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Improved the scheduling of ADM guidance and CFG mimicking for better visual quality in high frequency domain and small objects.

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Improved the scheduling of ADM guidance and CFG mimicking for better visual quality in high frequency domain and small objects.
This commit is contained in:
lllyasviel
2023-10-03 14:05:14 -07:00
committed by GitHub
parent 2f31d9e5a7
commit 480a7222c5
4 changed files with 40 additions and 39 deletions
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modules/patch.py
+32 -36
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@@ -147,27 +147,20 @@ def calculate_weight_patched(self, patches, weight, key):
return weight
def get_adaptive_weight_k(cfg_scale):
w = float(cfg_scale)
w -= 7.0
w /= 3.0
w = max(w, 0.01)
w = min(w, 0.99)
return w
def compute_cfg(uncond, cond, cfg_scale):
def compute_cfg(uncond, cond, cfg_scale, t):
global adaptive_cfg
k = adaptive_cfg * get_adaptive_weight_k(cfg_scale)
x_cfg = uncond + cfg_scale * (cond - uncond)
ro_pos = torch.std(cond, dim=(1, 2, 3), keepdim=True)
ro_cfg = torch.std(x_cfg, dim=(1, 2, 3), keepdim=True)
mimic_cfg = float(adaptive_cfg)
real_cfg = float(cfg_scale)
x_rescaled = x_cfg * (ro_pos / ro_cfg)
x_final = k * x_rescaled + (1.0 - k) * x_cfg
real_eps = uncond + real_cfg * (cond - uncond)
return x_final
if cfg_scale < adaptive_cfg:
return real_eps
mimicked_eps = uncond + mimic_cfg * (cond - uncond)
return real_eps * t + mimicked_eps * (1 - t)
def patched_sampler_cfg_function(args):
@@ -184,7 +177,7 @@ def patched_sampler_cfg_function(args):
positive_eps_degraded = anisotropic.adaptive_anisotropic_filter(x=positive_eps, g=positive_x0)
positive_eps_degraded_weighted = positive_eps_degraded * alpha + positive_eps * (1.0 - alpha)
return compute_cfg(uncond=negative_eps, cond=positive_eps_degraded_weighted, cfg_scale=cfg_scale)
return compute_cfg(uncond=negative_eps, cond=positive_eps_degraded_weighted, cfg_scale=cfg_scale, t=t)
def patched_discrete_eps_ddpm_denoiser_forward(self, input, sigma, **kwargs):
@@ -210,10 +203,8 @@ def sdxl_encode_adm_patched(self, **kwargs):
clip_pooled = comfy.model_base.sdxl_pooled(kwargs, self.noise_augmentor)
width = kwargs.get("width", 768)
height = kwargs.get("height", 768)
crop_w = kwargs.get("crop_w", 0)
crop_h = kwargs.get("crop_h", 0)
target_width = kwargs.get("target_width", width)
target_height = kwargs.get("target_height", height)
target_width = width
target_height = height
if kwargs.get("prompt_type", "") == "negative":
width = float(width) * negative_adm_scale
@@ -225,20 +216,22 @@ def sdxl_encode_adm_patched(self, **kwargs):
# Avoid artifacts
width = int(width)
height = int(height)
crop_w = int(crop_w)
crop_h = int(crop_h)
crop_w = 0
crop_h = 0
target_width = int(target_width)
target_height = int(target_height)
out = []
out.append(self.embedder(torch.Tensor([height])))
out.append(self.embedder(torch.Tensor([width])))
out.append(self.embedder(torch.Tensor([crop_h])))
out.append(self.embedder(torch.Tensor([crop_w])))
out.append(self.embedder(torch.Tensor([target_height])))
out.append(self.embedder(torch.Tensor([target_width])))
flat = torch.flatten(torch.cat(out)).unsqueeze(dim=0).repeat(clip_pooled.shape[0], 1)
return torch.cat((clip_pooled.to(flat.device), flat), dim=1)
out_a = [self.embedder(torch.Tensor([height])), self.embedder(torch.Tensor([width])),
self.embedder(torch.Tensor([crop_h])), self.embedder(torch.Tensor([crop_w])),
self.embedder(torch.Tensor([target_height])), self.embedder(torch.Tensor([target_width]))]
flat_a = torch.flatten(torch.cat(out_a)).unsqueeze(dim=0).repeat(clip_pooled.shape[0], 1)
out_b = [self.embedder(torch.Tensor([target_height])), self.embedder(torch.Tensor([target_width])),
self.embedder(torch.Tensor([crop_h])), self.embedder(torch.Tensor([crop_w])),
self.embedder(torch.Tensor([target_height])), self.embedder(torch.Tensor([target_width]))]
flat_b = torch.flatten(torch.cat(out_b)).unsqueeze(dim=0).repeat(clip_pooled.shape[0], 1)
return torch.cat((clip_pooled.to(flat_a.device), flat_a, clip_pooled.to(flat_b.device), flat_b), dim=1)
def encode_token_weights_patched_with_a1111_method(self, token_weight_pairs):
@@ -346,9 +339,12 @@ def patched_unet_forward(self, x, timesteps=None, context=None, y=None, control=
transformer_options["current_index"] = 0
transformer_patches = transformer_options.get("patches", {})
assert (y is not None) == (
self.num_classes is not None
), "must specify y if and only if the model is class-conditional"
if isinstance(y, torch.Tensor) and int(y.dim()) == 2 and int(y.shape[1]) == 5632:
t = (timesteps / 999.0)[:, None].clone().to(x) ** 2.0
ya = y[..., :2816].clone()
yb = y[..., 2816:].clone()
y = t * ya + (1 - t) * yb
hs = []
t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(self.dtype)
emb = self.time_embed(t_emb)