nodes/WJLUOXIAO-XB_ToolBox
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增加bernini支持

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AI_Architect
2026-07-04 15:02:03 +08:00
parent 139701c47f
commit bb69eefd12
2 changed files with 79 additions and 82 deletions
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locales/zh/nodeDefs.json
+7 -10
View File
@@ -3631,7 +3631,7 @@
"name": "批次大小"
},
"vae_tile_size": {
"name": "VAE分块大小"
"name": "VAE编码分块大小"
},
"source_video": {
"name": "源视频"
@@ -3639,6 +3639,12 @@
"reference_video": {
"name": "参考视频"
},
"ref_max_size": {
"name": "参考图最大尺寸"
},
"scale_method": {
"name": "缩放方法"
},
"reference_image_0": {
"name": "参考图0"
},
@@ -3662,15 +3668,6 @@
},
"reference_image_7": {
"name": "参考图7"
},
"ref_max_size": {
"name": "参考图最大尺寸"
},
"scale_method": {
"name": "缩放方法"
},
"crop_mode": {
"name": "裁剪模式"
}
},
"outputs": {
nodes_wan_vae.py
+72 -72
View File
@@ -1510,76 +1510,76 @@ class XB_WanVAEDecodeTiled:
samples=samples, vae=vae,
tile_size=tile_size, overlap=spatial_overlap,
temporal_size=temporal_chunk, temporal_overlap=temporal_overlap)
# ============================================================
# XB_BerniniConditioning - Bernini VAE分块编码
# ============================================================
def _resize_long_edge(image, max_size, stride=16, method="area"):
h, w = image.shape[1], image.shape[2]
scale = min(max_size / max(h, w), 1.0)
nh = max(stride, round(h * scale / stride) * stride)
nw = max(stride, round(w * scale / stride) * stride)
return comfy.utils.common_upscale(image[:, :, :, :3].movedim(-1, 1), nw, nh, method, "disabled").movedim(1, -1)
class XB_BerniniConditioning:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"positive": ("CONDITIONING",),
"negative": ("CONDITIONING",),
"vae": ("VAE",),
"width": ("INT", {"default": 832, "min": 16, "max": 8192, "step": 16}),
"height": ("INT", {"default": 480, "min": 16, "max": 8192, "step": 16}),
"length": ("INT", {"default": 81, "min": 1, "max": 8192, "step": 4}),
"batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}),
"vae_tile_size": ("INT", {"default": 256, "min": 64, "max": 3840, "step": 32}),
},
"optional": {
"source_video": ("IMAGE",),
"reference_video": ("IMAGE",),
"reference_image_0": ("IMAGE",),
"reference_image_1": ("IMAGE",),
"reference_image_2": ("IMAGE",),
"reference_image_3": ("IMAGE",),
"reference_image_4": ("IMAGE",),
"reference_image_5": ("IMAGE",),
"reference_image_6": ("IMAGE",),
"reference_image_7": ("IMAGE",),
"ref_max_size": ("INT", {"default": 848, "min": 16, "max": 8192, "step": 16}),
"scale_method": (_SCALE_METHODS, {"default": "area"}),
"crop_mode": (_CROP_MODES, {"default": "center"}),
}
}
RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT")
RETURN_NAMES = ("positive", "negative", "latent")
FUNCTION = "go"
CATEGORY = "XB_ToolBox/Wan"
def go(self, positive, negative, vae, width, height, length, batch_size, vae_tile_size,
source_video=None, reference_video=None,
reference_image_0=None, reference_image_1=None, reference_image_2=None,
reference_image_3=None, reference_image_4=None, reference_image_5=None,
reference_image_6=None, reference_image_7=None,
ref_max_size=848, scale_method="area", crop_mode="center"):
latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8],
device=comfy.model_management.intermediate_device())
context = []
if source_video is not None:
vid = comfy.utils.common_upscale(source_video[:length, :, :, :3].movedim(-1, 1), width, height, scale_method, crop_mode).movedim(1, -1)
context.append(_encode_vae(vae, vid[:, :, :, :3], vae_tile_size))
if reference_video is not None:
ref_vid = _resize_long_edge(reference_video[:length], ref_max_size, method=scale_method)
context.append(_encode_vae(vae, ref_vid[:, :, :, :3], vae_tile_size))
ref_imgs = [reference_image_0,reference_image_1,reference_image_2,reference_image_3,reference_image_4,reference_image_5,reference_image_6,reference_image_7]
for imgs in ref_imgs:
if imgs is None: continue
for i in range(imgs.shape[0]):
img = _resize_long_edge(imgs[i:i+1], ref_max_size, method=scale_method)
context.append(_encode_vae(vae, img[:,:,:,:3], vae_tile_size))
if context:
positive = node_helpers.conditioning_set_values(positive, {"context_latents": context})
negative = node_helpers.conditioning_set_values(negative, {"context_latents": context})
# ============================================================
# XB_BerniniConditioning - Bernini VAE分块编码
# ============================================================
def _resize_long_edge(image, max_size, stride=16, method="area"):
h, w = image.shape[1], image.shape[2]
scale = min(max_size / max(h, w), 1.0)
nh = max(stride, round(h * scale / stride) * stride)
nw = max(stride, round(w * scale / stride) * stride)
return comfy.utils.common_upscale(image[:, :, :, :3].movedim(-1, 1), nw, nh, method, "disabled").movedim(1, -1)
class XB_BerniniConditioning:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"positive": ("CONDITIONING",),
"negative": ("CONDITIONING",),
"vae": ("VAE",),
"width": ("INT", {"default": 832, "min": 16, "max": 8192, "step": 16}),
"height": ("INT", {"default": 480, "min": 16, "max": 8192, "step": 16}),
"length": ("INT", {"default": 81, "min": 1, "max": 8192, "step": 4}),
"batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}),
},
"optional": {
"source_video": ("IMAGE",),
"reference_video": ("IMAGE",),
"reference_image_0": ("IMAGE",),
"reference_image_1": ("IMAGE",),
"reference_image_2": ("IMAGE",),
"reference_image_3": ("IMAGE",),
"reference_image_4": ("IMAGE",),
"reference_image_5": ("IMAGE",),
"reference_image_6": ("IMAGE",),
"reference_image_7": ("IMAGE",),
"ref_max_size": ("INT", {"default": 848, "min": 16, "max": 8192, "step": 16}),
"vae_tile_size": ("INT", {"default": 256, "min": 64, "max": 3840, "step": 32}),
"scale_method": (_SCALE_METHODS, {"default": "area"}),
}
}
RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT")
RETURN_NAMES = ("positive", "negative", "latent")
FUNCTION = "go"
CATEGORY = "XB_ToolBox/Wan"
def go(self, positive, negative, vae, width, height, length, batch_size,
source_video=None, reference_video=None,
reference_image_0=None, reference_image_1=None,
reference_image_2=None, reference_image_3=None,
reference_image_4=None, reference_image_5=None,
reference_image_6=None, reference_image_7=None,
ref_max_size=848, vae_tile_size=256, scale_method="area"):
latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8],
device=comfy.model_management.intermediate_device())
context = []
if source_video is not None:
vid = comfy.utils.common_upscale(source_video[:length, :, :, :3].movedim(-1, 1), width, height, scale_method, "center").movedim(1, -1)
context.append(_encode_vae(vae, vid[:, :, :, :3], vae_tile_size))
if reference_video is not None:
ref_vid = _resize_long_edge(reference_video[:length], ref_max_size, method=scale_method)
context.append(_encode_vae(vae, ref_vid[:, :, :, :3], vae_tile_size))
ref_imgs = [reference_image_0,reference_image_1,reference_image_2,reference_image_3,reference_image_4,reference_image_5,reference_image_6,reference_image_7]
for imgs in ref_imgs:
if imgs is None: continue
for i in range(imgs.shape[0]):
img = _resize_long_edge(imgs[i:i+1], ref_max_size, method=scale_method)
context.append(_encode_vae(vae, img[:,:,:,:3], vae_tile_size))
if context:
positive = node_helpers.conditioning_set_values(positive, {"context_latents": context})
negative = node_helpers.conditioning_set_values(negative, {"context_latents": context})
return (positive, negative, {"samples": latent})