Delete pages/textimage.py

pages/textimage.py

@@ -1,104 +0,0 @@
-import streamlit as st
-import torch
-import numpy as np
-from PIL import Image
-import random
-import uuid
-from diffusers import PixArtAlphaPipeline
-
-# Check for CUDA availability
-device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-
-# # Load the PixArtAlphaPipeline
-# if torch.cuda.is_available():
-#     pipe = PixArtAlphaPipeline.from_pretrained(
-#         "PixArt-alpha/PixArt-LCM-XL-2-1024-MS",
-#         torch_dtype=torch.float16,
-#         use_safetensors=True,
-#     )
-#     pipe.to(device)
-#     st.write("Model loaded successfully!")
-# else:
-    # st.error("This demo requires GPU support, which is not available on this system.")
-
-# Load the PixArtAlphaPipeline
-
-pipe = PixArtAlphaPipeline.from_pretrained(
-    "PixArt-alpha/PixArt-LCM-XL-2-1024-MS",
-    torch_dtype=torch.float16,
-    use_safetensors=True,
-)
-pipe.to(device)
-st.write("Model loaded successfully!")
-
-# Constants
-MAX_SEED = np.iinfo(np.int32).max
-
-# Function to save image and return the path
-def save_image(img):
-    unique_name = str(uuid.uuid4()) + ".png"
-    img.save(unique_name)
-    return unique_name
-
-# Main function for image generation
-def generate_image(prompt, style, use_negative_prompt, negative_prompt, seed, width, height, inference_steps):
-    generator = torch.Generator().manual_seed(seed)
-
-    # Apply the selected style
-    if style == "(No style)":
-        prompt_text = prompt
-    else:
-        prompt_text, _ = apply_style(style, prompt, negative_prompt)
-
-    # Generate the image
-    images = pipe(
-        prompt=prompt_text,
-        negative_prompt=None,
-        width=width,
-        height=height,
-        guidance_scale=0,
-        num_inference_steps=inference_steps,
-        generator=generator,
-        num_images_per_prompt=1,
-        use_resolution_binning=True,
-        output_type="pil",
-    ).images
-
-    # Save the image and display
-    if images:
-        img_path = save_image(images[0])
-        img = Image.open(img_path)
-        st.image(img, caption="Generated Image", use_column_width=True)
-        st.success("Image generated successfully!")
-    else:
-        st.error("Failed to generate image. Please try again.")
-
-# Helper function to apply selected style
-def apply_style(style_name, positive, negative):
-    # Define styles dictionary (similar to your Gradio code)
-    styles = {
-        "(No style)": (positive, ""),
-        "Cinematic": ("cinematic still " + positive, "anime, cartoon, ..."),
-        "Realistic": ("Photorealistic " + positive, "drawing, painting, ..."),
-        # Add other styles here...
-    }
-    return styles.get(style_name, styles["(No style)"])
-
-# Streamlit UI
-st.title("Instant Image Generator")
-
-prompt = st.text_input("Prompt", "Enter your prompt")
-
-style_names = ["(No style)", "Cinematic", "Realistic"]  # Add other styles here...
-style = st.selectbox("Image Style", style_names)
-
-use_negative_prompt = st.checkbox("Use negative prompt")
-negative_prompt = st.text_input("Negative prompt", "")
-
-seed = st.slider("Seed", 0, MAX_SEED, 0)
-width = st.slider("Width", 256, 4192, 1024, step=32)
-height = st.slider("Height", 256, 4192, 1024, step=32)
-inference_steps = st.slider("Steps", 4, 20, 4)
-
-if st.button("Generate Image"):
-    generate_image(prompt, style, use_negative_prompt, negative_prompt, seed, width, height, inference_steps)