Bump version: 0.3.11 → 0.3.12

fix(openrouter): handle rate limit errors with OpenWebUI fallback

When OpenRouter returns a 429 error due to rate limiting, the application now attempts to fall back to using an OpenWebUI model instead of returning a default prompt. This provides better resilience when external API limits are exceeded while maintaining functionality through local models.

The changes include:
- Adding RateLimitError import from openai
- Implementing fallback logic in create_prompt_on_openrouter function
- Using OpenWebUI as secondary source for prompts when rate limiting occurs
- Proper error handling and logging for both primary and fallback scenarios

This change improves the robustness of prompt generation by ensuring that users receive generated content even when external services are temporarily unavailable due to rate limits. The fallback mechanism prioritizes configured local models if available, with a final default prompt as backup.

The implementation follows the existing pattern of using random selection from configured OpenWebUI models and includes comprehensive error handling for both primary and fallback operations. This ensures that all failure modes are gracefully handled while maintaining backward compatibility.

.bumpversion.toml

@@ -1,5 +1,5 @@
 [tool.bumpversion]
-current_version = "0.3.4"
+current_version = "0.3.12"
 parse = "(?P<major>\\d+)\\.(?P<minor>\\d+)\\.(?P<patch>\\d+)"
 serialize = ["{major}.{minor}.{patch}"]
 replace = "{new_version}"

.gitignore

@@ -12,3 +12,4 @@ test.py
 .vscode/launch.json
 favourites.json
 .vscode/launch.json
+venv/*

Dockerfile

@@ -4,7 +4,7 @@ FROM python:3.11-slim
 # Set the working directory in the container
 WORKDIR /app
 # Set version label
-ARG VERSION="0.3.4"
+ARG VERSION="0.3.12"
 LABEL version=$VERSION
 
 # Copy project files into the container

libs/comfyui.py

@@ -32,9 +32,17 @@ def get_available_models() -> list:
     response = requests.get(url)
     if response.status_code == 200:
         data = response.json()
-        general = data.get("CheckpointLoaderSimple", {}).get("input", {}).get("required", {}).get("ckpt_name", [])[0]
-        flux = data.get("UnetLoaderGGUF", {}).get("input", {}).get("required", {}).get("unet_name", [])[0]
-        return general + flux
+        # Get SDXL models from CheckpointLoaderSimple
+        general = data.get("CheckpointLoaderSimple", {}).get("input", {}).get("required", {}).get("ckpt_name", [[]])[0]
+        # Get FLUX models from UnetLoaderGGUF
+        flux = data.get("UnetLoaderGGUF", {}).get("input", {}).get("required", {}).get("unet_name", [[]])[0]
+        # Combine both lists, handling cases where one might be missing
+        all_models = []
+        if isinstance(general, list):
+            all_models.extend(general)
+        if isinstance(flux, list):
+            all_models.extend(flux)
+        return all_models
     else:
         print(f"Failed to fetch models: {response.status_code}")
         return []
@@ -122,20 +130,46 @@ 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"
+        # Create a list of available workflows based on configuration
+        available_workflows = []
+        if not only_flux:
+            available_workflows.append("SDXL")
+        if use_flux:
+            available_workflows.append("FLUX")
+        if use_qwen:
+            available_workflows.append("Qwen")
+        
+        # If no workflows are available, default to SDXL
+        if not available_workflows:
+            available_workflows.append("SDXL")
+        
+        # Randomly select a workflow
+        selected_workflow = random.choice(available_workflows)
     elif "flux" in model.lower():
         selected_workflow = "FLUX"
+    elif "qwen" in model.lower():
+        selected_workflow = "Qwen"
     else:
         selected_workflow = "SDXL"
 
     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))
+            # If no valid models found, fall back to configured models
+            if not valid_models:
+                valid_models = available_model_list
+        # Ensure we have at least one model to choose from
+        if not valid_models:
+            # Fallback to a default model
+            valid_models = ["zavychromaxl_v100.safetensors"]
         model = random.choice(valid_models)
 
     return selected_workflow, model
@@ -164,16 +198,95 @@ 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="Positive Prompt T5",
-            seed_node="Seed",
-            seed_param="seed",
-            save_node="CivitAI Image Saver",
-            save_param="filename",
-            model_node="UnetLoaderGGUFAdvancedDisTorchMultiGPU",
+            prompt_node="CLIP Text Encode (Positive Prompt)",
+            seed_node="RandomNoise",
+            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_param="filename_prefix",
+            model_node="Load Checkpoint",
+            model_param="ckpt_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['44']['inputs']['text']
-                model = data['35']['inputs']['unet_name'].split(".")[0]
+                prompt = data['6']['inputs']['text']
+                model = data['38']['inputs']['unet_name'].split(".")[0]
             except KeyError:
                 # SDXL workflow
                 data = json.loads(img.info["prompt"])
@@ -113,11 +113,28 @@ def get_current_version():
         return "unknown"
 
 def load_models_from_config():
-    flux_models = load_config()["comfyui:flux"]["models"].split(",")
-    sdxl_models = load_config()["comfyui"]["models"].split(",")
+    config = load_config()
+    
+    # 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)
-    return sorted_sdxl_models, sorted_flux_models
+    sorted_qwen_models = sorted(qwen_models, key=str.lower)
+    return sorted_sdxl_models, sorted_flux_models, sorted_qwen_models
 
 
 def load_topics_from_config():
@@ -223,7 +240,6 @@ def create_prompt_with_random_model(base_prompt: str, topic: str = "random"):
             logging.error(f"Error with OpenRouter: {e}")
             return "A colorful abstract composition"  # Default fallback prompt
     
-    return "A colorful abstract composition"  # Default fallback prompt
 
 user_config = load_config()
 output_folder = user_config["comfyui"]["output_dir"]

libs/openrouter.py

@@ -1,8 +1,9 @@
 import random
 import logging
-from openai import OpenAI
+from openai import OpenAI, RateLimitError
 import nest_asyncio
 from libs.generic import load_recent_prompts, load_config
+from libs.openwebui import create_prompt_on_openwebui
 import re
 nest_asyncio.apply()
 
@@ -90,6 +91,20 @@ def create_prompt_on_openrouter(prompt: str, topic: str = "random", model: str =
             prompt = match.group(1)
         logging.debug(prompt)
         return prompt
+    except RateLimitError as e:
+        logging.warning(f"OpenRouter rate limit exceeded (429): {e}. Falling back to local OpenWebUI model.")
+        # Try to use OpenWebUI as fallback
+        openwebui_models = [m.strip() for m in user_config["openwebui"]["models"].split(",") if m.strip()] if "openwebui" in user_config and "models" in user_config["openwebui"] else []
+        if openwebui_models:
+            selected_model = random.choice(openwebui_models)
+            try:
+                return create_prompt_on_openwebui(user_content, topic, selected_model)
+            except Exception as e2:
+                logging.error(f"OpenWebUI fallback also failed: {e2}")
+                return "A colorful abstract composition"  # Final fallback
+        else:
+            logging.error("No OpenWebUI models configured for fallback.")
+            return "A colorful abstract composition"  # Final fallback
     except Exception as e:
         logging.error(f"Error generating prompt with OpenRouter: {e}")
         return ""

routes/create_routes.py

@@ -1,6 +1,6 @@
 from flask import Blueprint, request, render_template, redirect, url_for, session
 import threading
-from libs.comfyui import create_image, select_model, get_available_models
+from libs.comfyui import create_image, select_model, get_available_models, get_queue_count
 from libs.openwebui 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
@@ -35,17 +35,20 @@ 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 and FLUX only)
-    sdxl_models, flux_models = load_models_from_config()
+    # Load all models (SDXL, FLUX, and Qwen)
+    sdxl_models, flux_models, qwen_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,
+                         sdxx_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():
@@ -62,17 +65,20 @@ 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 and FLUX only)
-    sdxl_models, flux_models = load_models_from_config()
+    # Load all models (SDXL, FLUX, and Qwen)
+    sdxl_models, flux_models, qwen_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/job_routes.py

@@ -1,8 +1,12 @@
-from flask import Blueprint
-from libs.comfyui import cancel_current_job
+from flask import Blueprint, jsonify
+from libs.comfyui import cancel_current_job, get_queue_details
 
 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/create_image.html

@@ -73,6 +73,7 @@
         background: #555;
     }
 
+
     #spinner-overlay {
         position: fixed;
         inset: 0;
@@ -131,10 +132,66 @@
             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>
@@ -157,6 +214,13 @@
                 {% 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 %}
@@ -262,5 +326,59 @@
                 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

@@ -1,12 +1,31 @@
 {
+  "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": [
-        "62",
-        1
+        "13",
+        0
       ],
       "vae": [
-        "73",
+        "41",
         0
       ]
     },
@@ -15,298 +34,157 @@
       "title": "VAE Decode"
     }
   },
-  "40": {
+  "9": {
     "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
-      ],
-      "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
-        ]
-      },
+      "filename_prefix": "ComfyUI",
       "images": [
-        "8",
+        "42",
         0
       ]
     },
-    "class_type": "Image Saver",
+    "class_type": "SaveImage",
     "_meta": {
-      "title": "CivitAI Image Saver"
+      "title": "Save Image"
     }
   },
-  "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": {
+  "13": {
     "inputs": {
       "noise": [
-        "65",
+        "25",
         0
       ],
       "guider": [
-        "67",
+        "22",
         0
       ],
       "sampler": [
-        "63",
+        "16",
         0
       ],
       "sigmas": [
-        "64",
+        "17",
         0
       ],
       "latent_image": [
-        "41",
-        5
+        "27",
+        0
       ]
     },
     "class_type": "SamplerCustomAdvanced",
     "_meta": {
-      "title": "Custom Sampler"
+      "title": "SamplerCustomAdvanced"
     }
   },
-  "63": {
+  "16": {
     "inputs": {
-      "sampler_name": [
-        "50",
-        0
-      ]
+      "sampler_name": "euler"
     },
     "class_type": "KSamplerSelect",
     "_meta": {
-      "title": "KSampler Select"
+      "title": "KSamplerSelect"
     }
   },
-  "64": {
+  "17": {
     "inputs": {
-      "scheduler": [
-        "49",
-        0
-      ],
-      "steps": [
-        "40",
-        0
-      ],
-      "denoise": [
-        "53",
-        0
-      ],
+      "scheduler": "simple",
+      "steps": 20,
+      "denoise": 1,
       "model": [
-        "35",
+        "30",
         0
       ]
     },
     "class_type": "BasicScheduler",
     "_meta": {
-      "title": "Sigma Generator"
+      "title": "BasicScheduler"
     }
   },
-  "65": {
-    "inputs": {
-      "noise_seed": [
-        "48",
-        0
-      ]
-    },
-    "class_type": "RandomNoise",
-    "_meta": {
-      "title": "Noise Generator"
-    }
-  },
-  "67": {
+  "22": {
     "inputs": {
       "model": [
-        "35",
+        "30",
         0
       ],
       "conditioning": [
-        "47",
+        "26",
         0
       ]
     },
     "class_type": "BasicGuider",
     "_meta": {
-      "title": "Prompt Guider"
+      "title": "BasicGuider"
     }
   },
-  "72": {
+  "25": {
+    "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": false,
+      "use_other_vram": true,
       "expert_mode_allocations": ""
     },
     "class_type": "DualCLIPLoaderGGUFDisTorchMultiGPU",
@@ -314,7 +192,7 @@
       "title": "DualCLIPLoaderGGUFDisTorchMultiGPU"
     }
   },
-  "73": {
+  "41": {
     "inputs": {
       "vae_name": "FLUX1/ae.safetensors",
       "device": "cuda:0"
@@ -324,20 +202,18 @@
       "title": "VAELoaderMultiGPU"
     }
   },
-  "35": {
+  "42": {
     "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": ""
+      "offload_model": true,
+      "offload_cache": true,
+      "anything": [
+        "8",
+        0
+      ]
     },
-    "class_type": "UnetLoaderGGUFAdvancedDisTorchMultiGPU",
+    "class_type": "VRAMCleanup",
     "_meta": {
-      "title": "UnetLoaderGGUFAdvancedDisTorchMultiGPU"
+      "title": "🎈VRAM-Cleanup"
     }
   }
 }

workflow_qwen.json

@@ -0,0 +1,161 @@
+{
+  "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": [
+        "129",
+        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"
+    }
+  },
+  "129": {
+    "inputs": {
+      "offload_model": true,
+      "offload_cache": true,
+      "anything": [
+        "98",
+        0
+      ]
+    },
+    "class_type": "VRAMCleanup",
+    "_meta": {
+      "title": "🎈VRAM-Cleanup"
+    }
+  }
+}

workflow_sdxl.json

@@ -52,6 +52,12 @@
   "6": {
     "inputs": {
       "text": "A bustling cyberpunk street at night, filled with neon signs, rain-soaked pavement, and futuristic street vendors. High detail, vivid neon colors, and realistic reflections.",
+      "speak_and_recognation": {
+        "__value__": [
+          false,
+          true
+        ]
+      },
       "clip": [
         "4",
         1
@@ -65,6 +71,12 @@
   "7": {
     "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
+        ]
+      },
       "clip": [
         "4",
         1
@@ -95,7 +107,7 @@
     "inputs": {
       "filename_prefix": "ComfyUI",
       "images": [
-        "8",
+        "10",
         0
       ]
     },
@@ -103,5 +115,19 @@
     "_meta": {
       "title": "Save Image"
     }
+  },
+  "10": {
+    "inputs": {
+      "offload_model": true,
+      "offload_cache": true,
+      "anything": [
+        "8",
+        0
+      ]
+    },
+    "class_type": "VRAMCleanup",
+    "_meta": {
+      "title": "🎈VRAM-Cleanup"
+    }
   }
 }