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Remove some trailing white space.

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This commit is contained in:
comfyanonymous
2024-12-27 18:02:21 -05:00
parent 9cfd185676
commit d170292594
15 changed files with 37 additions and 38 deletions
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2
comfy/controlnet.py
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@@ -120,7 +120,7 @@ class ControlBase:
if self.previous_controlnet is not None:
out += self.previous_controlnet.get_models()
return out
def get_extra_hooks(self):
out = []
if self.extra_hooks is not None:

10
comfy/extra_samplers/uni_pc.py
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@@ -80,7 +80,7 @@ class NoiseScheduleVP:
'linear' or 'cosine' for continuous-time DPMs.
Returns:
A wrapper object of the forward SDE (VP type).
===============================================================
Example:
@@ -208,7 +208,7 @@ def model_wrapper(
arXiv preprint arXiv:2202.00512 (2022).
[2] Ho, Jonathan, et al. "Imagen Video: High Definition Video Generation with Diffusion Models."
arXiv preprint arXiv:2210.02303 (2022).
4. "score": marginal score function. (Trained by denoising score matching).
Note that the score function and the noise prediction model follows a simple relationship:
```
@@ -245,7 +245,7 @@ def model_wrapper(
[4] Ho, Jonathan, and Tim Salimans. "Classifier-free diffusion guidance."
arXiv preprint arXiv:2207.12598 (2022).
The `t_input` is the time label of the model, which may be discrete-time labels (i.e. 0 to 999)
or continuous-time labels (i.e. epsilon to T).
@@ -621,7 +621,7 @@ class UniPC:
B_h = torch.expm1(hh)
else:
raise NotImplementedError()
for i in range(1, order + 1):
R.append(torch.pow(rks, i - 1))
b.append(h_phi_k * factorial_i / B_h)
@@ -870,4 +870,4 @@ def sample_unipc(model, noise, sigmas, extra_args=None, callback=None, disable=F
return x
def sample_unipc_bh2(model, noise, sigmas, extra_args=None, callback=None, disable=False):
return sample_unipc(model, noise, sigmas, extra_args, callback, disable, variant='bh2')
return sample_unipc(model, noise, sigmas, extra_args, callback, disable, variant='bh2')

2
comfy/hooks.py
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@@ -101,7 +101,7 @@ class WeightHook(Hook):
self.need_weight_init = True
self._strength_model = strength_model
self._strength_clip = strength_clip
@property
def strength_model(self):
return self._strength_model * self.strength

2
comfy/k_diffusion/sampling.py
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@@ -1230,7 +1230,7 @@ def sample_dpmpp_2m_cfg_pp(model, x, sigmas, extra_args=None, callback=None, dis
nonlocal uncond_denoised
uncond_denoised = args["uncond_denoised"]
return args["denoised"]
model_options = extra_args.get("model_options", {}).copy()
extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True)

2
comfy/ldm/modules/sub_quadratic_attention.py
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@@ -261,7 +261,7 @@ def efficient_dot_product_attention(
value=value,
mask=mask,
)
# TODO: maybe we should use torch.empty_like(query) to allocate storage in-advance,
# and pass slices to be mutated, instead of torch.cat()ing the returned slices
res = torch.cat([

2
comfy/ldm/pixart/pixartms.py
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@@ -223,7 +223,7 @@ class PixArtMS(nn.Module):
if self.micro_conditioning:
if c_size is None:
c_size = torch.tensor([H*8, W*8], dtype=x.dtype, device=x.device).repeat(B, 1)
if c_ar is None:
c_ar = torch.tensor([H/W], dtype=x.dtype, device=x.device).repeat(B, 1)

4
comfy/model_detection.py
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@@ -229,7 +229,7 @@ def detect_unet_config(state_dict, key_prefix):
if pe_key in state_dict_keys:
dit_config["input_size"] = int(math.sqrt(state_dict[pe_key].shape[1])) * patch_size
dit_config["pe_interpolation"] = dit_config["input_size"] // (512//8) # guess
ar_key = "{}ar_embedder.mlp.0.weight".format(key_prefix)
if ar_key in state_dict_keys:
dit_config["image_model"] = "pixart_alpha"
@@ -571,7 +571,7 @@ def unet_config_from_diffusers_unet(state_dict, dtype=None):
'transformer_depth': [0, 1, 1], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': -2, 'use_linear_in_transformer': False,
'context_dim': 768, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 1, 1, 1, 1],
'use_temporal_attention': False, 'use_temporal_resblock': False}
SD15_diffusers_inpaint = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'adm_in_channels': None,
'dtype': dtype, 'in_channels': 9, 'model_channels': 320, 'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0],
'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1, 'use_linear_in_transformer': False, 'context_dim': 768, 'num_heads': 8,

2
comfy/sample.py
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@@ -13,7 +13,7 @@ def prepare_noise(latent_image, seed, noise_inds=None):
generator = torch.manual_seed(seed)
if noise_inds is None:
return torch.randn(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, generator=generator, device="cpu")
unique_inds, inverse = np.unique(noise_inds, return_inverse=True)
noises = []
for i in range(unique_inds[-1]+1):

2
comfy/sampler_helpers.py
View File

@@ -42,7 +42,7 @@ def get_hooks_from_cond(cond, hooks_dict: dict[comfy.hooks.EnumHookType, dict[co
if cnet.previous_controlnet is None:
return _list
return get_extra_hooks_from_cnet(cnet.previous_controlnet, _list)
hooks_list = []
cnets = set(cnets)
for base_cnet in cnets:

10
comfy/utils.py
View File

@@ -455,7 +455,7 @@ def pixart_to_diffusers(mmdit_config, output_prefix=""):
for k in PIXART_MAP_BASIC:
key_map[k[1]] = "{}{}".format(output_prefix, k[0])
return key_map
def auraflow_to_diffusers(mmdit_config, output_prefix=""):
@@ -702,7 +702,7 @@ def get_attr(obj, attr):
def bislerp(samples, width, height):
def slerp(b1, b2, r):
'''slerps batches b1, b2 according to ratio r, batches should be flat e.g. NxC'''
c = b1.shape[-1]
#norms
@@ -730,13 +730,13 @@ def bislerp(samples, width, height):
res[dot > 1 - 1e-5] = b1[dot > 1 - 1e-5]
res[dot < 1e-5 - 1] = (b1 * (1.0-r) + b2 * r)[dot < 1e-5 - 1]
return res
def generate_bilinear_data(length_old, length_new, device):
coords_1 = torch.arange(length_old, dtype=torch.float32, device=device).reshape((1,1,1,-1))
coords_1 = torch.nn.functional.interpolate(coords_1, size=(1, length_new), mode="bilinear")
ratios = coords_1 - coords_1.floor()
coords_1 = coords_1.to(torch.int64)
coords_2 = torch.arange(length_old, dtype=torch.float32, device=device).reshape((1,1,1,-1)) + 1
coords_2[:,:,:,-1] -= 1
coords_2 = torch.nn.functional.interpolate(coords_2, size=(1, length_new), mode="bilinear")
@@ -747,7 +747,7 @@ def bislerp(samples, width, height):
samples = samples.float()
n,c,h,w = samples.shape
h_new, w_new = (height, width)
#linear w
ratios, coords_1, coords_2 = generate_bilinear_data(w, w_new, samples.device)
coords_1 = coords_1.expand((n, c, h, -1))