e76ebb35118504a7a9efa04c29a3ddf4a9bfebe2
prompt-generator-comfyui
Custom AI prompt generator node for ComfyUI. With this node, you can use text generation model to generate prompts. Before using, text generation model has to trained with prompt dataset.
Table Of Contents
- prompt-generator-comfyui
- Table Of Contents
- Setup
- Features
- Example Workflow
- Variables
- Example Outputs
Setup
For Portable Version of the ComfyUI
- Automatic installation is added for portable version.
- Clone the repository with
git clone https://github.com/alpertunga-bile/prompt-generator-comfyui.gitcommand undercustom_nodesfolder. - Run the run_nvidia_gpu.bat file
- Open the
hires.fixWithPromptGenerator.jsonorbasicWorkflowWithPromptGeneratorworkflow - Put your generator under
models/prompt_generatorsfolder. You can create your prompt generator with this repository. You have to put generator as folder. Do not just putpytorch_model.binfile for example. - Click
Refreshbutton in ComfyUI
For Manual Installation of the ComfyUI
- Clone the repository with
git clone https://github.com/alpertunga-bile/prompt-generator-comfyui.gitcommand undercustom_nodesfolder. - Run the ComfyUI
- Open the
hires.fixWithPromptGenerator.jsonorbasicWorkflowWithPromptGeneratorworkflow - Put your generator under
models/prompt_generatorsfolder. You can create your prompt generator with this repository. You have to put generator as folder. Do not just putpytorch_model.binfile for example. - Click
Refreshbutton in ComfyUI
With ComfyUI Manager
- Download the node with ComfyUI Manager
- Restart the ComfyUI
- Open the
hires.fixWithPromptGenerator.jsonorbasicWorkflowWithPromptGeneratorworkflow - Put your generator under
models/prompt_generatorsfolder. You can create your prompt generator with this repository. You have to put generator as folder. Do not just putpytorch_model.binfile for example. - Click
Refreshbutton in ComfyUI
Features
- Multiple output generation is added. You can choose from 5 outputs and check the generated prompts in the log file and terminal. The prompts are logged and printed in order.
- Optimizations are done with Optimum package.
- ONNX and transformers models are supported.
- Preprocessing outputs. See this section.
- Recursive generation is supported. See this section.
- Print generated text to terminal and log the node's state under
generated_promptsfolder with date as filename.
Example Workflow
-
You can found the used models in this link
-
For to use the model follow these steps:
- Download the model and unzip to
models/prompt_generatorsfolder. - Click
Refreshbutton in ComfyUI. - Then select the generator with the node's
model_namevariable (If you can't see the generator restart the ComfyUI).
- Download the model and unzip to
-
Prompt Generator Node may look different with final version but workflow in ComfyUI is not going to change
Variables
IMPORTANT NOTE
num_beams must be dividable by num_beam_groups otherwise you will get errors.
| Variable Names | Definitions |
|---|---|
| model_name | Folder name that contains the model |
| accelerate | Open optimizations. Some of the models are not supported by BetterTransformer (Check your model). If it is not supported switch this option to disable or convert your model to ONNX |
| prompt | Input prompt for the generator |
| cfg | CFG is enabled by setting guidance_scale > 1. Higher guidance scale encourages the model to generate samples that are more closely linked to the input prompt, usually at the expense of poorer quality |
| min_length | Minimum number of generated tokens |
| max_length | Maximum number of generated tokens |
| do_sample | When True, picks words based on their conditional probability |
| early_stopping | When True, generation finishes if the EOS token is reached |
| num_beams | Number of steps for each search path |
| num_beam_groups | Number of groups to divide num_beams into in order to ensure diversity among different groups of beams |
| diversity_penalty | This value is subtracted from a beam’s score if it generates a token same as any beam from other group at a particular time. Note that diversity_penalty is only effective if group beam search is enabled. |
| temperature | How sensitive the algorithm is to selecting low probability options |
| top_k | How many potential answers are considered when performing sampling |
| top_p | Min number of tokens are selected where their probabilities add up to top_p |
| repetition_penalty | The parameter for repetition penalty. 1.0 means no penalty |
| no_repeat_ngram_size | The size of an n-gram that cannot occur more than once. (0=infinity) |
| remove_invalid_values | Whether to remove possible nan and inf outputs of the model to prevent the generation method to crash. Note that using remove_invalid_values can slow down generation. |
| self_recursive | See this section |
| recursive_level | See this section |
| preprocess_mode | See this section |
How Recursive Works?
- Let's say we give
a,as seed and recursive level is 1. I am going to use the same outputs for this example to understand the functionality more accurately. - With self recursive, let's say generator's output is
b. So next seed is going to beband generator's output isc. Final output isa, c. It can be used for generating random outputs. - Without self recursive, let's say generator's output is
b. So next seed is going to bea, band generator's output isc. Final output isa, b, c. It can be used for more accurate prompts.
How Preprocess Mode Works?
- exact_keyword =>
(masterpiece), ((masterpiece))is not allowed. Checking the pure keyword without parantheses and weights. Adding prompts from the beginning of the generated text so add important prompts to seed. - exact_prompt =>
(masterpiece), ((masterpiece))is allowed but(masterpiece), (masterpiece)is not. Checking the exact match of the prompt. - none => Everything is allowed even the repeated prompts.
Example
# ---------------------------------------------------------------------- Original ---------------------------------------------------------------------- #
((masterpiece)), ((masterpiece:1.2)), (masterpiece), blahblah, blah, blah, ((blahblah)), (((((blah))))), ((same prompt)), same prompt, (masterpiece)
# ------------------------------------------------------------- Preprocess (Exact Keyword) ------------------------------------------------------------- #
((masterpiece)), blahblah, blah, ((same prompt))
# ------------------------------------------------------------- Preprocess (Exact Prompt) -------------------------------------------------------------- #
((masterpiece)), ((masterpiece:1.2)), (masterpiece), blahblah, blah, ((blahblah)), (((((blah))))), ((same prompt)), same prompt
Example Outputs
Languages
Python
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