Karl/ai-frame-image-server
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main ... 0.2.3

10 changed files with 172 additions and 678 deletions

2
.bumpversion.toml

@ -1,5 +1,5 @@
[tool.bumpversion]
current_version = "0.2.8"
current_version = "0.2.3"
parse = "(?P<major>\\d+)\\.(?P<minor>\\d+)\\.(?P<patch>\\d+)"
serialize = ["{major}.{minor}.{patch}"]
search = "{current_version}"

1
.gitignore vendored

@ -10,4 +10,3 @@ prompts_log.jsonl
publish.sh
test.py
.vscode/launch.json
favourites.json

2
libs/comfyui.py

@ -164,7 +164,7 @@ def create_image(prompt: str | None = None, model: str = "Random") -> None:
seed_param="seed",
save_node="CivitAI Image Saver",
save_param="filename",
model_node="UnetLoaderGGUFAdvancedDisTorchMultiGPU",
model_node="Unet Loader (GGUF)",
model_param="unet_name",
model=model
)

8
libs/ollama.py

@ -3,7 +3,6 @@ import logging
import litellm
import nest_asyncio
from libs.generic import load_recent_prompts, load_config
import re
nest_asyncio.apply()
logging.basicConfig(level=logging.INFO)
@ -75,10 +74,5 @@ def create_prompt_on_openwebui(prompt: str, topic: str = "random") -> str:
# api_key=user_config["openwebui"]["api_key"],
# )
# prompt = response["choices"][0]["message"]["content"].strip('"')
match = re.search(r'"([^"]+)"', prompt)
if not match:
match = re.search(r":\s*\n*\s*(.+)", prompt)
if match:
prompt = match.group(1)
logging.debug(prompt)
return prompt
return prompt.split(": ")[-1]

39
routes/gallery_routes.py

@ -1,50 +1,15 @@
from flask import Blueprint, render_template, jsonify, request
from flask import Blueprint, render_template
import os
import json
bp = Blueprint("gallery_routes", __name__)
image_folder = "./output"
favourites_file = "./favourites.json"
def get_favourites():
if not os.path.exists(favourites_file):
return []
with open(favourites_file, 'r') as f:
return json.load(f)
def save_favourites(favourites):
with open(favourites_file, 'w') as f:
json.dump(favourites, f)
@bp.route("/images", methods=["GET"])
def gallery():
favourites = get_favourites()
images = [
{"filename": f, "favourited": f in favourites}
{"filename": f}
for f in os.listdir(image_folder)
if f.lower().endswith(("png", "jpg", "jpeg", "gif"))
]
images = sorted(images, key=lambda x: os.path.getmtime(os.path.join(image_folder, x["filename"])), reverse=True)
return render_template("gallery.html", images=images)
@bp.route("/favourites", methods=["GET"])
def get_favourites_route():
return jsonify(get_favourites())
@bp.route("/favourites/toggle", methods=["POST"])
def toggle_favourite():
data = request.get_json()
filename = data.get("filename")
if not filename:
return jsonify({"status": "error", "message": "Filename missing"}), 400
favourites = get_favourites()
is_favourited = False
if filename in favourites:
favourites.remove(filename)
else:
favourites.append(filename)
is_favourited = True
save_favourites(favourites)
return jsonify({"status": "success", "favourited": is_favourited})

12
routes/settings_routes.py

@ -13,6 +13,7 @@ def config_editor():
config = configparser.ConfigParser()
config.read(CONFIG_PATH)
# Load from config directly — no helper functions needed anymore
topics = config.get('comfyui', 'topics', fallback='').split(',')
general_models = config.get('comfyui', 'models', fallback='').split(',')
flux_models = config.get('comfyui:flux', 'models', fallback='').split(',')
@ -63,12 +64,7 @@ def config_editor():
continue
form_key = f"{section}:{key}"
if form_key in request.form:
new_value = request.form[form_key]
# Prevent overwriting masked secrets unless actually changed
if key in ('password_for_auth', 'api_key') and new_value == "********":
continue # Skip overwriting
config[section][key] = new_value
config[section][key] = request.form[form_key]
# Save everything at once
with open(CONFIG_PATH, 'w') as configfile:
@ -91,8 +87,8 @@ def config_editor():
return render_template(
'settings.html',
topics=sorted(topics,key=str.lower),
models=sorted(general_models + flux_models,key=str.lower),
topics=topics,
models=general_models + flux_models,
config_sections=filtered_config.keys(),
config_values=filtered_config
)

137
templates/gallery.html

@ -116,22 +116,6 @@
/* lower than lightbox (999) */
}
.favourites-button {
position: fixed;
top: 20px;
right: 120px;
z-index: 500;
}
.favourite-heart {
position: absolute;
top: 20px;
left: 30px;
font-size: 30px;
color: white;
cursor: pointer;
}
.button-link {
background: #333;
color: white;
@ -143,7 +127,6 @@
transition: background 0.3s;
display: inline-block;
text-align: center;
border: none;
}
.button-link:hover {
@ -196,17 +179,15 @@
<body>
<a href="/" class="button-link home-button">Home</a>
<button class="button-link favourites-button" id="favourites-button" onclick="toggleFavouritesView()">Show Favourites</button>
<h1 id="page-title">Image Archive</h1>
<h1>Image Archive</h1>
<!-- Empty gallery container; images will be loaded incrementally -->
<div class="gallery" id="gallery"></div>
<!-- Lightbox -->
<div class="lightbox" id="lightbox" tabindex="-1" onkeyup="handleLightboxKeys(event)">
<div class="lightbox" id="lightbox">
<span class="close" onclick="closeLightbox()">&times;</span>
<span class="favourite-heart" id="favourite-heart" onclick="toggleFavourite()">&#9825;</span>
<span class="arrow left" onclick="prevImage()">&#10094;</span>
<img id="lightbox-img" src="" />
<p id="lightbox-prompt"></p>
@ -215,11 +196,11 @@
<!-- Pass image filenames from Flask to JS -->
<script>
let allImages = JSON.parse(`[
const allImages = [
{% for image in images %}
{ "filename": "{{ image.filename }}", "favourited": {{ 'true' if image.favourited else 'false' }} }{% if not loop.last %},{% endif %}
{% endfor %}
]`);
{ filename: "{{ image.filename }}" },
{% endfor %}
];
</script>
<script>
@ -228,15 +209,12 @@
let loadedCount = 0;
let currentIndex = 0;
const detailsCache = {}; // Cache for image details
let showingFavourites = false;
let filteredImages = allImages;
function createImageElement(image) {
const img = document.createElement('img');
img.src = `/images/thumbnails/${image.filename}`;
img.dataset.fullsrc = `/images/${image.filename}`;
img.dataset.filename = image.filename;
img.dataset.favourited = image.favourited;
img.loading = 'lazy';
img.style.cursor = 'pointer';
img.style.borderRadius = '10px';
@ -245,8 +223,7 @@
}
function loadNextBatch() {
const imagesToLoad = showingFavourites ? filteredImages : allImages;
const nextImages = imagesToLoad.slice(loadedCount, loadedCount + batchSize);
const nextImages = allImages.slice(loadedCount, loadedCount + batchSize);
nextImages.forEach(image => {
const imgEl = createImageElement(image);
gallery.appendChild(imgEl);
@ -254,35 +231,12 @@
loadedCount += nextImages.length;
}
function renderGallery() {
gallery.innerHTML = '';
loadedCount = 0;
loadNextBatch();
}
function toggleFavouritesView() {
showingFavourites = !showingFavourites;
const button = document.getElementById('favourites-button');
const pageTitle = document.getElementById('page-title');
if (showingFavourites) {
filteredImages = allImages.filter(img => img.favourited);
button.textContent = 'Show All';
pageTitle.textContent = 'Favourites';
} else {
filteredImages = allImages;
button.textContent = 'Show Favourites';
pageTitle.textContent = 'Image Archive';
}
renderGallery();
}
// Load initial batch
renderGallery();
loadNextBatch();
// Load more images when scrolling near bottom
window.addEventListener('scroll', () => {
const imagesToLoad = showingFavourites ? filteredImages : allImages;
if (loadedCount >= imagesToLoad.length) return; // all loaded
if (loadedCount >= allImages.length) return; // all loaded
if ((window.innerHeight + window.scrollY) >= (document.body.offsetHeight - 100)) {
loadNextBatch();
}
@ -297,45 +251,7 @@
const images = getGalleryImages();
currentIndex = images.indexOf(imgEl);
showImageAndLoadDetails(currentIndex);
const lightbox = document.getElementById("lightbox");
lightbox.style.display = "flex";
lightbox.focus();
}
function updateFavouriteHeart(isFavourited) {
const heart = document.getElementById('favourite-heart');
heart.innerHTML = isFavourited ? '&#9829;' : '&#9825;'; // solid vs outline heart
heart.style.color = isFavourited ? 'red' : 'white';
}
function toggleFavourite() {
const images = getGalleryImages();
const imgEl = images[currentIndex];
const filename = imgEl.dataset.filename;
fetch('/favourites/toggle', {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify({ filename: filename })
})
.then(response => response.json())
.then(data => {
if (data.status === 'success') {
const isFavourited = data.favourited;
imgEl.dataset.favourited = isFavourited;
updateFavouriteHeart(isFavourited);
const originalImage = allImages.find(img => img.filename === filename);
if (originalImage) {
originalImage.favourited = isFavourited;
}
if (showingFavourites) {
filteredImages = allImages.filter(img => img.favourited);
renderGallery();
}
}
});
document.getElementById("lightbox").style.display = "flex";
}
function showImageAndLoadDetails(index) {
@ -343,10 +259,8 @@
const imgEl = images[index];
const filename = imgEl.dataset.filename;
const fullsrc = imgEl.dataset.fullsrc;
const isFavourited = imgEl.dataset.favourited === 'true';
document.getElementById("lightbox-img").src = fullsrc;
updateFavouriteHeart(isFavourited);
if (detailsCache[filename]) {
document.getElementById("lightbox-prompt").textContent =
@ -373,16 +287,12 @@
function nextImage() {
const images = getGalleryImages();
const imagesToLoad = showingFavourites ? filteredImages : allImages;
if (currentIndex + 1 >= images.length && loadedCount < imagesToLoad.length) {
if (currentIndex + 1 >= images.length && loadedCount < allImages.length) {
loadNextBatch();
// Wait briefly to ensure DOM updates
setTimeout(() => {
const updatedImages = getGalleryImages();
if (currentIndex + 1 < updatedImages.length) {
currentIndex++;
showImageAndLoadDetails(currentIndex);
}
currentIndex++;
showImageAndLoadDetails(currentIndex);
}, 100);
} else {
currentIndex = (currentIndex + 1) % images.length;
@ -399,27 +309,6 @@
function closeLightbox() {
document.getElementById("lightbox").style.display = "none";
if (showingFavourites) {
// Refresh the gallery if a favourite was removed
const currentImage = getGalleryImages()[currentIndex];
const wasFavourited = currentImage.dataset.favourited === 'true';
const originalImage = allImages.find(img => img.filename === currentImage.dataset.filename);
if (originalImage && !originalImage.favourited) {
renderGallery();
}
}
}
function handleLightboxKeys(e) {
if (e.key === 'f') {
toggleFavourite();
} else if (e.key === 'Escape') {
closeLightbox();
} else if (e.key === 'ArrowLeft') {
prevImage();
} else if (e.key === 'ArrowRight') {
nextImage();
}
}
</script>
</body>

10
templates/settings.html

@ -161,19 +161,9 @@
<option value="False" {% if value.lower()=='false' %}selected{% endif %}>False</option>
</select>
{% else %}
{% if key in ['password_for_auth', 'api_key'] %}
<input type="text" name="{{ section }}:{{ key }}" value="********" placeholder="********">
{% elif value.lower() in ['true', 'false'] %}
<select name="{{ section }}:{{ key }}">
<option value="True" {% if value.lower()=='true' %}selected{% endif %}>True</option>
<option value="False" {% if value.lower()=='false' %}selected{% endif %}>False</option>
</select>
{% else %}
<input type="text" name="{{ section }}:{{ key }}" value="{{ value }}">
{% endif %}
{% endif %}
{% endfor %}
</div>
{% endfor %}

206
workflow_flux.json

@ -6,7 +6,7 @@
1
],
"vae": [
"73",
"27",
0
]
},
@ -15,6 +15,75 @@
"title": "VAE Decode"
}
},
"22": {
"inputs": {
"clip_name1": "t5/t5xxl_fp8_e4m3fn.safetensors",
"clip_name2": "clip_l.safetensors",
"type": "flux",
"device": "default"
},
"class_type": "DualCLIPLoader",
"_meta": {
"title": "DualCLIPLoader"
}
},
"27": {
"inputs": {
"vae_name": "FLUX1/ae.safetensors"
},
"class_type": "VAELoader",
"_meta": {
"title": "Load VAE"
}
},
"32": {
"inputs": {
"upscale_model": [
"33",
0
],
"image": [
"8",
0
]
},
"class_type": "ImageUpscaleWithModel",
"_meta": {
"title": "Upscale Image (using Model)"
}
},
"33": {
"inputs": {
"model_name": "4x-UltraSharp.pth"
},
"class_type": "UpscaleModelLoader",
"_meta": {
"title": "Load Upscale Model"
}
},
"34": {
"inputs": {
"upscale_method": "lanczos",
"scale_by": 0.5,
"image": [
"32",
0
]
},
"class_type": "ImageScaleBy",
"_meta": {
"title": "Half size"
}
},
"35": {
"inputs": {
"unet_name": "flux1-dev-Q4_0.gguf"
},
"class_type": "UnetLoaderGGUF",
"_meta": {
"title": "Unet Loader (GGUF)"
}
},
"40": {
"inputs": {
"int": 20
@ -57,7 +126,10 @@
"50",
1
],
"scheduler_name": "normal",
"scheduler": [
"49",
1
],
"positive": [
"44",
0
@ -100,7 +172,7 @@
]
},
"images": [
"8",
"34",
0
]
},
@ -111,7 +183,7 @@
},
"44": {
"inputs": {
"text": "Yautja Predator wielding flamethrower in smoky, cyberpunk alleyway darkness",
"text": "",
"speak_and_recognation": {
"__value__": [
false,
@ -152,18 +224,18 @@
]
},
"clip": [
"72",
0
"68",
1
]
},
"class_type": "CLIPTextEncode",
"_meta": {
"title": "Prompt Encoder"
"title": "CLIP Text Encode (Prompt)"
}
},
"48": {
"inputs": {
"seed": 47371998700984,
"seed": 903006749445372,
"increment": 1
},
"class_type": "Seed Generator (Image Saver)",
@ -177,7 +249,7 @@
},
"class_type": "Scheduler Selector (Comfy) (Image Saver)",
"_meta": {
"title": "Scheduler"
"title": "Scheduler Selector"
}
},
"50": {
@ -186,27 +258,66 @@
},
"class_type": "Sampler Selector (Image Saver)",
"_meta": {
"title": "Sampler"
"title": "Sampler Selector (Image Saver)"
}
},
"51": {
"inputs": {
"images": [
"8",
0
]
},
"class_type": "PreviewImage",
"_meta": {
"title": "Preview Image"
}
},
"52": {
"inputs": {
"float": 3.500000000000001
"float": 3.5
},
"class_type": "Float Literal (Image Saver)",
"_meta": {
"title": "CFG Scale"
"title": "CFG"
}
},
"53": {
"inputs": {
"float": 1.0000000000000002
"float": 1
},
"class_type": "Float Literal (Image Saver)",
"_meta": {
"title": "Denoise"
}
},
"60": {
"inputs": {
"clip_l": "",
"t5xxl": [
"44",
0
],
"guidance": [
"52",
0
],
"speak_and_recognation": {
"__value__": [
false,
true
]
},
"clip": [
"68",
1
]
},
"class_type": "CLIPTextEncodeFlux",
"_meta": {
"title": "CLIPTextEncodeFlux"
}
},
"62": {
"inputs": {
"noise": [
@ -232,7 +343,7 @@
},
"class_type": "SamplerCustomAdvanced",
"_meta": {
"title": "Custom Sampler"
"title": "SamplerCustomAdvanced"
}
},
"63": {
@ -244,7 +355,7 @@
},
"class_type": "KSamplerSelect",
"_meta": {
"title": "KSampler Select"
"title": "KSamplerSelect"
}
},
"64": {
@ -262,13 +373,13 @@
0
],
"model": [
"35",
"68",
0
]
},
"class_type": "BasicScheduler",
"_meta": {
"title": "Sigma Generator"
"title": "BasicScheduler"
}
},
"65": {
@ -280,13 +391,13 @@
},
"class_type": "RandomNoise",
"_meta": {
"title": "Noise Generator"
"title": "RandomNoise"
}
},
"67": {
"inputs": {
"model": [
"35",
"68",
0
],
"conditioning": [
@ -296,48 +407,31 @@
},
"class_type": "BasicGuider",
"_meta": {
"title": "Prompt Guider"
"title": "BasicGuider"
}
},
"72": {
"68": {
"inputs": {
"clip_name1": "t5-v1_1-xxl-encoder-Q4_K_M.gguf",
"clip_name2": "clip_l.safetensors",
"type": "flux",
"device": "cuda:0",
"virtual_vram_gb": 0,
"use_other_vram": false,
"expert_mode_allocations": ""
"lora_01": "None",
"strength_01": 1,
"lora_02": "None",
"strength_02": 1,
"lora_03": "None",
"strength_03": 1,
"lora_04": "None",
"strength_04": 1,
"model": [
"35",
0
],
"clip": [
"22",
0
]
},
"class_type": "DualCLIPLoaderGGUFDisTorchMultiGPU",
"class_type": "Lora Loader Stack (rgthree)",
"_meta": {
"title": "DualCLIPLoaderGGUFDisTorchMultiGPU"
}
},
"73": {
"inputs": {
"vae_name": "FLUX1/ae.safetensors",
"device": "cuda:0"
},
"class_type": "VAELoaderMultiGPU",
"_meta": {
"title": "VAELoaderMultiGPU"
}
},
"35": {
"inputs": {
"unet_name": "flux1-dev-Q4_0.gguf",
"dequant_dtype": "default",
"patch_dtype": "default",
"patch_on_device": false,
"device": "cuda:1",
"virtual_vram_gb": 0,
"use_other_vram": false,
"expert_mode_allocations": ""
},
"class_type": "UnetLoaderGGUFAdvancedDisTorchMultiGPU",
"_meta": {
"title": "UnetLoaderGGUFAdvancedDisTorchMultiGPU"
"title": "Lora Loader Stack (rgthree)"
}
}
}

433
workflow_flux_original.json

@ -1,433 +0,0 @@
{
"8": {
"inputs": {
"samples": [
"62",
1
],
"vae": [
"27",
0
]
},
"class_type": "VAEDecode",
"_meta": {
"title": "VAE Decode"
}
},
"22": {
"inputs": {
"clip_name1": "t5/t5xxl_fp8_e4m3fn.safetensors",
"clip_name2": "clip_l.safetensors",
"type": "flux",
"device": "default"
},
"class_type": "DualCLIPLoader",
"_meta": {
"title": "DualCLIPLoader"
}
},
"27": {
"inputs": {
"vae_name": "FLUX1/ae.safetensors"
},
"class_type": "VAELoader",
"_meta": {
"title": "Load VAE"
}
},
"32": {
"inputs": {
"upscale_model": [
"33",
0
],
"image": [
"8",
0
]
},
"class_type": "ImageUpscaleWithModel",
"_meta": {
"title": "Upscale Image (using Model)"
}
},
"33": {
"inputs": {
"model_name": "4x-UltraSharp.pth"
},
"class_type": "UpscaleModelLoader",
"_meta": {
"title": "Load Upscale Model"
}
},
"34": {
"inputs": {
"upscale_method": "lanczos",
"scale_by": 0.5,
"image": [
"32",
0
]
},
"class_type": "ImageScaleBy",
"_meta": {
"title": "Half size"
}
},
"35": {
"inputs": {
"unet_name": "flux1-dev-Q4_0.gguf"
},
"class_type": "UnetLoaderGGUF",
"_meta": {
"title": "Unet Loader (GGUF)"
}
},
"40": {
"inputs": {
"int": 20
},
"class_type": "Int Literal (Image Saver)",
"_meta": {
"title": "Generation Steps"
}
},
"41": {
"inputs": {
"width": 720,
"height": 1080,
"aspect_ratio": "custom",
"swap_dimensions": "Off",
"upscale_factor": 2,
"prescale_factor": 1,
"batch_size": 1
},
"class_type": "CR Aspect Ratio",
"_meta": {
"title": "CR Aspect Ratio"
}
},
"42": {
"inputs": {
"filename": "THISFILE",
"path": "",
"extension": "png",
"steps": [
"40",
0
],
"cfg": [
"52",
0
],
"modelname": "flux1-dev-Q4_0.gguf",
"sampler_name": [
"50",
1
],
"positive": [
"44",
0
],
"negative": [
"45",
0
],
"seed_value": [
"48",
0
],
"width": [
"41",
0
],
"height": [
"41",
1
],
"lossless_webp": true,
"quality_jpeg_or_webp": 100,
"optimize_png": false,
"counter": 0,
"denoise": [
"53",
0
],
"clip_skip": 0,
"time_format": "%Y-%m-%d-%H%M%S",
"save_workflow_as_json": true,
"embed_workflow": true,
"additional_hashes": "",
"download_civitai_data": true,
"easy_remix": true,
"speak_and_recognation": {
"__value__": [
false,
true
]
},
"images": [
"34",
0
]
},
"class_type": "Image Saver",
"_meta": {
"title": "CivitAI Image Saver"
}
},
"44": {
"inputs": {
"text": "",
"speak_and_recognation": {
"__value__": [
false,
true
]
}
},
"class_type": "ttN text",
"_meta": {
"title": "Positive Prompt T5"
}
},
"45": {
"inputs": {
"text": "text, watermark, deformed Avoid flat colors, poor lighting, and artificial elements. No unrealistic elements, low resolution, or flat colors. Avoid generic objects, poor lighting, and inconsistent styles, blurry, low-quality, distorted faces, overexposed lighting, extra limbs, bad anatomy, low contrast",
"speak_and_recognation": {
"__value__": [
false,
true
]
}
},
"class_type": "ttN text",
"_meta": {
"title": "Negative Prompt"
}
},
"47": {
"inputs": {
"text": [
"44",
0
],
"speak_and_recognation": {
"__value__": [
false,
true
]
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