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Move nodes_lt into expected spot for this fork

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doctorpangloss 2024-11-22 15:51:54 -08:00
parent f39b8dfebc
commit 63a00aedb5
1 changed files with 37 additions and 31 deletions
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68
comfy_extras/nodes_lt.py → comfy_extras/nodes/nodes_lt.py
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@ -1,17 +1,22 @@
import nodes import math
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 comfy.utils
from comfy import node_helpers
from comfy.nodes import base_nodes as nodes
class EmptyLTXVLatentVideo: class EmptyLTXVLatentVideo:
@classmethod @classmethod
def INPUT_TYPES(s): def INPUT_TYPES(s):
return {"required": { "width": ("INT", {"default": 768, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 32}), return {"required": {"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})}}
RETURN_TYPES = ("LATENT",) RETURN_TYPES = ("LATENT",)
FUNCTION = "generate" FUNCTION = "generate"
@ -19,14 +24,14 @@ class EmptyLTXVLatentVideo:
def generate(self, width, height, length, batch_size=1): def generate(self, width, height, length, batch_size=1):
latent = torch.zeros([batch_size, 128, ((length - 1) // 8) + 1, height // 32, width // 32], device=comfy.model_management.intermediate_device()) latent = torch.zeros([batch_size, 128, ((length - 1) // 8) + 1, height // 32, width // 32], device=comfy.model_management.intermediate_device())
return ({"samples": latent}, ) return ({"samples": latent},)
class LTXVImgToVideo: class LTXVImgToVideo:
@classmethod @classmethod
def INPUT_TYPES(s): def INPUT_TYPES(s):
return {"required": {"positive": ("CONDITIONING", ), return {"required": {"positive": ("CONDITIONING",),
"negative": ("CONDITIONING", ), "negative": ("CONDITIONING",),
"vae": ("VAE",), "vae": ("VAE",),
"image": ("IMAGE",), "image": ("IMAGE",),
"width": ("INT", {"default": 768, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 32}), "width": ("INT", {"default": 768, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 32}),
@ -49,16 +54,17 @@ class LTXVImgToVideo:
latent = torch.zeros([batch_size, 128, ((length - 1) // 8) + 1, height // 32, width // 32], device=comfy.model_management.intermediate_device()) latent = torch.zeros([batch_size, 128, ((length - 1) // 8) + 1, height // 32, width // 32], device=comfy.model_management.intermediate_device())
latent[:, :, :t.shape[2]] = t latent[:, :, :t.shape[2]] = t
return (positive, negative, {"samples": latent}, ) return (positive, negative, {"samples": latent},)
class LTXVConditioning: class LTXVConditioning:
@classmethod @classmethod
def INPUT_TYPES(s): def INPUT_TYPES(s):
return {"required": {"positive": ("CONDITIONING", ), return {"required": {"positive": ("CONDITIONING",),
"negative": ("CONDITIONING", ), "negative": ("CONDITIONING",),
"frame_rate": ("FLOAT", {"default": 25.0, "min": 0.0, "max": 1000.0, "step": 0.01}), "frame_rate": ("FLOAT", {"default": 25.0, "min": 0.0, "max": 1000.0, "step": 0.01}),
}} }}
RETURN_TYPES = ("CONDITIONING", "CONDITIONING") RETURN_TYPES = ("CONDITIONING", "CONDITIONING")
RETURN_NAMES = ("positive", "negative") RETURN_NAMES = ("positive", "negative")
FUNCTION = "append" FUNCTION = "append"
@ -74,10 +80,10 @@ class LTXVConditioning:
class ModelSamplingLTXV: class ModelSamplingLTXV:
@classmethod @classmethod
def INPUT_TYPES(s): def INPUT_TYPES(s):
return {"required": { "model": ("MODEL",), return {"required": {"model": ("MODEL",),
"max_shift": ("FLOAT", {"default": 2.05, "min": 0.0, "max": 100.0, "step":0.01}), "max_shift": ("FLOAT", {"default": 2.05, "min": 0.0, "max": 100.0, "step": 0.01}),
"base_shift": ("FLOAT", {"default": 0.95, "min": 0.0, "max": 100.0, "step":0.01}), "base_shift": ("FLOAT", {"default": 0.95, "min": 0.0, "max": 100.0, "step": 0.01}),
}, },
"optional": {"latent": ("LATENT",), } "optional": {"latent": ("LATENT",), }
} }
@ -109,7 +115,7 @@ class ModelSamplingLTXV:
model_sampling = ModelSamplingAdvanced(model.model.model_config) model_sampling = ModelSamplingAdvanced(model.model.model_config)
model_sampling.set_parameters(shift=shift) model_sampling.set_parameters(shift=shift)
m.add_object_patch("model_sampling", model_sampling) m.add_object_patch("model_sampling", model_sampling)
return (m, ) return (m,)
class LTXVScheduler: class LTXVScheduler:
@ -117,22 +123,22 @@ class LTXVScheduler:
def INPUT_TYPES(s): def INPUT_TYPES(s):
return {"required": return {"required":
{"steps": ("INT", {"default": 20, "min": 1, "max": 10000}), {"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
"max_shift": ("FLOAT", {"default": 2.05, "min": 0.0, "max": 100.0, "step":0.01}), "max_shift": ("FLOAT", {"default": 2.05, "min": 0.0, "max": 100.0, "step": 0.01}),
"base_shift": ("FLOAT", {"default": 0.95, "min": 0.0, "max": 100.0, "step":0.01}), "base_shift": ("FLOAT", {"default": 0.95, "min": 0.0, "max": 100.0, "step": 0.01}),
"stretch": ("BOOLEAN", { "stretch": ("BOOLEAN", {
"default": True, "default": True,
"tooltip": "Stretch the sigmas to be in the range [terminal, 1]." "tooltip": "Stretch the sigmas to be in the range [terminal, 1]."
}), }),
"terminal": ( "terminal": (
"FLOAT", "FLOAT",
{ {
"default": 0.1, "min": 0.0, "max": 0.99, "step": 0.01, "default": 0.1, "min": 0.0, "max": 0.99, "step": 0.01,
"tooltip": "The terminal value of the sigmas after stretching." "tooltip": "The terminal value of the sigmas after stretching."
}, },
), ),
}, },
"optional": {"latent": ("LATENT",), } "optional": {"latent": ("LATENT",), }
} }
RETURN_TYPES = ("SIGMAS",) RETURN_TYPES = ("SIGMAS",)
CATEGORY = "sampling/custom_sampling/schedulers" CATEGORY = "sampling/custom_sampling/schedulers"