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ltxv: add strength parameter to conditioning. (#7849)

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This commit adds strength parameter to the LTXVImgToVideo node.
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Andrew Kvochko 2025-04-28 19:59:17 +03:00 committed by GitHub
parent 8115a7895b
commit cb9ac3db58
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1 changed files with 3 additions and 2 deletions
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comfy_extras/nodes_lt.py
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@ -38,6 +38,7 @@ class LTXVImgToVideo:
"height": ("INT", {"default": 512, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 32}), "height": ("INT", {"default": 512, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 32}),
"length": ("INT", {"default": 97, "min": 9, "max": nodes.MAX_RESOLUTION, "step": 8}), "length": ("INT", {"default": 97, "min": 9, "max": nodes.MAX_RESOLUTION, "step": 8}),
"batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}),
"strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0}),
}} }}
RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT")
@ -46,7 +47,7 @@ class LTXVImgToVideo:
CATEGORY = "conditioning/video_models" CATEGORY = "conditioning/video_models"
FUNCTION = "generate" FUNCTION = "generate"
def generate(self, positive, negative, image, vae, width, height, length, batch_size): def generate(self, positive, negative, image, vae, width, height, length, batch_size, strength):
pixels = comfy.utils.common_upscale(image.movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1) pixels = comfy.utils.common_upscale(image.movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1)
encode_pixels = pixels[:, :, :, :3] encode_pixels = pixels[:, :, :, :3]
t = vae.encode(encode_pixels) t = vae.encode(encode_pixels)
@ -59,7 +60,7 @@ class LTXVImgToVideo:
dtype=torch.float32, dtype=torch.float32,
device=latent.device, device=latent.device,
) )
conditioning_latent_frames_mask[:, :, :t.shape[2]] = 0 conditioning_latent_frames_mask[:, :, :t.shape[2]] = 1.0 - strength
return (positive, negative, {"samples": latent, "noise_mask": conditioning_latent_frames_mask}, ) return (positive, negative, {"samples": latent, "noise_mask": conditioning_latent_frames_mask}, )