mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
0beb08686c
* add support for CFG rescale * fix typo * move rescale position and tweak docs * move input position * implement suggestions from github and discord * cleanup unused code * add back dropped FieldDescription * fix(ui): revert unrelated UI changes * chore(nodes): bump denoise_latents version 1.4.0 -> 1.5.0 * feat(nodes): add cfg_rescale_multiplier to metadata node * feat(ui): add cfg rescale multiplier to linear UI - add param to state - update graph builders - add UI under advanced - add metadata handling & recall - regen types * chore: black * fix(backend): make `StableDiffusionGeneratorPipeline._rescale_cfg()` staticmethod This doesn't need access to class. * feat(backend): add docstring for `_rescale_cfg()` method * feat(ui): update cfg rescale mult translation string --------- Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
68 lines
3.8 KiB
Python
68 lines
3.8 KiB
Python
class FieldDescriptions:
|
|
denoising_start = "When to start denoising, expressed a percentage of total steps"
|
|
denoising_end = "When to stop denoising, expressed a percentage of total steps"
|
|
cfg_scale = "Classifier-Free Guidance scale"
|
|
cfg_rescale_multiplier = "Rescale multiplier for CFG guidance, used for models trained with zero-terminal SNR"
|
|
scheduler = "Scheduler to use during inference"
|
|
positive_cond = "Positive conditioning tensor"
|
|
negative_cond = "Negative conditioning tensor"
|
|
noise = "Noise tensor"
|
|
clip = "CLIP (tokenizer, text encoder, LoRAs) and skipped layer count"
|
|
unet = "UNet (scheduler, LoRAs)"
|
|
vae = "VAE"
|
|
cond = "Conditioning tensor"
|
|
controlnet_model = "ControlNet model to load"
|
|
vae_model = "VAE model to load"
|
|
lora_model = "LoRA model to load"
|
|
main_model = "Main model (UNet, VAE, CLIP) to load"
|
|
sdxl_main_model = "SDXL Main model (UNet, VAE, CLIP1, CLIP2) to load"
|
|
sdxl_refiner_model = "SDXL Refiner Main Modde (UNet, VAE, CLIP2) to load"
|
|
onnx_main_model = "ONNX Main model (UNet, VAE, CLIP) to load"
|
|
lora_weight = "The weight at which the LoRA is applied to each model"
|
|
compel_prompt = "Prompt to be parsed by Compel to create a conditioning tensor"
|
|
raw_prompt = "Raw prompt text (no parsing)"
|
|
sdxl_aesthetic = "The aesthetic score to apply to the conditioning tensor"
|
|
skipped_layers = "Number of layers to skip in text encoder"
|
|
seed = "Seed for random number generation"
|
|
steps = "Number of steps to run"
|
|
width = "Width of output (px)"
|
|
height = "Height of output (px)"
|
|
control = "ControlNet(s) to apply"
|
|
ip_adapter = "IP-Adapter to apply"
|
|
t2i_adapter = "T2I-Adapter(s) to apply"
|
|
denoised_latents = "Denoised latents tensor"
|
|
latents = "Latents tensor"
|
|
strength = "Strength of denoising (proportional to steps)"
|
|
metadata = "Optional metadata to be saved with the image"
|
|
metadata_collection = "Collection of Metadata"
|
|
metadata_item_polymorphic = "A single metadata item or collection of metadata items"
|
|
metadata_item_label = "Label for this metadata item"
|
|
metadata_item_value = "The value for this metadata item (may be any type)"
|
|
workflow = "Optional workflow to be saved with the image"
|
|
interp_mode = "Interpolation mode"
|
|
torch_antialias = "Whether or not to apply antialiasing (bilinear or bicubic only)"
|
|
fp32 = "Whether or not to use full float32 precision"
|
|
precision = "Precision to use"
|
|
tiled = "Processing using overlapping tiles (reduce memory consumption)"
|
|
detect_res = "Pixel resolution for detection"
|
|
image_res = "Pixel resolution for output image"
|
|
safe_mode = "Whether or not to use safe mode"
|
|
scribble_mode = "Whether or not to use scribble mode"
|
|
scale_factor = "The factor by which to scale"
|
|
blend_alpha = (
|
|
"Blending factor. 0.0 = use input A only, 1.0 = use input B only, 0.5 = 50% mix of input A and input B."
|
|
)
|
|
num_1 = "The first number"
|
|
num_2 = "The second number"
|
|
mask = "The mask to use for the operation"
|
|
board = "The board to save the image to"
|
|
image = "The image to process"
|
|
tile_size = "Tile size"
|
|
inclusive_low = "The inclusive low value"
|
|
exclusive_high = "The exclusive high value"
|
|
decimal_places = "The number of decimal places to round to"
|
|
freeu_s1 = 'Scaling factor for stage 1 to attenuate the contributions of the skip features. This is done to mitigate the "oversmoothing effect" in the enhanced denoising process.'
|
|
freeu_s2 = 'Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to mitigate the "oversmoothing effect" in the enhanced denoising process.'
|
|
freeu_b1 = "Scaling factor for stage 1 to amplify the contributions of backbone features."
|
|
freeu_b2 = "Scaling factor for stage 2 to amplify the contributions of backbone features."
|