import gradio as gr
import torch
from diffusers import DiffusionPipeline
from transformers import AutoTokenizer,AutoModel
from diffusers.models import AutoencoderKL

pipeline = DiffusionPipeline.from_pretrained(
    "CompVis/stable-diffusion-v1-4",
     text_encoder = AutoModel.from_pretrained("csebuetnlp/banglabert"),
     custom_pipeline="gr33nr1ng3r/Mukh-Oboyob",
     
)
pipeline.unet.load_attn_procs("gr33nr1ng3r/Mukh-Oboyob")


def diffusion(text,num_inference_steps,guidance_scale):
    prompt=text
    image = pipeline(prompt, num_inference_steps=num_inference_steps, guidance_scale=guidance_scale,height=128,width=128).images[0]
    return image
    
mukh_biboron_app = gr.Interface(
    diffusion,
    [
        gr.Textbox(
            label="prompt text",
            lines=3,
        ),
        gr.Slider(1, 100, value=50),
        gr.Slider(1.0, 30.0, value=7.5),
    ],
    "image",
   
)
if __name__ == "__main__":
    mukh_biboron_app.queue(max_size=20).launch(show_error=True)