| |
| |
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|
|
| import torch |
| import ldm_patched.modules.model_management |
| import ldm_patched.modules.sample |
| import ldm_patched.modules.samplers |
| import ldm_patched.modules.utils |
|
|
|
|
| class PerpNeg: |
| @classmethod |
| def INPUT_TYPES(s): |
| return {"required": {"model": ("MODEL", ), |
| "empty_conditioning": ("CONDITIONING", ), |
| "neg_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0}), |
| }} |
| RETURN_TYPES = ("MODEL",) |
| FUNCTION = "patch" |
|
|
| CATEGORY = "_for_testing" |
|
|
| def patch(self, model, empty_conditioning, neg_scale): |
| m = model.clone() |
| nocond = ldm_patched.modules.sample.convert_cond(empty_conditioning) |
|
|
| def cfg_function(args): |
| model = args["model"] |
| noise_pred_pos = args["cond_denoised"] |
| noise_pred_neg = args["uncond_denoised"] |
| cond_scale = args["cond_scale"] |
| x = args["input"] |
| sigma = args["sigma"] |
| model_options = args["model_options"] |
| nocond_processed = ldm_patched.modules.samplers.encode_model_conds(model.extra_conds, nocond, x, x.device, "negative") |
|
|
| (noise_pred_nocond, _) = ldm_patched.modules.samplers.calc_cond_uncond_batch(model, nocond_processed, None, x, sigma, model_options) |
|
|
| pos = noise_pred_pos - noise_pred_nocond |
| neg = noise_pred_neg - noise_pred_nocond |
| perp = ((torch.mul(pos, neg).sum())/(torch.norm(neg)**2)) * neg |
| perp_neg = perp * neg_scale |
| cfg_result = noise_pred_nocond + cond_scale*(pos - perp_neg) |
| cfg_result = x - cfg_result |
| return cfg_result |
|
|
| m.set_model_sampler_cfg_function(cfg_function) |
|
|
| return (m, ) |
|
|
|
|
| NODE_CLASS_MAPPINGS = { |
| "PerpNeg": PerpNeg, |
| } |
|
|
| NODE_DISPLAY_NAME_MAPPINGS = { |
| "PerpNeg": "Perp-Neg", |
| } |
|
|
