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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}") | |