Superside ComfyUI Nodes
6ebd0aa6cd
Ported and curated from the internal fal-flux-nodes package: dropped 3 redundant nodes (Nano Banana v1, AnyVLM, SeedVR v1) that were fully superseded by newer variants already in the set. Every node now takes an explicit api_key STRING input instead of reading from a config file or environment variable, so no key is ever baked into this repo.
108 lines
3.7 KiB
Python
108 lines
3.7 KiB
Python
import logging
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from .base_node import (
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SupersideFalNode,
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ImageProcessingMixin,
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APIClientMixin,
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API_KEY_INPUT_SPEC,
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)
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logging.basicConfig(level=logging.DEBUG)
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logger = logging.getLogger(__name__)
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class PASDUpscalerNode(SupersideFalNode, ImageProcessingMixin, APIClientMixin):
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"""
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PASD Upscaler Node: Perform high-quality image super-resolution using PASD-SDXL.
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The model applies AI-enhanced super-resolution with:
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- Pixel-aware stable diffusion
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- ControlNet guidance for structure preservation
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- Wavelet color correction for natural results
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"""
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"image": ("IMAGE",),
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"api_key": API_KEY_INPUT_SPEC,
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},
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"optional": {
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"scale": ("INT", {"default": 2, "min": 1, "max": 4, "step": 1}),
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"steps": ("INT", {"default": 25, "min": 10, "max": 50, "step": 1}),
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"guidance_scale": (
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"FLOAT",
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{"default": 7.0, "min": 1.0, "max": 20.0, "step": 0.1},
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),
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"conditioning_scale": (
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"FLOAT",
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{"default": 0.8, "min": 0.1, "max": 1.0, "step": 0.1},
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),
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"prompt": (
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"STRING",
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{
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"multiline": True,
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"default": "",
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"placeholder": "Additional prompt to guide "
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"super-resolution (optional)",
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},
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),
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"negative_prompt": (
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"STRING",
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{
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"multiline": True,
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"default": "blurry, dirty, messy, frames, deformed, dotted, "
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"noise, raster lines, unclear, lowres, over-smoothed, "
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"painting, ai generated",
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"placeholder": "Negative prompt to avoid unwanted artifacts",
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},
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),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "upscale"
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DESCRIPTION = (
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"Perform high-quality image super-resolution using PASD-SDXL with "
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"pixel-aware stable diffusion and ControlNet guidance"
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)
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def prepare_arguments(self, client, image, **kwargs):
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"""Prepare arguments for the API call."""
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# Upload the input image
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image_url = self.upload_image(client, image)
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arguments = {"image_url": image_url}
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# Add optional parameters if provided
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if kwargs.get("scale"):
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arguments["scale"] = kwargs["scale"]
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if kwargs.get("steps"):
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arguments["steps"] = kwargs["steps"]
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if kwargs.get("guidance_scale"):
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arguments["guidance_scale"] = kwargs["guidance_scale"]
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if kwargs.get("conditioning_scale"):
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arguments["conditioning_scale"] = kwargs["conditioning_scale"]
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if kwargs.get("prompt") and kwargs["prompt"].strip():
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arguments["prompt"] = kwargs["prompt"].strip()
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if kwargs.get("negative_prompt") and kwargs["negative_prompt"].strip():
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arguments["negative_prompt"] = kwargs["negative_prompt"].strip()
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return arguments
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def upscale(self, image, api_key, **kwargs):
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"""Main upscaling function."""
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try:
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client = self.get_client(api_key)
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arguments = self.prepare_arguments(client, image, **kwargs)
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result = self.call_api(client, "fal-ai/pasd", arguments)
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return self.process_images(result)
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except Exception as e:
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logger.error(f"Upscaling failed: {str(e)}")
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raise
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