qnloft
/
ComfyUI
mirror of https://github.com/comfyanonymous/ComfyUI.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

Fix imports.

This commit is contained in:
comfyanonymous 2023-05-04 18:07:41 -04:00
parent 7e51bbd07f
commit bae4fb4a9d
13 changed files with 42 additions and 42 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

10
comfy/cldm/cldm.py
View File

@ -5,17 +5,17 @@ import torch
import torch as th
import torch.nn as nn
from ldm.modules.diffusionmodules.util import (
from ..ldm.modules.diffusionmodules.util import (
conv_nd,
linear,
zero_module,
timestep_embedding,
)
from ldm.modules.attention import SpatialTransformer
from ldm.modules.diffusionmodules.openaimodel import UNetModel, TimestepEmbedSequential, ResBlock, Downsample, AttentionBlock
from ldm.models.diffusion.ddpm import LatentDiffusion
from ldm.util import log_txt_as_img, exists, instantiate_from_config
from ..ldm.modules.attention import SpatialTransformer
from ..ldm.modules.diffusionmodules.openaimodel import UNetModel, TimestepEmbedSequential, ResBlock, Downsample, AttentionBlock
from ..ldm.models.diffusion.ddpm import LatentDiffusion
from ..ldm.util import log_txt_as_img, exists, instantiate_from_config
class ControlledUnetModel(UNetModel):

2
comfy/gligen.py
View File

@ -1,6 +1,6 @@
import torch
from torch import nn, einsum
from ldm.modules.attention import CrossAttention
from .ldm.modules.attention import CrossAttention
from inspect import isfunction

8
comfy/ldm/models/autoencoder.py
View File

@ -3,11 +3,11 @@ import torch
import torch.nn.functional as F
from contextlib import contextmanager
from ldm.modules.diffusionmodules.model import Encoder, Decoder
from ldm.modules.distributions.distributions import DiagonalGaussianDistribution
from comfy.ldm.modules.diffusionmodules.model import Encoder, Decoder
from comfy.ldm.modules.distributions.distributions import DiagonalGaussianDistribution
from ldm.util import instantiate_from_config
from ldm.modules.ema import LitEma
from comfy.ldm.util import instantiate_from_config
from comfy.ldm.modules.ema import LitEma
# class AutoencoderKL(pl.LightningModule):
class AutoencoderKL(torch.nn.Module):

2
comfy/ldm/models/diffusion/ddim.py
View File

@ -4,7 +4,7 @@ import torch
import numpy as np
from tqdm import tqdm
from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like, extract_into_tensor
from comfy.ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like, extract_into_tensor
class DDIMSampler(object):

12
comfy/ldm/models/diffusion/ddpm.py
View File

@ -19,12 +19,12 @@ from tqdm import tqdm
from torchvision.utils import make_grid
# from pytorch_lightning.utilities.distributed import rank_zero_only
from ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config
from ldm.modules.ema import LitEma
from ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution
from ldm.models.autoencoder import IdentityFirstStage, AutoencoderKL
from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like
from ldm.models.diffusion.ddim import DDIMSampler
from comfy.ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config
from comfy.ldm.modules.ema import LitEma
from comfy.ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution
from ..autoencoder import IdentityFirstStage, AutoencoderKL
from comfy.ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like
from .ddim import DDIMSampler
__conditioning_keys__ = {'concat': 'c_concat',

4
comfy/ldm/modules/attention.py
View File

@ -6,7 +6,7 @@ from torch import nn, einsum
from einops import rearrange, repeat
from typing import Optional, Any
from ldm.modules.diffusionmodules.util import checkpoint
from .diffusionmodules.util import checkpoint
from .sub_quadratic_attention import efficient_dot_product_attention
from comfy import model_management
@ -21,7 +21,7 @@ if model_management.xformers_enabled():
import os
_ATTN_PRECISION = os.environ.get("ATTN_PRECISION", "fp32")
from cli_args import args
from comfy.cli_args import args
def exists(val):
return val is not None

2
comfy/ldm/modules/diffusionmodules/model.py
View File

@ -6,7 +6,7 @@ import numpy as np
from einops import rearrange
from typing import Optional, Any
from ldm.modules.attention import MemoryEfficientCrossAttention
from ..attention import MemoryEfficientCrossAttention
from comfy import model_management
if model_management.xformers_enabled_vae():

6
comfy/ldm/modules/diffusionmodules/openaimodel.py
View File

@ -6,7 +6,7 @@ import torch as th
import torch.nn as nn
import torch.nn.functional as F
from ldm.modules.diffusionmodules.util import (
from .util import (
checkpoint,
conv_nd,
linear,
@ -15,8 +15,8 @@ from ldm.modules.diffusionmodules.util import (
normalization,
timestep_embedding,
)
from ldm.modules.attention import SpatialTransformer
from ldm.util import exists
from ..attention import SpatialTransformer
from comfy.ldm.util import exists
# dummy replace

4
comfy/ldm/modules/diffusionmodules/upscaling.py
View File

@ -3,8 +3,8 @@ import torch.nn as nn
import numpy as np
from functools import partial
from ldm.modules.diffusionmodules.util import extract_into_tensor, make_beta_schedule
from ldm.util import default
from .util import extract_into_tensor, make_beta_schedule
from comfy.ldm.util import default
class AbstractLowScaleModel(nn.Module):

2
comfy/ldm/modules/diffusionmodules/util.py
View File

@ -15,7 +15,7 @@ import torch.nn as nn
import numpy as np
from einops import repeat
from ldm.util import instantiate_from_config
from comfy.ldm.util import instantiate_from_config
def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):

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

@ -1,5 +1,5 @@
from ldm.modules.diffusionmodules.upscaling import ImageConcatWithNoiseAugmentation
from ldm.modules.diffusionmodules.openaimodel import Timestep
from ..diffusionmodules.upscaling import ImageConcatWithNoiseAugmentation
from ..diffusionmodules.openaimodel import Timestep
import torch
class CLIPEmbeddingNoiseAugmentation(ImageConcatWithNoiseAugmentation):

2
comfy/model_management.py
View File

@ -1,6 +1,6 @@
import psutil
from enum import Enum
from cli_args import args
from .cli_args import args
class VRAMState(Enum):
CPU = 0

26
comfy/sd.py
View File

@ -2,8 +2,8 @@ import torch
import contextlib
import copy
import sd1_clip
import sd2_clip
from . import sd1_clip
from . import sd2_clip
from comfy import model_management
from .ldm.util import instantiate_from_config
from .ldm.models.autoencoder import AutoencoderKL
@ -446,10 +446,10 @@ class CLIP:
else:
params = {}
if self.target_clip == "ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder":
if self.target_clip.endswith("FrozenOpenCLIPEmbedder"):
clip = sd2_clip.SD2ClipModel
tokenizer = sd2_clip.SD2Tokenizer
elif self.target_clip == "ldm.modules.encoders.modules.FrozenCLIPEmbedder":
elif self.target_clip.endswith("FrozenCLIPEmbedder"):
clip = sd1_clip.SD1ClipModel
tokenizer = sd1_clip.SD1Tokenizer
@ -896,9 +896,9 @@ def load_clip(ckpt_path, embedding_directory=None):
clip_data = utils.load_torch_file(ckpt_path)
config = {}
if "text_model.encoder.layers.22.mlp.fc1.weight" in clip_data:
config['target'] = 'ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder'
config['target'] = 'comfy.ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder'
else:
config['target'] = 'ldm.modules.encoders.modules.FrozenCLIPEmbedder'
config['target'] = 'comfy.ldm.modules.encoders.modules.FrozenCLIPEmbedder'
clip = CLIP(config=config, embedding_directory=embedding_directory)
clip.load_from_state_dict(clip_data)
return clip
@ -974,9 +974,9 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o
if output_clip:
clip_config = {}
if "cond_stage_model.model.transformer.resblocks.22.attn.out_proj.weight" in sd_keys:
clip_config['target'] = 'ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder'
clip_config['target'] = 'comfy.ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder'
else:
clip_config['target'] = 'ldm.modules.encoders.modules.FrozenCLIPEmbedder'
clip_config['target'] = 'comfy.ldm.modules.encoders.modules.FrozenCLIPEmbedder'
clip = CLIP(config=clip_config, embedding_directory=embedding_directory)
w.cond_stage_model = clip.cond_stage_model
load_state_dict_to = [w]
@ -997,7 +997,7 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o
noise_schedule_config["timesteps"] = sd[noise_aug_key].shape[0]
noise_schedule_config["beta_schedule"] = "squaredcos_cap_v2"
params["noise_schedule_config"] = noise_schedule_config
noise_aug_config['target'] = "ldm.modules.encoders.noise_aug_modules.CLIPEmbeddingNoiseAugmentation"
noise_aug_config['target'] = "comfy.ldm.modules.encoders.noise_aug_modules.CLIPEmbeddingNoiseAugmentation"
if size == 1280: #h
params["timestep_dim"] = 1024
elif size == 1024: #l
@ -1049,19 +1049,19 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o
unet_config["in_channels"] = sd['model.diffusion_model.input_blocks.0.0.weight'].shape[1]
unet_config["context_dim"] = sd['model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn2.to_k.weight'].shape[1]
sd_config["unet_config"] = {"target": "ldm.modules.diffusionmodules.openaimodel.UNetModel", "params": unet_config}
model_config = {"target": "ldm.models.diffusion.ddpm.LatentDiffusion", "params": sd_config}
sd_config["unet_config"] = {"target": "comfy.ldm.modules.diffusionmodules.openaimodel.UNetModel", "params": unet_config}
model_config = {"target": "comfy.ldm.models.diffusion.ddpm.LatentDiffusion", "params": sd_config}
if noise_aug_config is not None: #SD2.x unclip model
sd_config["noise_aug_config"] = noise_aug_config
sd_config["image_size"] = 96
sd_config["embedding_dropout"] = 0.25
sd_config["conditioning_key"] = 'crossattn-adm'
model_config["target"] = "ldm.models.diffusion.ddpm.ImageEmbeddingConditionedLatentDiffusion"
model_config["target"] = "comfy.ldm.models.diffusion.ddpm.ImageEmbeddingConditionedLatentDiffusion"
elif unet_config["in_channels"] > 4: #inpainting model
sd_config["conditioning_key"] = "hybrid"
sd_config["finetune_keys"] = None
model_config["target"] = "ldm.models.diffusion.ddpm.LatentInpaintDiffusion"
model_config["target"] = "comfy.ldm.models.diffusion.ddpm.LatentInpaintDiffusion"
else:
sd_config["conditioning_key"] = "crossattn"