"--image_encoder_path", type=str, default=None, # required=True, help="Path to CLIP image encoder", ) parser.add_argument( "--output_dir", type=str, default="sd-ip_adapter", help="The output directory where the model predictions and checkpoints will be written.", ) parser.add...
load_ip_adapter( "h94/IP-Adapter", subfolder="sdxl_models", weight_name="ip-adapter-plus_sdxl_vit-h.safetensors", image_encoder_folder="models/image_encoder") pipeline.enable_model_cpu_offload() image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/...
Integral Image In subject area: Computer Science An Integral Image is a transformed representation of an input image, where each pixel value corresponds to the sum of pixel values in a rectangular region of the original image. This transformation allows for efficient computation of the sum of pixe...
The subretinal fluid segmentation in spectral-domain optical coherence tomography (SD-OCT) with neurosensory retinal detachment plays a vital role in the assessment of CSC. Anand and Jayakumari [114], use FCM with Quick ABC (EFCM-QABC) for the segmentation of subretinal fluid. Initially, ...
Figure 5 shows the network structure of the Swinv2-Unet UBlock, which is the basic component of the upsampling path on the UNet encoder–decoder. The inputs to the UBlock include the output of the previous UBlock layer and the corresponding DBlock. The DBlock and UBlock are connected using...
It can be seen that the learning-based methods train the encoder with a whole dataset to learn the mapping from image to latent code, rather than optimize on a single image. Such a scheme yields several advantages. First, the optimization becomes smoother, which avoids the latent code from ...
After that, Karpathy et al. [47] modified their previous work using BRNN as sentence encoder instead of DT as in [33] to achieve better performance, where DT Parsers might be trained on unassociated text corpora that will affect the performance. Furthermore, the CNN is used to ...
Recent advances in deep learning techniques have led to improved diagnostic abilities in ophthalmology. A generative adversarial network (GAN), which consists of two competing types of deep neural networks, including a generator and a discriminator, has
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pip install peft==0.6.2 accelerate launch --num_processes=1 --main_process_port=36667 train_scripts/train_pixart_lora_hf.py --mixed_precision="fp16" \ --pretrained_model_name_or_path=PixArt-alpha/PixArt-XL-2-1024-MS \ --dataset_name=lambdalabs/pokemon-blip-captions --caption_column="...