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import os
import torch
import folder_paths
import yaml
import comfy.model_management as mm
from comfy.utils import ProgressBar, load_torch_file
from PIL import Image
from omegaconf import OmegaConf
from tqdm import tqdm
import numpy as np
import cv2
from .gimmvfi.generalizable_INR.gimmvfi_r import GIMMVFI_R
from .gimmvfi.generalizable_INR.gimmvfi_f import GIMMVFI_F
from .gimmvfi.generalizable_INR.configs import GIMMVFIConfig
from .gimmvfi.generalizable_INR.raft import RAFT
from .gimmvfi.generalizable_INR.flowformer.core.FlowFormer.LatentCostFormer.transformer import FlowFormer
from .gimmvfi.generalizable_INR.flowformer.configs.submission import get_cfg
from .gimmvfi.utils.flow_viz import flow_to_image
from .gimmvfi.utils.utils import InputPadder, RaftArgs, easydict_to_dict
import logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
log = logging.getLogger(__name__)
script_directory = os.path.dirname(os.path.abspath(__file__))
class DownloadAndLoadGIMMVFIModel:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"model": ([
"gimmvfi_r_arb_lpips_fp32.safetensors",
"gimmvfi_f_arb_lpips_fp32.safetensors"
],),
},
}
RETURN_TYPES = ("GIMMVIF_MODEL",)
RETURN_NAMES = ("gimmvfi_model",)
FUNCTION = "loadmodel"
CATEGORY = "GIMM-VFI"
def loadmodel(self, model):
device = mm.get_torch_device()
offload_device = mm.unet_offload_device()
download_path = os.path.join(folder_paths.models_dir, 'interpolation', 'gimm-vfi')
model_path = os.path.join(download_path, model)
if not os.path.exists(model_path):
log.info(f"Downloading GMMI-VFI model to: {model_path}")
from huggingface_hub import snapshot_download
snapshot_download(
repo_id="Kijai/GIMM-VFI_safetensors",
allow_patterns=[f"*{model}*"],
local_dir=download_path,
local_dir_use_symlinks=False,
)
if "gimmvfi_r" in model:
config_path = os.path.join(script_directory, "configs", "gimmvfi", "gimmvfi_r_arb.yaml")
flow_model = "raft-things_fp32.safetensors"
elif "gimmvfi_f" in model:
config_path = os.path.join(script_directory, "configs", "gimmvfi", "gimmvfi_f_arb.yaml")
flow_model = "flowformer_sintel_fp32.safetensors"
flow_model_path = os.path.join(folder_paths.models_dir, 'interpolation', 'gimm-vfi', flow_model)
if not os.path.exists(flow_model_path):
log.info(f"Downloading RAFT model to: {flow_model_path}")
from huggingface_hub import snapshot_download
snapshot_download(
repo_id="Kijai/GIMM-VFI_safetensors",
allow_patterns=[f"*{flow_model}*"],
local_dir=download_path,
local_dir_use_symlinks=False,
)
with open(config_path) as f:
config = yaml.load(f, Loader=yaml.FullLoader)
config = easydict_to_dict(config)
config = OmegaConf.create(config)
arch_defaults = GIMMVFIConfig.create(config.arch)
config = OmegaConf.merge(arch_defaults, config.arch)
# load model
if "gimmvfi_r" in model:
model = GIMMVFI_R(config)
#load RAFT
raft_args = RaftArgs(
small=False,
mixed_precision=False,
alternate_corr=False
)
raft_model = RAFT(raft_args)
raft_sd = load_torch_file(flow_model_path)
raft_model.load_state_dict(raft_sd, strict=True)
raft_model.to(device)
flow_estimator = raft_model
elif "gimmvfi_f" in model:
model = GIMMVFI_F(config)
cfg = get_cfg()
flowformer = FlowFormer(cfg.latentcostformer)
flowformer_sd = load_torch_file(flow_model_path)
flowformer.load_state_dict(flowformer_sd, strict=True)
flow_estimator = flowformer
sd = load_torch_file(model_path)
model.load_state_dict(sd, strict=False)
model.flow_estimator = flow_estimator
model = model.eval().to(device)
return (model,)
def load_image(img_path):
img = Image.open(img_path)
raw_img = np.array(img.convert("RGB"))
img = torch.from_numpy(raw_img.copy()).permute(2, 0, 1) / 255.0
return img.to(torch.float).unsqueeze(0)
#region Interpolate
class GIMMVFI_interpolate:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"gimmvfi_model": ("GIMMVIF_MODEL",),
"images": ("IMAGE", {"tooltip": "The images to interpolate between"}),
"ds_factor": ("FLOAT", {"default": 1.0, "min": 0.01, "max": 1.0, "step": 0.01}),
"interpolation_factor": ("INT", {"default": 8, "min": 1, "max": 100, "step": 1}),
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
},
}
RETURN_TYPES = ("IMAGE", "IMAGE",)
RETURN_NAMES = ("images", "flow_tensors",)
FUNCTION = "interpolate"
CATEGORY = "PyramidFlowWrapper"
def interpolate(self, gimmvfi_model, images, ds_factor, interpolation_factor,seed):
mm.soft_empty_cache()
images = images.permute(0, 3, 1, 2)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
device = mm.get_torch_device()
offload_device = mm.unet_offload_device()
gimmvfi_model.to(device)
out_images_list = []
flows = []
start = 0
end = images.shape[0] - 1
pbar = ProgressBar(images.shape[0] - 1)
for j in tqdm(range(start, end)):
I0 = images[j].unsqueeze(0)
I2 = images[j+1].unsqueeze(0)
if j == start:
out_images_list.append(I0.squeeze(0).permute(1, 2, 0))
padder = InputPadder(I0.shape, 32)
I0, I2 = padder.pad(I0, I2)
xs = torch.cat((I0.unsqueeze(2), I2.unsqueeze(2)), dim=2).to(device, non_blocking=True)
batch_size = xs.shape[0]
s_shape = xs.shape[-2:]
coord_inputs = [
(
gimmvfi_model.sample_coord_input(
batch_size,
s_shape,
[1 / interpolation_factor * i],
device=xs.device,
upsample_ratio=ds_factor,
),
None,
)
for i in range(1, interpolation_factor)
]
timesteps = [
i * 1 / interpolation_factor * torch.ones(xs.shape[0]).to(xs.device).to(torch.float)
for i in range(1, interpolation_factor)
]
all_outputs = gimmvfi_model(xs, coord_inputs, t=timesteps, ds_factor=ds_factor)
out_frames = [padder.unpad(im) for im in all_outputs["imgt_pred"]]
out_flowts = [padder.unpad(f) for f in all_outputs["flowt"]]
flowt_imgs = [
flow_to_image(
flowt.squeeze().detach().cpu().permute(1, 2, 0).numpy(),
convert_to_bgr=True,
)
for flowt in out_flowts
]
I1_pred_img = [
(I1_pred[0].detach().cpu().permute(1, 2, 0))
for I1_pred in out_frames
]
for i in range(interpolation_factor - 1):
out_images_list.append(I1_pred_img[i])
flows.append(flowt_imgs[i])
out_images_list.append(
((padder.unpad(I2)).squeeze().detach().cpu().permute(1, 2, 0))
)
pbar.update(1)
image_tensors = torch.stack(out_images_list)
image_tensors = image_tensors.cpu().float()
rgb_images = [cv2.cvtColor(flow, cv2.COLOR_BGR2RGB) for flow in flows]
flow_tensors = torch.stack([torch.from_numpy(image) for image in rgb_images])
flow_tensors = flow_tensors / 255.0
flow_tensors = flow_tensors.cpu().float()
return (image_tensors, flow_tensors)
NODE_CLASS_MAPPINGS = {
"DownloadAndLoadGIMMVFIModel": DownloadAndLoadGIMMVFIModel,
"GIMMVFI_interpolate": GIMMVFI_interpolate,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"DownloadAndLoadGIMMVFIModel": "(Down)Load GIMMVFI Model",
"GIMMVFI_interpolate": "GIMM-VFI Interpolate",
}
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