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16f0866 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 | import torch
import gradio as gr
from PIL import Image
from transformers import pipeline
from diffusers import StableDiffusion3Pipeline
model_path = "../Models/models--Salesforce--blip-image-captioning-base/snapshots/82a37760796d32b1411fe092ab5d4e227313294b"
device = "cuda" if torch.cuda.is_available() else "cpu"
caption_image = pipeline("image-to-text", model=model_path, device=device)
# caption_image = pipeline("image-to-text", model="Salesforce/blip-image-captioning-base", device=device)
def image_generation(prompt):
# is_cuda = False
pipeline = StableDiffusion3Pipeline.from_pretrained("stabilityai/stable-diffusion-3-medium-diffusers",
torch_dtype=torch.float32,
text_encoder_3=None,
tokenizer_3=None)
# pipeline.enable_model_cpu_offload()
pipeline.to('cpu')
image = pipeline(
prompt=prompt,
negative_prompt="blurred, ugly, watermark, low resolution, blurry",
num_inference_steps=15,
height=192,
width=192,
guidance_scale=7.0
).images[0]
return image
def caption_my_image(pil_image):
semantics = caption_image(images=pil_image)[0]['generated_text']
image = image_generation(semantics)
return image
gr.close_all()
demo = gr.Interface(fn=caption_my_image,
inputs=[gr.Image(label="Select Image",type="pil")],
outputs=[gr.Image(label="New Generated Image using SD3", type="pil")],
title="@GenAILearniverse Project 10: Generate Similar image",
description="THIS APPLICATION WILL BE USED TO GENERATE SIMILAR IMAGE BASED ON IMAGE UPLOADED.")
demo.launch() |