nodes/logtd-ComfyUI-InstanceDiffusion
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Possible optimizations and add DownloadInstanceDiffusionModels -node

Browse Source
This commit is contained in:
kijai
2024-05-04 19:17:14 +03:00
parent fe4f477d79
commit 72de57d2b0
6 changed files with 100 additions and 18 deletions
Download Patch File Download Diff File Expand all files Collapse all files
__init__.py
+4 -1
View File
@@ -3,6 +3,7 @@ from .nodes.load_instance_scaleu_node import LoadInstanceScaleUNode
from .nodes.load_instance_fusers_node import LoadInstanceFusersNode
from .nodes.load_instance_positionnet_node import LoadInstancePositionNetNode
from .nodes.instance_diffusion_tracking_prompt_node import InstanceDiffusionTrackingPromptNode
from .nodes.download_and_load_models import DownloadInstanceDiffusionModels
NODE_CLASS_MAPPINGS = {
@@ -11,6 +12,7 @@ NODE_CLASS_MAPPINGS = {
"LoadInstancePositionNetModel": LoadInstancePositionNetNode,
"LoadInstanceFusersNode": LoadInstanceFusersNode,
"InstanceDiffusionTrackingPrompt": InstanceDiffusionTrackingPromptNode,
"DownloadInstanceDiffusionModels": DownloadInstanceDiffusionModels
}
NODE_DISPLAY_NAME_MAPPINGS = {
@@ -18,5 +20,6 @@ NODE_DISPLAY_NAME_MAPPINGS = {
"LoadInstancePositionNetModel": "Load Instance PositionNet Model",
"LoadInstanceScaleUModel": "Load Instance ScaleU Model",
"LoadInstanceFusersNode": "Load Instance Fusers Model",
"InstanceDiffusionTrackingPrompt": "Instance Diffusion Tracking Prompt"
"InstanceDiffusionTrackingPrompt": "Instance Diffusion Tracking Prompt",
"DownloadInstanceDiffusionModels": "(Down)Load Instance Diffusion Models"
}
modules/convnext.py
+8 -6
View File
@@ -12,6 +12,8 @@ import torch.nn.functional as F
from timm.models.layers import trunc_normal_, DropPath
from timm.models.registry import register_model
import comfy.ops
ops = comfy.ops.manual_cast
class Block(nn.Module):
r""" ConvNeXt Block. There are two equivalent implementations:
@@ -27,13 +29,13 @@ class Block(nn.Module):
def __init__(self, dim, drop_path=0., layer_scale_init_value=1e-6):
super().__init__()
self.dwconv = nn.Conv2d(dim, dim, kernel_size=7,
self.dwconv = ops.Conv2d(dim, dim, kernel_size=7,
padding=3, groups=dim) # depthwise conv
self.norm = LayerNorm(dim, eps=1e-6)
# pointwise/1x1 convs, implemented with linear layers
self.pwconv1 = nn.Linear(dim, 4 * dim)
self.pwconv1 = ops.Linear(dim, 4 * dim)
self.act = nn.GELU()
self.pwconv2 = nn.Linear(4 * dim, dim)
self.pwconv2 = ops.Linear(4 * dim, dim)
self.gamma = nn.Parameter(layer_scale_init_value * torch.ones((dim)),
requires_grad=True) if layer_scale_init_value > 0 else None
self.drop_path = DropPath(
@@ -79,14 +81,14 @@ class ConvNeXt(nn.Module):
# stem and 3 intermediate downsampling conv layers
self.downsample_layers = nn.ModuleList()
stem = nn.Sequential(
nn.Conv2d(in_chans, dims[0], kernel_size=4, stride=4),
ops.Conv2d(in_chans, dims[0], kernel_size=4, stride=4),
LayerNorm(dims[0], eps=1e-6, data_format="channels_first")
)
self.downsample_layers.append(stem)
for i in range(3):
downsample_layer = nn.Sequential(
LayerNorm(dims[i], eps=1e-6, data_format="channels_first"),
nn.Conv2d(dims[i], dims[i + 1], kernel_size=2, stride=2),
ops.Conv2d(dims[i], dims[i + 1], kernel_size=2, stride=2),
)
self.downsample_layers.append(downsample_layer)
@@ -104,7 +106,7 @@ class ConvNeXt(nn.Module):
cur += depths[i]
def _init_weights(self, m):
if isinstance(m, (nn.Conv2d, nn.Linear)):
if isinstance(m, (ops.Conv2d, ops.Linear)):
trunc_normal_(m.weight, std=.02)
nn.init.constant_(m.bias, 0)
modules/text_grounding_net.py
+11 -9
View File
@@ -3,12 +3,14 @@ import torch.nn as nn
from .util import FourierEmbedder
from .convnext import convnext_tiny
import comfy.ops
ops = comfy.ops.manual_cast
class UniFusion(nn.Module):
def __init__(self, in_dim, out_dim, mid_dim=3072, fourier_freqs=8,
train_add_boxes=True, train_add_points=True, train_add_scribbles=True, train_add_masks=True,
test_drop_boxes=False, test_drop_points=False, test_drop_scribbles=True, test_drop_masks=False,
use_seperate_tokenizer=True):
use_seperate_tokenizer=True, use_segs=True):
super().__init__()
self.in_dim = in_dim
self.out_dim = out_dim
@@ -27,14 +29,14 @@ class UniFusion(nn.Module):
# Use instance masks as additional model inputs for mask conditioned image generation
#self.use_segs = True if self.add_masks else False
self.use_segs = False
self.use_segs = use_segs
if self.use_segs:
in_dim = 30
self.resize_input = 512
self.down_factor = 64 # determined by the convnext backbone
# from num_sem to 3 channels
self.in_conv = nn.Conv2d(in_dim, 3, 3, 1, 1)
self.in_conv = ops.Conv2d(in_dim, 3, 3, 1, 1)
self.convnext_tiny_backbone = convnext_tiny(pretrained=True)
self.num_tokens = (self.resize_input // self.down_factor) ** 2
self.convnext_feature_dim = 3072
@@ -80,19 +82,19 @@ class UniFusion(nn.Module):
for idx, input_dim_ in enumerate(input_dim_list):
mid_dim = self.mid_dim
self.linears_list.append(nn.Sequential(
nn.Linear(input_dim_, mid_dim),
ops.Linear(input_dim_, mid_dim),
nn.SiLU(),
nn.Linear(mid_dim, mid_dim),
ops.Linear(mid_dim, mid_dim),
nn.SiLU(),
nn.Linear(mid_dim, out_dim),
ops.Linear(mid_dim, out_dim),
))
else:
self.linears = nn.Sequential(
nn.Linear(input_dim, self.mid_dim),
ops.Linear(input_dim, self.mid_dim),
nn.SiLU(),
nn.Linear(self.mid_dim, self.mid_dim),
ops.Linear(self.mid_dim, self.mid_dim),
nn.SiLU(),
nn.Linear(self.mid_dim, out_dim),
ops.Linear(self.mid_dim, out_dim),
)
self.null_positive_feature = torch.nn.Parameter(
nodes/download_and_load_models.py
+72
View File
@@ -0,0 +1,72 @@
import os
import folder_paths
import comfy.utils
from .. import constants as constants
from ..model_helpers.prepare_positionnet import prepare_positionnet, get_positionnet_default_params
from ..model_helpers.prepare_scaleu import prepare_scaleu_nets
from ..model_helpers.prepare_fusers import prepare_fusers
from huggingface_hub import snapshot_download
INSTANCE_FUSERS_DIR = "fuser_models"
INSTANCE_SCALEU_DIR = "scaleu_models"
class DownloadInstanceDiffusionModels:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"use_segs": ("BOOLEAN", {"default": True}),
}}
RETURN_TYPES = ("POSITIONNET", "FUSERS", "SCALEU", )
FUNCTION = "load_model"
CATEGORY = "instance/loaders"
def load_model(self, use_segs: bool):
repo_id = "logtd/instance_diffusion"
instance_models_folder = os.path.join(folder_paths.models_dir, constants.INSTANCE_MODELS_DIR)
models_to_download = [
("position_net", constants.INSTANCE_POSITIONNET_DIR, "position_net.ckpt"),
("fusers", constants.INSTANCE_FUSERS_DIR, "fusers.ckpt"),
("scaleu", constants.INSTANCE_SCALEU_DIR, "scaleu.ckpt")
]
for model_name, model_folder, model_file in models_to_download:
model_folder_path = os.path.join(instance_models_folder, model_folder)
model_file_path = os.path.join(model_folder_path, model_file)
if not os.path.exists(model_file_path):
print(f"Selected model: {model_file_path} not found, downloading...")
allow_patterns = [f"*{model_name}*"]
snapshot_download(repo_id=repo_id,
allow_patterns=allow_patterns,
local_dir=model_folder_path,
local_dir_use_symlinks=False
)
positionnet_file = os.path.join(instance_models_folder, constants.INSTANCE_POSITIONNET_DIR, "position_net.ckpt")
fusers_file = os.path.join(instance_models_folder, constants.INSTANCE_FUSERS_DIR, "fusers.ckpt")
scaleu_file = os.path.join(instance_models_folder, constants.INSTANCE_SCALEU_DIR, "scaleu.ckpt")
pos_checkpoint = comfy.utils.load_torch_file(positionnet_file, safe_load=True)
params = get_positionnet_default_params()
params["use_segs"] = use_segs
model = prepare_positionnet(pos_checkpoint, params)
positionnet = {
'model': model,
}
fusers_checkpoint = comfy.utils.load_torch_file(fusers_file, safe_load=True)
fusers_list = prepare_fusers(fusers_checkpoint)
fusers = {
'model_list': fusers_list
}
scaleu_checkpoint = comfy.utils.load_torch_file(scaleu_file, safe_load=True)
scaleu_list = prepare_scaleu_nets(scaleu_checkpoint)
scaleu = {
'model_list': scaleu_list
}
return (positionnet, fusers, scaleu)
nodes/load_instance_positionnet_node.py
+4 -2
View File
@@ -8,17 +8,19 @@ class LoadInstancePositionNetNode:
def INPUT_TYPES(s):
return {"required": {
"model_filename": (get_model_list(constants.INSTANCE_POSITIONNET_DIR),),
"use_segs": ("BOOLEAN", {"default": True}),
}}
RETURN_TYPES = ("POSITIONNET",)
RETURN_TYPES = ("POSITIONNET", "FUSERS", "SCALEU",)
FUNCTION = "load_model"
CATEGORY = "instance/loaders"
def load_model(self, model_filename: str):
def load_model(self, model_filename: str, use_segs: bool):
checkpoint = load_checkpoint(
constants.INSTANCE_POSITIONNET_DIR, model_filename)
params = get_positionnet_default_params()
params["use_segs"] = use_segs
model = prepare_positionnet(checkpoint, params)
positionnet = {
'model': model,
requirements.txt
+1
View File
@@ -0,0 +1 @@
huggingface_hub