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LTXVideo: Add conditioning image compression.

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Compressing the conditioning image reduces the chances to get frozen
result.
This commit is contained in:
Michael.Kupchik 2024-12-01 15:42:22 +00:00
parent 8e4118c0de
commit 9add9bf443
1 changed files with 27 additions and 2 deletions
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29
comfy_extras/nodes_lt.py
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@ -1,9 +1,11 @@
import io
import nodes import nodes
import node_helpers import node_helpers
import torch import torch
import comfy.model_management import comfy.model_management
import comfy.model_sampling import comfy.model_sampling
import math import math
import PIL
class EmptyLTXVLatentVideo: class EmptyLTXVLatentVideo:
@classmethod @classmethod
@ -32,7 +34,8 @@ class LTXVImgToVideo:
"width": ("INT", {"default": 768, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 32}), "width": ("INT", {"default": 768, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 32}),
"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}),
"image_compression": ("INT", {"default": 60, "min": 0, "max": 100, "tooltip": "Quality of the input image compression to use."})}}
RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT")
RETURN_NAMES = ("positive", "negative", "latent") RETURN_NAMES = ("positive", "negative", "latent")
@ -40,9 +43,31 @@ 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 compress(self, images: torch.Tensor, quality=60):
result = torch.zeros_like(images)
for i, image in enumerate(images):
tensor = (image * 255).byte().cpu().numpy()
image = PIL.Image.fromarray(tensor)
buffer = io.BytesIO()
image.save(buffer, format="JPEG", quality=quality)
buffer.seek(0)
decompressed_image = PIL.Image.open(buffer)
decompressed_tensor = torch.tensor(
list(decompressed_image.getdata()), dtype=torch.uint8
).view(*decompressed_image.size[::-1], -1)
decompressed_tensor = decompressed_tensor.unsqueeze(0).float() / 255
result[i] = decompressed_tensor
return result
def generate(self, positive, negative, image, vae, width, height, length, batch_size, image_compression):
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]
if image_compression < 100:
encode_pixels = self.compress(encode_pixels)
t = vae.encode(encode_pixels) t = vae.encode(encode_pixels)
positive = node_helpers.conditioning_set_values(positive, {"guiding_latent": t}) positive = node_helpers.conditioning_set_values(positive, {"guiding_latent": t})
negative = node_helpers.conditioning_set_values(negative, {"guiding_latent": t}) negative = node_helpers.conditioning_set_values(negative, {"guiding_latent": t})