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TSC
2023-04-06 19:09:18 -05:00
committed by GitHub
parent 0b23c4f440
commit cbb640bbd9
1 changed files with 22 additions and 11 deletions
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33
efficiency_nodes.py
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@@ -20,7 +20,6 @@ import importlib
import matplotlib.pyplot as plt
import random
global my_dir, comfy_dir
# Get the absolute path of the parent directory of the current script
my_dir = os.path.dirname(os.path.abspath(__file__))
@@ -37,6 +36,8 @@ import comfy.samplers
import comfy.sd
import comfy.utils
MAX_RESOLUTION=8192
# Tensor to PIL (grabbed from WAS Suite)
def tensor2pil(image: torch.Tensor) -> Image.Image:
return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
@@ -51,30 +52,40 @@ class TSC_EfficientLoader:
@classmethod
def INPUT_TYPES(s):
return {"required": { "ckpt_name": (folder_paths.get_filename_list("checkpoints"), ),
"vae_name": (folder_paths.get_filename_list("vae"),), #.insert(0, "default.pt")
"vae_name": (folder_paths.get_filename_list("vae"),),
"clip_skip": ("INT", {"default": -1, "min": -24, "max": -1, "step": 1}),
"positive": ("STRING", {"default": "Positive prompt goes here.","multiline": True}),
"negative": ("STRING", {"default": "Negative prompt goes here.", "multiline": True}),
"positive": ("STRING", {"default": "Positive","multiline": True}),
"negative": ("STRING", {"default": "Negative", "multiline": True}),
"empty_latent_width": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 64}),
"empty_latent_height": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 64}),
"batch_size": ("INT", {"default": 1, "min": 1, "max": 64})
}}
RETURN_TYPES = ("MODEL", "CONDITIONING", "CONDITIONING", "CLIP", "VAE", )
RETURN_NAMES = ("MODEL", "CONDITIONING+", "CONDITIONING-", "CLIP", "VAE", )
RETURN_TYPES = ("MODEL", "CONDITIONING", "CONDITIONING", "LATENT", "VAE", "CLIP" ,)
RETURN_NAMES = ("MODEL", "CONDITIONING+", "CONDITIONING-", "LATENT", "VAE", "CLIP", )
FUNCTION = "efficientloader"
CATEGORY = "Efficiency Nodes/Loaders"
def efficientloader(self, ckpt_name, vae_name, clip_skip, positive, negative, output_vae=True, output_clip=True):
def efficientloader(self, ckpt_name, vae_name, clip_skip, positive, negative, empty_latent_width, empty_latent_height, batch_size,
output_vae=True, output_clip=True):
# Load Checkpoint
ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
out = comfy.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, embedding_directory=folder_paths.get_folder_paths("embeddings"))
# Create Empty Latent
latent = torch.zeros([batch_size, 4, empty_latent_height // 8, empty_latent_width // 8]).cpu()
# Load VAE
vae_path = folder_paths.get_full_path("vae", vae_name)
vae = comfy.sd.VAE(ckpt_path=vae_path)
# Extract CLIP
clip = out[1] #second entry
clip = clip.clone()
clip.clip_layer(clip_skip)
return (out[0], [[clip.encode(positive), {}]], [[clip.encode(negative), {}]], out[1], vae, )
return (out[0], [[clip.encode(positive), {}]], [[clip.encode(negative), {}]], {"samples":latent}, vae, out[1], )
# TSC KSampler (Efficient)
@@ -141,7 +152,7 @@ class TSC_KSampler:
print('\033[38;2;62;116;77mTSC_Nodes:\033[0m TSC_KSampler({}): no vae input detected, preview disabled'.format(my_unique_id))
return {"ui": {"images": list()}, "result": (model, positive, negative, {"samples": latent}, vae, empty_image, )}
images = vae.decode(latent)
images = vae.decode(latent).cpu()
last_helds["images"][my_unique_id] = images
filename_prefix = "TSC_KS_{:02d}".format(my_unique_id)
@@ -216,8 +227,8 @@ class TSC_ImageOverlay:
"overlay_resize": (["None", "Fit", "Resize by rescale_factor", "Resize to width & heigth"],),
"resize_method": (["nearest-exact", "bilinear", "area"],),
"rescale_factor": ("FLOAT", {"default": 1, "min": 0.01, "max": 16.0, "step": 0.01}),
"width": ("INT", {"default": 512, "min": 0, "max": 7680, "step": 64}),
"height": ("INT", {"default": 512, "min": 0, "max": 4320, "step": 64}),
"width": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 64}),
"height": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 64}),
"x_offset": ("INT", {"default": 0, "min": -48000, "max": 48000, "step": 1}),
"y_offset": ("INT", {"default": 0, "min": -48000, "max": 48000, "step": 1}),
"rotation": ("INT", {"default": 0, "min": -180, "max": 180, "step": 1}),