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
https://redd.it/1shjyg8
@rStableDiffusion
Reddit
From the StableDiffusion community on Reddit: Anyone interested in this .. or did someone else make it already? LTX 2.3 Desktop…
Explore this post and more from the StableDiffusion community
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
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
Reddit
From the StableDiffusion community on Reddit
Explore this post and more from the StableDiffusion community
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
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
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
* "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
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
GitHub
GitHub - jd-opensource/JoyAI-Image: JoyAI-Image is the unified multimodal foundation model for image understanding, text-to-image…
JoyAI-Image is the unified multimodal foundation model for image understanding, text-to-image generation, and instruction-guided image editing. - jd-opensource/JoyAI-Image
Live AI video is doing too much lifting as a term. Here's a breakdown of what people actually mean.
The phrase is everywhere right now, but it's covering at least three meaningfully different things that keep getting conflated:
1. Faster post-production. The model still generates a discrete clip, it just does it quicker than it used to. Useful, but this is throughput improvement, not liveness.
2. Low-latency iteration. You can tweak and regenerate fast enough that it feels interactive. Still clip-based under the hood. Great UX, but the model still isn't responding to a continuous stream.
3. Actual real-time inference on a live stream. The model is continuously generating frames in response to incoming input, not producing clips at all. This is a fundamentally different architecture and a much harder problem.
The third category is where things get genuinely interesting from a technical standpoint. Decart is one of the few doing this for real, but because demos for all three can look superficially similar, the distinction gets lost. Vendors have every incentive to let it stay lost.Worth being precise about which one you're actually evaluating if you're building anything serious on top of this.
https://redd.it/1shogaz
@rStableDiffusion
The phrase is everywhere right now, but it's covering at least three meaningfully different things that keep getting conflated:
1. Faster post-production. The model still generates a discrete clip, it just does it quicker than it used to. Useful, but this is throughput improvement, not liveness.
2. Low-latency iteration. You can tweak and regenerate fast enough that it feels interactive. Still clip-based under the hood. Great UX, but the model still isn't responding to a continuous stream.
3. Actual real-time inference on a live stream. The model is continuously generating frames in response to incoming input, not producing clips at all. This is a fundamentally different architecture and a much harder problem.
The third category is where things get genuinely interesting from a technical standpoint. Decart is one of the few doing this for real, but because demos for all three can look superficially similar, the distinction gets lost. Vendors have every incentive to let it stay lost.Worth being precise about which one you're actually evaluating if you're building anything serious on top of this.
https://redd.it/1shogaz
@rStableDiffusion
Reddit
From the StableDiffusion community on Reddit
Explore this post and more from the StableDiffusion community
Qwen3.5-4B-Base-ZitGen-V1
Hi,
I'd like to share a fine-tuned LLM I've been working on. It's optimized for image-to-prompt and is only 4B parameters.
Model: https://huggingface.co/lolzinventor/Qwen3.5-4B-Base-ZitGen-V1
I thought some of you might find it interesting. It is an image captioning fine-tune optimized for Stable Diffusion prompt generation (i.e., image-to-prompt). Is there a comfy UI custom node that would allow this to be added to a cui workflow? i.e. LLM based captioning.
# What Makes This Unique
What makes this fine-tune unique is that the dataset (images + prompts) were generated by LLMs tasked with using the ComfyUI API to regenerate a target image.
# The Process
The process is as follows:
1. The target image and the last generated image (blank if it's the first step) are provided to an LLM with a comparison prompt.
2. The LLM outputs a detailed description of each image and the key differences between them.
3. The comparison results and the last generated prompt (empty if it's the first step) are provided to an LLM with an SD generation prompt.
4. The output prompt is sent to the ComfyUI API using Z-Image Turbo, and the output image is captured.
5. Repeat N times.
# Training Details
The system employed between 4 and 6 rounds of comparison and correction to generate each prompt-image pair. In theory, this process adapts the prompt to minimize the difference between the target image and the generated image, thereby tailoring the prompt to the specific SD model being used.
The prompts were then ranked and filtered to remove occasional LLM errors, such as residuals from the original prompt or undesirable artifacts (e.g., watermarks). Finally, the prompts and images were formatted into the ShareGPT dataset format and used to train Qwen 3.5 4B.
https://redd.it/1shvuxa
@rStableDiffusion
Hi,
I'd like to share a fine-tuned LLM I've been working on. It's optimized for image-to-prompt and is only 4B parameters.
Model: https://huggingface.co/lolzinventor/Qwen3.5-4B-Base-ZitGen-V1
I thought some of you might find it interesting. It is an image captioning fine-tune optimized for Stable Diffusion prompt generation (i.e., image-to-prompt). Is there a comfy UI custom node that would allow this to be added to a cui workflow? i.e. LLM based captioning.
# What Makes This Unique
What makes this fine-tune unique is that the dataset (images + prompts) were generated by LLMs tasked with using the ComfyUI API to regenerate a target image.
# The Process
The process is as follows:
1. The target image and the last generated image (blank if it's the first step) are provided to an LLM with a comparison prompt.
2. The LLM outputs a detailed description of each image and the key differences between them.
3. The comparison results and the last generated prompt (empty if it's the first step) are provided to an LLM with an SD generation prompt.
4. The output prompt is sent to the ComfyUI API using Z-Image Turbo, and the output image is captured.
5. Repeat N times.
# Training Details
The system employed between 4 and 6 rounds of comparison and correction to generate each prompt-image pair. In theory, this process adapts the prompt to minimize the difference between the target image and the generated image, thereby tailoring the prompt to the specific SD model being used.
The prompts were then ranked and filtered to remove occasional LLM errors, such as residuals from the original prompt or undesirable artifacts (e.g., watermarks). Finally, the prompts and images were formatted into the ShareGPT dataset format and used to train Qwen 3.5 4B.
https://redd.it/1shvuxa
@rStableDiffusion
huggingface.co
lolzinventor/Qwen3.5-4B-Base-ZitGen-V1 · Hugging Face
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
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LTX 2.3 - Image + Audio + Video ControlNet (IC-LoRA) to Video
https://redd.it/1shxv8n
@rStableDiffusion
https://redd.it/1shxv8n
@rStableDiffusion
Ace Step 1.5 XL ComfyUI automation workflow without lama for generating random tags using qwen, generate song and then give it a rating by using waveform analysis
The idea came to me after sorting trough a lot of Ace Step 1.5 XL outputs and trying to find best styles and tags for songs. Why not automate the generation process AND the review process, or at least make it easier. So as usual I used Qwen LM and Qwen VL (compared to something like olama these ones run directly in comfy and do not require a server) to randomize the tags on each run, but more importantly to try and rate the output. How ? By converting the audio output into a set of waveforms for 4 segments of the song that I feed into Qwen VL as an image and ask it to subjectively look at the waveform and give it feedback and rating, rating that is used then to also name the output file. Like this. I am not sure it works properly but the A+ rated songs were indeed better than B rated ones.
Workflow is here. Install the missing extensions and add the qwen models.
Here is part of the working flow, including output folder.
https://preview.redd.it/kpar4blijfug1.jpg?width=1280&format=pjpg&auto=webp&s=cf2b4e5491c8b237d29e9649d90d40c6172090a9
https://preview.redd.it/oxtxaf8kjfug1.jpg?width=1400&format=pjpg&auto=webp&s=643c100c7fe05bb5184551edd0b7a34d99476ddf
https://preview.redd.it/3old46smjfug1.jpg?width=1592&format=pjpg&auto=webp&s=07b366afe5ae259b11fbd86cf2332c56ab9192ea
https://redd.it/1shzm63
@rStableDiffusion
The idea came to me after sorting trough a lot of Ace Step 1.5 XL outputs and trying to find best styles and tags for songs. Why not automate the generation process AND the review process, or at least make it easier. So as usual I used Qwen LM and Qwen VL (compared to something like olama these ones run directly in comfy and do not require a server) to randomize the tags on each run, but more importantly to try and rate the output. How ? By converting the audio output into a set of waveforms for 4 segments of the song that I feed into Qwen VL as an image and ask it to subjectively look at the waveform and give it feedback and rating, rating that is used then to also name the output file. Like this. I am not sure it works properly but the A+ rated songs were indeed better than B rated ones.
Workflow is here. Install the missing extensions and add the qwen models.
Here is part of the working flow, including output folder.
https://preview.redd.it/kpar4blijfug1.jpg?width=1280&format=pjpg&auto=webp&s=cf2b4e5491c8b237d29e9649d90d40c6172090a9
https://preview.redd.it/oxtxaf8kjfug1.jpg?width=1400&format=pjpg&auto=webp&s=643c100c7fe05bb5184551edd0b7a34d99476ddf
https://preview.redd.it/3old46smjfug1.jpg?width=1592&format=pjpg&auto=webp&s=07b366afe5ae259b11fbd86cf2332c56ab9192ea
https://redd.it/1shzm63
@rStableDiffusion
Dystalgia - Aurel Manea Photography (Aurel Manega)
Ace Step 1.5 XL ComfyUI workflow for generating random tags, generate song and then give it a rating by using waveform analysis…
The idea came to me after sorting trough a lot of Ace Step 1.5 XL outputs and trying to find best styles and tags for songs. Why not automate the […]
Just installed ForgeNeo and I'm facing this issue *failed to recognize model type*
https://redd.it/1si419g
@rStableDiffusion
https://redd.it/1si419g
@rStableDiffusion
[Release] ComfyUI Image Conveyor — sequential drag-and-drop image queue node
https://redd.it/1sibmrf
@rStableDiffusion
https://redd.it/1sibmrf
@rStableDiffusion
New nodes to handle/visualize bboxes
Hello community, I'd like to introduce my ComfyUI nodes I recently created, which I hope you find useful. They are designed to work with BBoxes coming from face/pose detectors, but not only that. I tried my best but didn't find any custom nodes that allow selecting particular bboxes (per frame) during processing videos with multiple persons present on the video. The thing is - face detector perfectly detects bboxes (BoundingBox) of people's faces, but, when you want to use it for Wan 2.2. Animation or other purposes, there is no way to choose particular person on the video to crop their face for animation, when multiple characters present on the video/image. Face/Pose detectors do their job just fine, but further processing of bboxes they produce jump from one person to another sometimes, causing inconsistency. My nodes allow to pick particular bbox per frame, in order to crop their faces with precision for Wan2.2 animation, when multiple persons are present in the frame. Hence, you can choose particular face(bbox) per frame.
I haven't found any nodes that allow that so I created these for this purpose.
Please let me know if they would be helpful for your creations.
https://registry.comfy.org/publishers/masternc80/nodes/bboxnodes
Description of the nodes is in repository:
https://github.com/masternc80/ComfyUI-BBoxNodes
https://redd.it/1sidcv5
@rStableDiffusion
Hello community, I'd like to introduce my ComfyUI nodes I recently created, which I hope you find useful. They are designed to work with BBoxes coming from face/pose detectors, but not only that. I tried my best but didn't find any custom nodes that allow selecting particular bboxes (per frame) during processing videos with multiple persons present on the video. The thing is - face detector perfectly detects bboxes (BoundingBox) of people's faces, but, when you want to use it for Wan 2.2. Animation or other purposes, there is no way to choose particular person on the video to crop their face for animation, when multiple characters present on the video/image. Face/Pose detectors do their job just fine, but further processing of bboxes they produce jump from one person to another sometimes, causing inconsistency. My nodes allow to pick particular bbox per frame, in order to crop their faces with precision for Wan2.2 animation, when multiple persons are present in the frame. Hence, you can choose particular face(bbox) per frame.
I haven't found any nodes that allow that so I created these for this purpose.
Please let me know if they would be helpful for your creations.
https://registry.comfy.org/publishers/masternc80/nodes/bboxnodes
Description of the nodes is in repository:
https://github.com/masternc80/ComfyUI-BBoxNodes
https://redd.it/1sidcv5
@rStableDiffusion
registry.comfy.org
ComfyUI Registry
Discover and install ComfyUI custom nodes.
ComfyUI Tutorial: Create Mind Blowing Video With LTX 2.3 Transition LORA
https://youtu.be/egQb_iHc05Q
https://redd.it/1sidsdf
@rStableDiffusion
https://youtu.be/egQb_iHc05Q
https://redd.it/1sidsdf
@rStableDiffusion
YouTube
ComfyUI Tutorial: Create Mind Blowing Video With LTX 2.3 Transition LORA #comfyui #ltx2.3
In this tutorial, I show you how to create stunning ai transition videos with the new LTX2.3 TRANSITION LORA inside ComfyUI — all running on a low VRAM setup (works even with 6GB GPUs!). You’ll learn how to build a complete workflow that combines image generation…
Trying to inpaint using Z-image Turbo BF16; what am I doing wrong?
https://preview.redd.it/3krmmy345jug1.png?width=1787&format=png&auto=webp&s=359dfa4e2515bd33e40090f986e4a597a00d06d6
Fairly new to the SD scene. I've been trying to do inpainting for an hour or so with no luck. The model, CLIP and VAE are in the screenshot. The output image always looks incredibly similar to the input image, as if I had zero denoise. the prompt also seems to do nothing. Here, I tried to make LeBron scream by masking just his face. The node connections seem to be all correct too. Is there another explanation? Sampler? The model itself?
https://redd.it/1siefug
@rStableDiffusion
https://preview.redd.it/3krmmy345jug1.png?width=1787&format=png&auto=webp&s=359dfa4e2515bd33e40090f986e4a597a00d06d6
Fairly new to the SD scene. I've been trying to do inpainting for an hour or so with no luck. The model, CLIP and VAE are in the screenshot. The output image always looks incredibly similar to the input image, as if I had zero denoise. the prompt also seems to do nothing. Here, I tried to make LeBron scream by masking just his face. The node connections seem to be all correct too. Is there another explanation? Sampler? The model itself?
https://redd.it/1siefug
@rStableDiffusion
