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Use docs python package

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Benjamin Lu 2025-05-24 18:49:05 -04:00
parent 5249ad0280
commit 9f40d03051
5 changed files with 20 additions and 45 deletions
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13
app/frontend_management.py
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@ -205,6 +205,19 @@ comfyui-workflow-templates is not installed.
""".strip()
)
@classmethod
def embedded_docs_path(cls) -> str:
"""Get the path to embedded documentation"""
try:
import comfyui_embedded_docs
return str(
importlib.resources.files(comfyui_embedded_docs) / "docs"
)
except ImportError:
logging.info("comfyui-embedded-docs package not found")
return None
@classmethod
def parse_version_string(cls, value: str) -> tuple[str, str, str]:
"""

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docs/FluxProUltraImageNode/assets/flux-1-1-pro-ultra-image.jpg
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40
docs/FluxProUltraImageNode/en.md
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@ -1,40 +0,0 @@
![ComfyUI Native Flux 1.1 [pro] Ultra Image node](assets/flux-1-1-pro-ultra-image.jpg)
The Flux 1.1 [pro] Ultra Image node allows you to generate ultra-high-resolution images through text prompts, directly connecting to Black Forest Labs' latest image generation API.
This node supports two main usage modes:
1. **Text-to-Image**: Generate high-quality images from text prompts (when no image input is used)
2. **Image-to-Image**: Combine existing images with prompts to create new images that blend features from both (Remix mode)
This node supports Ultra mode through API calls, capable of generating images at 4 times the resolution of standard Flux 1.1 [pro] (up to 4MP), without sacrificing prompt adherence, and maintaining super-fast generation times of just 10 seconds. Compared to other high-resolution models, it's more than 2.5 times faster.
## Parameter Description
### Basic Parameters
| Parameter | Type | Default | Description |
| ----------------- | ------- | ------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| prompt | String | "" | Text description for generating the image |
| prompt_upsampling | Boolean | False | Whether to use prompt upsampling technique to enhance details. When enabled, automatically modifies prompts for more creative generation, but results become non-deterministic (same seed won't produce exactly the same result) |
| seed | Integer | 0 | Random seed value, controls generation randomness |
| aspect_ratio | String | "16:9" | Width-to-height ratio of the image, must be between 1:4 and 4:1 |
| raw | Boolean | False | When set to True, generates less processed, more natural-looking images |
### Optional Parameters
| Parameter | Type | Default | Description |
| --------------------- | ----- | ------- | --------------------------------------------------------------------------------------------------------------------------------------------------------- |
| image_prompt | Image | None | Optional input, used for Image-to-Image (Remix) mode |
| image_prompt_strength | Float | 0.1 | Active when `image_prompt` is input, adjusts the blend between prompt and image prompt. Higher values make output closer to input image, range is 0.0-1.0 |
### Output
| Output | Type | Description |
| ------ | ----- | -------------------------------------- |
| IMAGE | Image | Generated high-resolution image result |
## How It Works
Flux 1.1 [pro] Ultra mode uses optimized deep learning architecture and efficient GPU acceleration technology to achieve high-resolution image generation without sacrificing speed. When a request is sent to the API, the system parses the prompt, applies appropriate parameters, then computes the image in parallel, finally generating and returning the high-resolution result.
Compared to regular models, Ultra mode particularly focuses on detail preservation and consistency at large scales, ensuring impressive quality even at 4MP high resolution.

1
requirements.txt
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@ -1,5 +1,6 @@
comfyui-frontend-package==1.19.9
comfyui-workflow-templates==0.1.14
comfyui-embedded-docs==0.1.0
torch
torchsde
torchvision

11
server.py
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@ -746,11 +746,12 @@ class PromptServer():
web.static('/templates', workflow_templates_path)
])
# Serve node documentation markdown from the docs directory
docs_dir = os.path.join(os.path.dirname(__file__), 'docs')
self.app.add_routes([
web.static('/docs', docs_dir)
])
# Serve embedded documentation from the package
embedded_docs_path = FrontendManager.embedded_docs_path()
if embedded_docs_path:
self.app.add_routes([
web.static('/docs', embedded_docs_path)
])
self.app.add_routes([
web.static('/', self.web_root),