Update app.py

app.py

@@ -1,55 +1,3 @@
 import gradio as gr
-from PIL import Image
-import torch
-from torch import autocast
-from diffusers import StableDiffusionImg2ImgPipeline
-from torch import autocast
-from tqdm.auto import tqdm
-import requests
-from io import BytesIO
-from PIL import Image
-from typing import List, Optional, Union
-import inspect
-import warnings
-import sentence_transformers
 
-# Load the Stable Diffusion model
-modelid = "CompVis/stable-diffusion-v1-4"
-device = "cuda" if torch.cuda.is_available() else "cpu"
-pipe = StableDiffusionImg2ImgPipeline.from_pretrained(modelid, revision="fp16", torch_dtype=torch.float16)
-pipe.to(device)
-
-url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"
-# Load the Sentence-BERT model for text embeddings
-text_embedding_model = sentence_transformers.SentenceTransformer("paraphrase-MiniLM-L6-v2")
-
-
-def generate_image(prompt):
-    response = requests.get(url)
-    init_img = Image.open(BytesIO(response.content)).convert("RGB")
-    init_img = init_img.resize((768, 512))
-
-    generator = torch.Generator(device=device).manual_seed(1024)
-    with autocast("cuda"):
-        prompt_embedding = text_embedding_model.encode([str(prompt)])[0]
-        prompt_tensor = torch.tensor(prompt_embedding, device=device).half()
-        image = pipe(prompt=prompt_tensor, init_image=init_img, strength=0.75, guidance_scale=7.5, generator=generator).images[0]
-
-    return image
-
-# Define the input and output components
-input_text = gr.inputs.Textbox(lines=10, label="Enter a prompt")
-output_image = gr.outputs.Image(type="pil", label="Generated Image")
-
-# Create the Gradio interface
-iface = gr.Interface(
-    fn=generate_image,
-    inputs=input_text,
-    outputs=output_image,
-    title="Stable Bud",
-    description="Generate images using Stable Diffusion",
-    layout="vertical",
-)
-
-if __name__ == "__main__":
-    iface.launch()
+gr.Interface.load("models/CompVis/stable-diffusion-v1-4").launch()