Spaces:
Running
on
Zero
Running
on
Zero
File size: 2,869 Bytes
77f10a3 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 |
import torch
def project(v0, v1):
v1 = torch.nn.functional.normalize(v1, dim=[-1, -2, -3])
v0_parallel = (v0 * v1).sum(dim=[-1, -2, -3], keepdim=True) * v1
v0_orthogonal = v0 - v0_parallel
return v0_parallel, v0_orthogonal
class APG:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"model": ("MODEL",),
"eta": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01, "tooltip": "Controls the scale of the parallel guidance vector. Default CFG behavior at a setting of 1."}),
"norm_threshold": ("FLOAT", {"default": 5.0, "min": 0.0, "max": 50.0, "step": 0.1, "tooltip": "Normalize guidance vector to this value, normalization disable at a setting of 0."}),
"momentum": ("FLOAT", {"default": 0.0, "min": -5.0, "max": 1.0, "step": 0.01, "tooltip":"Controls a running average of guidance during diffusion, disabled at a setting of 0."}),
}
}
RETURN_TYPES = ("MODEL",)
FUNCTION = "patch"
CATEGORY = "sampling/custom_sampling"
def patch(self, model, eta, norm_threshold, momentum):
running_avg = 0
prev_sigma = None
def pre_cfg_function(args):
nonlocal running_avg, prev_sigma
if len(args["conds_out"]) == 1: return args["conds_out"]
cond = args["conds_out"][0]
uncond = args["conds_out"][1]
sigma = args["sigma"][0]
cond_scale = args["cond_scale"]
if prev_sigma is not None and sigma > prev_sigma:
running_avg = 0
prev_sigma = sigma
guidance = cond - uncond
if momentum != 0:
if not torch.is_tensor(running_avg):
running_avg = guidance
else:
running_avg = momentum * running_avg + guidance
guidance = running_avg
if norm_threshold > 0:
guidance_norm = guidance.norm(p=2, dim=[-1, -2, -3], keepdim=True)
scale = torch.minimum(
torch.ones_like(guidance_norm),
norm_threshold / guidance_norm
)
guidance = guidance * scale
guidance_parallel, guidance_orthogonal = project(guidance, cond)
modified_guidance = guidance_orthogonal + eta * guidance_parallel
modified_cond = (uncond + modified_guidance) + (cond - uncond) / cond_scale
return [modified_cond, uncond] + args["conds_out"][2:]
m = model.clone()
m.set_model_sampler_pre_cfg_function(pre_cfg_function)
return (m,)
NODE_CLASS_MAPPINGS = {
"APG": APG,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"APG": "Adaptive Projected Guidance",
}
|