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Workflow with the sole purpose of testing each weighttype of the IpAdapter.
Runtime profile
Source description
Workflow with the sole purpose of testing each weight_type of the IpAdapter.
This workflow allows us to see each option of IpAdapter's weight_type side by side and determine which is the best choice depending on the intended use.
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A brief explanation of each IpAdapter's option:
weight_type – Defines the profile of how the embeddings are applied over time or space:
linear: Applies the weight in a constant and linear manner.
ease in: Starts weak and gradually increases the strength of the weight.
AI-generated commentary
AI-generated explanation based on source and configuration details. Suggestions are clearly labeled.
IpAdapter Test is for comparing IpAdapter weight_type options side by side; it can take an image, text conditioning, a checkpoint, and an empty latent image, vary embedding settings, and produce image previews and saved images.
Its processing path combines IPAdapterAdvanced and IPAdapterUnifiedLoader with KSampler, VAEDecode, PreviewImage, and SaveImage.
Provide an image, text conditioning, a checkpoint, and an empty latent image through LoadImage, CLIPTextEncode, CheckpointLoaderSimple, and EmptyLatentImage.
The named outputs are image previews and saved images through PreviewImage and SaveImage.
It is listed with base model SD 1.5.
Listed model files are LCM_LoRA_Weights_SD15.safetensors, realisticVisionV60B1_v51HyperVAE.safetensors, and vae-ft-mse-840000-ema-pruned.safetensors.
Listed components include Anything Everywhere; caching_condition; caching_image; CheckpointLoaderSimple; CLIPTextEncode; easy float; EmptyLatentImage; GetNode; ImageResize+; IPAdapterAdvanced; and IPAdapterUnifiedLoader.
It also includes KSampler; LoadImage; Lora Loader Stack (rgthree); LoraTagLoader; PreviewImage; SaveImage; Seed; SetNode; ttN text; VAEDecode; and VAELoader.
Choose weight_type to vary embedding strength with linear, ease in, ease out, ease in-out, reverse in-out, weak input, weak output, weak middle, strong middle, style transfer, composition, or strong style transfer.
Choose embeds_scaling from V only, K+V, K_V w/ C penalty, or K+mean(V) w/ C penalty. These use the value vector, key and value vectors, or consistency-penalty variants described for each option.
Choose combine embeddings from concat, add, subtract, average, or norm average to join, add, subtract, average, or normalize before averaging embeddings, respectively.
Before using it, check that the listed model files and components are available in your environment.
Suggestion · not verified
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Sources
1 sourceSource excerpts
1 excerptSource context: 272 downloads · Type Workflows · Base model SD 1.5
ease out: Starts strong and softens over time.
ease in-out: Smooth transition at both ends (start and finish).
reverse in-out: The inverse of ease in-out, with emphasis in the middle.
weak input: Applies less strength at the beginning.
weak output: Applies less strength at the end.
weak middle: The strength is lower in the middle of the process.
strong middle: The strength is higher in the middle and lower at the ends.
style transfer: Focuses on preserving the style of the original image, with smooth and gradual emphasis.
composition: Attempts to blend different embeddings in a balanced way, with harmonious transitions.
strong style transfer: Strongly enforces the style embeddings of the reference image.
embeds_scaling – Defines how the embeddings are integrated into the attention mechanism:
V only: Uses only the value vector (V) in cross-attention. Less intrusive.
K+V: Uses both key (K) and value (V). Exerts more influence on the results.
K_V w/ C penalty: Same as K+V, but with a consistency penalty (C) to avoid distortions.
K+mean(V) w/ C penalty: Uses K and the mean of V, with consistency penalty — balances smoothness with control.
combine embeddings – Methods for merging multiple embeddings:
concat: Direct concatenation — simply joins the embeddings along the feature dimension, increasing total size. Preserves all individual information.
add: Element-wise addition — sums the corresponding values of the vectors. Directly blends the representations.
subtract: Subtracts embeddings — useful to highlight differences between them (e.g., style A - style B). Can produce distinct visual variations.
average: Simple average — smooths the embeddings, producing a balanced blend. Less prone to distortions.
norm average: Normalized average — same idea as average, but normalizes the vectors before combining, keeping magnitude consistent among them. Helps prevent embeddings with vastly different weights from dominating the blend.
Estimated VRAM requirement
Estimate unavailable
128 MB across 1 of 5 model files. Model file total + 25% loading overhead + 2 GB execution buffer, rounded up.
Requirements
25 requirementsLoRA · 128 MB · SAFETENSOR · Unknown
realisticVisionV60B1_v51HyperVAE.safetensors
Not resolvedVAE · Unknown
vae-ft-mse-840000-ema-pruned.safetensors
File unverifiedVAE · Unknown · vae-ft-mse-840000-ema-pruned | 840000 | 840k SD1.5 VAE
VAEDecode
PossibleVAE · Unknown
VAELoader
PossibleVAE · Unknown
Node pack · Unknown
Node pack · Unknown
Node pack · Unknown
CheckpointLoaderSimple
PossibleNode pack · Unknown
CLIPTextEncode
PossibleNode pack · Unknown
Node pack · Unknown
EmptyLatentImage
PossibleNode pack · Unknown
GetNode
Not resolvedNode pack · Unknown
ImageResize+
PossibleNode pack · Unknown
IPAdapterAdvanced
PossibleNode pack · Unknown
IPAdapterUnifiedLoader
PossibleNode pack · Unknown
KSampler
PossibleNode pack · Unknown
LoadImage
PossibleNode pack · Unknown
Lora Loader Stack (rgthree)
Not resolvedNode pack · Unknown
LoraTagLoader
PossibleNode pack · Unknown
PreviewImage
PossibleNode pack · Unknown
SaveImage
PossibleNode pack · Unknown
Seed
PossibleNode pack · Unknown
SetNode
Not resolvedNode pack · Unknown
Node pack · Unknown
