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Init support tile control lora and regional prompt

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bbaudio-2025
2025-07-11 23:03:49 +08:00
committed by GitHub
parent b96ae77325
commit 2cec8e138e
1 changed files with 108 additions and 23 deletions
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nodes.py
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@@ -5,6 +5,7 @@ import comfy.sample
import nodes
import node_helpers
import latent_preview
from comfy.comfy_types import IO
def emptyimage(width, height, batch_size=1, color=(0,0,0)):
r = torch.full([batch_size, height, width, 1], color[0] / 255, dtype=torch.float32, device="cpu")
@@ -17,7 +18,7 @@ def imagecrop(image, width, height, x, y):
y = min(y, image.shape[1] - 1)
to_x = width + x
to_y = height + y
img = image[:,y:to_y, x:to_x, :]
img = image[:,y:to_y, x:to_x, :].clone()
return img
def feather(mask, left=0, top=0, right=0, bottom=0):
@@ -132,13 +133,25 @@ def temporalistgen(num_total_frame, length, num_crossfade, num_loopback_crossfad
raise ValueError("temporalistgen: loopback_crossfade数值过大，尝试减小\nloopback_crossfade too large")
return slice_list
def corssfadevideos(video1, video2, num_corssfade_frame):
def crop_resize_img_list(croparea_list, image):
image_list = []
init_width = croparea_list[0]['width_crop']
init_height = croparea_list[0]['height_crop']
for item in croparea_list:
cropped_image = imagecrop(image, item['width_crop'], item['height_crop'], item['offset_x'], item['offset_y'])
if cropped_image.shape[1] != init_height or cropped_image.shape[2] != init_width:
cropped_image = comfy.utils.common_upscale(cropped_image.movedim(-1, 1), init_width, init_height, "bilinear", "center").movedim(1, -1)
image_list.append(cropped_image)
result = torch.cat(image_list, dim=0)
return result
def crossfadevideos(video1, video2, num_corssfade_frame):
if video1.ndim != video2.ndim:
raise ValueError("corssfadevideos: 拼接图片类型不一致\nImageType Mismatch")
raise ValueError("crossfadevideos: 拼接图片类型不一致\nImageType Mismatch")
if video1[[0],].shape != video2[[0],].shape:
raise ValueError("corssfadevideos: 拼接图片尺寸不一致\nImageSize Mismatch")
raise ValueError("crossfadevideos: 拼接图片尺寸不一致\nImageSize Mismatch")
if num_corssfade_frame > video1.shape[0] or num_corssfade_frame > video2.shape[0]:
raise ValueError("corssfadevideos: 拼接图片数目应大于过渡数目\nVideoLength should be longer than CrossLength")
raise ValueError("crossfadevideos: 拼接图片数目应大于过渡数目\nVideoLength should be longer than CrossLength")
video_slice1 = video1[:-num_corssfade_frame]
video_slice2 = video1[-num_corssfade_frame:]
video_slice3 = video2[:num_corssfade_frame]
@@ -160,7 +173,7 @@ def corssfadevideos(video1, video2, num_corssfade_frame):
return result
def vace_sample(model, positive, negative, vae, width, height, length, strength, seed, cfg, sampler_name, scheduler, steps, denoise, video,
control_video=None, control_masks=None, reference_image=None):
control_video=None, control_masks=None, reference_image=None, tile_control_video=None):
# from comfyui
latent_length = ((length - 1) // 4) + 1
if control_video is not None:
@@ -213,16 +226,23 @@ def vace_sample(model, positive, negative, vae, width, height, length, strength,
mask = mask.unsqueeze(0)
positive = node_helpers.conditioning_set_values(positive, {"vace_frames": [control_video_latent], "vace_mask": [mask], "vace_strength": [strength]}, append=True)
negative = node_helpers.conditioning_set_values(negative, {"vace_frames": [control_video_latent], "vace_mask": [mask], "vace_strength": [strength]}, append=True)
# sample
latent = vae.encode(video[:,:,:,:3])
if reference_image is not None:
latent = torch.cat((reference_image_vaed, latent), dim=2)
noise = comfy.sample.prepare_noise(latent, seed)
# add "concat_latent_image" to support tile control lora
if tile_control_video is not None:
tile_control_latent = vae.encode(tile_control_video[:,:,:,:3])
if reference_image is not None:
tile_control_latent = torch.cat((reference_image_vaed, tile_control_latent), dim=2)
positive = node_helpers.conditioning_set_values(positive, {"vace_frames": [control_video_latent], "vace_mask": [mask], "vace_strength": [strength], "concat_latent_image": tile_control_latent}, append=True)
negative = node_helpers.conditioning_set_values(negative, {"vace_frames": [control_video_latent], "vace_mask": [mask], "vace_strength": [strength], "concat_latent_image": tile_control_latent}, append=True)
else:
positive = node_helpers.conditioning_set_values(positive, {"vace_frames": [control_video_latent], "vace_mask": [mask], "vace_strength": [strength]}, append=True)
negative = node_helpers.conditioning_set_values(negative, {"vace_frames": [control_video_latent], "vace_mask": [mask], "vace_strength": [strength]}, append=True)
noise = comfy.sample.prepare_noise(latent, seed)
callback = latent_preview.prepare_callback(model, steps)
disable_pbar = not comfy.utils.PROGRESS_BAR_ENABLED
samples = comfy.sample.sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent,
@@ -266,6 +286,7 @@ class UltimateVideoUpscaler:
"croparea_list": ("LIST", ),
"reference_image": ("IMAGE", ),
"control_video": ("IMAGE", ),
"tile_control_video": ("IMAGE", ),
}
}
@@ -286,18 +307,23 @@ QQ群：948626609
def upscale_video(self, model, width_upscale, height_upscale, width, height, length, pad_mask_limit, crossfade_frame, loopback_crossfade,
crop_ref, ref_as_init_frame, noise_aug, input_video, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise, vae,
croparea_list=None, reference_image=None, control_video=None):
croparea_list=None, reference_image=None, control_video=None, tile_control_video=None):
if control_video is not None and control_video.shape[0] != input_video.shape[0]:
raise ValueError("控制视频帧数与输入视频帧数应当一致\nFrame count of ControlVideo and InputVideo should be the same")
if tile_control_video is not None and tile_control_video.shape[0] != input_video.shape[0]:
raise ValueError("tile控制视频帧数与输入视频帧数应当一致\nFrame count of TileControlVideo and InputVideo should be the same")
if loopback_crossfade > 0:
cross_slice = input_video[:loopback_crossfade].clone()
input_video = torch.cat((input_video, cross_slice), dim=0)
if control_video is not None:
control_cross_slice = control_video[:loopback_crossfade].clone()
control_video = torch.cat((control_video, control_cross_slice), dim=0)
if tile_control_video is not None:
tile_control_cross_slice = tile_control_video[:loopback_crossfade].clone()
tile_control_video = torch.cat((tile_control_video, tile_control_cross_slice), dim=0)
total_frame = input_video.shape[0]
if total_frame > length and crossfade_frame == 0:
raise ValueError("视频帧数大于length，需要设置crossfade_frame以启用时间分割\nFrame count of input video is larger than length, need set a proper value for crossfade_frame to enable temporal tiling")
raise ValueError("视频帧数大于length，需要设置crossfade_frame以启用时间分割\nFrame count of input video is larger than length, need set a proper value for crossfade_frame to enable temporal tiling")
strength = 1 # VACE Strength
temporalist = temporalistgen(total_frame, length, crossfade_frame, loopback_crossfade)
upscaled_videos_list = []
@@ -309,8 +335,11 @@ QQ群：948626609
upscaled_video = comfy.utils.common_upscale(input_video[start_index:(start_index + length_n)].movedim(-1, 1), width_upscale, height_upscale, "bilinear", "center").movedim(1, -1)
if control_video is not None:
up_scaled_control = comfy.utils.common_upscale(control_video[start_index:(start_index + length_n)].movedim(-1, 1), width_upscale, height_upscale, "bilinear", "center").movedim(1, -1)
if tile_control_video is not None:
up_scaled_tile_control = comfy.utils.common_upscale(tile_control_video[start_index:(start_index + length_n)].movedim(-1, 1), width_upscale, height_upscale, "bilinear", "center").movedim(1, -1)
upscaled_video = add_noise(upscaled_video, noise_aug, seed)
if turn_index > 0 and cross_fade > 0:
# replace crossfade frames
upscaled_video[:cross_fade] = upscaled_videos_list[turn_index-1][-cross_fade:].clone()
if croparea_list is None:
croparea_list = spatialistgen(width_upscale, height_upscale, width, height)
@@ -336,6 +365,10 @@ QQ群：948626609
controls = imgcomposite(crop_ctl, crop_gen, 0, 0, 1-mask_ctl) if index != 0 else crop_ctl
else:
controls = crop_gen if index != 0 else torch.full((length_n, height_crop_n, width_crop_n, 3), 0.5, device='cpu')
if tile_control_video is not None:
crop_tile_ctl = imagecrop(up_scaled_tile_control, width_crop_n, height_crop_n, offset_x_n, offset_y_n)
else:
crop_tile_ctl = None
if reference_image is not None:
reference_image = comfy.utils.common_upscale(reference_image.movedim(-1, 1), width_upscale, height_upscale, "bilinear", "center").movedim(1, -1)
if crop_ref is True:
@@ -352,12 +385,17 @@ QQ群：948626609
init_ctl = imagecrop(upscaled_videos_list[turn_index-1][-cross_fade:].clone(), width_crop_n, height_crop_n, offset_x_n, offset_y_n)
controls[:cross_fade] = init_ctl
mask_ctl[:cross_fade] = torch.full((1, height_crop_n, width_crop_n), 0.0, device='cpu')
# if crop_tile_ctl is not None:
# init_tile_ctl = imagecrop(upscaled_videos_list[turn_index-1][-cross_fade:].clone(), width_crop_n, height_crop_n, offset_x_n, offset_y_n)
# controls[:cross_fade] = init_ctl
if turn['flag_final_slice'] is True and loopback_crossfade > 0:
end_ctl = imagecrop(upscaled_videos_list[0][:loopback_crossfade].clone(), width_crop_n, height_crop_n, offset_x_n, offset_y_n)
controls[-loopback_crossfade:] = end_ctl
mask_ctl[-loopback_crossfade:] = torch.full((1, height_crop_n, width_crop_n), 0.0, device='cpu')
if 'cond_p' in item:
positive = item['cond_p']
sampled_video = vace_sample(model, positive, negative, vae, width_crop_n, height_crop_n, length_n, strength, seed, cfg, sampler_name, scheduler, steps, denoise, video,
controls, mask_ctl, refimg)
controls, mask_ctl, refimg, crop_tile_ctl)
mask_feather = feather(torch.full((1, height_crop_n, width_crop_n), 1.0, device='cpu'), item['feather_left'], item['feather_top'], item['feather_right'], item['feather_bottom'])
mask_feather = repeat_tensor(mask_feather, length_n)
result_video = imgcomposite(result_video, sampled_video, offset_x_n, offset_y_n, mask_feather)
@@ -370,10 +408,10 @@ QQ群：948626609
index = 0
while index < len(upscaled_videos_list):
cross_fade = temporalist[index + 1]['num_crossfade']
result_video = corssfadevideos(result_video, upscaled_videos_list[index], cross_fade)
result_video = crossfadevideos(result_video, upscaled_videos_list[index], cross_fade)
index += 1
if loopback_crossfade > 0:
crossed_start = corssfadevideos(result_video[-loopback_crossfade:], result_video[:loopback_crossfade], loopback_crossfade)
crossed_start = crossfadevideos(result_video[-loopback_crossfade:], result_video[:loopback_crossfade], loopback_crossfade)
result_video[:loopback_crossfade] = crossed_start
result_video = result_video[:(total_frame - loopback_crossfade)]
return (result_video, )
@@ -385,19 +423,33 @@ class CustomCropArea:
"required": {
"width_upscale": ("INT", {"default": 1280, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
"height_upscale": ("INT", {"default": 720, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
"presets": (["'H' for wide screen", "'三' for long narrow screen"], {
"default": "'H' for wide screen",
"width": ("INT", {"default": 832, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}),
"height": ("INT", {"default": 480, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}),
"presets": (["default", "'H' for wide screen", "'三' for long narrow screen"], {
"default": "default",
"tooltip": "预设分割方案\nPresets of cropping plan"
}),
},
"optional": {
"reference_image": ("IMAGE", ),
}
}
RETURN_TYPES = ("LIST",)
RETURN_TYPES = ("LIST", "IMAGE")
RETURN_NAMES = ("croparea_list", "IMAGE")
FUNCTION = "custom_croplist_gen"
CATEGORY = "SuperUltimateVaceTools"
DESCRIPTION = "Use preset of cropping plan"
def custom_croplist_gen(self, width_upscale, height_upscale, presets):
if presets == "'H' for wide screen":
def custom_croplist_gen(self, width_upscale, height_upscale, width, height, presets, reference_image=None):
if reference_image is not None:
reference_image = comfy.utils.common_upscale(reference_image.movedim(-1, 1), width_upscale, height_upscale, "bilinear", "center").movedim(1, -1)
if presets == "default":
# same as SuperUltimateVACEUpscale
result = spatialistgen(width_upscale, height_upscale, width, height)
elif presets == "'H' for wide screen":
# left&right cropped image will be stretched
if height_upscale%16 != 0:
raise ValueError("‘H’方案下放大高度必须为16的倍数\n'H' plan requires height_upscale to be multiplier of 16")
result = [
@@ -523,15 +575,48 @@ class CustomCropArea:
'feather_bottom': 0,
},
]
return (result,)
if reference_image is not None:
img_batch = crop_resize_img_list(result, reference_image)
else:
img_batch = None
return (result, img_batch)
class BatchPrompt:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"clip": (IO.CLIP, {"tooltip": "The CLIP model used for encoding the text."}),
"prompt_list": ("STRING", ),
"croparea_list": ("LIST", ),
}
}
RETURN_TYPES = ("LIST", )
RETURN_NAMES = ("croparea_list", )
FUNCTION = "func"
CATEGORY = "SuperUltimateVaceTools"
DESCRIPTION = "batch conditioning prompt list"
def func(self, clip, prompt_list, croparea_list):
if len(prompt_list) != len(croparea_list):
raise ValueError("提示词队列长度与切割队列长度不一致，检查节点连接是否正确\nLength of prompt_list is not same as croparea_list, check nodes connection")
index = 0
for prompt in prompt_list:
tokens = clip.tokenize(prompt)
croparea_list[index]['cond_p'] = clip.encode_from_tokens_scheduled(tokens)
index += 1
return (croparea_list, )
NODE_CLASS_MAPPINGS = {
"SuperUltimateVACEUpscale": UltimateVideoUpscaler,
"CustomCropArea": CustomCropArea
"CustomCropArea": CustomCropArea,
"BatchPromptCropArea": BatchPrompt
}
NODE_DISPLAY_NAME_MAPPINGS = {
"SuperUltimateVACEUpscale": "SuperUltimate VACE Upscale",
"CustomCropArea": "Custom Crop Area"
"CustomCropArea": "Custom Crop Area",
"BatchPromptCropArea": "Batch Prompt Crop Area"
}