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https://git.datalinker.icu/kijai/ComfyUI-KJNodes.git
synced 2026-04-05 05:06:59 +08:00
Cleanup
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Kijai
parent
8a7d3b9d58
commit
656feb2c14
161
nodes.py
161
nodes.py
@ -1,8 +1,10 @@
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import nodes
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import torch
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import torch.nn.functional as F
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from torchvision.transforms import Resize, CenterCrop, InterpolationMode
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from torchvision.transforms import functional as TF
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from scipy.interpolate import CubicSpline
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import scipy.ndimage
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from scipy.spatial import Voronoi
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import matplotlib.pyplot as plt
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@ -1154,7 +1156,7 @@ class VRAM_Debug:
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}
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RETURN_TYPES = ("IMAGE", "MODEL","INT", "INT",)
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RETURN_NAMES = ("image_passthrough", "freemem_before", "freemem_after")
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RETURN_NAMES = ("image_passthrough", "model_passthrough", "freemem_before", "freemem_after")
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FUNCTION = "VRAMdebug"
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CATEGORY = "KJNodes"
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@ -1254,9 +1256,6 @@ class EmptyLatentImagePresets:
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return (latent, int(width), int(height),)
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#https://github.com/hahnec/color-matcher/
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from color_matcher import ColorMatcher
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#from color_matcher.normalizer import Normalizer
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class ColorMatch:
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@classmethod
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@ -1287,6 +1286,10 @@ class ColorMatch:
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FUNCTION = "colormatch"
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def colormatch(self, image_ref, image_target, method):
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try:
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from color_matcher import ColorMatcher #https://github.com/hahnec/color-matcher/
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except:
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raise Exception("Can't import color-matcher, did you install requirements.txt? Manual install: pip install color-matcher")
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cm = ColorMatcher()
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image_ref = image_ref.cpu()
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image_target = image_target.cpu()
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@ -2282,7 +2285,6 @@ class ResizeMask:
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return(outputs, outputs.shape[2], outputs.shape[1],)
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from torch.nn.functional import pad
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class OffsetMask:
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@classmethod
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def INPUT_TYPES(s):
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@ -2355,23 +2357,23 @@ class OffsetMask:
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if padding_mode == 'empty':
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mask[i] = torch.cat([torch.zeros((height, temp_x)), mask[i, :, :-temp_x]], dim=1)
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elif padding_mode in ['replicate', 'reflect']:
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mask[i] = pad(mask[i, :, :-temp_x], (0, temp_x), mode=padding_mode)
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mask[i] = F.pad(mask[i, :, :-temp_x], (0, temp_x), mode=padding_mode)
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elif temp_x < 0:
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if padding_mode == 'empty':
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mask[i] = torch.cat([mask[i, :, :temp_x], torch.zeros((height, -temp_x))], dim=1)
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elif padding_mode in ['replicate', 'reflect']:
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mask[i] = pad(mask[i, :, -temp_x:], (temp_x, 0), mode=padding_mode)
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mask[i] = F.pad(mask[i, :, -temp_x:], (temp_x, 0), mode=padding_mode)
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if temp_y > 0:
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if padding_mode == 'empty':
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mask[i] = torch.cat([torch.zeros((temp_y, width)), mask[i, :-temp_y, :]], dim=0)
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elif padding_mode in ['replicate', 'reflect']:
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mask[i] = pad(mask[i, :-temp_y, :], (0, temp_y), mode=padding_mode)
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mask[i] = F.pad(mask[i, :-temp_y, :], (0, temp_y), mode=padding_mode)
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elif temp_y < 0:
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if padding_mode == 'empty':
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mask[i] = torch.cat([mask[i, :temp_y, :], torch.zeros((-temp_y, width))], dim=0)
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elif padding_mode in ['replicate', 'reflect']:
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mask[i] = pad(mask[i, -temp_y:, :], (temp_y, 0), mode=padding_mode)
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mask[i] = F.pad(mask[i, -temp_y:, :], (temp_y, 0), mode=padding_mode)
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return mask,
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@ -2895,52 +2897,6 @@ class AddLabel:
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return (combined_images,)
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class ReferenceOnlySimple3:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": { "model": ("MODEL",),
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"reference": ("LATENT",),
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"reference2": ("LATENT",),
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"input": ("LATENT",),
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"batch_size": ("INT", {"default": 1, "min": 1, "max": 64})
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}}
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RETURN_TYPES = ("MODEL", "LATENT")
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FUNCTION = "reference_only"
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CATEGORY = "KJNodes/experiments"
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def reference_only(self, model, reference, reference2, input, batch_size):
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model_reference = model.clone()
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size_latent = list(reference["samples"].shape)
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size_latent[0] = batch_size
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latent = input
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batch = latent["samples"].shape[0] + reference["samples"].shape[0] + reference2["samples"].shape[0]
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def reference_apply(q, k, v, extra_options):
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k = k.clone().repeat(1, 2, 1)
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offset = 0
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if q.shape[0] > batch:
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offset = batch
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re = extra_options["transformer_index"] % 2
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for o in range(0, q.shape[0], batch):
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for x in range(1, batch):
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k[x + o, q.shape[1]:] = q[o + re,:]
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return q, k, k
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model_reference.set_model_attn1_patch(reference_apply)
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out_latent = torch.cat((reference["samples"], reference2["samples"], latent["samples"]))
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if "noise_mask" in latent:
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mask = latent["noise_mask"]
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else:
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mask = torch.ones((64,64), dtype=torch.float32, device="cpu")
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mask = mask.repeat(latent["samples"].shape[0], 1, 1)
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out_mask = torch.zeros((1,mask.shape[1],mask.shape[2]), dtype=torch.float32, device="cpu")
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return (model_reference, {"samples": out_latent, "noise_mask": torch.cat((out_mask,out_mask, mask))})
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class SoundReactive:
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@classmethod
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@ -3428,8 +3384,6 @@ def interpolate_coordinates(coordinates_dict, batch_size):
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return interpolated
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from scipy.interpolate import CubicSpline
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import numpy as np
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def interpolate_coordinates_with_curves(coordinates_dict, batch_size):
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sorted_coords = sorted(coordinates_dict.items())
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@ -3582,67 +3536,7 @@ class ImageUpscaleWithModelBatched:
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t = torch.cat(t, dim=0).permute(0, 2, 3, 1).cpu()
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return (t,)
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import torchvision
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from torch import nn
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from safetensors import safe_open
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class EfficientNetEncoder(nn.Module):
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def __init__(self, c_latent=16):
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super().__init__()
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self.backbone = torchvision.models.efficientnet_v2_s(weights='DEFAULT').features.eval()
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self.mapper = nn.Sequential(
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nn.Conv2d(1280, c_latent, kernel_size=1, bias=False),
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nn.BatchNorm2d(c_latent, affine=False), # then normalize them to have mean 0 and std 1
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)
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def forward(self, x):
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return self.mapper(self.backbone(x))
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class EffnetEncode:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {
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"image": ("IMAGE",),
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}}
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RETURN_TYPES = ("LATENT",)
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FUNCTION = "encode"
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CATEGORY = "KJNodes"
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def encode(self, image):
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device = comfy.model_management.get_torch_device()
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image = image.permute(0, 3, 1, 2).to(device)
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effnet_preprocess = torchvision.transforms.Compose([
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torchvision.transforms.Normalize(
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mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)
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)
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])
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image = effnet_preprocess(image)
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effnet = EfficientNetEncoder()
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effnet_checkpoint = os.path.join(folder_paths.models_dir,"vae", "Stable-cascade","effnet_encoder.safetensors")
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if not os.path.exists(effnet_checkpoint):
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try:
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from huggingface_hub import snapshot_download
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download_to = os.path.join(folder_paths.models_dir,'vae', "Stable-cascade")
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snapshot_download(repo_id="stabilityai/stable-cascade", allow_patterns=["effnet_encoder.safetensors"],
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local_dir=download_to, local_dir_use_symlinks=False)
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except:
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raise Exception("Model not found and download failed. (https://huggingface.co/stabilityai/stable-cascade, effnet_encoder.safetensors)")
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effnet_state_dict = {}
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with safe_open(effnet_checkpoint, framework="pt", device="cpu") as f:
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for key in f.keys():
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effnet_state_dict[key] = f.get_tensor(key)
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effnet.load_state_dict(effnet_state_dict if 'state_dict' not in effnet_checkpoint else effnet_checkpoint['state_dict'])
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effnet.eval().requires_grad_(False).to(device)
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t = effnet(image).cpu()
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return ({"samples":t}, )
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return (t,)
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class ImageNormalize_Neg1_To_1:
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@classmethod
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@ -3659,11 +3553,9 @@ class ImageNormalize_Neg1_To_1:
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def normalize(self,images):
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images = images * 2.0 - 1.0
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return (images,)
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import comfy.sample
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from nodes import CLIPTextEncode
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folder_paths.add_model_folder_path("intristic_loras", os.path.join(script_dir, "intristic_loras"))
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@ -3822,19 +3714,16 @@ class LoadResAdapterNormalization:
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model_clone = model.clone()
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norm_state_dict = comfy.utils.load_torch_file(resadapter_path)
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new_values = {key[len(prefix_to_remove):]: value for key, value in norm_state_dict.items() if key.startswith(prefix_to_remove)}
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# Replace the values for the keys in the model's state dict
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for key in model.model.diffusion_model.state_dict().keys():
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if key in new_values:
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original_tensor = model.model.diffusion_model.state_dict()[key]
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new_tensor = new_values[key].to(model.model.diffusion_model.dtype)
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if original_tensor.shape == new_tensor.shape:
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model_clone.add_object_patch(f"diffusion_model.{key}.data", new_tensor)
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print(f"Replaced key: {key}")
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else:
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print(f"Shape mismatch for key: {key}. Did not replace.")
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try:
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for key in model.model.diffusion_model.state_dict().keys():
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if key in new_values:
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original_tensor = model.model.diffusion_model.state_dict()[key]
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new_tensor = new_values[key].to(model.model.diffusion_model.dtype)
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if original_tensor.shape == new_tensor.shape:
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model_clone.add_object_patch(f"diffusion_model.{key}.data", new_tensor)
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except:
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raise Exception("Could not patch model, this way of patching was added to ComfyUI on March 3rd 2024, is your ComfyUI up to date?")
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print("Added resnet normalization patch.")
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return (model_clone, )
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class Superprompt:
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@ -3922,7 +3811,6 @@ NODE_CLASS_MAPPINGS = {
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"FlipSigmasAdjusted": FlipSigmasAdjusted,
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"InjectNoiseToLatent": InjectNoiseToLatent,
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"AddLabel": AddLabel,
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"ReferenceOnlySimple3": ReferenceOnlySimple3,
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"SoundReactive": SoundReactive,
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"GenerateNoise": GenerateNoise,
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"StableZero123_BatchSchedule": StableZero123_BatchSchedule,
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@ -3937,7 +3825,6 @@ NODE_CLASS_MAPPINGS = {
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"CondPassThrough": CondPassThrough,
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"ImageUpscaleWithModelBatched": ImageUpscaleWithModelBatched,
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"ScaleBatchPromptSchedule": ScaleBatchPromptSchedule,
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"EffnetEncode": EffnetEncode,
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"ImageNormalize_Neg1_To_1": ImageNormalize_Neg1_To_1,
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"Intrinsic_lora_sampling": Intrinsic_lora_sampling,
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"RemapMaskRange": RemapMaskRange,
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@ -3994,7 +3881,6 @@ NODE_DISPLAY_NAME_MAPPINGS = {
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"FlipSigmasAdjusted": "FlipSigmasAdjusted",
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"InjectNoiseToLatent": "InjectNoiseToLatent",
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"AddLabel": "AddLabel",
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"ReferenceOnlySimple3": "ReferenceOnlySimple3",
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"SoundReactive": "SoundReactive",
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"GenerateNoise": "GenerateNoise",
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"StableZero123_BatchSchedule": "StableZero123_BatchSchedule",
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@ -4009,7 +3895,6 @@ NODE_DISPLAY_NAME_MAPPINGS = {
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"CondPassThrough": "CondPassThrough",
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"ImageUpscaleWithModelBatched": "ImageUpscaleWithModelBatched",
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"ScaleBatchPromptSchedule": "ScaleBatchPromptSchedule",
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"EffnetEncode": "EffnetEncode",
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"ImageNormalize_Neg1_To_1": "ImageNormalize_Neg1_To_1",
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"Intrinsic_lora_sampling": "Intrinsic_lora_sampling",
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"RemapMaskRange": "RemapMaskRange",
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