| from typing_extensions import override |
| import torch |
| import torch.nn.functional as F |
|
|
| from comfy_api.latest import ComfyExtension, io |
|
|
|
|
| class Mahiro(io.ComfyNode): |
| @classmethod |
| def define_schema(cls): |
| return io.Schema( |
| node_id="Mahiro", |
| display_name="Positive-Biased Guidance", |
| category="_for_testing", |
| description="Modify the guidance to scale more on the 'direction' of the positive prompt rather than the difference between the negative prompt.", |
| inputs=[ |
| io.Model.Input("model"), |
| ], |
| outputs=[ |
| io.Model.Output(display_name="patched_model"), |
| ], |
| is_experimental=True, |
| search_aliases=[ |
| "mahiro", |
| "mahiro cfg", |
| "similarity-adaptive guidance", |
| "positive-biased cfg", |
| ], |
| ) |
|
|
| @classmethod |
| def execute(cls, model) -> io.NodeOutput: |
| m = model.clone() |
|
|
| def mahiro_normd(args): |
| scale: float = args["cond_scale"] |
| cond_p: torch.Tensor = args["cond_denoised"] |
| uncond_p: torch.Tensor = args["uncond_denoised"] |
| |
| leap = cond_p * scale |
| |
| u_leap = uncond_p * scale |
| cfg = args["denoised"] |
| merge = (leap + cfg) / 2 |
| normu = torch.sqrt(u_leap.abs()) * u_leap.sign() |
| normm = torch.sqrt(merge.abs()) * merge.sign() |
| sim = F.cosine_similarity(normu, normm).mean() |
| simsc = 2 * (sim + 1) |
| wm = (simsc * cfg + (4 - simsc) * leap) / 4 |
| return wm |
|
|
| m.set_model_sampler_post_cfg_function(mahiro_normd) |
| return io.NodeOutput(m) |
|
|
|
|
| class MahiroExtension(ComfyExtension): |
| @override |
| async def get_node_list(self) -> list[type[io.ComfyNode]]: |
| return [ |
| Mahiro, |
| ] |
|
|
|
|
| async def comfy_entrypoint() -> MahiroExtension: |
| return MahiroExtension() |
|
|
