VideoTriangleCFGGuidance

Documentation

• Class name: VideoTriangleCFGGuidance
• Category: sampling/video_models
• Output node: False

This node applies a triangular configuration guidance patch to a video model, dynamically adjusting the conditioning scale over time to modulate the influence of conditional inputs. It's designed to enhance the generation of video content by applying a specific pattern of guidance that varies linearly within a defined period, aiming to achieve a balance between conditioned and unconditioned content.

Input types

Required

• model
  • The video model to which the triangular CFG guidance patch will be applied. This model is cloned and modified with a new sampler CFG function.
  • Comfy dtype: MODEL
  • Python dtype: torch.nn.Module
• min_cfg
  • The minimum conditioning scale factor to be used in the triangular CFG guidance. It defines the lowest point of scale adjustment, influencing the balance between conditioned and unconditioned content generation.
  • Comfy dtype: FLOAT
  • Python dtype: float

Output types

• model
  • Comfy dtype: MODEL
  • The modified video model with the triangular CFG guidance patch applied, capable of generating video content with dynamically adjusted conditioning.
  • Python dtype: torch.nn.Module

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

class VideoTriangleCFGGuidance:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": { "model": ("MODEL",),
                              "min_cfg": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.5, "round": 0.01}),
                              }}
    RETURN_TYPES = ("MODEL",)
    FUNCTION = "patch"

    CATEGORY = "sampling/video_models"

    def patch(self, model, min_cfg):
        def linear_cfg(args):
            cond = args["cond"]
            uncond = args["uncond"]
            cond_scale = args["cond_scale"]
            period = 1.0
            values = torch.linspace(0, 1, cond.shape[0], device=cond.device)
            values = 2 * (values / period - torch.floor(values / period + 0.5)).abs()
            scale = (values * (cond_scale - min_cfg) + min_cfg).reshape((cond.shape[0], 1, 1, 1))

            return uncond + scale * (cond - uncond)

        m = model.clone()
        m.set_model_sampler_cfg_function(linear_cfg)
        return (m, )