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import gradio as gr | |
import torch | |
from diffusers import StableDiffusionPipeline | |
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, | |
negative_prompt="blurred, ugly, watermark, low resolution, blurry, nude", | |
num_inference_steps=40, | |
height=1024, | |
width=1024, | |
guidance_scale=8.0 | |
).images[0] | |
return image | |
interface = gr.Interface( | |
fn=image_generation, | |
inputs=gr.Textbox(lines=2, placeholder="Enter Your Prompt ..."), | |
outputs=gr.Image(type="pil"), | |
title="AI Text Generation By SD-3M" | |
) | |
interface.launch() | |
# import gradio as gr | |
# import torch | |
# from diffusers import StableDiffusers3Pipeline | |
# def image_generation(prompt): | |
# device = "cuda" if torch.cuda.is_available() else "cpu" | |
# pipeline = StableDiffusers3Pipeline.from_pretrained("stabilityai/stable-diffusion-3-medium-diffusers", | |
# torch_dtype=torch.float16 if device == "cuda" else torch.float32, | |
# text_encoder_3 = None, | |
# tokenizer_3 = None) | |
# # pipeline.to(device) | |
# pipeline.enable_model_cpu_offload() | |
# image = pipeline( | |
# prompt = prompt, | |
# negative_prompt = "blurred, ugly, watermark, low resolution, blurry, nude", | |
# num_inference_steps = 40, | |
# height=1024, | |
# width=1024, | |
# guidance_scale=8.0 | |
# ).images[0] | |
# image.show() | |
# interface= gr.interface( | |
# fn=image_generation, | |
# inputs = gr.Textbox(lines="2", placeholder="Enter Your Prompt ..."), | |
# outputs = gr.Image(type="pil"), | |
# title = "AI Text Generation By SD-3M" | |
# ) | |
# interface.launch() |