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comfyui_svd.py
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# from einops import rearrange, repeat
# from omegaconf import OmegaConf
# import math
# import torch
# import importlib
# import comfy.model_management
# import os
# def get_obj_from_str(string, reload=False, invalidate_cache=True):
# module, cls = string.rsplit(".", 1)
# if invalidate_cache:
# importlib.invalidate_caches()
# if reload:
# module_imp = importlib.import_module(module)
# importlib.reload(module_imp)
# return getattr(importlib.import_module(module, package=None), cls)
# def instantiate_from_config(config):
# if not "target" in config:
# if config == "__is_first_stage__":
# return None
# elif config == "__is_unconditional__":
# return None
# raise KeyError("Expected key `target` to instantiate.")
# return get_obj_from_str(config["target"])(**config.get("params", dict()))
# def get_unique_embedder_keys_from_conditioner(conditioner):
# return list(set([x.input_key for x in conditioner.embedders]))
# def get_batch(keys, value_dict, N, T, device):
# batch = {}
# batch_uc = {}
# for key in keys:
# if key == "fps_id":
# batch[key] = (
# torch.tensor([value_dict["fps_id"]])
# .to(device)
# .repeat(int(math.prod(N)))
# )
# elif key == "motion_bucket_id":
# batch[key] = (
# torch.tensor([value_dict["motion_bucket_id"]])
# .to(device)
# .repeat(int(math.prod(N)))
# )
# elif key == "cond_aug":
# batch[key] = repeat(
# torch.tensor([value_dict["cond_aug"]]).to(device),
# "1 -> b",
# b=math.prod(N),
# )
# elif key == "cond_frames":
# batch[key] = repeat(value_dict["cond_frames"], "1 ... -> b ...", b=N[0])
# elif key == "cond_frames_without_noise":
# batch[key] = repeat(
# value_dict["cond_frames_without_noise"], "1 ... -> b ...", b=N[0]
# )
# else:
# batch[key] = value_dict[key]
# if T is not None:
# batch["num_video_frames"] = T
# for key in batch.keys():
# if key not in batch_uc and isinstance(batch[key], torch.Tensor):
# batch_uc[key] = torch.clone(batch[key])
# return batch, batch_uc
# def load_model(
# config: str,
# script_directory: str,
# device: str,
# num_frames: int,
# num_steps: int,
# lowvram_mode: bool
# ):
# config = OmegaConf.load(config)
# config.model.params.ckpt_path = os.path.join(script_directory, config.model.params.ckpt_path)
# config.model.params.conditioner_config.params.emb_models[0].params.open_clip_embedding_config.params.init_device = device
# config.model.params.sampler_config.params.num_steps = num_steps
# config.model.params.sampler_config.params.guider_config.params.num_frames = (num_frames)
# model = instantiate_from_config(config.model).to(device).eval()
# if lowvram_mode:
# model.model.half()
# return model
# class SVDimg2vid:
# @classmethod
# def INPUT_TYPES(cls):
# return {
# "required": {
# "version": (
# [ 'svd',
# 'svd_xt',
# 'svd_image_decoder',
# 'svd_xt_image_decoder',
# ],
# {
# "default": 'svd'
# }),
# "image": ("IMAGE",),
# "num_frames": ("INT", {"default": 14, "min": 2, "max": 1000}),
# "num_steps": ("INT", {"default": 24, "min": 1, "max": 10000}),
# "fps_id": ("INT", {"default": 6, "min": 1, "max": 100}),
# "motion_bucket_id": ("INT", {"default": 127, "min": 1, "max": 10000}),
# "cond_aug": ("FLOAT", {"default": 0.02, "step":0.001}),
# "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
# "decoding_t": ("INT", {"default": 1, "min": 1, "max": 10000}),
# "lowvram_mode": ("BOOLEAN", {"default": True}),
# },
# }
# RETURN_TYPES = ("IMAGE",)
# RETURN_NAMES = ("image",)
# FUNCTION = "generate"
# CATEGORY = "KJNodes/experimental"
# def generate(self, image, version, num_frames, num_steps, fps_id, motion_bucket_id, cond_aug, seed, decoding_t, lowvram_mode):
# w, h = image.shape[2], image.shape[1]
# if h % 64 != 0 or w % 64 != 0:
# raise ValueError(f"SVD: ERROR: Your image is of size {w}x{h} which is not divisible by 64")
# #since this is so memory intensive, try to get everything free
# comfy.model_management.cleanup_models()
# torch.cuda.empty_cache()
# torch.cuda.ipc_collect()
# device: str = "cuda"
# script_directory = os.path.dirname(os.path.abspath(__file__))
# model_config = os.path.join(script_directory, "svd", "configs", f"{version}.yaml")
# model = load_model(
# model_config,
# script_directory,
# device,
# num_frames,
# num_steps,
# lowvram_mode,
# )
# torch.manual_seed(seed)
# image = image.permute(0, 3, 1, 2)
# image = image * 2.0 - 1.0
# image = image.to(device)
# B, C, H, W = image.shape
# assert C == 3
# F = 8
# C = 4
# shape = (num_frames, C, H // F, W // F)
# if (H, W) != (576, 1024):
# print(
# "WARNING: The conditioning frame you provided is not 1024x576. This leads to suboptimal performance as model was only trained on 1024x576. Consider increasing `cond_aug`."
# )
# if motion_bucket_id > 255:
# print(
# "WARNING: High motion bucket! This may lead to suboptimal performance."
# )
# if fps_id < 5:
# print("WARNING: Small fps value! This may lead to suboptimal performance.")
# if fps_id > 30:
# print("WARNING: Large fps value! This may lead to suboptimal performance.")
# value_dict = {}
# value_dict["motion_bucket_id"] = motion_bucket_id
# value_dict["fps_id"] = fps_id
# value_dict["cond_aug"] = cond_aug
# value_dict["cond_frames_without_noise"] = image
# value_dict["cond_frames"] = image + cond_aug * torch.randn_like(image)
# value_dict["cond_aug"] = cond_aug
# with torch.no_grad():
# with torch.autocast(device):
# model.conditioner.to(device)
# batch, batch_uc = get_batch(
# get_unique_embedder_keys_from_conditioner(model.conditioner),
# value_dict,
# [1, num_frames],
# T=num_frames,
# device=device,
# )
# c, uc = model.conditioner.get_unconditional_conditioning(
# batch,
# batch_uc=batch_uc,
# force_uc_zero_embeddings=[
# "cond_frames",
# "cond_frames_without_noise",
# ],
# )
# if lowvram_mode:
# model.conditioner.cpu()
# torch.cuda.empty_cache()
# for k in ["crossattn", "concat"]:
# uc[k] = repeat(uc[k], "b ... -> b t ...", t=num_frames)
# uc[k] = rearrange(uc[k], "b t ... -> (b t) ...", t=num_frames)
# c[k] = repeat(c[k], "b ... -> b t ...", t=num_frames)
# c[k] = rearrange(c[k], "b t ... -> (b t) ...", t=num_frames)
# randn = torch.randn(shape, device=device)
# additional_model_inputs = {}
# additional_model_inputs["image_only_indicator"] = torch.zeros(
# 2, num_frames
# ).to(device)
# additional_model_inputs["num_video_frames"] = batch["num_video_frames"]
# def denoiser(input, sigma, c):
# if lowvram_mode:
# input = input.half()
# return model.denoiser(model.model, input, sigma, c, **additional_model_inputs)
# model.denoiser.to(device)
# model.model.to(device)
# samples_z = model.sampler(denoiser, randn, cond=c, uc=uc)
# if lowvram_mode:
# model.model.cpu()
# model.denoiser.cpu()
# torch.cuda.empty_cache()
# model.en_and_decode_n_samples_a_time = decoding_t
# samples_x = model.decode_first_stage(samples_z)
# samples = torch.clamp((samples_x + 1.0) / 2.0, min=0.0, max=1.0)
# samples = samples.permute(0, 2, 3, 1)
# results = samples.cpu()
# return (results,)
# NODE_CLASS_MAPPINGS = {
# "SVDimg2vid": SVDimg2vid,
# }
# NODE_DISPLAY_NAME_MAPPINGS = {
# "SVDimg2vid": "SVDimg2vid",
# }