298977887/ComfyUI
1
0
Fork 0
mirror of https://github.com/comfyanonymous/ComfyUI.git synced 2025-04-20 03:13:30 +00:00
Code Issues Packages Projects Releases Wiki Activity

Make unclip more deterministic.

Browse Source 
Pass a seed argument note that this might make old unclip images different.
This commit is contained in:
comfyanonymous 2024-01-14 17:25:21 -05:00
parent 270daa02a8
commit 2395ae740a
2 changed files with 6 additions and 6 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

4
comfy/ldm/modules/encoders/noise_aug_modules.py
View File

@ -23,13 +23,13 @@ class CLIPEmbeddingNoiseAugmentation(ImageConcatWithNoiseAugmentation):
x = (x * self.data_std.to(x.device)) + self.data_mean.to(x.device) x = (x * self.data_std.to(x.device)) + self.data_mean.to(x.device)
return x return x
def forward(self, x, noise_level=None): def forward(self, x, noise_level=None, seed=None):
if noise_level is None: if noise_level is None:
noise_level = torch.randint(0, self.max_noise_level, (x.shape[0],), device=x.device).long() noise_level = torch.randint(0, self.max_noise_level, (x.shape[0],), device=x.device).long()
else: else:
assert isinstance(noise_level, torch.Tensor) assert isinstance(noise_level, torch.Tensor)
x = self.scale(x) x = self.scale(x)
z = self.q_sample(x, noise_level) z = self.q_sample(x, noise_level, seed=seed)
z = self.unscale(z) z = self.unscale(z)
noise_level = self.time_embed(noise_level) noise_level = self.time_embed(noise_level)
return z, noise_level return z, noise_level

8
comfy/model_base.py
View File

@ -210,7 +210,7 @@ class BaseModel(torch.nn.Module):
return (((area * 0.6) / 0.9) + 1024) * (1024 * 1024) return (((area * 0.6) / 0.9) + 1024) * (1024 * 1024)
def unclip_adm(unclip_conditioning, device, noise_augmentor, noise_augment_merge=0.0): def unclip_adm(unclip_conditioning, device, noise_augmentor, noise_augment_merge=0.0, seed=None):
adm_inputs = [] adm_inputs = []
weights = [] weights = []
noise_aug = [] noise_aug = []
@ -219,7 +219,7 @@ def unclip_adm(unclip_conditioning, device, noise_augmentor, noise_augment_merge
weight = unclip_cond["strength"] weight = unclip_cond["strength"]
noise_augment = unclip_cond["noise_augmentation"] noise_augment = unclip_cond["noise_augmentation"]
noise_level = round((noise_augmentor.max_noise_level - 1) * noise_augment) noise_level = round((noise_augmentor.max_noise_level - 1) * noise_augment)
c_adm, noise_level_emb = noise_augmentor(adm_cond.to(device), noise_level=torch.tensor([noise_level], device=device)) c_adm, noise_level_emb = noise_augmentor(adm_cond.to(device), noise_level=torch.tensor([noise_level], device=device), seed=seed)
adm_out = torch.cat((c_adm, noise_level_emb), 1) * weight adm_out = torch.cat((c_adm, noise_level_emb), 1) * weight
weights.append(weight) weights.append(weight)
noise_aug.append(noise_augment) noise_aug.append(noise_augment)
@ -245,11 +245,11 @@ class SD21UNCLIP(BaseModel):
if unclip_conditioning is None: if unclip_conditioning is None:
return torch.zeros((1, self.adm_channels)) return torch.zeros((1, self.adm_channels))
else: else:
return unclip_adm(unclip_conditioning, device, self.noise_augmentor, kwargs.get("unclip_noise_augment_merge", 0.05)) return unclip_adm(unclip_conditioning, device, self.noise_augmentor, kwargs.get("unclip_noise_augment_merge", 0.05), kwargs.get("seed", 0) - 10)
def sdxl_pooled(args, noise_augmentor): def sdxl_pooled(args, noise_augmentor):
if "unclip_conditioning" in args: if "unclip_conditioning" in args:
return unclip_adm(args.get("unclip_conditioning", None), args["device"], noise_augmentor)[:,:1280] return unclip_adm(args.get("unclip_conditioning", None), args["device"], noise_augmentor, seed=args.get("seed", 0) - 10)[:,:1280]
else: else:
return args["pooled_output"] return args["pooled_output"]