrices are fused. This API is 🧪 experimental. unfuse_qkv_projections < source > ( unet: bool = True vae: bool = True ) Parameters unet (bool, defaults to True) — To apply fusion on the UNet. vae (bool, defaults to True) — To apply ... |
DeepFloyd IF Overview DeepFloyd IF is a novel state-of-the-art open-source text-to-image model with a high degree of photorealism and language understanding. |
The model is a modular composed of a frozen text encoder and three cascaded pixel diffusion modules: Stage 1: a base model that generates 64x64 px image based on text prompt, Stage 2: a 64x64 px => 256x256 px super-resolution model, and Stage 3: a 256x256 px => 1024x1024 px super-resolution model |
Stage 1 and Stage 2 utilize a frozen text encoder based on the T5 transformer to extract text embeddings, which are then fed into a UNet architecture enhanced with cross-attention and attention pooling. |
Stage 3 is Stability AI’s x4 Upscaling model. |
The result is a highly efficient model that outperforms current state-of-the-art models, achieving a zero-shot FID score of 6.66 on the COCO dataset. |
Our work underscores the potential of larger UNet architectures in the first stage of cascaded diffusion models and depicts a promising future for text-to-image synthesis. Usage Before you can use IF, you need to accept its usage conditions. To do so: Make sure to have a Hugging Face account and be logged in. Accept t... |
login() and enter your Hugging Face Hub access token. Next we install diffusers and dependencies: Copied pip install -q diffusers accelerate transformers The following sections give more in-detail examples of how to use IF. Specifically: Text-to-Image Generation Image-to-Image Generation Inpainting Reusing model weig... |
from diffusers.utils import pt_to_pil, make_image_grid |
import torch |
# stage 1 |
stage_1 = DiffusionPipeline.from_pretrained("DeepFloyd/IF-I-XL-v1.0", variant="fp16", torch_dtype=torch.float16) |
stage_1.enable_model_cpu_offload() |
# stage 2 |
stage_2 = DiffusionPipeline.from_pretrained( |
"DeepFloyd/IF-II-L-v1.0", text_encoder=None, variant="fp16", torch_dtype=torch.float16 |
) |
stage_2.enable_model_cpu_offload() |
# stage 3 |
safety_modules = { |
"feature_extractor": stage_1.feature_extractor, |
"safety_checker": stage_1.safety_checker, |
"watermarker": stage_1.watermarker, |
} |
stage_3 = DiffusionPipeline.from_pretrained( |
"stabilityai/stable-diffusion-x4-upscaler", **safety_modules, torch_dtype=torch.float16 |
) |
stage_3.enable_model_cpu_offload() |
prompt = 'a photo of a kangaroo wearing an orange hoodie and blue sunglasses standing in front of the eiffel tower holding a sign that says "very deep learning"' |
generator = torch.manual_seed(1) |
# text embeds |
prompt_embeds, negative_embeds = stage_1.encode_prompt(prompt) |
# stage 1 |
stage_1_output = stage_1( |
prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_embeds, generator=generator, output_type="pt" |
).images |
#pt_to_pil(stage_1_output)[0].save("./if_stage_I.png") |
# stage 2 |
stage_2_output = stage_2( |
image=stage_1_output, |
prompt_embeds=prompt_embeds, |
negative_prompt_embeds=negative_embeds, |
generator=generator, |
output_type="pt", |
).images |
#pt_to_pil(stage_2_output)[0].save("./if_stage_II.png") |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.