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ModèleCivitai

Winter Style

This is a DreamArtist Textual Inversion Style Embedding trained on images of a snowy winter forest/cabin scene. You can get amazing winter colors and snow effects in your images by calling Style-Winter in the...

TextualInversionSD 1.5

Qwen-Image-Edit1024x10248GB VRAM

Reference-guided relighting

Generate one relit image from a subject image and a lighting reference, or from the subject and text instructions alone. For a first run, upload the subject as image 1 and a lighting reference as image 2. Keep the lighting-reference stage enabled, retain the supplied prompt, and leave Lightning mode off. The supplied Chinese prompt asks to remove image 1's original lighting and relight it using image 2's lighting and color tone. Standard sampling uses 20 steps and CFG 4. Enable Lightning mode to select the additional 4-step Lightning LoRA and its separate settings, initially 4 steps and CFG 1. Switching modes selects the current values for that mode; it does not reset edited values. Each mode's steps and CFG remain independently adjustable. Lightning mode uses the 4-step model artifact, even if an upstream source note mentions an 8-step artifact. The light-migration LoRA remains active in both modes. To relight without a reference, disable the lighting-reference stage and rewrite the instructions to describe the desired lighting directly, without referring to image 2. No reference upload is needed in this mode. A subject upload is always required; when the reference stage is enabled, upload a reference too. Advanced controls include an optional negative prompt, both LoRA strengths, denoise, seed, sampler, scheduler, guidance normalization and sampling shift. Lower denoise retains more of the initial subject's structure and can limit the lighting change. Use seed -1 for a fresh random seed, or a fixed seed to compare edits with the same initial noise. LoRA strengths control their model contributions; Lightning strength only affects Lightning mode. Changing an inactive mode's steps or CFG affects subsequent runs in that mode. The subject is automatically scaled before conditioning and sampling, with no independent output-size control. The Pipe returns one generated image. Relighting may change subject details, and uploaded transparency is not preserved because only the image loaders' RGB outputs are connected.

Stable Diffusion 3.5 Large1024x10248GB VRAM

Grok image edit

Create one or more Grok-edited images from an uploaded source image and text instructions. For a first run, upload the image you want to edit, replace the robot example prompt with your own instructions, and use the default grok-imagine-image model, 1K resolution, and one result. Describe both the changes you want and any details or framing you want retained, then run the tool. Choose among grok-imagine-image, grok-imagine-image-quality, and grok-imagine-image-pro. Resolution offers the provider's 1K or 2K setting rather than an exact width and height. Request 1–10 edited results from the same source image and instructions; this does not add source images. Aspect ratio stays automatic because the provider's explicit aspect-ratio setting requires multiple source images, while this tool supplies one. Seed controls whether the provider node re-runs; results remain nondeterministic even when a seed is repeated. Leave it at -1 to randomize the re-run seed each run. Uploaded transparency is not preserved: only the loader's RGB image output reaches the editor.

Magnific portrait skin enhancement

Produce a skin-enhanced portrait using Magnific's creative mode. Upload a portrait, leave sharpening at 0 and smart grain at 15 for your first run, then run the Pipe to receive the enhanced image. Sharpening controls sharpening intensity; smart grain controls grain intensity. Both use a 0–100 scale and can be adjusted independently. Creative mode allows artistic changes to the portrait's appearance. This Pipe supports creative mode only; faithful and flexible modes are unavailable. Uploaded transparency is not preserved because processing uses the loader's RGB image output. Processing sends the portrait to the Magnific partner API, requires provider authentication configured in the runtime, and uses provider-priced processing.

SD3.5 Large blur-guided image generation

Generate new images from a prompt and an already preprocessed blur reference using SD3.5 Large and its blur ControlNet. Upload the required reference and describe the desired subject, appearance, materials, and details in the positive prompt. This tool does not blur or otherwise preprocess the upload. For a first run, replace the example prompt with your description, upload a reference with the intended composition, and choose output dimensions matching its aspect ratio. Width and height are in pixels and use multiples of 16. Start with one image and the defaults: 1024 × 1024, control strength 1, 30 steps, CFG 4, Euler sampler, Simple scheduler, and denoise fraction 1. Control strength determines the influence of the reference. Lower it for more freedom; set it to 0 for generation without reference guidance, while still supplying the required upload. Control start and end select the portion of sampling during which guidance applies: 0 is the beginning and 1 is the end. Keep start below end. Open Sampling for variation and tuning. Seed -1 selects a random seed each run; use a fixed seed when comparing settings with the same initial noise. CFG controls prompt guidance. Steps, sampler, scheduler, and denoise fraction change the sampling process. Keep denoise at 1 for the initial run. Image count generates a batch using the same prompt, reference, and dimensions; larger batches and dimensions require more memory. The checkpoint and blur ControlNet are fixed in this tool. Generation starts from an empty latent rather than an encoded copy of the upload, so this is not pixel-preserving restoration. Lowering denoise does not turn it into image-to-image restoration. There is no editable negative prompt: its text conditioning is zeroed internally before ControlNet is applied. Uploaded alpha is not used.

Seedance 2.01024x10248GB VRAM

Seedance 2.0 text to video

Generate one video from a scene description, with optional generated audio. No uploaded media is required. For a first run, use the supplied five-second fantasy prompt with Seedance 2.0, 720p, 16:9 and Generate audio enabled. Alternatively, replace the prompt with your subject, setting, action, visual style and camera movement. Include desired sound when audio is enabled. This workflow calls the ByteDance provider API, requires runtime authentication and uses provider-priced generation. Choose Seedance 2.0, Seedance 2.0 Fast or Seedance 2.0 Mini. Resolution is a text field: enter a value supported by the selected model rather than assuming every resolution works with every model. Aspect ratio sets the frame shape; adaptive leaves that choice to the provider. Duration accepts 4–15 seconds. When changing it, update any duration or shot timestamps in the prompt as well. Prompt wording such as '8K' does not replace the Resolution control. Disable Generate audio for a silent result. Watermark requests a provider watermark. Seed defaults to -1 for randomization on each run. A fixed seed does not reproduce the same video; the source describes generation as non-deterministic regardless of seed. The generated video is returned as one preview result. Under advanced Video encoding, leave File type and Codec at auto for automatic selection, or choose a supported container and compatible codec. This output uses Sweet Tea's preview transport rather than the original SaveVideo stage.

Text to videoLTX-Video1024x10248GB VRAM

GPT Image edit and generation

Edit a reference image or generate images from text using GPT Image. For a first run, upload one source image, keep the Source image stage enabled, and describe the changes you want and the features to retain. The supplied example requests anime styling while retaining identity. Keep the defaults: GPT Image 1.5, Auto output size, Auto background, High quality and one image. For text-only generation, disable the Source image stage and replace the editing instruction with a complete scene description. An upload is required while that stage is enabled. Model selects GPT Image 1, GPT Image 1.5 or GPT Image 2. Output size requests pixel dimensions or Auto. GPT Image 2 enables additional sizes and custom dimensions. To use custom dimensions, select GPT Image 2 and Custom output size, then enter width and height from 1024 to 3840 pixels in multiples of 16. These fields have no effect with other output-size selections. Background requests automatic, opaque or transparent background handling. Quality selects Low, Medium or High and affects generation time and cost. Image count requests 1 to 8 images using the same instruction and optional reference; the selected output returns those images. This tool connects one RGB reference image. Uploaded alpha transparency is not preserved, and the loader's separate alpha-mask output is unused. Although the generation node supports an optional inpainting mask, this tool does not connect or expose one, so it has no mask-based editing mode. The source seed setting is hidden because the backend does not implement it; it cannot make results reproducible.

fal.ai LTX Video

Describe a scene and its motion to generate a video through fal.ai's LTX Video 13B Distilled endpoint. This workflow accepts text only and returns the generated video. For a first run, enter a prompt describing the subject, setting, action, and camera movement. Keep the source defaults of 480p, 16:9, and 121 frames. The negative prompt provides a starting list of visual and motion defects to discourage. Resolution and aspect ratio set the video shape. Frame count sets its length in frames; this workflow does not expose FPS, so frame count is not a duration in seconds. Frame count must be entered as an integer, without units. Alternative shape values and frame counts must be supported by the provider. Seed defaults to randomization on each run. A fixed seed is available for exploring variations under otherwise similar settings, without a guarantee of identical provider results. Generation requires configured fal.ai access.

Seedance text to video

Generate one short video from a scene description with Seedance, with optional generated audio on Seedance 1.5 Pro. No uploaded media is required. For a first run, keep Seedance 1.5 Pro selected and use the example prompt or describe your own subject, setting, action, and camera movement. The defaults request a five-second, 720p, widescreen video with generated audio. Include dialogue or sound intent in the prompt when audio is enabled, and keep the action and dialogue appropriate for the chosen duration. Choose a duration from 3–12 seconds, a resolution of 480p, 720p, or 1080p, and the aspect ratio for your intended frame shape. Model lets you select the listed Seedance 1.0 alternatives; those models ignore Generate audio. With Seedance 1.5 Pro, turn Generate audio off for a silent result. Fixed camera adds a stationary-camera instruction to the prompt but does not guarantee camera lock. Remove conflicting camera movement instructions when enabling it. AI watermark requests an “AI generated” watermark. Seed -1 randomizes each run; a nonnegative seed controls the generation's random input. The generated video is returned through Sweet Tea's video output transport with automatic format and codec selection by default. Generation sends your prompt to the ByteDance provider and uses provider-priced billing.

Amateur Snapshot Photo - [STYLE] [LORA] [FLUX]

Latest page update: Updated recommended workflow again to a version 2, which is slightly even better.

bad X (bad sdxl negative embedding)

This is a textual inversion based on SDXL 1.0 images with the refiner, tested and trained to enhance the effectiveness of the output of human figures. It is meant to correct mutation like symptoms of the face and...

TextualInversionSDXL 1.0

Wan 2.1 VACE 14B1024x10248GB VRAM

Wan 2.1 VACE text to video

Generate a silent video from text using Wan 2.1 VACE 14B. For a first run, replace the supplied car-scene prompt with your subject, setting, visual style and intended motion. Keep the remaining defaults: 1280 × 720 pixels, 81 frames, 16 FPS, batch size 1, CausVid enabled, 4 sampling steps and CFG 1. The output lasts about 5.1 seconds. No media upload is required. Width and height use multiples of 16. Frame count advances by four from 1. Frame count determines how many frames are generated; FPS changes playback speed and duration without adding frames. Keep batch size 1 for a single sequence: this Pipe assembles one video output and does not offer separate files per batch item. The advanced negative prompt discourages unwanted traits; its supplied Chinese text includes static frames, blur, overexposure, text overlays and visual artifacts. CausVid uses a fixed LoRA with separate model and text-encoder strengths. The supplied guidance recommends testing model strength between 0.3 and 0.7 if motion is shaky or blurry. To use the base-model mode, bypass CausVid and manually set Sampling steps to 20 and CFG to 6. Bypassing does not update these settings automatically. Use seed -1 for randomization or a fixed nonnegative seed to reuse starting noise. Sampler, scheduler, denoising amount and schedule shift remain available for advanced adjustment; keep their supplied defaults for a first run. Video playback includes an 8- or 10-bit choice; 10-bit may reduce gradient banding but is not supported by every player or downstream node. Video export provides container, codec and CRF controls. Keep automatic container and codec selection and CRF -1 for the first run; these settings affect encoding rather than generated content. This is a text-only configuration: no reference image, control video, control mask or audio is connected. The VACE strength field does not add those inputs. The model and CausVid LoRA are fixed; smaller-model alternatives and animated WebP export are not selectable modes in this Pipe.

Qwen-Image1024x10248GB VRAM

Qwen-Image edge-guided generation

Upload a source image and describe the image you want to create. The Pipe resizes the upload to approximately the chosen megapixel count while retaining its aspect ratio, extracts Canny edges, and uses the encoded edge map as both reference conditioning and the sampler's starting latent. It returns the generated image and the edge-map preview. For a first run, keep the 1 MP size, default edge thresholds, full denoise, and standard mode. Replace the example positive prompt with your desired subject, appearance, and style. Inspect the edge preview: lower thresholds admit more edges, while higher thresholds suppress weaker details. Keep the low threshold below the high threshold. Enable Lightning mode to select the supplied four-step Lightning LoRA together with its separate steps and CFG settings. Standard mode defaults to 20 steps and CFG 2.5; Lightning defaults to 4 steps and CFG 1. Each mode retains its own tuning fields, and only the selected mode's fields affect a run. Denoise operates on the edge-map latent, not the original photograph. Lower values can leave more of that edge-map appearance in the result; this is not a control for preserving the source image's colors. Union LoRA strength adjusts the model adaptation used for reference guidance rather than guaranteeing an exact edge match. Keep Lightning strength at 1 initially. The resize stage can be disabled to work at the upload's native dimensions; megapixel and resampling settings then have no effect, and large uploads can substantially increase resource use. This graph implements Canny guidance only, despite the Union model supporting other control types. It does not use the upload's alpha mask.

FLUX.1 [dev]1024x10248GB VRAM

Flux.1 Inpaint

Fill missing parts of images using Flux.1 Fill Inpainting.

VérifiéUnrated

Magnific reference style transfer

Create a restyled image from a source image, a visual style reference and a descriptive prompt. For a first run, upload both images and replace the fantasy example prompt with a description of your subject and intended appearance. Keep style strength 70, structure strength 41, faithful flavor, balanced engine and fixed generation enabled, then run the Pipe to view the resulting image. Style strength controls the reference style's influence from 0 to 100 percent. Structure strength controls retention of the original image's structure on a separate 0–100 scale. Adjust these independently to balance restyling with composition retention. Flavor selects the service's style-transfer treatment, while engine selects its processing engine. Both offer alternative treatments of the same inputs. Disable fixed generation to introduce more randomness across runs; enabling it does not guarantee identical results. Processing sends the images and prompt to the Magnific partner service through its API node, requires service authentication and uses provider-priced generation. Portrait enhancements are unavailable in this Pipe and remain disabled. Uploaded alpha masks are not connected, so source transparency is not preserved.

Gemini image refinement

Refine and upscale a reference image with Gemini, or generate a new image from a prompt. For a first run, upload a Source image, leave that stage enabled, and use the default prompt to request refined details, textures and materials while retaining composition and text. The default resolution is 4K. This is generative editing: exact text, details and composition may change. Uploaded transparency is not preserved because only the loader's IMAGE output is connected. Change Prompt to describe other edits and what should remain. Aspect ratio set to auto follows the reference image's proportions; an explicit ratio requests a different output shape. Resolution selects a target size tier of 1K, 2K or 4K rather than exact pixel dimensions. The 2K and 4K settings use Gemini's native upscaler, and dimensions also depend on aspect ratio. For text-to-image generation, bypass Source image and replace the refinement prompt with a complete description of the image you want. Choose an explicit aspect ratio to specify the output proportions. This Pipe accepts one reference image. Model selects the Gemini image model. Seed -1 requests a new random seed each run; a fixed seed requests best-effort repeatability, but identical results are not guaranteed. System instructions set foundational provider behavior alongside Prompt; the default directs Gemini to produce an image. Provider response format requests either IMAGE or IMAGE+TEXT, but only the image is connected to this Pipe's result. Generation uses the Gemini provider and sends the prompt and any reference image to that service through a paid provider call. The generated image is returned through Sweet Tea's preview transport; this workflow does not permanently save an image file.

MoGe valid geometry mask

Estimate a geometry-validity mask from one perspective image. For a first run, upload an image, leave Automatic resizing enabled and keep Detail level at 2. Each run performs MoGe geometry inference, renders predicted validity and converts the rendered red channel into the output mask. The mask identifies regions with valid predicted geometry. It is not foreground segmentation or an object cutout; sky and other regions with undefined geometry may be excluded. Prediction uses the uploaded RGB image, independently of uploaded transparency. This Pipe exports a mask, not depth, normal maps or a mesh. Automatic resizing checks image width. Images wider than 2048 pixels are resized to a fixed 2048-pixel longer edge before inference. Images at most 2048 pixels wide remain unchanged, even when their height exceeds 2048. Disable Automatic resizing to use the original image. The mask follows the dimensions used for inference, so resizing can change its dimensions relative to the upload. Open Geometry estimation for inference controls. Detail level ranges from 0 to 9; the source identifies 0 as fastest and 9 as most detailed. Horizontal field of view is measured in degrees: leave 0 for automatic recovery from predicted points, or enter a known camera value for geometry unprojection. Exclude invalid geometry sets invalid points and depth to infinity when enabled; disabling it retains raw predicted geometry. The validity mask is returned separately in either mode. Inference batch size controls images per inference call and does not create additional variants from the single upload. Force projection starts enabled; its precise effect is not documented in the supplied source, so retain the default for a first run. These settings do not select other output types. Mask rendering and red-channel conversion are fixed to preserve the validity-mask result. The workflow uses the fixed model moge_2_vitl_normal_fp16.safetensors. For a manually managed ComfyUI installation, the source specifies models/geometry_estimation/ as its location and https://huggingface.co/Comfy-Org/MoGe/resolve/main/geometry_estimation/moge_2_vitl_normal_fp16.safetensors as its download source.

Princess Style

This is a Style embedding which turns your image subjects into bonafide Princesses! Think pearls, tiaras, and ballgowns! Simply throw Style-Princess on to the end of your prompt (or the start, if the effect doesn't...

TextualInversionSD 1.5

Z-Image Turbo1024x10248GB VRAM

Z Image Turbo pixel-art image generator

Generate one image or a batch from text using the fixed Z Image Turbo model, with optional pixel-art styling. No uploaded image is required. For a first run, leave pixel-art style enabled, edit the pixel-art prompt, and keep the 1024 × 1024 size, batch size 1, LoRA strength 1 and four sampling steps. The source prompt begins with “Pixel art style, retro”; use that as a starting point when describing your own subject. Run the Pipe to receive the generated images. The style toggle switches both the model branch and the prompt. When disabled, the base model uses the separate unstyled prompt. Edit both prompts when comparing modes; changes to one do not update the other. LoRA strength affects only the enabled pixel-art branch. Width and height are output pixels and must be multiples of 16. Batch size controls how many images are returned. The output stage does not permanently save images to the ComfyUI output folder. Advanced sampling controls include seed, steps, guidance scale, sampler, scheduler and denoise fraction. Seed -1 randomizes each run; use a fixed seed to compare changes with otherwise unchanged settings. The source defaults are four steps, guidance 1, res_multistep sampling, the simple scheduler and denoise fraction 1. Negative conditioning is zeroed automatically, so there is no editable negative prompt. The optional sampling-shift stage starts bypassed, matching the source workflow. Enable it before adjusting its shift value; the source value is 3. While bypassed, shift has no effect. Generation starts from an empty latent and does not support image-to-image input. Reducing denoise fraction changes sampling but does not preserve an uploaded image. The model, text encoder, VAE and pixel-art LoRA are fixed by this Pipe.