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Cherry Blossom Mind + With Krea , I’m training visual models that echo and unfold like petals in dialogue. Each output feels like a fragment of a larger poetic system.

Karoline Georges

7,893 subscribers

26,246 次观看 • 1 年前 •via X (Twitter)

艺术科学技术

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Wow… the more I use #klingai 2.6, the more it blows my mind 🤯🎥 Every output feels like a visual level-up. It’s seriously next level.

Wow… the more I use #klingai 2.6, the more it blows my mind 🤯🎥 Every output feels like a visual level-up. It’s seriously next level.

Pierrick Chevallier | IA

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For 3.11Labs, we created a visual identity that reflects the studio’s balance of structure and experimentation. The system supports their AI creations with a distinct visual style while revealing more about the technology behind each piece.

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Holographik

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my issue with Heated Rivalry is that every second of dialogue feels like this. i actually do find it endearing and fun but really can only endure 1-2 episodes of it at a time

my issue with Heated Rivalry is that every second of dialogue feels like this. i actually do find it endearing and fun but really can only endure 1-2 episodes of it at a time

satchel maloney

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Kling 3.0 is now available in ComfyUI via Partner Nodes! You can now generate multiple shots in a single run with precise duration control for each. A new level of consistency, multilingual dialogue, and up to 15 seconds of cinematic output. Fully composable with ComfyUI’s ecosystem of nodes and extensions!

Kling 3.0 is now available in ComfyUI via Partner Nodes! You can now generate multiple shots in a single run with precise duration control for each. A new level of consistency, multilingual dialogue, and up to 15 seconds of cinematic output. Fully composable with ComfyUI’s ecosystem of nodes and extensions!

ComfyUI

14,650 次观看 • 5 个月前

ghost already sounds so good in the mashup, can’t wait to hear the original song but the lyrics though 🥹 “i feel like i’m a ghost, i’m out of my mind i don’t know why i’m lost in space and time i feel like i’m a ghost, i’ll say that i’m fine i’m fine, i’m done”

ghost already sounds so good in the mashup, can’t wait to hear the original song but the lyrics though 🥹 “i feel like i’m a ghost, i’m out of my mind i don’t know why i’m lost in space and time i feel like i’m a ghost, i’ll say that i’m fine i’m fine, i’m done”

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"Do not miss the once in a lifetime chance to see the largest collection of 329 ‘Taihaku’ cherry blossom trees in the entire world all in their two week blossom window this spring at Alnwick Garden. Such a rare sight and no where else in the world has this beauty at such a scale but right here in Northumberland you can visit the cherry tree orchard and take it all in. The view is breathtaking and something you have to see with your own eyes. In 1926 Captain Collingwood Ingram was invited to Japan to give a lecture to the Sakurakai (cherry society). Whilst there he was shown a picture in an 18th Century book of a large, white cherry blossom that had become extinct. He recognised the same blossom on a cherry tree which had been imported to a Sussex garden in 1899 and was able to take cuttings from it and reintroduce the lost Taihaku to Japan in 1932.All of the Taihaku in cultivation today are off-spring of that cherry tree in Sussex!" -Emma.

"Do not miss the once in a lifetime chance to see the largest collection of 329 ‘Taihaku’ cherry blossom trees in the entire world all in their two week blossom window this spring at Alnwick Garden. Such a rare sight and no where else in the world has this beauty at such a scale but right here in Northumberland you can visit the cherry tree orchard and take it all in. The view is breathtaking and something you have to see with your own eyes. In 1926 Captain Collingwood Ingram was invited to Japan to give a lecture to the Sakurakai (cherry society). Whilst there he was shown a picture in an 18th Century book of a large, white cherry blossom that had become extinct. He recognised the same blossom on a cherry tree which had been imported to a Sussex garden in 1899 and was able to take cuttings from it and reintroduce the lost Taihaku to Japan in 1932.All of the Taihaku in cultivation today are off-spring of that cherry tree in Sussex!" -Emma.

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it's crazy what a 1.5B model can do these days! "VibeThinker-1.5B is a 1.5-billion parameter dense language model. With a total training cost of only $7,800 USD, it achieves reasoning performance comparable to larger models like GPT OSS-20B Medium." runs perfectly on device!

it's crazy what a 1.5B model can do these days! "VibeThinker-1.5B is a 1.5-billion parameter dense language model. With a total training cost of only $7,800 USD, it achieves reasoning performance comparable to larger models like GPT OSS-20B Medium." runs perfectly on device!

Maziyar PANAHI

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white mem go through life knowing nothing … mind U it’s a shows he’s on and is a producer for. Sydney has more than enough “formal” training and this feels like a full on aggression

white mem go through life knowing nothing … mind U it’s a shows he’s on and is a producer for. Sydney has more than enough “formal” training and this feels like a full on aggression

ayo edebiri stan🫐

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im ctfu not a moa wiggling kai’s favorite snack in front of him like they’re calling a puppy😭 and the cherry on top is that he’s actually reacting like a puppy

im ctfu not a moa wiggling kai’s favorite snack in front of him like they’re calling a puppy😭 and the cherry on top is that he’s actually reacting like a puppy

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Most video tools can generate clips. Very few can maintain identity. That has been the real bottleneck in AI video creation. Kling O1 changes that. For the first time, creators can carry a character, style, and visual language across scenes without constant fixes. You can reference past clips, assets, or images and the output stays consistently on-model. No visual drift. No rework loops. No “this doesn’t look like the last shot” moments. It feels less like prompting a tool and more like working with a creative collaborator that remembers context. The impact is practical, not theoretical: → Faster production cycles → Lower iteration costs → Noticeably higher output quality This is what mature AI tooling looks like. Not louder features. Not bigger claims. Just reliability where it actually matters. Consistency is no longer the problem.

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Playing with scale in Simulon is so magical and a lot of fun. It feels like being a kid again!

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Both the face and the mind have become like that of a buffalo. #Trump

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Most recent diffusion language model research (that I’ve seen) seems to be using masking as the noising process. It looks like, however, most closed-source models (Google Gemini Diffusion and possibly Inception Labs’ Mercury) use a different noising process, where instead of masking tokens, they replace them with different tokens (either with a random token or a semantically similar token). I wondered how they were getting such high throughput with the latter noising process, since I believed that optimizing inference with KVCache approximation would be more difficult (for various reasons). I visualized this noising process with tiny-diffusion and compared it to normal unmasking, and was very surprised to see how fast the generation “settles” into a reasonable output, and then only slightly refines afterwards, requiring much fewer steps in total. Unmasking (where tokens are never remasked, the typical implementation) is inherently limited in generation speed by the fact that an increase in tokens decoded per step leads to more errors due to the mismatch between individual and marginal token probability distributions we sample from. The token replacement noising process seems to have a much different set of characteristics. Because we sample each token per step, every token makes “progress” towards the final output each iteration (in addition to potentially giving other tokens more information in future steps). Generally, masking has outperformed other noising processes, which is probably why most research focused on it (using smaller models). But the paper referred to in the retweet shows that random replacement as a noising process may scale better as model size increases. Big labs might have noticed these results much earlier (due to having drastically more training resources and being able to test larger models), which may explain the discrepancy in the choice of noising process. I’m gonna test this with larger models, since tiny-diffusion only has 10M parameters.

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I’ve been obsessively training a Wan 2.2 LoRA for morphing transitions for a couple of months and it feels like I’m getting closer now It’s been deceptively hard training something so conceptual, since you don’t want the model to learn any specific movements or design elements

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