09e0d1cb3a
* Reworked SAG, removed unnecessary patch * Reworked anisotropic filters for faster compute. * Replaced with guided anisotropic filter for less distribution.
74 lines
2.3 KiB
Python
74 lines
2.3 KiB
Python
import torch
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import comfy.model_base
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import comfy.ldm.modules.diffusionmodules.openaimodel
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import comfy.samplers
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import comfy.k_diffusion.external
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import modules.anisotropic as anisotropic
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from comfy.k_diffusion import utils
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sharpness = 2.0
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cfg_x0 = 0.0
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cfg_s = 1.0
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def cfg_patched(args):
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global cfg_x0, cfg_s
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positive_eps = args['cond'].clone()
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positive_x0 = args['cond'] * cfg_s + cfg_x0
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uncond = args['uncond'] * cfg_s + cfg_x0
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cond_scale = args['cond_scale']
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t = args['timestep']
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alpha = 1.0 - (t / 999.0)[:, None, None, None].clone()
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alpha *= 0.001 * sharpness
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eps_degraded = anisotropic.adaptive_anisotropic_filter(x=positive_eps, g=positive_x0)
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eps_degraded_weighted = eps_degraded * alpha + positive_eps * (1.0 - alpha)
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cond = eps_degraded_weighted * cfg_s + cfg_x0
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return uncond + (cond - uncond) * cond_scale
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def patched_discrete_eps_ddpm_denoiser_forward(self, input, sigma, **kwargs):
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global cfg_x0, cfg_s
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c_out, c_in = [utils.append_dims(x, input.ndim) for x in self.get_scalings(sigma)]
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cfg_x0 = input
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cfg_s = c_out
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return self.get_eps(input * c_in, self.sigma_to_t(sigma), **kwargs)
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def sdxl_encode_adm_patched(self, **kwargs):
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clip_pooled = kwargs["pooled_output"]
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width = kwargs.get("width", 768)
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height = kwargs.get("height", 768)
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crop_w = kwargs.get("crop_w", 0)
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crop_h = kwargs.get("crop_h", 0)
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target_width = kwargs.get("target_width", width)
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target_height = kwargs.get("target_height", height)
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if kwargs.get("prompt_type", "") == "negative":
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width *= 0.8
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height *= 0.8
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elif kwargs.get("prompt_type", "") == "positive":
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width *= 1.5
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height *= 1.5
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out = []
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out.append(self.embedder(torch.Tensor([height])))
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out.append(self.embedder(torch.Tensor([width])))
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out.append(self.embedder(torch.Tensor([crop_h])))
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out.append(self.embedder(torch.Tensor([crop_w])))
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out.append(self.embedder(torch.Tensor([target_height])))
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out.append(self.embedder(torch.Tensor([target_width])))
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flat = torch.flatten(torch.cat(out))[None, ]
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return torch.cat((clip_pooled.to(flat.device), flat), dim=1)
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def patch_all():
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comfy.k_diffusion.external.DiscreteEpsDDPMDenoiser.forward = patched_discrete_eps_ddpm_denoiser_forward
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comfy.model_base.SDXL.encode_adm = sdxl_encode_adm_patched
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