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Seedance 2 vs Seedance 2 Mini - a full comparison thread 🧵 I ran both models through 9 very different scenes. Sword & sandal epics, vampire seduction, kung fu, futuristic bars, J-horror and more. Left is Seedance 2. Right is Mini. How does the smaller model hold up? Judge...

15,689 Aufrufe • vor 3 Monaten •via X (Twitter)

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Wait? Is this Seedance 2 Mini? The mystery Seedance 2 model IS HERE! No wait this time. About 55% of the cost of the 2.0 Fast is about 75% Quality wise they say to expect Fast level output and that seems fair? Thoughts? Left is Seedance 2 Mini Right is Seedance 2.0 Full Prompt Image To Video Use the uploaded images as identity, costume, and lighting reference. Same naturally confident charismatic age 25 woman throughout performing a sold-out arena concert. Real concert documentary feel, handheld camera energy, practical stage lighting only. gritty, real, fun, clean editing, english language high energy modern concert performance 0: 00–0:01 — Open tight on her face mid-performance, eyes bright, genuine laugh breaking through a lyric, hair catching stage light. Crowd noise swells under faint mic hum. 0: 01–0:06 — She steps forward to the edge of the stage, singing into the mic with playful energy, she frowns, sad "Wait! Is this Seedance 2 Mini?" and then smiles and laughs free hand reaching out toward the crowd, fingers brushing toward raised phones and hands. The crowd repeats the lyric back to her as she laughs. Warm stage lights and lens flares in the background, slight handheld camera sway following her movement. 0: 06–0:09 — Wide shot from a low angle near the crowd barrier — she's framed against haze and colored stage lights, band visible in soft focus behind her (guitarist stage left, keyboard player stage right). She throws her free arm out wide, laughing between lines, hair whipping slightly as she turns. 0: 10–0:12 — Cut to a closer three-quarter shot — she playfully points the mic toward the crowd for a sing-along moment, grinning, eyes scanning the audience with real warmth and connection. Visible sweat sheen, natural skin texture, slight camera shake as if shot from the pit. — Final beat: close-up, she looks directly into the lens with a bright, genuine smile Camera: handheld documentary-style movement throughout, natural focus pulls, no perfectly smooth gimbal shots, slight shake and imperfection consistent with a real concert film crew. Practical stage lighting only — no CGI glow, no artificial bloom, no glossy skin, visible natural texture and sweat under hot lights. Audio: live arena ambience — crowd roar, cheering, phone-camera flash pops, her voice carrying clearly over a full live band mix (electric guitar, keys, drums, bass). No studio polish — natural live-mix dynamics, slight room reverb. No on-screen text, no logos, no subtitles, no watermarks, no identity drift, no extra performers beyond the band already in frame, no slideshow stillness, no overly smooth or glossy rendering, no jarring cuts, no akward moments, no jerky moves, no duplicates, no clones

Brent Lynch

12,605 Aufrufe • vor 3 Monaten

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 | AI Ads Guy

11,288 Aufrufe • vor 3 Monaten

Batch Normalization by hand ✍️ ~ 7 steps walkthrough below Batch normalization is common practice for improving training and achieving faster convergence. It sounds simple. But it is often misunderstood. 🤔 Does batch normalization involve trainable parameters, tunable hyper-parameters, or both? 🤔 Is batch normalization applied to inputs, features, weights, biases, or outputs? 🤔 How is batch normalization different from layer normalization? So I drew and calculated one entirely by hand. Goal: normalize a mini-batch of 4 examples to mean 0 and variance 1, then let the network scale it back. = 1. Given = A mini-batch of 4 training examples, each with 3 features. = 2. Linear layer = Let us multiply by the weights and add the biases. Batch norm sits after this, which answers the second question: what gets normalized is features, not inputs, weights or biases. = 3. ReLU = We apply the activation, and -2 becomes 0. Negative values are suppressed before any statistic is taken. = 4. Batch statistics = Let us compute the sum, mean, variance and standard deviation, one row at a time. A row is a feature and the four columns are the four examples, so every number here measures one feature against the rest of the batch. That is the "batch" in batch normalization, and it is exactly what layer normalization does not do. The statistics are rounded to whole numbers, which is what keeps the rest of the page doable in pen. = 5. Shift to mean 0 = We subtract the mean, in green. The four values in each feature now average to zero. = 6. Scale to variance 1 = Let us divide by the standard deviation, in orange. Each feature now has variance one, whatever scale it arrived at. = 7. Scale and shift = We multiply by a linear transformation and pass the result on. The diagonal and the last column are trainable, so having just forced every feature to mean 0 and variance 1, we hand the network the means to undo it. The outputs: Mean of each feature = [2, 1, 2] Std dev of each feature = [1, 1, 2] To the next layer = [2, -2, 2, 0], [-3, 3, 6, -3], [2, 0, 1, 2] The answers: 🤔 Both. The scale and shift are trainable, the statistics are not. Epsilon and the momentum on the running statistics are the hyper-parameters, and one mini-batch by hand needs neither. 🤔 Features, after the linear layer, not inputs, weights or biases. 🤔 Batch norm measures across the batch, one feature at a time. Layer norm measures across the features, one example at a time. 💾 Save this post!

Tom Yeh

20,848 Aufrufe • vor 2 Monaten