.bumpversion.toml

@@ -1,5 +1,5 @@
 [tool.bumpversion]
-current_version = "0.3.9"
+current_version = "0.2.21"
 parse = "(?P<major>\\d+)\\.(?P<minor>\\d+)\\.(?P<patch>\\d+)"
 serialize = ["{major}.{minor}.{patch}"]
 replace = "{new_version}"

Dockerfile

@@ -4,7 +4,7 @@ FROM python:3.11-slim
 # Set the working directory in the container
 WORKDIR /app
 # Set version label
-ARG VERSION="0.3.9"
+ARG VERSION="0.2.21"
 LABEL version=$VERSION
 
 # Copy project files into the container

ai_frame_image_server.py

@@ -18,13 +18,6 @@ user_config = load_config()
 app = Flask(__name__)
 app.secret_key = os.environ.get("SECRET_KEY")
 
-# Make version available to all templates
-from libs.generic import get_current_version
-@app.context_processor
-def inject_version():
-    version = get_current_version()
-    return dict(version=version)
-
 # Inject config into routes that need it
 create_routes.init_app(user_config)
 auth_routes.init_app(user_config)
@@ -46,16 +39,7 @@ from libs.comfyui import create_image
 
 def scheduled_task():
     print(f"Executing scheduled task at {time.strftime('%Y-%m-%d %H:%M:%S')}")
-    # Generate a random prompt using either OpenWebUI or OpenRouter
+    create_image(None)
-    from libs.generic import create_prompt_with_random_model
-    prompt = create_prompt_with_random_model("Generate a random detailed prompt for stable diffusion.")
-    if prompt:
-        # Select a random model
-        import random
-        model = "Random Image Model"
-        create_image(prompt, model)
-    else:
-        print("Failed to generate a prompt for the scheduled task.")
 
 if user_config["frame"]["auto_regen"] == "True":
     if os.environ.get("WERKZEUG_RUN_MAIN") == "true":

libs/comfyui.py

@@ -15,7 +15,7 @@ from tenacity import (
 import nest_asyncio
 from libs.generic import rename_image, load_config, save_prompt
 from libs.create_thumbnail import generate_thumbnail
-from libs.openwebui import create_prompt_on_openwebui
+from libs.ollama import create_prompt_on_openwebui
 nest_asyncio.apply()
 
 logging.basicConfig(level=logging.INFO)
@@ -122,7 +122,6 @@ def generate_image(
 def select_model(model: str) -> tuple[str, str]:
     use_flux = json.loads(user_config["comfyui"].get("FLUX", "false").lower())
     only_flux = json.loads(user_config["comfyui"].get("ONLY_FLUX", "false").lower())
-    use_qwen = json.loads(user_config["comfyui"].get("Qwen", "false").lower())
 
     if model == "Random Image Model":
         selected_workflow = "FLUX" if (use_flux and (only_flux or random.choice([True, False]))) else "SDXL"
@@ -134,8 +133,6 @@ def select_model(model: str) -> tuple[str, str]:
     if model == "Random Image Model":
         if selected_workflow == "FLUX":
             valid_models = user_config["comfyui:flux"]["models"].split(",")
-        elif selected_workflow == "Qwen":
-            valid_models = user_config["comfyui:qwen"]["models"].split(",")
         else:  # SDXL
             available_model_list = user_config["comfyui"]["models"].split(",")
             valid_models = list(set(get_available_models()) & set(available_model_list))
@@ -148,11 +145,7 @@ def create_image(prompt: str | None = None, model: str = "Random Image Model") -
     """Generate an image with a chosen workflow (Random, FLUX*, or SDXL*)."""
 
     if prompt is None:
-        # Generate a random prompt using either OpenWebUI or OpenRouter
+        logging.error("No prompt provided.")
-        from libs.generic import create_prompt_with_random_model
-        prompt = create_prompt_with_random_model("Generate a random detailed prompt for stable diffusion.")
-        if not prompt:
-            logging.error("Failed to generate a prompt.")
         return
 
     if not prompt:
@@ -167,95 +160,16 @@ def create_image(prompt: str | None = None, model: str = "Random Image Model") -
             file_name="image",
             comfy_prompt=prompt,
             workflow_path="./workflow_flux.json",
-            prompt_node="CLIP Text Encode (Positive Prompt)",
+            prompt_node="Positive Prompt T5",
-            seed_node="RandomNoise",
+            seed_node="Seed",
-            seed_param="noise_seed",
-            save_node="Save Image",
-            save_param="filename_prefix",
-            model_node="UnetLoaderGGUFDisTorchMultiGPU",
-            model_param="unet_name",
-            model=model
-        )
-    elif selected_workflow == "Qwen":
-        generate_image(
-            file_name="image",
-            comfy_prompt=prompt,
-            workflow_path="./workflow_qwen.json",
-            prompt_node="Positive",
-            seed_node="KSampler",
             seed_param="seed",
-            save_node="Save Image",
+            save_node="CivitAI Image Saver",
-            save_param="filename_prefix",
+            save_param="filename",
-            model_node="Load Checkpoint",
+            model_node="UnetLoaderGGUFAdvancedDisTorchMultiGPU",
-            model_param="ckpt_name",
+            model_param="unet_name",
             model=model
         )
     else:  # SDXL
         generate_image("image", comfy_prompt=prompt, model=model)
 
     logging.info(f"{selected_workflow} generation started with prompt: {prompt}")
-
-def get_queue_count() -> int:
-    """Fetches the current queue count from ComfyUI (pending + running jobs)."""
-    url = user_config["comfyui"]["comfyui_url"] + "/queue"
-    try:
-        response = requests.get(url)
-        response.raise_for_status()
-        data = response.json()
-        pending = len(data.get("queue_pending", []))
-        running = len(data.get("queue_running", []))
-        return pending + running
-    except Exception as e:
-        logging.error(f"Error fetching queue count: {e}")
-        return 0
-
-def get_queue_details() -> list:
-    """Fetches detailed queue information including model names and prompts."""
-    url = user_config["comfyui"]["comfyui_url"] + "/queue"
-    try:
-        response = requests.get(url)
-        response.raise_for_status()
-        data = response.json()
-        jobs = []
-        for job_list in [data.get("queue_running", []), data.get("queue_pending", [])]:
-            for job in job_list:
-                # Extract prompt data (format: [priority, time, prompt])
-                prompt_data = job[2]
-                model = "Unknown"
-                prompt = "No prompt"
-                
-                # Find model loader node (works for SDXL/FLUX/Qwen workflows)
-                for node in prompt_data.values():
-                    if node.get("class_type") in ["CheckpointLoaderSimple", "UnetLoaderGGUFAdvancedDisTorchMultiGPU"]:
-                        model = node["inputs"].get("ckpt_name", "Unknown")
-                        break
-                
-                # Find prompt node using class_type pattern and title matching
-                for node in prompt_data.values():
-                    class_type = node.get("class_type", "")
-                    if "CLIPTextEncode" in class_type and "text" in node["inputs"]:
-                        meta = node.get('_meta', {})
-                        title = meta.get('title', '').lower()
-                        if 'positive' in title or 'prompt' in title:
-                            prompt = node["inputs"]["text"]
-                            break
-                
-                jobs.append({
-                    "id": job[0],
-                    "model": model.split(".")[0] if model != "Unknown" else model,
-                    "prompt": prompt
-                })
-        return jobs
-    except Exception as e:
-        logging.error(f"Error fetching queue details: {e}")
-        return []
-    try:
-        response = requests.get(url)
-        response.raise_for_status()
-        data = response.json()
-        pending = len(data.get("queue_pending", []))
-        running = len(data.get("queue_running", []))
-        return pending + running
-    except Exception as e:
-        logging.error(f"Error fetching queue count: {e}")
-        return 0

libs/generic.py

@@ -84,8 +84,8 @@ def get_details_from_png(path):
             try:
                 # Flux workflow
                 data = json.loads(img.info["prompt"])
-                prompt = data['6']['inputs']['text']
+                prompt = data['44']['inputs']['text']
-                model = data['38']['inputs']['unet_name'].split(".")[0]
+                model = data['35']['inputs']['unet_name'].split(".")[0]
             except KeyError:
                 # SDXL workflow
                 data = json.loads(img.info["prompt"])
@@ -110,31 +110,14 @@ def get_current_version():
         return version
     except subprocess.CalledProcessError as e:
         print("Error running bump-my-version:", e)
-        return "unknown"
+        return None
 
 def load_models_from_config():
-    config = load_config()
+    flux_models = load_config()["comfyui:flux"]["models"].split(",")
-    
+    sdxl_models = load_config()["comfyui"]["models"].split(",")
-    # Only load FLUX models if FLUX feature is enabled
-    use_flux = config["comfyui"].get("flux", "False").lower() == "true"
-    if use_flux and "comfyui:flux" in config and "models" in config["comfyui:flux"]:
-        flux_models = config["comfyui:flux"]["models"].split(",")
-    else:
-        flux_models = []
-    
-    sdxl_models = config["comfyui"]["models"].split(",")
-    
-    # Only load Qwen models if Qwen feature is enabled
-    use_qwen = config["comfyui"].get("qwen", "False").lower() == "true"
-    if use_qwen and "comfyui:qwen" in config and "models" in config["comfyui:qwen"]:
-        qwen_models = config["comfyui:qwen"]["models"].split(",")
-    else:
-        qwen_models = []
-    
     sorted_flux_models = sorted(flux_models, key=str.lower)
     sorted_sdxl_models = sorted(sdxl_models, key=str.lower)
-    sorted_qwen_models = sorted(qwen_models, key=str.lower)
+    return sorted_sdxl_models, sorted_flux_models
-    return sorted_sdxl_models, sorted_flux_models, sorted_qwen_models
 
 
 def load_topics_from_config():
@@ -175,10 +158,7 @@ def load_prompt_models_from_config():
 
 
 def create_prompt_with_random_model(base_prompt: str, topic: str = "random"):
-    """Create a prompt using a randomly selected model from OpenWebUI or OpenRouter.
+    """Create a prompt using a randomly selected model from OpenWebUI or OpenRouter."""
-    
-    If OpenWebUI fails, it will retry once. If it fails again, it will fallback to OpenRouter.
-    """
     prompt_models = load_prompt_models_from_config()
     
     if not prompt_models:
@@ -188,59 +168,16 @@ def create_prompt_with_random_model(base_prompt: str, topic: str = "random"):
     # Randomly select a model
     service, model = random.choice(prompt_models)
     
-    # Import here to avoid circular imports
-    from libs.openwebui import create_prompt_on_openwebui
-    from libs.openrouter import create_prompt_on_openrouter
-    
     if service == "openwebui":
-        try:
+        # Import here to avoid circular imports
-            # First attempt with OpenWebUI
+        from libs.ollama import create_prompt_on_openwebui
-            logging.info(f"Attempting to generate prompt with OpenWebUI using model: {model}")
+        return create_prompt_on_openwebui(base_prompt, topic)
-            result = create_prompt_on_openwebui(base_prompt, topic, model)
-            if result:
-                return result
-            
-            # If first attempt returns None, try again
-            logging.warning("First OpenWebUI attempt failed. Retrying...")
-            result = create_prompt_on_openwebui(base_prompt, topic, model)
-            if result:
-                return result
-            
-            # If second attempt fails, fallback to OpenRouter
-            logging.warning("Second OpenWebUI attempt failed. Falling back to OpenRouter...")
-            openrouter_models = [m for m in prompt_models if m[0] == "openrouter"]
-            if openrouter_models:
-                _, openrouter_model = random.choice(openrouter_models)
-                return create_prompt_on_openrouter(base_prompt, topic, openrouter_model)
-            else:
-                logging.error("No OpenRouter models configured for fallback.")
-                return "A colorful abstract composition"  # Default fallback prompt
-                
-        except Exception as e:
-            logging.error(f"Error with OpenWebUI: {e}")
-            # Fallback to OpenRouter on exception
-            logging.warning("OpenWebUI exception. Falling back to OpenRouter...")
-            openrouter_models = [m for m in prompt_models if m[0] == "openrouter"]
-            if openrouter_models:
-                _, openrouter_model = random.choice(openrouter_models)
-                try:
-                    return create_prompt_on_openrouter(base_prompt, topic, openrouter_model)
-                except Exception as e2:
-                    logging.error(f"Error with OpenRouter fallback: {e2}")
-                    return "A colorful abstract composition"  # Default fallback prompt
-            else:
-                logging.error("No OpenRouter models configured for fallback.")
-                return "A colorful abstract composition"  # Default fallback prompt
-    
     elif service == "openrouter":
-        try:
+        # Import here to avoid circular imports
-            # Use OpenRouter
+        from libs.openrouter import create_prompt_on_openrouter
-            return create_prompt_on_openrouter(base_prompt, topic, model)
+        return create_prompt_on_openrouter(base_prompt, topic)
-        except Exception as e:
-            logging.error(f"Error with OpenRouter: {e}")
-            return "A colorful abstract composition"  # Default fallback prompt
     
-    return "A colorful abstract composition"  # Default fallback prompt
+    return None
 
 user_config = load_config()
 output_folder = user_config["comfyui"]["output_dir"]

libs/ollama.py

@@ -1,11 +1,9 @@
 import random
 import logging
+import litellm
 import nest_asyncio
 from libs.generic import load_recent_prompts, load_config
 import re
-from openwebui_chat_client import OpenWebUIClient
-from datetime import datetime
-
 nest_asyncio.apply()
 
 logging.basicConfig(level=logging.INFO)
@@ -35,27 +33,22 @@ def create_prompt_on_openwebui(prompt: str, topic: str = "random", model: str =
         topic_instruction = f" Incorporate the theme of '{selected_topic}' into the new prompt."
 
     user_content = (
-        "Can you generate me a really random image idea, Do not exceed 10 words. Use clear language, not poetic metaphors."
+        "Can you generate me a really random image idea, Do not exceed 10 words. Use clear language, not poetic metaphors.”"
         + topic_instruction
         + "Avoid prompts similar to the following:"
         + "\n".join(f"{i+1}. {p}" for i, p in enumerate(recent_prompts))
     )
 
+
     if model:
         # Use the specified model
         model = model
     else:
         # Select a random model
-        model = random.choice(user_config["openwebui"]["models"].split(",")).strip()
+        model = random.choice(user_config["openwebui"]["models"].split(","))
-
+    response = litellm.completion(
-    # Create OpenWebUI client
+        api_base=user_config["openwebui"]["base_url"],
-    client = OpenWebUIClient(
+        model="openai/" + model,
-        base_url=user_config["openwebui"]["base_url"],
-        token=user_config["openwebui"]["api_key"],
-        default_model_id=model
-    )
-
-    # Prepare messages for the chat
         messages=[
             {
                 "role": "system",
@@ -70,26 +63,23 @@ def create_prompt_on_openwebui(prompt: str, topic: str = "random", model: str =
                 "role": "user",
                 "content": user_content,
             },
-    ]
+        ],
-
+        api_key=user_config["openwebui"]["api_key"],
-    # Send the chat request
-    try:
-        result = client.chat(
-            question=user_content,
-            chat_title=datetime.now().strftime("%Y-%m-%d %H:%M"),
-            folder_name="ai-frame-image-server"
     )
 
-        if result:
+    prompt = response["choices"][0]["message"]["content"].strip('"')
-            prompt = result["response"].strip('"')
+    # response = litellm.completion(
-        else:
+    #     api_base=user_config["openwebui"]["base_url"],
-            # Return None if the request fails
+    #     model="openai/brxce/stable-diffusion-prompt-generator:latest",
-            logging.warning(f"OpenWebUI request failed with model: {model}")
+    #     messages=[
-            return None
+    #         {
-    except Exception as e:
+    #             "role": "user",
-        logging.error(f"Error in OpenWebUI request with model {model}: {e}")
+    #             "content": prompt,
-        return None
+    #         },
-
+    #     ],
+    #     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) 

routes/create_routes.py

@@ -1,7 +1,7 @@
 from flask import Blueprint, request, render_template, redirect, url_for, session
 import threading
-from libs.comfyui import create_image, select_model, get_available_models, get_queue_count
+from libs.comfyui import create_image, select_model, get_available_models
-from libs.openwebui import create_prompt_on_openwebui
+from libs.ollama import create_prompt_on_openwebui
 from libs.generic import load_models_from_config, load_topics_from_config, load_openrouter_models_from_config, load_openwebui_models_from_config, create_prompt_with_random_model
 import os
 
@@ -23,7 +23,7 @@ def create():
                 # Use the specified prompt model
                 service, service_model = prompt_model.split(":", 1) if ":" in prompt_model else (prompt_model, "")
                 if service == "openwebui":
-                    from libs.openwebui import create_prompt_on_openwebui
+                    from libs.ollama import create_prompt_on_openwebui
                     prompt = create_prompt_on_openwebui(user_config["comfyui"]["prompt"], topic, service_model)
                 elif service == "openrouter":
                     from libs.openrouter import create_prompt_on_openrouter
@@ -35,20 +35,17 @@ def create():
         threading.Thread(target=lambda: create_image(prompt, model)).start()
         return redirect(url_for("create_routes.image_queued", prompt=prompt, model=model.split(".")[0]))
 
-    # Load all models (SDXL, FLUX, and Qwen)
+    # Load all models (SDXL and FLUX only)
-    sdxl_models, flux_models, qwen_models = load_models_from_config()
+    sdxl_models, flux_models = load_models_from_config()
     openwebui_models = load_openwebui_models_from_config()
     openrouter_models = load_openrouter_models_from_config()
     
-    queue_count = get_queue_count()
     return render_template("create_image.html",
-                         sdxx_models=sdxl_models,
+                         sdxl_models=sdxl_models,
                          flux_models=flux_models,
-                         qwen_models=qwen_models,
                          openwebui_models=openwebui_models,
                          openrouter_models=openrouter_models,
-                         topics=load_topics_from_config(),
+                         topics=load_topics_from_config())
-                         queue_count=queue_count)
 
 @bp.route("/image_queued")
 def image_queued():
@@ -65,20 +62,17 @@ def create_image_page():
     if user_config["frame"]["create_requires_auth"] == "True" and not session.get("authenticated"):
         return redirect(url_for("auth_routes.login", next=request.path))
     
-    # Load all models (SDXL, FLUX, and Qwen)
+    # Load all models (SDXL and FLUX only)
-    sdxl_models, flux_models, qwen_models = load_models_from_config()
+    sdxl_models, flux_models = load_models_from_config()
     openwebui_models = load_openwebui_models_from_config()
     openrouter_models = load_openrouter_models_from_config()
     
-    queue_count = get_queue_count()
     return render_template("create_image.html",
                          sdxl_models=sdxl_models,
                          flux_models=flux_models,
-                         qwen_models=qwen_models,
                          openwebui_models=openwebui_models,
                          openrouter_models=openrouter_models,
-                         topics=load_topics_from_config(),
+                         topics=load_topics_from_config())
-                         queue_count=queue_count)
 
 
 def init_app(config):

routes/index_routes.py

@@ -11,10 +11,12 @@ def index():
     image_filename = "./image.png"
     image_path = os.path.join(image_folder, image_filename)
     prompt = get_details_from_png(image_path)["p"]
+    version = get_current_version()
 
     return render_template(
         "index.html",
         image=image_filename,
         prompt=prompt,
         reload_interval=user_config["frame"]["reload_interval"],
+        version=version,
     )

routes/job_routes.py

@@ -1,12 +1,8 @@
-from flask import Blueprint, jsonify
+from flask import Blueprint
-from libs.comfyui import cancel_current_job, get_queue_details
+from libs.comfyui import cancel_current_job
 
 bp = Blueprint("job_routes", __name__)
 
 @bp.route("/cancel", methods=["GET"])
 def cancel_job():
     return cancel_current_job()
-
-@bp.route("/api/queue", methods=["GET"])
-def api_queue():
-    return jsonify(get_queue_details())

templates/base.html

@@ -12,7 +12,7 @@
 
     <!-- Version number at bottom right -->
     <div class="version">
-        <a href="{{ url_for('settings_route.config_editor') }}">{% if version and version != 'unknown' %}v{{ version }}{% else %}v?.?.?{% endif %}</a>
+        <a href="{{ url_for('settings_route.config_editor') }}">v{{ version }}</a>
     </div>
 
     {% block scripts %}{% endblock %}

templates/create_image.html

@@ -73,7 +73,6 @@
         background: #555;
     }
 
-
     #spinner-overlay {
         position: fixed;
         inset: 0;
@@ -132,66 +131,10 @@
             height: 150px;
         }
     }
-    .queue-dropdown {
-        position: absolute;
-        top: 100%;
-        right: 0;
-        background: #222;
-        border: 1px solid #444;
-        border-radius: 5px;
-        padding: 10px;
-        z-index: 1001;
-        display: none;
-        max-height: 300px;
-        overflow-y: auto;
-        width: 400px;
-    }
-
-    .queue-item {
-        margin-bottom: 5px;
-        padding: 5px;
-        border-bottom: 1px solid #333;
-    }
-
-    .queue-item:last-child {
-        border-bottom: none;
-    }
-
-    .queue-item .prompt {
-        font-size: 0.9em;
-        color: #aaa;
-        white-space: normal;
-        word-wrap: break-word;
-        position: relative;
-        cursor: pointer;
-    }
-    
-    .queue-item .prompt:hover::after {
-        content: "Model: " attr(data-model);
-        position: absolute;
-        bottom: 100%;
-        left: 0;
-        background: #333;
-        color: #00aaff;
-        padding: 4px 8px;
-        border-radius: 4px;
-        font-size: 0.8em;
-        white-space: nowrap;
-        z-index: 1002;
-        box-shadow: 0 2px 4px rgba(0,0,0,0.3);
-    }
 </style>
 {% endblock %}
 
 {% block content %}
-<div class="queue-container" style="position: fixed; top: 20px; right: 20px; z-index: 1000;">
-    <button id="queue-btn" style="background: #333; color: white; border: none; padding: 5px 10px; border-radius: 5px; cursor: pointer;">
-        Queue: <span id="queue-count">{{ queue_count | default(0) }}</span>
-    </button>
-    <div id="queue-dropdown" class="queue-dropdown">
-        <!-- Queue items will be populated here -->
-    </div>
-</div>
 <h1 style="margin-bottom: 20px;">Create An Image</h1>
 
 <textarea id="prompt-box" placeholder="Enter your custom prompt here..."></textarea>
@@ -214,13 +157,6 @@
                 {% endfor %}
             </optgroup>
             {% endif %}
-            {% if qwen_models %}
-            <optgroup label="Qwen">
-                {% for m in qwen_models %}
-                <option value="{{ m }}">{{ m.rsplit('.', 1)[0] if '.' in m else m }}</option>
-                {% endfor %}
-            </optgroup>
-            {% endif %}
             {% if sdxl_models %}
             <optgroup label="SDXL">
                 {% for m in sdxl_models %}
@@ -326,59 +262,5 @@
                 alert("Error requesting random prompt: " + error);
             });
     }
-    document.addEventListener('DOMContentLoaded', function() {
-        const queueBtn = document.getElementById('queue-btn');
-        const queueDropdown = document.getElementById('queue-dropdown');
-        const queueCountSpan = document.getElementById('queue-count');
-
-        // Toggle dropdown visibility
-        queueBtn.addEventListener('click', function(e) {
-            e.stopPropagation();
-            if (queueDropdown.style.display === 'block') {
-                queueDropdown.style.display = 'none';
-            } else {
-                fetchQueueDetails();
-                queueDropdown.style.display = 'block';
-            }
-        });
-
-        // Close dropdown when clicking outside
-        document.addEventListener('click', function() {
-            queueDropdown.style.display = 'none';
-        });
-
-        // Prevent dropdown from closing when clicking inside it
-        queueDropdown.addEventListener('click', function(e) {
-            e.stopPropagation();
-        });
-
-        function fetchQueueDetails() {
-            fetch('/api/queue')
-                .then(response => response.json())
-                .then(jobs => {
-                    queueCountSpan.textContent = jobs.length;
-                    const container = queueDropdown;
-                    container.innerHTML = '';
-
-                    if (jobs.length === 0) {
-                        container.innerHTML = '<div class="queue-item">No jobs in queue</div>';
-                        return;
-                    }
-
-                    jobs.forEach(job => {
-                        const item = document.createElement('div');
-                        item.className = 'queue-item';
-                        item.innerHTML = `
-                            <div class="prompt" data-model="${job.model}">${job.prompt}</div>
-                        `;
-                        container.appendChild(item);
-                    });
-                })
-                .catch(error => {
-                    console.error('Error fetching queue:', error);
-                    queueDropdown.innerHTML = '<div class="queue-item">Error loading queue</div>';
-                });
-        }
-    });
 </script>
 {% endblock %}

workflow_flux.json

@@ -1,31 +1,12 @@
 {
-  "6": {
-    "inputs": {
-      "text": "Terminator endoskeleton riding a bmx bike",
-      "speak_and_recognation": {
-        "__value__": [
-          false,
-          true
-        ]
-      },
-      "clip": [
-        "39",
-        0
-      ]
-    },
-    "class_type": "CLIPTextEncode",
-    "_meta": {
-      "title": "CLIP Text Encode (Positive Prompt)"
-    }
-  },
   "8": {
     "inputs": {
       "samples": [
-        "13",
+        "62",
-        0
+        1
       ],
       "vae": [
-        "41",
+        "73",
         0
       ]
     },
@@ -34,157 +15,298 @@
       "title": "VAE Decode"
     }
   },
-  "9": {
+  "40": {
     "inputs": {
-      "filename_prefix": "ComfyUI",
+      "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
+      ],
+      "scheduler_name": "normal",
+      "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": [
         "8",
         0
       ]
     },
-    "class_type": "SaveImage",
+    "class_type": "Image Saver",
     "_meta": {
-      "title": "Save Image"
+      "title": "CivitAI Image Saver"
     }
   },
-  "13": {
+  "44": {
+    "inputs": {
+      "text": "Yautja Predator wielding flamethrower in smoky, cyberpunk alleyway darkness",
+      "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
+        ]
+      },
+      "clip": [
+        "72",
+        0
+      ]
+    },
+    "class_type": "CLIPTextEncode",
+    "_meta": {
+      "title": "Prompt Encoder"
+    }
+  },
+  "48": {
+    "inputs": {
+      "seed": 47371998700984,
+      "increment": 1
+    },
+    "class_type": "Seed Generator (Image Saver)",
+    "_meta": {
+      "title": "Seed"
+    }
+  },
+  "49": {
+    "inputs": {
+      "scheduler": "beta"
+    },
+    "class_type": "Scheduler Selector (Comfy) (Image Saver)",
+    "_meta": {
+      "title": "Scheduler"
+    }
+  },
+  "50": {
+    "inputs": {
+      "sampler_name": "euler"
+    },
+    "class_type": "Sampler Selector (Image Saver)",
+    "_meta": {
+      "title": "Sampler"
+    }
+  },
+  "52": {
+    "inputs": {
+      "float": 3.500000000000001
+    },
+    "class_type": "Float Literal (Image Saver)",
+    "_meta": {
+      "title": "CFG Scale"
+    }
+  },
+  "53": {
+    "inputs": {
+      "float": 1.0000000000000002
+    },
+    "class_type": "Float Literal (Image Saver)",
+    "_meta": {
+      "title": "Denoise"
+    }
+  },
+  "62": {
     "inputs": {
       "noise": [
-        "25",
+        "65",
         0
       ],
       "guider": [
-        "22",
+        "67",
         0
       ],
       "sampler": [
-        "16",
+        "63",
         0
       ],
       "sigmas": [
-        "17",
+        "64",
         0
       ],
       "latent_image": [
-        "27",
+        "41",
-        0
+        5
       ]
     },
     "class_type": "SamplerCustomAdvanced",
     "_meta": {
-      "title": "SamplerCustomAdvanced"
+      "title": "Custom Sampler"
     }
   },
-  "16": {
+  "63": {
     "inputs": {
-      "sampler_name": "euler"
+      "sampler_name": [
+        "50",
+        0
+      ]
     },
     "class_type": "KSamplerSelect",
     "_meta": {
       "title": "KSampler Select"
     }
   },
-  "17": {
+  "64": {
     "inputs": {
-      "scheduler": "simple",
+      "scheduler": [
-      "steps": 20,
+        "49",
-      "denoise": 1,
+        0
+      ],
+      "steps": [
+        "40",
+        0
+      ],
+      "denoise": [
+        "53",
+        0
+      ],
       "model": [
-        "30",
+        "35",
         0
       ]
     },
     "class_type": "BasicScheduler",
     "_meta": {
-      "title": "BasicScheduler"
+      "title": "Sigma Generator"
     }
   },
-  "22": {
+  "65": {
+    "inputs": {
+      "noise_seed": [
+        "48",
+        0
+      ]
+    },
+    "class_type": "RandomNoise",
+    "_meta": {
+      "title": "Noise Generator"
+    }
+  },
+  "67": {
     "inputs": {
       "model": [
-        "30",
+        "35",
         0
       ],
       "conditioning": [
-        "26",
+        "47",
         0
       ]
     },
     "class_type": "BasicGuider",
     "_meta": {
-      "title": "BasicGuider"
+      "title": "Prompt Guider"
     }
   },
-  "25": {
+  "72": {
-    "inputs": {
-      "noise_seed": 707623342760804
-    },
-    "class_type": "RandomNoise",
-    "_meta": {
-      "title": "RandomNoise"
-    }
-  },
-  "26": {
-    "inputs": {
-      "guidance": 3.5,
-      "conditioning": [
-        "6",
-        0
-      ]
-    },
-    "class_type": "FluxGuidance",
-    "_meta": {
-      "title": "FluxGuidance"
-    }
-  },
-  "27": {
-    "inputs": {
-      "width": 720,
-      "height": 1088,
-      "batch_size": 1
-    },
-    "class_type": "EmptySD3LatentImage",
-    "_meta": {
-      "title": "CR Aspect Ratio"
-    }
-  },
-  "30": {
-    "inputs": {
-      "max_shift": 1.15,
-      "base_shift": 0.5,
-      "width": 720,
-      "height": 1088,
-      "model": [
-        "38",
-        0
-      ]
-    },
-    "class_type": "ModelSamplingFlux",
-    "_meta": {
-      "title": "ModelSamplingFlux"
-    }
-  },
-  "38": {
-    "inputs": {
-      "unet_name": "flux1-dev-Q4_0.gguf",
-      "device": "cuda:1",
-      "virtual_vram_gb": 0,
-      "use_other_vram": true,
-      "expert_mode_allocations": ""
-    },
-    "class_type": "UnetLoaderGGUFDisTorchMultiGPU",
-    "_meta": {
-      "title": "UnetLoaderGGUFDisTorchMultiGPU"
-    }
-  },
-  "39": {
     "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": true,
+      "use_other_vram": false,
       "expert_mode_allocations": ""
     },
     "class_type": "DualCLIPLoaderGGUFDisTorchMultiGPU",
@@ -192,7 +314,7 @@
       "title": "DualCLIPLoaderGGUFDisTorchMultiGPU"
     }
   },
-  "41": {
+  "73": {
     "inputs": {
       "vae_name": "FLUX1/ae.safetensors",
       "device": "cuda:0"
@@ -201,5 +323,21 @@
     "_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"
+    }
   }
 }

workflow_qwen.json

@@ -1,147 +0,0 @@
-{
-  "93": {
-    "inputs": {
-      "text": "jpeg compression",
-      "speak_and_recognation": {
-        "__value__": [
-          false,
-          true
-        ]
-      },
-      "clip": [
-        "126",
-        0
-      ]
-    },
-    "class_type": "CLIPTextEncode",
-    "_meta": {
-      "title": "CLIP Text Encode (Prompt)"
-    }
-  },
-  "95": {
-    "inputs": {
-      "seed": 22,
-      "steps": 10,
-      "cfg": 4.5,
-      "sampler_name": "euler",
-      "scheduler": "normal",
-      "denoise": 1,
-      "model": [
-        "127",
-        0
-      ],
-      "positive": [
-        "100",
-        0
-      ],
-      "negative": [
-        "93",
-        0
-      ],
-      "latent_image": [
-        "97",
-        0
-      ]
-    },
-    "class_type": "KSampler",
-    "_meta": {
-      "title": "KSampler"
-    }
-  },
-  "97": {
-    "inputs": {
-      "width": 1280,
-      "height": 768,
-      "length": 1,
-      "batch_size": 1
-    },
-    "class_type": "EmptyHunyuanLatentVideo",
-    "_meta": {
-      "title": "EmptyHunyuanLatentVideo"
-    }
-  },
-  "98": {
-    "inputs": {
-      "samples": [
-        "95",
-        0
-      ],
-      "vae": [
-        "128",
-        0
-      ]
-    },
-    "class_type": "VAEDecode",
-    "_meta": {
-      "title": "VAE Decode"
-    }
-  },
-  "100": {
-    "inputs": {
-      "text": "Terminator riding a push bike",
-      "speak_and_recognation": {
-        "__value__": [
-          false,
-          true
-        ]
-      },
-      "clip": [
-        "126",
-        0
-      ]
-    },
-    "class_type": "CLIPTextEncode",
-    "_meta": {
-      "title": "CLIP Text Encode (Prompt)"
-    }
-  },
-  "102": {
-    "inputs": {
-      "images": [
-        "98",
-        0
-      ]
-    },
-    "class_type": "PreviewImage",
-    "_meta": {
-      "title": "Preview Image"
-    }
-  },
-  "126": {
-    "inputs": {
-      "clip_name": "Qwen2.5-VL-7B-Instruct-Q3_K_M.gguf",
-      "type": "qwen_image",
-      "device": "cuda:1",
-      "virtual_vram_gb": 6,
-      "use_other_vram": true,
-      "expert_mode_allocations": ""
-    },
-    "class_type": "CLIPLoaderGGUFDisTorchMultiGPU",
-    "_meta": {
-      "title": "CLIPLoaderGGUFDisTorchMultiGPU"
-    }
-  },
-  "127": {
-    "inputs": {
-      "unet_name": "qwen-image-Q2_K.gguf",
-      "device": "cuda:0",
-      "virtual_vram_gb": 6,
-      "use_other_vram": true,
-      "expert_mode_allocations": ""
-    },
-    "class_type": "UnetLoaderGGUFDisTorchMultiGPU",
-    "_meta": {
-      "title": "UnetLoaderGGUFDisTorchMultiGPU"
-    }
-  },
-  "128": {
-    "inputs": {
-      "vae_name": "qwen_image_vae.safetensors",
-      "device": "cuda:1"
-    },
-    "class_type": "VAELoaderMultiGPU",
-    "_meta": {
-      "title": "VAELoaderMultiGPU"
-    }
-  }
-}