New changes at CivitAI
https://civitai.com/articles/28369/two-front-doors-civitaicom-civitaired-and-whats-next

https://redd.it/1sh7imc
@rStableDiffusion

Are there any simple paths to local image generation on Linux?

I've had no luck so far. To note, I have some general familiarity with the command line.

That said, I've tried ComfyUI, Foooocus, SwarmUI...I've had no luck getting any of those to even successfully install. Missing dependency that, can't find that, can't install that. All these wgets and git clones and 'throw it in python's seem to end badly for me.

I have managed to download and launch Invoke AI successfully. But I haven't had any luck generating an actual image: I got word of ROCm issues from the error messages, and it seems Fedora messes with that. Trying to fix that up still got me nowhere.

\--------

Is there anything a bit simpler to use, just to get started? I run LM Studio on this computer just fine, and as it stands I'm hoping they'll one day branch out into image / video gen. I don't care if it can barely do a smiley face, I just want it to be local, and FOSS.


Bonus Info:
GPU | Radeon 7600
CPU | Ryzen 5 7600
RAM | 16GB DDR5
OS | Fedora 43, Plasma 6.6


If you have ideas, let me know. Thank you for your time.

https://redd.it/1shagk4
@rStableDiffusion

Bad news on Happy Horse from twitter
https://redd.it/1shcgox
@rStableDiffusion

Which video model learns face likeness best when training LoRA?

Hey, I’m trying to train LoRAs for real human likeness and was wondering which video model currently does the best job at learning and preserving identity.

I’ve tried a bit with LTX and Wan, but still not sure which one is actually better for likeness. Would love to hear what people are getting the best results with right now

https://redd.it/1shbfra
@rStableDiffusion

ACE-Step 1.5 XL Base — BF16 version (converted from FP32)

I converted the ACE-Step 1.5 XL Base model from FP32 to BF16. The original weights were \~18.8 GB in FP32, this version is \~7.5 GB — same quality, lower VRAM usage.

The Base model is the go-to starting point for fine-tuning (LoRA, etc.) — if you want to train your own style, this is the one to use. A great tool for that is Side Step.

🤗 https://huggingface.co/marcorez8/acestep-v15-xl-base-bf16

I also converted the XL Turbo variant yesterday: Reddit post | Model

https://redd.it/1shfihr
@rStableDiffusion

HappyHorse is from Alibaba ATH, not Grok / Veo 3.2 / Wan 2.7 / Seedance 2

I finally found what looks like the official clarification.

According to the verified HappyHorse twitter account, HappyHorse is a product currently in internal testing under Alibaba's ATH innovation division. It also says the product is not officially launched yet, and that the so-called "official websites" circulating online are fake.

https://preview.redd.it/s0yc372pjbug1.png?width=760&format=png&auto=webp&s=77cb530ff67fbb68537c0a7417fa782b88c3981a

https://preview.redd.it/zlpry4m0jbug1.png?width=1337&format=png&auto=webp&s=4756801907a9adcbcad4dc8c3c859615fcc6a208

https://redd.it/1shfzip
@rStableDiffusion

VoxCPM TTS model + LoRa training abilities right in Comfy
https://redd.it/1shfwcg
@rStableDiffusion

Happy Horse deceiving practices

Kinda lame that Happy Horse was pushed as open weights early on, got people interested, and now it’s apparently becoming closed-source API only, they knew what they were doing.

Way less people are interested in closed video models but make a promise it’s open weights and you get way more traction… then have it closed.

A paid, censored, all you data stolen, closed video model is way less useful for a lot of us. The whole appeal was being able to run it ourselves, experiment freely, fine-tune, make loras, and build on top of it without being stuck behind someone else’s rules and pricing.

Feels like they used the open-weights angle to build hype and traction, then pulled the ladder up and i relly believe that. Also saying that the sources stating it’s open weights are fake also seem super fishy.

Like at this point alibaba just uses the name they built by releasing super good local models to promote closed models (that imo are not even close to other closed models)

https://redd.it/1shi6ca
@rStableDiffusion

I can finally run LTX Desktop after the last update.

https://redd.it/1shizzu
@rStableDiffusion

LTX 2.3 Lip Sync Music Clip -- Drake - Toosie Slide

https://redd.it/1shjibe
@rStableDiffusion

Anyone interested in this .. or did someone else make it already? LTX 2.3 Desktop - Lora injector + my own prompt tool..

https://redd.it/1shjyg8
@rStableDiffusion

ComfyUI - disappearing workflows

gentlemen, what am I doing wrong? For some time now, whenever I launch COMFYUI, there is always only one project open, even though I had multiple tabs open when closing it. And this is not a problem, but sometimes for some reason unclosed tabs overwrite one another...

I made a beautiful SDXL table workflow and today there is an old workflow saved on it, which yesterday I turned on for literally only 5 seconds to copy one element... What am I doing wrong? How to protect yourself against uncontrolled overwriting?

https://redd.it/1shnqi4
@rStableDiffusion

After ~400 Z-Image Turbo gens I finally figured out why everyone's portraits look plastic

Been using Z-Image Turbo pretty heavily since it dropped and wanted to dump some notes here because I kept seeing the same complaints I had on day one and nobody was really answering them properly.

The thing I kept running into: every portrait looked like a skincare ad. Glossy skin, symmetrical face, that weird "influencer default" look. I tried every SDXL trick I knew. "Average person", "realistic", "not a model", "amateur photo", "candid". Basically nothing moved the needle. I was ready to write the model off as another Flux-lite.

Then I saw 90hex's post here a while back about using actual photography vocabulary and something clicked. I'd been prompting Z-Image like it was SDXL when the encoder is clearly trained on way more specific stuff. Once I started naming actual cameras and film stocks instead of emotional modifiers, the plastic problem basically evaporated.

A few things that genuinely surprised me:

1. "Point-and-shoot film camera" is the single highest-leverage phrase I've found. Drops the model out of beauty-default mode faster than any combination of "realistic/candid/amateur" ever did. "35mm film camera" works too. "iPhone snapshot with handheld imperfection" works. "Disposable camera" works. The common thread is naming a physical piece of gear with a real visual fingerprint.
2. Words like "masterpiece, 8k, etc" do almost nothing. I ran A/B tests on 20 prompts with and without the usual quality spam and the outputs were basically indistinguishable. The S3-DiT encoder clearly wasn't trained on that vocabulary the way SD1.5 was. Replace that whole block with one camera + one film stock and you get way more signal per token.
3. Negative prompts are legitimately dead at cfg 0. I know the docs say this but I didn't fully believe it until I tested. Putting "blurry, ugly, deformed, bad anatomy" in the negative field does absolutely nothing at the default cfg. If you bump cfg to 1.2-2.0 in Comfy some effect comes back but Turbo starts overcooking and the speed advantage evaporates. Just write constraints as presence instead. "Clean studio background, sharp focus, plain seamless backdrop" is way more effective than any negative prompt I tried.
4. The bracket trick is the best-kept secret in this community. 90hex mentioned it in passing and I don't think people realize how powerful it is for building character consistency without training a LoRA. Wrap alternatives in {this|that|the other} inside one prompt, batch 32, and you get an entire photoshoot of the same person across different cameras, lighting, poses, and moods. I've been using it to build reference libraries for characters I want to stay consistent across a short series. Zero training required. It's absurd.
5. Attention cap is real. Past about 75-100 effective tokens the model starts to drift. If you're writing 400-word prompts (I was) you're actively hurting yourself. 3-5 strong concepts, subject first, any quoted text second. The rest is gravy.
6. Prefix/suffix style presets are a cheat code. Saw DrStalker's 70-styles post a while back and started building my own table. Same base scene wrapped in different style prefix/suffix pairs gives you a pile of completely different looks with zero rewriting. Cinematic photo, medium format, analog film, Ansel Adams landscape, neon noir, dieselpunk, Ghibli-like, Moebius-like, pixel art, stained glass. Game changer for iteration speed.

The prompt that finally unstuck me:

>

First time I got an output that looked like an actual person I'd see on the street and not a magazine cover. The trick is stacking "realistic ordinary everyday" (which does nothing alone) with a specific equipment spec (which does everything). The equipment word is the anchor. The ordinary words only work once the anchor is there.

A few more things I've been testing that seem to work:

"Shot on Kodak Portra 400" for warm skin tones that don't look airbrushed
"Ilford HP5 black and white" for actual film B&W grain

After ~400 Z-Image Turbo gens I finally figured out why everyone's portraits look plastic

Been using Z-Image Turbo pretty heavily since it dropped and wanted to dump some notes here because I kept seeing the same complaints I had on day one and nobody was really answering them properly.

The thing I kept running into: every portrait looked like a skincare ad. Glossy skin, symmetrical face, that weird "influencer default" look. I tried every SDXL trick I knew. "Average person", "realistic", "not a model", "amateur photo", "candid". Basically nothing moved the needle. I was ready to write the model off as another Flux-lite.

Then I saw 90hex's post here a while back about using actual photography vocabulary and something clicked. I'd been prompting Z-Image like it was SDXL when the encoder is clearly trained on way more specific stuff. Once I started naming actual cameras and film stocks instead of emotional modifiers, the plastic problem basically evaporated.

**A few things that genuinely surprised me:**

1. **"Point-and-shoot film camera" is the single highest-leverage phrase I've found.** Drops the model out of beauty-default mode faster than any combination of "realistic/candid/amateur" ever did. "35mm film camera" works too. "iPhone snapshot with handheld imperfection" works. "Disposable camera" works. The common thread is naming a physical piece of gear with a real visual fingerprint.
2. **Words like "masterpiece, 8k, etc" do almost nothing.** I ran A/B tests on 20 prompts with and without the usual quality spam and the outputs were basically indistinguishable. The S3-DiT encoder clearly wasn't trained on that vocabulary the way SD1.5 was. Replace that whole block with one camera + one film stock and you get way more signal per token.
3. **Negative prompts are legitimately dead at cfg 0.** I know the docs say this but I didn't fully believe it until I tested. Putting "blurry, ugly, deformed, bad anatomy" in the negative field does absolutely nothing at the default cfg. If you bump cfg to 1.2-2.0 in Comfy some effect comes back but Turbo starts overcooking and the speed advantage evaporates. Just write constraints as presence instead. "Clean studio background, sharp focus, plain seamless backdrop" is way more effective than any negative prompt I tried.
4. **The bracket trick is the best-kept secret in this community.** 90hex mentioned it in passing and I don't think people realize how powerful it is for building character consistency without training a LoRA. Wrap alternatives in {this|that|the other} inside one prompt, batch 32, and you get an entire photoshoot of the same person across different cameras, lighting, poses, and moods. I've been using it to build reference libraries for characters I want to stay consistent across a short series. Zero training required. It's absurd.
5. **Attention cap is real.** Past about 75-100 effective tokens the model starts to drift. If you're writing 400-word prompts (I was) you're actively hurting yourself. 3-5 strong concepts, subject first, any quoted text second. The rest is gravy.
6. **Prefix/suffix style presets are a cheat code.** Saw DrStalker's 70-styles post a while back and started building my own table. Same base scene wrapped in different style prefix/suffix pairs gives you a pile of completely different looks with zero rewriting. Cinematic photo, medium format, analog film, Ansel Adams landscape, neon noir, dieselpunk, Ghibli-like, Moebius-like, pixel art, stained glass. Game changer for iteration speed.

**The prompt that finally unstuck me:**

>

First time I got an output that looked like an actual person I'd see on the street and not a magazine cover. The trick is stacking "realistic ordinary everyday" (which does nothing alone) with a specific equipment spec (which does everything). The equipment word is the anchor. The ordinary words only work once the anchor is there.

**A few more things I've been testing that seem to work:**

* "Shot on Kodak Portra 400" for warm skin tones that don't look airbrushed
* "Ilford HP5 black and white" for actual film B&W grain

that looks better than any "monochrome high contrast" prompt I tried
* "Cinestill 800T" for night scenes with that halation glow around lights
* Adding "slightly asymmetrical features" or "faint laugh lines" to portraits kills the symmetry default
* "On-board flash falloff" gives you that candid snapshot look with the harsh foreground light and falling-off background

**Stuff I'm still figuring out:**

* LoRA weights feel different than SDXL. Anything above 0.85 tends to overcook. Anyone else seeing this?
* Text rendering is good but seems to tank if the prompt is too long. I think the model budgets attention between scene description and typography and long prompts starve the text encoder. Curious if others have tested this.
* Bilingual prompts (EN + CN in the same prompt) sometimes produce better English typography than pure EN prompts. No idea why. Might be a training data quirk.
* Hands are genuinely fixed but feet still look weird like 30% of the time. Haven't found a reliable fix yet.

https://preview.redd.it/zrkeynx1ndug1.jpg?width=1920&format=pjpg&auto=webp&s=6ca058e66cc4c7e174f2f07ce5f6499cb15694d7

https://preview.redd.it/v557bkw7pdug1.jpg?width=1920&format=pjpg&auto=webp&s=250b92caf4634f2e40cc588728bcfdb96ec1ad2d

https://preview.redd.it/jhtxz9ecpdug1.jpg?width=1920&format=pjpg&auto=webp&s=3ba407eb55529659d95e8aca043076eea025ce3f

https://preview.redd.it/4ezi3rmhpdug1.jpg?width=1920&format=pjpg&auto=webp&s=5df585e2ced71d89e5b826941155e62a046a7f1e

https://preview.redd.it/ymibzw0lpdug1.jpg?width=1920&format=pjpg&auto=webp&s=13a51528f6849298b25e69054e3335eb65bdf741

https://preview.redd.it/c740vz9ppdug1.jpg?width=1920&format=pjpg&auto=webp&s=078a0239cc2a424c27a9b75c5a35881310b22b54



https://redd.it/1shpbbb
@rStableDiffusion

JoyAI-Image-Edit now has ComfyUI support

https://github.com/jd-opensource/JoyAI-Image

Its very good at spatial awareness.
Would be interesting to do a more detailed comparison with qwen image edit.

https://redd.it/1show8s
@rStableDiffusion