mirror of
https://github.com/comfyanonymous/ComfyUI.git
synced 2025-09-14 13:35:05 +00:00
Fix imports.
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@@ -3,11 +3,11 @@ import torch
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import torch.nn.functional as F
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from contextlib import contextmanager
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from ldm.modules.diffusionmodules.model import Encoder, Decoder
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from ldm.modules.distributions.distributions import DiagonalGaussianDistribution
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from comfy.ldm.modules.diffusionmodules.model import Encoder, Decoder
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from comfy.ldm.modules.distributions.distributions import DiagonalGaussianDistribution
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from ldm.util import instantiate_from_config
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from ldm.modules.ema import LitEma
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from comfy.ldm.util import instantiate_from_config
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from comfy.ldm.modules.ema import LitEma
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# class AutoencoderKL(pl.LightningModule):
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class AutoencoderKL(torch.nn.Module):
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@@ -4,7 +4,7 @@ import torch
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import numpy as np
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from tqdm import tqdm
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from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like, extract_into_tensor
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from comfy.ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like, extract_into_tensor
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class DDIMSampler(object):
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@@ -19,12 +19,12 @@ from tqdm import tqdm
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from torchvision.utils import make_grid
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# from pytorch_lightning.utilities.distributed import rank_zero_only
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from ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config
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from ldm.modules.ema import LitEma
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from ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution
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from ldm.models.autoencoder import IdentityFirstStage, AutoencoderKL
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from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like
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from ldm.models.diffusion.ddim import DDIMSampler
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from comfy.ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config
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from comfy.ldm.modules.ema import LitEma
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from comfy.ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution
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from ..autoencoder import IdentityFirstStage, AutoencoderKL
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from comfy.ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like
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from .ddim import DDIMSampler
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__conditioning_keys__ = {'concat': 'c_concat',
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@@ -6,7 +6,7 @@ from torch import nn, einsum
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from einops import rearrange, repeat
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from typing import Optional, Any
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from ldm.modules.diffusionmodules.util import checkpoint
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from .diffusionmodules.util import checkpoint
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from .sub_quadratic_attention import efficient_dot_product_attention
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from comfy import model_management
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@@ -21,7 +21,7 @@ if model_management.xformers_enabled():
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import os
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_ATTN_PRECISION = os.environ.get("ATTN_PRECISION", "fp32")
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from cli_args import args
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from comfy.cli_args import args
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def exists(val):
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return val is not None
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@@ -6,7 +6,7 @@ import numpy as np
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from einops import rearrange
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from typing import Optional, Any
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from ldm.modules.attention import MemoryEfficientCrossAttention
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from ..attention import MemoryEfficientCrossAttention
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from comfy import model_management
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if model_management.xformers_enabled_vae():
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@@ -6,7 +6,7 @@ import torch as th
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import torch.nn as nn
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import torch.nn.functional as F
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from ldm.modules.diffusionmodules.util import (
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from .util import (
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checkpoint,
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conv_nd,
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linear,
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@@ -15,8 +15,8 @@ from ldm.modules.diffusionmodules.util import (
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normalization,
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timestep_embedding,
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)
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from ldm.modules.attention import SpatialTransformer
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from ldm.util import exists
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from ..attention import SpatialTransformer
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from comfy.ldm.util import exists
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# dummy replace
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@@ -3,8 +3,8 @@ import torch.nn as nn
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import numpy as np
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from functools import partial
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from ldm.modules.diffusionmodules.util import extract_into_tensor, make_beta_schedule
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from ldm.util import default
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from .util import extract_into_tensor, make_beta_schedule
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from comfy.ldm.util import default
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class AbstractLowScaleModel(nn.Module):
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@@ -15,7 +15,7 @@ import torch.nn as nn
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import numpy as np
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from einops import repeat
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from ldm.util import instantiate_from_config
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from comfy.ldm.util import instantiate_from_config
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def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
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@@ -1,5 +1,5 @@
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from ldm.modules.diffusionmodules.upscaling import ImageConcatWithNoiseAugmentation
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from ldm.modules.diffusionmodules.openaimodel import Timestep
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from ..diffusionmodules.upscaling import ImageConcatWithNoiseAugmentation
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from ..diffusionmodules.openaimodel import Timestep
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import torch
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class CLIPEmbeddingNoiseAugmentation(ImageConcatWithNoiseAugmentation):
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