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Gemini video editing

Generate an edited video from an uploaded reference clip and text instructions using Gemini Omni Flash. For a first run, upload your source video and replace the astronaut-and-sheep example prompt with instructions describing what to change and what should remain recognizable. Include desired style, motion, and audio instructions as needed. Leave temperature, top-p, and output encoding at their defaults. Set aspect ratio to 16:9 for landscape or 9:16 for portrait. Request a duration from 3s to 10s in whole seconds. These controls append instructions to the prompt rather than performing a crop or trim; avoid conflicting framing or duration requests in your editing text. The source model specifies 720p video at 24 FPS with audio. Writing 8K in the prompt does not change that specification. Temperature controls sampling variation, and top-p controls the cumulative probability threshold used for nucleus sampling. Seed -1 randomizes each run; a fixed seed does not make the generated video reproducible. Advanced output settings offer file type and codec choices. This Pipe uses a hosted Gemini API and requires provider access through ComfyUI. Your reference video and instructions are sent to the provider, and generation uses provider billing. The selected result is the generated video; the model's separate text output is not displayed.

[SDXL] FLYX3_MIX (AnimagineXL31)

Mage-Flow Turbo image edit

Edit one uploaded image with written instructions using the fixed Mage-Flow Edit Turbo model. Describe what to keep and what to change, such as content, background, lighting or style; no painted mask is used. For a first run, upload your source image, replace the example instruction with an edit suited to it, and keep Image count at 1. Leave the source sampling configuration at four steps, guidance scale (CFG) 1, Euler, the simple scheduler and denoise fraction 1. Seed -1 chooses new sampling noise each run; use a fixed seed when comparing instructions or settings. Resize source image is enabled by default at approximately one megapixel. Target megapixels controls the processed reference area, retaining aspect ratio and aligning dimensions to multiples of 16. Reference resampling selects the interpolation used for this resizing. Turn off Resize source image to condition on the original reference dimensions. Match source size makes the output follow the processed reference dimensions, including any reference resizing. Turn it off to use Width and Height instead. Enter manual dimensions in pixels divisible by 16; the supplied guidance recommends 512–2048 pixels per side. Very wide or tall references can exceed that range under automatic sizing, so choose manual dimensions when needed. Image count generates a batch of edited images; larger batches require more memory. Negative prompt describes attributes to discourage and has little or no influence at CFG 1. Increasing guidance makes negative conditioning relevant. Advanced sampling controls remain available for comparisons, but more steps or stronger guidance do not guarantee a better Turbo edit. Denoise fraction changes the sampling schedule; it is not established here as a reliable source-image preservation strength. This graph accepts one reference image and returns the decoded edited image batch. It does not expose multiple references or a selection mask. Uploaded transparency is not preserved because only the loader's RGB image output is connected.

Qwen 2511 image edit

Edit a source image with a written instruction using Qwen Image Edit 2511. For a first run, upload an image, describe the changes you want and the features to retain, and keep the supplied defaults: denoise strength 1, 40 sampling steps, CFG guidance 4, Euler sampler and Simple scheduler. The example instruction requests anime styling while retaining identity. Leave the negative prompt empty unless you want to describe attributes to discourage. Seed -1 randomizes the sampling noise. The image is automatically resized before editing. The resized RGB image supplies both positive and negative reference conditioning and is encoded as the initial sampling latent. Lower denoise values retain more of that initial image's structure. CFG guidance adjusts prompt conditioning separately from denoise strength. A fixed nonnegative seed reuses the noise setting; changing the image, instruction or sampling controls can still change the result. Output dimensions come from the automatically scaled source image. This tool has no manual output-size control, text-only mode, mask-based editing input or separate output-upscaling stage. Uploaded alpha transparency is not preserved because the loader's alpha-mask output is not connected. The selected output returns the decoded edited image. Advanced settings include negative prompting, guidance normalization strength, normalization before CFG and sampling shift. Normalization defaults to strength 1 with pre-CFG normalization off, using post-CFG attenuation. Enabling pre-CFG normalization rescales noise before CFG combination without clamping and can amplify it. Sampling shift defaults to 3.1 and changes the model's sampling configuration independently of denoise and CFG. The workflow uses qwen_image_edit_2511_bf16.safetensors, qwen_2.5_vl_7b_fp8_scaled.safetensors as its Qwen image text encoder, and qwen_image_vae.safetensors.

Replace video background

Replace an uploaded video's background with a selected solid color using Bria. For a first run, upload your source video, keep Background color set to Black, and run the Pipe to download the processed video. Choose another listed background color to change the fill behind the foreground subject. The output uses a solid background; no transparent-output mode is provided. Rerun seed controls whether Bria processes the video again. Its default of -1 selects a random value on each run. Bria results remain non-deterministic even when you reuse a seed. Under advanced Video output settings, you can choose the file type and codec or leave both on auto. Quality (CRF) defaults to -1 for automatic quality and accepts explicit values from 0 to 63. These settings configure output encoding rather than background removal.

HiDream I1 Fast image generator: mechanisms and practical controls

Start with one image from text, follow the exact conditioning, sampling and decoding path, and configure size, starting noise and guidance with the recipe's actual controls.

Qwen Image Edit 2509

Edit an uploaded image with natural-language instructions and receive an edited RGB image in Sweet Tea. For a first run, upload your source image, replace the example instruction with the change you want, describe details to retain, and leave Lightning mode and the default sampling settings enabled. Lightning mode selects the supplied acceleration LoRA and the Lightning steps and CFG settings, initially 4 and 1. Disable it to use the base model and the separate standard settings, initially 20 steps and CFG 4. Each mode retains its own values; inactive-mode settings take effect when you switch to that mode. Keep Euler and the simple scheduler for the source configuration. The configured Qwen editing model, text encoder and VAE are required, along with the Lightning adapter for Lightning mode. Denoise ranges from 0 to 1: lower values retain more source structure, while 1 permits stronger changes. Seed -1 chooses a new random variation each run; use a fixed seed to compare settings with the same initial noise. Negative prompt describes unwanted content. At the default Lightning CFG of 1, negative conditioning has no ordinary classifier-free guidance effect; use standard mode with CFG above 1 when negative guidance is needed. Both modes expose their own steps and CFG controls. The downstream sampler accepts 1–10000 steps and CFG from 0 to 100. Advanced settings also expose Lightning adapter strength, guidance normalization strength, normalization before CFG, and sampling shift. Adapter strength affects Lightning mode only; the normalization and sampling patches apply in both modes. Keep their source defaults for a first run: adapter strength 1, normalization strength 1, normalization before CFG disabled, and sampling shift 3. Enabling normalization before CFG removes its attenuation-only behavior and can amplify. The source image is automatically scaled before conditioning and latent encoding. There is no manual output-size control or mask input, and the result may have different pixel dimensions from the upload. Uploaded transparency is not preserved. The selected output returns an image preview rather than permanently saving a PNG file.

DnD Darkest Fantasy

FLUX.1 Schnell text to image

Create images from text using the fixed FLUX.1 Schnell FP8 checkpoint. No source image is required. The runtime needs flux1-schnell-fp8.safetensors in its checkpoints directory. For a first run, replace the example prompt with your subject, setting and visual style. Keep 1024 × 1024 pixels, one image, four steps, CFG 1, Euler sampler, simple scheduler and denoise strength 1. Run the tool to receive the generated image batch through Sweet Tea's preview output; this graph does not permanently save files with SaveImage. Width and height must be multiples of 16. Adjust them together to choose the aspect ratio. Image count determines the number of images returned per run. Larger dimensions and batches require more memory. Seed -1 chooses fresh noise each run; a fixed seed lets you compare changes with the same starting noise. Advanced sampling controls expose steps, CFG, sampler, scheduler and denoise strength. Start with the source defaults. The optional negative prompt feeds negative conditioning but has no effect at CFG 1. Raising CFG allows that conditioning to influence sampling and is not a guaranteed improvement for this Schnell setup. This tool starts from an empty latent and supports text-to-image generation only. Lower denoise strength changes sampling of that empty latent; it does not provide image editing. The checkpoint is fixed, with no model-selection or source-image mode.

FLUX virtual try-on: mechanisms and practical controls

Create a try-on preview from a person photo and one garment reference, configure the actual styling and seed inputs, and understand the provider path and preservation limits.

Chromatic aberration

Create an image with chromatic-aberration color fringing from an uploaded source image. For a first run, upload your image and run with the saved settings: Amount 31 and Pattern Linear. These settings apply horizontal color separation rather than a neutral preview. Set Amount to 0 to preview the loaded image without the effect, then change the amount or pattern and run again. Amount uses a unitless 0 to 100 scale, not pixels or a percentage. Higher values increase the displacement of red and blue samples in opposite directions, while green stays at its original position. The same amount produces different displacement across patterns. Choose Linear for a uniform horizontal shift. Radial produces separation along directions radiating from the image center, increasing toward the edges. Barrel increases separation with the square of distance from the center; it separates color channels rather than warping the whole image. Swirl shifts perpendicular to the radial direction, producing separation around the center. Diagonal shifts along both image axes in normalized image coordinates, so its apparent angle depends on the image proportions. If Pattern appears as a text field, enter Linear, Radial, Barrel, Swirl or Diagonal exactly. Radial, Barrel and Swirl account for the source aspect ratio and have no displacement at the center. Amount 0 disables every pattern. Strong settings sample farther from each pixel and can produce pronounced edge artifacts. Output dimensions follow the source image. Uploaded transparency is not preserved: the image loader's separate alpha mask is unused, even though the shader copies its input texture's alpha. This tool applies only chromatic aberration; the other image adjustments in this family are separate tools.

【FLUX】INPAINT — v3.1

Optimize for 1080p screen and some correction

ACE-Step music generator

Describe genre, instruments, mood, and tempo in the music prompt. Supply lyrics and section markers for vocals, or retain [instrumental] and arrangement cues for instrumental music. Lyrics strength controls lyric conditioning and remains available in either mode. The workflow uses zeroed negative conditioning, so there is no separate negative prompt. The source note advises manually transliterating multilingual lyrics and adding language markers such as [zh], [ja], or [ko].

Vidu start and end frame video

Generate a five-second 1080p video connecting two uploaded images with prompted motion using Vidu Q1. Upload both a start frame and an end frame. Replace the example Motion prompt with the action, camera movement, and transition you want between those images. For a first run, leave Movement amplitude on auto and Seed at -1. Movement amplitude controls the amount of object movement in the frame, with auto, small, medium, and large choices. The images supply the visual endpoints; the prompt directs the transition. This configuration fixes the model to Vidu Q1, duration to five seconds, and resolution to 1080p. Seed -1 randomizes each run. A positive integer sets the generation seed; Vidu also treats 0 as random. The generated video is returned for preview with automatic file type and codec settings by default. Output controls offer auto, MP4, MKV, or WebM file types and auto, H.264, or AV1 codecs. Generation sends the endpoint images and prompt to the Vidu partner API and requires an authenticated account with sufficient credits.

Melody "Celesty McMarden" Wave - Heroine? Saint? No, I'm an All-Works Maid (And Proud of It)! / メロディ・ウェーブ / Heroine? Seijo? Iie, All Works Maid desu (Hokori)! , ヒロイン?聖女?いいえ、オールワークスメイドです(誇)!

Melody "Celesty McMarden" Wave - Heroine? Saint? No, I'm an All-Works Maid (And Proud of It)! / メロディ・ウェーブ / Heroine? Seijo? Iie, All Works Maid desu (Hokori)! , ヒロイン?聖女?いいえ、オールワークスメイドです(誇)!

Unlearn Cl Ucf101 Videomae Base Random Label 10 42

This model is a fine-tuned version of MCG-NJU/videomae-base on the ucf101 dataset. It achieves the following results on the evaluation set: Loss: 0.8124 Accuracy: 0.7958 Test Accuracy: 0.7958 Df Accuracy: 0.9353...

Chikage Ootori (Real Eroge Situation! 2 )

Chikage Ootori (Real Eroge Situation! 2 )

ZI Andrea Botez (vrtlAndreaBotez).safetensors

ZI Emily Osment (vrtlEmilyOsment).safetensors

Z-Image / Z-Image-Turbo + Lora T2I

Two workspaces for Comfy UI