Full refresh with additional resolutions, custom options and latent output.
This commit is contained in:
BRADSEC
@@ -119,3 +119,6 @@ venv.bak/
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Thumbs.db
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ehthumbs.db
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Desktop.ini
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# Claude Code files
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.claude/
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@@ -1,43 +1,72 @@
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# ResolutionSelector for ComfyUI
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# Resolution Selector for ComfyUI
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A custom node for Stable Diffusion [ComfyUI](https://github.com/comfyanonymous/ComfyUI) to enable easy selection of image resolutions for SDXL SD15 SD21
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Custom node for [ComfyUI](https://github.com/comfyanonymous/ComfyUI) providing easy resolution and empty latent selection for image generation workflows.
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- Select base SDXL resolution, width and height are returned as `INT` values which can be connected to latent image inputs or other inputs such as the `CLIPTextEncodeSDXL` `width, height, target_width, target_height`.
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- Resolution list based off what is currently being used in the [Fooocus SDXL Web UI](https://github.com/lllyasviel/Fooocus).
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- If using older models such as SD 1.5 or SD 2.1 use the `base_adjustment` dropdown. This will reduce the returned width and height values to suit the selected model whilst maintaining the image aspect ratio.
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```terminal
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# Example
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SDXL base_resolution 1024x1024
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SD21 adjustment returns 768x768
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SD15 adjustment returns 512x512
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```
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## Features
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### Resolution Selector
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- **Model-optimised resolution presets** (portrait, landscape, square)
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- **Empty latent output** for direct KSampler connection
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- **Custom width/height inputs** with **independent multiplier and latent**
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### Installation
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## Installation
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```
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```bash
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# Change to the directory you installed ComfyUI
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cd pathTo/ComfyUI
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# Change to the custom_nodes directory ie.
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# Change to the custom_nodes directory
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cd custom_nodes
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```
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```terminal
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# Clone the repo into custom_nodes
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git clone https://github.com/bradsec/ComfyUI_ResolutionSelector.git
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# Restart ComfyUI
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```
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### Usage after install
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`Add Node > utils > Resolution Selector`
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## Usage
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#### SDXL hookup example
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### Resolution Selector
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**Add Node:** `Add Node > utils > Resolution Selector`
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**Inputs:**
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- `model` (dropdown) - Select your image generation model or "All" for all resolutions
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- `resolution` (dropdown) - Choose from model-optimized presets with aspect ratios (e.g., "1920x1080 (16:9 Landscape)")
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- `resolution_multiplier` (dropdown) - Multiply preset resolution (1x, 2x, 3x, 4x)
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- `custom_width` (optional) - Override with custom width (0-4096, step 8)
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- `custom_height` (optional) - Override with custom height (0-4096, step 8)
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- `custom_multiplier` (optional) - Independent multiplier for custom dimensions (1x, 2x, 3x, 4x)
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**Outputs:**
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- `width` (INT) - Preset resolution width in pixels
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- `height` (INT) - Preset resolution height in pixels
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- `latent` (LATENT) - Empty latent tensor for preset resolution
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- `custom_width` (INT) - Custom width (0 if not set)
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- `custom_height` (INT) - Custom height (0 if not set)
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- `custom_latent` (LATENT) - Empty latent tensor for custom resolution
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**Example Workflows:**
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**Basic preset with multiplier:**
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```
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Resolution Selector (model: SDXL, resolution: "1024x1024 (1:1 Square)", multiplier: 2x)
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→ width: 2048, height: 2048
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→ latent → KSampler
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```
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**Custom dimensions with independent multiplier:**
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```
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Resolution Selector (custom_width: 1024, custom_height: 768, custom_multiplier: 2x)
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→ custom_width: 2048, custom_height: 1536
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→ custom_latent → KSampler
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```
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**Using "All" model option:**
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```
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Resolution Selector (model: All, resolution: "1920x1080 (16:9 Landscape)")
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→ Shows all 34 unique resolutions from all models
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```
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#### SD15 hookup example
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from .resolution_selector import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
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__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']
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WEB_DIRECTORY = "./web"
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__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS', 'WEB_DIRECTORY']
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[project]
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name = "comfyui_resolutionselector"
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description = "A custom node for Stable Diffusion ComfyUI to enable easy selection of image resolutions for SDXL SD15 SD21"
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version = "1.0.0"
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name = "comfyui_resolutionselector""
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description = "Custom node for ComfyUI providing easy resolution and empty latent selection for image generation workflows."
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version = "2.0.0"
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license = "MIT"
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[project.urls]
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+397
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# Resolution presets
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BASE_RESOLUTIONS = [
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(1024, 1024),
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(704, 1408),
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(704, 1344),
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(768, 1344),
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(768, 1280),
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(832, 1216),
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(832, 1152),
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(896, 1152),
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(896, 1088),
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(960, 1088),
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(960, 1024),
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(1024, 960),
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(1088, 960),
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(1088, 896),
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(1152, 896),
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(1152, 832),
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(1216, 832),
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(1280, 768),
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(1344, 768),
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(1344, 704),
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(1408, 704),
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(1472, 704),
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(1536, 640),
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(1600, 640),
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(1664, 576),
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(1728, 576)
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]
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import torch
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try:
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import comfy.model_management
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COMFY_AVAILABLE = True
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except ImportError:
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COMFY_AVAILABLE = False
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# Model-specific resolution presets with constraints
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MODEL_RESOLUTIONS = {
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"Flux": {
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"square": [(512, 512), (768, 768), (1024, 1024), (1280, 1280), (1536, 1536), (1920, 1920), (2048, 2048)],
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"portrait": [(768, 1344), (832, 1216), (896, 1152), (928, 1664), (1024, 1536), (1024, 1792), (1080, 1920), (1152, 2048)],
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"landscape": [(1344, 768), (1216, 832), (1152, 896), (1536, 1024), (1664, 928), (1792, 1024), (1920, 1080), (2048, 1152)],
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"constraints": {"divisible_by": 16, "min": 256, "max": 2048}
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},
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"Qwen Image": {
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"square": [(1024, 1024), (1280, 1280), (1328, 1328), (1536, 1536), (1920, 1920), (2048, 2048)],
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"portrait": [(928, 1664), (1024, 1536), (1080, 1920), (1140, 1472), (1152, 2048)],
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"landscape": [(1472, 1140), (1536, 1024), (1664, 928), (1920, 1080), (2048, 1152)],
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"constraints": {"divisible_by": 8, "min": 256, "max": 2048}
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},
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"Z-Image": {
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"square": [(512, 512), (768, 768), (1024, 1024), (1280, 1280), (1536, 1536), (1920, 1920), (2048, 2048)],
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"portrait": [(720, 1280), (768, 1024), (1080, 1920), (1152, 2048)],
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"landscape": [(1024, 768), (1280, 720), (1920, 1080), (2048, 1152)],
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"constraints": {"divisible_by": 8, "min": 256, "max": 2048}
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},
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"SD 1.5": {
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"square": [(512, 512), (768, 768), (1024, 1024), (1280, 1280), (1536, 1536)],
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"portrait": [(512, 768), (512, 682), (512, 1024), (768, 1024), (768, 1344), (1080, 1920)],
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"landscape": [(768, 512), (1024, 512), (1024, 768), (1344, 768), (1536, 512), (1920, 1080)],
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"constraints": {"divisible_by": 8, "min": 256, "max": 2048}
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},
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"SDXL": {
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"square": [(1024, 1024), (1280, 1280), (1536, 1536), (1920, 1920), (2048, 2048)],
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"portrait": [(640, 1536), (768, 1344), (832, 1216), (896, 1152), (1024, 1536), (1080, 1920), (1152, 2048)],
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"landscape": [(1152, 896), (1216, 832), (1344, 768), (1536, 640), (1536, 1024), (1920, 1080), (2048, 1152)],
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"constraints": {"divisible_by": 8, "min": 256, "max": 2048}
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}
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}
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def gcd(a, b):
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"""
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Calculate greatest common divisor using Euclidean algorithm.
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Args:
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a (int): First number
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b (int): Second number
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Returns:
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int: Greatest common divisor
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"""
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while b != 0:
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a, b = b, a % b
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return a
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def calculate_aspect_ratio(width, height):
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"""
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Calculate simplified aspect ratio from dimensions, rounding to common ratios.
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Args:
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width (int): Width in pixels
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height (int): Height in pixels
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Returns:
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str: Aspect ratio like "16:9" or "1:1"
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"""
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# Calculate actual ratio as decimal
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actual_ratio = width / height
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# Common aspect ratios (ratio_value, "width:height" string)
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common_ratios = [
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(1.0, "1:1"), # Square
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(1.25, "5:4"), # 1.25
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(1.33, "4:3"), # 1.333...
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(1.5, "3:2"), # 1.5
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(1.6, "16:10"), # 1.6
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(1.78, "16:9"), # 1.777...
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(2.0, "2:1"), # 2.0
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(2.35, "21:9"), # 2.333... (ultrawide)
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(2.4, "12:5"), # 2.4
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(3.0, "3:1"), # 3.0
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# Portrait ratios
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(0.75, "3:4"), # 0.75
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(0.67, "2:3"), # 0.666...
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(0.625, "5:8"), # 0.625
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(0.56, "9:16"), # 0.5625
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(0.5, "1:2"), # 0.5
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(0.42, "5:12"), # 0.4166...
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]
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# Find closest common ratio (within 5% tolerance)
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tolerance = 0.05
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for ratio_value, ratio_str in common_ratios:
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if abs(actual_ratio - ratio_value) / ratio_value < tolerance:
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return ratio_str
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# If no close match, calculate GCD-based ratio
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divisor = gcd(width, height)
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ratio_width = width // divisor
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ratio_height = height // divisor
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return f"{ratio_width}:{ratio_height}"
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def format_resolution(width, height):
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"""
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Format resolution tuple into display string with aspect ratio and orientation.
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Uses fixed-width formatting for better alignment in dropdowns.
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Args:
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width (int): Width in pixels
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height (int): Height in pixels
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Returns:
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str: Formatted string like "1920x1080 (16:9 Landscape)" with consistent spacing
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"""
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aspect_ratio = calculate_aspect_ratio(width, height)
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if width == height:
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orientation = "Square"
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elif width < height:
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orientation = "Portrait"
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else:
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orientation = "Landscape"
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# Format with fixed width for better alignment (e.g., "1920x1080 ")
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# Most resolutions are 4 digits, so we pad to 9 characters (4x4 + 'x')
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resolution_str = f"{width}x{height}"
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padded_resolution = resolution_str.ljust(13) # Pad to 13 chars for alignment
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return f"{padded_resolution}({aspect_ratio} {orientation})"
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def get_resolution_list(model_name):
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"""
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Generate ordered list of resolution strings for a specific model.
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Args:
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model_name (str): Name of the model (or "All" for all unique resolutions)
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Returns:
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list: Formatted resolution strings in order: square, portrait, landscape
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"""
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if model_name == "All":
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return get_all_resolutions()
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if model_name not in MODEL_RESOLUTIONS:
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return []
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model_data = MODEL_RESOLUTIONS[model_name]
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resolutions = []
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# Order: square first, then portrait, then landscape
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for category in ["square", "portrait", "landscape"]:
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for width, height in model_data[category]:
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resolutions.append(format_resolution(width, height))
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return resolutions
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def get_default_resolution(model_name):
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"""
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Get the default/native resolution for a specific model.
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Args:
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model_name (str): Name of the model
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Returns:
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str: Formatted resolution string for the model's native resolution
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"""
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# Model-specific native/optimal resolutions
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default_resolutions = {
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"Flux": (1024, 1024), # Flux native
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"Qwen Image": (1328, 1328), # Qwen native
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"Z-Image": (1024, 1024), # Z-Image native
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"SD 1.5": (512, 512), # SD 1.5 native
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"SDXL": (1024, 1024), # SDXL native
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"All": (1024, 1024), # Default for "All"
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}
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if model_name in default_resolutions:
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width, height = default_resolutions[model_name]
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return format_resolution(width, height)
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# Fallback to 1024x1024
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return format_resolution(1024, 1024)
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def get_all_resolutions():
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"""
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Get all unique resolution strings across all models, sorted by dimensions.
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Returns:
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list: All unique resolution strings sorted by total pixels
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"""
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unique_resolutions = {}
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# Collect all unique width×height pairs
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for model_name in MODEL_RESOLUTIONS.keys():
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model_data = MODEL_RESOLUTIONS[model_name]
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for category in ["square", "portrait", "landscape"]:
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for width, height in model_data[category]:
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key = (width, height)
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if key not in unique_resolutions:
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unique_resolutions[key] = format_resolution(width, height)
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# Sort by total pixels, then by width
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sorted_resolutions = sorted(
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unique_resolutions.items(),
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key=lambda item: (item[0][0] * item[0][1], item[0][0])
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)
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return [res_str for _, res_str in sorted_resolutions]
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def parse_resolution_string(resolution_str):
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"""
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Parse formatted resolution string back to width, height integers.
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Args:
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resolution_str (str): Format "1920x1080 (16:9 Landscape)" with possible padding
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Returns:
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tuple: (width, height)
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Raises:
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ValueError: If string format is invalid
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"""
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try:
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# Format: "1920x1080 (16:9 Landscape)" - may have padding spaces
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# Extract the dimension part (before the opening parenthesis)
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dimension_part = resolution_str.split("(")[0].strip()
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if not dimension_part or 'x' not in dimension_part:
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raise ValueError(f"No dimension part found in: {resolution_str}")
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# Split on 'x' and convert to integers
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width, height = map(int, dimension_part.split("x"))
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return (width, height)
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except (ValueError, IndexError) as e:
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raise ValueError(f"Invalid resolution format: {resolution_str}")
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class ResolutionSelector:
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"""
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A node to provide a drop-down list of resolutions and returns two int values (width and height).
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Enhanced resolution selector supporting multiple image generation models.
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Provides model-specific resolution presets, custom dimension inputs, and empty latent output.
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"""
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def __init__(self):
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pass
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"""Initialize device for latent tensor generation."""
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if COMFY_AVAILABLE:
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self.device = comfy.model_management.intermediate_device()
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else:
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self.device = torch.device("cpu")
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@classmethod
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def INPUT_TYPES(cls):
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"""
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Return a dictionary which contains config for all input fields.
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"""
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# Create a list of resolution strings for the drop-down menu
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resolution_strings = [
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f"{width} x {height}" for width, height in BASE_RESOLUTIONS]
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Returns:
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dict: Input configuration with required and optional fields
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"""
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model_list = ["All"] + list(MODEL_RESOLUTIONS.keys())
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# Get all possible resolutions across all models (JavaScript will filter dynamically)
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all_resolutions = get_all_resolutions()
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return {
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"required": {
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"base_resolution": (resolution_strings,),
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"base_adjustment": (["SDXL (None)", "SD21 (75%)", "SD15 (50%)"],),
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"model": (model_list, {
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"default": "SDXL"
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}),
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"resolution": (all_resolutions, {
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"default": "1024x1024 (1:1 Square)"
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}),
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"resolution_multiplier": (["1x", "2x", "3x", "4x"], {
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"default": "1x"
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}),
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},
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"optional": {
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"custom_width": ("INT", {
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"default": 0,
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"min": 0,
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"max": 4096,
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"step": 8,
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"display": "number"
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}),
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"custom_height": ("INT", {
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"default": 0,
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"min": 0,
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"max": 4096,
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"step": 8,
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"display": "number"
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}),
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"custom_multiplier": (["1x", "2x", "3x", "4x"], {
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"default": "1x"
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}),
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}
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}
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|
||||
RETURN_TYPES = ("INT", "INT")
|
||||
RETURN_NAMES = ("width", "height")
|
||||
RETURN_TYPES = ("INT", "INT", "LATENT", "INT", "INT", "LATENT")
|
||||
RETURN_NAMES = ("width", "height", "latent", "custom_width", "custom_height", "custom_latent")
|
||||
FUNCTION = "select_resolution"
|
||||
CATEGORY = 'utils'
|
||||
CATEGORY = "utils"
|
||||
|
||||
def select_resolution(self, base_resolution, base_adjustment):
|
||||
def select_resolution(self, model, resolution, resolution_multiplier="1x", custom_width=0, custom_height=0, custom_multiplier="1x"):
|
||||
"""
|
||||
Returns the width and height based on the selected resolution and adjustment.
|
||||
Select and validate resolution, generate outputs.
|
||||
|
||||
Args:
|
||||
base_resolution (str): Selected resolution in the format "width x height".
|
||||
base_adjustment (str): Selected adjustment (resolution value reduction) based on SD version.
|
||||
model (str): Selected model name
|
||||
resolution (str): Selected preset resolution string
|
||||
resolution_multiplier (str): Multiplier for resolution (1x-4x)
|
||||
custom_width (int, optional): Custom width override
|
||||
custom_height (int, optional): Custom height override
|
||||
custom_multiplier (str, optional): Multiplier for custom dimensions (1x-4x)
|
||||
|
||||
Returns:
|
||||
Tuple[int, int]: Adjusted width and height.
|
||||
tuple: (width: int, height: int, latent: dict, custom_width: int, custom_height: int, custom_latent: dict)
|
||||
"""
|
||||
try:
|
||||
width, height = map(int, base_resolution.split(' x '))
|
||||
except ValueError:
|
||||
raise ValueError("Invalid base_resolution format.")
|
||||
# Parse multipliers (e.g., "2x" -> 2)
|
||||
multiplier = int(resolution_multiplier.replace("x", ""))
|
||||
custom_mult = int(custom_multiplier.replace("x", ""))
|
||||
|
||||
adjustment_factors = {
|
||||
"SDXL (None)": 1, "SD21 (75%)": 0.75, "SD15 (50%)": 0.5}
|
||||
factor = adjustment_factors.get(base_adjustment)
|
||||
# Parse preset resolution
|
||||
width, height = parse_resolution_string(resolution)
|
||||
|
||||
if factor is None:
|
||||
raise ValueError("Invalid base_adjustment value.")
|
||||
# Apply multiplier to preset resolution
|
||||
width *= multiplier
|
||||
height *= multiplier
|
||||
|
||||
width = int(width * factor)
|
||||
height = int(height * factor)
|
||||
# Generate latent for preset resolution
|
||||
latent = self._generate_empty_latent(width, height)
|
||||
|
||||
return width, height
|
||||
# Determine final custom dimensions
|
||||
if custom_width > 0 and custom_height > 0:
|
||||
# Use custom dimensions with custom multiplier
|
||||
final_custom_width = custom_width * custom_mult
|
||||
final_custom_height = custom_height * custom_mult
|
||||
|
||||
# Validate against model constraints (if not "All" model)
|
||||
if model != "All":
|
||||
self._validate_dimensions(model, final_custom_width, final_custom_height)
|
||||
|
||||
# Generate custom latent
|
||||
custom_latent = self._generate_empty_latent(final_custom_width, final_custom_height)
|
||||
|
||||
return (width, height, latent, final_custom_width, final_custom_height, custom_latent)
|
||||
else:
|
||||
# No custom dimensions, return zeros and minimal empty custom latent
|
||||
custom_latent = self._generate_empty_latent(1, 1)
|
||||
|
||||
return (width, height, latent, 0, 0, custom_latent)
|
||||
|
||||
def _validate_dimensions(self, model, width, height):
|
||||
"""
|
||||
Validate dimensions against model-specific constraints.
|
||||
|
||||
Args:
|
||||
model (str): Model name
|
||||
width (int): Width in pixels
|
||||
height (int): Height in pixels
|
||||
|
||||
Raises:
|
||||
ValueError: If dimensions violate model constraints
|
||||
"""
|
||||
if model not in MODEL_RESOLUTIONS:
|
||||
return
|
||||
|
||||
constraints = MODEL_RESOLUTIONS[model]["constraints"]
|
||||
divisible_by = constraints.get("divisible_by", 8)
|
||||
min_dim = constraints.get("min", 64)
|
||||
max_dim = constraints.get("max", 4096)
|
||||
|
||||
# Check divisibility
|
||||
if width % divisible_by != 0:
|
||||
raise ValueError(
|
||||
f"{model} requires width divisible by {divisible_by}. "
|
||||
f"Got {width} (remainder: {width % divisible_by})"
|
||||
)
|
||||
|
||||
if height % divisible_by != 0:
|
||||
raise ValueError(
|
||||
f"{model} requires height divisible by {divisible_by}. "
|
||||
f"Got {height} (remainder: {height % divisible_by})"
|
||||
)
|
||||
|
||||
# Check bounds
|
||||
if width < min_dim or width > max_dim:
|
||||
raise ValueError(
|
||||
f"{model} requires width between {min_dim} and {max_dim}. Got {width}"
|
||||
)
|
||||
|
||||
if height < min_dim or height > max_dim:
|
||||
raise ValueError(
|
||||
f"{model} requires height between {min_dim} and {max_dim}. Got {height}"
|
||||
)
|
||||
|
||||
def _generate_empty_latent(self, width, height, batch_size=1):
|
||||
"""
|
||||
Generate empty latent tensor for VAE input.
|
||||
|
||||
Args:
|
||||
width (int): Image width in pixels
|
||||
height (int): Image height in pixels
|
||||
batch_size (int): Number of latent samples (default: 1)
|
||||
|
||||
Returns:
|
||||
dict: LATENT dict with 'samples' key containing empty tensor
|
||||
"""
|
||||
# Latent space is 1/8 the image dimensions for SD-based models
|
||||
latent_width = width // 8
|
||||
latent_height = height // 8
|
||||
|
||||
# Shape: [batch_size, channels=4, height//8, width//8]
|
||||
latent_tensor = torch.zeros(
|
||||
[batch_size, 4, latent_height, latent_width],
|
||||
device=self.device
|
||||
)
|
||||
|
||||
return {"samples": latent_tensor}
|
||||
|
||||
|
||||
NODE_CLASS_MAPPINGS = {
|
||||
|
||||
Binary file not shown.
|
Before Width: | Height: | Size: 57 KiB |
Binary file not shown.
|
Before Width: | Height: | Size: 152 KiB |
Binary file not shown.
|
Before Width: | Height: | Size: 257 KiB |
@@ -0,0 +1,151 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Test script for ResolutionSelector enhancements
|
||||
"""
|
||||
|
||||
import sys
|
||||
sys.path.insert(0, '.')
|
||||
|
||||
# Mock torch module for testing without ComfyUI environment
|
||||
class MockTorch:
|
||||
class device:
|
||||
def __init__(self, name):
|
||||
self.name = name
|
||||
|
||||
sys.modules['torch'] = MockTorch()
|
||||
sys.modules['comfy'] = type('module', (), {'model_management': None})()
|
||||
|
||||
from resolution_selector import (
|
||||
gcd,
|
||||
calculate_aspect_ratio,
|
||||
format_resolution,
|
||||
get_resolution_list,
|
||||
get_all_resolutions,
|
||||
parse_resolution_string,
|
||||
MODEL_RESOLUTIONS
|
||||
)
|
||||
|
||||
def test_gcd():
|
||||
"""Test GCD function"""
|
||||
print("Testing GCD function:")
|
||||
assert gcd(1920, 1080) == 120, "GCD of 1920, 1080 should be 120"
|
||||
assert gcd(1024, 1024) == 1024, "GCD of 1024, 1024 should be 1024"
|
||||
assert gcd(16, 9) == 1, "GCD of 16, 9 should be 1"
|
||||
print(" ✓ GCD tests passed")
|
||||
|
||||
def test_aspect_ratio():
|
||||
"""Test aspect ratio calculation"""
|
||||
print("\nTesting aspect ratio calculation:")
|
||||
assert calculate_aspect_ratio(1920, 1080) == "16:9", "1920x1080 should be 16:9"
|
||||
assert calculate_aspect_ratio(1024, 1024) == "1:1", "1024x1024 should be 1:1"
|
||||
assert calculate_aspect_ratio(1280, 720) == "16:9", "1280x720 should be 16:9"
|
||||
assert calculate_aspect_ratio(1536, 1024) == "3:2", "1536x1024 should be 3:2"
|
||||
print(" ✓ Aspect ratio tests passed")
|
||||
|
||||
def test_format_resolution():
|
||||
"""Test resolution formatting"""
|
||||
print("\nTesting resolution formatting:")
|
||||
result = format_resolution(1920, 1080)
|
||||
print(f" 1920x1080 → '{result}'")
|
||||
# With padding, should be "1920x1080 (16:9 Landscape)" - 13 chars total for resolution part
|
||||
assert "(16:9 Landscape)" in result, f"Should contain aspect ratio and orientation"
|
||||
assert result.startswith("1920x1080"), f"Should start with resolution"
|
||||
|
||||
result = format_resolution(1024, 1024)
|
||||
print(f" 1024x1024 → '{result}'")
|
||||
assert "(1:1 Square)" in result, f"Should contain aspect ratio and orientation"
|
||||
assert result.startswith("1024x1024"), f"Should start with resolution"
|
||||
|
||||
result = format_resolution(1080, 1920)
|
||||
print(f" 1080x1920 → '{result}'")
|
||||
assert "(9:16 Portrait)" in result, f"Should contain aspect ratio and orientation"
|
||||
assert result.startswith("1080x1920"), f"Should start with resolution"
|
||||
print(" ✓ Format resolution tests passed")
|
||||
|
||||
def test_parse_resolution():
|
||||
"""Test resolution string parsing"""
|
||||
print("\nTesting resolution string parsing:")
|
||||
# Test with padding (as it will be in the actual dropdown)
|
||||
width, height = parse_resolution_string("1920x1080 (16:9 Landscape)")
|
||||
assert width == 1920 and height == 1080, "Should parse 1920x1080 with padding"
|
||||
|
||||
width, height = parse_resolution_string("1024x1024 (1:1 Square)")
|
||||
assert width == 1024 and height == 1024, "Should parse 1024x1024 with padding"
|
||||
|
||||
# Test without padding (backward compatibility)
|
||||
width, height = parse_resolution_string("1920x1080 (16:9 Landscape)")
|
||||
assert width == 1920 and height == 1080, "Should parse 1920x1080 without padding"
|
||||
print(" ✓ Parse resolution tests passed")
|
||||
|
||||
def test_model_resolutions():
|
||||
"""Test model resolution lists"""
|
||||
print("\nTesting model resolution lists:")
|
||||
|
||||
# Test individual model
|
||||
flux_res = get_resolution_list("Flux")
|
||||
print(f" Flux has {len(flux_res)} resolutions")
|
||||
assert len(flux_res) > 0, "Flux should have resolutions"
|
||||
assert any("1920x1080" in r for r in flux_res), "Flux should have 1920x1080"
|
||||
|
||||
# Test All model
|
||||
all_res = get_resolution_list("All")
|
||||
print(f" 'All' has {len(all_res)} unique resolutions")
|
||||
assert len(all_res) > len(flux_res), "'All' should have more resolutions than individual models"
|
||||
|
||||
# Verify no duplicates in All
|
||||
assert len(all_res) == len(set(all_res)), "'All' should have no duplicates"
|
||||
print(" ✓ Model resolution tests passed")
|
||||
|
||||
def test_new_resolutions():
|
||||
"""Test that new resolutions were added"""
|
||||
print("\nTesting new resolutions up to 1920x1080:")
|
||||
|
||||
for model_name, model_data in MODEL_RESOLUTIONS.items():
|
||||
resolutions = get_resolution_list(model_name)
|
||||
has_1080p = any("1920x1080" in r or "1080x1920" in r for r in resolutions)
|
||||
print(f" {model_name}: {len(resolutions)} resolutions, has 1080p: {has_1080p}")
|
||||
|
||||
print(" ✓ New resolutions verified")
|
||||
|
||||
def test_all_resolutions_unique():
|
||||
"""Test that 'All' model returns unique resolutions"""
|
||||
print("\nTesting 'All' model uniqueness:")
|
||||
all_res = get_all_resolutions()
|
||||
|
||||
# Count occurrences
|
||||
dimensions = []
|
||||
for res in all_res:
|
||||
width, height = parse_resolution_string(res)
|
||||
dimensions.append((width, height))
|
||||
|
||||
# Check for duplicates
|
||||
unique_dimensions = set(dimensions)
|
||||
assert len(dimensions) == len(unique_dimensions), "Should have no duplicate dimensions"
|
||||
print(f" ✓ All {len(all_res)} resolutions are unique")
|
||||
|
||||
if __name__ == "__main__":
|
||||
print("=" * 60)
|
||||
print("ResolutionSelector Enhancement Tests")
|
||||
print("=" * 60)
|
||||
|
||||
try:
|
||||
test_gcd()
|
||||
test_aspect_ratio()
|
||||
test_format_resolution()
|
||||
test_parse_resolution()
|
||||
test_model_resolutions()
|
||||
test_new_resolutions()
|
||||
test_all_resolutions_unique()
|
||||
|
||||
print("\n" + "=" * 60)
|
||||
print("✓ ALL TESTS PASSED!")
|
||||
print("=" * 60)
|
||||
|
||||
except AssertionError as e:
|
||||
print(f"\n✗ TEST FAILED: {e}")
|
||||
sys.exit(1)
|
||||
except Exception as e:
|
||||
print(f"\n✗ ERROR: {e}")
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
sys.exit(1)
|
||||
@@ -0,0 +1,236 @@
|
||||
import { app } from "/scripts/app.js";
|
||||
|
||||
const MODEL_RESOLUTIONS = {
|
||||
"Flux": {
|
||||
"square": [[512, 512], [768, 768], [1024, 1024], [1280, 1280], [1536, 1536], [1920, 1920], [2048, 2048]],
|
||||
"portrait": [[768, 1344], [832, 1216], [896, 1152], [928, 1664], [1024, 1536], [1024, 1792], [1080, 1920], [1152, 2048]],
|
||||
"landscape": [[1344, 768], [1216, 832], [1152, 896], [1536, 1024], [1664, 928], [1792, 1024], [1920, 1080], [2048, 1152]],
|
||||
},
|
||||
"Qwen Image": {
|
||||
"square": [[1024, 1024], [1280, 1280], [1328, 1328], [1536, 1536], [1920, 1920], [2048, 2048]],
|
||||
"portrait": [[928, 1664], [1024, 1536], [1080, 1920], [1140, 1472], [1152, 2048]],
|
||||
"landscape": [[1472, 1140], [1536, 1024], [1664, 928], [1920, 1080], [2048, 1152]],
|
||||
},
|
||||
"Z-Image": {
|
||||
"square": [[512, 512], [768, 768], [1024, 1024], [1280, 1280], [1536, 1536], [1920, 1920], [2048, 2048]],
|
||||
"portrait": [[720, 1280], [768, 1024], [1080, 1920], [1152, 2048]],
|
||||
"landscape": [[1024, 768], [1280, 720], [1920, 1080], [2048, 1152]],
|
||||
},
|
||||
"SD 1.5": {
|
||||
"square": [[512, 512], [768, 768], [1024, 1024], [1280, 1280], [1536, 1536]],
|
||||
"portrait": [[512, 768], [512, 682], [512, 1024], [768, 1024], [768, 1344], [1080, 1920]],
|
||||
"landscape": [[768, 512], [1024, 512], [1024, 768], [1344, 768], [1536, 512], [1920, 1080]],
|
||||
},
|
||||
"SDXL": {
|
||||
"square": [[1024, 1024], [1280, 1280], [1536, 1536], [1920, 1920], [2048, 2048]],
|
||||
"portrait": [[640, 1536], [768, 1344], [832, 1216], [896, 1152], [1024, 1536], [1080, 1920], [1152, 2048]],
|
||||
"landscape": [[1152, 896], [1216, 832], [1344, 768], [1536, 640], [1536, 1024], [1920, 1080], [2048, 1152]],
|
||||
}
|
||||
};
|
||||
|
||||
function gcd(a, b) {
|
||||
while (b !== 0) {
|
||||
const temp = b;
|
||||
b = a % b;
|
||||
a = temp;
|
||||
}
|
||||
return a;
|
||||
}
|
||||
|
||||
function calculateAspectRatio(width, height) {
|
||||
// Calculate actual ratio as decimal
|
||||
const actualRatio = width / height;
|
||||
|
||||
// Common aspect ratios [ratio_value, "width:height" string]
|
||||
const commonRatios = [
|
||||
[1.0, "1:1"], // Square
|
||||
[1.25, "5:4"], // 1.25
|
||||
[1.33, "4:3"], // 1.333...
|
||||
[1.5, "3:2"], // 1.5
|
||||
[1.6, "16:10"], // 1.6
|
||||
[1.78, "16:9"], // 1.777...
|
||||
[2.0, "2:1"], // 2.0
|
||||
[2.35, "21:9"], // 2.333... (ultrawide)
|
||||
[2.4, "12:5"], // 2.4
|
||||
[3.0, "3:1"], // 3.0
|
||||
// Portrait ratios
|
||||
[0.75, "3:4"], // 0.75
|
||||
[0.67, "2:3"], // 0.666...
|
||||
[0.625, "5:8"], // 0.625
|
||||
[0.56, "9:16"], // 0.5625
|
||||
[0.5, "1:2"], // 0.5
|
||||
[0.42, "5:12"], // 0.4166...
|
||||
];
|
||||
|
||||
// Find closest common ratio (within 5% tolerance)
|
||||
const tolerance = 0.05;
|
||||
for (const [ratioValue, ratioStr] of commonRatios) {
|
||||
if (Math.abs(actualRatio - ratioValue) / ratioValue < tolerance) {
|
||||
return ratioStr;
|
||||
}
|
||||
}
|
||||
|
||||
// If no close match, calculate GCD-based ratio
|
||||
const divisor = gcd(width, height);
|
||||
const ratioWidth = width / divisor;
|
||||
const ratioHeight = height / divisor;
|
||||
return `${ratioWidth}:${ratioHeight}`;
|
||||
}
|
||||
|
||||
function formatResolution(width, height) {
|
||||
const aspectRatio = calculateAspectRatio(width, height);
|
||||
let orientation;
|
||||
|
||||
if (width === height) {
|
||||
orientation = "Square";
|
||||
} else if (width < height) {
|
||||
orientation = "Portrait";
|
||||
} else {
|
||||
orientation = "Landscape";
|
||||
}
|
||||
|
||||
// Format with fixed width for better alignment
|
||||
const resolutionStr = `${width}x${height}`;
|
||||
const paddedResolution = resolutionStr.padEnd(13, ' '); // Pad to 13 chars for alignment
|
||||
|
||||
return `${paddedResolution}(${aspectRatio} ${orientation})`;
|
||||
}
|
||||
|
||||
function getAllResolutions() {
|
||||
const uniqueResolutions = new Map();
|
||||
|
||||
// Collect all unique width×height pairs
|
||||
for (const modelName in MODEL_RESOLUTIONS) {
|
||||
const modelData = MODEL_RESOLUTIONS[modelName];
|
||||
for (const category of ["square", "portrait", "landscape"]) {
|
||||
if (modelData[category]) {
|
||||
for (const [w, h] of modelData[category]) {
|
||||
const key = `${w}x${h}`;
|
||||
if (!uniqueResolutions.has(key)) {
|
||||
uniqueResolutions.set(key, { width: w, height: h, pixels: w * h });
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Sort by total pixels, then by width
|
||||
const sorted = Array.from(uniqueResolutions.values())
|
||||
.sort((a, b) => {
|
||||
if (a.pixels !== b.pixels) return a.pixels - b.pixels;
|
||||
return a.width - b.width;
|
||||
});
|
||||
|
||||
return sorted.map(({ width, height }) => formatResolution(width, height));
|
||||
}
|
||||
|
||||
function getResolutionsForModel(model) {
|
||||
if (model === "All") {
|
||||
return getAllResolutions();
|
||||
}
|
||||
|
||||
const modelData = MODEL_RESOLUTIONS[model];
|
||||
if (!modelData) return [];
|
||||
|
||||
const resolutions = [];
|
||||
for (const category of ["square", "portrait", "landscape"]) {
|
||||
if (modelData[category]) {
|
||||
for (const [w, h] of modelData[category]) {
|
||||
resolutions.push(formatResolution(w, h));
|
||||
}
|
||||
}
|
||||
}
|
||||
return resolutions;
|
||||
}
|
||||
|
||||
function getDefaultResolution(model) {
|
||||
// Model-specific native/optimal resolutions
|
||||
const defaultResolutions = {
|
||||
"Flux": [1024, 1024],
|
||||
"Qwen Image": [1328, 1328],
|
||||
"Z-Image": [1024, 1024],
|
||||
"SD 1.5": [512, 512],
|
||||
"SDXL": [1024, 1024],
|
||||
"All": [1024, 1024]
|
||||
};
|
||||
|
||||
if (defaultResolutions[model]) {
|
||||
const [w, h] = defaultResolutions[model];
|
||||
return formatResolution(w, h);
|
||||
}
|
||||
|
||||
// Fallback
|
||||
return formatResolution(1024, 1024);
|
||||
}
|
||||
|
||||
app.registerExtension({
|
||||
name: "ResolutionSelector.DynamicDropdown",
|
||||
|
||||
async nodeCreated(node) {
|
||||
if (node.comfyClass !== "ResolutionSelector") return;
|
||||
|
||||
// Set wider default node width to prevent text cutoff
|
||||
node.setSize([400, node.size[1]]);
|
||||
|
||||
const modelWidget = node.widgets.find(w => w.name === "model");
|
||||
const resolutionWidget = node.widgets.find(w => w.name === "resolution");
|
||||
|
||||
if (!modelWidget || !resolutionWidget) {
|
||||
console.error("ResolutionSelector: Required widgets not found");
|
||||
return;
|
||||
}
|
||||
|
||||
const origCallback = modelWidget.callback;
|
||||
|
||||
const updateResolutions = (modelValue) => {
|
||||
const resolutions = getResolutionsForModel(modelValue);
|
||||
|
||||
if (resolutions.length === 0) {
|
||||
console.warn(`ResolutionSelector: No resolutions found for model ${modelValue}`);
|
||||
return;
|
||||
}
|
||||
|
||||
resolutionWidget.options.values = resolutions;
|
||||
|
||||
// Set to model-specific default resolution
|
||||
const defaultRes = getDefaultResolution(modelValue);
|
||||
if (resolutions.includes(defaultRes)) {
|
||||
resolutionWidget.value = defaultRes;
|
||||
} else if (!resolutions.includes(resolutionWidget.value)) {
|
||||
// Fallback to first resolution if default not found
|
||||
resolutionWidget.value = resolutions[0];
|
||||
}
|
||||
|
||||
node.setDirtyCanvas(true, true);
|
||||
};
|
||||
|
||||
modelWidget.callback = function(value) {
|
||||
if (origCallback) {
|
||||
origCallback.apply(this, arguments);
|
||||
}
|
||||
updateResolutions(value);
|
||||
};
|
||||
|
||||
setTimeout(() => {
|
||||
updateResolutions(modelWidget.value);
|
||||
}, 10);
|
||||
},
|
||||
|
||||
async loadedGraphNode(node) {
|
||||
if (node.comfyClass !== "ResolutionSelector") return;
|
||||
|
||||
const modelWidget = node.widgets.find(w => w.name === "model");
|
||||
if (modelWidget) {
|
||||
const resolutions = getResolutionsForModel(modelWidget.value);
|
||||
const resolutionWidget = node.widgets.find(w => w.name === "resolution");
|
||||
|
||||
if (resolutionWidget && resolutions.length > 0) {
|
||||
resolutionWidget.options.values = resolutions;
|
||||
|
||||
if (!resolutions.includes(resolutionWidget.value)) {
|
||||
resolutionWidget.value = resolutions[0];
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
});
|
||||
Reference in New Issue
Block a user