lokesh0802 commited on
Commit
6ab762d
1 Parent(s): edbcada

Update app.py

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Files changed (1) hide show
  1. app.py +23 -19
app.py CHANGED
@@ -1,32 +1,36 @@
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  import streamlit as st
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  import torch
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  from diffusers import StableDiffusionPipeline
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- import matplotlib.pyplot as plt
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- st.title('Text-to-Image Generation using Stable Diffusion')
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-
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- # Load the diffusion model
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  model_id1 = "runwayml/stable-diffusion-v1-5"
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  pipe = StableDiffusionPipeline.from_pretrained(model_id1, torch_dtype=torch.float16, use_safetensors=True)
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  pipe = pipe.to("cuda")
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- # Define function to generate images
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- def generate_image(prompt, negative_prompt, num_inference_steps=50, weight=640):
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- params = {'prompt': prompt, 'num_inference_steps': num_inference_steps, 'num_images_per_prompt': 2, 'height': int(1.2 * weight),
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- 'weight': weight, 'negative_prompt': negative_prompt}
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-
 
 
 
 
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  img = pipe(**params).images
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  return img[0], img[1]
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- # Create Streamlit interface
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- st.sidebar.subheader("Enter Prompts")
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- prompt = st.sidebar.text_area("Enter Prompt")
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- negative_prompt = st.sidebar.text_area("Enter Negative Prompt")
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- num_inference_steps = st.sidebar.slider("Number of Inference Steps", 1, 100, 50)
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- weight = st.sidebar.slider("Image Width", 512, 640, 640)
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- if st.sidebar.button("Generate Images"):
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- image1, image2 = generate_image(prompt, negative_prompt, num_inference_steps, weight)
 
 
 
 
 
 
 
 
 
 
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- st.image(image1, caption='Image 1', use_column_width=True)
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- st.image(image2, caption='Image 2', use_column_width=True)
 
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  import streamlit as st
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  import torch
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  from diffusers import StableDiffusionPipeline
 
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  model_id1 = "runwayml/stable-diffusion-v1-5"
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  pipe = StableDiffusionPipeline.from_pretrained(model_id1, torch_dtype=torch.float16, use_safetensors=True)
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  pipe = pipe.to("cuda")
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+ def generate_image(prompt, negative_prompt, num_inference_steps=50, width=640):
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+ params = {
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+ 'prompt': prompt,
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+ 'num_inference_steps': num_inference_steps,
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+ 'num_images_per_prompt': 2,
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+ 'height': int(1.2 * width),
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+ 'width': width,
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+ 'negative_prompt': negative_prompt
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+ }
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  img = pipe(**params).images
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  return img[0], img[1]
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+ def main():
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+ st.title("Diffuser Image Generator")
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+ prompt = st.text_input("Enter the prompt:")
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+ negative_prompt = st.text_input("Enter the negative prompt:")
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+ num_inference_steps = st.slider("Number of inference steps", 1, 100, 50)
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+ width = st.slider("Width", 512, 640, 640)
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+
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+ if st.button("Generate Image"):
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+ image1, image2 = generate_image(prompt, negative_prompt, num_inference_steps, width)
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+ st.image(image1, caption="Generated Image 1", use_column_width=True)
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+ st.image(image2, caption="Generated Image 2", use_column_width=True)
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+
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+ if __name__ == "__main__":
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+ main()