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from diffusers import DiffusionPipeline
import torch
import re
from PIL import Image
import io
from dotenv import load_dotenv
import os
load_dotenv()
# Ensure GPU is used if available
device = "cuda" if torch.cuda.is_available() else "cpu"
pipeline = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0")
pipeline = pipeline.to(device)
def generate_image_prompts(script):
# Split the script into sentences
sentences = re.split(r'(?<=[.!?]) +', script)
# Generate prompts for each sentence
prompts = []
for sentence in sentences:
if sentence.strip(): # Ensure the sentence is not empty
prompts.append(sentence.strip())
return prompts
def hf_pipeline(prompt):
API_URL = "https://api-inference.huggingface.co/models/Shakker-Labs/AWPortrait-FL"
headers = {"Authorization": f"Bearer {os.getenv('HF_API_TOKEN')}"}
response = requests.post(API_URL, headers=headers, json={"inputs": prompt})
if response.status_code == 200:
return Image.open(io.BytesIO(response.content)) # Return the image directly
else:
raise Exception(f"Failed to generate image. Status code: {response.status_code}, {response.text}")
def generate_images(prompts):
image_files = []
for idx, prompt in enumerate(prompts):
print(f"Generating image for prompt: {prompt}")
# Ensure the prompt is processed on the correct device
image = hf_pipeline(prompt).images[0]
filename = f"generated_image_{idx}.png"
image.save(filename)
image_files.append(filename)
return image_files |