RES4LYF
Advanced samplers with new noise scaling math to enable SDE sampling with all publicly available native models; new unsampling/noise inversion methods and other advanced…
Nodes (299)
Pick the noise your sampler starts from
Rebuild a CONDITIONING from an encoded string
The no-fuss RES sampler drop-in
Encode a Flux prompt without baking in guidance
Style transfer by matching feature statistics
Style transfer through attention injection
RES4LYF's answer to img2img and ad-hoc ControlNet
Apply a style guide to low or high detail bands
Match the overall color and tone of a reference
Drive a generation with two latent guides at once
Steer composition and style with guide images
Refine an image against its own estimate, no reference
Bundle masked and unmasked latent guides for RES4LYF
The kitchen-sink guide behind RES4LYF faceswaps
Reference-image style transfer inside RES4LYF
Soften scattersort seams with an edge mask
Scattersort with per-component targeting
Tile scattersort for tighter reference adherence
Mask a video by time range for guided sampling
RES4LYF inpainting with separate masked and background guidance
The no-fuss RES4LYF inpainting node
Load model, CLIP(s) and VAE in one node with fp8 casting
Schedule sampler parameters over time (and per frame)
Merge several RES4LYF option nodes into one
Unsample-and-resample loops for refinement
Add detail during sampling, on a step window
The text box for RES4LYF's hidden flags
The sync_eps control for RES4LYF flow guiding
Shape guidance across the frames of a video
Feed a raw per-frame weight curve to RES4LYF
Bongmath and implicit refinement, as an option node
Rescale the latent during RES4LYF sampling
Add momentum to the RES4LYF sampling trajectory
Tune the noise injection for SDE sampling
Add SDE noise to only part of the image
Feed your own latent as the SDE noise
Noise scaling and lying sigmas for RES4LYF
Overshoot control for RES4LYF samplers
Switch solvers mid-denoise
Tiled sampling settings for RES4LYF samplers
Tiled sampling for RES4LYF
Torch.compile speedups for Wan video
Different prompts for different regions
Mask-based regional prompts, unlimited zones
Two masked regions plus an unmasked catch-all
Two prompts, two masked regions, one image
Three masked prompt regions in one image
Unlimited regional prompt zones via a bundle
The sampler you build, from an older workflow
The RES4LYF sampler with every knob exposed
Every sampling knob the pack has
The sampler you build, not the one you run
Pick one solver from all 119 of them
A sigma schedule you can shape by hand
Hand one sampler's latent to the next
The all-in-one flow-matching sampler
Schedule eta and noise across the steps
The legacy schedule bundler for the old ClownsharKSampler
The all-in-one sampler for flow-matching models
Steer a generation with a guide latent
Dual guides for subject + background
RES4LYF's noise settings, bundled into one legacy node
Style transfer at the attention level for Flux, SD3.5 and HiDream
Style transfer at the attention level for SD1.5 and SDXL
Full transformer-block control for style transfer on MMDiT models
Style transfer control at the ResBlock level for classic U-Net models
Push RES4LYF style transfer harder
Reference-image style transfer for Flux, SD3.5, HiDream and Chroma
Inject style at specific UNet blocks
Style transfer inside a U-Net's spatial transformer
The finest-grained style-transfer dial RES4LYF has
The node that actually turns a reference image into a style guide
Layer one conditioning onto another at a controlled strength
Crossfade two prompts over the course of a run, not in one jump
Combine up to four conditionings in one node
Combine up to eight conditionings without a chain of combiners
Trim a bloated T5 conditioning down for speed
An experimental T5/CLIP-weighted conditioning blend
Cast your conditioning to double precision
Dump a conditioning object out as text you can actually look at
The fix for SD3.5's silent 77-token quality cliff
The SD3.5-safe replacement for ConditioningZeroOut
A flat (or linear) sigma ramp
Erase and replace concepts in HiDream's two text encoders
A plain empty-latent source for RES4LYF workflows
Empty latents for Cascade, 16-channel, and odd shapes
Kill the plasticky AI look with real grain
Turning off Flux's baked-in guidance so real CFG works
One node to load a whole Flux stack
Keeping real CFG from burning Flux images
Stitch two image batches into one sequence
Stitch two latents into one sequence
Stitch two latent clips together, no re-encode
Stitching per-frame masks into RES4LYF's temporal mask
Pull one frame out of a batch
Pull one frame out of a batch as a latent
Pull one frame out as a latent
Flipping a video latent's frame order
Shrink a per-frame mask to Wan's latent frame rate
Excluding a single frame from RES4LYF's temporal mask
Trim a range of frames out of an image batch
Trim video frames before the VAE decode
Cutting a frame range out of a video latent
Splitting detail from color, Photoshop-style
Split detail from tone, edit, recombine
The gentler frequency-separation blend
Split an image into luminance and color
Crop by pixel coordinates, keep the paste-back data
A one-knob blur for masks and guides
Turning a flat-color layout into ordered zones
Kill the plastic look with film grain
Edge-friendly blur for cleanup and detail work
Pulling two batched images back apart
Fill a canvas from a small tile
A sharpen pass with median or gaussian character
Color-grading a 4-channel latent directly
Channel-wise grading for Flux-style 16-channel latents
Turning one latent into a batch of copies
Copy channel data between two latents
Wiping RES4LYF's stashed metadata off a latent
Keeping guide-crop bookkeeping in sync
Printing what RES4LYF stashed on your latent
Read where a RES4LYF sampler left off
Reading a latent's per-channel average
Matching one latent's color stats to another
Colored noise as a standalone starting point
Plain noise, with mean and spread under your control
Hand-tuned starting noise, one channel at a time
An Empty Latent Image that starts from structured noise
Hand-control the noise you inject into a latent
One latent in, a batch of noised variants out
Fix color and contrast drift in the latent
Blend two latents by structure and by texture, separately
Scale a latent's structure and texture, per channel
Nudge a latent's structure and texture, per channel
A nonlinear curve over a latent's structure and texture
Selectively overwrite a chained sampler's hidden state
Move a latent onto (or off) the GPU
Pull the sampler's raw diffusion state out of a latent
Reattach a chained sampler's hidden state to a new latent
Trim frames off a video latent without breaking sampler state
Scale a latent by a factor without leaving latent space
Upscale a latent through a real VAE roundtrip
Patch specific transformer layers of a model at high precision
The standalone SAMPLER version of RES4LYF's solver, kept for old workflows
The original all-in-one, with shift built in
Bundling img2img guidance and schedule for the old sampler
The older RES4LYF sampler, kept for old workflows
The linear-quadratic schedule with real knobs
Crop to a mask at the ratio you want
Extract just the boundary of a mask, for feathering hard region edges
Turn a soft, grayscale mask into a hard on/off one
Turn a color map into region masks for regional prompting
Turn a flat-color region map into one mask per zone
Draw a mask by hand, right inside the node
Split a mask list into sixteen wireable outputs
Split a mask list into four wireable outputs
Split a mask list into eight wireable outputs
An on/off switch for a mask, without rewiring your graph
The shift knob that actually matters on flow-matching models
Shift that scales with your image size
Set the timestep shift for flow-matching models
Getting your images, masks, and latents ready for RES4LYF unsampling
RES4LYF's style-transfer patch for AuraFlow
Block-level control over RES4LYF's AuraFlow style patch
Prep a Chroma model for RES4LYF's style tricks
RES4LYF's block-level style patch for Chroma
Turn Flux into a style/reference engine for RES4LYF
RES4LYF's style/faceswap patch for Flux, block by block
RES4LYF's style-transfer patch for HiDream
Block-level control over RES4LYF's HiDream style patch
RES4LYF's style-transfer patch for LTX Video
Block-level control over RES4LYF's LTX Video style patch
Precision control for Flux Redux inside RES4LYF's style-transfer system
RES4LYF's style-transfer patch for SD3.5
Block-level control over RES4LYF's SD3.5 style patch
Enable RES4LYF's style engine on your model at high precision
Unlock RES4LYF's Wan video features
Sliding-window attention for long Wan videos
Trade sampling speed for fewer OOM crashes
Rewriting the sigma-to-timestep math for Runge-Kutta samplers
One node for SD3.5's model, CLIP, and VAE
One seed, plus the next one, on demand
One place to set width and height, wired everywhere
One node for a base resolution and its scaled-up counterpart
Run RES4LYF sampling in float32 or float64
Casting a latent to a specific numeric precision, and inspecting all three at once
Force fp16/fp32/fp64 through your sampling graph
Hand a half-sampled latent to the next sampler and change the rules
The noise and denoise settings for RES4LYF samplers
Attaching independent conditioning and CFG to a RES4LYF guide
Separate conditioning and CFG for masked vs. unmasked regions
Feed a custom GUIDER into a RES4LYF sampler
Chaining sampler passes without losing your place
Set the compressed latent size for UltraCascade sampling
The node that actually runs the sampling
Elementwise addition for two noise schedules
Use one schedule as an envelope for another
Force a noise schedule back to positive
A noise floor that reacts to the schedule instead of a fixed number
Spend more steps where the schedule is changing fastest
Nudge every noise level in your schedule up or down
Pad extra values onto the end of a schedule
Reshape a noise schedule with an inverse-trig curve
Another inverse-trig reshape for a noise schedule
The one inverse-trig reshape that doesn't need normalizing
Run a noise schedule through a Lorenz system
Smooth and resample a noise schedule with spline interpolation
Perturb a noise schedule with a classic chaotic map
The hygiene node for after you've been editing sigmas by hand
Remap a schedule's timing through a flow curve
Yes, that Collatz conjecture, applied to your noise schedule
Glue two noise schedules end to end
Generate a noise schedule from an integer sequence
How many steps is that schedule, actually?
The vector-math node in the sigma toolbox
Trim the tail off a noise schedule instead of clamping it
Clean up a sigma schedule with repeated values
Vector-math for two noise schedules, undocumented territory
Bend a noise schedule with easing curves
Wrap a noise schedule into a repeating sawtooth
Keep only the fractional part of every noise value
Type a noise schedule by hand
Reshape a noise schedule with the gamma and beta functions
Run a noise schedule through the bell curve, five different ways
The S-curve version of a noise schedule
Generate a noise schedule from a number-theory conjecture about primes
Generate a schedule that falls off like the harmonic series
Reshape a noise schedule with sinh, cosh, tanh and friends
Pick per-step between two schedules based on a condition
Turn raw sigma values into a 0-1 position within a range
Build an up-then-down noise schedule for unsampling loops
The poly-exponential sibling of the up-down chainsampler schedule
Smooth out a jagged noise schedule
Reshape a schedule with the inverse of x times e^x
A noise schedule generated from the physics diffusion models are built on
Blend two noise schedules by a proportion
Ripple a sine wave into your noise schedule
Compress a noise schedule with a base-2 log
Write your own formula to generate a noise schedule
Write your own sigma-schedule formula
Wrap a sigma schedule with a remainder operation
Scale a whole noise schedule by one number
The two schedules an unsample-then-resample pass needs
A schedule generator built from ML's normalizing flows
Append a value onto a sigma schedule
Rescale a schedule off its typical values, not its extremes
A schedule generator borrowed from topology
Reshape a schedule with a single exponent
Actually see the schedule you built
Make one schedule statistically match another
Scale a whole schedule by one number
Run a sigma schedule through a Turing-pattern simulation
Change a sigma schedule's floating-point precision
Stretch or compress a schedule to a different step count
Set denoise by noise level, not by slicing steps
Pace a schedule out with curved-space geometry
Actually see what a scheduler does to your sigmas
Swap a schedule's minimum value for a different one
Reshape a noise schedule through an S-curve
Ease a schedule in and out like a graphics engineer would
Cut a noise schedule in two for chain sampling
Cut a noise schedule in two for two-stage sampling
Compress a schedule's high end with a square root
Take the front slice of a schedule for chained samplers
Build a multi-stage schedule in one node
Transform a schedule by timestep and decay
Cut a schedule down to its first N steps
Undo whatever Sigmas Pad added
Clamp steps too big for variance-locked SDE
Transform a schedule through the Riemann zeta family
Wiring Stage C's output into Stage B
Encoding an image into Stage C's latent space at a size you actually chose
RES4LYF's Flux Redux applicator, with an actual strength dial
A tangent-shaped noise schedule you can bend
Hand-shape your denoising curve
Shape your own sigma curve with a tangent function
Windowing which frames actually hear your prompt in Wan
The simplest way to split a Wan clip into two prompts
Windowing self-attention and cross-attention separately in Wan
Splitting self- and cross-attention at one pivot point each
A plain multiline text node for your prompts
Two prompt boxes in one node, and that's the whole pitch
Three multiline text boxes in one node, nothing fancier than that
Build prompts out of parts instead of retyping the whole thing
Glue two strings together in RES4LYF
Load a .txt off disk into your workflow
Randomize word order in a prompt, reproducibly
Shuffle your tags and cut the prompt to fit the encoder
Know exactly what CLIP is going to cut before it cuts it
Free-ish speed for AuraFlow, if you'll eat a slow first run
Torch.compile for Flux, with a scalpel instead of an on/off switch
The generic torch.compile node for whatever doesn't have a dedicated one
Torch.compile scoped to SD3.5's MMDiT
The legacy all-in-one sampler for Stable Cascade and UltraCascade
The tiled UltraCascade sampler in RES4LYF
Bake a patched model to disk so you stop re-running the merge
Encode with resizing and masks built in
The style transfer node that works across almost every model family
SUPERIOR SAMPLING WITH RES4LYF: THE POWER OF BONGMATH
RES_3M vs. Uni-PC (WAN). Typically only 20 steps are needed with RES samplers. Far more are needed with Uni-PC and other common samplers, and they never reach the same level of quality.
INSTALLATION
If you are using a venv, you will need to first run from within your ComfyUI folder (that contains your "venv" folder):
Linux:
source venv/bin/activate
Windows:
venv\Scripts\activate
Then, "cd" into your "custom_nodes" folder and run the following commands:
git clone https://github.com/ClownsharkBatwing/RES4LYF/
cd RES4LYF
If you are using a venv, run these commands:
pip install -r requirements.txt
Alternatively, if you are using the portable version of ComfyUI you will need to replace "pip" with the path to your embedded pip executable. For example, on Windows:
X:\path\to\your\comfy_portable_folder\python_embedded\Scripts\pip.exe install -r requirements.txt
IMPORTANT UPDATE INFO
The previous versions will remain available but with "Legacy" prepended to their names.
If you wish to use the sampler menu shown below, you will need to install https://github.com/rgthree/rgthree-comfy (which I highly recommend you have regardless).
If these menus do not show up after restarting ComfyUI and refreshing the page (hit F5, not just "r") verify that these menus are enabled in the rgthree settings (click the gear in the bottom left of ComfyUI, select rgthree, and ensure "Auto Nest Subdirectories" is checked):
NEW VERSION DOCUMENTATION
I have prepared a detailed explanation of many of the concepts of sampling with exmaples in this workflow. There's also many tips, explanations of parameters, and all of the most important nodes are laid out for you to see. Some new workflow-enhancing tricks like "chainsamplers" are demonstrated, and regional AND temporal prompting are explained (supporting Flux, HiDream, SD3.5, AuraFlow, and WAN - you can even change the conditioning on a frame-by-frame basis!).
[example_workflows/intro to clownsampling.json ](https://github.com/ClownsharkBatwing/RES4LYF/blob/main/example_workflows/intro%20to%20clownsampling.json)
STYLE TRANSFER
Supported models: HiDream, Flux, Chroma, AuraFlow, SD1.5, SDXL, SD3.5, Stable Cascade, LTXV, and WAN. Also supported: Stable Cascade (and UltraPixel) which has an excellent understanding of style (https://github.com/ClownsharkBatwing/UltraCascade).
Currently, best results are with HiDream or Chroma, or Flux with a style lora (Flux Dev is very lacking with style knowledge). Include some mention of the style you wish to use in the prompt. (Try with the guide off to confirm the prompt is not doing the heavy lifting!)
For example, the prompt for the below was simply "a gritty illustration of a japanese woman with traditional hair in traditional clothes". Mostly you just need to make clear whether it's supposed to be a photo or an illustration, etc. so that the conditioning isn't fighting the style guide (every model has its inherent biases).
COMPOSITION GUIDE; OUTPUT; STYLE GUIDE
KILL FLUX BLUR (and HiDream blur)
Consecutive seeds, no cherrypicking.
REGIONAL CONDITIONING
Unlimited zones! Over 10 zones have been used in one image before.
Currently supported models: HiDream, Flux, Chroma, SD3.5, SD1.5, SDXL, AuraFlow, and WAN.
Masks can be drawn freely, or more traditional rigid ones may be used, such as in this example:
TEMPORAL CONDITIONING
Unlimited zones! Ability to change the prompt for each frame.
Currently supported models: WAN.
VIDEO 2 VIDEO EDITING
Viable with any video model, demo with WAN:
PREVIOUS VERSION NODE DOCUMENTATION
At the heart of this repository is the "ClownsharKSampler", which was specifically designed to support both rectified flow and probability flow models. It features 69 different selectible samplers (44 explicit, 18 fully implicit, 7 diagonally implicit) all available in both ODE or SDE modes with 20 noise types, 9 noise scaling modes, and options for implicit Runge-Kutta sampling refinement steps. Several new explicit samplers are implemented, most notably RES_2M, RES_3S, and RES_5S. Additionally, img2img capabilities include both latent image guidance and unsampling/resampling (via new forms of rectified noise inversion).
A particular emphasis of this project has been to facilitate modulating parameters vs. time, which can facilitate large gains in image quality from the sampling process. To this end, a wide variety of sigma, latent, and noise manipulation nodes are included.
Much of this work remains experimental and is subject to further changes.
ClownSampler
SharkSampler
ClownsharKSampler
This is an all-in-one sampling node designed for convenience without compromising on control or quality.
There are several key sections to the parameters which will be explained below.
INPUTS
The only two mandatory inputs here are "model" and "latent_image".
POSITIVE and NEGATIVE: If you connect nothing to either of these inputs, the node will automatically generate null conditioning. If you are unsampling, you actually don't need to hook up any conditioning at all (and will set CFG = 1.0). In most cases, merely using the positive conditioning will suffice, unless you really need to use a specific negative prompt.
SIGMAS: If a sigmas scheduler node is connected to this input, it will override the scheduler and steps settings chosen within the node.
NOISE SETTINGS
NOISE_TYPE_INIT: This sets the initial noise type applied to the latent image.
NOISE_TYPE_SDE: This sets the noise type used during SDE sampling. Note that SDE sampling is identical to ODE sampling in most ways - the difference is that noise is added after each step. It's like a form of carefully controlled continuous noise injection.
NOISE_MODE_SDE: This determines what method is used for scaling the amount of noise to be added based on the "eta" setting below. They are listed in order of strength of the effect.
ETA: This controls how much noise is added after each step. Note that for most of the noise modes, anything equal to or greater than 1.0 will trigger internal scaling to prevent NaN errors. The exception is the noise mode "exp" which allows for settings far above 1.0.
NOISE_SEED: Largely identical to the setting in KSampler. Set to -1 to have it increment the most recently used seed (by the workflow) by 1.
CONTROL_AFTER_GENERATE: Self-explanatory. I recommend setting to "fixed" or "increment" (as you don't have to reload the workflow to regenerate something, you can just decement it by one).
SAMPLER SETTINGS
SAMPLER_MODE: In virtually all situations, use "standard". However, if you are unsampling, set to "unsample", and if you are resampling (the stage after unsampling), set to "resample". Both of these modes will disable noise addition within ComfyUI, which is essential for these methods to work properly.
SAMPLER_NAME: This is used similarly to the KSampler setting. This selects the explicit sampler type. Note the use of numbers and letters at the end of each sampler name: "2m, 3m, 2s, 3s, 5s, etc."
Samplers that end in "s" use substeps between each step. One ending with "2s" has two stages per step, therefore costs two model calls per step (Euler costs one - model calls are what determine inference time). "3s" would take three model calls per step, and therefore take three times as long to run as Euler. However, the increase in accuracy can be very dramatic, especially when using noise (SDE sampling). The "res" family of samplers are particularly notable (they are effectively refinements of the dpmpp family, with new, higher order, much more accurate versions implemented here).
Samplers that end in "m" are "multistep" samplers, which instead of issuing new model calls for substeps, recycle previous steps as estimations for these substeps. They're less accurate, but all run at Euler speed (one model call per step). Sometimes this can be an advantage, as multistep samplers tend to converge more linearly toward a target image. This can be useful for img2img transformations, unsampling, or when using latent image guides.
IMPLICIT_SAMPLER_NAME: This is very useful with SD3.5 Medium for improving coherence, reducing artifacts and mutations, etc. It may be difficult to use with a model like Flux unless you plan on setting up a queue of generations and walking away. It will use the explicit step type as a predictor for each of the implicit substeps, so if you choose a slow explicit sampler, you will be waiting a long time. Euler, res_2m, deis_2m, etc. will often suffice as a predictor for implicit sampling, though any sampler may be used. Try "res_5s" as your explicit sampler type, and "gauss-legendre_5s", if you wish to demonstrate your commitment to climate change (and image quality).
Setting this to "none" has the same effect as setting implicit_steps = 0.
SCHEDULER AND DENOISE SETTINGS
These are identical in most ways to the settings by the same name in KSampler.
SCHEDULER: There is one extra sigma scheduler offered by default: "beta57" which is the beta schedule with modified parameters (alpha = 0.5, beta = 0.7).
IMPLICIT_STEPS: This controls the number of implicit steps to run. Note that it will double, triple, etc. the runtime as you increase the stepcount. Typically, gains diminish quickly after 2-3 implicit steps.
DENOISE: This is identical to the KSampler setting. Controls the amount of noise removed from the image. Note that with this method, the effect will change significantly depending on your choice of scheduler.
DENOISE_ALT: Instead of splitting the sigma schedule like "denoise", this multiplies them. The results are different, but track more closely from one scheduler to another when using the same value. This can be particularly useful for img2img workflows.
CFG: This is identical to the KSampler setting. Typically, you'll set this to 1.0 (to disable it) when using Flux, if you're using Flux guidance. However, the effect is quite nice when using dedistilled models if you use "CLIP Text Encode" without any Flux guidance, and set CFG to 3.0.
If you've never quite understood CFG, you can think of it this way. Imagine you're walking down the street and see what looks like an enticing music festival in the distance (your positive conditioning). You're on the fence about attending, but then, suddenly, a horde of pickleshark cannibals come storming out of a nearby bar (your negative conditioning). Together, the two team up to drive you toward the music festival. That's CFG.
SHIFT SETTINGS
These are present for convenience as they are used in virtually every workflow.
SHIFT: This is the same as "shift" for the ModelSampling nodes for SD3.5, AuraFlow, etc., and is equivalent to "max_shift" for Flux. Set this value to -1 to disable setting shift (or max_shift) within the node.
BASE_SHIFT: This is only used by Flux. Set this value to -1 to disable setting base_shift within the node.
SHIFT_SCALING: This changes how the shift values are calculated. "exponential" is the default used by Flux, whereas "linear" is the default used by SD3.5 and AuraFlow. In most cases, "exponential" leads to better results, though "linear" has some niche uses.
Sampler and noise mode list
Explicit samplers
Bolded samplers are added as options to the sampler dropdown in ComyfUI (an ODE and SDE version for each).
res_2m
res_2/3/5s
deis_2/3/4m
ralston_2/3/4s
dpmpp_2/3m
dpmpp_sde_2s
dpmpp_2/3s
midpoint_2s
heun_2/3s
houwen-wray_3s
kutta_3s
ssprk3_3s
rk38_4s
rk4_4s
dormand-prince_6s
dormand-prince_13s
bogacki-shampine_7s
ddim
euler
Fully Implicit Samplers
gauss-legendre_2/3/4/5s
radau_(i/ii)a_2/3s
lobatto_iii(a/b/c/d/star)_2/3s
Diagonally Implicit Samplers
kraaijevanger_spijker_2s
qin_zhang_2s
pareschi_russo_2s
pareschi_russo_alt_2s
crouzeix_2/3s
irk_exp_diag_2s (features an exponential integrator)
PREVIOUS FLUX WORKFLOWS
TXT2IMG:
This uses my amateur cell phone lora, which is freely available (https://huggingface.co/ClownsharkBatwing/CSBW_Style/blob/main/amateurphotos_1_amateurcellphonephoto_recapt2.safetensors). It significantly reduces the plastic, blurred look of Flux Dev.

INPAINTING:

UNSAMPLING (Dual guides with masks):

PREVIOUS WORKFLOWS
THE FOLLOWING WORKFLOWS ARE FOR A PREVIOUS VERSION OF THE NODE. These will still work! You will, however, need to manually delete and recreate the sampler and guide nodes and input the settings as they appear in the screenshots. The layout of the nodes has been changed slightly. To replicate their behavior precisely, add to the new extra_options box in ClownsharKSampler: truncate_conditioning=true (if that setting was used in the screenshot for the node).
TXT2IMG Workflow:


TXT2IMG Workflow (Latent Image Guides):


Input image: https://github.com/ClownsharkBatwing/RES4LYF/blob/main/workflows/txt2img%20guided%20SD35M%20input.png
TXT2IMG Workflow (Dual Guides with Masking):


Input images and mask: https://github.com/ClownsharkBatwing/RES4LYF/blob/main/workflows/txt2img%20dual%20guides%20with%20mask%20SD35M%20input1.png https://github.com/ClownsharkBatwing/RES4LYF/blob/main/workflows/txt2img%20dual%20guides%20with%20mask%20SD35M%20input2.png https://github.com/ClownsharkBatwing/RES4LYF/blob/main/workflows/txt2img%20dual%20guides%20with%20mask%20SD35M%20mask.png
IMG2IMG Workflow (Unsampling):


Input image: https://github.com/ClownsharkBatwing/RES4LYF/blob/main/workflows/img2img%20unsampling%20SD35L%20input.png
IMG2IMG Workflow (Unsampling with SDXL):


Input image: https://github.com/ClownsharkBatwing/RES4LYF/blob/main/workflows/img2img%20unsampling%20SDXL%20input.png
IMG2IMG Workflow (Unsampling with latent image guide):


Input image: https://github.com/ClownsharkBatwing/RES4LYF/blob/main/workflows/img2img%20guided%20unsampling%20SD35M%20input.png
IMG2IMG Workflow (Unsampling with dual latent image guides and masking):


Input images and mask: https://github.com/ClownsharkBatwing/RES4LYF/blob/main/workflows/img2img%20dual%20guided%20masked%20unsampling%20SD35M%20input1.png https://github.com/ClownsharkBatwing/RES4LYF/blob/main/workflows/img2img%20dual%20guided%20masked%20unsampling%20SD35M%20input2.png https://github.com/ClownsharkBatwing/RES4LYF/blob/main/workflows/img2img%20dual%20guided%20masked%20unsampling%20SD35M%20mask.png