nodes/BlenderNeko-ComfyUI_TiledKSampler
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account for cnet refactor

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BlenderNeko
2023-08-26 12:02:28 +02:00
parent 05d09e6db6
commit c6b352fbd2
1 changed files with 5 additions and 4 deletions
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nodes.py
+5 -4
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@@ -9,6 +9,7 @@ import torch
sys.path.insert(0, os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy"))
import comfy.sd
import comfy.controlnet
import comfy.model_management
import comfy.sample
from . import tiling
@@ -79,12 +80,12 @@ def slice_gligen(tile_h, tile_h_len, tile_w, tile_w_len, cond, gligen):
else:
cond['gligen'] = (gligen_type, gligen_model, gligen_areas_new)
def slice_cnet(h, h_len, w, w_len, model:comfy.sd.ControlNet, img):
def slice_cnet(h, h_len, w, w_len, model:comfy.controlnet.ControlBase, img):
if img is None:
img = model.cond_hint_original
model.cond_hint = tiling.get_slice(img, h*8, h_len*8, w*8, w_len*8).to(model.control_model.dtype).to(model.device)
def slices_T2I(h, h_len, w, w_len, model:comfy.sd.T2IAdapter, img):
def slices_T2I(h, h_len, w, w_len, model:comfy.controlnet.ControlBase, img):
model.control_input = None
if img is None:
img = model.cond_hint_original
@@ -133,7 +134,7 @@ def sample_common(model, add_noise, noise_seed, tile_width, tile_height, tiling_
#cnets
cnets = comfy.sample.get_models_from_cond(positive, 'control') + comfy.sample.get_models_from_cond(negative, 'control')
cnets = [m for m in cnets if isinstance(m, comfy.sd.ControlNet)]
cnets = [m for m in cnets if isinstance(m, comfy.controlnet.ControlNet)]
cnets = list(set([x for m in cnets for x in recursion_to_list(m, "previous_controlnet")]))
cnet_imgs = [
torch.nn.functional.interpolate(m.cond_hint_original, (shape[-2] * 8, shape[-1] * 8), mode='nearest-exact').to('cpu')
@@ -142,7 +143,7 @@ def sample_common(model, add_noise, noise_seed, tile_width, tile_height, tiling_
#T2I
T2Is = comfy.sample.get_models_from_cond(positive, 'control') + comfy.sample.get_models_from_cond(negative, 'control')
T2Is = [m for m in T2Is if isinstance(m, comfy.sd.T2IAdapter)]
T2Is = [m for m in T2Is if isinstance(m, comfy.controlnet.T2IAdapter)]
T2Is = [x for m in T2Is for x in recursion_to_list(m, "previous_controlnet")]
T2I_imgs = [
torch.nn.functional.interpolate(m.cond_hint_original, (shape[-2] * 8, shape[-1] * 8), mode='nearest-exact').to('cpu')