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