Norod

Norod

Norod

from transformers import AutoTokenizer
from transformers import LlamaTokenizerFast



#tokenizer_yam = AutoTokenizer.from_pretrained("yam-peleg/Hebrew-Gemma-11B-V2")
tokenizer_grok = LlamaTokenizerFast.from_pretrained('Xenova/grok-1-tokenizer')
tokenizer_gemma = AutoTokenizer.from_pretrained("google/gemma-7b-it")
tokenizer_aya101 = AutoTokenizer.from_pretrained("CohereForAI/aya-101")
tokenizer_gpt2 = AutoTokenizer.from_pretrained("gpt2")

Norod

import torch
import gradio as gr
from threading import Thread
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, TextIteratorStreamer

checkpoint = "CohereForAI/aya-101"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint, device_map='auto', torch_dtype=torch.bfloat16)

Norod

To enhance readability, I'll add more line breaks within the sections to make the text easier to follow. Here's a revised version with additional spacing:

"You are ChatGPT, a large language model trained by OpenAI, based on the GPT-4 architecture."

"Image input capabilities: Enabled"

"Conversation start date: 2023-12-19T01:17:10.597024"

"Deprecated knowledge cutoff: 2023-04-01"

Norod

Head Crop with Face Landmarks

This Python script utilizes the dlib library for face detection and facial landmarks to detect and crop a human head in an image. The detect_and_crop_head function takes an input image file, detects the face using a pre-trained 5-point facial landmarks model, and crops the image to include the entire head with a 1:1 aspect ratio. The cropping is adjusted based on the roll and yaw angles calculated from the facial landmarks.

Dependencies

• Python 3
• OpenCV
• dlib
• NumPy

Norod

Detect and Crop Head in Image

This Python script leverages the OpenCV library for face detection and the PIL (Pillow) library for image processing to detect and crop a human head in an image. The detect_and_crop_head function takes an input image file, detects the face using a pre-trained Haar Cascade classifier, and crops the image to include the entire head with a 1:1 aspect ratio. The cropping is achieved by specifying a factor that determines the size of the cropped region around the detected face. Adjust the factor parameter based on the specific requirements of your images.

Usage:

input_image_path = "path/to/your/input/image.jpg"
output_image_path = "path/to/save/cropped/head.jpg"
detect_and_crop_head(input_image_path, output_image_path, factor=1.5)

Norod

Norod

from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
import torch


#////////////////////////////////////////////////////////////////
guidance_scale=7.5
steps=40
width=1024
height=1024
base_model_id_str = "sdxl_10"

Norod