Create requirements.txt

requirements.txt

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+import streamlit as st
+from PIL import Image
+import torch
+from diffusers import AutoPipelineForInpainting
+from diffusers.utils import load_image
+
+pipe = AutoPipelineForInpainting.from_pretrained("diffusers/stable-diffusion-xl-1.0-inpainting-0.1", torch_dtype=torch.float16, variant="fp16")
+generator = torch.Generator(device="cuda").manual_seed(0)
+
+def main():
+    st.title("Image and Text Preview App")
+
+    # Upload images
+    st.sidebar.subheader("Upload Images")
+    uploaded_image1 = st.sidebarfile_uploader("Upload Image", type=["jpg", "jpeg", "png"])
+    uploaded_image2 = st.sidebarfile_uploader("Upload Mask", type=["jpg", "jpeg", "png"])
+
+    # Input text
+    st.subheader("Text Prompts")
+    prompt = st.text_input("Text prompt", "")
+    negative_prompt = st.text_input("Negative prompt", "")
+
+    guidance_scale = st.text_input("Guidance scale", 7.5)
+    steps = st.text_input("Steps", 20)
+    strength = st.text_input("Strength", 1)
+    
+    btn_submit = st.button("Generate")
+     
+    if btn_submit:
+        init_image = Image.open(uploaded_image1).convert("RGB")
+        st.image(init_image, caption="Uploaded Image", use_column_width=True)
+        mask_image = Image.open(uploaded_image2).convert("RGB")
+        st.image(mask_image, caption="Uploaded Mask", use_column_width=True)
+    
+        output = pipe(prompt = prompt, 
+                      negative_prompt=negative_prompt, 
+                      image=init_image, 
+                      mask_image=mask_image, 
+                      guidance_scale=int(guidance_scale), 
+                      num_inference_steps=int(steps), 
+                      strength=strength,
+                      generator=generator)
+        
+        st.text("Generated Image:")
+        st.image(output.images[0])
+
+
+if __name__ == "__main__":
+    main()