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import torch
import gradio as gr
from PIL import Image
from diffusers import StableDiffusionPipeline
# Use a pipeline as a high-level helper
from transformers import pipeline
device = "cuda" if torch.cuda.is_available() else "cpu"
caption_image = pipeline("image-to-text",
model="Salesforce/blip-image-captioning-large", device=device)
def image_generation(prompt):
device = "cuda" if torch.cuda.is_available() else "cpu"
pipeline = StableDiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-3-medium",
torch_dtype=torch.float16 if device == "cuda" else torch.float32,
)
#pipeline.to(device)
pipeline.enable_model_cpu_offload()
image = pipeline(
prompt=prompt + " 8K, Ultra HD",
negative_prompt="blurred, ugly, watermark, low resolution, blurry, nude",
num_inference_steps=40,
height=1024,
width=1024,
guidance_scale=9.0
).images[0]
return image
def caption_my_image(pil_image):
semantics = caption_image(images=pil_image)[0]['generated_text']
images = image_generation(semantics)
return images
demo = gr.Interface(fn=caption_my_image,
inputs=[gr.Image(label="Select Image",type="pil")],
outputs=[gr.Image(label="New Image genrated using SD3",type="pil")],
title="PicTalker | ImageNarrator | SnapSpeech | SpeakScene",
description="🌟 Transform Ordinary Photos into Extraordinary Art!")
demo.launch() |