refactor: cleanup/remove code for testing video stuff

add basically all inputs to one node, venice parameters etc, use dynamic combo for hiding unsupported capabilities
fix encode tensor for vision
add text model spec get to utils/catalog

.gitignore

@@ -3,6 +3,8 @@ test.py
 js/test.js
 veniceai_config.json
 data/*
+testing_video/*
+nodes/test/*
 
 # Ignore Mac system files
 .DS_Store

README.md

@@ -1,110 +1,4 @@
-# ComfyUI Venice.AI API Custom Nodes
+### Hi, hello
 
-An unofficial custom node implementation for ComfyUI that integrates with venice.ai's Generative AI services such as Image, Text and TTS Generation models (as well as Upscale and Enhance). This project is adapted from [ComfyUI-FLUX-TOGETHER-API](https://github.com/BZcreativ/ComfyUI-FLUX-TOGETHER-API) to work with the venice.ai API.  
-**Note**: TTS is in Beta per Venice (as of 15th June 2025). The node for it "Generate Speech (Venice)" is set to BETA/Experimental too. To be able to find it with node search you have to enable "Show experimental nodes in search" setting in ComfyUI.
-
-Disclaimer: I originally made this on a whim because someone wanted something similar to Together.AI custom nodes but have them use venice instead. I'm also not affiliated with Venice.AI. Idk is this good enough for a disclaimer or something
-
-## Nodes: (Text gen node is missing but will be updated soon)
-
-![all nodes showcased](./gh_assets/nodes_showcase.png)  
-Image and Text models (as of 15th June 2025 | for Speech gen only tts-kokoro is available)  
-For updated text generation experience please use `Generate Text Advanced BETA (Venice)` node and additionally `Textgen Parameters (Venice) for extra venice.ai specific parameters to pass onto the generation process.  
-![Image gen models](./gh_assets/image_gen_models_15june.png) ![Text gen models](./gh_assets/text_gen_models15june.png)
-
-~~# todo: inpainting~~  Deprecated by venice, new thing is coming for it at some point
-
-todo: actual log, maybe separate logging file for less clutter from comfyui stuff, maybe, maybe... eeeee
-
-todo: add settings to set default model by user
-
-todo: less convoluted approach to downloading and loading model/character/styles lists
-
-~~todo: LLM characters list~~ done, but its in beta so subject to big changes, use with `Textgen Parameters (Venice)` node
-
-todo: use variants api (currently in beta) | idk what happened to this, probably gone
-
-Todo: chat history/memory/context for LLM | got a vague idea but thats pretty much it
-
-todo
-
-- Some Validation on queue for API limits like prompt max length or width/height
-  - these are different for different models so this isnt planned to be implemented unless the api exposes those limits somehow
-    - i'd have an error thrown before it gets sent to api about some value being too high or too low (though api should send error back for now either way)
-    - maybe the api already exposes those limits, the steps limit is afaik at least, need to look into it
-
-### Below ReadMe text is only slightly altered from original Flux Together API readme, it was not really reworked or anything so its likely not correct or up to date
-
-### Installation - these instructions are a mess
-
-0. Before proceeding, check if you can find these nodes through ComfyUI-Manager interface rather than following the instructions below.
-
-<details><summary>Expand me to see harder instructions</summary>
-1. Clone this repository into your ComfyUI custom_nodes directory:
-
-```bash
-cd ComfyUI/custom_nodes
-git clone https://github.com/DraconicDragon/ComfyUI-Venice-API.git
-```
-
-2. Install the required dependencies: (this might be done by comfyui automatically on restart already?)
-
-```bash
-pip install -r requirements.txt
-```
-
-OR From the Comfyui Folder (this one is usually preferred if you have portable edition)
-
-```bash
- ./python_embeded\python.exe -m pip install -r ComfyUI\custom_nodes\ComfyUI-Venice-API\requirements.txt
-```
-
-</details>
-
-### Configuration
-
-1. Get your API key from [venice.ai](https://venice.ai)
-2. Add your API key in ComfyUI settings > VeniceAI
-
-### Parameters for Generate Image (Venice) node
-
-| Parameter       | Type    | Range      | Default | Description                             |
-|-----------------|---------|------------|---------|-----------------------------------------|
-| prompt          | string  | 1-1500     | "A flying cat made of lettuce" | Main generation prompt |
-| negative_prompt | string  | 0-1500     | ""      | Elements to avoid                       |
-| width           | integer | 0-1280?    | 1024    | Image width                             |
-| height          | integer | 0-1280?    | 1024    | Image height                            |
-| batch_size      | integer | 1-4        | 1       | Number of Images to gen in a single run |
-| steps           | integer | 1-30 or 50 | 20      | Number of generation steps              |
-| cfg/guidance    | float   | 0-20.0     | 3.0     | Guidance scale                          |
-| style_preset    | string  | N/A        | none    | The Style preset to apply               |
-| hide_watermark  | boolean | N/A        | true    | Whether to hide watermark (NSFW = false)|
-| safe_mode       | boolean | N/A        | false   | Whether to blur NSFW images             |
-| seed            | integer | -999999999 to 999999999 | -1  | Generation seed                |
-
-### License
-
-MIT License - see [LICENSE](LICENSE) file for details.
-
-### Credits
-
-- This project is adapted from [ComfyUI-FLUX-TOGETHER-API](https://github.com/BZcreativ/ComfyUI-FLUX-TOGETHER-API)
-- venice.ai for providing the generative AI services and API
-- [ComfyUI-FLUX-TOGETHER-API](https://github.com/BZcreativ/ComfyUI-FLUX-TOGETHER-API) for their work
-- ComfyUI team for the amazing framework
-
-### Author
-
-Created by [BZcreativ](https://github.com/BZcreativ)
-
-venice.ai rewrite by [DraconicDragon](https://github.com/DraconicDragon)
-
-### Contributing
-
-Contributions are welcome! Feel free to submit a Pull Request.
-
-### Example
-
-todo
-
-For detailed usage instructions, see [USAGE.md](USAGE.md) (not reworked)
+WIP dev branch changing things woohee  
+see [TODO](TODO.md) for some sparse todo stuff

TODO.md

@@ -0,0 +1,7 @@
+# TODO
+
+- [ ] Support quote video gen button label for price estimates
+- [ ] Convert to node schema v3
+- [ ] Use DynamicCombo for all applicable nodes
+  - [ ] Combine DynamicCombo with other limits such as prompt length
+- [ ] Check for TODOs in code files

USAGE.md

@@ -1,128 +0,0 @@
-# dont follow stuff thats in here i dont think its correct 
-
-
-# ComfyUI venice.ai API Node Usage Guide
-
-### Disclaimer: I just made this on a whim because someone wanted something similar to Together.AI custom nodes but use venice instead and I don't have access to any API keys for any of the mentioned services.
-
-## Setup
-
-1. Ensure you have a venice.ai account and API key
-2. Configure your API key in `config.ini`
-3. Install all required dependencies (should be done automatically by comfy?)
-
-## Node Configuration
-
-### Input Parameters
-
-#### Required Parameters:
-- **Prompt** (String)
-  - Your main generation prompt
-  - Be specific and detailed for best results if using Flux
-
-- **Negative Prompt** (String)
-  - Elements you want to avoid in the generation
-  - Leave empty if not needed | Will be ignored if flux-dev or flux-dev-uncensored is selected as model
-
-- **Steps** (Integer)
-  - Range: 1-30
-  - Default: 20
-
-- **Width** (Integer)
-  - Range: 512-2048
-  - Default: 1024
-  - Must be a multiple of 32
-  - Common values: 512, 768, 1024 (1MP), 1440 (Flux | 2MP)
-
-- **Height** (Integer)
-  - Range: 512-2048
-  - Default: 1024
-  - Must be a multiple of 32
-  - Common values: 512, 768, 1024 (1MP), 1440 (Flux | 2MP)
-
-- **Seed** (Integer)
-  - Range: 0 to max 64-bit integer
-  - Default: -1 | Random
-  - Reuse a seed with same prompt to reproduce an image 
-
-- **CFG (Guidance Scale)** (Float)
-  - Range: 0.1-15.0
-  - Default: 3.5
-  - Recommended range: 5.0-10.0 (SDXL) | ~3.5 (Flux)
-
-### Output
-
-The node outputs a single image tensor compatible with other ComfyUI nodes.
-
-## Best Practices
-
-1. **Prompt Engineering**
-   - Be specific and detailed in your prompts
-   - Use descriptive adjectives
-   - Include style references when needed
-
-2. **Performance**
-   - Start with lower step counts (20-30) for testing
-   - Increase steps for final generations
-   - Use reasonable image dimensions (1024x1024 is standard)
-
-3. **Error Handling/Troubleshooting**
-   - Check console for error messages
-   - Verify API key is correctly configured
-   - Ensure parameters are within valid ranges
-
-## Common Workflows
-
-### Basic Image Generation
-1. Add Together API Node to workspace
-2. Connect to a Load Image node
-3. Configure prompt and basic parameters
-4. Execute workflow
-
-### Advanced Usage
-1. Combine with other ComfyUI nodes
-2. Use seed control for consistent results
-3. Experiment with guidance scale for style control
-
-## Troubleshooting
-
-### Common Issues
-
-1. **API Key Errors**
-   - Verify key in config.ini
-   - Check API key validity
-   - Ensure proper formatting
-
-2. **Generation Errors**
-   - Verify parameter ranges
-   - Check prompt length
-   - Monitor API rate limits
-
-3. **Image Quality Issues**
-   - Adjust step count
-   - Modify guidance scale
-   - Refine prompt
-
-## Examples
-
-### Basic Prompt Example
-```
-A beautiful landscape with mountains and lakes, cinematic lighting, high detail
-```
-
-### Advanced Prompt Example
-```
-A stunning mountain landscape at sunset, volumetric lighting, 
-golden hour, ultra detailed, professional photography, 
-8k resolution, artistic composition
-```
-
-### Negative Prompt Example
-```
-blur, haze, low quality, distortion, bad composition, 
-oversaturated, unrealistic lighting
-```
-
-## Support
-
-For issues and feature requests, please use the GitHub issue tracker.

__init__.py

@@ -1,39 +1,31 @@
-import importlib
-import logging
 import os
 
-from .pyserver import (
-    get_key_from_jssetting,  # noqa: F401
-    update_characters,  # noqa: F401
-    update_models,  # noqa: F401
-    update_styles,  # noqa: F401
-)
+from comfy_api.latest import ComfyExtension, io
 
-node_list = [
-    # "things_n_stuff_node",
-    "gen_image_node",
-    # "gen_image_inpaint_node",
-    "gen_text_node",
-    "gen_text_advanced_node",
-    "gen_text_venice_params_node",
-    "i2i_enhance_upscale_node",
-    "gen_speech_node",
-    "util_nodes",
-]
+from .nodes.gen_image_node import GenerateImage
+from .nodes.gen_speech_node import GenerateSpeech
+from .nodes.gen_text_node import GenerateTextAdvanced
+from .nodes.gen_video_from_text_node import GenerateVideoFromText
+from .nodes.i2i_enhance_upscale import I2IEnhanceUpscale
+from .nodes.test_node import DCTestNode
 
-NODE_CLASS_MAPPINGS = {}
-NODE_DISPLAY_NAME_MAPPINGS = {}
 
-for module_name in node_list:
-    try:
-        imported_module = importlib.import_module(f".nodes.{module_name}", __name__)
-        NODE_CLASS_MAPPINGS.update(imported_module.NODE_CLASS_MAPPINGS)
-        NODE_DISPLAY_NAME_MAPPINGS.update(imported_module.NODE_DISPLAY_NAME_MAPPINGS)
-    except ImportError as e:
-        logging.warning(f"Could not import module '{module_name}': {e}")
+class VeniceExtension(ComfyExtension):
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            DCTestNode,
+            GenerateImage,
+            GenerateSpeech,
+            GenerateTextAdvanced,
+            GenerateVideoFromText,
+            I2IEnhanceUpscale,
+        ]
+
+
+async def comfy_entrypoint() -> VeniceExtension:
+    return VeniceExtension()
 
 
 WEB_DIRECTORY = os.path.join(os.path.dirname(__file__), "js")
 
-
-__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS", "WEB_DIRECTORY"]
+__all__ = ["WEB_DIRECTORY"]

globals.py

@@ -1,3 +1,5 @@
+import os
+
 API_ENDPOINTS = {
     "list_models": "/models",  # response type is list of strings
     "list_styles": "/image/styles",  #
@@ -6,9 +8,17 @@ API_ENDPOINTS = {
     "upscale_image": "/image/upscale",  # NOTE: apparently doesnt even work yet? idk; response type is image/png file, content type is multipart/form-data
     "text_generate": "/chat/completions",  # has much info, text response is in choices: content, can have multiple choices apparently but dosnt seem to be utilized
     "speech_generate": "/audio/speech",  # type: file (audio/aac; audio/mpeg; audio/wav.. etc)
+    "video_queue": "/video/queue",  #
+    "video_quote": "/video/quote",  # price estimate, takes same payload as video_queue
+    "video_retrieve": "/video/retrieve",  # get video file by job id
+    "list_api_keys": "/api_keys",
 }
 
 VENICEAI_BASE_URL = "https://api.venice.ai/api/v1"
 
-# unused right now
-headers = {"User-Agent": "ComfyUI-Venice-API/1.0 (by draconicdragon on github)"}
+# request hygiene
+USER_AGENT = "ComfyUI-Venice-API/1.0 (by draconicdragon on github)"
+
+
+os.environ["VENICE_CLIENT_DRY_RUN"] = "0"
+os.environ["VENICE_CLIENT_DEBUG"] = "1"

js/nodespawn.js

@@ -1,125 +0,0 @@
-import { api } from "../../scripts/api.js";
-import { app } from "../../scripts/app.js";
-
-
-// Helper function to fetch data and assign to widget
-async function fetchAndAssignWidget(widget, url, dataKey, logMsg, errorMsg) {
-    if (!widget) return;
-    try {
-        console.log(`(VeniceAI.NodeSpawn) ${logMsg}`);
-        const response = await api.fetchApi(url);
-
-        if (!response.ok) {
-            throw new Error(`HTTP error: ${response.status} ${response.statusText}`);
-        }
-
-        const rawText = await response.text();
-
-        let data;
-        try {
-            data = JSON.parse(rawText);
-        } catch (jsonError) {
-            throw new Error(`Failed to parse JSON: ${jsonError.message}. Raw response: ${rawText}`);
-        }
-
-        widget.options.values = data[dataKey];
-        if (widget.onChange) {
-            widget.onChange();
-        }
-
-        this.setDirtyCanvas(true);
-    } catch (error) {
-        console.error(`(VeniceAI.NodeSpawn) ${errorMsg}:`, error);
-        alert(`(VeniceAI.NodeSpawn) ${errorMsg}:\n${error}`);
-    }
-}
-
-app.registerExtension({
-    name: "VeniceAI.NodeSpawn",
-    async beforeRegisterNodeDef(nodeType, nodeData) {
-        if (nodeData.name === "GenerateImage_VENICE" || nodeData.name === "InpaintImage_VENICE") {
-            const originalOnNodeCreated = nodeType.prototype.onNodeCreated;
-            nodeType.prototype.onNodeCreated = async function () {
-                if (originalOnNodeCreated) {
-                    originalOnNodeCreated.apply(this);
-                }
-                await fetchAndAssignWidget.call(
-                    this,
-                    this.widgets.find(w => w.name === "model"),
-                    "/veniceai/get_models_list",
-                    "image_models",
-                    "Trying to fetch image models...",
-                    "Failed to fetch image models"
-                );
-                await fetchAndAssignWidget.call(
-                    this,
-                    this.widgets.find(w => w.name === "style_preset"),
-                    "/veniceai/get_styles_list",
-                    "data",
-                    "Trying to fetch styles...",
-                    "Failed to fetch styles"
-                );
-            };
-        }
-
-        if (nodeData.name === "GenerateText_VENICE" || nodeData.name === "GenerateTextAdvanced_VENICE") {
-            const originalOnNodeCreated = nodeType.prototype.onNodeCreated;
-            nodeType.prototype.onNodeCreated = async function () {
-                if (originalOnNodeCreated) {
-                    originalOnNodeCreated.apply(this);
-                }
-                await fetchAndAssignWidget.call(
-                    this,
-                    this.widgets.find(w => w.name === "model"),
-                    "/veniceai/get_models_list",
-                    "text_models",
-                    "Trying to fetch text models...",
-                    "Failed to fetch text models"
-                );
-            };
-        }
-
-        if (nodeData.name === "GenerateTextVeniceParameters_VENICE") {
-            const originalOnNodeCreated = nodeType.prototype.onNodeCreated;
-            nodeType.prototype.onNodeCreated = async function () {
-                if (originalOnNodeCreated) {
-                    originalOnNodeCreated.apply(this);
-                }
-                await fetchAndAssignWidget.call(
-                    this,
-                    this.widgets.find(w => w.name === "character_slug"),
-                    "/veniceai/get_characters_list",
-                    "characters",
-                    "Trying to fetch character slugs...",
-                    "Failed to fetch character slugs"
-                );
-            };
-        }
-
-        if (nodeData.name === "GenerateSpeech_VENICE") {
-            const originalOnNodeCreated = nodeType.prototype.onNodeCreated;
-            nodeType.prototype.onNodeCreated = async function () {
-                if (originalOnNodeCreated) {
-                    originalOnNodeCreated.apply(this);
-                }
-                await fetchAndAssignWidget.call(
-                    this,
-                    this.widgets.find(w => w.name === "model"),
-                    "/veniceai/get_models_list",
-                    "tts_models",
-                    "Trying to fetch tts models...",
-                    "Failed to fetch tts models"
-                );
-                await fetchAndAssignWidget.call(
-                    this,
-                    this.widgets.find(w => w.name === "voice"),
-                    "/veniceai/get_models_list",
-                    "tts_voices",
-                    "Trying to fetch tts voices...",
-                    "Failed to fetch tts voices"
-                );
-            }
-        }
-    }
-});
-

js/settings.js

@@ -5,11 +5,15 @@ app.registerExtension({
 	name: "VeniceAI.Settings",
 	settings: [
 		{
+			category: ["VeniceAI", "API Key", "VeniceAI API Key"],
 			id: "VeniceAI.apikey",
 			name: "VeniceAI API Key",
 			type: "text",
 			defaultValue: "your_venice_api_key_here",
-			tooltip: "Enter your VeniceAI API Bearer Token Key here",
+			tooltip: "Enter your VeniceAI API Key/Bearer Token here",
+			attrs: {
+				type: "password",
+			},
 			onChange: async (newVal) => {
 				api.fetchApi("/veniceai/save_apikey", {
 					method: "POST",
@@ -19,4 +23,3 @@ app.registerExtension({
 		},
 	],
 });
-

js/startup.js

@@ -5,51 +5,13 @@ app.registerExtension({
     name: "VeniceAI.Startup",
 
     async setup() {
-        // Load saved value from server on startup
-        console.log("(VeniceAI.Startup) Fetching VeniceAI API key from config file...");
-        const api_key_response = await api.fetchApi("/veniceai/get_apikey");
-        const savedKey = await api_key_response.json();
-
-        // update the settings UI
-        app.extensionManager.setting.set("VeniceAI.apikey", savedKey.apikey);
-
         try {
-            // update the model list
-            console.log("(VeniceAI.Startup) Updating model list...");
-            //alert("fetching model list")
-            const response = await api.fetchApi("/veniceai/update_models_list");
-            const data = await response.json();
-            //alert(`response status ${JSON.stringify(data)}`);
-            if (data.error) {
-                alert(`${data.message}`);
-                console.log(`(VeniceAI.Startup) ${data.message}`);
-            }
-            else{
-                // update the style list if not model list error
-                console.log("(VeniceAI.Startup) Updating styles list...");
-                //alert("fetching styles list")
-                const response_s = await api.fetchApi("/veniceai/update_styles_list");
-                //alert(`response status ${await response_s.text()}`);
-                const data_s = await response_s.json();
-                if (data_s.error) {
-                    alert(`${data_s.message}`);
-                    console.log(`(VeniceAI.Startup) ${data_s.message}`);
-                }
-
-                // update the characters list
-                console.log("(VeniceAI.Startup) Updating characters list...");
-                const response_c = await api.fetchApi("/veniceai/update_characters_list");
-                const data_c = await response_c.json();
-                if (data_c.error) {
-                    alert(`${data_c.message}`);
-                    console.log(`(VeniceAI.Startup) ${data_c.message}`);
-                }
-            }
+            console.log("(VeniceAI.Startup) Fetching VeniceAI API key from config file...");
+            const api_key_response = await api.fetchApi("/veniceai/get_apikey");
+            const savedKey = await api_key_response.json();
+            app.extensionManager.setting.set("VeniceAI.apikey", savedKey.apikey);
         } catch (error) {
-            // Handle any unexpected errors
-            alert(`(VeniceAI.Startup) Unexpected Error: ${error.message}`);
-            console.error("(VeniceAI.Startup) Unexpected Error:", error);
+            console.error("(VeniceAI.Startup) Failed to load Venice API key", error);
         }
-
     },
 });

nodes/catalog_utils.py

@@ -0,0 +1,89 @@
+from __future__ import annotations
+
+import logging
+from typing import Any, Dict, Iterable, Sequence, Tuple
+
+from ..venice_catalog import get_characters, get_models, get_styles
+
+log = logging.getLogger(__name__)
+
+
+def _safe_values(
+    loader,
+    key: str,
+    fallback: Sequence[str] = ("This list is unavailable; check logs for details?",),
+) -> Tuple[str, ...]:
+    """
+    Safely retrieves a sequence of string values from a loader payload keyed by `key`.
+
+    Attempts to call the provided `loader` callable to obtain a payload, logging failures
+    and falling back to the provided default values. Extracts and normalizes the value
+    associated with `key`, ensuring it is returned as a tuple of strings. If the extracted
+    value is missing, empty, or otherwise falsy, the fallback values are returned instead.
+    """
+    try:
+        payload = loader()
+    except Exception as exc:
+        log.debug("Failed to load %s catalog: %s", key, exc)
+        return tuple(fallback)
+
+    raw = payload.get(key)
+    if raw is None:
+        return tuple(fallback)
+
+    if isinstance(raw, Iterable) and not isinstance(raw, (str, bytes)):
+        values = tuple(str(item) for item in raw if item)
+    else:
+        values = tuple(str(raw)) if raw else ()
+
+    return values or tuple(fallback)
+
+
+def image_model_choices() -> Tuple[str, ...]:
+    return _safe_values(lambda: get_models(), "image_models")
+
+
+def text2video_model_choices() -> Tuple[str, ...]:
+    return _safe_values(lambda: get_models(), "text2video_models")
+
+
+def image2video_model_choices() -> Tuple[str, ...]:
+    return _safe_values(lambda: get_models(), "image2video_models")
+
+
+def video_model_specs() -> Dict[str, Dict[str, Any]]:
+    """Return a by-id mapping of video model specs with constraints for DynamicCombo use."""
+    models = get_models()
+    return models.get("video_models_by_id", {})
+
+
+def image_model_specs() -> Dict[str, Dict[str, Any]]:
+    """Return a by-id mapping of image model specs and constraints for UI validation."""
+    models = get_models()
+    return models.get("image_models_by_id", {})
+
+
+def text_model_specs() -> Dict[str, Dict[str, Any]]:
+    """Return a by-id mapping of text model specs and constraints for UI validation."""
+    models = get_models()
+    return models.get("text_models_by_id", {})
+
+
+def text_model_choices() -> Tuple[str, ...]:
+    return _safe_values(lambda: get_models(), "text_models")
+
+
+def tts_model_choices() -> Tuple[str, ...]:
+    return _safe_values(lambda: get_models(), "tts_models")
+
+
+def tts_voice_choices() -> Tuple[str, ...]:
+    return _safe_values(lambda: get_models(), "tts_voices")
+
+
+def style_choices() -> Tuple[str, ...]:
+    return _safe_values(lambda: get_styles(), "data")
+
+
+def character_choices() -> Tuple[str, ...]:
+    return _safe_values(lambda: get_characters(), "characters")

nodes/gen_image_base.py

@@ -2,9 +2,7 @@ import base64
 import io
 import logging
 
-import numpy as np
-import torch # type: ignore
-import torchvision.transforms as transforms # type: ignore
+import torchvision.transforms as transforms
 from PIL import Image
 
 
@@ -33,13 +31,3 @@ class GenerateImageBase:
 
         except Exception as e:
             raise Exception(f"Error processing image result: {str(e)}") from e
-
-    def create_blank_image(self):
-        blank_img = Image.new("RGB", (64, 64), color="black")
-        img_array = np.array(blank_img).astype(np.float32) / 255.0
-        img_tensor = torch.from_numpy(img_array)[None,]
-        return (img_tensor,)
-
-    def check_multiple_of_32(self, width, height):
-        if width % 32 != 0 or height % 32 != 0:
-            raise ValueError(f"Width {width} and height {height} must be multiples of 32.")

nodes/gen_image_node.py

@@ -1,168 +1,324 @@
 import logging
-import os
-import re
+from typing import Any, Dict
 
-import requests
-import torch  # type: ignore
+import torch
 
-from ..globals import API_ENDPOINTS, VENICEAI_BASE_URL
+from comfy_api.latest import io
+
+from ..globals import API_ENDPOINTS
+from ..nodes.catalog_utils import image_model_specs, style_choices
 from ..nodes.gen_image_base import GenerateImageBase
+from ..nodes.utils import ensure_multiple_of, ensure_prompt_length
+from ..venice_client import client
+
+LOG = logging.getLogger(__name__)
 
 
-class GenerateImage(GenerateImageBase):
+class GenerateImage(io.ComfyNode):
+    _processor = GenerateImageBase()
+
     @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "model": (
-                    "COMBO",
-                    {
-                        "default": "flux-dev",
-                        "tooltip": "Model to use for image generation, if this just says flux-dev or C O M B O then something failed oopsie.",
-                    },
-                ),
-                "prompt": ("STRING", {"default": "A flying cat made of lettuce", "multiline": True}),
-                "neg_prompt": (
-                    "STRING",
-                    {
-                        "placeholder": "Negative Prompt. (Ignored for Flux based models.)\nBad composition, rating_explicit, Text, signature, lowres, faded image, out of focus, cropped, out of frame, vacant scene, bad quality, worst quality,",
-                        "multiline": True,
-                        "tooltip": "Negative prompt. This is ignored when using flux-dev or flux-dev-uncensored",
-                    },
-                ),
-                "width": (
-                    "INT",
-                    {
-                        "default": 1024,
-                        "min": 0,
-                        "max": 2048,  # limit is 1280 but i dont want to restrict this in case of future updates, https://docs.venice.ai/api-reference/endpoint/image/generate#body-height
-                        "step": 16,
-                        "tooltip": "Must be a multiple of 32. Maximum allowed by venice.ai at time of writing is 1280",
-                    },
-                ),
-                "height": (
-                    "INT",
-                    {
-                        "default": 1024,
-                        "min": 0,
-                        "max": 2048,
-                        "step": 16,
-                        "tooltip": "Must be a multiple of 32. Maximum allowed by venice.ai at time of writing is 1280",
-                    },
-                ),
-                "batch_size": ("INT", {"default": 1, "min": 1, "max": 4}),
-                "steps": (
-                    "INT",
-                    {
-                        "default": 20,
-                        "min": 1,
-                        "max": 50,
-                        "tooltip": (
-                            "Number of inference steps. The following models have reduced max steps from "
-                            "the global max: venice-sd35: 30 max steps, hidream: 50 max steps, fluently-xl: 50 max steps, "
-                            "flux-dev: 30 max steps, flux-dev-uncensored: 30 max steps, getphat-flux: 50 max steps, "
-                            "lustify-sdxl: 50 max steps, pony-realism: 50 max steps, stable-diffusion-3.5: 30 max steps, "
-                            "juggernaut-xi: 50 max steps."
-                        ),
-                    },
-                ),
-                "guidance": ("FLOAT", {"default": 3.0, "min": 0.0, "max": 20.0, "step": 0.05}),
-                # "lora_strength": ("INT", {"default": 50, "min": 0, "max": 100}), # check docs idk how to work this yet
-                "style_preset": ("COMBO", {"default": "none"}),
-                "hide_watermark": (
-                    "BOOLEAN",
-                    {
-                        "default": True,
-                        "tooltip": "Whether to hide the Venice watermark. Venice may ignore this parameter for certain generated content (mainl NSFW seems like).",
-                    },
-                ),
-                "safe_mode": (
-                    "BOOLEAN",
-                    {
-                        "default": False,
-                        "tooltip": "Whether to use safe mode. If enabled, this will blur images that are classified as having adult content.",
-                    },
-                ),
-                # "format": (["png", "jpeg", "webp"], {"default": "png",}),
-            },
-            "optional": {
-                "seed": ("INT", {"default": -1, "min": -0x3B9AC9FF, "max": 0x3B9AC9FF})
-            },  # 0xffffffffffffffff is 64 bit integer limit, current hex is 999999999, venice max
-        }
+    def _image_specs(cls, require: bool = False) -> Dict[str, Dict[str, Any]]:
+        specs = image_model_specs() or {}
+        if require and not specs:
+            raise ValueError(
+                "No Venice image model specs available; refresh the catalog in VeniceAI settings and retry."
+            )
+        return specs
 
-    def generate(
-        self,
+    @staticmethod
+    def _style_options() -> tuple[str, ...]:
+        options = list(style_choices())
+        if not options:
+            return ("none_available",)
+        return tuple(options)
+
+    @staticmethod
+    def _option_input_id(model_id: str, field: str) -> str:
+        sanitized = "".join(ch if ch.isalnum() or ch in {"_", "-"} else "_" for ch in model_id)
+        return f"{sanitized}__{field}"
+
+    @classmethod
+    def _get_option_value(cls, model_payload: Dict[str, Any], model_id: str, field: str) -> Any:
+        candidates = (
+            cls._option_input_id(model_id, field),
+            field,
+            f"{field}__{model_id}",
+            f"{model_id}__{field}",
+        )
+        for key in candidates:
+            if key in model_payload:
+                return model_payload.get(key)
+        return None
+
+    @classmethod
+    def _resolve_option_value(cls, model_payload: Dict[str, Any], model_id: str, field: str, default: Any) -> Any:
+        value = cls._get_option_value(model_payload, model_id, field)
+        return default if value is None else value
+
+    @staticmethod
+    def _coerce_positive_int(value: Any) -> int | None:
+        try:
+            candidate = int(value)
+        except (TypeError, ValueError):
+            return None
+        return candidate if candidate > 0 else None
+
+    @staticmethod
+    def _width_height_divisor(constraints: Dict[str, Any]) -> int:
+        divisor = constraints.get("widthHeightDivisor")
+        if isinstance(divisor, int) and divisor > 0:
+            return divisor
+        return 16
+
+    @classmethod
+    def _steps_limits(cls, constraints: Dict[str, Any]) -> tuple[int, int]:
+        steps = constraints.get("steps") or {}
+        default = cls._coerce_positive_int(steps.get("default"))
+        max_value = cls._coerce_positive_int(steps.get("max"))
+        default = default if default is not None else 20
+        max_steps = max_value if max_value is not None else 50
+        if default > max_steps:
+            max_steps = default
+        return default, max_steps
+
+    @classmethod
+    def _model_option_inputs(
+        cls,
+        model_id: str,
+        width_divisor: int,
+        steps_default: int,
+        steps_max: int,
+    ) -> list[io.Input]:
+        return [
+            io.Int.Input(
+                cls._option_input_id(model_id, "width"),
+                display_name="width",
+                default=1024,
+                min=0,
+                max=2048,
+                step=width_divisor,
+                tooltip="Target width for the generated image; stepping is tied to the model's `widthHeightDivisor`. Defaults to 16",
+            ),
+            io.Int.Input(
+                cls._option_input_id(model_id, "height"),
+                display_name="height",
+                default=1024,
+                min=0,
+                max=2048,
+                step=width_divisor,
+                tooltip="Target height for the generated image; stepping is tied to the model's `widthHeightDivisor`. Defaults to 16",
+            ),
+            io.Int.Input(
+                cls._option_input_id(model_id, "steps"),
+                display_name="steps",
+                default=steps_default,
+                min=1,
+                max=steps_max,
+                tooltip="Number of inference steps. Model constraints can reduce the range and have different defaults.",
+            ),
+        ]
+
+    @classmethod
+    def _build_model_options(cls) -> list[io.DynamicCombo.Option]:
+        specs = cls._image_specs(require=False)
+        options: list[io.DynamicCombo.Option] = []
+
+        def _sorted_model_items() -> list[tuple[str, Dict[str, Any]]]:
+            return sorted(specs.items())
+
+        for model_id, spec in _sorted_model_items():
+            if (
+                model_id == "nano-banana"
+            ):  # todo: implement ui for nano-banana, might be able to use code from video node
+                continue
+            constraints = spec.get("constraints") or {}
+            width_divisor = cls._width_height_divisor(constraints)
+            steps_default, steps_max = cls._steps_limits(constraints)
+            option_inputs = cls._model_option_inputs(
+                model_id,
+                width_divisor,
+                steps_default,
+                steps_max,
+            )
+            options.append(io.DynamicCombo.Option(model_id, option_inputs))
+
+        if not options:
+            option_inputs = cls._model_option_inputs(
+                "none_available",
+                16,
+                20,
+                50,
+            )
+            options.append(io.DynamicCombo.Option("none_available", option_inputs))
+
+        return options
+
+    @staticmethod
+    def _prompt_limit_from_spec(spec: Dict[str, Any] | None, default: int = 1500) -> int:
+        if not spec:
+            return default
+        constraints = spec.get("constraints") or {}
+        limit = constraints.get("promptCharacterLimit")
+        if isinstance(limit, int) and limit > 0:
+            return limit
+        try:
+            normalized = int(limit)
+        except (TypeError, ValueError):
+            return default
+        return normalized if normalized > 0 else default
+
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        model_options = cls._build_model_options()
+        style_options = cls._style_options()
+
+        return io.Schema(
+            node_id="GenerateImage_VENICE",
+            display_name="Generate Image (Venice)",
+            category="venice.ai",
+            inputs=[
+                io.String.Input(
+                    "prompt",
+                    default="A flying cat made of lettuce",
+                    multiline=True,
+                    placeholder="Positive Prompt. Example: A flying cat made of lettuce",
+                    tooltip="The text prompt to guide the image generation. Character limit depends on model (usually around 1500-7500 characters).",
+                ),
+                io.String.Input(
+                    "neg_prompt",
+                    default="",
+                    multiline=True,
+                    placeholder="Negative Prompt. Example: low quality, vacant scene",
+                    tooltip=(
+                        "Negative prompt (ignored for models that do not support CFG - z-image-turbo, flux-dev, etc.). "
+                        "Character limit depends on model (usually around 1500-7500 characters)."
+                    ),
+                ),
+                io.DynamicCombo.Input(
+                    "model",
+                    options=model_options,
+                    tooltip="Select a Venice image model to auto-populate valid parameters",
+                ),
+                io.Int.Input(
+                    "batch_size",
+                    default=1,
+                    min=1,
+                    max=4,
+                    tooltip="Number of images to generate in a single batch (sequential requests, does not use variants api (yet?)).",
+                ),
+                io.Float.Input(
+                    "guidance",
+                    default=6.0,
+                    min=0.0,
+                    max=20.0,
+                    step=0.05,
+                    tooltip=(
+                        "CFG scale (SDXL based models work well with 6.0, most newer ones work with 3-4. "
+                        "Closed Source models may ignore this setting and distilled models too, such as z-image-turbo or flux-dev and similar)."
+                    ),
+                ),
+                io.Combo.Input(
+                    "style_preset",
+                    options=list(style_options),
+                    default=style_options[0],
+                    tooltip="Venice.ai style preset to apply to the generated image.",
+                ),
+                io.Boolean.Input(
+                    "hide_watermark",
+                    default=True,
+                    tooltip="Hide the Venice watermark when possible.",
+                ),
+                io.Boolean.Input(
+                    "safe_mode",
+                    default=False,
+                    tooltip="Enable safe mode (blurs NSFW content).",
+                ),
+                io.Int.Input(
+                    "seed",
+                    optional=True,
+                    default=42,
+                    min=-0x3B9AC9FF,
+                    max=0x3B9AC9FF,
+                    tooltip="Seed for reproducibility.",
+                ),
+            ],
+            outputs=[io.Image.Output(id="image", display_name="Image")],
+        )
+
+    @classmethod
+    def execute(
+        cls,
         model,
         prompt,
         neg_prompt,
-        width,
-        height,
-        batch_size,
-        steps,
         guidance,
-        # lora_strength,
+        batch_size,
         style_preset,
         hide_watermark,
         safe_mode,
-        # format,
         seed=-1,
-    ):
-        if prompt == "" or len(prompt) > 1500:
-            raise ValueError("VeniceAI Generate Image Node: Prompt cannot be empty or above 1500 characters")
-        if len(neg_prompt) > 1500:
-            raise ValueError("VeniceAI Generate Image Node: Negative prompt cannot be above 1500 characters")
-        if re.match(r"^flux.*", model):
-            logging.info(f"VeniceAPI INFO: Ignoring negative prompt for {model}.")
-            neg_prompt = ""
+    ) -> io.NodeOutput:
+        if not isinstance(model, dict) or "model" not in model:
+            raise ValueError("Model selection is required")
 
-        images_tensor = ()  # empty tuple for tensors
+        model_id = model.get("model")
+        specs = cls._image_specs(require=True)
+        spec = specs.get(model_id)
+        if not spec:
+            raise ValueError("Selected model is missing from the Venice catalog; refresh the catalog and try again.")
+
+        constraints = spec.get("constraints") or {}
+        prompt_limit = cls._prompt_limit_from_spec(spec)
+        width_height_divisor = cls._width_height_divisor(constraints)
+        steps_default, _steps_max = cls._steps_limits(constraints)
+
+        width = int(cls._resolve_option_value(model, model_id, "width", 1024))
+        height = int(cls._resolve_option_value(model, model_id, "height", 1024))
+        steps = int(cls._resolve_option_value(model, model_id, "steps", steps_default))
+        guidance = float(guidance)
+        batch_size = int(batch_size)
+        style_options = cls._style_options()
+        if style_preset not in style_options:
+            style_preset = style_options[0]
+        hide_watermark = bool(hide_watermark)
+        safe_mode = bool(safe_mode)
+        seed = seed
+
+        ensure_multiple_of(width, height, multiple=width_height_divisor)
+        ensure_prompt_length(prompt, prompt_limit, "Prompt")
+        ensure_prompt_length(neg_prompt, prompt_limit, "Negative Prompt", allow_empty=True)
+
+        seed_value = -1 if seed is None else int(seed)
+        images_tensor = ()
 
         try:
-            self.check_multiple_of_32(width, height)  # todo: make this be validate node instead
-
-            headers = {"Authorization": f"Bearer {os.getenv('VENICEAI_API_KEY')}", "Content-Type": "application/json"}
-            url = VENICEAI_BASE_URL + API_ENDPOINTS["image_generate"]
-
             payload = {
-                "model": model,
+                "model": model_id,
                 "prompt": prompt,
                 "negative_prompt": neg_prompt,
-                # "lora_strength": lora_strength,
                 "style_preset": style_preset,
                 "height": height,
                 "width": width,
                 "steps": steps,
                 "cfg_scale": guidance,
-                "seed": seed,
+                "seed": seed_value,
                 "return_binary": False,
                 "hide_watermark": hide_watermark,
                 "safe_mode": safe_mode,
-                "format": "png",  # hardcoded because, change to format var and uncomment related stuff above if want dynamic
+                "format": "png",
+                "embed_exif_metadata": True,
             }
-            if style_preset == "none":
-                del payload["style_preset"]
+            if style_preset in ("none", "none_available"):
+                payload.pop("style_preset", None)
 
             for i in range(batch_size):
-                payload["seed"] = seed + i
-                response = requests.request("POST", url, json=payload, headers=headers)
-
-                if response.status_code != 200:
-                    raise requests.exceptions.HTTPError(
-                        f"HTTP error: {response.status_code}, Response: {response.text}"
-                    )
-
-                images_tensor += self.process_result(response.json())
+                payload["seed"] = seed_value + i
+                response_json = client.post_json(API_ENDPOINTS["image_generate"], payload)
+                images_tensor += cls._processor.process_result(response_json)
 
             merged = torch.cat(images_tensor, dim=0)
-            return (merged,)
+            return io.NodeOutput(merged)
 
-        except Exception as e:
-            raise Exception(f"Error processing image result: {str(e)}") from e
-
-
-NODE_CLASS_MAPPINGS = {
-    "GenerateImage_VENICE": GenerateImage,
-}
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "GenerateImage_VENICE": "Generate Image (Venice)",
-}
+        except Exception as exc:
+            raise Exception(f"Error processing image result: {str(exc)}") from exc

nodes/gen_speech_node.py

@@ -1,169 +1,224 @@
+import logging
 import os
+import subprocess
 import tempfile
 
 import requests
-import torch  # type: ignore
-import torchaudio  # type: ignore
+import torch
 
-from ..globals import API_ENDPOINTS, VENICEAI_BASE_URL
+from comfy_api.latest import io
+
+try:
+    import imageio_ffmpeg
+except ImportError:  # pragma: no cover
+    imageio_ffmpeg = None
+
+from ..globals import API_ENDPOINTS
+from ..nodes.catalog_utils import tts_model_choices, tts_voice_choices
+from ..nodes.utils import ensure_prompt_length
+from ..venice_client import client
+
+try:
+    from torchaudio import sox_io_backend
+except ImportError:  # pragma: no cover - default backend may not be available everywhere
+    sox_io_backend = None
+
+try:
+    from torchaudio import soundfile_backend
+except ImportError:  # pragma: no cover
+    soundfile_backend = None
 
 
-class GenerateSpeech:
+LOG = logging.getLogger(__name__)
+
+
+class GenerateSpeech(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "model": (
-                    "COMBO",
-                    {
-                        "default": "tts-kokoro",
-                    },
+    def _model_options(cls) -> list[str]:
+        options = list(tts_model_choices())
+        return options or ["none_available"]
+
+    @classmethod
+    def _voice_options(cls) -> list[str]:
+        options = list(tts_voice_choices())
+        return options or ["none_available"]
+
+    @staticmethod
+    def _ffmpeg_decode(temp_path: str) -> tuple[torch.Tensor, int]:
+        if imageio_ffmpeg is None:
+            LOG.error("imageio-ffmpeg is not installed; ffmpeg fallback unavailable")
+            raise RuntimeError("imageio-ffmpeg is not installed; install it to enable ffmpeg fallback.")
+
+        try:
+            ffmpeg = imageio_ffmpeg.get_ffmpeg_exe()
+        except Exception as exc:
+            LOG.error("Unable to download ffmpeg via imageio-ffmpeg: %s", exc)
+            raise RuntimeError("Failed to download ffmpeg via imageio-ffmpeg") from exc
+
+        LOG.info("Decoding Venice audio via ffmpeg executable %s", ffmpeg)
+        try:
+            process = subprocess.run(
+                [
+                    ffmpeg,
+                    "-hide_banner",
+                    "-loglevel",
+                    "error",
+                    "-i",
+                    temp_path,
+                    "-acodec",
+                    "pcm_f32le",
+                    "-f",
+                    "f32le",
+                    "-ac",
+                    "1",
+                    "-ar",
+                    "44100",
+                    "-",
+                ],
+                stdout=subprocess.PIPE,
+                stderr=subprocess.PIPE,
+                check=True,
+            )
+        except subprocess.CalledProcessError as exc:
+            LOG.error("ffmpeg failed to decode %s: %s", temp_path, exc.stderr.decode(errors="ignore"))
+            raise RuntimeError(f"ffmpeg failed to decode {temp_path}: {exc.stderr.decode(errors='ignore')}") from exc
+
+        audio_data = torch.frombuffer(process.stdout, dtype=torch.float32).clone()
+        audio_data = audio_data.reshape(-1, 1).transpose(0, 1)
+        return audio_data, 44100
+
+    @staticmethod
+    def _load_with_torchaudio_backends(temp_path: str, response_format: str) -> tuple[torch.Tensor, int]:
+        backends = [sox_io_backend, soundfile_backend]
+        errors: list[str] = []
+        for backend in backends:
+            if backend is None:
+                continue
+            backend_name = getattr(backend, "__name__", "torchaudio_backend")
+            try:
+                return backend.load(temp_path)
+            except Exception as exc:
+                error = str(exc)
+                errors.append(error)
+                LOG.warning("torchaudio backend %s failed to load %s: %s", backend_name, response_format, error)
+        if errors:
+            raise RuntimeError(f"Failed to load audio format '{response_format}' with all methods: {', '.join(errors)}")
+        raise RuntimeError(
+            f"Failed to load audio format '{response_format}' because no torchaudio backend is available."
+        )
+
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        response_formats = ["mp3", "opus", "aac", "flac", "wav", "pcm"]
+        model_options = cls._model_options()
+        voice_options = cls._voice_options()
+
+        return io.Schema(
+            node_id="GenerateSpeech_VENICE",
+            display_name="Generate Speech (Venice)",
+            category="venice.ai",
+            inputs=[
+                io.Combo.Input(
+                    "model",
+                    options=model_options,
+                    default=model_options[0],
+                    tooltip="Model to use for text-to-speech",
                 ),
-                "input": (
-                    "STRING",
-                    {
-                        "default": "",
-                        "multiline": True,
-                        "tooltip": ("The text to generate audio for. The maximum length is 4096 characters."),
-                    },
+                io.String.Input(
+                    "input",
+                    default="",
+                    multiline=True,
+                    placeholder="Text to speak",
+                    tooltip="The text prompt used for speech generation (max 4096 chars)",
                 ),
-                "response_format": (
-                    [
-                        # todo: some dont work because idk implementing would be ass
-                        "mp3",
-                        # "opus",
-                        # "aac",
-                        # "flac",
-                        "wav",
-                        "pcm",
-                    ],
-                    {
-                        "default": "mp3",
-                        "tooltip": (
-                            "mp3: widely supported, lossy; "
-                            # "opus: very good quality at low bitrate; "
-                            # "aac: lossy, good for streaming; "
-                            # "flac: lossless compressed audio; "
-                            "wav: lossless raw audio; "
-                            "pcm: uncompressed raw audio."
-                        ),
-                    },
+                io.Combo.Input(
+                    "response_format",
+                    options=response_formats,
+                    default=response_formats[0],
+                    tooltip="Audio format to request from the Venice TTS API",
                 ),
-                "speed": (
-                    "FLOAT",
-                    {
-                        "default": 1.0,
-                        "min": 0.25,
-                        "max": 4,
-                        "step": 0.01,
-                        "tooltip": (
-                            "The text to image style to apply during prompt enhancement. "
-                            "Does best with short descriptive prompts, like gold, marble or angry, menacing."
-                        ),
-                    },
+                io.Float.Input(
+                    "speed",
+                    default=1.0,
+                    min=0.25,
+                    max=4.0,
+                    step=0.01,
+                    tooltip="Playback speed multiplier (1.0 = normal speed)",
                 ),
-                # "streaming": (
-                #     "BOOLEAN",
-                #     {
-                #         "default": False,
-                #         "tooltip": (
-                #             "Should the content stream back sentence by sentence or be processed and returned as a complete audio file."
-                #
-                # ),
-                #     },
-                # ),
-                "voice": (
-                    "COMBO",
-                    {
-                        "default": "af_sky - tts-kokoro",
-                    },
+                io.Combo.Input(
+                    "voice",
+                    options=voice_options,
+                    default=voice_options[0],
+                    tooltip="Voice preset to use for the TTS model",
                 ),
-            }
-        }
+            ],
+            outputs=[io.Audio.Output(id="audio", display_name="audio")],
+        )
 
-    RETURN_TYPES = ("AUDIO",)
-    RETURN_NAMES = ("audio",)
-    FUNCTION = "gen_speech"
-    CATEGORY = "venice.ai"
+    @classmethod
+    def execute(cls, model, input, response_format, speed, voice) -> io.NodeOutput:
+        ensure_prompt_length(input, 4096, label="TTS input")
 
-    EXPERIMENTAL = True
-
-    def gen_speech(self, model, input, response_format, speed, voice):
-        if len(input) > 4096 or len(input) == 0:
-            raise ValueError("Generate Speech (Venice) Input exceeds the max length of 4096 characters or is empty.")
-
-        url = VENICEAI_BASE_URL + API_ENDPOINTS["speech_generate"]
-
-        # remove everything from voice string after and including the hyphen " - blabla"
-        voice = voice.split(" - ")[0] if " - " in voice else voice
-
-        # Prepare JSON payload
+        # todo: currently models' voices show up as "model-name - voice_name"
+        # todo: this can be in dynamiccombo so the single combo dropdown is not cluttered with all the voices of all selectable models
+        normalized_voice = voice.split(" - ")[-1].strip() if " - " in voice else voice
         payload = {
             "model": model,
             "input": input,
             "speed": speed,
-            "voice": voice,
+            "voice": normalized_voice,
             "response_format": response_format,
             "streaming": False,
         }
 
-        headers = {"Authorization": f"Bearer {os.getenv('VENICEAI_API_KEY')}", "Content-Type": "application/json"}
-
-        # Send request
         try:
-            response = requests.post(url, json=payload, headers=headers)
-            response.raise_for_status()
-        except requests.exceptions.RequestException as e:
-            raise RuntimeError(f"Generate Speech (Venice) API request failed: {str(e)}")
+            response = client.request(
+                "POST",
+                API_ENDPOINTS["speech_generate"],
+                json=payload,
+                headers={"Content-Type": "application/json"},
+            )
+        except requests.exceptions.RequestException as exc:
+            raise RuntimeError(f"Generate Speech (Venice) API request failed: {str(exc)}")
 
-        # Convert the audio response to a ComfyUI-compatible tensor and return it
-        if not response.content or len(response.content) == 0:
+        if not response.content:
             raise RuntimeError("No audio data received from Venice API.")
 
-        # Save to temporary file and load with torchaudio for better format support
-        with tempfile.NamedTemporaryFile(suffix=f".{response_format}", delete=False) as temp_file:
-            temp_file.write(response.content)
-            temp_file_path = temp_file.name
-
+        temp_path = None
+        waveform = None
+        sample_rate = None
         try:
-            # Load audio using torchaudio from the temporary file
-            waveform, sample_rate = torchaudio.load(temp_file_path)
-        except Exception as e:
-            # Fallback: try different approaches for problematic formats
+            # Save to temp file and load with torchaudio for better format support
+            with tempfile.NamedTemporaryFile(suffix=f".{response_format}", delete=False) as temp_file:
+                temp_file.write(response.content)
+                temp_path = temp_file.name
+
             try:
-                if response_format == "pcm":
-                    # For PCM, we need to handle it as raw audio data
-                    # Assume 16-bit PCM, mono, 16kHz (adjust as needed based on API response)
+                waveform, sample_rate = cls._load_with_torchaudio_backends(temp_path, response_format)
+            except RuntimeError as audio_exc:
+                LOG.warning("torchaudio decoding failed for %s: %s", response_format, audio_exc)
+                if response_format == "pcm" and response.content:
+                    LOG.info("Falling back to raw PCM interpretation for %s", response_format)
                     audio_data = torch.frombuffer(response.content, dtype=torch.int16).float() / 32768.0
-                    waveform = audio_data.unsqueeze(0)  # Add channel dimension
-                    sample_rate = 16000  # Default sample rate, adjust if needed
+                    waveform = audio_data.unsqueeze(0)
+                    sample_rate = 16000  # Default sample rate, might need change?
                 else:
-                    # For other formats, try loading without specifying format
-                    waveform, sample_rate = torchaudio.load(temp_file_path, format=None)
-            except Exception as e2:
-                raise RuntimeError(
-                    f"Failed to load audio format '{response_format}' with all methods. Errors: {str(e)}, {str(e2)}"
-                )
+                    LOG.info("Attempting ffmpeg fallback for %s audio", response_format)
+                    waveform, sample_rate = cls._ffmpeg_decode(temp_path)
         finally:
-            # Clean up temporary file
-            try:
-                os.unlink(temp_file_path)
-            except:
-                pass
+            if temp_path:
+                try:
+                    os.unlink(temp_path)
+                except OSError:
+                    pass
+
+        if waveform is None or sample_rate is None:
+            raise RuntimeError("Unable to decode Venice speech response.")
 
-        # Ensure shape is [B, C, T] (batch size 1)
         if waveform.dim() == 2:
             waveform = waveform.unsqueeze(0)  # [1, C, T]
         elif waveform.dim() == 1:
             waveform = waveform.unsqueeze(0).unsqueeze(0)  # [1, 1, T]
 
-        return ({"waveform": waveform, "sample_rate": sample_rate},)
-
-
-NODE_CLASS_MAPPINGS = {
-    "GenerateSpeech_VENICE": GenerateSpeech,
-}
-
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "GenerateSpeech_VENICE": "Generate Speech [BETA] (Venice)",
-}
+        audio_value: io.Audio.Type = {"waveform": waveform, "sample_rate": sample_rate}
+        return io.NodeOutput(audio_value)

nodes/gen_text_advanced_node.py

@@ -1,354 +0,0 @@
-import base64
-import io
-import os
-
-import numpy as np
-import requests
-from PIL import Image
-
-from ..globals import API_ENDPOINTS, VENICEAI_BASE_URL
-
-
-class GenerateTextAdvanced:
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "model": ("COMBO", {"default": "llama-3.1-405b", "tooltip": ("The model to use for text generation.")}),
-                "prompt": (
-                    "STRING",
-                    {
-                        "default": "",
-                        "multiline": True,
-                        "tooltip": ("The prompt to generate text from. Ask, command or chat with the model."),
-                    },
-                ),
-                "system_prompt": (
-                    "STRING",
-                    {
-                        "default": "",
-                        "multiline": True,
-                        "tooltip": ("Optional system prompt to guide the model's behavior."),
-                    },
-                ),
-                "enable_system_prompt": (
-                    "BOOLEAN",
-                    {
-                        "default": True,
-                        "tooltip": ("Enable or disable system prompt being passed on."),
-                    },
-                ),
-                "frequency_penalty": (
-                    "FLOAT",
-                    {
-                        "default": 0.0,
-                        "min": -2.0,
-                        "max": 2.0,
-                        "step": 0.05,
-                        "tooltip": (
-                            "Positive values penalize new tokens based on their existing frequency in the text so far, "
-                            "decreasing the model's likelihood to repeat the same line verbatim."
-                        ),
-                    },
-                ),
-                "presence_penalty": (
-                    "FLOAT",
-                    {
-                        "default": 0.0,
-                        "min": -2.0,
-                        "max": 2.0,
-                        "step": 0.05,
-                        "tooltip": (
-                            "Positive values penalize new tokens based on whether they appear in the text so far, "
-                            "increasing the model's likelihood to talk about new topics."
-                        ),
-                    },
-                ),
-                "repetition_penalty": (
-                    "FLOAT",
-                    {
-                        "default": 1.2,
-                        "min": 0.0,
-                        "max": 2.0,
-                        "step": 0.05,
-                        "tooltip": ("1.0 means no penalty. Values > 1.0 discourage repetition."),
-                    },
-                ),
-                "max_temp": (
-                    "FLOAT",
-                    {
-                        "default": 1.5,
-                        "min": 0.0,
-                        "max": 2.0,
-                        "step": 0.05,
-                        "tooltip": ("Maximum temperature value for dynamic temperature scaling."),
-                    },
-                ),
-                "min_temp": (
-                    "FLOAT",
-                    {
-                        "default": 0.1,
-                        "min": 0.0,
-                        "max": 2.0,
-                        "step": 0.05,
-                        "tooltip": ("Minimum temperature value for dynamic temperature scaling."),
-                    },
-                ),
-                "max_completion_tokens": (
-                    "INT",
-                    {
-                        "default": 420,
-                        "min": 1,
-                        "max": 131072,
-                        "step": 1,
-                        "tooltip": (
-                            "An upper bound for the number of tokens that can be generated for "
-                            "a completion, including visible output tokens and reasoning tokens."
-                        ),
-                    },
-                ),
-                "temperature": (
-                    "FLOAT",
-                    {
-                        "default": 0.5,
-                        "min": 0.0,
-                        "max": 2.0,
-                        "step": 0.05,
-                        "tooltip": (
-                            "Higher values like 0.8 will make the output more random, "
-                            "while lower values like 0.2 will make it more focused and deterministic. "
-                            "We generally recommend altering this or top_p but not both."
-                        ),
-                    },
-                ),
-                "top_k": (
-                    "INT",
-                    {
-                        "default": 40,
-                        "min": 0,
-                        "tooltip": ("The number of highest probability vocabulary tokens to keep for top-k-filtering."),
-                    },
-                ),
-                "top_p": (
-                    "FLOAT",
-                    {
-                        "default": 0.8,
-                        "min": 0.0,
-                        "max": 2.0,
-                        "step": 0.01,
-                        "tooltip": (
-                            "An alternative to sampling with temperature, called nucleus sampling, "
-                            "where the model considers the results of the tokens with top_p probability mass. "
-                            "So 0.1 means only the tokens comprising the top 10% probability mass are considered."
-                        ),
-                    },
-                ),
-                "min_p": (
-                    "FLOAT",
-                    {
-                        "default": 0.05,
-                        "min": 0.0,
-                        "max": 1.0,
-                        "step": 0.01,
-                        "tooltip": (
-                            "Sets a minimum probability threshold for token selection. "
-                            "Tokens with probabilities below this value are filtered out."
-                        ),
-                    },
-                ),
-                # "stop": ("STRING", {"default": "", "tooltip": "Up to 4 sequences where the API will stop generating further tokens. Defaults to null.", "placeholder": "stop: [\"\\n\"]"}),
-                # "stop_token_ids": ("STRING", {"default": "", "tooltip": "Array of token IDs where the API will stop generating further tokens. Example: [151643, 151645]", "placeholder": "151643, 151645, ..."}),
-                "enable_vision": (
-                    "BOOLEAN",
-                    {
-                        "default": False,
-                        "tooltip": (
-                            "Enable or disable vision tasks. "
-                            "Requires image_for_vision input to be populated and "
-                            "for the LLM to actually support vision tasks to process."
-                        ),
-                    },
-                ),
-            },
-            "optional": {
-                "venice_parameters": (
-                    "STRING",
-                    {
-                        "forceInput": True,
-                        "tooltip": (
-                            "Optional input. "
-                            "Use the Textgen Parameters (Venice) node to use "
-                            "extra, venice specific parameters for text generation."
-                        ),
-                    },
-                ),
-                "image_for_vision": (
-                    "IMAGE",
-                    {
-                        "tooltip": (
-                            "Optional input. "
-                            "Add an image for vision-supported LLMs to process. "
-                            "Will only be processed if 'enable_vision' is 'True' and "
-                            "if the LLM actually supports vision tasks."
-                        ),
-                    },
-                ),
-            },
-        }
-
-    RETURN_TYPES = ("STRING",)
-    RETURN_NAMES = ("response",)
-    FUNCTION = "generate_text"
-    CATEGORY = "venice.ai"
-    DESCRIPTION = (
-        "Text Generation node that makes use of Venice.AI's text generation API. "
-        "Use Textgen Parameters (Venice) node to pass on extra Venice.AI specific parameters."
-        "Does not have chat history context. "
-    )
-
-    def generate_text(
-        # region params
-        self,
-        model,
-        prompt,
-        system_prompt,
-        enable_system_prompt,
-        frequency_penalty,
-        presence_penalty,
-        repetition_penalty,
-        max_temp,
-        min_temp,
-        max_completion_tokens,
-        temperature,
-        top_k,
-        top_p,
-        min_p,
-        enable_vision,
-        **kwargs,
-        # endregion
-    ):
-
-        url = VENICEAI_BASE_URL + API_ENDPOINTS["text_generate"]
-
-        user_content = []
-        venice_parameters = kwargs.get("venice_parameters", None)
-        image_for_vision = kwargs.get("image_for_vision", None)
-
-        if image_for_vision is not None and enable_vision:
-            # Convert tensor to PIL Image
-            image_tensor = image_for_vision[0]  # shape: (H, W, 3)
-            image_np = image_tensor.cpu().numpy()  # Still in (H, W, 3)
-            image_np = (image_np * 255).astype(np.uint8)  # Scale from [0, 1] to [0, 255] if needed
-            pil_image = Image.fromarray(image_np)
-
-            # Resize image to meet constraints
-            original_width, original_height = pil_image.size
-            aspect_ratio = original_width / original_height
-
-            # Determine target dimensions
-            if original_width > original_height:
-                target_width = 1024
-                target_height = int(target_width / aspect_ratio)
-                if target_height < 256:
-                    target_height = 256
-                    target_width = int(target_height * aspect_ratio)
-            else:
-                target_height = 1024
-                target_width = int(target_height * aspect_ratio)
-                if target_width < 256:
-                    target_width = 256
-                    target_height = int(target_width / aspect_ratio)
-
-            # Round dimensions to multiples of 14
-            def round_down_to_multiple(value, multiple):
-                return (value // multiple) * multiple
-
-            target_width = round_down_to_multiple(target_width, 14)
-            target_height = round_down_to_multiple(target_height, 14)
-
-            # Ensure minimum dimension is 256 after rounding
-            if min(target_width, target_height) < 256:
-                if target_width < target_height:
-                    target_width = ((256 + 13) // 14) * 14
-                    target_height = round_down_to_multiple(int(target_width / aspect_ratio), 14)
-                else:
-                    target_height = ((256 + 13) // 14) * 14
-                    target_width = round_down_to_multiple(int(target_height * aspect_ratio), 14)
-
-            pil_image = pil_image.resize((target_width, target_height), Image.LANCZOS)  # type: ignore
-
-            # Convert to base64 and check size
-            buffered = io.BytesIO()
-            pil_image.save(buffered, format="PNG")
-            img_base64 = base64.b64encode(buffered.getvalue()).decode("utf-8")
-
-            # Resize further if base64 exceeds 4.5MB
-            while len(img_base64) > 4500000:
-                scaling_factor = (4500000 / len(img_base64)) ** 0.5
-                new_width = int(target_width * scaling_factor)
-                new_height = int(target_height * scaling_factor)
-
-                new_width = max(round_down_to_multiple(new_width, 14), 256)
-                new_height = max(round_down_to_multiple(new_height, 14), 256)
-
-                pil_image = pil_image.resize((new_width, new_height), Image.LANCZOS)  # type: ignore
-                target_width, target_height = new_width, new_height
-
-                buffered = io.BytesIO()
-                pil_image.save(buffered, format="PNG")
-                img_base64 = base64.b64encode(buffered.getvalue()).decode("utf-8")
-
-            user_content.extend(
-                [
-                    {"type": "text", "text": prompt},
-                    {"type": "image_url", "image_url": {"url": f"data:image/png;base64,{img_base64}"}},
-                ]
-            )
-        else:
-            user_content.append({"type": "text", "text": prompt})
-
-        if not enable_system_prompt:
-            system_prompt = ""
-
-        messages = [{"role": "system", "content": system_prompt}]
-        messages.append({"role": "user", "content": user_content})
-
-        payload = {
-            "model": model,
-            "messages": messages,
-            "frequency_penalty": frequency_penalty,
-            "presence_penalty": presence_penalty,
-            "repetition_penalty": repetition_penalty,
-            "max_temp": max_temp,
-            "min_temp": min_temp,
-            "max_completion_tokens": max_completion_tokens,
-            "temperature": temperature,
-            "top_k": top_k,
-            "top_p": top_p,
-            "min_p": min_p,
-        }
-        if venice_parameters is not None:
-            payload["venice_parameters"] = venice_parameters
-
-        headers = {"Authorization": f"Bearer {os.getenv('VENICEAI_API_KEY')}", "Content-Type": "application/json"}
-        response = requests.post(url, json=payload, headers=headers)
-
-        if response.status_code != 200:
-            raise requests.exceptions.HTTPError(f"HTTP error: {response.status_code}, Response: {response.text}")
-
-        json_response = response.json()
-        try:
-            content = json_response["choices"][0]["message"]["content"]
-        except (KeyError, IndexError, TypeError) as e:
-            raise ValueError(f"Unexpected API response format: {json_response}") from e
-
-        return (content,)
-
-
-NODE_CLASS_MAPPINGS = {
-    "GenerateTextAdvanced_VENICE": GenerateTextAdvanced,
-}
-
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "GenerateTextAdvanced_VENICE": "Generate Text Advanced BETA (Venice)",
-}

nodes/gen_text_node.py

@@ -1,161 +1,498 @@
-import base64
-import io
-import os
+import json
+import logging
+from typing import Any, Dict, Iterable
 
-import numpy as np
-import requests
-from PIL import Image
+from comfy_api.latest import io
 
-from ..globals import API_ENDPOINTS, VENICEAI_BASE_URL
+from ..globals import API_ENDPOINTS
+from ..nodes.catalog_utils import character_choices, text_model_specs
+from ..nodes.utils import encode_tensor_for_vision
+from ..venice_client import client
+
+logger = logging.getLogger(__name__)
 
 
-class GenerateText:
+class GenerateTextAdvanced(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "model": (
-                    "COMBO",
-                    {
-                        "default": "llama-3.3-70b",
-                    },
+    def _option_input_id(cls, model_id: str, field: str) -> str:
+        return f"{model_id}__{field}"
+
+    @staticmethod
+    def _constraint_default(value: Any, fallback: float) -> float:
+        if isinstance(value, dict):
+            default = value.get("default")
+        else:
+            default = value
+        if isinstance(default, (int, float)):
+            return float(default)
+        return fallback
+
+    @classmethod
+    def _get_option_value(cls, model_payload: Dict[str, Any], model_id: str, field: str) -> Any:
+        candidates = (
+            cls._option_input_id(model_id, field),
+            field,
+            f"{field}__{model_id}",
+            f"{model_id}__{field}",
+        )
+        for key in candidates:
+            if key in model_payload:
+                return model_payload.get(key)
+        return None
+
+    @classmethod
+    def _text_specs(cls) -> Dict[str, Dict[str, Any]]:
+        specs = text_model_specs() or {}
+        if not specs:
+            raise ValueError("No Venice text model specs available")
+        return specs
+
+    @staticmethod
+    def _normalize_stop_tokens(value: str | Iterable[str] | None) -> list[str]:
+        tokens: list[str] = []
+        if not value:
+            return tokens
+        segments: Iterable[str] = value.splitlines() if isinstance(value, str) else value
+        for segment in segments:
+            for raw_token in str(segment).split(","):
+                trimmed = raw_token.strip()
+                if trimmed:
+                    tokens.append(trimmed)
+        return tokens
+
+    @classmethod
+    def _build_model_options(cls) -> list[io.DynamicCombo.Option]:
+        specs = cls._text_specs()
+        options: list[io.DynamicCombo.Option] = []
+        for model_id, spec in sorted(specs.items(), key=lambda item: item[0]):
+            constraints = spec.get("constraints") or {}
+            capabilities = spec.get("capabilities") or {}
+            temperature_default = cls._constraint_default(constraints.get("temperature"), 0.5)
+            top_p_default = cls._constraint_default(constraints.get("top_p"), 0.8)
+
+            option_inputs: list[io.Input] = [
+                io.Float.Input(
+                    cls._option_input_id(model_id, "temperature"),
+                    display_name="temperature",
+                    default=temperature_default,
+                    min=0.0,
+                    max=2.0,
+                    step=0.01,
+                    tooltip="Sampling temperature (per-model default taken from the catalog).",
                 ),
-                "system_prompt": ("STRING", {"default": "", "multiline": True}),
-                "prompt": ("STRING", {"default": "", "multiline": True}),
-                "frequency_penalty": ("FLOAT", {"default": 1.5, "min": 0.0, "max": 2.0, "step": 0.1}),
-                "presence_penalty": ("FLOAT", {"default": 1.5, "min": 0.0, "max": 2.0, "step": 0.1}),
-                "temperature": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 2.0, "step": 0.1}),
-                "top_p": ("FLOAT", {"default": 0.9, "min": 0.0, "max": 1.0, "step": 0.1}),
-                "enable_vision": ("BOOLEAN", {"default": False}),
-            },
-            "optional": {
-                "image_for_vision": ("IMAGE",),
-            },
-        }
+                io.Float.Input(
+                    cls._option_input_id(model_id, "top_p"),
+                    display_name="top_p",
+                    default=top_p_default,
+                    min=0.0,
+                    max=1.0,
+                    step=0.01,
+                    tooltip="Nucleus sampling probability (per-model default taken from the catalog).",
+                ),
+            ]
 
-    RETURN_TYPES = ("STRING",)
-    RETURN_NAMES = ("response",)
-    FUNCTION = "generate_text"
-    CATEGORY = "venice.ai"
+            if capabilities.get("supportsVision"):
+                option_inputs.append(
+                    io.Boolean.Input(
+                        cls._option_input_id(model_id, "enable_vision"),
+                        display_name="enable_vision",
+                        default=False,
+                        tooltip="Enable vision inputs when the model supports vision.",
+                    )
+                )
 
-    def generate_text(
-        self,
+            if capabilities.get("supportsReasoning"):
+                option_inputs.append(
+                    io.Boolean.Input(
+                        cls._option_input_id(model_id, "reasoning"),
+                        display_name="reasoning",
+                        default=True,
+                        tooltip="Toggle reasoning capabilities for this model.",
+                    )
+                )
+
+            option_inputs.extend(cls._venice_parameter_inputs(model_id, capabilities))
+
+            options.append(io.DynamicCombo.Option(model_id, option_inputs))
+
+        return options
+
+    @classmethod
+    def _venice_parameter_inputs(cls, model_id: str, capabilities: Dict[str, Any]) -> list[io.Input]:
+        character_options = ["none", *character_choices()]
+        inputs: list[io.Input] = [
+            io.Combo.Input(
+                cls._option_input_id(model_id, "vp_character_slug"),
+                display_name="vp_character_slug",
+                options=character_options,
+                default=character_options[0],
+                optional=True,
+                tooltip="Select a Venice character slug (public ID) from the catalog.",
+            ),
+        ]
+
+        if capabilities.get("supportsReasoning"):
+            inputs.extend(
+                [
+                    io.Boolean.Input(
+                        cls._option_input_id(model_id, "vp_strip_thinking_response"),
+                        display_name="vp_strip_thinking_response",
+                        default=False,
+                        tooltip="Strip thinking blocks from the response on reasoning models.",
+                    ),
+                    io.Boolean.Input(
+                        cls._option_input_id(model_id, "vp_disable_thinking"),
+                        display_name="vp_disable_thinking",
+                        default=False,
+                        tooltip="Disable thinking blocks for supported reasoning models.",
+                    ),
+                ]
+            )
+
+        if capabilities.get("supportsWebSearch"):
+            inputs.extend(
+                [
+                    io.Combo.Input(
+                        cls._option_input_id(model_id, "vp_enable_web_search"),
+                        display_name="vp_enable_web_search",
+                        options=["auto", "off", "on"],
+                        default="off",
+                        optional=True,
+                        tooltip="Set to auto/off/on to control Venice web search for this request.",
+                    ),
+                    io.Boolean.Input(
+                        cls._option_input_id(model_id, "vp_enable_web_scraping"),
+                        display_name="vp_enable_web_scraping",
+                        default=False,
+                        tooltip="Enable Venice web scraping for URLs found in the latest user message.",
+                    ),
+                    io.Boolean.Input(
+                        cls._option_input_id(model_id, "vp_enable_web_citations"),
+                        display_name="vp_enable_web_citations",
+                        default=False,
+                        tooltip="Request citations when web search returns sources.",
+                    ),
+                ]
+            )
+
+        inputs.append(
+            io.Boolean.Input(
+                cls._option_input_id(model_id, "vp_include_venice_system_prompt"),
+                display_name="vp_include_venice_system_prompt",
+                default=False,
+                tooltip="Include Venice-supplied system prompts alongside your own.",
+            )
+        )
+
+        return inputs
+
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        model_options = cls._build_model_options()
+
+        return io.Schema(
+            node_id="GenerateTextAdvanced_VENICE",
+            display_name="Generate Text Advanced (Venice)",
+            category="venice.ai",
+            inputs=[
+                io.String.Input(
+                    "prompt",
+                    default="",
+                    multiline=True,
+                    tooltip="The prompt to generate text from. Ask, command or chat with the model.",
+                ),
+                io.String.Input(
+                    "system_prompt",
+                    default="",
+                    multiline=True,
+                    tooltip="Optional system prompt to guide the model's behavior.",
+                ),
+                io.Image.Input(
+                    "vision_image",
+                    display_name="vision_image",
+                    optional=True,
+                    tooltip="Optional image for vision-capable models. Enable the vision toggle to send it.",
+                ),
+                io.DynamicCombo.Input(
+                    "model",
+                    options=model_options,
+                    tooltip="The model to use for text generation.",
+                ),
+                io.Float.Input(
+                    "frequency_penalty",
+                    default=0.0,
+                    min=-2.0,
+                    max=2.0,
+                    step=0.05,
+                    tooltip=(
+                        "Positive values penalize new tokens based on their existing frequency in the text so far, "
+                        "decreasing the model's likelihood to repeat the same line verbatim."
+                    ),
+                ),
+                io.Float.Input(
+                    "presence_penalty",
+                    default=0.0,
+                    min=-2.0,
+                    max=2.0,
+                    step=0.05,
+                    tooltip=(
+                        "Positive values penalize new tokens based on whether they appear in the text so far, "
+                        "increasing the model's likelihood to talk about new topics."
+                    ),
+                ),
+                io.Float.Input(
+                    "repetition_penalty",
+                    default=1.2,
+                    min=0.0,
+                    max=2.0,
+                    step=0.05,
+                    tooltip="1.0 means no penalty. Values > 1.0 discourage repetition.",
+                ),
+                io.Float.Input(
+                    "max_temp",
+                    default=1.5,
+                    min=0.0,
+                    max=2.0,
+                    step=0.05,
+                    tooltip="Maximum temperature value for dynamic temperature scaling.",
+                ),
+                io.Float.Input(
+                    "min_temp",
+                    default=0.1,
+                    min=0.0,
+                    max=2.0,
+                    step=0.05,
+                    tooltip="Minimum temperature value for dynamic temperature scaling.",
+                ),
+                io.Int.Input(
+                    "max_completion_tokens",
+                    default=420,
+                    min=1,
+                    max=131072,
+                    step=1,
+                    tooltip=(
+                        "An upper bound for the number of tokens that can be generated for "
+                        "a completion, including visible output tokens and reasoning tokens."
+                    ),
+                ),
+                io.Int.Input(
+                    "top_k",
+                    default=40,
+                    min=0,
+                    tooltip="The number of highest probability vocabulary tokens to keep for top-k-filtering.",
+                ),
+                io.Float.Input(
+                    "min_p",
+                    default=0.05,
+                    min=0.0,
+                    max=1.0,
+                    step=0.01,
+                    tooltip=(
+                        "Sets a minimum probability threshold for token selection. "
+                        "Tokens with probabilities below this value are filtered out."
+                    ),
+                ),
+                io.String.Input(
+                    "stop_tokens",
+                    default="",
+                    tooltip="Optional comma- or newline-separated tokens to stop generation on (requires at least one).",
+                ),
+                    io.Int.Input(
+                        "seed",
+                        default=42,
+                        min=1,
+                        tooltip="Seed for Venice randomness; must be 1 or greater.",
+                    ),
+                io.Boolean.Input(
+                    "enable_system_prompt",
+                    default=True,
+                    tooltip="Enable or disable system prompt being passed on.",
+                ),
+                io.Boolean.Input(
+                    "debug_append_response",
+                    default=False,
+                    tooltip="Append the raw Venice response after three newlines for debugging.",
+                ),
+            ],
+            outputs=[io.String.Output(id="response", display_name="response")],
+        )
+
+    @classmethod
+    def execute(
+        cls,
         model,
-        system_prompt,
         prompt,
+        system_prompt,
+        vision_image,
         frequency_penalty,
         presence_penalty,
-        temperature,
-        top_p,
-        enable_vision,
-        **kwargs,
-    ):
-        url = VENICEAI_BASE_URL + API_ENDPOINTS["text_generate"]
+        repetition_penalty,
+        max_temp,
+        min_temp,
+        max_completion_tokens,
+        top_k,
+        min_p,
+        stop_tokens,
+        seed,
+        enable_system_prompt,
+        debug_append_response,
+    ) -> io.NodeOutput:
+        if isinstance(model, str):
+            model = {"model": model}
+        if not isinstance(model, dict) or "model" not in model:
+            raise ValueError("Model selection is required")
+
+        model_id = model.get("model")
+        specs = cls._text_specs()
+        spec = specs.get(model_id)
+        if not spec:
+            raise ValueError("Selected model is missing from the Venice catalog; refresh the catalog and try again.")
+
+        constraints = spec.get("constraints") or {}
+        capabilities = spec.get("capabilities") or {}
+
+        temperature_value = cls._get_option_value(model, model_id, "temperature")
+        if temperature_value is None:
+            temperature_value = cls._constraint_default(constraints.get("temperature"), 0.5)
+        try:
+            temperature_value = float(temperature_value)
+        except (TypeError, ValueError):
+            temperature_value = 0.5
+
+        top_p_value = cls._get_option_value(model, model_id, "top_p")
+        if top_p_value is None:
+            top_p_value = cls._constraint_default(constraints.get("top_p"), 0.8)
+        try:
+            top_p_value = float(top_p_value)
+        except (TypeError, ValueError):
+            top_p_value = 0.8
+
+        reasoning_value = cls._get_option_value(model, model_id, "reasoning")
+        reasoning_enabled = (
+            bool(reasoning_value) if reasoning_value is not None else bool(capabilities.get("supportsReasoning"))
+        )
+        reasoning_effort_value = "medium"
+        vision_enabled = (
+            bool(cls._get_option_value(model, model_id, "enable_vision"))
+            if capabilities.get("supportsVision")
+            else False
+        )
+        vision_tensor = None
+        if vision_image is not None:
+            candidates = vision_image if isinstance(vision_image, (list, tuple)) else (vision_image,)
+            for candidate in candidates:
+                if candidate is not None:
+                    vision_tensor = candidate
+                    break
+        normalized_stop_tokens = cls._normalize_stop_tokens(stop_tokens)
+        try:
+            seed_value = int(seed)
+        except (TypeError, ValueError):
+            seed_value = 42
+        if seed_value < 1:
+            seed_value = 1
+
+        venice_parameters: Dict[str, Any] = {}
+
+        def _set_bool(field: str, key: str) -> None:
+            value = cls._get_option_value(model, model_id, field)
+            if value is not None:
+                venice_parameters[key] = bool(value)
+
+        slug_value = cls._get_option_value(model, model_id, "vp_character_slug")
+        if isinstance(slug_value, str):
+            trimmed = slug_value.strip()
+            if trimmed and trimmed.lower() not in {"", "none"}:
+                venice_parameters["character_slug"] = trimmed
+
+        if capabilities.get("supportsReasoning"):
+            _set_bool("vp_strip_thinking_response", "strip_thinking_response")
+            _set_bool("vp_disable_thinking", "disable_thinking")
+
+        if capabilities.get("supportsWebSearch"):
+            web_search = cls._get_option_value(model, model_id, "vp_enable_web_search")
+            if isinstance(web_search, str):
+                trimmed = web_search.strip()
+                if trimmed:
+                    venice_parameters["enable_web_search"] = trimmed
+            elif web_search is not None:
+                venice_parameters["enable_web_search"] = str(web_search)
+            _set_bool("vp_enable_web_scraping", "enable_web_scraping")
+            _set_bool("vp_enable_web_citations", "enable_web_citations")
+
+        _set_bool("vp_include_venice_system_prompt", "include_venice_system_prompt")
+
+        if vision_tensor is not None and (not capabilities.get("supportsVision") or not vision_enabled):
+            logger.warning(
+                "Vision image provided but model %s does not support vision, or enable_vision is disabled",
+                model_id,
+            )
+        if vision_enabled and vision_tensor is None:
+            raise ValueError("Vision input is enabled but no image was provided")
 
         user_content = []
-        image_for_vision = kwargs.get("image_for_vision", None)
-
-        if image_for_vision is not None and enable_vision:
-            # Convert tensor to PIL Image
-            image_tensor = image_for_vision[0]  # shape: (H, W, 3)
-            image_np = image_tensor.cpu().numpy()  # Still in (H, W, 3)
-            image_np = (image_np * 255).astype(np.uint8)  # Scale from [0, 1] to [0, 255] if needed
-            pil_image = Image.fromarray(image_np)
-
-            # Resize image to meet constraints
-            original_width, original_height = pil_image.size
-            aspect_ratio = original_width / original_height
-
-            # Determine target dimensions
-            if original_width > original_height:
-                target_width = 1024
-                target_height = int(target_width / aspect_ratio)
-                if target_height < 256:
-                    target_height = 256
-                    target_width = int(target_height * aspect_ratio)
-            else:
-                target_height = 1024
-                target_width = int(target_height * aspect_ratio)
-                if target_width < 256:
-                    target_width = 256
-                    target_height = int(target_width / aspect_ratio)
-
-            # Round dimensions to multiples of 14
-            def round_down_to_multiple(value, multiple):
-                return (value // multiple) * multiple
-
-            target_width = round_down_to_multiple(target_width, 14)
-            target_height = round_down_to_multiple(target_height, 14)
-
-            # Ensure minimum dimension is 256 after rounding
-            if min(target_width, target_height) < 256:
-                if target_width < target_height:
-                    target_width = ((256 + 13) // 14) * 14
-                    target_height = round_down_to_multiple(int(target_width / aspect_ratio), 14)
-                else:
-                    target_height = ((256 + 13) // 14) * 14
-                    target_width = round_down_to_multiple(int(target_height * aspect_ratio), 14)
-
-            pil_image = pil_image.resize((target_width, target_height), Image.LANCZOS)  # type: ignore
-
-            # Convert to base64 and check size
-            buffered = io.BytesIO()
-            pil_image.save(buffered, format="PNG")
-            img_base64 = base64.b64encode(buffered.getvalue()).decode("utf-8")
-
-            # Resize further if base64 exceeds 4.5MB
-            while len(img_base64) > 4500000:
-                scaling_factor = (4500000 / len(img_base64)) ** 0.5
-                new_width = int(target_width * scaling_factor)
-                new_height = int(target_height * scaling_factor)
-
-                new_width = max(round_down_to_multiple(new_width, 14), 256)
-                new_height = max(round_down_to_multiple(new_height, 14), 256)
-
-                pil_image = pil_image.resize((new_width, new_height), Image.LANCZOS)  # type: ignore
-                target_width, target_height = new_width, new_height
-
-                buffered = io.BytesIO()
-                pil_image.save(buffered, format="PNG")
-                img_base64 = base64.b64encode(buffered.getvalue()).decode("utf-8")
-
+        if vision_enabled and vision_tensor is not None:
+            encoded_image = encode_tensor_for_vision(vision_tensor)
             user_content.extend(
                 [
                     {"type": "text", "text": prompt},
-                    {"type": "image_url", "image_url": {"url": f"data:image/png;base64,{img_base64}"}},
+                    {"type": "image_url", "image_url": {"url": encoded_image}},
                 ]
             )
         else:
             user_content.append({"type": "text", "text": prompt})
 
+        if not enable_system_prompt:
+            system_prompt = ""
+
         messages = [{"role": "system", "content": system_prompt}]
         messages.append({"role": "user", "content": user_content})
 
         payload = {
-            "model": model,
+            "model": model_id,
             "messages": messages,
             "frequency_penalty": frequency_penalty,
+            "logprobs": False,  # unused, not supported by all models
+            "top_logprobs": 0,  # x >= 0
+            "max_completion_tokens": max_completion_tokens,
+            "max_temp": max_temp,
+            "min_p": min_p,
+            "min_temp": min_temp,
+            "n": 1,  # basically batch size
             "presence_penalty": presence_penalty,
-            "temperature": temperature,
-            "top_p": top_p,
+            "repetition_penalty": repetition_penalty,
+            "seed": seed_value,
+            "stream": False,
+            "temperature": temperature_value,
+            "top_k": top_k,
+            "top_p": top_p_value,
+            "parallel_tool_calls": True,
         }
+        if reasoning_enabled:
+            payload["reasoning"] = {"mode": reasoning_effort_value}
+            payload["reasoning_effort"] = reasoning_effort_value
+        if normalized_stop_tokens:
+            payload["stop"] = normalized_stop_tokens
+        if venice_parameters:
+            payload["venice_parameters"] = venice_parameters
 
-        headers = {"Authorization": f"Bearer {os.getenv('VENICEAI_API_KEY')}", "Content-Type": "application/json"}
-        response = requests.post(url, json=payload, headers=headers)
+        json_response = client.post_json(API_ENDPOINTS["text_generate"], payload)
+        try:
+            choice = json_response["choices"][0]
+            logger.info(
+                "Venice LLM finish_reason=%s native_finish_reason=%s stop_reason=%s",
+                choice.get("finish_reason"),
+                choice.get("native_finish_reason"),
+                choice.get("stop_reason"),
+            )
+            content = choice["message"]["content"]
+        except (KeyError, IndexError, TypeError) as exc:
+            raise ValueError(f"Unexpected API response format: {json_response}") from exc
+        if debug_append_response:
+            try:
+                raw_dump = json.dumps(json_response, indent=2)
+            except (TypeError, ValueError):
+                raw_dump = str(json_response)
+            content = f"{content}\n\n\n{raw_dump}"
 
-        if response.status_code != 200:
-            raise requests.exceptions.HTTPError(f"HTTP error: {response.status_code}, Response: {response.text}")
-
-        json_response = response.json()
-        content = json_response["choices"][0]["message"]["content"]
-        # print(content)
-        return (content,)
-
-
-NODE_CLASS_MAPPINGS = {
-    "GenerateText_VENICE": GenerateText,
-}
-
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "GenerateText_VENICE": "Generate Text (Venice)",
-}
+        return io.NodeOutput(content)

nodes/gen_text_venice_params_node.py

@@ -1,118 +0,0 @@
-class GenerateTextVeniceParameters:
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "character_slug": (
-                    "COMBO",
-                    {
-                        "default": "strawberry-the-cat",
-                        "tooltip": ("The character slug of a public Venice character."),
-                    },
-                ),
-                "enable_character": (
-                    "BOOLEAN",
-                    {
-                        "default": False,
-                        "tooltip": ("Enable or disable character parameter being passed on."),
-                    },
-                ),
-                "strip_thinking_response": (
-                    "BOOLEAN",
-                    {
-                        "default": False,
-                        "tooltip": (
-                            "Strip <think></think> blocks from the response. "
-                            "Applicable only to reasoning / thinking models. "
-                            "Also available to use as a model feature suffix. "
-                            "Defaults to false."
-                        ),
-                    },
-                ),
-                "disable_thinking": (
-                    "BOOLEAN",
-                    {
-                        "default": False,
-                        "tooltip": (
-                            "On supported reasoning models, will disable thinking and "
-                            "strip the <think></think> blocks from the response. "
-                            "Defaults to false."
-                        ),
-                    },
-                ),
-                "web_search": (
-                    ["auto", "on", "off"],
-                    {
-                        "default": "auto",
-                        "tooltip": (
-                            "Auto will enable it based on the model's discretion. "
-                            "On will force web search on the request. "
-                            "Citations will be returned either in the first chunk of a "
-                            "streaming result, or in the non streaming response."
-                            "Defaults to off. "
-                        ),
-                    },
-                ),
-                "enable_web_citations": (
-                    "BOOLEAN",
-                    {
-                        "default": False,
-                        "tooltip": (
-                            "When web search is enabled, this will request that the LLM cite "
-                            "its sources using a [REF]0[/REF] format. "
-                            "Defaults to false."
-                        ),
-                    },
-                ),  # NOTE: include_search_results_in_stream not implemented
-                "use_venice_system_prompt": (
-                    "BOOLEAN",
-                    {
-                        "default": False,
-                        "tooltip": (
-                            "Whether to include the Venice supplied system prompts "
-                            "alongside specified system prompts. "
-                            "Defaults to true."
-                        ),
-                    },
-                ),
-            },
-        }
-
-    RETURN_TYPES = ("STRING",)
-    RETURN_NAMES = ("venice_parameters",)
-    FUNCTION = "pass_on_params"
-    CATEGORY = "venice.ai"
-    DESCRIPTION = (
-        "Passes on parameters unique parameters to Venice's API implementation for Venice text generation nodes."
-    )
-
-    def pass_on_params(
-        self,
-        character_slug,
-        enable_character,
-        strip_thinking_response,
-        disable_thinking,
-        web_search,
-        enable_web_citations,
-        use_venice_system_prompt,
-    ):
-        venice_params = {
-            "strip_thinking_response": strip_thinking_response,
-            "disable_thinking": disable_thinking,
-            "enable_web_search": web_search,
-            "enable_web_citations": enable_web_citations,
-            "include_venice_system_prompt": use_venice_system_prompt,
-        }
-        if enable_character:
-            venice_params["character_slug"] = character_slug
-
-        return (venice_params,)
-
-
-NODE_CLASS_MAPPINGS = {
-    "GenerateTextVeniceParameters_VENICE": GenerateTextVeniceParameters,
-}
-
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "GenerateTextVeniceParameters_VENICE": "Textgen Parameters (Venice)",
-}

nodes/gen_video_from_text_node.py

@@ -0,0 +1,241 @@
+import logging
+from typing import Any, Dict, Iterable
+
+from comfy_api.latest import InputImpl, io
+
+from ..nodes.catalog_utils import video_model_specs
+from ..nodes.utils import encode_tensor_for_vision, ensure_prompt_length
+from ..nodes.video_utils import poll_video_until_ready, queue_video_job
+
+LOG = logging.getLogger(__name__)
+
+
+class GenerateVideoFromText(io.ComfyNode):
+
+    @staticmethod
+    def _option_input_id(model_id: str, field: str) -> str:
+        sanitized = "".join(ch if ch.isalnum() or ch in {"_", "-"} else "_" for ch in model_id)
+        return f"{sanitized}__{field}"
+
+    @staticmethod
+    def _constraint_values(value: Iterable | None) -> list[str]:
+        if not value:
+            return []
+        if isinstance(value, (str, bytes)):
+            normalized = str(value).strip()
+            return [normalized] if normalized else []
+        return [str(item) for item in value if item]
+
+    @classmethod
+    def _video_specs(cls) -> Dict[str, Dict[str, Any]]:
+        specs = video_model_specs() or {}
+        if not specs:
+            raise ValueError(
+                "No Venice video model specs available; refresh the catalog in VeniceAI settings and retry."
+            )
+        return specs
+
+    @classmethod
+    def _get_option_value(cls, model_payload: Dict[str, Any], model_id: str, field: str) -> Any:
+        candidates = (
+            cls._option_input_id(model_id, field),
+            field,
+            f"{field}__{model_id}",
+            f"{model_id}__{field}",
+        )
+        for key in candidates:
+            if key in model_payload:
+                return model_payload.get(key)
+        return None
+
+    @classmethod
+    def _build_model_options(cls) -> list[io.DynamicCombo.Option]:
+        specs = cls._video_specs()
+        options: list[io.DynamicCombo.Option] = []
+
+        def _sorted_models_by_group(group: str) -> list[tuple[str, Dict[str, Any]]]:
+            return sorted(
+                (
+                    (model_id, spec)
+                    for model_id, spec in specs.items()
+                    if spec.get("constraints", {}).get("model_type") == group
+                ),
+                key=lambda item: item[0],
+            )
+
+        ordered_specs = [
+            # didnt know about this, is same as [] + []
+            *_sorted_models_by_group("text-to-video"),
+            *_sorted_models_by_group("image-to-video"),
+        ]
+
+        for model_id, spec in ordered_specs:
+            constraints = spec.get("constraints") or {}
+            aspect_ratios = cls._constraint_values(constraints.get("aspect_ratios"))
+            resolutions = cls._constraint_values(constraints.get("resolutions"))
+            durations = cls._constraint_values(constraints.get("durations"))
+            audio_default = bool(constraints.get("audio")) if constraints.get("audio") is not None else False
+
+            option_inputs: list[io.Input] = []
+            if constraints.get("model_type") == "image-to-video":
+                option_inputs.append(
+                    io.Image.Input(
+                        cls._option_input_id(model_id, "image"),
+                        display_name="image",
+                        tooltip="Source image for image-to-video models",
+                    )
+                )
+
+            if durations:
+                option_inputs.append(
+                    io.Combo.Input(
+                        id=cls._option_input_id(model_id, "duration"),
+                        display_name="duration",
+                        options=durations,
+                        default=durations[0],
+                        tooltip="Duration allowed by the selected model",
+                    )
+                )
+            if aspect_ratios:
+                option_inputs.append(
+                    io.Combo.Input(
+                        cls._option_input_id(model_id, "aspect_ratio"),
+                        display_name="aspect_ratio",
+                        options=aspect_ratios,
+                        default=aspect_ratios[0],
+                        tooltip="Aspect ratios allowed by the selected model",
+                    )
+                )
+            if resolutions:
+                option_inputs.append(
+                    io.Combo.Input(
+                        cls._option_input_id(model_id, "resolution"),
+                        display_name="resolution",
+                        options=resolutions,
+                        default=resolutions[0],
+                        tooltip="Resolutions allowed by the selected model",
+                    )
+                )
+
+            if constraints.get("audio_configurable"):
+                option_inputs.append(
+                    io.Boolean.Input(
+                        cls._option_input_id(model_id, "audio"),
+                        display_name="audio",
+                        default=audio_default,
+                        tooltip="Generate audio (only when the model allows toggling)",
+                    )
+                )
+
+            options.append(io.DynamicCombo.Option(model_id, option_inputs))
+
+        return options
+
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        model_options = cls._build_model_options()
+
+        return io.Schema(
+            node_id="TextToVideo_VENICE",
+            display_name="Generate Video from Text (Venice)",
+            category="venice.ai",
+            inputs=[
+                io.DynamicCombo.Input(
+                    "model",
+                    options=model_options,
+                    tooltip="Select a Venice video model to auto-populate valid parameters",
+                ),
+                io.String.Input(
+                    "prompt",
+                    default="A cat made of lettuce flying through space",
+                    placeholder="Positive Prompt. Example: A cat made of lettuce flying through space",
+                    tooltip="Text prompt to generate the video from",
+                    multiline=True,
+                ),
+                io.String.Input(
+                    "negative_prompt",
+                    default="low resolution, error, worst quality, low quality, defects",
+                    placeholder="Negative Prompt",
+                    tooltip="Negative prompt to avoid elements in the video",
+                    multiline=True,
+                ),
+            ],
+            outputs=[
+                io.Video.Output(id="video", display_name="Video"),
+            ],
+        )
+
+    @classmethod
+    def execute(
+        cls,
+        model,
+        prompt,
+        negative_prompt,
+    ) -> io.NodeOutput:
+        ensure_prompt_length(prompt, 2500, label="Prompt")
+        ensure_prompt_length(negative_prompt, 2500, label="Negative Prompt", allow_empty=True)
+
+        if not isinstance(model, dict) or "model" not in model:
+            raise ValueError("Model selection is required")
+
+        model_id = model.get("model")
+        specs = cls._video_specs()
+        spec = specs.get(model_id)
+        if not spec:
+            raise ValueError("Selected model is missing from the Venice catalog; refresh the catalog and try again.")
+        constraints = spec.get("constraints") or {}
+
+        durations = cls._constraint_values(constraints.get("durations"))
+        aspect_ratios = cls._constraint_values(constraints.get("aspect_ratios"))
+        resolutions = cls._constraint_values(constraints.get("resolutions"))
+
+        duration = cls._get_option_value(model, model_id, "duration")
+        if durations:
+            if duration is None:
+                raise ValueError(f"Model {model_id} requires a duration selection")
+            if duration not in durations:
+                raise ValueError(f"Duration '{duration}' is not supported by model {model_id}")
+
+        aspect_ratio = cls._get_option_value(model, model_id, "aspect_ratio")
+        if aspect_ratios:
+            if aspect_ratio is None:
+                raise ValueError(f"Model {model_id} requires an aspect ratio selection")
+            if aspect_ratio not in aspect_ratios:
+                raise ValueError(f"Aspect ratio '{aspect_ratio}' is not supported by model {model_id}")
+
+        resolution = cls._get_option_value(model, model_id, "resolution")
+        if resolutions:
+            if resolution is None:
+                raise ValueError(f"Model {model_id} requires a resolution selection")
+            if resolution not in resolutions:
+                raise ValueError(f"Resolution '{resolution}' is not supported by model {model_id}")
+
+        audio_configurable = bool(constraints.get("audio_configurable"))
+        audio_default = bool(constraints.get("audio")) if constraints.get("audio") is not None else False
+        audio_value = cls._get_option_value(model, model_id, "audio") if audio_configurable else None
+        audio = audio_value if audio_value is not None else audio_default
+
+        payload = {
+            "model": model_id,
+            "prompt": prompt,
+            "negative_prompt": negative_prompt,
+        }
+        if durations:
+            payload["duration"] = duration
+        if aspect_ratios:
+            payload["aspect_ratio"] = aspect_ratio
+        if resolutions:
+            payload["resolution"] = resolution
+        if audio is not None:
+            payload["audio"] = audio
+
+        if constraints.get("model_type") == "image-to-video":
+            image = cls._get_option_value(model, model_id, "image")
+            if image is None:
+                raise ValueError(f"Model {model_id} requires an input image")
+            payload["image_url"] = encode_tensor_for_vision(image)
+
+        model_id_resp, queue_id = queue_video_job(payload)
+        video_path, _ = poll_video_until_ready(model=model_id_resp, queue_id=queue_id)
+
+        return io.NodeOutput(InputImpl.VideoFromFile(video_path))

nodes/i2i_enhance_upscale.py

@@ -0,0 +1,155 @@
+import base64
+import io as python_io
+import logging
+
+import requests
+from PIL import Image
+from torchvision.transforms import ToPILImage, ToTensor  # type: ignore
+
+from comfy_api.latest import io
+
+from ..globals import API_ENDPOINTS
+from ..nodes.utils import ensure_prompt_length
+from ..venice_client import client
+
+LOG = logging.getLogger(__name__)
+
+
+class I2IEnhanceUpscale(io.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id="I2IEnhanceUpscale_VENICE",
+            display_name="Img2Img Enhance + Upscale (Venice)",
+            category="venice.ai",
+            inputs=[
+                io.Image.Input(
+                    "image",
+                    tooltip="Image tensor to enhance or upscale",
+                ),
+                io.Float.Input(
+                    "scale",
+                    default=2.0,
+                    min=1.0,
+                    max=4.0,
+                    step=0.01,
+                    tooltip=(
+                        "Scale factor for upscaling the image. Valid values are 1, 2, 3, or 4.\n"
+                        "If set to 1, the image will not be upscaled but enhanced, 'enhance' must be set to 'True'."
+                    ),
+                ),
+                io.Boolean.Input(
+                    "enhance",
+                    default=False,
+                    tooltip=(
+                        "Whether to enhance the image using Venice's image engine during upscaling.\n"
+                        "Must be set to 'True' if scale is set to 1."
+                    ),
+                ),
+                io.Float.Input(
+                    "enhance_creativity",
+                    default=0.5,
+                    min=0.0,
+                    max=1.0,
+                    step=0.01,
+                    tooltip=(
+                        "Higher values let the enhancement AI change the image more. "
+                        "Setting this to 1 effectively creates an entirely new image."
+                    ),
+                ),
+                io.String.Input(
+                    "enhance_prompt",
+                    default="",
+                    multiline=True,
+                    placeholder="Prompt for enhance. Example: gold, graffiti, minimalistic",
+                    tooltip=(
+                        "The text to image style to apply during prompt enhancement. "
+                        "Does best with short descriptive prompts, like gold, marble or angry, menacing."
+                    ),
+                ),
+                io.Float.Input(
+                    "replication",
+                    default=0.1,
+                    min=0.0,
+                    max=1.0,
+                    step=0.01,
+                    tooltip=(
+                        "How strongly lines and noise in the base image are preserved. "
+                        "Higher values are noisier but less plastic/AI 'generated'/hallucinated"
+                    ),
+                ),
+            ],
+            outputs=[io.Image.Output(id="image", display_name="Image")],
+        )
+
+    @classmethod
+    def execute(
+        cls,
+        image,
+        scale,
+        enhance,
+        enhance_creativity,
+        enhance_prompt,
+        replication,
+    ) -> io.NodeOutput:
+        ensure_prompt_length(enhance_prompt, 1500, "Enhance prompt", allow_empty=True)
+        if scale == 1 and not enhance:
+            raise ValueError("Upscale Image (Venice) 'enhance' must be set to 'True' if scale is 1.")
+        if scale == 4:
+            LOG.info(
+                (
+                    "Upscale Image (Venice) A scale of 4 with large images will result "
+                    "in the scale being dynamically set (by venice) to ensure the "
+                    "final image stays within the maximum size limits."
+                )
+            )
+
+        # Convert tensor to PIL Image
+        try:
+            # Get first image from batch
+            img_tensor = image[0].detach().cpu()  # Shape: (H, W, C)
+
+            # Ensure RGB format by taking first 3 channels
+            if img_tensor.shape[-1] > 3:
+                img_tensor = img_tensor[:, :, :3]
+
+            # Convert to CHW format and create PIL Image
+            pil_image = ToPILImage()(img_tensor.permute(2, 0, 1))
+        except Exception as exc:
+            raise ValueError(f"Upscale Image (Venice) Failed to convert tensor to PIL image: {str(exc)}")
+
+        # Convert image to base64
+        byte_io = python_io.BytesIO()
+        pil_image.save(byte_io, format="PNG")
+        byte_io.seek(0)
+        image_base64 = base64.b64encode(byte_io.read()).decode("utf-8")
+
+        payload = {
+            "image": image_base64,
+            "scale": scale,
+            "enhance": enhance,
+            "enhanceCreativity": enhance_creativity,
+            "enhancePrompt": enhance_prompt,
+            "replication": replication,
+        }
+
+        response = None
+        try:
+            response = client.request(
+                "POST",
+                API_ENDPOINTS["upscale_image"],
+                json=payload,
+                headers={"Content-Type": "application/json"},
+            )
+        except requests.exceptions.RequestException as exc:
+            raise RuntimeError(f"Upscale Image (Venice) API request failed: {str(exc)}")
+
+        try:
+            upscaled_image = Image.open(python_io.BytesIO(response.content))
+            tensor = ToTensor()(upscaled_image)  # Converts to (C, H, W)
+            tensor = tensor.permute(1, 2, 0)  # Convert to (H, W, C)
+            tensor = tensor.unsqueeze(0)  # Add batch dimension (1, H, W, C)
+        except Exception as exc:
+            raise ValueError(f"Upscale Image (Venice) Failed to process response image: {str(exc)}")
+
+        return io.NodeOutput(tensor)

nodes/i2i_enhance_upscale_node.py

@@ -1,164 +0,0 @@
-import base64
-import io
-import logging
-import os
-
-import requests
-from PIL import Image
-from torchvision.transforms import ToPILImage, ToTensor # type: ignore
-
-from ..globals import API_ENDPOINTS, VENICEAI_BASE_URL
-
-
-class I2IEnhanceUpscale:
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "image": ("IMAGE",),
-                "scale": (
-                    "FLOAT",
-                    {
-                        "default": 2,
-                        "min": 1,
-                        "max": 4,
-                        "step": 0.01,
-                        "tooltip": (
-                            "Scale factor for upscaling the image. Valid values are 1, 2, 3, or 4.\n"
-                            "If set to 1, the image will not be upscaled but enhanced, 'enhanced setting must be set to 'True'."
-                        ),
-                    },
-                ),
-                "enhance": (
-                    "BOOLEAN",
-                    {
-                        "default": False,
-                        "tooltip": (
-                            "Whether to enhance the image using Venice's image engine during upscaling.\n"
-                            "Must be set to 'True' if scale is set to 1."
-                        ),
-                    },
-                ),
-                "enhance_creativity": (
-                    "FLOAT",
-                    {
-                        "default": 0.5,
-                        "min": 0.0,
-                        "max": 1.0,
-                        "step": 0.01,
-                        "tooltip": (
-                            "Higher values let the enhancement AI change the image more. "
-                            "Setting this to 1 effectively creates an entirely new image."
-                        ),
-                    },
-                ),
-                "enhance_prompt": (
-                    "STRING",
-                    {
-                        "default": "",
-                        "placeholder": "gold, graffiti, minimalistic",
-                        "multiline": True,
-                        "tooltip": (
-                            "The text to image style to apply during prompt enhancement. "
-                            "Does best with short descriptive prompts, like gold, marble or angry, menacing."
-                        ),
-                    },
-                ),
-                "replication": (
-                    "FLOAT",
-                    {
-                        "default": 0.1,
-                        "min": 0.0,
-                        "max": 1.0,
-                        "step": 0.01,
-                        "tooltip": (
-                            "How strongly lines and noise in the base image are preserved. "
-                            "Higher values are noisier but less plastic/AI 'generated'/hallucinated"
-                        ),
-                    },
-                ),
-            }
-        }
-
-    RETURN_TYPES = ("IMAGE",)
-    RETURN_NAMES = ("image",)
-    FUNCTION = "i2i_enhance_upscale"
-    CATEGORY = "venice.ai"
-
-    def i2i_enhance_upscale(self, image, scale, enhance, enhance_creativity, enhance_prompt, replication):
-        url = VENICEAI_BASE_URL + API_ENDPOINTS["upscale_image"]
-
-        if len(enhance_prompt) > 1500:
-            raise ValueError("Upscale Image (Venice) enhance_prompt cannot be above 1500 characters")
-        if scale == 1:
-            raise ValueError("Upscale Image (Venice) 'enhance' must be set to 'True' if scale is 1.")
-        if scale == 4:
-            logging.info(
-                (
-                    "Upscale Image (Venice) A scale of 4 with large images will result "
-                    "in the scale being dynamically set (by venice) to ensure the "
-                    "final image stays within the maximum size limits."
-                )
-            )
-
-        # if not api_key:
-        #     raise ValueError("VENICEAI_API_KEY environment variable not set")
-
-        # Convert tensor to PIL Image
-        try:
-            # Get first image from batch
-            img_tensor = image[0].detach().cpu()  # Shape: (H, W, C)
-
-            # Ensure RGB format by taking first 3 channels
-            if img_tensor.shape[-1] > 3:
-                img_tensor = img_tensor[:, :, :3]
-
-            # Convert to CHW format and create PIL Image
-            pil_image = ToPILImage()(img_tensor.permute(2, 0, 1))
-        except Exception as e:
-            raise ValueError(f"Upscale Image (Venice) Failed to convert tensor to PIL image: {str(e)}")
-
-        # Convert image to base64
-        byte_io = io.BytesIO()
-        pil_image.save(byte_io, format="PNG")
-        byte_io.seek(0)
-        image_base64 = base64.b64encode(byte_io.read()).decode("utf-8")
-
-        # Prepare JSON payload
-        payload = {
-            "image": image_base64,
-            "scale": scale,
-            "enhance": enhance,
-            "enhanceCreativity": enhance_creativity,
-            "enhancePrompt": enhance_prompt,
-            "replication": replication,
-        }
-
-        headers = {"Authorization": f"Bearer {os.getenv('VENICEAI_API_KEY')}", "Content-Type": "application/json"}
-
-        # Send request
-        try:
-            response = requests.post(url, json=payload, headers=headers)
-            response.raise_for_status()
-        except requests.exceptions.RequestException as e:
-            raise RuntimeError(f"Upscale Image (Venice) API request failed: {str(e)}")
-
-        # Convert response to tensor
-        try:
-            upscaled_image = Image.open(io.BytesIO(response.content))
-            tensor = ToTensor()(upscaled_image)  # Converts to (C, H, W)
-            tensor = tensor.permute(1, 2, 0)  # Convert to (H, W, C)
-            tensor = tensor.unsqueeze(0)  # Add batch dimension (1, H, W, C)
-        except Exception as e:
-            raise ValueError(f"Upscale Image (Venice) Failed to process response image: {str(e)}")
-
-        return (tensor,)
-
-
-NODE_CLASS_MAPPINGS = {
-    "I2IEnhanceUpscale_VENICE": I2IEnhanceUpscale,
-}
-
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "I2IEnhanceUpscale_VENICE": "Img2Img Enhance + Upscale (Venice)",
-}

nodes/things_n_stuff_node.py

@@ -1,185 +0,0 @@
-# Todo: chat history/memory/context for LLM
-
-# todo: inpainting
-
-# todo: LLM characters
-
-# todo: use variants api (currently in beta)
-
-
-# import base64
-# import io
-# import os
-
-# import numpy as np
-# import requests
-# import torch
-# from PIL import Image
-
-# from ..globals import API_ENDPOINTS, VENICEAI_BASE_URL
-# from ..nodes.gen_image_base import GenerateImageBase
-
-# todo: https://docs.comfy.org/custom-nodes/backend/more_on_inputs#dynamically-created-inputs
-# for model list update?
-
-
-# region inpaint
-# class InpaintImage(GenerateImageBase):
-#     @classmethod
-#     def INPUT_TYPES(cls):
-#         return {
-#             "required": {
-#                 "image": ("IMAGE",),
-#                 "model": (
-#                     "COMBO",
-#                     {"default": "flux-dev"},
-#                 ),
-#                 "prompt": ("STRING", {"default": "A flying cat made of lettuce", "multiline": True}),
-#                 "neg_prompt": (
-#                     "STRING",
-#                     {
-#                         "placeholder": "Bad composition, rating_explicit, Text, signature, lowres, lowres, low details, faded image, out of focus, cropped, clipped, cut-off, out of frame, deserted scene, empty scene, vacant scene, desolate scene, sparse décor, bad quality, worst quality,",
-#                         "multiline": True,
-#                         "tooltip": "Negative prompt. This is ignored when using flux-dev or flux-dev-uncensored",
-#                     },
-#                 ),
-#                 "width": (
-#                     "INT",
-#                     {
-#                         "default": 1024,
-#                         "min": 256,
-#                         "max": 2048,  # limit is 1280 but i dont want to restrict this in case of future updates, https://docs.venice.ai/api-reference/endpoint/image/generate#body-height
-#                         "step": 32,
-#                         "tooltip": "Must be a multiple of 32. Maximum allowed by venice.ai is 1280",
-#                     },
-#                 ),
-#                 "height": (
-#                     "INT",
-#                     {
-#                         "default": 1024,
-#                         "min": 256,
-#                         "max": 2048,
-#                         "step": 32,
-#                         "tooltip": "Must be a multiple of 32. Maximum allowed by venice.ai is 1280",
-#                     },
-#                 ),
-#                 "batch_size": ("INT", {"default": 1, "min:": 1, "max": 4}),
-#                 "steps": ("INT", {"default": 20, "min": 1, "max": 30}),
-#                 "guidance": ("FLOAT", {"default": 3.0, "min": 0.1, "max": 15.0}),
-#                 "style_preset": ("COMBO", {"default": "none"}),
-#                 "hide_watermark": ("BOOLEAN", {"default": True}),
-#                 "inpaint_strength": ("INT", {"default": 50, "min": 0, "max": 100}),
-#             },
-#             "optional": {"seed": ("INT", {"default": -1})},
-#         }
-
-#     def generate_image(
-#         self,
-#         image,
-#         model,
-#         prompt,
-#         neg_prompt,
-#         width,
-#         height,
-#         batch_size,
-#         steps,
-#         guidance,
-#         style_preset,
-#         hide_watermark,
-#         inpaint_strength,
-#         seed=-1,
-#     ):
-#         images_tensor = ()  # empty tuple for tensors
-
-#         if model in ["flux-dev", "flux-dev-uncensored"]:
-#             print(f"VeniceAPI INFO: Ignoring negative prompt for {model}.")
-#             neg_prompt = ""
-
-#         try:
-#             self.check_multiple_of_32(width, height)  # todo: make this be validate node instead
-
-#             # Convert input image tensor to base64
-#             if image is None or image.size(0) == 0:
-#                 raise ValueError("Input image is required for inpainting")
-
-#             # Process first image in the batch
-#             img_tensor = image[0].cpu()  # Convert to CPU tensor
-#             np_image = img_tensor.numpy()
-#             np_image = (np_image * 255).astype(np.uint8)  # Convert to 0-255 range
-
-#             # Create PIL Image and convert to base64
-#             pil_image = Image.fromarray(np_image, "RGB")
-#             buffered = io.BytesIO()
-#             pil_image.save(buffered, format="PNG")
-#             img_base64 = base64.b64encode(buffered.getvalue()).decode("utf-8")
-#             source_image_base64 = f"data:image/png;base64,{img_base64}"
-
-#             headers = {"Authorization": f"Bearer {os.getenv('VENICEAI_API_KEY')}", "Content-Type": "application/json"}
-#             url = VENICEAI_BASE_URL + API_ENDPOINTS["image_generate"]
-
-#             payload = {
-#                 "model": model,
-#                 "prompt": prompt,
-#                 "negative_prompt": neg_prompt,
-#                 "style_preset": style_preset,
-#                 "height": height,
-#                 "width": width,
-#                 "steps": steps,
-#                 "cfg_scale": guidance,
-#                 "seed": seed,
-#                 "return_binary": False,
-#                 "hide_watermark": hide_watermark,
-#                 "format": "png",
-#                 "inpaint": {
-#                     "strength": inpaint_strength,
-#                     "source_image_base64": source_image_base64,
-#                     "mask": {
-#                         "image_prompt": "Generate a high-resolution image...",
-#                         "object_target": "rabbit's face",
-#                         "inferred_object": "rabbit's face wearing round silver spectacles",
-#                     },
-#                 },
-#             }
-#             if style_preset == "none":
-#                 del payload["style_preset"]
-
-#             for i in range(batch_size):
-#                 payload["seed"] = seed + i
-#                 response = requests.request("POST", url, json=payload, headers=headers)
-
-#                 if response.status_code != 200:
-#                     raise requests.exceptions.HTTPError(
-#                         f"HTTP error: {response.status_code}, Response: {response.text}"
-#                     )
-
-#                 images_tensor += self.process_result(response.json())
-
-#             merged = torch.cat(images_tensor, dim=0)
-#             return (merged,)
-
-#         except Exception as e:
-#             raise Exception(f"Error processing image result: {str(e)}") from e
-
-
-# endregion
-
-
-# region text gen
-
-
-# endregion
-
-
-# region upscale img
-
-
-# endregion
-
-
-# NODE_CLASS_MAPPINGS = {
-#     "InpaintImage_VENICE": InpaintImage,
-# }
-
-# NODE_DISPLAY_NAME_MAPPINGS = {
-#     "InpaintImage_VENICE": "Inpaint Image (Venice)",
-# }

nodes/util_nodes.py

@@ -1,27 +0,0 @@
-class CharCountTextBox:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "input_text": ("STRING", {"default": "", "multiline": True}),
-            }
-        }
-
-    CATEGORY = "venice.ai"
-
-    RETURN_TYPES = ("STRING",)
-    RETURN_NAMES = ("text",)
-    FUNCTION = "return_same_text"
-
-    def return_same_text(self, input_text):
-
-        return {"ui": {"text": input_text}, "result": (input_text,)}
-
-
-NODE_CLASS_MAPPINGS = {
-    "CharCountTextBox": CharCountTextBox,
-}
-
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "CharCountTextBox": "Textbox w/ char count",
-}

nodes/utils.py

@@ -0,0 +1,136 @@
+from __future__ import annotations
+
+import base64
+import io
+
+import numpy as np
+import torch
+from PIL import Image
+from requests import RequestException
+
+from ..globals import API_ENDPOINTS
+from ..venice_client import VeniceAPIError, client
+from ..venice_config import config as venice_config
+
+
+def _round_down(value: int, multiple: int) -> int:
+    return (value // multiple) * multiple
+
+
+def ensure_prompt_length(text: str, maximum: int, label: str, *, allow_empty: bool = False) -> None:
+    if not text:
+        if allow_empty:
+            return
+        raise ValueError(f"{label} is required and cannot be empty")
+    if len(text) > maximum:
+        raise ValueError(f"{label} exceeds the maximum length of {maximum} characters")
+
+
+def encode_tensor_for_vision(image_tensor: torch.Tensor, *, max_encoded_bytes: int = 4_500_000) -> str:
+    """
+    Encode a vision tensor into a Base64-encoded PNG string suitable for transmission.
+
+    Parameters
+    ----------
+    image_tensor : torch.Tensor
+        A height×width×(3 or 4) tensor representing an image in the range [0, 1].
+    max_encoded_bytes : int, optional
+        Maximum allowed length of the Base64 payload. Defaults to 4,500,000 bytes.
+
+    Returns
+    -------
+    str
+        A data URI containing the PNG image encoded in Base64.
+
+    Raises
+    ------
+    ValueError
+        If the tensor does not have 3 dimensions or the last dimension is not 3 or 4.
+    """
+    tensor = image_tensor.detach().cpu()
+    if tensor.ndim == 4 and tensor.shape[0] == 1:
+        tensor = tensor[0]
+    if tensor.ndim == 3 and tensor.shape[-1] not in {3, 4} and tensor.shape[0] in {3, 4}:
+        tensor = tensor.permute(1, 2, 0)
+    if tensor.ndim != 3 or tensor.shape[-1] not in {3, 4}:
+        raise ValueError("Vision images must be height×width×(3 or 4 channels)")
+
+    if tensor.shape[-1] > 3:
+        tensor = tensor[:, :, :3]
+
+    array = (tensor.numpy() * 255).clip(0, 255).astype(np.uint8)
+    pil_image = Image.fromarray(array, "RGB")
+
+    original_width, original_height = pil_image.size
+    aspect_ratio = original_width / original_height
+
+    if original_width > original_height:
+        target_width = 1024
+        target_height = int(target_width / aspect_ratio)
+        if target_height < 256:
+            target_height = 256
+            target_width = int(target_height * aspect_ratio)
+    else:
+        target_height = 1024
+        target_width = int(target_height * aspect_ratio)
+        if target_width < 256:
+            target_width = 256
+            target_height = int(target_width / aspect_ratio)
+
+    target_width = _round_down(target_width, 14)
+    target_height = _round_down(target_height, 14)
+
+    if min(target_width, target_height) < 256:
+        if target_width < target_height:
+            target_width = _round_down(256 + 13, 14)
+            target_height = _round_down(int(target_width / aspect_ratio), 14)
+        else:
+            target_height = _round_down(256 + 13, 14)
+            target_width = _round_down(int(target_height * aspect_ratio), 14)
+
+    pil_image = pil_image.resize((target_width, target_height), Image.LANCZOS)
+
+    buffered = io.BytesIO()
+    pil_image.save(buffered, format="PNG")
+    img_base64 = base64.b64encode(buffered.getvalue()).decode("utf-8")
+
+    while len(img_base64) > max_encoded_bytes:
+        scaling_factor = (max_encoded_bytes / len(img_base64)) ** 0.5
+        new_width = max(_round_down(int(target_width * scaling_factor), 14), 256)
+        new_height = max(_round_down(int(target_height * scaling_factor), 14), 256)
+
+        pil_image = pil_image.resize((new_width, new_height), Image.LANCZOS)
+        target_width, target_height = new_width, new_height
+
+        buffered = io.BytesIO()
+        pil_image.save(buffered, format="PNG")
+        img_base64 = base64.b64encode(buffered.getvalue()).decode("utf-8")
+
+    return f"data:image/png;base64,{img_base64}"
+
+
+def ensure_multiple_of(width: int, height: int, *, multiple: int = 32) -> None:
+    bad_dimensions = []
+    if width % multiple != 0:
+        bad_dimensions.append(f"width ({width})")
+    if height % multiple != 0:
+        bad_dimensions.append(f"height ({height})")
+    if bad_dimensions:
+        dimensions = " and ".join(bad_dimensions)
+        raise ValueError(f"{dimensions} must be multiples of {multiple}")
+
+
+# unused right now, might be useful, or not
+def ensure_api_key_valid() -> None:
+    key = venice_config.apikey.strip()
+    if not key:
+        raise ValueError("VeniceAI API key is missing; set it in the VeniceAI settings first.")
+
+    try:
+        client.get_json(API_ENDPOINTS["list_api_keys"])
+    except VeniceAPIError as exc:
+        raise ValueError("Unable to validate the VeniceAI API key.", exc) from exc
+    except RequestException as exc:
+        raise ValueError("Unable to reach VeniceAI while validating the API key.", exc) from exc
+
+    return None

nodes/video_utils.py

@@ -0,0 +1,130 @@
+import logging
+import tempfile
+import time
+from typing import Optional, Tuple
+
+from comfy.utils import ProgressBar  # type: ignore
+
+from ..globals import API_ENDPOINTS
+from ..venice_client import VeniceAPIError, client
+
+LOG = logging.getLogger(__name__)
+
+# Defaults
+POLL_INTERVAL_SECONDS = 5
+MAX_POLLS = 100  # MAX_POLLS * POLL_INTERVAL_SECONDS = x minutes total wait time
+PROGRESS_BAR_TOTAL = 100
+
+
+def queue_video_job(payload: dict) -> Tuple[str, str]:
+    """
+    Enqueue a Venice video generation job and return (model, queue_id).
+    Raises VeniceAPIError on failure.
+    """
+    resp = client.post_json(API_ENDPOINTS["video_queue"], payload)
+    model = resp.get("model") or payload.get("model") or ""
+    queue_id = resp.get("queue_id")
+    if not queue_id:
+        raise VeniceAPIError(f"Video queue response missing queue_id: {resp}")
+    LOG.info("Venice video queued: model=%s queue_id=%s", model, queue_id)
+    return model, queue_id
+
+
+def _guess_suffix(content_type: str) -> str:
+    if "mp4" in content_type:
+        return ".mp4"
+    if "webm" in content_type:
+        return ".webm"
+    if "quicktime" in content_type or "mov" in content_type:
+        return ".mov"
+    return ".bin"
+
+
+def poll_video_until_ready(
+    *,
+    model: str,
+    queue_id: str,
+    progress_bar: Optional[ProgressBar] = None,
+    delete_on_completion: bool = True,
+    max_polls: int = MAX_POLLS,
+    poll_interval: float = POLL_INTERVAL_SECONDS,
+) -> Tuple[str, str]:
+    """
+    Poll Venice /video/retrieve until the video is ready.
+    Returns (video_filepath, queue_id). Raises VeniceAPIError on failure/timeout.
+    """
+    pbar = progress_bar or ProgressBar(PROGRESS_BAR_TOTAL)
+    last_progress = 0
+
+    for attempt in range(max_polls):
+        time.sleep(poll_interval)
+
+        retrieve_payload = {
+            "model": model,
+            "queue_id": queue_id,
+            "delete_media_on_completion": delete_on_completion,
+        }
+
+        resp = client.request(
+            "POST",
+            API_ENDPOINTS["video_retrieve"],
+            json=retrieve_payload,
+            headers={
+                "Content-Type": "application/json",
+                "Accept": "*/*",  # allow binary video or JSON status
+            },
+        )
+        ctype = (resp.headers or {}).get("Content-Type", "").lower()
+
+        is_status_json = ctype.startswith("application/json") or ctype.startswith("text/")
+        if is_status_json:
+            data = resp.json()
+            status = (data.get("status") or "UNKNOWN").upper()
+            exec_dur = float(data.get("execution_duration") or 0)
+            avg_exec = float(data.get("average_execution_time") or 0)
+
+            LOG.debug(
+                "Venice video status: status=%s queue_id=%s exec_ms=%s avg_ms=%s attempt=%s/%s",
+                status,
+                queue_id,
+                exec_dur,
+                avg_exec,
+                attempt + 1,
+                max_polls,
+            )
+
+            if avg_exec and avg_exec > 0:
+                progress_value = min(int(exec_dur / avg_exec * PROGRESS_BAR_TOTAL), PROGRESS_BAR_TOTAL)
+            else:
+                progress_value = min(last_progress + 1, PROGRESS_BAR_TOTAL)
+
+            delta = progress_value - last_progress
+            if delta > 0:
+                pbar.update(delta)
+                last_progress = progress_value
+
+            if status not in {"PROCESSING", "QUEUED"}:
+                raise VeniceAPIError(f"Video job reported unexpected status '{status}' for queue_id {queue_id}")
+            continue
+
+        # Got binary content (video)
+        suffix = _guess_suffix(ctype)
+        with tempfile.NamedTemporaryFile(delete=False, suffix=suffix) as tmp_file:
+            tmp_file.write(resp.content)
+            video_path = tmp_file.name
+
+        LOG.debug(
+            "Venice video ready: queue_id=%s saved_to=%s content_type=%s",
+            queue_id,
+            video_path,
+            ctype,
+        )
+
+        if last_progress < PROGRESS_BAR_TOTAL:
+            pbar.update(PROGRESS_BAR_TOTAL - last_progress)
+
+        return str(video_path), queue_id
+
+    raise VeniceAPIError(
+        f"Timed out waiting for Venice video. queue_id={queue_id} after {max_polls * poll_interval:.0f}s"
+    )

pyserver/__init__.py

@@ -0,0 +1 @@
+from . import routes  # noqa: F401

pyserver/get_key_from_jssetting.py

@@ -1,47 +0,0 @@
-import json
-import os
-from pathlib import Path
-
-from aiohttp import web
-from server import PromptServer
-
-routes = PromptServer.instance.routes
-
-CONFIG_FILE = Path(__file__).parent.parent / "veniceai_config.json"
-
-
-def ensure_config_file_exists():
-    # Create the config file with a default value if it doesn't exist
-    if not CONFIG_FILE.exists():
-        default_key = "your_venice_api_key_here"  # Set a default value (e.g., empty string)
-        set_venice_key(default_key)
-
-
-def set_venice_key(apikey):
-    os.environ["VENICEAI_API_KEY"] = apikey
-
-    with open(CONFIG_FILE, "w") as f:
-        json.dump({"apikey": apikey}, f)
-
-
-@routes.post("/veniceai/save_apikey")
-async def post_key_server(request):
-    data = await request.json()
-
-    set_venice_key(data.get("apikey", ""))
-
-    return web.json_response({})
-
-
-@routes.get("/veniceai/get_apikey")
-async def get_key_server(request):
-    return web.json_response({"apikey": os.getenv("VENICEAI_API_KEY")})
-
-
-ensure_config_file_exists()
-
-
-if CONFIG_FILE.exists():
-    with open(CONFIG_FILE) as f:
-        saved_key = json.load(f).get("apikey", "")
-        set_venice_key(saved_key)

pyserver/routes.py

@@ -0,0 +1,89 @@
+import asyncio
+
+from aiohttp import web
+
+from server import PromptServer  # type: ignore
+
+from ..venice_catalog import (
+    get_characters,
+    get_models,
+    get_styles,
+    refresh_characters,
+    refresh_models,
+    refresh_styles,
+)
+from ..venice_config import config as venice_config
+
+routes = PromptServer.instance.routes
+
+
+def _error_response(message: str) -> web.Response:
+    return web.json_response({"message": message, "error": True})
+
+
+@routes.post("/veniceai/save_apikey")
+async def save_key_server(request: web.Request) -> web.Response:
+    payload = await request.json()
+    venice_config.save_apikey(payload.get("apikey", ""))
+    return web.json_response({"message": "API key saved", "error": False})
+
+
+@routes.get("/veniceai/get_apikey")
+async def get_key_server(_: web.Request) -> web.Response:
+    return web.json_response({"apikey": venice_config.apikey})
+
+
+# @routes.get("/veniceai/update_models_list")
+# async def update_models_list_server(request: web.Request) -> web.Response:
+#     model_type = request.rel_url.query.get("type")
+#     try:
+#         payload = await asyncio.to_thread(refresh_models, model_type)
+#     except Exception as exc:
+#         return _error_response(str(exc))
+#     return web.json_response({"message": "Model list updated", "error": False, "data": payload})
+
+
+# @routes.get("/veniceai/get_models_list")
+# async def get_models_list_server(request: web.Request) -> web.Response:
+#     model_type = request.rel_url.query.get("type")
+#     try:
+#         payload = await asyncio.to_thread(get_models, model_type)
+#     except Exception as exc:
+#         return _error_response(str(exc))
+#     return web.json_response(payload)
+
+
+# @routes.get("/veniceai/update_styles_list")
+# async def update_styles_list_server(_: web.Request) -> web.Response:
+#     try:
+#         payload = await asyncio.to_thread(refresh_styles)
+#     except Exception as exc:
+#         return _error_response(str(exc))
+#     return web.json_response({"message": "Styles updated", "error": False, "data": payload})
+
+
+# @routes.get("/veniceai/get_styles_list")
+# async def get_styles_list_server(_: web.Request) -> web.Response:
+#     try:
+#         payload = await asyncio.to_thread(get_styles)
+#     except Exception as exc:
+#         return _error_response(str(exc))
+#     return web.json_response(payload)
+
+
+# @routes.get("/veniceai/update_characters_list")
+# async def update_characters_list_server(_: web.Request) -> web.Response:
+#     try:
+#         payload = await asyncio.to_thread(refresh_characters)
+#     except Exception as exc:
+#         return _error_response(str(exc))
+#     return web.json_response({"message": "Characters updated", "error": False, "data": payload})
+
+
+# @routes.get("/veniceai/get_characters_list")
+# async def get_characters_list_server(_: web.Request) -> web.Response:
+#     try:
+#         payload = await asyncio.to_thread(get_characters)
+#     except Exception as exc:
+#         return _error_response(str(exc))
+#     return web.json_response(payload)

pyserver/update_characters.py

@@ -1,56 +0,0 @@
-import json
-import os
-from pathlib import Path
-
-import requests
-from aiohttp import web
-
-from server import PromptServer  # type: ignore
-
-from ..globals import API_ENDPOINTS, VENICEAI_BASE_URL
-
-routes = PromptServer.instance.routes
-
-script_dir = Path(__file__).resolve().parent
-data_dir = script_dir.parent / "data"
-data_dir.mkdir(exist_ok=True)
-characters_list_path = data_dir / "characters_list.json"
-
-
-@routes.get("/veniceai/update_characters_list")
-async def update_characters_list_server(request):
-    try:
-        headers = {"Authorization": f"Bearer {os.getenv('VENICEAI_API_KEY')}"}
-        url = f"{VENICEAI_BASE_URL}{API_ENDPOINTS['characters']}"
-
-        response = requests.get(url, headers=headers)
-        response.raise_for_status()  # Raises HTTPError for bad responses
-
-        response_data = response.json()
-
-        # remove "object" key from response_data
-        response_data.pop("object", None)
-
-        response_data["data"] = sorted(response_data.get("data", []), key=lambda item: item.get("slug", ""))
-
-        with open(characters_list_path, "w") as f:
-            json.dump(response_data, f, indent=4)
-
-    except Exception as e:
-        return (f"Unexpected error: {e}", True)
-
-    response = (response_data, False)
-    return web.json_response({"message": response[0], "error": response[1]})
-
-
-@routes.get("/veniceai/get_characters_list")
-async def get_local_characters_list(request):
-    with open(characters_list_path, "r") as f:
-        characters_list_json = json.load(f)
-
-    characters = []
-    for item in characters_list_json.get("data", []):
-        if isinstance(item, dict) and item.get("slug"):
-            characters.append(item.get("slug"))
-
-    return web.json_response({"characters": characters})

pyserver/update_models.py

@@ -1,155 +0,0 @@
-import json
-import os
-import re
-from pathlib import Path
-
-import requests
-from aiohttp import web
-
-from server import PromptServer  # type: ignore
-
-from ..globals import API_ENDPOINTS, VENICEAI_BASE_URL
-
-routes = PromptServer.instance.routes
-
-script_dir = Path(__file__).resolve().parent
-data_dir = script_dir.parent / "data"
-data_dir.mkdir(exist_ok=True)
-all_model_list_path = data_dir / "all_model_list.json"
-
-
-async def fetch_models_list():
-    try:
-        headers = {"Authorization": f"Bearer {os.getenv('VENICEAI_API_KEY')}"}
-        url = f"{VENICEAI_BASE_URL}{API_ENDPOINTS['list_models']}"
-        params = {"type": "all"}
-
-        response = requests.get(url, headers=headers, params=params)
-        response.raise_for_status()  # Raises HTTPError for bad responses
-
-        response_data = response.json()
-
-        # params = {"type": "embedding"}
-
-        # r2 = requests.get(url, headers=headers, params=params)
-        # r2.raise_for_status()  # Raises HTTPError for bad responses
-
-        # r2_data = r2.json()
-
-        # # concatenate response_data and r2_data
-        # if isinstance(response_data, list) and isinstance(r2_data, list):
-        #     response_data = response_data + r2_data
-
-    # except requests.exceptions.HTTPError as http_err:
-    #     print(f"HTTP error occurred: {http_err}")
-    # except requests.exceptions.RequestException as req_err:
-    #     print(f"Request error occurred: {req_err}")
-    # except (ValueError, KeyError) as data_err:
-    #     print(f"Data parsing error: {data_err}")
-    except Exception as e:
-        return (f"Unexpected error: {e}", True)
-
-    return (response_data, False)
-
-
-async def humanize_name(model_id: str) -> str:
-
-    # Handle number-letter combinations without space
-    model_id = re.sub(r"(\d+)([a-zA-Z]+)", lambda m: f"{m.group(1)}{m.group(2).upper()}", model_id)
-
-    # Replace remaining separators with spaces and title case
-    model_id = re.sub(r"[-_]", " ", model_id).title()
-
-    humanized = model_id.replace("xl", "XL").replace("vl", "VL").replace("sd", "SD").replace("llama", "LLaMA")
-
-    return humanized
-
-
-@routes.get("/veniceai/update_models_list")
-async def update_models_list_server(request):
-    # img_model_json = await fetch_model_list("image")
-    # txt_model_json = await fetch_model_list("text")
-    # merged_json = {"object": "list", "data": img_model_json.get("data", []) + txt_model_json.get("data", [])}
-
-    response = await fetch_models_list()
-
-    if response[1]:  # true = error happened
-        return response
-
-    merged_json = response[0]
-
-    # Validating response structure
-    if not isinstance(merged_json, dict) or "data" not in merged_json:
-        raise ValueError("Unexpected API response format")
-
-    # Create lookup dictionary and enhanced JSON
-    enhanced_data = []
-    model_dict = {}
-
-    # Process all models for enhanced JSON
-    for model in merged_json["data"]:
-        model_id = model["id"]
-        model_spec = model.get("model_spec", {})
-
-        # Format traits excluding function_calling_default
-        traits = [
-            trait.replace("_", " ").title()
-            for trait in model_spec.get("traits", [])
-            if trait != "function_calling_default"
-        ]
-
-        # Create humanized description
-        parts = [await humanize_name(model_id)]
-        if "availableContextTokens" in model_spec:
-            parts.append(f"ctx: {model_spec['availableContextTokens']}")
-        if traits:
-            parts.append(" | ".join(traits))
-
-        # Add to enhanced data
-        enhanced_model = model.copy()
-        enhanced_model["humanized"] = " | ".join(parts)
-        enhanced_data.append(enhanced_model)
-        model_dict[model_id] = enhanced_model
-
-    # Save enhanced JSON
-    enhanced_json = {"object": "list", "data": enhanced_data}
-    with open(all_model_list_path, "w") as f:
-        json.dump(enhanced_json, f, indent=2)
-
-    response = ("Model list update success", False)
-    return web.json_response({"message": response[0], "error": response[1]})
-
-
-# NOTE: routes are frozen and don't update with ComfyUI-HotReloadHack
-@routes.get("/veniceai/get_models_list")
-async def get_local_models_list_server(request):
-    with open(all_model_list_path, "r") as f:
-        model_list_json = json.load(f)
-
-    data = model_list_json["data"]
-
-    # Create final sorted lists of names/ids
-    img_models = [m["id"] for m in data if m.get("type") == "image"]
-    txt_models = [m["id"] for m in data if m.get("type") == "text"]
-    tts_models = [m["id"] for m in data if m.get("type") == "tts"]
-
-    # Collect voices with humanized model name
-    tts_voices = []
-    for m in data:
-        if m.get("type") == "tts":
-            voices = m.get("model_spec", {}).get("voices", [])
-            # humanized = m.get("humanized", m.get("id", ""))
-            humanized = m.get("id", "")
-            tts_voices.extend([f"{humanized} - {voice}" for voice in voices])
-
-    # if tts_voices:
-    #     print(f"TTS Voices: {', '.join(tts_voices[:100])}")
-
-    data = {
-        "image_models": sorted(img_models),
-        "text_models": sorted(txt_models),
-        "tts_models": sorted(tts_models),
-        "tts_voices": sorted(tts_voices),
-        "model_list_json": model_list_json,
-    }
-    return web.json_response(data)

pyserver/update_styles.py

@@ -1,58 +0,0 @@
-import json
-import os
-from pathlib import Path
-
-import requests
-from aiohttp import web
-
-from server import PromptServer  # type: ignore
-
-from ..globals import API_ENDPOINTS, VENICEAI_BASE_URL
-
-routes = PromptServer.instance.routes
-
-script_dir = Path(__file__).resolve().parent
-data_dir = script_dir.parent / "data"
-data_dir.mkdir(exist_ok=True)
-styles_list_path = data_dir / "styles_list.json"
-
-
-@routes.get("/veniceai/update_styles_list")
-async def update_styles_list_server(request):
-    try:
-        headers = {"Authorization": f"Bearer {os.getenv('VENICEAI_API_KEY')}"}
-        url = f"{VENICEAI_BASE_URL}{API_ENDPOINTS['list_styles']}"
-
-        response = requests.get(url, headers=headers)
-        response.raise_for_status()  # Raises HTTPError for bad responses
-
-        response_data = response.json()
-
-        # remove "object" key from response_data
-        response_data.pop("object", None)
-
-        response_data["data"] = sorted(response_data.get("data", []))
-        response_data["data"].insert(0, "none")
-
-        with open(styles_list_path, "w") as f:
-            json.dump(response_data, f, indent=2)
-
-    # except requests.exceptions.HTTPError as http_err:
-    #     print(f"HTTP error occurred: {http_err}")
-    # except requests.exceptions.RequestException as req_err:
-    #     print(f"Request error occurred: {req_err}")
-    # except (ValueError, KeyError) as data_err:
-    #     print(f"Data parsing error: {data_err}")
-    except Exception as e:
-        return (f"Unexpected error: {e}", True)
-
-    response = (response_data, False)
-    return web.json_response({"message": response[0], "error": response[1]})
-
-
-@routes.get("/veniceai/get_styles_list")
-async def get_local_styles_list(requests):
-    with open(styles_list_path, "r") as f:
-        styles_list_json = json.load(f)
-
-    return web.json_response(styles_list_json)

requirements.txt

@@ -1,4 +1,7 @@
 requests>=2.31.0
 Pillow>=10.0.0
 numpy>=1.24.0
-configparser>=5.3.0
+configparser>=5.3.0
+torchaudio
+torchvision
+imageio-ffmpeg

venice_catalog.py

@@ -0,0 +1,343 @@
+import json
+import logging
+import os
+import time
+from pathlib import Path
+from typing import Any, Callable, Dict, Iterable, List, Optional
+
+from .venice_client import VeniceAPIError, VeniceClient
+
+DATA_DIR = Path(__file__).with_name("data")
+DATA_DIR.mkdir(exist_ok=True)
+
+
+class CatalogStore:
+    def __init__(
+        self,
+        name: str,
+        default_factory: Optional[Callable[[], Dict[str, Any]]] = None,
+        legacy_name: Optional[str] = None,
+    ) -> None:
+        self.file = DATA_DIR / name
+        self._default_factory = default_factory or (lambda: {"object": "list", "data": []})
+        self._legacy_name = legacy_name
+
+    def load(self) -> Dict[str, Any]:
+        if not self.file.exists():
+            return self._load_legacy() or self._default_factory()
+        try:
+            with self.file.open("r", encoding="utf-8") as fp:
+                return json.load(fp)
+        except (ValueError, json.JSONDecodeError):
+            return self._load_legacy() or self._default_factory()
+
+    def save(self, payload: Dict[str, Any]) -> None:
+        with self.file.open("w", encoding="utf-8") as fp:
+            json.dump(payload, fp, indent=2)
+
+    def _load_legacy(self) -> Dict[str, Any] | None:
+        if not self._legacy_name:
+            return None
+        legacy_file = self.file.with_name(self._legacy_name)
+        if not legacy_file.exists():
+            return None
+        try:
+            with legacy_file.open("r", encoding="utf-8") as fp:
+                return json.load(fp)
+        except (ValueError, json.JSONDecodeError):
+            return None
+
+    def filter_by_type(self, payload: Dict[str, Any], *types: str) -> List[Dict[str, Any]]:
+        data = payload.get("data") or []
+        return [entry for entry in data if entry.get("type") in types]
+
+
+_model_store = CatalogStore("models_list.json", legacy_name="all_model_list.json")
+_styles_store = CatalogStore(
+    "styles_list.json",
+    lambda: {"data": ["none"], "object": "list"},
+    legacy_name="styles_list.json",
+)
+_characters_store = CatalogStore(
+    "characters_list.json",
+    lambda: {"data": [], "object": "list"},
+    legacy_name="characters_list.json",
+)
+_client = VeniceClient()
+
+# to prevent spamming api.
+# can be bypassed by setting force_refresh=True in get_* calls
+_CACHE_TTL = float(os.environ.get("VENICE_CATALOG_TTL", "900"))
+_models_last_refresh = 0.0
+_styles_last_refresh = 0.0
+_characters_last_refresh = 0.0
+LOG = logging.getLogger(__name__)
+
+
+def _enrich_models(raw: Dict[str, Any]) -> Dict[str, Any]:
+    return raw.copy()
+
+
+def _extract_video_models(payload: Dict[str, Any]) -> Dict[str, Any]:
+    """
+    Extract Venice video models and expose a by-id mapping of their constraints.
+
+    The mapping is intentionally lightweight (id, name, model_type, constraints) so downstream
+    callers (nodes) can build DynamicCombo inputs without re-parsing the raw payload.
+    """
+    data = payload.get("data", []) or []
+    by_id: Dict[str, Any] = {}
+
+    for model in data:
+        if model.get("type") != "video":
+            continue
+        model_id = model.get("id")
+        if not model_id:
+            continue
+
+        model_spec = model.get("model_spec") or {}
+        constraints = model_spec.get("constraints") or {}
+        model_type = constraints.get("model_type")
+        if not isinstance(constraints, dict):
+            constraints = {}
+
+        entry = {
+            "id": model_id,
+            "name": model_spec.get("name"),
+            "model_type": model_type,
+            "constraints": {
+                "aspect_ratios": constraints.get("aspect_ratios") or [],
+                "resolutions": constraints.get("resolutions") or [],
+                "durations": constraints.get("durations") or [],
+                "audio": constraints.get("audio"),
+                "audio_configurable": constraints.get("audio_configurable"),
+                "model_type": model_type,
+            },
+            "raw": model,
+        }
+        by_id[model_id] = entry
+
+    return by_id
+
+
+def _extract_image_models(payload: Dict[str, Any]) -> Dict[str, Any]:
+    data = payload.get("data", []) or []
+    by_id: Dict[str, Any] = {}
+
+    for model in data:
+        if model.get("type") != "image":
+            continue
+        model_id = model.get("id")
+        if not model_id:
+            continue
+
+        model_spec = model.get("model_spec") or {}
+        constraints = model_spec.get("constraints") or {}
+        if not isinstance(constraints, dict):
+            constraints = {}
+
+        entry = {
+            "id": model_id,
+            "name": model_spec.get("name"),
+            "constraints": dict(constraints),
+            "raw": model,
+        }
+        by_id[model_id] = entry
+
+    return by_id
+
+
+def _extract_text_models(payload: Dict[str, Any]) -> Dict[str, Any]:
+    data = payload.get("data", []) or []
+    by_id: Dict[str, Any] = {}
+
+    for model in data:
+        if model.get("type") != "text":
+            continue
+        model_id = model.get("id")
+        if not model_id:
+            continue
+
+        model_spec = model.get("model_spec") or {}
+        constraints = model_spec.get("constraints") or {}
+        capabilities = model_spec.get("capabilities") or {}
+        if not isinstance(constraints, dict):
+            constraints = {}
+        if not isinstance(capabilities, dict):
+            capabilities = {}
+
+        entry = {
+            "id": model_id,
+            "name": model_spec.get("name"),
+            "constraints": dict(constraints),
+            "capabilities": dict(capabilities),
+            "raw": model,
+        }
+        by_id[model_id] = entry
+
+    return by_id
+
+
+def _should_refresh(last_refresh: float) -> bool:
+    if _CACHE_TTL <= 0:
+        return False
+    return (time.monotonic() - last_refresh) > _CACHE_TTL
+
+
+def _record_models_refresh() -> None:
+    global _models_last_refresh
+    _models_last_refresh = time.monotonic()
+
+
+def _record_styles_refresh() -> None:
+    global _styles_last_refresh
+    _styles_last_refresh = time.monotonic()
+
+
+def _record_characters_refresh() -> None:
+    global _characters_last_refresh
+    _characters_last_refresh = time.monotonic()
+
+
+def _should_attempt_refresh(payload: Dict[str, Any], should_refresh: bool) -> bool:
+    if not should_refresh:
+        return False
+    if _client.dry_run and payload.get("data"):
+        LOG.debug("Dry-run mode skipping catalog refresh because cached data is available.")
+        return False
+    return True
+
+
+def refresh_models(model_type: str = "all") -> Dict[str, Any]:
+    try:
+        raw = _client.list_models(model_type or "all")
+    except VeniceAPIError as exc:
+        LOG.error("Failed to refresh Venice model list: %s", exc)
+        raise
+    enriched = _enrich_models(raw)
+    if not enriched.get("data"):
+        raise VeniceAPIError("Venice returned an empty model catalog")
+    _model_store.save(enriched)
+    _record_models_refresh()
+    return enriched
+
+
+def refresh_styles() -> Dict[str, Any]:
+    try:
+        raw = _client.list_styles()
+    except VeniceAPIError as exc:
+        LOG.error("Failed to refresh Venice styles list: %s", exc)
+        raise
+
+    data = raw.get("data")
+    if not data:
+        raise VeniceAPIError("Venice returned an empty styles catalog")
+
+    sorted_data = sorted(data)
+    sorted_data.insert(0, "none")
+    payload = {"object": raw.get("object", "list"), "data": sorted_data}
+    _styles_store.save(payload)
+    _record_styles_refresh()
+    return payload
+
+
+def refresh_characters() -> Dict[str, Any]:
+    try:
+        raw = _client.list_characters()
+    except VeniceAPIError as exc:
+        LOG.error("Failed to refresh Venice characters list: %s", exc)
+        raise
+
+    items = raw.get("data")
+    if not items:
+        raise VeniceAPIError("Venice returned an empty characters catalog")
+
+    sorted_items = sorted(items, key=lambda item: item.get("slug", "")) if isinstance(items, Iterable) else []
+    payload = {"object": raw.get("object", "list"), "data": sorted_items}
+    _characters_store.save(payload)
+    _record_characters_refresh()
+    return payload
+
+
+def get_models(model_type: Optional[str] = None, *, force_refresh: bool = False) -> Dict[str, Any]:
+    """
+    Return model metadata from the Venice catalog, optionally filtered by type.
+
+    Parameters
+    ----------
+    model_type : Optional[str]
+        If provided, filters models by the requested type (e.g., "image", "text", "tts") and
+        returns only that subset; when omitted, the response contains categorized lists
+        of model IDs, available TTS voices, and the raw payload.
+
+    Returns
+    -------
+    Dict[str, Any]
+        The filtered or fully categorized model information, keyed by categories such as
+        "image_models", "text_models", "tts_models", "tts_voices", and "model_list_json".
+        When a model_type filter is applied, returns a single "models" key with matching entries.
+    """
+    payload = _model_store.load()
+    should_refresh = force_refresh or not payload.get("data") or _should_refresh(_models_last_refresh)
+    if _should_attempt_refresh(payload, should_refresh):
+        payload = refresh_models()
+    if model_type:
+        return {"models": _model_store.filter_by_type(payload, model_type)}
+
+    video_models_by_id = _extract_video_models(payload)
+    image_models_by_id = _extract_image_models(payload)
+    text_models_by_id = _extract_text_models(payload)
+
+    # todo: maybe dataclass is better for this
+    filtered = {
+        # todo: the *_by_id might be enough so this stuff below can be removed
+        "image_models": sorted([m.get("id") for m in payload.get("data", []) if m.get("type") == "image"]),
+        "text_models": sorted([m.get("id") for m in payload.get("data", []) if m.get("type") == "text"]),
+        # todo: voices should be linked to models like in models json
+        "tts_models": sorted([m.get("id") for m in payload.get("data", []) if m.get("type") == "tts"]),
+        "tts_voices": sorted(
+            [
+                f"{m.get('id', '')} - {voice}"
+                for m in payload.get("data", [])
+                if m.get("type") == "tts"
+                for voice in (m.get("model_spec", {}).get("voices") or [])
+            ]
+        ),
+        "text2video_models": sorted(
+            [
+                model_id
+                for model_id, spec in video_models_by_id.items()
+                if spec.get("constraints", {}).get("model_type") == "text-to-video"
+            ]
+        ),
+        "image2video_models": sorted(
+            [
+                model_id
+                for model_id, spec in video_models_by_id.items()
+                if spec.get("constraints", {}).get("model_type") == "image-to-video"
+            ]
+        ),
+        "video_models_by_id": video_models_by_id,
+        "image_models_by_id": image_models_by_id,
+        "model_list_json": payload,
+        "text_models_by_id": text_models_by_id,
+    }
+    return filtered
+
+
+def get_styles(*, force_refresh: bool = False) -> Dict[str, Any]:
+    payload = _styles_store.load()
+    should_refresh = force_refresh or not payload.get("data") or _should_refresh(_styles_last_refresh)
+    if _should_attempt_refresh(payload, should_refresh):
+        payload = refresh_styles()
+    return payload
+
+
+def get_characters(*, force_refresh: bool = False) -> Dict[str, Any]:
+    payload = _characters_store.load()
+    should_refresh = force_refresh or not payload.get("data") or _should_refresh(_characters_last_refresh)
+    if _should_attempt_refresh(payload, should_refresh):
+        payload = refresh_characters()
+    data = payload.get("data", [])
+    characters = [item.get("slug") for item in data if isinstance(item, dict) and item.get("slug")]
+    return {"characters": characters}

venice_client.py

@@ -0,0 +1,156 @@
+from __future__ import annotations
+
+import logging
+import os
+from typing import Any, Dict, Mapping, Optional
+
+import requests
+
+from .globals import API_ENDPOINTS, USER_AGENT, VENICEAI_BASE_URL
+from .venice_config import config as venice_config
+
+LOG = logging.getLogger(__name__)
+if os.environ.get("VENICE_CLIENT_DEBUG", "").lower() in {"1", "true"}:
+    LOG.setLevel(logging.DEBUG)
+
+
+class VeniceAPIError(Exception):
+    pass
+
+
+class DummyResponse:
+    def __init__(self, payload: dict | None = None, status_code: int = 200) -> None:
+        self._payload = payload or {}
+        self.status_code = status_code
+
+    def raise_for_status(self) -> None:
+        return None
+
+    def json(self) -> dict:
+        return self._payload
+
+    @property
+    def text(self) -> str:
+        return ""
+
+    def __getattr__(self, item: str) -> Any:
+        return None
+
+
+class VeniceClient:
+    def __init__(self) -> None:
+        self._session = requests.Session()
+        self._session.headers.update({"User-Agent": USER_AGENT})
+        self._dry_run = os.environ.get("VENICE_CLIENT_DRY_RUN", "").lower() in {"1", "true"}
+
+    @property
+    def dry_run(self) -> bool:
+        return self._dry_run
+
+    def _ensure_api_key(self) -> str:
+        key = venice_config.apikey.strip()
+        if not key:
+            raise VeniceAPIError(
+                "VeniceAI API key is missing. Set it via the VeniceAI settings before using the nodes."
+            )
+        return key
+
+    def _build_headers(self, extra: Optional[Mapping[str, str]] = None) -> Dict[str, str]:
+        headers: Dict[str, str] = {"Authorization": f"Bearer {self._ensure_api_key()}"}
+        if extra:
+            headers.update(extra)
+        return headers
+
+    def _friendly_status_hint(self, status_code: int) -> str:
+        if status_code == 401:
+            return "The API key may be invalid, expired, or lack permissions."
+        if status_code == 404:
+            return "The requested VeniceAI endpoint was not found. Ensure the node and catalog data are up to date."
+        if status_code == 429:
+            return "Request rate limits were hit. Wait a moment before retrying."
+        if 500 <= status_code < 600:
+            return "VeniceAI appears to be experiencing server issues; try again in a bit."
+        return "Check your request parameters and ensure your API key is valid."
+
+    def _friendly_network_hint(self) -> str:
+        return "Unable to reach VeniceAI. Confirm your internet connection and that api.venice.ai is reachable."
+
+    def request(self, method: str, endpoint: str, **kwargs: Any) -> requests.Response:
+        if self._dry_run:
+            logging.debug("VeniceClient dry run skipping %s %s", method, endpoint)
+            return DummyResponse()
+
+        url = VENICEAI_BASE_URL + endpoint
+        headers = kwargs.pop("headers", None)
+        kwargs.setdefault("timeout", 30)
+        try:
+            response = self._session.request(method, url, headers=self._build_headers(headers), **kwargs)
+            response.raise_for_status()
+        except requests.HTTPError as exc:
+            self._log_response(method, endpoint, exc.response)
+            hint = (
+                self._friendly_status_hint(exc.response.status_code)
+                if exc.response is not None
+                else "Unexpected response from VeniceAI."
+            )
+            message = f"Venice request failed ({method} {endpoint}): {exc}. \n{hint}"
+            LOG.debug("Venice request failed: %s %s %s", method, endpoint, exc)
+            raise VeniceAPIError(message) from exc
+        except requests.RequestException as exc:
+            message = f"{self._friendly_network_hint()} Details: {exc}"
+            LOG.debug("Venice network error: %s %s %s", method, endpoint, exc)
+            raise VeniceAPIError(message) from exc
+        self._log_response(method, endpoint, response)
+        return response
+
+    def _log_response(self, method: str, endpoint: str, response: Optional[requests.Response | DummyResponse]) -> None:
+        if not LOG.isEnabledFor(logging.DEBUG) or response is None:
+            return
+
+        headers = getattr(response, "headers", None)
+        header_snapshot: Dict[str, Any] = dict(headers) if headers else {}
+        content_type = (header_snapshot.get("Content-Type") or "").lower()
+
+        body = ""
+        readable_body = content_type.startswith("application/json") or content_type.startswith("text/")
+        if readable_body or not content_type:
+            try:
+                body = response.text or ""
+            except Exception as exc:
+                body = f"<unable to read body: {exc}>"
+            if len(body) > 2000:
+                body = body[:2000] + "...[truncated]"
+        else:
+            body = f"<{content_type} response body omitted>"
+
+        LOG.debug(
+            "Venice response %s %s status=%s headers=%s body=%s",
+            method,
+            endpoint,
+            getattr(response, "status_code", "<?>"),
+            header_snapshot,
+            body,
+        )
+
+    def post_json(self, endpoint: str, payload: Mapping[str, Any], **kwargs: Any) -> Dict[str, Any]:
+        headers = {"Content-Type": "application/json"}
+        headers.update(kwargs.pop("headers", {}))
+        response = self.request("POST", endpoint, json=payload, headers=headers, **kwargs)
+        return response.json()
+
+    def get_json(self, endpoint: str, params: Optional[Mapping[str, Any]] = None) -> Dict[str, Any]:
+        response = self.request("GET", endpoint, params=params)
+        return response.json()
+
+    def list_models(self, model_type: Optional[str] = None) -> Dict[str, Any]:
+        params = {"type": model_type} if model_type else None
+        return self.get_json(API_ENDPOINTS["list_models"], params=params)
+
+    def list_styles(self) -> Dict[str, Any]:
+        return self.get_json(API_ENDPOINTS["list_styles"])
+
+    def list_characters(self) -> Dict[str, Any]:
+        return self.get_json(API_ENDPOINTS["characters"])
+
+
+client = VeniceClient()

venice_config.py

@@ -0,0 +1,57 @@
+from __future__ import annotations
+
+import json
+import os
+from pathlib import Path
+from typing import Any, Dict
+
+DEFAULT_CONFIG: Dict[str, Any] = {"apikey": ""}
+
+
+class VeniceConfig:
+    _instance: "VeniceConfig" | None = None
+
+    def __new__(cls) -> "VeniceConfig":
+        if cls._instance is None:
+            cls._instance = super().__new__(cls)
+            cls._instance._initialized = False
+        return cls._instance
+
+    def __init__(self) -> None:
+        if self._initialized:
+            return
+        self._initialized = True
+        self.path = Path(__file__).with_name("veniceai_config.json")
+        self.path.parent.mkdir(parents=True, exist_ok=True)
+        self._data = self._read()
+        self._sync_env()
+
+    def _read(self) -> Dict[str, Any]:
+        if not self.path.exists():
+            self._write(DEFAULT_CONFIG)
+            return DEFAULT_CONFIG.copy()
+        try:
+            with self.path.open("r", encoding="utf-8") as fp:
+                return json.load(fp)
+        except (ValueError, json.JSONDecodeError):
+            self._write(DEFAULT_CONFIG)
+            return DEFAULT_CONFIG.copy()
+
+    def _write(self, data: Dict[str, Any]) -> None:
+        with self.path.open("w", encoding="utf-8") as fp:
+            json.dump(data, fp, indent=2)
+
+    def _sync_env(self) -> None:
+        os.environ.setdefault("VENICEAI_API_KEY", self._data.get("apikey", ""))
+
+    @property
+    def apikey(self) -> str:
+        return self._data.get("apikey", "") or ""
+
+    def save_apikey(self, key: str) -> None:
+        self._data["apikey"] = key
+        self._write(self._data)
+        os.environ["VENICEAI_API_KEY"] = key
+
+
+config = VeniceConfig()

workflows/workflow_together_old.json

@@ -1,310 +0,0 @@
-{
-  "last_node_id": 22,
-  "last_link_id": 6,
-  "nodes": [
-    {
-      "id": 20,
-      "type": "SaveImage",
-      "pos": {
-        "0": 645,
-        "1": 514
-      },
-      "size": {
-        "0": 315,
-        "1": 270
-      },
-      "flags": {},
-      "order": 4,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "images",
-          "type": "IMAGE",
-          "link": 6
-        }
-      ],
-      "outputs": [],
-      "properties": {},
-      "widgets_values": [
-        "ComfyUIPro"
-      ]
-    },
-    {
-      "id": 15,
-      "type": "SaveImage",
-      "pos": {
-        "0": 641,
-        "1": 913
-      },
-      "size": {
-        "0": 315,
-        "1": 270
-      },
-      "flags": {},
-      "order": 6,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "images",
-          "type": "IMAGE",
-          "link": 5
-        }
-      ],
-      "outputs": [],
-      "properties": {},
-      "widgets_values": [
-        "ComfyUIP11"
-      ]
-    },
-    {
-      "id": 5,
-      "type": "SaveImage",
-      "pos": {
-        "0": 624,
-        "1": 91
-      },
-      "size": {
-        "0": 315,
-        "1": 270
-      },
-      "flags": {},
-      "order": 5,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "images",
-          "type": "IMAGE",
-          "link": 4
-        }
-      ],
-      "outputs": [],
-      "properties": {},
-      "widgets_values": [
-        "ComfyUIDev"
-      ]
-    },
-    {
-      "id": 9,
-      "type": "FluxPro_TOGETHER",
-      "pos": {
-        "0": 116,
-        "1": 513
-      },
-      "size": {
-        "0": 400,
-        "1": 292
-      },
-      "flags": {},
-      "order": 0,
-      "mode": 0,
-      "inputs": [],
-      "outputs": [
-        {
-          "name": "IMAGE",
-          "type": "IMAGE",
-          "links": [
-            6
-          ]
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "FluxPro_TOGETHER"
-      },
-      "widgets_values": [
-        "The image showcases a striking 3D CGI representation of a stylized angry muscle Doberman, characterized by a sleek black and silver coat that glistens under the soft lighting, enhancing its muscular physique. The dog is prominently positioned in the center of the composition against a stark black background, which accentuates its fierce expression and well-defined features. Its piercing eyes are shielded by trendy green sunglasses, adding a fashionable flair that contrasts with the dark tones of its fur. The meticulous detailing highlights the texture of the Doberman’s coat, revealing subtle variations in color that suggest a glossy sheen. Surrounding the Doberman, hints of a countryside landscape can be inferred in the background, possibly indicating an advertising theme, although it remains blurred and understated to maintain focus on the dog itself. The overall quality of the image is exceptional, marked by ultra-detailed craftsmanship that embodies a masterpiece in high-resolution, making it a captivating visual spectacle.\n\n",
-        1024,
-        1024,
-        28,
-        true,
-        "2",
-        2.5,
-        2,
-        324,
-        "randomize"
-      ]
-    },
-    {
-      "id": 6,
-      "type": "FluxDev_TOGETHER",
-      "pos": {
-        "0": 112,
-        "1": 91
-      },
-      "size": {
-        "0": 400,
-        "1": 268
-      },
-      "flags": {},
-      "order": 1,
-      "mode": 0,
-      "inputs": [],
-      "outputs": [
-        {
-          "name": "IMAGE",
-          "type": "IMAGE",
-          "links": [
-            4
-          ]
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "FluxDev_TOGETHER"
-      },
-      "widgets_values": [
-        "The image showcases a striking 3D CGI representation of a stylized angry muscle Doberman, characterized by a sleek black and silver coat that glistens under the soft lighting, enhancing its muscular physique. The dog is prominently positioned in the center of the composition against a stark black background, which accentuates its fierce expression and well-defined features. Its piercing eyes are shielded by trendy green sunglasses, adding a fashionable flair that contrasts with the dark tones of its fur. The meticulous detailing highlights the texture of the Doberman’s coat, revealing subtle variations in color that suggest a glossy sheen. Surrounding the Doberman, hints of a countryside landscape can be inferred in the background, possibly indicating an advertising theme, although it remains blurred and understated to maintain focus on the dog itself. The overall quality of the image is exceptional, marked by ultra-detailed craftsmanship that embodies a masterpiece in high-resolution, making it a captivating visual spectacle.\n\n",
-        1024,
-        1024,
-        4,
-        true,
-        "1",
-        2.5,
-        675,
-        "randomize"
-      ]
-    },
-    {
-      "id": 14,
-      "type": "FluxPro11_TOGETHER",
-      "pos": {
-        "0": 115,
-        "1": 912
-      },
-      "size": {
-        "0": 400,
-        "1": 244
-      },
-      "flags": {},
-      "order": 2,
-      "mode": 0,
-      "inputs": [],
-      "outputs": [
-        {
-          "name": "IMAGE",
-          "type": "IMAGE",
-          "links": [
-            5
-          ]
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "FluxPro11_TOGETHER"
-      },
-      "widgets_values": [
-        "The image showcases a striking 3D CGI representation of a stylized angry muscle Doberman, characterized by a sleek black and silver coat that glistens under the soft lighting, enhancing its muscular physique. The dog is prominently positioned in the center of the composition against a stark black background, which accentuates its fierce expression and well-defined features. Its piercing eyes are shielded by trendy green sunglasses, adding a fashionable flair that contrasts with the dark tones of its fur. The meticulous detailing highlights the texture of the Doberman’s coat, revealing subtle variations in color that suggest a glossy sheen. Surrounding the Doberman, hints of a countryside landscape can be inferred in the background, possibly indicating an advertising theme, although it remains blurred and understated to maintain focus on the dog itself. The overall quality of the image is exceptional, marked by ultra-detailed craftsmanship that embodies a masterpiece in high-resolution, making it a captivating visual spectacle.\n",
-        1024,
-        1024,
-        true,
-        1,
-        "2",
-        1148,
-        "randomize"
-      ]
-    },
-    {
-      "id": 22,
-      "type": "Fast Groups Muter (rgthree)",
-      "pos": {
-        "0": -142,
-        "1": 36
-      },
-      "size": {
-        "0": 226.8000030517578,
-        "1": 130
-      },
-      "flags": {},
-      "order": 3,
-      "mode": 0,
-      "inputs": [],
-      "outputs": [
-        {
-          "name": "OPT_CONNECTION",
-          "type": "*",
-          "links": null
-        }
-      ],
-      "properties": {
-        "matchColors": "",
-        "matchTitle": "",
-        "showNav": true,
-        "sort": "position",
-        "customSortAlphabet": "",
-        "toggleRestriction": "default"
-      }
-    }
-  ],
-  "links": [
-    [
-      4,
-      6,
-      0,
-      5,
-      0,
-      "IMAGE"
-    ],
-    [
-      5,
-      14,
-      0,
-      15,
-      0,
-      "IMAGE"
-    ],
-    [
-      6,
-      9,
-      0,
-      20,
-      0,
-      "IMAGE"
-    ]
-  ],
-  "groups": [
-    {
-      "title": "Flux Pro",
-      "bounding": [
-        102,
-        440,
-        872,
-        376
-      ],
-      "color": "#8A8",
-      "font_size": 24,
-      "flags": {}
-    },
-    {
-      "title": "Flux Dev",
-      "bounding": [
-        103,
-        11,
-        871,
-        407
-      ],
-      "color": "#A88",
-      "font_size": 24,
-      "flags": {}
-    },
-    {
-      "title": "Flux Pro 1.1",
-      "bounding": [
-        100,
-        834,
-        879,
-        373
-      ],
-      "color": "#444",
-      "font_size": 24,
-      "flags": {}
-    }
-  ],
-  "config": {},
-  "extra": {
-    "ds": {
-      "scale": 0.8264462809917354,
-      "offset": [
-        442.1956952821454,
-        170.1053343944367
-      ]
-    }
-  },
-  "version": 0.4
-}