pipeLoader v1 (Legacy)¶
Documentation¶
- Class name:
ttN pipeLoader - Category:
ttN/legacy - Output node:
False
The ttN pipeLoader node is designed to initialize and manage the loading process for various data pipelines within the tinyterraNodes framework. It abstracts the complexities involved in setting up and configuring the data flow, ensuring seamless integration and efficient handling of data across different stages of the pipeline.
Input types¶
Required¶
ckpt_name- unknown
- Comfy dtype:
COMBO[STRING] - Python dtype:
unknown
config_name- unknown
- Comfy dtype:
COMBO[STRING] - Python dtype:
unknown
vae_name- unknown
- Comfy dtype:
COMBO[STRING] - Python dtype:
unknown
clip_skip- unknown
- Comfy dtype:
INT - Python dtype:
unknown
lora1_name- unknown
- Comfy dtype:
COMBO[STRING] - Python dtype:
unknown
lora1_model_strength- unknown
- Comfy dtype:
FLOAT - Python dtype:
unknown
lora1_clip_strength- unknown
- Comfy dtype:
FLOAT - Python dtype:
unknown
lora2_name- unknown
- Comfy dtype:
COMBO[STRING] - Python dtype:
unknown
lora2_model_strength- unknown
- Comfy dtype:
FLOAT - Python dtype:
unknown
lora2_clip_strength- unknown
- Comfy dtype:
FLOAT - Python dtype:
unknown
lora3_name- unknown
- Comfy dtype:
COMBO[STRING] - Python dtype:
unknown
lora3_model_strength- unknown
- Comfy dtype:
FLOAT - Python dtype:
unknown
lora3_clip_strength- unknown
- Comfy dtype:
FLOAT - Python dtype:
unknown
positive- Defines the positive conditioning input for the pipeline, influencing the direction and nature of the data processing.
- Comfy dtype:
STRING - Python dtype:
str
positive_token_normalization- unknown
- Comfy dtype:
COMBO[STRING] - Python dtype:
unknown
positive_weight_interpretation- unknown
- Comfy dtype:
COMBO[STRING] - Python dtype:
unknown
negative- Sets the negative conditioning input, providing a counterbalance to the positive conditioning and further refining the pipeline's output.
- Comfy dtype:
STRING - Python dtype:
str
negative_token_normalization- unknown
- Comfy dtype:
COMBO[STRING] - Python dtype:
unknown
negative_weight_interpretation- unknown
- Comfy dtype:
COMBO[STRING] - Python dtype:
unknown
empty_latent_width- unknown
- Comfy dtype:
INT - Python dtype:
unknown
empty_latent_height- unknown
- Comfy dtype:
INT - Python dtype:
unknown
batch_size- unknown
- Comfy dtype:
INT - Python dtype:
unknown
seed- Sets the seed for random number generation, ensuring reproducibility and consistency in the pipeline's operations.
- Comfy dtype:
INT - Python dtype:
int
Optional¶
model_override- unknown
- Comfy dtype:
MODEL - Python dtype:
unknown
clip_override- unknown
- Comfy dtype:
CLIP - Python dtype:
unknown
optional_lora_stack- unknown
- Comfy dtype:
LORA_STACK - Python dtype:
unknown
Output types¶
pipe- Comfy dtype:
PIPE_LINE - Outputs the configured pipeline, ready for further processing or analysis.
- Python dtype:
Dict[str, Any]
- Comfy dtype:
model- Comfy dtype:
MODEL - Outputs the model component of the pipeline.
- Python dtype:
str
- Comfy dtype:
positive- Comfy dtype:
CONDITIONING - Outputs the positive conditioning component of the pipeline.
- Python dtype:
str
- Comfy dtype:
negative- Comfy dtype:
CONDITIONING - Outputs the negative conditioning component of the pipeline.
- Python dtype:
str
- Comfy dtype:
latent- Comfy dtype:
LATENT - Outputs the latent space representation used in the pipeline.
- Python dtype:
str
- Comfy dtype:
vae- Comfy dtype:
VAE - Outputs the VAE model integrated into the pipeline.
- Python dtype:
str
- Comfy dtype:
clip- Comfy dtype:
CLIP - Outputs the CLIP model component of the pipeline.
- Python dtype:
str
- Comfy dtype:
seed- Comfy dtype:
INT - Outputs the seed used for random number generation in the pipeline.
- Python dtype:
int
- Comfy dtype:
Usage tips¶
- Infra type:
CPU - Common nodes:
Source code¶
class ttN_TSC_pipeLoader:
version = '1.1.2'
@classmethod
def INPUT_TYPES(cls):
return {"required": {
"ckpt_name": (folder_paths.get_filename_list("checkpoints"), ),
"config_name": (["Default",] + folder_paths.get_filename_list("configs"), {"default": "Default"} ),
"vae_name": (["Baked VAE"] + folder_paths.get_filename_list("vae"),),
"clip_skip": ("INT", {"default": -1, "min": -24, "max": 0, "step": 1}),
"lora1_name": (["None"] + folder_paths.get_filename_list("loras"),),
"lora1_model_strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
"lora1_clip_strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
"lora2_name": (["None"] + folder_paths.get_filename_list("loras"),),
"lora2_model_strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
"lora2_clip_strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
"lora3_name": (["None"] + folder_paths.get_filename_list("loras"),),
"lora3_model_strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
"lora3_clip_strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
"positive": ("STRING", {"default": "Positive","multiline": True}),
"positive_token_normalization": (["none", "mean", "length", "length+mean"],),
"positive_weight_interpretation": (["comfy", "A1111", "compel", "comfy++", "down_weight"],),
"negative": ("STRING", {"default": "Negative", "multiline": True}),
"negative_token_normalization": (["none", "mean", "length", "length+mean"],),
"negative_weight_interpretation": (["comfy", "A1111", "compel", "comfy++", "down_weight"],),
"empty_latent_width": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 8}),
"empty_latent_height": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 8}),
"batch_size": ("INT", {"default": 1, "min": 1, "max": 64}),
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
},
"optional": {"model_override": ("MODEL",), "clip_override": ("CLIP",), "optional_lora_stack": ("LORA_STACK",),},
"hidden": {"prompt": "PROMPT", "ttNnodeVersion": ttN_TSC_pipeLoader.version}, "my_unique_id": "UNIQUE_ID",}
RETURN_TYPES = ("PIPE_LINE" ,"MODEL", "CONDITIONING", "CONDITIONING", "LATENT", "VAE", "CLIP", "INT",)
RETURN_NAMES = ("pipe","model", "positive", "negative", "latent", "vae", "clip", "seed",)
FUNCTION = "adv_pipeloader"
CATEGORY = "ttN/legacy"
def adv_pipeloader(self, ckpt_name, config_name, vae_name, clip_skip,
lora1_name, lora1_model_strength, lora1_clip_strength,
lora2_name, lora2_model_strength, lora2_clip_strength,
lora3_name, lora3_model_strength, lora3_clip_strength,
positive, positive_token_normalization, positive_weight_interpretation,
negative, negative_token_normalization, negative_weight_interpretation,
empty_latent_width, empty_latent_height, batch_size, seed, model_override=None, clip_override=None, optional_lora_stack=None, prompt=None, my_unique_id=None):
model: ModelPatcher | None = None
clip: CLIP | None = None
vae: VAE | None = None
# Create Empty Latent
latent = sampler.emptyLatent(None, batch_size, empty_latent_width, empty_latent_height)
samples = {"samples":latent}
# Clean models from loaded_objects
loader.update_loaded_objects(prompt)
# Load models
model, clip, vae = loader.load_checkpoint(ckpt_name, config_name)
if model_override is not None:
model = model_override
if clip_override is not None:
clip = clip_override
if optional_lora_stack is not None:
for lora in optional_lora_stack:
model, clip = loader.load_lora(lora[0], model, clip, lora[1], lora[2])
if lora1_name != "None":
model, clip = loader.load_lora(lora1_name, model, clip, lora1_model_strength, lora1_clip_strength)
if lora2_name != "None":
model, clip = loader.load_lora(lora2_name, model, clip, lora2_model_strength, lora2_clip_strength)
if lora3_name != "None":
model, clip = loader.load_lora(lora3_name, model, clip, lora3_model_strength, lora3_clip_strength)
# Check for custom VAE
if vae_name != "Baked VAE":
vae = loader.load_vae(vae_name)
# CLIP skip
if not clip:
raise Exception("No CLIP found")
clipped = clip.clone()
if clip_skip != 0:
clipped.clip_layer(clip_skip)
positive = loader.nsp_parse(positive, seed, title='pipeLoader Positive', my_unique_id=my_unique_id)
positive_embeddings_final, positive_pooled = advanced_encode(clipped, positive, positive_token_normalization, positive_weight_interpretation, w_max=1.0, apply_to_pooled='enable')
positive_embeddings_final = [[positive_embeddings_final, {"pooled_output": positive_pooled}]]
negative = loader.nsp_parse(negative, seed, title='pipeLoader Negative', my_unique_id=my_unique_id)
negative_embeddings_final, negative_pooled = advanced_encode(clipped, negative, negative_token_normalization, negative_weight_interpretation, w_max=1.0, apply_to_pooled='enable')
negative_embeddings_final = [[negative_embeddings_final, {"pooled_output": negative_pooled}]]
image = ttNsampler.pil2tensor(Image.new('RGB', (1, 1), (0, 0, 0)))
pipe = {"model": model,
"positive": positive_embeddings_final,
"negative": negative_embeddings_final,
"vae": vae,
"clip": clip,
"samples": samples,
"images": image,
"seed": seed,
"loader_settings": {"ckpt_name": ckpt_name,
"vae_name": vae_name,
"lora1_name": lora1_name,
"lora1_model_strength": lora1_model_strength,
"lora1_clip_strength": lora1_clip_strength,
"lora2_name": lora2_name,
"lora2_model_strength": lora2_model_strength,
"lora2_clip_strength": lora2_clip_strength,
"lora3_name": lora3_name,
"lora3_model_strength": lora3_model_strength,
"lora3_clip_strength": lora3_clip_strength,
"refiner_ckpt_name": None,
"refiner_vae_name": None,
"refiner_lora1_name": None,
"refiner_lora1_model_strength": None,
"refiner_lora1_clip_strength": None,
"refiner_lora2_name": None,
"refiner_lora2_model_strength": None,
"refiner_lora2_clip_strength": None,
"clip_skip": clip_skip,
"positive": positive,
"positive_l": None,
"positive_g": None,
"positive_token_normalization": positive_token_normalization,
"positive_weight_interpretation": positive_weight_interpretation,
"positive_balance": None,
"negative": negative,
"negative_l": None,
"negative_g": None,
"negative_token_normalization": negative_token_normalization,
"negative_weight_interpretation": negative_weight_interpretation,
"negative_balance": None,
"empty_latent_width": empty_latent_width,
"empty_latent_height": empty_latent_height,
"batch_size": batch_size,
"seed": seed,
"empty_samples": samples,}
}
return (pipe, model, positive_embeddings_final, negative_embeddings_final, samples, vae, clip, seed)