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Fix model_management import so it doesn't get executed twice.

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comfyanonymous 2023-04-15 18:55:17 -04:00
parent 81d1f00df3
commit 73c3e11e83
7 changed files with 14 additions and 14 deletions
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2
comfy/ldm/modules/attention.py
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@ -9,7 +9,7 @@ from typing import Optional, Any
from ldm.modules.diffusionmodules.util import checkpoint from ldm.modules.diffusionmodules.util import checkpoint
from .sub_quadratic_attention import efficient_dot_product_attention from .sub_quadratic_attention import efficient_dot_product_attention
import model_management from comfy import model_management
from . import tomesd from . import tomesd

2
comfy/ldm/modules/diffusionmodules/model.py
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@ -7,7 +7,7 @@ from einops import rearrange
from typing import Optional, Any from typing import Optional, Any
from ldm.modules.attention import MemoryEfficientCrossAttention from ldm.modules.attention import MemoryEfficientCrossAttention
import model_management from comfy import model_management
if model_management.xformers_enabled_vae(): if model_management.xformers_enabled_vae():
import xformers import xformers

2
comfy/ldm/modules/sub_quadratic_attention.py
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@ -24,7 +24,7 @@ except ImportError:
from torch import Tensor from torch import Tensor
from typing import List from typing import List
import model_management from comfy import model_management
def dynamic_slice( def dynamic_slice(
x: Tensor, x: Tensor,

2
comfy/samplers.py
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@ -3,7 +3,7 @@ from .k_diffusion import external as k_diffusion_external
from .extra_samplers import uni_pc from .extra_samplers import uni_pc
import torch import torch
import contextlib import contextlib
import model_management from comfy import model_management
from .ldm.models.diffusion.ddim import DDIMSampler from .ldm.models.diffusion.ddim import DDIMSampler
from .ldm.modules.diffusionmodules.util import make_ddim_timesteps from .ldm.modules.diffusionmodules.util import make_ddim_timesteps

4
comfy/sd.py
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@ -4,7 +4,7 @@ import copy
import sd1_clip import sd1_clip
import sd2_clip import sd2_clip
import model_management from comfy import model_management
from .ldm.util import instantiate_from_config from .ldm.util import instantiate_from_config
from .ldm.models.autoencoder import AutoencoderKL from .ldm.models.autoencoder import AutoencoderKL
import yaml import yaml
@ -388,7 +388,7 @@ class CLIP:
return cond return cond
def encode(self, text): def encode(self, text):
tokens = self.tokenizer.tokenize_with_weights(text) tokens = self.tokenize(text)
return self.encode_from_tokens(tokens) return self.encode_from_tokens(tokens)
class VAE: class VAE:

2
comfy_extras/nodes_upscale_model.py
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@ -1,6 +1,6 @@
import os import os
from comfy_extras.chainner_models import model_loading from comfy_extras.chainner_models import model_loading
import model_management from comfy import model_management
import torch import torch
import comfy.utils import comfy.utils
import folder_paths import folder_paths

14
nodes.py
View File

@ -21,16 +21,16 @@ import comfy.utils
import comfy.clip_vision import comfy.clip_vision
import model_management import comfy.model_management
import importlib import importlib
import folder_paths import folder_paths
def before_node_execution(): def before_node_execution():
model_management.throw_exception_if_processing_interrupted() comfy.model_management.throw_exception_if_processing_interrupted()
def interrupt_processing(value=True): def interrupt_processing(value=True):
model_management.interrupt_current_processing(value) comfy.model_management.interrupt_current_processing(value)
MAX_RESOLUTION=8192 MAX_RESOLUTION=8192
@ -241,7 +241,7 @@ class DiffusersLoader:
model_path = os.path.join(search_path, model_path) model_path = os.path.join(search_path, model_path)
break break
return comfy.diffusers_convert.load_diffusers(model_path, fp16=model_management.should_use_fp16(), output_vae=output_vae, output_clip=output_clip, embedding_directory=folder_paths.get_folder_paths("embeddings")) return comfy.diffusers_convert.load_diffusers(model_path, fp16=comfy.model_management.should_use_fp16(), output_vae=output_vae, output_clip=output_clip, embedding_directory=folder_paths.get_folder_paths("embeddings"))
class unCLIPCheckpointLoader: class unCLIPCheckpointLoader:
@ -680,7 +680,7 @@ class SetLatentNoiseMask:
def common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent, denoise=1.0, disable_noise=False, start_step=None, last_step=None, force_full_denoise=False): def common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent, denoise=1.0, disable_noise=False, start_step=None, last_step=None, force_full_denoise=False):
latent_image = latent["samples"] latent_image = latent["samples"]
noise_mask = None noise_mask = None
device = model_management.get_torch_device() device = comfy.model_management.get_torch_device()
if disable_noise: if disable_noise:
noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu") noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu")
@ -696,7 +696,7 @@ def common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive,
noise_mask = noise_mask.to(device) noise_mask = noise_mask.to(device)
real_model = None real_model = None
model_management.load_model_gpu(model) comfy.model_management.load_model_gpu(model)
real_model = model.model real_model = model.model
noise = noise.to(device) noise = noise.to(device)
@ -726,7 +726,7 @@ def common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive,
control_net_models = [] control_net_models = []
for x in control_nets: for x in control_nets:
control_net_models += x.get_control_models() control_net_models += x.get_control_models()
model_management.load_controlnet_gpu(control_net_models) comfy.model_management.load_controlnet_gpu(control_net_models)
if sampler_name in comfy.samplers.KSampler.SAMPLERS: if sampler_name in comfy.samplers.KSampler.SAMPLERS:
sampler = comfy.samplers.KSampler(real_model, steps=steps, device=device, sampler=sampler_name, scheduler=scheduler, denoise=denoise, model_options=model.model_options) sampler = comfy.samplers.KSampler(real_model, steps=steps, device=device, sampler=sampler_name, scheduler=scheduler, denoise=denoise, model_options=model.model_options)