Create app.py

app.py

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+import streamlit as st
+import torch
+from diffusers import StableDiffusion3Pipeline
+import random
+
+# Load model
+device = "cuda" if torch.cuda.is_available() else "cpu"
+pipe = StableDiffusion3Pipeline.from_pretrained(
+    "stabilityai/stable-diffusion-3-medium-diffusers",
+    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32
+).to(device)
+
+st.title("🎨 Stable Diffusion 3 Medium - Streamlit")
+
+prompt = st.text_input("Prompt", "Astronaut in a jungle, cold color palette, 8k")
+negative_prompt = st.text_input("Negative Prompt", "blurry, low quality, text")
+guidance_scale = st.slider("Guidance Scale", 0.0, 10.0, 5.0)
+num_inference_steps = st.slider("Inference Steps", 1, 50, 28)
+width = st.slider("Image Width", 256, 1344, 1024, step=64)
+height = st.slider("Image Height", 256, 1344, 1024, step=64)
+random_seed = st.checkbox("Randomize Seed", value=True)
+seed = random.randint(0, 2**32 - 1) if random_seed else st.number_input("Seed", value=0, step=1)
+
+if st.button("Generate Image"):
+    with st.spinner("Generating..."):
+        generator = torch.Generator().manual_seed(seed)
+        image = pipe(
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            guidance_scale=guidance_scale,
+            num_inference_steps=num_inference_steps,
+            width=width,
+            height=height,
+            generator=generator
+        ).images[0]
+        st.image(image, caption="Generated Image")
+        st.write(f"Seed used: {seed}")