Update app.py

app.py

@@ -1,12 +1,15 @@
+from diffusers import StableDiffusionPipeline
 import torch
-from diffusers import FluxPipeline
 import streamlit as st
 from transformers import pipeline
 
-pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16)
-pipe.enable_model_cpu_offload()  # Save VRAM by offloading to CPU (remove if you have enough GPU power)
+model_id = "runwayml/stable-diffusion-v1-5"
+pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float32)
+pipe.to("cpu")
 
 st.set_page_config(page_title="🔥 MemeForge - Text-to-Meme Generator 🔥", page_icon="🔥", layout="wide")
+
+# Custom styling for the page
 st.markdown("""
     <style>
         .title {
@@ -54,28 +57,20 @@ st.markdown("""
 
 st.markdown('<div class="title">🔥 MemeForge - Text-to-Meme Generator 🔥</div>', unsafe_allow_html=True)
 st.markdown('<div class="description">Enter a description, and the AI will generate a meme image for you!</div>', unsafe_allow_html=True)
+
 input_text = st.text_input("Enter meme description:", key="input", placeholder="e.g., A funny dog looking confused.")
+
 if st.button("Generate Meme", key="generate", help="Click to generate a meme from your description"):
     if input_text:
         modified_prompt = "MEME of an Image of " + input_text
 
-        image = pipe(
-            modified_prompt,
-            height=1024,
-            width=1024,
-            guidance_scale=3.5,
-            num_inference_steps=50,
-            max_sequence_length=512,
-            generator=torch.Generator("cpu").manual_seed(0)
-        ).images[0]
+        # Generate image
+        image = pipe(modified_prompt, num_inference_steps=10).images[0]
 
+        # Display the generated image
         st.image(image, caption="Your Generated Meme", use_column_width=True)
 
         # Save the image
         image.save("generated_meme.png")
     else:
         st.warning("Please enter a description to generate a meme.")
-
-
-
-