Create app.py

app.py

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+import streamlit as st
+from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler, AutoencoderKL
+import torch
+
+# Load the model and scheduler
+model_id = "sam749/Photon-v1"
+vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse", torch_dtype=torch.float16).to("cuda")
+pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
+pipe.vae = vae
+pipe.to("cuda")
+
+pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
+
+# Streamlit interface
+st.title("Text-to-Image Generator")
+
+# Prompt inputs
+prompt = st.text_input("Enter the prompt for the image:")
+negative_prompt = "cartoon, painting, illustration, (worst quality, low quality, normal quality:2)"
+
+cfg_scale = 5.5
+width = 512
+height = 768
+
+num_inference_steps = 24
+
+if st.button("Generate Image"):
+    with st.spinner("Generating..."):
+        # Generate image with the given parameters
+        generator = torch.Generator("cuda").manual_seed(0)
+        image = pipe(prompt, negative_prompt=negative_prompt, num_inference_steps=num_inference_steps,
+                     guidance_scale=cfg_scale, width=width, height=height, generator=generator).images[0]
+
+        # Display the generated image
+        st.image(image, caption="Generated Image", use_column_width=True)
+        image.save("generated_image.png")