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feat: overhaul upscale with bug fixes and advanced features

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- Fix multi-image: process selected file, not always first
- Fix progress bar: asymptotic fill prevents visual stalling
- Fix slider: write results to captured index, not current selection
- Add model selection (Auto/AI/Fast), face enhancement, denoise
- Add output format (PNG/JPEG/WebP) with quality control
- Add Upscale All for sequential batch processing with queue
- More granular Python progress stages for smoother UX
This commit is contained in:
Siddharth Kumar Sah
2026-04-12 19:04:23 +08:00
parent 7995d13c81
commit fe376aebd2
4 changed files with 367 additions and 71 deletions
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packages/ai/python/upscale.py
+155 -52
View File
@@ -14,6 +14,34 @@ REALESRGAN_MODEL_PATH = os.environ.get(
"/opt/models/realesrgan/RealESRGAN_x4plus.pth",
)
GFPGAN_MODEL_PATH = os.environ.get(
"GFPGAN_MODEL_PATH",
"/opt/models/gfpgan/GFPGANv1.3.pth",
)
def apply_denoise(img, strength):
"""Apply denoising to a PIL image. Uses OpenCV when available, falls back to PIL."""
if strength <= 0:
return img
try:
import numpy as np
import cv2
arr = np.array(img)
# Map 0-1 strength to filter parameter (3-15 range)
h = int(3 + strength * 12)
if len(arr.shape) == 3 and arr.shape[2] >= 3:
denoised = cv2.fastNlMeansDenoisingColored(arr, None, h, h, 7, 21)
else:
denoised = cv2.fastNlMeansDenoising(arr, None, h, 7, 21)
return type(img).fromarray(denoised)
except ImportError:
from PIL import ImageFilter
radius = max(0.5, strength * 1.5)
return img.filter(ImageFilter.GaussianBlur(radius=radius))
def main():
input_path = sys.argv[1]
@@ -21,79 +49,154 @@ def main():
settings = json.loads(sys.argv[3]) if len(sys.argv) > 3 else {}
scale = settings.get("scale", 2)
model_choice = settings.get("model", "auto")
face_enhance = settings.get("faceEnhance", False)
denoise_strength = float(settings.get("denoise", 0))
output_format = settings.get("format", "png")
quality = int(settings.get("quality", 95))
try:
emit_progress(10, "Loading upscale model")
emit_progress(5, "Opening image")
from PIL import Image
img = Image.open(input_path)
new_size = (img.width * scale, img.height * scale)
# Try Real-ESRGAN first
try:
# Redirect stdout to stderr so basicsr/realesrgan init messages
# cannot contaminate our JSON result on stdout.
stdout_fd = os.dup(1)
os.dup2(2, 1)
method = "lanczos"
result = None
# Try Real-ESRGAN if requested
if model_choice in ("auto", "realesrgan"):
try:
from basicsr.archs.rrdbnet_arch import RRDBNet
from realesrgan import RealESRGANer
from gpu import gpu_available
import numpy as np
import torch
finally:
# Restore stdout after imports
os.dup2(stdout_fd, 1)
os.close(stdout_fd)
emit_progress(10, "Loading AI model")
if not os.path.exists(REALESRGAN_MODEL_PATH):
raise FileNotFoundError(f"RealESRGAN model not found: {REALESRGAN_MODEL_PATH}")
# Redirect stdout to stderr so basicsr/realesrgan init messages
# cannot contaminate our JSON result on stdout.
stdout_fd = os.dup(1)
os.dup2(2, 1)
use_gpu = gpu_available()
device = torch.device("cuda" if use_gpu else "cpu")
try:
from basicsr.archs.rrdbnet_arch import RRDBNet
from realesrgan import RealESRGANer
from gpu import gpu_available
import numpy as np
import torch
finally:
# Restore stdout after imports
os.dup2(stdout_fd, 1)
os.close(stdout_fd)
# RealESRGAN_x4plus is a 4x model internally
model = RRDBNet(
num_in_ch=3,
num_out_ch=3,
num_feat=64,
num_block=23,
num_grow_ch=32,
scale=4,
)
upsampler = RealESRGANer(
scale=4,
model_path=REALESRGAN_MODEL_PATH,
model=model,
half=use_gpu,
device=device,
)
emit_progress(20, "Model ready")
img_array = np.array(img.convert("RGB"))
emit_progress(25, "Upscaling image")
output, _ = upsampler.enhance(img_array, outscale=scale)
emit_progress(90, "Upscaling complete")
result = Image.fromarray(output)
emit_progress(95, "Saving result")
result.save(output_path)
method = "realesrgan"
except (ImportError, FileNotFoundError, RuntimeError, OSError):
# RealESRGAN unavailable or failed - fall back to Lanczos
if not os.path.exists(REALESRGAN_MODEL_PATH):
raise FileNotFoundError(
f"RealESRGAN model not found: {REALESRGAN_MODEL_PATH}"
)
use_gpu = gpu_available()
device = torch.device("cuda" if use_gpu else "cpu")
# RealESRGAN_x4plus is a 4x model internally
model = RRDBNet(
num_in_ch=3,
num_out_ch=3,
num_feat=64,
num_block=23,
num_grow_ch=32,
scale=4,
)
upsampler = RealESRGANer(
scale=4,
model_path=REALESRGAN_MODEL_PATH,
model=model,
half=use_gpu,
device=device,
)
emit_progress(20, "AI model loaded")
img_array = np.array(img.convert("RGB"))
emit_progress(30, "Enhancing image with AI")
output_array, _ = upsampler.enhance(img_array, outscale=scale)
emit_progress(80, "AI enhancement complete")
result = Image.fromarray(output_array)
method = "realesrgan"
# Face enhancement with GFPGAN
if face_enhance:
emit_progress(82, "Enhancing faces")
try:
from gfpgan import GFPGANer
if os.path.exists(GFPGAN_MODEL_PATH):
face_enhancer = GFPGANer(
model_path=GFPGAN_MODEL_PATH,
upscale=scale,
arch="clean",
channel_multiplier=2,
bg_upsampler=upsampler,
)
_, _, face_output = face_enhancer.enhance(
img_array,
has_aligned=False,
only_center_face=False,
paste_back=True,
)
result = Image.fromarray(face_output)
emit_progress(88, "Face enhancement complete")
else:
emit_progress(88, "Face model not found, skipping")
except (ImportError, RuntimeError, OSError):
emit_progress(88, "Face enhancement unavailable, skipping")
except (ImportError, FileNotFoundError, RuntimeError, OSError):
# RealESRGAN unavailable or failed
if model_choice == "realesrgan":
emit_progress(15, "AI model not available, using fast resize")
result = None
# Fall back to Lanczos
if result is None:
emit_progress(50, "Upscaling with Lanczos")
img_upscaled = img.resize(new_size, Image.LANCZOS)
emit_progress(95, "Saving result")
img_upscaled.save(output_path)
result = img.resize(new_size, Image.LANCZOS)
method = "lanczos"
# Denoise
if denoise_strength > 0:
emit_progress(90, "Reducing noise")
result = apply_denoise(result, denoise_strength)
# Determine final output path based on format
base_path = output_path.rsplit(".", 1)[0]
if output_format == "jpeg":
final_path = base_path + ".jpg"
elif output_format == "webp":
final_path = base_path + ".webp"
else:
final_path = base_path + ".png"
# Save with format-specific options
emit_progress(95, "Saving result")
save_kwargs = {}
if output_format == "jpeg":
result = result.convert("RGB") # Strip alpha for JPEG
save_kwargs["quality"] = quality
save_kwargs["optimize"] = True
elif output_format == "webp":
save_kwargs["quality"] = quality
result.save(final_path, **save_kwargs)
# Get actual dimensions of the saved result
actual_w, actual_h = result.size
print(
json.dumps(
{
"success": True,
"scale": scale,
"width": new_size[0],
"height": new_size[1],
"width": actual_w,
"height": actual_h,
"method": method,
"output_path": final_path,
"format": output_format,
}
)
)
packages/ai/src/upscaling.ts
+10 -1
View File
@@ -4,6 +4,11 @@ import { type ProgressCallback, runPythonWithProgress } from "./bridge.js";
export interface UpscaleOptions {
scale?: number;
model?: string;
faceEnhance?: boolean;
denoise?: number;
format?: string;
quality?: number;
}
export interface UpscaleResult {
@@ -11,6 +16,7 @@ export interface UpscaleResult {
width: number;
height: number;
method: string;
format: string;
}
export async function upscale(
@@ -34,11 +40,14 @@ export async function upscale(
throw new Error(result.error || "Upscaling failed");
}
const buffer = await readFile(outputPath);
// Python may write to a different path when the output format changes
const actualOutputPath = result.output_path || outputPath;
const buffer = await readFile(actualOutputPath);
return {
buffer,
width: result.width,
height: result.height,
method: result.method ?? "unknown",
format: result.format ?? "png",
};
}