| 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}), |
| "rescaling_scale": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 10.0, "step": 0.01}), |
| }} |
| 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="", rescaling_scale=0): |
| |
| 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 |
| if rescaling_scale != 0: |
| factor = cond_pred.std() / cfg_result.std() |
| factor = rescaling_scale * factor + (1 - rescaling_scale) |
| cfg_result *= factor |
|
|
| return cfg_result |
|
|
| m = model.clone() |
| m.set_model_sampler_post_cfg_function(post_cfg_function) |
|
|
| return (m, ) |
|
|
|
|
| NODE_CLASS_MAPPINGS = { |
| "SkipLayerGuidanceDiT": SkipLayerGuidanceDiT, |
| } |
|
|
