INNER CODE UNIT · Python

map_sigma

Extraltodeus/ComfyUI-AutomaticCFG · nodes.py:102

def map_sigma(sigma, sigmax, sigmin):
    return 1 + ((sigma - sigmax) * (0 - 1)) / (sigmin - sigmax)

def center_latent_mean_values(latent, per_channel, mult):
    for b in range(len(latent)):
        if per_channel:
            for c in range(len(latent[b])):
                latent[b][c] -= latent[b][c].mean() * mult
        else:
            latent[b] -= latent[b].mean() * mult
    return latent

def get_denoised_ranges(latent, measure="hard", top_k=0.25):
    chans = []
    for x in range(len(latent)):
        max_values = torch.topk(latent[x] - latent[x].mean() if measure == "range" else latent[x], k=int(len(latent[x])*top_k), largest=True).values
        min_values = torch.topk(latent[x] - latent[x].mean() if measure == "range" else latent[x], k=int(len(latent[x])*top_k), largest=False).values
        max_val = torch.mean(max_values).item()

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