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17dd751 e6c83b5 17dd751 e6c83b5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 | import streamlit as st
from PIL import Image, ImageEnhance
from rembg import remove
import io
import torch
import numpy as np
from transformers import AutoImageProcessor, Swin2SRForImageSuperResolution, pipeline
# Page Configuration
st.set_page_config(layout="wide", page_title="AI Image Lab")
# --- Caching AI Models ---
# We use separate functions for 2x and 4x to avoid loading both into memory if not needed.
@st.cache_resource
def load_upscaler_x2():
"""Loads the Swin2SR model for 2x upscale."""
model_id = "caidas/swin2SR-classical-sr-x2-64"
processor = AutoImageProcessor.from_pretrained(model_id)
model = Swin2SRForImageSuperResolution.from_pretrained(model_id)
return processor, model
@st.cache_resource
def load_upscaler_x4():
"""Loads the Swin2SR model for 4x upscale."""
# This model is heavier and takes longer to run
model_id = "caidas/swin2SR-classical-sr-x4-63"
processor = AutoImageProcessor.from_pretrained(model_id)
model = Swin2SRForImageSuperResolution.from_pretrained(model_id)
return processor, model
@st.cache_resource
def load_depth_pipeline():
"""Loads a lightweight Depth Estimation pipeline."""
pipe = pipeline(task="depth-estimation", model="vinvino02/glpn-nyu")
return pipe
def ai_upscale(image, processor, model):
"""Runs the super-resolution model."""
inputs = processor(image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
output = outputs.reconstruction.data.squeeze().float().cpu().clamp_(0, 1).numpy()
output = np.moveaxis(output, 0, -1)
output = (output * 255.0).round().astype(np.uint8)
return Image.fromarray(output)
def convert_image_to_bytes(img):
buf = io.BytesIO()
img.save(buf, format="PNG")
return buf.getvalue()
def main():
st.title("✨ AI Image Lab: Transformers Edition")
st.markdown("Processing pipeline: **Background Removal** → **AI Modifiers** → **Geometry**")
# --- Sidebar Controls ---
st.sidebar.header("Processing Pipeline")
# 1. Background
st.sidebar.subheader("1. Cleanup")
remove_bg = st.sidebar.checkbox("Remove Background (rembg)", value=False)
# 2. AI Enhancements
st.sidebar.subheader("2. AI Enhancements")
ai_mode = st.sidebar.radio(
"Choose AI Modification:",
["None", "AI Super-Resolution (2x)", "AI Super-Resolution (4x)", "Depth Estimation"]
)
# 3. Geometry & Color
st.sidebar.subheader("3. Final Adjustments")
rotate_angle = st.sidebar.slider("Rotate", -180, 180, 0, 1)
contrast_val = st.sidebar.slider("Contrast", 0.5, 1.5, 1.0, 0.1)
# --- Main Content ---
uploaded_file = st.file_uploader("Upload an image...", type=["jpg", "jpeg", "png", "webp"])
if uploaded_file is not None:
image = Image.open(uploaded_file).convert("RGB")
processed_image = image.copy()
# --- STEP 1: Background Removal ---
if remove_bg:
with st.spinner("Removing background..."):
processed_image = remove(processed_image)
# --- STEP 2: AI Enhancements ---
if ai_mode == "AI Super-Resolution (2x)":
st.info("Loading Swin2SR (2x) model... (Fast)")
try:
processor, model = load_upscaler_x2()
with st.spinner("Upscaling (2x)..."):
processed_image = ai_upscale(processed_image, processor, model)
except Exception as e:
st.error(f"Error loading Upscaler: {e}")
elif ai_mode == "AI Super-Resolution (4x)":
st.warning("Loading Swin2SR (4x) model... (This is computationally heavy!)")
# Added a warning because 4x on CPU can be quite slow for large images
try:
processor, model = load_upscaler_x4()
with st.spinner("Upscaling (4x)... please wait"):
processed_image = ai_upscale(processed_image, processor, model)
except Exception as e:
st.error(f"Error loading Upscaler: {e}")
elif ai_mode == "Depth Estimation":
st.info("Generating Depth Map...")
try:
depth_pipe = load_depth_pipeline()
with st.spinner("Estimating depth..."):
result = depth_pipe(processed_image)
processed_image = result["depth"]
except Exception as e:
st.error(f"Error loading Depth Model: {e}")
# --- STEP 3: Geometry/Color ---
# Rotation
if rotate_angle != 0:
processed_image = processed_image.rotate(rotate_angle, expand=True)
# Contrast
if contrast_val != 1.0:
enhancer = ImageEnhance.Contrast(processed_image)
processed_image = enhancer.enhance(contrast_val)
# --- Display ---
col1, col2 = st.columns(2)
with col1:
st.subheader("Original")
st.image(image, use_container_width=True)
st.caption(f"Size: {image.size}")
with col2:
st.subheader("Result")
st.image(processed_image, use_container_width=True)
st.caption(f"Size: {processed_image.size}")
# --- Download ---
st.markdown("---")
btn = st.download_button(
label="💾 Download Result",
data=convert_image_to_bytes(processed_image),
file_name="ai_enhanced_image.png",
mime="image/png",
)
if __name__ == "__main__":
main() |