init

app.py

@@ -0,0 +1,53 @@
+import streamlit as st
+import torch
+from diffusers import DiffusionPipeline
+
+# Load both base & refiner
+base = DiffusionPipeline.from_pretrained(
+    "ageraustine/stable-diffusion", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
+)
+base.to("cuda")
+refiner = DiffusionPipeline.from_pretrained(
+    "ageraustine/stable-diffusion",
+    text_encoder_2=base.text_encoder_2,
+    vae=base.vae,
+    torch_dtype=torch.float16,
+    use_safetensors=True,
+    variant="fp16",
+)
+refiner.to("cuda")
+
+# Define how many steps and what % of steps to be run on each expert (80/20) here
+n_steps = 40
+high_noise_frac = 0.8
+
+# Streamlit app
+st.title("Text-to-Image Generation App")
+
+# Text input
+user_text = st.text_input("Enter a text prompt for image generation")
+
+# Generate image based on user input
+if st.button("Generate Image"):
+    if user_text:
+        # Run both experts
+        image = base(
+            prompt=user_text,
+            num_inference_steps=n_steps,
+            denoising_end=high_noise_frac,
+            output_type="latent",
+        ).images
+        image = refiner(
+            prompt=user_text,
+            num_inference_steps=n_steps,
+            denoising_start=high_noise_frac,
+            image=image,
+        ).images[0]
+
+        # Display the generated image
+        st.image(image, caption="Generated Image", use_column_width=True)
+    else:
+        st.warning("Please enter a text prompt for image generation.")
+
+ 
+ 

requirements.txt

@@ -0,0 +1,3 @@
+torch
+diffusers
+streamlit