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Fix remaining relative versus absolute namespace errors

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doctorpangloss 2024-06-13 13:37:23 -07:00
parent 3cc3173625
commit 8be5134f4c
2 changed files with 3 additions and 3 deletions
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4
comfy/cmd/server.py
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@ -25,7 +25,7 @@ from aiohttp import web
from can_ada import URL, parse as urlparse from can_ada import URL, parse as urlparse
from typing_extensions import NamedTuple from typing_extensions import NamedTuple
import comfy.interruption from .. import interruption
from .latent_preview_image_encoding import encode_preview_image from .latent_preview_image_encoding import encode_preview_image
from .. import model_management from .. import model_management
from .. import utils from .. import utils
@ -527,7 +527,7 @@ class PromptServer(ExecutorToClientProgress):
@routes.post("/interrupt") @routes.post("/interrupt")
async def post_interrupt(request): async def post_interrupt(request):
comfy.interruption.interrupt_current_processing() interruption.interrupt_current_processing()
return web.Response(status=200) return web.Response(status=200)
@routes.post("/free") @routes.post("/free")

2
comfy/sample.py
View File

@ -29,7 +29,7 @@ def prepare_noise(latent_image, seed, noise_inds=None):
def fix_empty_latent_channels(model, latent_image): def fix_empty_latent_channels(model, latent_image):
latent_channels = model.get_model_object("latent_format").latent_channels #Resize the empty latent image so it has the right number of channels latent_channels = model.get_model_object("latent_format").latent_channels #Resize the empty latent image so it has the right number of channels
if latent_channels != latent_image.shape[1] and torch.count_nonzero(latent_image) == 0: if latent_channels != latent_image.shape[1] and torch.count_nonzero(latent_image) == 0:
latent_image = comfy.utils.repeat_to_batch_size(latent_image, latent_channels, dim=1) latent_image = utils.repeat_to_batch_size(latent_image, latent_channels, dim=1)
return latent_image return latent_image
def prepare_sampling(model, noise_shape, positive, negative, noise_mask): def prepare_sampling(model, noise_shape, positive, negative, noise_mask):