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Wan 2.1 video inpainting
Generate an edited video segment with SAM3 object selection and Wan 2.1 VACE masked video conditioning. For a first run, upload a source video, replace Object to edit with a description of existing content in that clip, and replace Edit prompt with the desired replacement, scene and motion. Leave Reference image disabled for text-only editing. To add appearance guidance, enable that stage and upload an image. Start frame is a zero-based source-frame index. Frame count requests the segment to edit, and the actual selected frame batch determines generation length. Begin with Start frame 0 and the default 81 frames when the clip has enough frames. Approximate segment duration is frame count divided by the source frame rate. This tool outputs the generated segment, without reinserting it into the full source video. Output width and height are in pixels and are rounded down to multiples of 16. Use positive dimensions of at least 16. Center framing crops while preserving proportions; Disabled stretches to fit. SAM3 selects content after resizing. Mask margin expands that selection in resized-frame pixels; negative margins shrink it. Detection threshold and refinement passes adjust selection. Combined masks are the normal video setting; separate object masks have no dedicated alignment stage here. Fast sampling selects the fixed CausVid LoRA and the Fast steps and Fast CFG controls. Turning it off selects the base model and Standard steps and Standard CFG. CausVid LoRA strength affects only fast mode. Both modes share the seed, sampler, scheduler, denoise strength and sampling shift. Use seed -1 for randomization. VACE control strength is separate from prompt guidance. Keep batch size 1 for the normal single-sequence output; larger batches are not split into separate video outputs. The output uses the source video's frame rate and passes through its audio. Audio is not explicitly trimmed or offset to match Start frame and Frame count, so synchronization and duration may differ for a selected segment. Generated frames are decoded directly, without a final composite that guarantees unchanged pixels outside the mask. Video encoding controls select the output container, codec and compression setting. The workflow uses fixed SAM3, Wan VACE, text-encoder and VAE models; fast mode additionally uses the fixed CausVid LoRA. These model assets must be available to the execution environment. The supplied dependency evidence marks them missing; this review establishes the connected tool and its guidance, not a completed runtime test.
Wan 2.2 text to video
Generate one silent video from a text description with Wan 2.2. No uploaded image or video is required. For a first run, edit the positive prompt to describe your subject, movement, camera, lighting and setting, then keep the 640 × 640 size, 5-second duration and 16 FPS defaults. The negative prompt independently discourages unwanted content and visual traits; its supplied Chinese text includes artifacts, static frames and clutter. Standard mode uses 20 total steps, CFG 3.5 and a handoff at step 10. Enable Lightning mode to select both Lightx2v LoRAs together with the separate Lightning settings: 4 total steps, CFG 1 and a handoff at step 2. Only the selected mode's steps, CFG and handoff settings apply. LoRA strength controls apply only in Lightning mode. Each handoff must remain strictly between zero and its mode's total step count. The downstream samplers accept 1–10000 total steps and CFG values from 0–100. Width and height are pixels in multiples of 16. Use a positive duration and a frame rate from 1–120 FPS. Requested frame count is floor(duration × FPS) + 1; choose a combination whose floored product is a multiple of 4 for the expected 4n + 1 frame layout. FPS also sets playback speed. The defaults request 81 frames, giving approximately 5.06 seconds of encoded playback. Larger dimensions, longer duration and higher FPS increase generation work and memory use. Seed -1 randomizes each run; use a fixed seed to compare settings. Advanced initial and finishing sampler, scheduler and schedule-shift controls apply in both modes. The initial stage adds noise and runs the high-noise model; the finishing stage continues the same latent without adding new noise. Both stages use the selected total step count, with the handoff dividing their work. The supplied sampler, scheduler and shift values match across stages. Keep file type, codec and encoding quality on automatic selection for a first run. Explicit container and codec choices require compatible encoding and playback support. Video assembly defaults to 8-bit; optional 10-bit assembly can retain smoother gradients but requires downstream support. CRF controls encoding quality separately from generation. Latent batch size remains one; this tool returns one video and does not offer separate batch files. Audio is not generated.
Psycho Style
This is a DreamArtist Textual Inversion Style Embedding trained on an image of a colorful cyberpunk-style skeleton (wearing headphones). You can expect to get some super weird colors, and intricate robotic cyberpunk...
CyberRealistic Negative - Pony v1.0
Join The Tinkerer on Whop. Membership gets you early releases, private tools and a bunch of extra stuff.
Bernini-R image editing
Edit an uploaded image with text instructions using Bernini-R. Upload a source image, describe the changes in English and state what should remain unchanged. For a first run, use “make it night,” a 1280-pixel long edge, Turbo enabled, batch size 1 and frame count 1. Run the Pipe to receive an edited image. The resized source supplies the output dimensions and retains the source aspect ratio. Turbo selects the LightX2V LoRAs and defaults to 6 total steps, a 3-step high-noise stage and CFG 1. Turning it off uses the same Bernini model pair without those LoRAs and defaults to 40 total steps, a 20-step high-noise stage and CFG 5. Each mode has separate advanced step, split and guidance controls; changing controls for the inactive mode does not affect the current run. LoRA strengths apply only with Turbo enabled. Use total steps from 1 to 10000, a positive split strictly below the corresponding total, and CFG from 0 to 100. The negative prompt describes unwanted characteristics. The supplied Chinese text includes artifacts, malformed anatomy and motion-related exclusions; revise it for your intended result. The sampler and noise schedule are shared by both stages. Denoise changes the schedule range rather than directly blending the source and output. Initial-stage noise is enabled by default; its seed uses -1 for a new random seed each run. Set a fixed seed to compare settings. Finishing-stage noise is disabled by default so the second stage continues the existing latent; enabling it makes the separate finishing seed active. Batch size requests multiple results. Frame count 1 produces still images; 5, 9 and subsequent increments of 4 request sequences conditioned on the uploaded still. Results are returned as individual images through the preview output, without video encoding or a playback rate. Prompt prefix offers Default, Text to Image, Text to Video, Image Editing and Subject to Image. These choices only select system prompt wording. The uploaded image remains connected in every selection; reference-image composition and text-only generation are not supported by this selector. The graph uses rendering without a separate prompt planner. Uploaded transparency is not preserved because only the loader’s RGB IMAGE output is connected.
LTX 2.3 first-to-last frame transition
Generate a video transition guided by two uploaded images, with jointly generated audio. Upload both a start image and an end image, then replace the example prompt with your subject, scene, movement, camera motion and transformation. Describe desired sounds or dialogue in the same prompt. The result is one video containing generated audio; there is no audio upload input. Endpoint images guide generation rather than guaranteeing exact copies, and exact dialogue or synchronization is not guaranteed. For a first run, use images with similar framing and keep 720 × 1280 pixels, a requested duration of 6 whole seconds, 25 FPS, both image strengths at 0.7, transition LoRA strength at 1 and CFG at 1. The example prompt describes photography changing into watercolor and includes zhuanchang, the token suggested by the source notes for the transition LoRA. Replace its scene description to match your images. Width and height feed both image resizers; the resized start image establishes the video dimensions. Use explicit positive integer dimensions of at least 64 pixels. Avoid 0: it calculates aspect ratio independently for each image and can produce incompatible guide sizes. Both images are center-cropped by default. Advanced framing controls independently choose center cropping or stretching and interpolation for each image. Area interpolation suits downscaling, lanczos suits upscaling and nearest-exact suits pixel art. Uploaded transparency is not preserved because only the loaders' RGB image outputs are connected. Advanced preprocessing controls independently set compression for each resized image on a 0–100 scale; both default to 25. Duration is requested in positive whole seconds. The graph requests Duration × FPS + 1 frames and shares FPS across audio timing, video conditioning and playback. Use FPS from 1 to 120 and keep Duration × FPS + 1 at or below 1000, the supplied audio latent limit. The extra frame and model frame handling mean requested duration is not an exact final running time. Start and end guide indices default to 0 and -1. Keep these for endpoint guidance, or change them to move the guides within the clip. Negative indices count backward from the end; choose positions within the generated clip. Their strengths independently control image influence. The transition LoRA can be bypassed or set to strength 0 while retaining both image guides. Seed -1 requests a fresh random seed each run; use a fixed nonnegative seed when comparing settings. CFG defaults to 1, where conventional negative-prompt guidance has no effect. If you raise CFG, review the negative prompt first: its supplied exclusions include scene-specific details and restrictions that may conflict with stylized imagery or your intended audio. Advanced sampling controls expose ancestral noise and its noise scale. Ancestral noise defaults to off at 0; noise scale becomes relevant when ancestral noise is nonzero. The manual sigma schedule contains nine values defining eight sampling intervals, with no separate Steps control. Keep the supplied schedule for a first run. Video bit depth defaults to 8-bit. The supported 10-bit option can retain smoother gradients, but some players or downstream tools may not support it. Advanced video file controls offer automatic or explicit container and codec choices. Keep both on auto for a first run; explicit choices must be compatible.
PA7_Portrait_Tools
Portrait promptography is an art form that goes beyond simply capturing someone's physical appearance. It delves deep into the core of their being, aiming to encapsulate their essence, personality, and emotions in a...
FLUX.2 Klein single-image edit
Generate an edited image from one uploaded source image and written instructions with FLUX.2 Klein 9B Distilled. For a first run, upload your source image, then describe what to change and which details to retain. The supplied instruction replaces the background with a coastal cliff; replace it if that edit does not suit your image. Start with 1 megapixel, one image per run, 4 steps, guidance scale 1 and Euler sampling. Seed -1 chooses a new random seed each run; use a fixed seed when comparing instruction or control changes. Output size approximately follows the source image's aspect ratio at the chosen megapixel count. This setting resizes the reference and determines the generated canvas dimensions. Resize method controls interpolation of the uploaded reference before encoding; it is available alongside output size. Advanced controls expose image count, seed, steps, guidance scale and sampler. Larger sizes and batches require more memory. The uploaded image supplies reference conditioning. Generation starts from a fresh latent rather than directly denoising the upload, so exact preservation of identity, pose or unchanged regions is not guaranteed. There is no mask, edit-strength slider or separate negative prompt. Text conditioning for the negative branch is zeroed automatically, while the image reference is supplied to both conditioning branches. Uploaded transparency is not preserved because only the loader's RGB image output is connected. This tool accepts one source image. Combining a main image with a second visual reference belongs to the separate multi-reference edit tool.
AloeVera's SuperNegative embeddings
0.VERY IMPORTANT -> All the versions of the embeddings are available on the huggingface link. (Civitai has upload limitations). Also click on "Show more" down below for all the examples/comparisons + tips. ☺️
Multi-keyframe video
Create one silent video by generating five prompted transitions between six ordered keyframe images with Wan 2.2 and its paired four-step Lightx2v LoRAs. The generated frame batches are joined in sequence. For a first run, keep all six keyframes enabled and upload an image to each slot. Describe the subject, motion and camera action for each adjacent pair in its transition prompt. Replace transition 1's cat example and fill the other four prompts. Leave size, frame counts and output encoding at their defaults. The defaults generate five 25-frame segments at 720 × 720, joined into 125 frames played at 24 FPS: approximately 5.21 seconds. In advanced controls, each transition has its own frame count and dimensions. Use frame counts of 4n + 1, such as 25, 49 or 81. If changing size, set identical widths and identical heights across all five transitions, using multiples of 16; mismatched dimensions can prevent frame joining. Playback FPS changes how fast the existing frames play, without generating more frames. Duration is the sum of all five frame counts divided by FPS. To omit an endpoint image, disable its keyframe node. Every enabled keyframe needs an upload. Disabling an interior keyframe removes the preceding transition's end-image condition and the following transition's start-image condition. It does not remove either transition or shorten the sequence. A transition with both endpoints disabled still generates from its prompt. All five prompts remain independently editable in these modes. Each transition has an independent initial noise seed. Use -1 for a new random seed each run, or a fixed seed to reuse that transition's noise. Negative prompts are independently connected, but the packaged CFG value of 1 does not apply conventional negative-prompt guidance. The two sampling stages use a coordinated four-step schedule with a handoff after step two; retain the packaged preset for normal use. Video output offers automatic, MP4, MKV or WebM file selection and automatic, H.264 or AV1 encoding. Leave automatic selection and CRF -1 for the first run; these controls affect encoding rather than generated content. The graph joins full frame batches without trimming shared endpoints or blending seams. Repeated boundary frames or visible seams between independently generated transitions may occur. The result has no audio or transparency; uploaded alpha masks are not connected.
Runway image generation
Generate images with Runway from a text prompt and an optional reference image. For a first run, replace the example prompt with your desired subject, setting, composition, and visual style. Keep Output shape at 1024:1024 for a square image. Reference image is enabled initially: upload one image, or bypass that stage to generate from text alone. Run to preview the generated result in Sweet Tea. With a reference image enabled, describe the result you want that image to guide. Only one reference image is accepted, and no image transformation strength control is exposed. Other output shape values must be supported by the Runway node; the draft does not enumerate them. Running requires configured ComfyUI partner API access and sufficient account credits. Generation sends the prompt and any enabled reference image to Runway and uses a paid provider call. The output stage returns images for preview without permanently saving them through ComfyUI.
Causal Forcing image to video
Animate an uploaded starting image into one silent video with Causal Forcing Framewise. For a first run, upload an image and replace the example motion prompt with a description of your subject's movement and the desired camera behavior. Choose width and height in multiples of 16 with an aspect ratio similar to the image. Start with 832 × 480, duration 5, batch size 1 and playback at 16 FPS. The image is used as the starting-frame input; uploaded transparency is not preserved through the connected RGB image output. Duration is measured on a fixed 16 FPS generation basis. The graph calculates min(duration × 16 + 1, 81) and uses its integer output as the requested frame count. Use positive duration values up to 5 seconds; larger values do not add frames. At 16 FPS, 81 frames play for about 5.06 seconds. Playback FPS changes how quickly the generated frames play, independently of their count: higher FPS shortens playback and lower FPS lengthens it. Seed -1 requests fresh random variation each run. A fixed seed reuses the sampling noise. CFG defaults to 1, where the negative prompt has no effect. For experiments with CFG above 1, describe unwanted appearance or motion in the negative prompt. Changing guidance departs from the supplied checkpoint preset. Advanced sampling controls expose steps, noise schedule and denoise fraction. Begin with the source configuration: 4 steps, simple schedule and denoise 1. Denoise controls the sampling schedule rather than directly specifying how closely to preserve the uploaded image. The fixed model is causal_forcing-framewise.safetensors with the Wan VAE and text encoder; its autoregressive sampler is fixed to one frame per block, matching the Framewise mode. Keep batch size 1 for a single sequence. The graph has one video assembly path and does not return separate videos for batch members. File type and codec control output encoding and default to auto. No audio input is connected, so the result is silent.
CyberRealistic Negative - Pony v2.0
Join The Tinkerer on Whop. Membership gets you early releases, private tools and a bunch of extra stuff.
Luma text to video
Generate one video from a scene description through Luma, with optional camera motion concepts. Enter a text prompt describing the subject, setting, action, lighting, and movement. This tool has no image input. Generation sends the request to Luma and requires API access and sufficient account credits. For a first run, replace the example prompt and keep Ray 2, 16:9, 720p, and 5s. Leave both camera stages bypassed and run the tool to receive the video. Enable Loop to request a looping clip. For camera concepts, enable Camera motion in the advanced controls. Its initial choices are zoom_in and orbit_left; change the four slots as needed, using None for unused slots. Enable Additional camera motion as well when you want to combine up to four more concepts with the primary stage. Its slots initially contain None. Bypassed stages do not add their own selected concepts. The model affects supported output settings: Ray 1.6 ignores duration and resolution. Re-run seed controls whether the API generation runs again, not reproducibility. Keep -1 to randomize each run; identical seeds do not reproduce the same video. Video output returns the generated clip through Sweet Tea. Keep File type and Codec at Auto for the first run, or use the advanced output controls to choose a container and codec. These settings affect output encoding, rather than the scene generated by Luma.
花想容/Chinese style/古风/中国風です Lora
推荐的质量词: masterpiece, best quality, score7, score8, highres, extremely detailed 8k, illustration, great quality, amazing quality, very aesthetic, absurdres, Chinese style,
SeedVR2 image upscale
Upscale and restore an uploaded still image with SeedVR2, returning the restored image directly. For a first run, upload an image, leave the target width at 1080 pixels and guidance at 1.01, then run. Compare the result with your original: restoration can reinterpret fine details. The target-width control accepts 1–4320 pixels. The seed defaults to -1 for a fresh random seed each run. Choose a fixed seed from 0 to 5000 when comparing guidance or other settings. Guidance accepts 0.01–2. GPU-memory conservation is enabled by default and can be toggled. Processing batch size defaults to 1; keep that starting value for the single uploaded image. It does not select the number of output variants. The fixed seedvr2_ema_3b_fp16.safetensors model and the SeedVR2 custom node must be available in the runtime. This Pipe accepts still images and has no video input or encoding mode. Uploaded transparency is not preserved because the loader's separate alpha-mask output is not connected.
veryBadImageNegative
veryBadImageNegativev1.3 uses a new training atlas and performs well when used in AOM3 and viewer-mixv1.7 .
LongCat Image Edit
Create an edited image with LongCat Image Edit by providing a source image and describing the desired change. Set output resolution in megapixels while retaining the source aspect ratio. Advanced controls include edit strength, positive and negative guidance, sampling settings, and seed. An optional negative prompt describes elements to avoid.
JoyAI Image Edit
Generate edited versions of a reference image using natural-language instructions. For a first run, upload the image you want to edit, replace the example instruction with your requested change, and state what should remain unchanged. For example: “Change the background to a glacial scene; keep the bottle and its label.” Leave Output resolution at 1 megapixel, Variations at 1, and the sampling defaults unchanged, then run. Output resolution sets the approximate total pixel count. The reference is resized before conditioning, and its resized dimensions determine the generated image dimensions, aligned to multiples of 16. Resize method changes how the reference is resampled. Increasing resolution or the number of variations increases memory use. This is a generated edit, not a masked, pixel-preserving retouch: changes can extend beyond the requested region. Uploaded transparency is not preserved because only the loader's RGB IMAGE output is connected. Negative prompt describes unwanted content and can remain empty. Both positive and negative conditioning receive the resized reference. Variations generates multiple edited images in one batch. Seed -1 selects a new random seed each run; use a fixed seed when comparing control changes. Advanced sampling controls include Steps, CFG, Sampler and Scheduler. Start with 40 steps, CFG 4, Euler and Normal. Denoise controls the sampling schedule fraction; retain 1 initially. Since generation starts from an empty latent, lowering denoise is not a direct reference-preservation control. Guidance normalization exposes independent strength and timing settings; begin with strength 1 and Normalize before CFG enabled. The Pipe returns the edited image batch through Sweet Tea's image output. It does not return a separate side-by-side comparison. The original save node has been replaced, so its file-format and permanent-save settings do not describe this selected output.
