Spaces:
Running
on
L40S
Running
on
L40S
File size: 3,221 Bytes
4450790 |
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 78 79 |
import comfy.model_patcher
import comfy.samplers
import re
class SkipLayerGuidanceDiT:
'''
Enhance guidance towards detailed dtructure by having another set of CFG negative with skipped layers.
Inspired by Perturbed Attention Guidance (https://arxiv.org/abs/2403.17377)
Original experimental implementation for SD3 by Dango233@StabilityAI.
'''
@classmethod
def INPUT_TYPES(s):
return {"required": {"model": ("MODEL", ),
"double_layers": ("STRING", {"default": "7, 8, 9", "multiline": False}),
"single_layers": ("STRING", {"default": "7, 8, 9", "multiline": False}),
"scale": ("FLOAT", {"default": 3.0, "min": 0.0, "max": 10.0, "step": 0.1}),
"start_percent": ("FLOAT", {"default": 0.01, "min": 0.0, "max": 1.0, "step": 0.001}),
"end_percent": ("FLOAT", {"default": 0.15, "min": 0.0, "max": 1.0, "step": 0.001})
}}
RETURN_TYPES = ("MODEL",)
FUNCTION = "skip_guidance"
EXPERIMENTAL = True
DESCRIPTION = "Generic version of SkipLayerGuidance node that can be used on every DiT model."
CATEGORY = "advanced/guidance"
def skip_guidance(self, model, scale, start_percent, end_percent, double_layers="", single_layers=""):
# check if layer is comma separated integers
def skip(args, extra_args):
return args
model_sampling = model.get_model_object("model_sampling")
sigma_start = model_sampling.percent_to_sigma(start_percent)
sigma_end = model_sampling.percent_to_sigma(end_percent)
double_layers = re.findall(r'\d+', double_layers)
double_layers = [int(i) for i in double_layers]
single_layers = re.findall(r'\d+', single_layers)
single_layers = [int(i) for i in single_layers]
if len(double_layers) == 0 and len(single_layers) == 0:
return (model, )
def post_cfg_function(args):
model = args["model"]
cond_pred = args["cond_denoised"]
cond = args["cond"]
cfg_result = args["denoised"]
sigma = args["sigma"]
x = args["input"]
model_options = args["model_options"].copy()
for layer in double_layers:
model_options = comfy.model_patcher.set_model_options_patch_replace(model_options, skip, "dit", "double_block", layer)
for layer in single_layers:
model_options = comfy.model_patcher.set_model_options_patch_replace(model_options, skip, "dit", "single_block", layer)
model_sampling.percent_to_sigma(start_percent)
sigma_ = sigma[0].item()
if scale > 0 and sigma_ >= sigma_end and sigma_ <= sigma_start:
(slg,) = comfy.samplers.calc_cond_batch(model, [cond], x, sigma, model_options)
cfg_result = cfg_result + (cond_pred - slg) * scale
return cfg_result
m = model.clone()
m.set_model_sampler_post_cfg_function(post_cfg_function)
return (m, )
NODE_CLASS_MAPPINGS = {
"SkipLayerGuidanceDiT": SkipLayerGuidanceDiT,
}
|