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Versatile_Text_to_Image_Generator / app.py

Update app.py

302cd1a verified about 2 years ago

1.55 kB

import streamlit as st
from diffusers import DiffusionPipeline
from peft import PeftModel  # Import PEFT model loader
from PIL import Image
import torch

# Load the diffusion pipeline model
@st.cache_resource
def load_pipeline():
    pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
    
    # Ensure PEFT is available before loading LoRA weights
    try:
        pipe.load_lora_weights("Melonie/text_to_image_finetuned")
    except ValueError as e:
        st.error("PEFT backend is required but not properly set up.")
        raise e
    
    if torch.cuda.is_available():
        pipe.to("cuda")  # Move pipeline to GPU if available
    return pipe

pipe = load_pipeline()

# Streamlit app
st.title("Text-to-Image Generation App")

# User input for prompt
user_prompt = st.text_input(
    "Enter your image prompt", 
    value="Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
    help="Provide a detailed description of the image you'd like to generate."
)

# Button to generate the image
if st.button("Generate Image"):
    if user_prompt:
        with st.spinner("Generating image..."):
            try:
                # Generate the image
                image = pipe(user_prompt).images[0]
                
                # Display the generated image
                st.image(image, caption="Generated Image", use_column_width=True)
            except Exception as e:
                st.error(f"Error generating image: {str(e)}")
    else:
        st.error("Please enter a valid prompt.")

	import streamlit as st
	from diffusers import DiffusionPipeline
	from peft import PeftModel # Import PEFT model loader
	from PIL import Image
	import torch

	# Load the diffusion pipeline model
	@st.cache_resource
	def load_pipeline():
	pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")

	# Ensure PEFT is available before loading LoRA weights
	try:
	pipe.load_lora_weights("Melonie/text_to_image_finetuned")
	except ValueError as e:
	st.error("PEFT backend is required but not properly set up.")
	raise e

	if torch.cuda.is_available():
	pipe.to("cuda") # Move pipeline to GPU if available
	return pipe

	pipe = load_pipeline()

	# Streamlit app
	st.title("Text-to-Image Generation App")

	# User input for prompt
	user_prompt = st.text_input(
	"Enter your image prompt",
	value="Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
	help="Provide a detailed description of the image you'd like to generate."
	)

	# Button to generate the image
	if st.button("Generate Image"):
	if user_prompt:
	with st.spinner("Generating image..."):
	try:
	# Generate the image
	image = pipe(user_prompt).images[0]

	# Display the generated image
	st.image(image, caption="Generated Image", use_column_width=True)
	except Exception as e:
	st.error(f"Error generating image: {str(e)}")
	else:
	st.error("Please enter a valid prompt.")