netadmin/UI_latest
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Release on 09.11.24

Browse Source
This commit is contained in:
netadmin
2024-11-09 17:19:42 +03:00
commit 3c64fe0c3e
339 changed files with 456101 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files

167
comfy_extras/nodes_sd3.py Normal file
View File

@@ -0,0 +1,167 @@
import folder_paths
import comfy.sd
import comfy.model_management
import nodes
import torch
import re
class TripleCLIPLoader:
@classmethod
def INPUT_TYPES(s):
return {"required": { "clip_name1": (folder_paths.get_filename_list("text_encoders"), ), "clip_name2": (folder_paths.get_filename_list("text_encoders"), ), "clip_name3": (folder_paths.get_filename_list("text_encoders"), )
}}
RETURN_TYPES = ("CLIP",)
FUNCTION = "load_clip"
CATEGORY = "advanced/loaders"
def load_clip(self, clip_name1, clip_name2, clip_name3):
clip_path1 = folder_paths.get_full_path_or_raise("text_encoders", clip_name1)
clip_path2 = folder_paths.get_full_path_or_raise("text_encoders", clip_name2)
clip_path3 = folder_paths.get_full_path_or_raise("text_encoders", clip_name3)
clip = comfy.sd.load_clip(ckpt_paths=[clip_path1, clip_path2, clip_path3], embedding_directory=folder_paths.get_folder_paths("embeddings"))
return (clip,)
class EmptySD3LatentImage:
def __init__(self):
self.device = comfy.model_management.intermediate_device()
@classmethod
def INPUT_TYPES(s):
return {"required": { "width": ("INT", {"default": 1024, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}),
"height": ("INT", {"default": 1024, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}),
"batch_size": ("INT", {"default": 1, "min": 1, "max": 4096})}}
RETURN_TYPES = ("LATENT",)
FUNCTION = "generate"
CATEGORY = "latent/sd3"
def generate(self, width, height, batch_size=1):
latent = torch.zeros([batch_size, 16, height // 8, width // 8], device=self.device)
return ({"samples":latent}, )
class CLIPTextEncodeSD3:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"clip": ("CLIP", ),
"clip_l": ("STRING", {"multiline": True, "dynamicPrompts": True}),
"clip_g": ("STRING", {"multiline": True, "dynamicPrompts": True}),
"t5xxl": ("STRING", {"multiline": True, "dynamicPrompts": True}),
"empty_padding": (["none", "empty_prompt"], )
}}
RETURN_TYPES = ("CONDITIONING",)
FUNCTION = "encode"
CATEGORY = "advanced/conditioning"
def encode(self, clip, clip_l, clip_g, t5xxl, empty_padding):
no_padding = empty_padding == "none"
tokens = clip.tokenize(clip_g)
if len(clip_g) == 0 and no_padding:
tokens["g"] = []
if len(clip_l) == 0 and no_padding:
tokens["l"] = []
else:
tokens["l"] = clip.tokenize(clip_l)["l"]
if len(t5xxl) == 0 and no_padding:
tokens["t5xxl"] = []
else:
tokens["t5xxl"] = clip.tokenize(t5xxl)["t5xxl"]
if len(tokens["l"]) != len(tokens["g"]):
empty = clip.tokenize("")
while len(tokens["l"]) < len(tokens["g"]):
tokens["l"] += empty["l"]
while len(tokens["l"]) > len(tokens["g"]):
tokens["g"] += empty["g"]
cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True)
return ([[cond, {"pooled_output": pooled}]], )
class ControlNetApplySD3(nodes.ControlNetApplyAdvanced):
@classmethod
def INPUT_TYPES(s):
return {"required": {"positive": ("CONDITIONING", ),
"negative": ("CONDITIONING", ),
"control_net": ("CONTROL_NET", ),
"vae": ("VAE", ),
"image": ("IMAGE", ),
"strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
"start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
"end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001})
}}
CATEGORY = "conditioning/controlnet"
DEPRECATED = True
class SkipLayerGuidanceSD3:
'''
Enhance guidance towards detailed dtructure by having another set of CFG negative with skipped layers.
Inspired by Perturbed Attention Guidance (https://arxiv.org/abs/2403.17377)
Experimental implementation by Dango233@StabilityAI.
'''
@classmethod
def INPUT_TYPES(s):
return {"required": {"model": ("MODEL", ),
"layers": ("STRING", {"default": "7, 8, 9", "multiline": False}),
"scale": ("FLOAT", {"default": 3.0, "min": 0.0, "max": 10.0, "step": 0.1}),
"start_percent": ("FLOAT", {"default": 0.01, "min": 0.0, "max": 1.0, "step": 0.001}),
"end_percent": ("FLOAT", {"default": 0.15, "min": 0.0, "max": 1.0, "step": 0.001})
}}
RETURN_TYPES = ("MODEL",)
FUNCTION = "skip_guidance"
CATEGORY = "advanced/guidance"
def skip_guidance(self, model, layers, scale, start_percent, end_percent):
if layers == "" or layers == None:
return (model, )
# check if layer is comma separated integers
def skip(args, extra_args):
return args
model_sampling = model.get_model_object("model_sampling")
sigma_start = model_sampling.percent_to_sigma(start_percent)
sigma_end = model_sampling.percent_to_sigma(end_percent)
def post_cfg_function(args):
model = args["model"]
cond_pred = args["cond_denoised"]
cond = args["cond"]
cfg_result = args["denoised"]
sigma = args["sigma"]
x = args["input"]
model_options = args["model_options"].copy()
for layer in layers:
model_options = comfy.model_patcher.set_model_options_patch_replace(model_options, skip, "dit", "double_block", layer)
model_sampling.percent_to_sigma(start_percent)
sigma_ = sigma[0].item()
if scale > 0 and sigma_ >= sigma_end and sigma_ <= sigma_start:
(slg,) = comfy.samplers.calc_cond_batch(model, [cond], x, sigma, model_options)
cfg_result = cfg_result + (cond_pred - slg) * scale
return cfg_result
layers = re.findall(r'\d+', layers)
layers = [int(i) for i in layers]
m = model.clone()
m.set_model_sampler_post_cfg_function(post_cfg_function)
return (m, )
NODE_CLASS_MAPPINGS = {
"TripleCLIPLoader": TripleCLIPLoader,
"EmptySD3LatentImage": EmptySD3LatentImage,
"CLIPTextEncodeSD3": CLIPTextEncodeSD3,
"ControlNetApplySD3": ControlNetApplySD3,
"SkipLayerGuidanceSD3": SkipLayerGuidanceSD3,
}
NODE_DISPLAY_NAME_MAPPINGS = {
# Sampling
"ControlNetApplySD3": "Apply Controlnet with VAE",
}