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you don't need Seedance 2.5, here's cheaper and more efficient workflow we made this entire game shooting scene in one shot for $1.97 with DeepSeek V4 Flash 0731 + MiniMax H3 we use DeepSeek to code the raw Three.js scene first: exact camera movement, character motion, timing, everything. keep... show more

GMI Cloud

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146,085 次观看 • 3 天前 •via X (Twitter)

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📖THE STEP MOST CREATORS SKIP IS WHY THEIR AI ANIMATION LOOKS INCONSISTENT Consistency across clips doesn't come from prompting — it comes from the reference image. The pipeline, step by step: ▪ Start with ChatGPT Image 2 — generate a full character design sheet first, not just a single frame. Multiple angles, expressions, and outfit variations in one image keeps the character consistent across every scene ▪ Build a storyboard inside ChatGPT Image 2 as well — define each shot, camera angle, action, and mood before touching Seedance at all. This is the step most people skip and it's the reason clips look disconnected ▪ Define a color palette and lighting mood early — golden afternoon light, soft warm tones, dramatic shadows. Lock those values and repeat them across every prompt ▪ Take each storyboard frame into Seedance 2.0 as the reference image — one frame becomes one clip ▪ Write the Seedance prompt around the character action, not the scene description. The scene is already in the image. The prompt handles motion, camera behavior, and timing ▪ Keep clip duration between 4-6 seconds per shot — shorter clips give more control over pacing and reduce motion drift on character faces ▪ Match camera movement type across consecutive clips — if one shot dollies in, the next should hold or pull back, not dolly again The consistency across these frames comes from the character design sheet, not from luck. Seedance reads the reference image and the prompt together — if the reference is detailed enough, the output stays on-model. This video was created by ALOKXMEHTA 📥 tomorrow: the exact ChatGPT Image 2 prompt structure used to generate a multi-angle character design sheet like this one 🔖One article covers the entire workflow — it is pinned below, do not scroll past it.

📖THE STEP MOST CREATORS SKIP IS WHY THEIR AI ANIMATION LOOKS INCONSISTENT Consistency across clips doesn't come from prompting — it comes from the reference image. The pipeline, step by step: ▪ Start with ChatGPT Image 2 — generate a full character design sheet first, not just a single frame. Multiple angles, expressions, and outfit variations in one image keeps the character consistent across every scene ▪ Build a storyboard inside ChatGPT Image 2 as well — define each shot, camera angle, action, and mood before touching Seedance at all. This is the step most people skip and it's the reason clips look disconnected ▪ Define a color palette and lighting mood early — golden afternoon light, soft warm tones, dramatic shadows. Lock those values and repeat them across every prompt ▪ Take each storyboard frame into Seedance 2.0 as the reference image — one frame becomes one clip ▪ Write the Seedance prompt around the character action, not the scene description. The scene is already in the image. The prompt handles motion, camera behavior, and timing ▪ Keep clip duration between 4-6 seconds per shot — shorter clips give more control over pacing and reduce motion drift on character faces ▪ Match camera movement type across consecutive clips — if one shot dollies in, the next should hold or pull back, not dolly again The consistency across these frames comes from the character design sheet, not from luck. Seedance reads the reference image and the prompt together — if the reference is detailed enough, the output stays on-model. This video was created by ALOKXMEHTA 📥 tomorrow: the exact ChatGPT Image 2 prompt structure used to generate a multi-angle character design sheet like this one 🔖One article covers the entire workflow — it is pinned below, do not scroll past it.

Zentrix⌚️

14,015 次观看 • 1 个月前

One trick we discovered for avoiding realistic face moderation issues in Seedance 2.0 is using character turnaround sheets (front / side / back views). The first video is one of our experiment results — and it runs successfully. We’ve now integrated character turnarounds directly into our workflow + canvas system: 1. If your artwork was generated on our site, you can drag the image into the canvas directly from the Assets tab 2. Click the “Character Turnaround” button above the image to automatically generate a 3-view turnaround sheet 3. Create a new video node and use the turnaround sheet directly with Seedance 2.0 inside the workflow I’ve shared the workflow link in the comments if you want to explore the exact prompts, setup, and workflow details.

One trick we discovered for avoiding realistic face moderation issues in Seedance 2.0 is using character turnaround sheets (front / side / back views). The first video is one of our experiment results — and it runs successfully. We’ve now integrated character turnarounds directly into our workflow + canvas system: 1. If your artwork was generated on our site, you can drag the image into the canvas directly from the Assets tab 2. Click the “Character Turnaround” button above the image to automatically generate a 3-view turnaround sheet 3. Create a new video node and use the turnaround sheet directly with Seedance 2.0 inside the workflow I’ve shared the workflow link in the comments if you want to explore the exact prompts, setup, and workflow details.

underwood

32,883 次观看 • 2 个月前

I tried MiniMax Design (H3) to see how it handles real content creation. The workflow is simple. You just write a prompt or drop in an image, and it turns that into a dynamic video with motion, framing, and scene depth. No timeline to manage. No editing setup. No back and forth. What stood out to me: • Text to video and image to video both feel smooth. • It handles motion, camera angles, and flow on its own. • Output is fast, usually within seconds. • Works well for reels, quick ads, storytelling, and idea testing. It removes the hardest part: starting from scratch and turns your ideas into content in minutes. Instead of thinking, “How do I make this video?” You start with, “What do I want to create?” That shift alone makes it worth exploring. Try it here: #Hailuo

I tried MiniMax Design (H3) to see how it handles real content creation. The workflow is simple. You just write a prompt or drop in an image, and it turns that into a dynamic video with motion, framing, and scene depth. No timeline to manage. No editing setup. No back and forth. What stood out to me: • Text to video and image to video both feel smooth. • It handles motion, camera angles, and flow on its own. • Output is fast, usually within seconds. • Works well for reels, quick ads, storytelling, and idea testing. It removes the hardest part: starting from scratch and turns your ideas into content in minutes. Instead of thinking, “How do I make this video?” You start with, “What do I want to create?” That shift alone makes it worth exploring. Try it here: #Hailuo

Manish Kumar Shah

27,680 次观看 • 4 个月前

Beauty ads just changed forever. Free Claude Opus 4.8 + GPT Image 2 + Seedance 2.0 workflow to spin up 100s of video ads. No studio, no model, no macro lens, no shoot day. Here's what nobody in beauty marketing wants to say out loud. That glossy lip shot. The droplet hitting the surface in slow motion. The whip-pan into the next scene. The crystalline product splash. All the stuff that used to need a real set, a real camera op, and a full shoot day. You can generate every frame of it from a text prompt now, and stitch it into a finished ad before your coffee goes cold. The workflow is almost stupidly simple: → Tell Claude Opus 4.8 the beauty shot you want (dewy skin macro, gloss-on-lips contact, ripple transition, the works) → Claude turns it into a shot-by-shot storyboard plus a prompt for every frame → GPT Image 2 generates the photoreal stills, frame by frame → Seedance 2.0 animates each one into a clip with that buttery slow-mo glide → You drop the clips into HeyOz and assemble the full ad in one place The real unlock is volume. This isn't one hero video. Once the workflow is dialed, you spin up hundreds of variations. Different shades, different models, different hooks, different transitions. The exact creative volume Meta rewards, minus the production cost that used to make it impossible. Old way: one shoot, one look, $10k+, weeks of waiting. New way: a hundred angles, any look, a few dollars each, same afternoon. I wrote up the entire workflow. The Claude storyboard prompt, the GPT Image 2 frame prompts, the Seedance motion settings, the full assembly flow. Completely free, no email gate. Want it? Comment "GLOSS" and I'll send it straight over. (make sure you're following so it can actually reach you)

Beauty ads just changed forever. Free Claude Opus 4.8 + GPT Image 2 + Seedance 2.0 workflow to spin up 100s of video ads. No studio, no model, no macro lens, no shoot day. Here's what nobody in beauty marketing wants to say out loud. That glossy lip shot. The droplet hitting the surface in slow motion. The whip-pan into the next scene. The crystalline product splash. All the stuff that used to need a real set, a real camera op, and a full shoot day. You can generate every frame of it from a text prompt now, and stitch it into a finished ad before your coffee goes cold. The workflow is almost stupidly simple: → Tell Claude Opus 4.8 the beauty shot you want (dewy skin macro, gloss-on-lips contact, ripple transition, the works) → Claude turns it into a shot-by-shot storyboard plus a prompt for every frame → GPT Image 2 generates the photoreal stills, frame by frame → Seedance 2.0 animates each one into a clip with that buttery slow-mo glide → You drop the clips into HeyOz and assemble the full ad in one place The real unlock is volume. This isn't one hero video. Once the workflow is dialed, you spin up hundreds of variations. Different shades, different models, different hooks, different transitions. The exact creative volume Meta rewards, minus the production cost that used to make it impossible. Old way: one shoot, one look, $10k+, weeks of waiting. New way: a hundred angles, any look, a few dollars each, same afternoon. I wrote up the entire workflow. The Claude storyboard prompt, the GPT Image 2 frame prompts, the Seedance motion settings, the full assembly flow. Completely free, no email gate. Want it? Comment "GLOSS" and I'll send it straight over. (make sure you're following so it can actually reach you)

Ahad Shams

11,126 次观看 • 2 个月前

Every creator has been here: an idea in your head, no time to build it, no clean way to visualize it. So I tried something different… I opened Freepik and built the entire concept as a short video, start to finish, in one place. I wrote a simple scene, dropped in my product, and let Freepik generate a cinematic first draft. Then I refined it: better framing, smoother motion, cleaner lighting. The kind of polish you’d normally need a full editing stack for, all handled inside the same workflow. One idea. One tool. A finished video in minutes. Try it now:

Every creator has been here: an idea in your head, no time to build it, no clean way to visualize it. So I tried something different… I opened Freepik and built the entire concept as a short video, start to finish, in one place. I wrote a simple scene, dropped in my product, and let Freepik generate a cinematic first draft. Then I refined it: better framing, smoother motion, cleaner lighting. The kind of polish you’d normally need a full editing stack for, all handled inside the same workflow. One idea. One tool. A finished video in minutes. Try it now:

Chidanand Tripathi

93,250 次观看 • 8 个月前

AI video has changed so fast most people don't realize it yet The average agency still charges $500 for one UGC ad with a real human, lighting setup, post-production, the works I just made one in under 20 minutes. Character, voice, scene, everything generated. Same output. 1/25 the cost. No casting, no shoots, no studio time. RT this and comment "prompt" and I'll DM you the exact workflow I used.

AI video has changed so fast most people don't realize it yet The average agency still charges $500 for one UGC ad with a real human, lighting setup, post-production, the works I just made one in under 20 minutes. Character, voice, scene, everything generated. Same output. 1/25 the cost. No casting, no shoots, no studio time. RT this and comment "prompt" and I'll DM you the exact workflow I used.

Kiyoro

132,410 次观看 • 3 个月前

wan 2.2 makes lighting feel programmable you start with one normal clip drop a single reference frame and the model rebuilds the scene same framing, same motion, same timing but now you can swap mood change light direction and create cinematic variants without reshooting brands are using this to turn one talking-head ad into multiple high-end looks from the same source video i broke down the exact workflow rt + comment “wan” and i’ll send it (follow for dm)

wan 2.2 makes lighting feel programmable you start with one normal clip drop a single reference frame and the model rebuilds the scene same framing, same motion, same timing but now you can swap mood change light direction and create cinematic variants without reshooting brands are using this to turn one talking-head ad into multiple high-end looks from the same source video i broke down the exact workflow rt + comment “wan” and i’ll send it (follow for dm)

Daffy

24,979 次观看 • 7 个月前

i just recreated this viral vid in 30 seconds using seedance + cloudbot everything you see in this video was generated the character the environment the motion the visual effects seedance handles scene generation and realism cloudbot structures the workflow and automates variations what used to take editors hours of animation and compositing now takes minutes with the right prompt structure it’s trained on short form platforms so it understands pacing, hooks, and retention patterns you don’t guess formats anymore you call them out and replicate them this stack is about to remove creative bottlenecks entirely rt + comment “stack” and i’ll send the setup (follow for dm)

i just recreated this viral vid in 30 seconds using seedance + cloudbot everything you see in this video was generated the character the environment the motion the visual effects seedance handles scene generation and realism cloudbot structures the workflow and automates variations what used to take editors hours of animation and compositing now takes minutes with the right prompt structure it’s trained on short form platforms so it understands pacing, hooks, and retention patterns you don’t guess formats anymore you call them out and replicate them this stack is about to remove creative bottlenecks entirely rt + comment “stack” and i’ll send the setup (follow for dm)

Florin

14,528 次观看 • 6 个月前

Seedance 2.0 - Advanced Workflows Series 14. Compositing and Layering Some shots may have many characters that need specific movements. When we start adding characters, it becomes very difficult to achieve the exact animation, gesture, or movement we are looking of all of them in a single generation. In addition, the more characters are added, the more rigid Seedance 2.0 animations become in order to maintain consistency. This is especially visible in styles with squash and stretch and rubber hose animations, where the characters deform their body parts as if they were made of rubber, or in highly expressive movement animations in anime. For these cases, it is better to work on each animation separately and recompose it at the end into a video. Workflow + Prompts 👇

Seedance 2.0 - Advanced Workflows Series 14. Compositing and Layering Some shots may have many characters that need specific movements. When we start adding characters, it becomes very difficult to achieve the exact animation, gesture, or movement we are looking of all of them in a single generation. In addition, the more characters are added, the more rigid Seedance 2.0 animations become in order to maintain consistency. This is especially visible in styles with squash and stretch and rubber hose animations, where the characters deform their body parts as if they were made of rubber, or in highly expressive movement animations in anime. For these cases, it is better to work on each animation separately and recompose it at the end into a video. Workflow + Prompts 👇

VoxelPlot

13,595 次观看 • 1 个月前

Honestly... this makes a lot of sense. I've lost count of how many times I've regenerated an AI video just because someone was standing in the wrong place or the camera angle felt off. If I can quickly lay out the scene first and then let the model generate from that, I'd much rather work that way than keep tweaking prompts over and over. Seems like a really useful addition for anyone making AI videos. Props to TopviewAI for building this.

Honestly... this makes a lot of sense. I've lost count of how many times I've regenerated an AI video just because someone was standing in the wrong place or the camera angle felt off. If I can quickly lay out the scene first and then let the model generate from that, I'd much rather work that way than keep tweaking prompts over and over. Seems like a really useful addition for anyone making AI videos. Props to TopviewAI for building this.

Gina Acosta

103,147 次观看 • 1 个月前

kling 2.6 motion control is actually insane for AI ads you can swap any person in a video with a different person and it looks completely realistic, and its way better than wan 2.2 this unlocks massive testing potential for ads you can test the same ad concept across multiple different ICPs instantly here's the workflow: >generate your new character with nano banana pro >use kling 2.6 motion control to replace the person in your existing video generate a new voice with >elevenlabs to match the character you can take one winning ad and instantly create 10 variations targeting: -different age groups -different genders -different ethnicities -different aesthetics this is the future of ad testing at scale

kling 2.6 motion control is actually insane for AI ads you can swap any person in a video with a different person and it looks completely realistic, and its way better than wan 2.2 this unlocks massive testing potential for ads you can test the same ad concept across multiple different ICPs instantly here's the workflow: >generate your new character with nano banana pro >use kling 2.6 motion control to replace the person in your existing video generate a new voice with >elevenlabs to match the character you can take one winning ad and instantly create 10 variations targeting: -different age groups -different genders -different ethnicities -different aesthetics this is the future of ad testing at scale

Miko

29,467 次观看 • 7 个月前

Yeaah with Kartel.ai and Jesse Wellens ! So training Lora for character on Flux and also for Wan 2.1 14B ( using dataset video ) make a Huge difference. So here you are seeing a volumetric capture of Jesse Wellens that we did in at the spatial studio of Kartel.ai We use a a World Labs gaussian splatting to create a simple environment that we put with the volumetric capture in our own webgl viewer ( out soon for everyone ). After that, We use after this output in a ComfyUI workflow with Wan Fun control ( a bit similar Vace but its working for Wan 14B) with the double loras , the first to generate the first frame with flux, and the second one to guide the generation of Wan 2.1 Control. So we keep very good consistency of character and position, and creating amazing worlds :) ! Hope you will find it cool !

Yeaah with Kartel.ai and Jesse Wellens ! So training Lora for character on Flux and also for Wan 2.1 14B ( using dataset video ) make a Huge difference. So here you are seeing a volumetric capture of Jesse Wellens that we did in at the spatial studio of Kartel.ai We use a a World Labs gaussian splatting to create a simple environment that we put with the volumetric capture in our own webgl viewer ( out soon for everyone ). After that, We use after this output in a ComfyUI workflow with Wan Fun control ( a bit similar Vace but its working for Wan 14B) with the double loras , the first to generate the first frame with flux, and the second one to guide the generation of Wan 2.1 Control. So we keep very good consistency of character and position, and creating amazing worlds :) ! Hope you will find it cool !

Lovis Odin

23,066 次观看 • 1 年前

🤯 Depth videos perfectly solve false flags on reference videos — and can perfectly recreate both martial arts and dance! Turned a Guan Dao martial arts clip into a Depth motion reference, swapped the fighter for a market auntie, and moved the scene to a T-junction inside a local market 🤣 🌟 Workflow: 1. Pick a reference clip under 15 seconds 2. Convert it to Depth with Depth Anything V2 3. Generate a new character + scene 4. Feed everything into Seedance with the prompt below 🌟 Depth video conversion: You can build a local Depth converter with Codex — prompt in the comments. This goes way beyond trending dances. Martial arts, polearms, and other complex movements can all be transferred cleanly with Depth! Workflow + Prompt below 👇

🤯 Depth videos perfectly solve false flags on reference videos — and can perfectly recreate both martial arts and dance! Turned a Guan Dao martial arts clip into a Depth motion reference, swapped the fighter for a market auntie, and moved the scene to a T-junction inside a local market 🤣 🌟 Workflow: 1. Pick a reference clip under 15 seconds 2. Convert it to Depth with Depth Anything V2 3. Generate a new character + scene 4. Feed everything into Seedance with the prompt below 🌟 Depth video conversion: You can build a local Depth converter with Codex — prompt in the comments. This goes way beyond trending dances. Martial arts, polearms, and other complex movements can all be transferred cleanly with Depth! Workflow + Prompt below 👇

Larus Canus

50,222 次观看 • 25 天前

🤯 I didn’t expect Depth to track rooftop parkour this cleanly! Used a fast climbing clip as the motion reference, then swapped in a student with twin tails and a backpack on a coastal school rooftop. The running path, wall contact, jumps, landings, and full-body continuity all hold together surprisingly well. 🌟 Workflow: 1. Pick a reference clip under 15 seconds 2. Convert it to Depth with Depth Anything V2 3. Generate a new character + scene 4. Feed everything into Seedance with the prompt below 🌟 Depth video conversion: You can build a local Depth converter with Codex — prompt in the comments. Depth opens up a lot more possibilities for parkour, climbing, martial arts, dance, and other complex full-body motion. Workflow + Prompt below 👇

🤯 I didn’t expect Depth to track rooftop parkour this cleanly! Used a fast climbing clip as the motion reference, then swapped in a student with twin tails and a backpack on a coastal school rooftop. The running path, wall contact, jumps, landings, and full-body continuity all hold together surprisingly well. 🌟 Workflow: 1. Pick a reference clip under 15 seconds 2. Convert it to Depth with Depth Anything V2 3. Generate a new character + scene 4. Feed everything into Seedance with the prompt below 🌟 Depth video conversion: You can build a local Depth converter with Codex — prompt in the comments. Depth opens up a lot more possibilities for parkour, climbing, martial arts, dance, and other complex full-body motion. Workflow + Prompt below 👇

Larus Canus

24,613 次观看 • 21 天前

Gemini Omni's motion control is f*cking cracked i just figured out how to turn 1 reference video into 50+ AI videos with the exact same movements... you have a video of someone eating, dancing, using a product, doing whatever complex motion you need. you feed it to Gemini Omni and it recreates that exact motion with a completely new AI character in literally one prompt i've tested this against Kling motion control and it's not even close. Kling falls apart the moment you try anything complex. eating scenes look weird, hand movements get mangled, anything multi-step breaks down completely. Gemini Omni handles all of it if you're still using kling motion control or paying creators to split test your videos, this replaces that entire workflow here's the thing though. you can't just prompt this out of the box. if you try to do motion transfer with default prompting you're going to get errors or the motion won't transfer properly. there's a specific prompting method that makes it work every time so i packaged up the whole system.. here's what you're getting: > full step by step video breakdown > how to find the best reference videos to use > the exact prompting system that allows for motion control transfer so you never get errors > the workflow for batching this out at scale (1 video → 50+) RT + reply "MOTION" and i'll send it over (must follow so i can dm)

Gemini Omni's motion control is f*cking cracked i just figured out how to turn 1 reference video into 50+ AI videos with the exact same movements... you have a video of someone eating, dancing, using a product, doing whatever complex motion you need. you feed it to Gemini Omni and it recreates that exact motion with a completely new AI character in literally one prompt i've tested this against Kling motion control and it's not even close. Kling falls apart the moment you try anything complex. eating scenes look weird, hand movements get mangled, anything multi-step breaks down completely. Gemini Omni handles all of it if you're still using kling motion control or paying creators to split test your videos, this replaces that entire workflow here's the thing though. you can't just prompt this out of the box. if you try to do motion transfer with default prompting you're going to get errors or the motion won't transfer properly. there's a specific prompting method that makes it work every time so i packaged up the whole system.. here's what you're getting: > full step by step video breakdown > how to find the best reference videos to use > the exact prompting system that allows for motion control transfer so you never get errors > the workflow for batching this out at scale (1 video → 50+) RT + reply "MOTION" and i'll send it over (must follow so i can dm)

Miko

59,157 次观看 • 1 个月前

recreated this exact hook with AI in 3 minutes generated first frame with nano banana -> uploaded it along with reference video to Kling motion control, done when the quality is actually there, AI UGC beats real creators every time we built the best system for this right now: - realistic visuals - realistic voice - realistic movement - all optimized for virality and conversions way cheaper than a real creator and built for scale across thousands of posts and multiple accounts this isn't generic AI slop, these are high quality systems built with real effort and ready to scale your business it works across ecom,apps, saas, coaching, DTC... everything yet people are still paying $300-500 per UGC video from creators who ghost halfway through the project

recreated this exact hook with AI in 3 minutes generated first frame with nano banana -> uploaded it along with reference video to Kling motion control, done when the quality is actually there, AI UGC beats real creators every time we built the best system for this right now: - realistic visuals - realistic voice - realistic movement - all optimized for virality and conversions way cheaper than a real creator and built for scale across thousands of posts and multiple accounts this isn't generic AI slop, these are high quality systems built with real effort and ready to scale your business it works across ecom,apps, saas, coaching, DTC... everything yet people are still paying $300-500 per UGC video from creators who ghost halfway through the project

MAX

171,662 次观看 • 5 个月前

Fine-tune DeepSeek-OCR on your own language! (100% local) DeepSeek-OCR is a 3B-parameter vision model that achieves 97% precision while using 10× fewer vision tokens than text-based LLMs. It handles tables, papers, and handwriting without killing your GPU or budget. Why it matters: Most vision models treat documents as massive sequences of tokens, making long-context processing expensive and slow. DeepSeek-OCR uses context optical compression to convert 2D layouts into vision tokens, enabling efficient processing of complex documents. The best part? You can easily fine-tune it for your specific use case on a single GPU. I used Unsloth to run this experiment on Persian text and saw an 88.26% improvement in character error rate. ↳ Base model: 149% character error rate (CER) ↳ Fine-tuned model: 60% CER (57% more accurate) ↳ Training time: 60 steps on a single GPU Persian was just the test case. You can swap in your own dataset for any language, document type, or specific domain you're working with. I've shared the complete guide in the next tweet - all the code, notebooks, and environment setup ready to run with a single click. Everything is 100% open-source!

Fine-tune DeepSeek-OCR on your own language! (100% local) DeepSeek-OCR is a 3B-parameter vision model that achieves 97% precision while using 10× fewer vision tokens than text-based LLMs. It handles tables, papers, and handwriting without killing your GPU or budget. Why it matters: Most vision models treat documents as massive sequences of tokens, making long-context processing expensive and slow. DeepSeek-OCR uses context optical compression to convert 2D layouts into vision tokens, enabling efficient processing of complex documents. The best part? You can easily fine-tune it for your specific use case on a single GPU. I used Unsloth to run this experiment on Persian text and saw an 88.26% improvement in character error rate. ↳ Base model: 149% character error rate (CER) ↳ Fine-tuned model: 60% CER (57% more accurate) ↳ Training time: 60 steps on a single GPU Persian was just the test case. You can swap in your own dataset for any language, document type, or specific domain you're working with. I've shared the complete guide in the next tweet - all the code, notebooks, and environment setup ready to run with a single click. Everything is 100% open-source!

Akshay 🚀

126,122 次观看 • 9 个月前

Dreamina Seedance 2.5 is almost here!🔥— today — while US rollout begins August 7. Transitioning from "prompt-based guessing" to **"reference-driven creation"** — this provides the exact stellar upgrade creators have been waiting for. ✅ All of the Seedance family (2.5, 2.0, and future releases) operates at its lowest-ever tier ✅ Dreamina proves to be not just the perfect platform for Seedance 2.5 — it remains the most affordable way to run it, full-capacity, skipping any add-ons What makes it so remarkable? First video model to accept 50 multimodal references — drag in characters, scenes, reference shots, and tunes all in one go. Eliminate the nightmare of endless prompt writing. 🎬 30-second native clips — unfold complete stories, instead of mere fragments. You get 2 extensions ready in post-editing, plus a robust ultra-long mode extending up to 3 minutes. ✂️ Professional editing— powerful 3D white model preview, integrated green screen workflow, alongside Blender/Maya plugin support. 🌏 10+ languages — making sure lip-sync and subtitles align perfectly. It means zero translation tools needed. Dreamina Seedance 2.5 goes live globally first on Dreamina — undoubtedly the 2026 premier AI video model for creators. Coming soon. Create alongside Seedance 2.0 today and master the ultimate workflow! #dreamina #dreaminapartner #seedance #dreaminaseedance25

Dreamina Seedance 2.5 is almost here!🔥— today — while US rollout begins August 7. Transitioning from "prompt-based guessing" to "reference-driven creation" — this provides the exact stellar upgrade creators have been waiting for. ✅ All of the Seedance family (2.5, 2.0, and future releases) operates at its lowest-ever tier ✅ Dreamina proves to be not just the perfect platform for Seedance 2.5 — it remains the most affordable way to run it, full-capacity, skipping any add-ons What makes it so remarkable? First video model to accept 50 multimodal references — drag in characters, scenes, reference shots, and tunes all in one go. Eliminate the nightmare of endless prompt writing. 🎬 30-second native clips — unfold complete stories, instead of mere fragments. You get 2 extensions ready in post-editing, plus a robust ultra-long mode extending up to 3 minutes. ✂️ Professional editing— powerful 3D white model preview, integrated green screen workflow, alongside Blender/Maya plugin support. 🌏 10+ languages — making sure lip-sync and subtitles align perfectly. It means zero translation tools needed. Dreamina Seedance 2.5 goes live globally first on Dreamina — undoubtedly the 2026 premier AI video model for creators. Coming soon. Create alongside Seedance 2.0 today and master the ultimate workflow! #dreamina #dreaminapartner #seedance #dreaminaseedance25

ETHERSON

17,038 次观看 • 14 天前

$We are in an insane run of open-weight drops. Every modality, open source is winning. This is what an open source AI summer ☀️ looks like: 🧠 LLMs & Reasoning → DeepSeek-V4-Flash-0731 (my king 👑): 304B MoE refresh, Terminal-Bench 2.1 jumps 61.8→82.7 over the preview, DeepSWE 7.3→54.4. Closes in on Opus-4.8 on Agents' Last Exam (25.2 vs 25.7). MIT. → Muse-Glimmer-30B, from Meta (they are back!!): their first open agentic model. ~29.6B dense + perception encoder, 131k+ context, built to run fully local, no cloud. Apache 2.0. → Liquid AI LFM2.5-2.6B: 2.69B params, 131k context, 220 tok/s on an M5 Max in under 2.5GB RAM. Competitive with models 4x larger on agentic tasks. → inclusionAI Ling-3.0-flash: 124B total, only 5.1B active, ~12% the size of their old 1T flagship Ring-2.6, matches it on key benchmarks. MIT. → inclusionAI Ling-3.0-tiny: 7.9B total, 1.3B active, 86-90 tok/s on an M4 Pro MacBook at ~8GB peak memory. MIT. → NVIDIA Nemotron-3.5-Lightning-30B-A3B: hybrid Mamba-2+MoE+Attention, up to 1M context, runs on a single H100 or DGX Spark, SWE-bench Verified 52.8. → deepgrove maple-preview: 20B-A1B ternary-weight reasoner, 218 tok/s on a Mac mini M4, 5.3GB checkpoint. MIT. → BigBang-v1 (endless-frontier): fine-tuned from Qwen3.6-35B-A3B via a self-evolving generator/critic synthetic-data loop. Lands aggregate performance between DeepSeek V4 Flash (284B) and V4 Pro (1.6T), at 35B. Apache 2.0. 🎬 Video → MiniMax-H3: 33B dense omni model, native stereo audio, up to 2K/15s. 3.6k+ likes already. → Minimax-H3-Turbo (lightx2v): Apache-2.0 turbo distillation of H3 for fast inference. → Lightricks LTX-2.5: image-to-video update, custom Gemma-4-12B text encoder, a markedly stronger distilled model. 🔊 Voice → NVIDIA NemotronLabs VoiceChat-11B: full-duplex speech-to-speech, ~450ms turn-taking, #2 on open VoiceBench, and the first open full-duplex model with live tool-calling mid-conversation. 🛡️ Safety → Mistral Shieldstral-1.0-3B: 3B multimodal guardrail that takes your safety policy as plain text instead of fixed categories. Beats LlamaGuard-4-12B and ShieldGemma-9B on HarmBench (99.4) and ToxicChat (84.1) at a fraction of the size. Apache 2.0.$

We are in an insane run of open-weight drops. Every modality, open source is winning. This is what an open source AI summer ☀️ looks like: 🧠 LLMs & Reasoning → DeepSeek-V4-Flash-0731 (my king 👑): 304B MoE refresh, Terminal-Bench 2.1 jumps 61.8→82.7 over the preview, DeepSWE 7.3→54.4. Closes in on Opus-4.8 on Agents' Last Exam (25.2 vs 25.7). MIT. → Muse-Glimmer-30B, from Meta (they are back!!): their first open agentic model. ~29.6B dense + perception encoder, 131k+ context, built to run fully local, no cloud. Apache 2.0. → Liquid AI LFM2.5-2.6B: 2.69B params, 131k context, 220 tok/s on an M5 Max in under 2.5GB RAM. Competitive with models 4x larger on agentic tasks. → inclusionAI Ling-3.0-flash: 124B total, only 5.1B active, ~12% the size of their old 1T flagship Ring-2.6, matches it on key benchmarks. MIT. → inclusionAI Ling-3.0-tiny: 7.9B total, 1.3B active, 86-90 tok/s on an M4 Pro MacBook at ~8GB peak memory. MIT. → NVIDIA Nemotron-3.5-Lightning-30B-A3B: hybrid Mamba-2+MoE+Attention, up to 1M context, runs on a single H100 or DGX Spark, SWE-bench Verified 52.8. → deepgrove maple-preview: 20B-A1B ternary-weight reasoner, 218 tok/s on a Mac mini M4, 5.3GB checkpoint. MIT. → BigBang-v1 (endless-frontier): fine-tuned from Qwen3.6-35B-A3B via a self-evolving generator/critic synthetic-data loop. Lands aggregate performance between DeepSeek V4 Flash (284B) and V4 Pro (1.6T), at 35B. Apache 2.0. 🎬 Video → MiniMax-H3: 33B dense omni model, native stereo audio, up to 2K/15s. 3.6k+ likes already. → Minimax-H3-Turbo (lightx2v): Apache-2.0 turbo distillation of H3 for fast inference. → Lightricks LTX-2.5: image-to-video update, custom Gemma-4-12B text encoder, a markedly stronger distilled model. 🔊 Voice → NVIDIA NemotronLabs VoiceChat-11B: full-duplex speech-to-speech, ~450ms turn-taking, #2 on open VoiceBench, and the first open full-duplex model with live tool-calling mid-conversation. 🛡️ Safety → Mistral Shieldstral-1.0-3B: 3B multimodal guardrail that takes your safety policy as plain text instead of fixed categories. Beats LlamaGuard-4-12B and ShieldGemma-9B on HarmBench (99.4) and ToxicChat (84.1) at a fraction of the size. Apache 2.0.

Victor M

39,217 次观看 • 1 天前