A set of ComfyUI custom nodes designed for ultimate laziness. Generate diverse images with a single click using randomized prompts and dynamic weights.
- Probabilistic Prompts: Set custom probability thresholds for specific keywords/prompts to appear.
- Randomized Weights: Automatically assign randomized weights to prompts within your preferred range to discover unexpected variations.
Honestly, I'm too lazy to write a detailed guide, and let's be real—nobody actually opens the README to read it anyway. Just plug it into your workflow and figure it out. It's not rocket science. Enjoy!
This is a conditional text node designed for ComfyUI batch processing (e.g., animations, video frames, or batch image generation). It outputs a specified text based on whether the current batch_index meets the range conditions, under a custom execution probability.
📌 Key Features Auto-Switching Range Modes:
If greater_than < less_than ➡️ Inside Range mode (e.g., between 25 and 40).
If greater_than > less_than ➡️ Outside Range mode (e.g., less than 25 OR greater than 40).
If they are equal ➡️ Single Point mode (e.g., exactly equal to 40).
Independent Probability: Adjust the trigger rate via probability (0.0 - 1.0). By combining seed and batch_index, the randomness for each frame remains unique yet perfectly reproducible.
Cache Busting: Built-in IS_CHANGED mechanism ensures ComfyUI evaluates the probability on every single run instead of frozen by cache.
💬 Examples Ex 1 (Outside Range): Output good day with a 30% chance when batch is less than 25 or greater than 40.
greater_than: 40, less_than: 25, probability: 0.30, text: good day
Ex 2 (Inside Range): Output bad day with a 50% chance when batch is between 25 and 40.
greater_than: 25, less_than: 40, probability: 0.50, text: bad day
※Newly added random weight within order range
Allows you to input multiple text strings and select one randomly based on a custom weight ratio (e.g., inputting weights 1, 2, 3 gives the third text the highest probability of being chosen).
Fault Tolerance: Automatically handles mismatched weight counts and invalid weight inputs.