Create app.py

app.py

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+from diffusers import DiffusionPipeline
+import torch
+import streamlit as st
+
+# pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0")
+# pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0")
+
+# sdxl_base_model_path = ("../Models/models--stabilityai--stable-diffusion-xl-base-1.0/snapshots"
+#                         "/462165984030d82259a11f4367a4eed129e94a7b")
+#
+# sdxl_refiner_model_path = ("../Models/models--stabilityai--stable-diffusion-xl-refiner-1.0/snapshots/"
+#                            "5d4cfe854c9a9a87939ff3653551c2b3c99a4356")
+@st.cache_resource
+def load_pipeline():
+
+    # pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0",
+    #                                          torch_dtype=torch.float16 if device == "cuda" else torch.float32,
+    #                                          use_safetensors=True,
+    #                                          variant="fp16" if device =="cuda" else None)
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0",
+                                             torch_dtype=torch.float16 if device == "cuda" else torch.float32,
+                                             use_safetensors=True,
+                                             variant="fp16" if device == "cuda" else None)
+    # if device == "cuda":
+    #     pipe.to(device)
+    # else:
+    #     pipe.enable_model_cpu_offload()
+    return  pipe
+
+def image_generation(pipe, prompt, negative_prompt):
+    try:
+        image = pipe(
+            prompt = prompt,
+            negative_prompt = "blurred, ugly, watermark, low resolution" + negative_prompt,
+            num_inference_steps= 20,
+            guidance_scale=9.0
+        ).images[0]
+        return image
+    except Exception as e:
+        st.error(f"Error generating image: {str(e)}")
+        return None
+
+
+import streamlit as st
+
+# Define the table as a list of dictionaries with the provided data
+table = [
+    {
+        "name": "sai-neonpunk",
+        "prompt": "neonpunk style . cyberpunk, vaporwave, neon, vibes, vibrant, stunningly beautiful, crisp, detailed, sleek, ultramodern, magenta highlights, dark purple shadows, high contrast, cinematic, ultra detailed, intricate, professional",
+        "negative_prompt": "painting, drawing, illustration, glitch, deformed, mutated, cross-eyed, ugly, disfigured"
+    },
+    {
+        "name": "futuristic-retro cyberpunk",
+        "prompt": "retro cyberpunk. 80's inspired, synthwave, neon, vibrant, detailed, retro futurism",
+        "negative_prompt": "modern, desaturated, black and white, realism, low contrast"
+    },
+    {
+        "name": "Dark Fantasy",
+        "prompt": "Dark Fantasy Art, dark, moody, dark fantasy style",
+        "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, bright, sunny"
+    },
+    {
+        "name": "Double Exposure",
+        "prompt": "Double Exposure Style, double image ghost effect, image combination, double exposure style",
+        "negative_prompt": "ugly, deformed, noisy, blurry, low contrast"
+    }
+]
+
+# Convert the list of dictionaries to a dictionary with 'name' as key for easy lookup
+styles_dict = {entry["name"]: entry for entry in table}
+
+
+
+
+st.title("Application 11: @GenAiLearniverse Image Generation using SD XL")
+prompt = st.text_input("Enter your Prompt", value="A futuristic superhero cat")
+
+pipeline = load_pipeline()
+# Dropdown for selecting a style
+style_name = st.selectbox("Select a Style", options=list(styles_dict.keys()))
+
+# Display the selected style's prompt and negative prompt
+if style_name:
+    selected_entry = styles_dict[style_name]
+    selected_style_prompt = selected_entry["prompt"];
+    selected_style_negative_prompt = selected_entry["negative_prompt"]
+if st.button("Generate Awesome Image"):
+    with st.spinner("Generating your awesome image..."):
+        image =image_generation(pipeline,prompt + selected_style_prompt, selected_style_negative_prompt)
+        if image:
+            st.image(image)