This guy makes $12,000 a month from his own program through an AI girl who never existed and the sign-ups close themselves while he is offline. He got tired of watching course sellers pay a creator $400 a clip to be the face and lose him the second he raised his rate. He built a video-to-video setup where a live stand-in hands over the motion and the system drops a face he owns on top. No hired creator. No studio. No reshoot. Here is the exact breakdown: → First he records a clean source himself: even light, a front camera, real hand and face movement and the little window in the corner is left in on purpose to prove the girl on the left is a live person driving her, not animation → Tracking lifts the face points, the lips, the head turns and the shoulders. A locked character he built earlier goes on top of that skeleton with the same hair and face and outfit every time → It is not full CGI but cheap video-to-video: the room and most of the motion stay real and the model only repaints the looks and pulls the frame up to an influencer grade → Hair and fingers and the mouth want to float between frames so he keeps it close up. ComfyUI with AnimateDiff and LivePortrait hold her steady while ElevenLabs gives the voice → One recording becomes a week of clips with the same face and different scripts and each one drops a link to the program in the caption The move that 92% skip: they rent their face to a brand instead of pointing it at their own offer. Take a brand check for one clip and you start over next week. Point a face you own at a program you own and every clip sends warm traffic to the same place. A viewer who trusts her comes back to buy. The face is synthetic but the trust is real. And the trust is the only asset that compounds. The math is quiet: a clip is a couple of cents of compute. A member pays $40 a month for the program. And 300 members is $12,000 a month off a face he never has to pay. Her first clip on fixing a morning routine pulled 480,000 views in 12 hours. By the next day 210 people had joined the program for a girl who three days earlier did not exist yet. There was only a guy on the right of the frame in a grey sweater. Under the clip people argue whether the girl is even real. And that fight is what keeps handing the clip to new viewers. No casting. No studio. No payroll. Just one recorded take. One face he owns. And the discipline to point it at his own offer instead of renting it out. Half of you are already typing that there has to be a real human behind a face like that. The other half already hit record on their first source clip. Which half are you in?

Ceramic never asked AnimateDiff to move. LiquidWarp showed up anyway and the glaze started negotiating edges with the projector. Lightbrush FFGL just keeps the conversation civil while the loop learns to land.

I've built AI TV (Copy) with @base44! ai-tv-copy-15946780.base44.a… ai-tv-copy-15946780.base44.a… # AI Creative Studio Build Plan ## Boundary This build should not promise "no limits, no credits" for third-party APIs. APIs have terms, quotas, billing, and safety rules. The realistic free path is a local-first app that uses open-source tools on the user's own hardware, with optional paid/cloud connectors clearly labeled. This build should also add explicit sexual generation or sexualized avatar behavior. A compliant version can support adult-owned original characters, fashion, dance, performance, glamour, cinematic scenes, and age-gated mature themes without generating explicit sexual content or non-consensual likenesses. ## Product Create a local-first AI creative studio for: - UHD image generation and upscaling - Short video generation and animation workflows - Music, voice, ambience, and sound-effect generation - User-created character library with names, bios, visual references, voices, outfits, relationships, and style notes - Reusable memory so characters can be selected in future generations - Asset uploads for text, image, audio, and video references - Game-builder mode for interactive scenes, visual novels, mini-games, and 3D character demos - Active desktop AI agent that can queue jobs, manage assets, remember project context, and guide the user through generation steps ## Recommended Stack - App shell: Tauri or Electron for desktop, with React or Svelte for the UI. - Local image workflows: ComfyUI as the node-based generation backend. - Local models: Stable Diffusion/SDXL-compatible models, FLUX-compatible workflows where licensing permits, ControlNet, IP-Adapter, LoRA, and upscalers. - Video workflows: ComfyUI video nodes, AnimateDiff-style workflows, image-to-video adapters, and FFmpeg for rendering, trimming, encoding, and thumbnails. - Music and SFX: AudioCraft/MusicGen-style local generation where available, plus local sample libraries, procedural SFX, and FFmpeg processing. - 3D and avatar creation: Blender for character/scene authoring and rendering; VRM/GLB export for interactive avatars. - Game builder: Godot for exported games and interactive scenes; Three.js for lightweight in-app 3D previews. - Storage: SQLite for library metadata, project memory, prompt history, and character profiles; local filesystem for large assets. - Agent layer: local orchestration service that turns user requests into queued jobs, selects workflows, tracks outputs, and writes memory entries. ## Core Screens - Studio Dashboard: job queue, recent outputs, active character, model status, storage usage. - Character Library: create character, upload references, define profile, select voice/style/outfits, review generation history. - Generator: prompt box, character selector, workflow selector, resolution, seed, style controls, safety/licensing warnings. - Video Lab: storyboard frames, motion controls, reference video upload, render queue, timeline export. - Audio Lab: song prompt, mood, tempo, loops, stems, sound effects, ambience, export. - Game Builder: choose character, scene, interaction type, dialogue, goal, export to Godot or web preview. - Memory: searchable prompt history, character facts, approved traits, blocked traits, favorite outputs. ## Data Model - Character: id, name, aliases, age-confirmed-adult flag, bio, traits, visual tags, voice tags, relationship notes, consent/source notes. - Asset: id, file path, media type, owner/source, license, linked character, tags, created date. - Generation: id, prompt, negative prompt, workflow, model, seed, character ids, output paths, status, created date. - Memory: id, scope, character id, fact, confidence, source generation, pinned flag. - Project: id, title, genre, linked characters, target format, exports. ## Safety and Rights - Require explicit confirmation that uploaded people are the user, licensed talent, or fictional/original characters. - Block non-consensual likeness generation, minors, sexual exploitation, and attempts to evade model/API limits. - Store license/source metadata for each upload and model. - Add per-model license warnings before commercial export. - Keep all generated assets local unless the user explicitly enables a cloud connector. ## MVP Build Order 1. Desktop app shell with local SQLite library. 2. Upload library for images, videos, audio, and text notes. 3. Character profile creation and reusable memory. 4. ComfyUI integration for image generation and upscaling. 5. Output gallery with prompt history and character linking. 6. Audio/SFX generation or import workflow. 7. Video render queue using image sequences and FFmpeg. 8. Game-builder export format for Godot scenes or web-based Three.js previews. 9. Agent planner that queues multi-step jobs and records memory. ## Practical Hardware For local UHD generation, the app should detect GPU/VRAM and offer presets: - Low: CPU or small GPU, lower resolution, longer queue times. - Medium: 8-12 GB VRAM, SDXL-sized workflows, moderate upscaling. - High: 16-24 GB VRAM, larger batches, video workflows, UHD upscale pipelines. ## Deliverable Name Suggested name: IO Glitch Studio. Tagline: Local AI character, media, and game creation studio. create the app @grok @imagine

Brands pay this guy $500 a video for a beauty influencer who never existed, and her account brings in $7,700 a month while brand requests land in her DMs on their own. He got tired of watching stores burn $300 on a single UGC clip with a live model and makeup and reshoots. He built a video-to-video pipeline where a live stand-in hands over the motion and the system drops any face on top. No model. No makeup. No reshoot. Here is the exact breakdown: → First he records a clean source: even light, a front camera, expressive hands and face, and the little window on the right is left on purpose and proves that the left side is not animation but a live stand-in → Then tracking reads the face landmarks, the lips, the head turns and the torso, and a ready female character with a fixed hair, face and outfit goes on top of that skeleton → This is not full CGI but cheap video-to-video: the background and part of the motion stay from the original, the system swaps only the looks and pulls the frame up to an influencer grade → The main risk is hair and fingers and the mouth floating between frames, so everything is shot close up, and ComfyUI with AnimateDiff and LivePortrait hold the consistency and ElevenLabs gives the voice → One take turns into a batch of versions with different faces and voices and archetypes, and each one goes into its own tags for a live test The move that 95% skip: they hand the network both the face and the motion at once. Build her fully in a generator and the viewer leaves before she finishes her first line. Leave the motion to a live human, his weight and his timing, and give the network only the looks, and the frame reads as alive, because you can draw a picture but not the inertia of a real gesture. The economics are funny: a clip is a couple of cents of machine time, a studio charges $175 for it, and two a day is $7,700 a month off one operator, and it is ready before a live model would even reach the studio. On the very first night her first clip pulled 520,000 views in 11 hours, and by morning 30 brand requests had dropped into the DMs for a creator who did not exist three days ago, there was only a stand-in in front of the camera. Under the clip people argue "is this a live girl or is he moving for her", and every argument like that just adds to his reach. No casting. No operator. No shoot day. Just one recorded take. One locked character. And the discipline to give the motion to a human and the face to the machine. Half of you are already typing that soon no one on camera can be trusted. The other half already turned the camera on for their first take. Which half are you in?

He's 22. He's standing under a popcorn ceiling in a bedroom that smells like instant noodles. He snaps his fingers and becomes Sabrina Carpenter. Then Billie Eilish. Then Taylor Swift. Then Harry Styles. Not a filter. Not a TikTok mask. The hair covers the cheap RGB speakers behind him without a single glitch. The clothes change. The shoulders change. The jawline changes. The popcorn ceiling stays. 18 seconds. Four of the most expensive faces on earth, rendered in a room where the curtains are closed because the window faces a parking lot. He used ComfyUI. Free. AnimateDiff. Free. A face model trained on a $300 GPU he already owned for gaming. The setup that broke him for three weeks now takes one prompt in Claude Code. "Install the pipeline. Resolve the CUDA conflicts. Wire the nodes." It does. He renders. Here's the part nobody talks about. A girl in Manila is running 4 virtual influencers right now using this exact stack. Each account posts twice a day. Each one has brand deals. She cleared $11,400 last month and has never been on camera. A guy in Poland sells "AI UGC packages" to DTC brands. $8,000/month retainer. He delivers 30 videos. None of the people in them exist. The agencies still charge $15,000 for one celebrity-style ad shoot. He made four of them between lunch and a nap. The kid in the popcorn ceiling bedroom isn't the threat. The threat is how many of him are already uploading, and you've been liking their posts for months.

most people see another "ai girlfriend" account. he sees a $2,100/month creator studio for the cost of a coffee. pause at 0:05. look at the identical bedroom background across all clips. that's the leverage. one person running 30 "girl-next-door" personas for UGC brand deals. each persona averages 2-3 paid posts per month at ~$35 per post. total: ~$2,100. workflow is a local ollama animatediff pipeline on a mac studio, generating a week's content in an afternoon. zero model fees, zero actor costs, 100% owned IP. brands don't buy a person; they buy attention and conversion. AI UGC delivers both at 1/10th the cost and infinite scale. comment "guide" and i'll send the workflow checklist.

Okay, here's how you do it. It depends on your workflow, and what medium you're trying to work in. First thing you need to understand is that you don’t “make AI art” with one button, unless you’re deliberately using the most boring version of the tool. The process has parts: source material, model, prompt, sampler, scheduler, seed, latent noise, VAE, masks, control images, reference images, adapters, upscalers, and post-processing. Those are not magic words. They are control surfaces. If you’re doing a transformation workflow, especially one with heavy inpainting, you usually start in Photoshop, Krita, Procreate, Blender, a camera, a scanner, or whatever tool gets you source material. You create a thing first. A drawing, a doodle, a collage, a render, a photograph, a pile of visual scraps. Doodles are useful here, as long as they’re recognizable enough to trigger the model’s learned associations. At a high enough denoise level, a rough concept can become a finished element fairly quickly. I like collages because the outputs are less predictable. I’ll build a source image out of clashing textures, old drawings, photos, generated scraps, painted marks, broken perspective, or anything discordant that gives the model something interesting to chew on. That source work does not disappear. It affects the entire project. The model reacts to it, argues with it, preserves some of it, misunderstands some of it, and sometimes turns the accident into the best part. Then you take that into the generation system. In ComfyU, the main thing you’re doing before you even start, is building out your node graph. A basic text-to-image graph starts with a checkpoint loader. That loads the model, usually along with CLIP and the VAE. The text prompt goes into a text encoder. The negative prompt goes into another text encoder. An empty latent image node creates the starting latent space at whatever resolution you choose. Those conditionings, the latent, the model, the seed, the sampler, the scheduler, the step count, and the CFG value all feed into the sampler node. The sampler denoises the latent. The VAE decode node turns that latent into pixels. Then the save image node writes it out. That is your most basic possible node graph. Model loader into sampler. Prompts into sampler. Latent into sampler. Sampler into VAE decode. Decode into save. Even there, you have decisions. Change the checkpoint or “model” and the image vocabulary changes. Change the sampler and the path through noise changes. Change the scheduler and the timing of the denoise changes. Change the seed and the initial noise field changes. Change the CFG and the model listens to the prompt more or less aggressively. Change the VAE and the final pixel interpretation can shift color, contrast, or detail. Still with me, or are your eyes starting to glaze? Let’s keep going. For inpainting, the graph gets another branch. Instead of starting from an empty latent, you load a source image and a mask. The source image gets encoded into latent space by the VAE. The mask tells the sampler which part of that latent is allowed to change. You pass the masked latent into an inpaint sampler or an inpaint conditioning path, depending on the workflow. The prompt describes what should exist in the edited region. Then the denoise value controls how strongly the model is allowed to reinterpret it, while the CFG adjusts your semantic weight. That’s the important part. Denois is the amount of permission you are giving the model. Low denoise says, “stay close to the source.” Medium denoise says, “improvise, but keep the structure.” High denoise says, “use this as a launch ramp.” The better mind model, is think of like improvising against a blurry surface. Light blur gives you minor changes, heavy blur gives you radical improv. Inpainting is a sort of controlled reinterpretation. The mask is the boundary between what you are protecting and what you are letting the model predict. You can make that mask in Photoshop. You can paint it by hand. You can blur it, sharpen it, grow it, shrink it, feather it, invert it, combine it with alpha, or generate it from segmentation. You can use SAM (or segmentation anywhere model) to isolate a person, a face, a shirt, a background object, or a region of the frame. You can use depth or edge maps to build a mask. You can roto it manually if you hate yourself or love precision enough that the difference becomes academic. But once you care about structure, you start to care about ControlNet. That means the graph grows another conditioning branch (or several). You load a ControlNet model, load or generate a control image, preprocess that image into the right kind of map, and feed the resulting control conditioning into the sampler alongside the prompt conditioning. If you want pose, you use OpenPose or something like it. If you want to preserve linework, you use Canny, HED, Scribble, or Lineart. It depends, you sort of have to feel that one out. If you want composition, you use a depth model like Midas. For artistic compositions, I like balanced combinations of normal maps, depth, and HED, because together they can preserve form, spatial layout, and edge energy without freezing the whole image into a dead little technical diagram. ControlNet is a family of structural constraints. You can stack them. Pose can control the body, depth can control the space, HED can control the graphic outline, and the prompt can control the interpretation. Each one gets a strength value. Each one can start and stop at different points in the diffusion process. Maybe you want the pose to matter the whole time, but the edge map only matters early. Maybe you want depth to keep the room intact, but you want the surface detail to drift. Congratulations my friend! You are mixing constraints across the denoising schedule. Feels good, don’t it. Then there are identity and consistency tools. A LoRA is a small adapter trained to bias the model toward a subject, style, object, outfit, rendering habit, or visual vocabulary. So yes, you can have models in your models while you’re modeling things. If that isn’t enough, you can use embeddings, IPAdapter, reference conditioning, face reference tools, or a retrained checkpoint. If you want to get really ridiculous, you can start talking about hypernetworks, which are another layer of “AI that modifies the behavior of the AI,” because apparently the recursion monster was not done building things. The graph for this is usually another load-and-attach pattern. You load the LoRA and apply it to the model and CLIP before sampling. You load IPAdapter, feed it a reference image, encode that image through a vision model, and pass that conditioning into the sampler. You use reference tools when you want the output to inherit some visual identity from an image without being trapped by the source composition. Upscaling is another stage. So, now you’ve got this beautiful image you’ve made, but it’s low resolution compared to the usable professional output you want. Whatever will you do? Well, a simple upscale might take the decoded image, enlarge it with an upscale model, then run another img2img pass at low denoise to restore detail. A more serious upscale graph might tile the image, process each tile, blend the seams, preserve the global composition, and then do a final cleanup pass. Latent upscale changes the latent before decoding. Pixel upscale enlarges after decoding. Tiled diffusion tries to get more detail without blowing up VRAM. And now, there’s a new one that skips the latent step altogether, and just goes straight into absurdly heavy direct image editing, which… looks beautiful. New problem: “make it bigger” and “add plausible detail” are not the same operation. So you have to weigh that one when you get there. Now video. Video is where the machine starts smoking in funny colors. A still image only has to be coherent once. Video has to stay coherent across time, which means every frame inherits problems from the frame before it and invents new ones for the frame after it. If your workflow does not account for temporal consistency, weird shit happens. Y’know, there are half a dozen people who are doing this intentionally with their videos right here on X, and it’s pretty wild. You should check it out. My favorite is the exploding body parts guy. It looks like a japanese action movie… until it doesn’t. Assuming you’re not into body horror or hard surrealism, there are a few ways to approach it. In an image-to-video workflow, you start with a still image or keyframe and use a video model to generate motion from it. That can be great for short shots, animated loops, camera drift, character motion, or atmospheric movement. The graph usually loads the video model, encodes the starting image, sets motion parameters, samples a sequence of latent frames, decodes them, and combines them into a video file. The challenge is that you are giving up some control to the video model’s idea of motion. In a frame-by-frame workflow, you start with source footage. You extract the frames, process them through img2img or inpainting, then reassemble them. This gives you more control, but it creates temporal problems. Each frame can drift. Small inconsistencies become flicker. The character’s face may mutate. The texture may crawl. The background may breathe like it has opinions. To fight that, you use constraints. You keep seeds stable or vary them carefully. You use low denoise when you need preservation. You use ControlNet from the original footage, often with depth, lineart, or pose maps. You use optical flow or motion guidance when available. You track masks across frames instead of repainting them randomly. You may render keyframes first, then interpolate between them. You may use EbSynth-style propagation, video ControlNet, AnimateDiff-style motion modules, or a video model to stabilize the motion. In video inpainting, the mask becomes a time-based object. It is not just “paint this region.” If you’ve ever used something like Davinci Resolve or Premier, it’s the same general concept. You might generate masks with segmentation, clean them manually, track them through the shot, feather them to avoid hard seams, and then inpaint the masked area with enough denoise to change it but not so much that it detaches from the footage. This is where the workflow becomes very medium-specific. If I’m changing a shirt in a still image, I can mask the shirt, prompt the new garment, inpaint, clean the edges, and upscale. If I’m changing a shirt in a video, I need the mask to follow the torso, survive arm motion, handle folds, avoid eating the neck and hair, maintain fabric identity across frames, and not shimmer like a cursed napkin. Same idea. Different difficulty class. The Comfy graph for video usually becomes a chain of smaller graphs. One graph extracts or loads frames. Another generates control maps. Another handles masks. Another performs img2img or inpainting. Another upscales or interpolates. Another combines frames back into video. You can do some of this inside Comfy, some outside it, and some in a video editor. The cleanest workflows are rarely one giant graph. They are usually staged pipelines. So there you go. Now you know how to do it.

Seedance 2 steamrolls AnimateDiff on prompt fidelity runtimewire.com/article/seed…

noteで記事を書きました！この投稿をリポストするとお得に記事を読むことができます。 [ComfyMaster36] AnimateDiffで最初と最後のフレームの画像を指定して動画を生成しよう！ #ComfyUI | AICU media @AICUai #note note.com/aicu/n/n633ae36958a…

I learned the ropes with Runway Academy, then moved to ComfyUI AnimateDiff for better control. Prompt engineering is the real skill there though.

This 15yo kid just completely nuked the $5B egirl industry right from his gaming chair There's a wooden crucifix on the wall behind him. A black gaming chair. A 15 year old in a tshirt. Bedroom door closed. In ten seconds he becomes six different women. The chair doesn't move. The lamp doesn't flicker. Only the body and the face swap, one frame to the next, with no glitch where his shoulders used to be. He posted it as a meme. "When she asks how many girls I'm speaking to." He doesn't know what he just shipped. Somewhere else, the same pipeline runs ten Instagram accounts that never existed. Top earner cleared $34,890 last month from gated content alone. ComfyUI on his desktop. AnimateDiff for the motion. A Turkish rap track trending on TikTok. The room stays frozen. Six different women appear and vanish in his frame. A virtual model agency used to need photographers, contracts, three weeks of shoots, rent. Now it fits between a desk and a crucifix. The OnlyFans market paid out $6.6 billion last year. A growing share lands in accounts where nobody on the other side is real. His mom thinks he's gaming. He thinks he made a joke. The crucifix is the only thing in that room that watched the whole thing.

taking me all the way back! Some of the first animation I did in Comfy was thanks to some cool workflows purz made back in late 2023/early24 what a difference an animateDiff can make

May the road rise to meet you. #AnimateDiff

This 27-year-old runs UGC creatives for 7 fashion and beauty brands and pulls $12,000 per month from one $900 GPU and free open-source software. Every ecom brand right now hits the same wall. You need fresh UGC every week for TikTok Shop and Meta Ads, but a single UGC creator charges $300 to $800 per clip, takes a week to deliver, and cloud-based AI generators swap the model's face every two seconds and never let you build a unified brand look. This pipeline outputs 30 plus seconds of consistent video UGC from one selfie clip and one product photo in 20 minutes. Here is what it does: > Locks the model's face in every frame through a ControlNet face anchor in ComfyUI (one face across every clip is how a brand builds recognition. Cloud AI gives you a new girl every 2 seconds, and the audience never remembers the page) > Transfers movement, expressions, and lip sync from a single driver clip through AnimateDiff plus OpenPose (most operators rewrite the prompt for every angle and burn 4 hours on something that should take 20 minutes) > Automatically adjusts light and shadow to match whatever scene the model lands in (the step every tutorial skips. That is why most outputs look like a cardboard cutout pasted on a background) > Runs the whole graph through a local batch via ComfyUI Lists for one-driver-many-outputs processing (this is the difference between a one-off TikTok meme and a real ad production line) > Outputs ready cuts for TikTok Shop, Instagram Reels, Reels for fashion brands, and creatives for Meta Ads, all with the same AI model personality Real case: one weekly content drop, 5 product angles by 7 video variations equals 35 UGC clips → 1 day of work, $0 in API costs after the $900 GPU is paid off versus $1,500 to $5,000 for classic UGC with 5 different creators. The output goes straight into TikTok Shop, ad accounts, and Shopify product pages. The math for ecom founders and media buyers: One classic UGC clip = $300 to $800, a week to deliver, creator booking plus zero reusability One cloud AI render = $0.50 to $2.00 per second of video, face swap every 2 seconds, zero brand consistency One local GPU at $900 = unlimited generations, $0 per clip after the second week, one AI model as the face of the brand for years One solo operator on 7 retainers = $12,000 per month for 25 hours of work For dropshippers, marketplace sellers, fashion and beauty brands, SMMA owners, fitness influencers, and anyone running paid traffic to physical products → UGC is right now the only creative format that has not burned out on Meta and TikTok Ads. In most cases the choice is this: A classic UGC shoot at $300 to $800 per clip that takes a week and locks you into one creator in the niche Or cloud AI that swaps the model's face every 2 seconds and kills any brand recognition This fixes all of it. No creator bookings. No location rentals. No rewriting prompts for every scene. No face swap every 2 seconds. One AI model face, one product photo, and one $900 GPU, and your ad account has fresh creatives every morning.

This guy built an AI pipeline that generates 3 different hyperrealistic models in 7 seconds, and now dropshippers pay him $1,700 to clone the whole system. He got tired of watching ecom brands burn $8K on a photoshoot every time one product angle changes, so he built a $900 hardware setup (GPU plus RAM) that runs a local station generating 127 product videos without hiring a single model. Here is the exact breakdown: → Claude writes a 34-parameter JSON brand DNA before a single frame is generated. Target psychographics, price anchor, vibe matrix, anti-inspiration blacklist → He loads a short video with the desired pose and facial expression as a motion source. These are movement timecodes, not the final frame → Stable Diffusion plus AnimateDiff running locally on his hardware generate new models and transfer the motion onto them frame by frame, synced to the beat of a phonk track → A Negative Prompt node runs 41 exclusions: no plastic skin, no CGI glow, no melting fingers, no doll face, no floating chair, no morphing furniture → This one step kills the "AI look" that drops engagement 67 percent in the first 3 seconds → The source lighting is preserved automatically. Turquoise neon lands on the generated model's face like it belongs there, and the scene does not reek of generation → TikTok Studio uploads 19 videos in one batch with zero manual copy, because the brand voice was locked in on the first step → Atlas scrapes product links from Amazon and auto-generates a Shopify store with hero images, price tiers, scarcity copy, and mobile checkout in 90 seconds → The store goes live before the first TikTok video finishes rendering The key move 94 percent skip: you cannot animate the character before you lock in the negative prompt. If you throw raw video-to-video through without a filter, the face melts into a wax figure, the fabric loses texture, the fingers fuse together, a chair appears out of nowhere behind the model, the table the hand was resting on dissolves, and the hand floats mid-air. The scene screams "AI" and CTR dies. His system runs the exclusion filter as the first step, so the AI model moves like it was shot on an iPhone 15 Pro in a room with natural light, even if the original was filmed under cold neon. One brand pulled 2.6M views on TikTok in 11 days with zero spend on paid ads, and converted at 3.7 percent because the videos looked like organic UGC instead of glossy studio production. Brands now pay him $1,700 for the full pipeline setup plus $340 per month to sync the store with new drops and seasonal video batches. The entire system runs on $23 per month in API costs and one $900 workstation. No photographer. No model agency. No product samples. Just a prompt template, a motion reference, and the discipline to filter AI artifacts before you render the movement.

Beautiful #AnimateDiff stuff from years ago! Available at Opensea (link in first comment). "Back2Nature"

16.3万星神级项目！一秒生成AI艺术 脑子里有画面，手上画不出来，这种憋屈很多人都经历过。 龙哥折腾过好几个 AI 绘画工具，最后留在电脑里的，始终是这个 16.3 万星的 Stable Diffusion WebUI。 它把原本需要命令行和复杂参数操作的模型，包装成了一个浏览器就能用的界面，本机运行，打开就能画。 安装现在已经被社区简化到了极致。 下载一个整合包，解压，运行，浏览器地址栏输入一行地址，一个完整的创作工作台就出现在面前。 左边是提示词输入框，右边是图片预览区，上面摆满各种功能标签：文生图、图生图、局部重绘、附加网络。 真正让它经久不衰的，是插件生态。 ControlNet 让你可以用线稿、姿态骨架、深度图来控制构图。AnimateDiff 让它能生成动态画面。还有无数的脚本扩展，自动补全提示词、批量处理图片、放大分辨率。这些插件把工具的能力边界一拓再拓，几乎每隔一阵子就会带来新的玩法。 模型就更不用说了。 社区有成千上万个基础模型和 LoRA，风格从写实摄影、二次元插画到国风水墨，涵盖你能想到的各种视觉方向。下载后放进指定文件夹，界面里一键切换。我平时写文章要配图，不再去图库翻找，而是把主题描述清楚，它出十几张候选，总能挑出合适的。 有一点很值得提，它跑在本地。 这意味着出图速度取决于你的显卡，而不是云端的排队队列。没有生成次数限制，不用担心隐私图片被传上服务器。调整参数随便试错，画废了就再来一张，成本只有一点电费。 如果你对视觉表达有需求，但一直卡在技术门槛前面，Stable Diffusion WebUI 是很合适的入口。 它既有专业工具的可控性，又保留了“一句话出图”的简单模式。装上它，你会发现自己脑子里那些漂浮的图像，居然真的能具象成画面，一张一张铺在眼前。

saedance2.0で動画を作るコストを計算してもらった。 3分の動画作るにしても ハイブリッドでもかなりの金額がかかるな（約19000円） 趣味でやるには、なかなかのコストだ SeaDance 2.0で「720p・3分間動画」を作る場合のコスト比較レポート 結論から言うと、ハイブリッド環境（API＋ローカルRTX4060）が圧倒的にお得です。 試算条件 •目標：720p（16:9）、30fps、音声同期ありの3分（180秒）動画 •打率10%想定（10本生成して1本採用）→合計1,800秒分の生成コストで計算 •為替：1ドル＝150円 ① 全行程をAPIでやる場合（Fal.ai / Segmindなど） •720p単価：約22.7円/秒 •ストレート3分：約4,080円 •現実コスト（ガチャ込み）：約40,824円 ※1回の上限が5〜15秒のため、12〜36カットに分割生成＋調整が必要。結果として4万円前後かかります。 ② ハイブリッド環境（480p API＋RTX4060ローカル） ワークフロー 1API側：480p・最短5秒でガチャを回して構図・モーションだけ確定 2ローカル側：採用した動画をComfyUIに取り込み、AnimateDiff＋ControlNet（Tile/IP-Adapter）＋Real-ESRGANで720p以上に高画質化 コスト •480p API単価：約10.5円/秒 •APIガチャ分：約18,981円 •ローカル電気代（RTX4060 160W）：数十〜100円程度 合計：約19,100円（53%オフ） 比較まとめ 項目 全API利用 ハイブリッド（推奨） 現実コスト 約40,800円 約19,100円 作業時間 数分 数時間 画質コントロール 運任せ ◎細かく調整可能 VRAM負荷 ほぼなし フル稼働 技術アドバイス
RTX4060をお持ちなら絶対にハイブリッドをおすすめします。
480pでSeaDanceを出力→ローカルでSDXL/FluxのControlNetや超解像を噛ませると、API単体の720pよりも遥かにシャープで高品質な映像に仕上がります。 コスト半分以下＋クオリティ大幅アップで、クリエイティブ作業がかなり快適になります🔥 必要ならComfyUIのワークフロー例も後日共有します！