From 97384ee6aa7ca229002bfc525f1016c1743275b7 Mon Sep 17 00:00:00 2001
From: smthemex <138738845+smthemex@users.noreply.github.com>
Date: Wed, 22 May 2024 10:43:55 +0800
Subject: [PATCH] Delete custom_net/customnet.py

add
---
 custom_net/customnet.py | 177 ----------------------------------------
 1 file changed, 177 deletions(-)
 delete mode 100644 custom_net/customnet.py

diff --git a/custom_net/customnet.py b/custom_net/customnet.py
deleted file mode 100644
index be6fa19..0000000
--- a/custom_net/customnet.py
+++ /dev/null
@@ -1,177 +0,0 @@
-import sys
-import os
-current_path = os.getcwd()
-paths = os.path.join(current_path,"custom_nodes","Comfyui_CustomNet")
-sys.path.append(paths)
-
-
-import einops
-import torch
-import torch as th
-import torch.nn as nn
-import cv2
-from pytorch_lightning.utilities.rank_zero import rank_zero_only
-import numpy as np
-from torch.optim.lr_scheduler import LambdaLR
-
-from customnet_modules.util import (
-    conv_nd,
-    linear,
-    zero_module,
-    timestep_embedding,
-)
-
-from einops import rearrange, repeat
-from torchvision.utils import make_grid
-from customnet_modules.attention import SpatialTransformer
-from customnet_modules.openaimodel import UNetModel, TimestepEmbedSequential, ResBlock, Downsample, AttentionBlock
-from models.ddpm import LatentDiffusion
-from customnet_util import log_txt_as_img, exists, instantiate_from_config
-
-from models.ddim import DDIMSampler
-from customnet_util import load_state_dict
-
-
-
-
-
-
-class CustomNet(LatentDiffusion):
-    def __init__(self, 
-                text_encoder_config,
-                sd_15_ckpt=None,
-                use_cond_concat=False,
-                use_bbox_mask=False,
-                use_bg_inpainting=False,
-                learning_rate_scale=10,
-                *args, **kwargs):
-        super().__init__(*args, **kwargs)
-
-
-        self.text_encoder = instantiate_from_config(text_encoder_config)
-
-        if sd_15_ckpt is not None:
-            self.load_model_from_ckpt(ckpt=sd_15_ckpt)
-
-        self.use_cond_concat = use_cond_concat
-        self.use_bbox_mask = use_bbox_mask
-        self.use_bg_inpainting = use_bg_inpainting
-        self.learning_rate_scale = learning_rate_scale
-
-
-    def load_model_from_ckpt(self, ckpt, verbose=True):
-        print(" =========================== init Stable Diffusion pretrained checkpoint =========================== ")
-        print(f"Loading model from {ckpt}")
-        pl_sd = torch.load(ckpt, map_location="cpu")
-        if "global_step" in pl_sd:
-            print(f"Global Step: {pl_sd['global_step']}")
-        sd = pl_sd["state_dict"]
-        sd_keys = sd.keys()
-
-
-        missing = []
-        text_encoder_sd = self.text_encoder.state_dict()
-        for k in text_encoder_sd.keys():
-            sd_k = "cond_stage_model."+ k
-            if sd_k in sd_keys:
-                text_encoder_sd[k] =  sd[sd_k]
-            else:
-                missing.append(k)
-
-        self.text_encoder.load_state_dict(text_encoder_sd)
-
-
-
-
-    def configure_optimizers(self):
-        lr = self.learning_rate
-        params = []
-        params += list(self.cc_projection.parameters())
-
-
-        params_dualattn = []
-        for k, v in self.model.named_parameters():
-            if "to_k_text" in k or "to_v_text" in k:
-                params_dualattn.append(v)
-                print("training weight: ", k)
-            else:
-                params.append(v)
-
-        
-        opt = torch.optim.AdamW([
-                                 {'params':params_dualattn, 'lr': lr*self.learning_rate_scale},
-                                 {'params': params, 'lr': lr}
-                                 ])
-
-
-        if self.use_scheduler:
-            assert 'target' in self.scheduler_config
-            scheduler = instantiate_from_config(self.scheduler_config)
-
-            print("Setting up LambdaLR scheduler...")
-            scheduler = [
-                {
-                    'scheduler': LambdaLR(opt, lr_lambda=scheduler.schedule),
-                    'interval': 'step',
-                    'frequency': 1
-                }]
-            return [opt], scheduler
-        return opt
-
-    def training_step(self, batch, batch_idx):
-        loss, loss_dict = self.shared_step(batch)
-
-        self.log_dict(loss_dict, prog_bar=True,
-                      logger=True, on_step=True, on_epoch=True)
-
-        self.log("global_step", self.global_step,
-                 prog_bar=True, logger=True, on_step=True, on_epoch=False)
-
-        if self.use_scheduler:
-            lr = self.optimizers().param_groups[0]['lr']
-            self.log('lr_abs', lr, prog_bar=True, logger=True, on_step=True, on_epoch=False)
-
-        return loss
-
-
-   
-
-    def shared_step(self, batch, **kwargs):
-
-        if 'txt' in self.ucg_training:
-            k = 'txt'
-            p = self.ucg_training[k]
-            for i in range(len(batch[k])):
-                if self.ucg_prng.choice(2, p=[1 - p, p]):
-                    if isinstance(batch[k], list):     
-                        batch[k][i] = ""
-        
-        with torch.no_grad():
-            text = batch['txt']
-            text_embedding = self.text_encoder(text)
-
-
-        x, c = self.get_input(batch, self.first_stage_key)
-
-        c["c_crossattn"].append(text_embedding)
-        loss = self(x, c,)
-        return loss
-    
-
-    def apply_model(self, x_noisy, t, cond, return_ids=False,  **kwargs):
-
-        if isinstance(cond, dict):
-            # hybrid case, cond is exptected to be a dict
-            pass
-        else:
-            if not isinstance(cond, list):
-                cond = [cond]
-            key = 'c_concat' if self.model.conditioning_key == 'concat' else 'c_crossattn'
-            cond = {key: cond}
-
-        x_recon = self.model(x_noisy, t, **cond)
-
-        if isinstance(x_recon, tuple) and not return_ids:
-            return x_recon[0]
-        else:
-            return x_recon
\ No newline at end of file