LoRA used for this post:
Tifa Lockhart [Krea2] - Improved Likeness
Workflow:
Krea2 Uncensored - Image-to-Prompt + Prompt Enhancer + 4K Upscaler + CivitAI Metadata

Can you help me or give me some advice on how to fix this?

I don't know what I'm doing wrong. I've tried so many prompts, but for some reason, it's always cropping the image, with her head missing.
I could use some help with this one, please.

Here's the prompt: A painting in a contemporary impressionist style featuring a woman wearing a pink summer dress with lace trim, her body positioned amidst a landscape of wildflowers and butterflies. The artwork incorporates thick oil paint textures and watercolor-style washes across the scene. The color palette is composed of teal, blue, pink, purple, and golden yellow. A shallow depth of field creates a soft blur on the distant background elements. Layered transparency effects create highlights on the edges of the wildflowers. Digital texture overlays are visible throughout the composition to simulate an aged, luminous aesthetic. The main subject is the woman, full body, uncropped, on the right side of the image,

https://civitai.red/models/2699283/wan-scail-2-segmentation-control

Features:

• Image Analyzer
• LoRA Support
• Interpolate | Upscale | Color Match
• Color Correction
• Sage Attention
• Choose between 2 Samplers
• Background Remover (RMBG) to keep the background of the input video
• SCAIL-2 Identity Tracker
• Load an alternative audio file for the final video output
• Installation Paths & Download Links (in the Workflow)

Note:

If you have any questions, please first read the information in the red boxes within the workflow.

Additional options are available within the subgraph. Click the icon in the top-right corner of the Main Settings node to open it. Help is available by hovering your mouse cursor over the values inside the subgraph.

The workflow offers two samplers. Both deliver similar results. For testing purposes, the workflow allows you to easily switch between them. Personally, I use SCAIL-2 Infinity, This one seems to have fewer color shifts.

Wan SCAIL-2 is not perfect, but it delivers good results in most cases. If you encounter issues, setting a new seed or switching the sampler usually helps.

The scale of the scenes is extremely detailed. I tried replicating with zimage, krea, flux but didn't success even after trying 100s of prompt variations and different loras. Can we generate similar images locally.

how to maintain image consistency and preserve identity of the characters?

For contest my last post is here

https://www.reddit.com/r/StableDiffusion/s/fqdfn2RUQv

I put out an update on my socials about an upcoming release so I thought you guys may get a kick out of it given the response from the first release.

The model will be a fully playable text-to-keybed exportable to any DAW with rich prompting / metadata. Ill also put together a longer video on how I did it for other researchers to replicate (training strategies and the like)

Kyutai dropped Pocket TTS a bit ago and I've been sitting on it for a benchmark. Finally ran it head to head against the three CPU TTS models that have been getting attention (Kokoro 82M, Supertonic 3, Inflect-Nano-v1). 180 timed runs, 36 audio samples, objective MOS scores via UTMOS.

Short version: Pocket TTS is the slowest of the six configs I tested, and it's still the most interesting model in the field. Here's why.

What Pocket TTS actually is:

It's a ~100M param streaming language model that generates audio tokens over Kyutai's Mimi neural codec, then decodes to 24kHz. So instead of the usual acoustic-model-plus-vocoder setup, it's more like an autoregressive LLM but for audio. Token by token.

Two consequences of that architecture:

1. Latency is dead flat across text lengths. Its RTF is 0.69 to 0.76 whether you feed it 12 chars or 1712 chars. No fixed overhead to amortize. Compare with Kokoro PyTorch which climbs from 0.49 on tiny text to 0.83 on long text.
2. It streams. Which matters if you're building anything interactive.

Zero-shot voice cloning from 5 seconds. On CPU.

This is the headline feature. Hand it a 5-second reference clip of any voice and it speaks in that voice. Accent, timbre, pacing, even the mic character of the reference. No fine-tuning. No GPU. MIT license.

None of the other CPU-friendly models can do this at all. Kokoro and Inflect-Nano ship fixed voice sets, Supertonic same. If you want a user-supplied voice on a CPU box, Pocket TTS is currently in a category of one.

I ran the benchmark with Pocket TTS pinned to a preset voice (alba) for a fair speed/quality comparison. The cloning capability isn't in the numbers below because you can't benchmark it against models that don't have it.

Full results:

Hardware: Intel Xeon 8272CL, 4 cores, 16GB RAM, no GPU. UTMOS is utmos22_strong, an objective MOS predictor, so it's not just my ears this time.

The Inflect-Nano asterisk: UTMOS gave it 3.48 but to the ear it's buzzy and robotic. Known UTMOS failure mode where it over-rates small HiFi-GAN vocoders for being clean rather than natural. Also it has a hard ~15 second output cap I discovered mid-benchmark, so its RTF on long inputs is inflated.

Practical picks:

• Need voice cloning on CPU → Pocket TTS, no other option in this field
• Fixed voice, highest quality → Kokoro 82M
• Latency-critical with acceptable quality → Supertonic 3 at 5 steps
• Tiny footprint for short utterances → Inflect-Nano-v1, if you can live with the buzz and the 15s cap
• Prototyping only → Supertonic 3 at 2 steps

Two things worth calling out:

Pocket TTS install is genuinely painless. pip install pocket-tts, no CUDA build, no HuggingFace-repo-plus-sys.path wiring. Downloads weights on first load. The least fussy of the six.

The MIT license is a big deal. Kokoro is Apache 2.0 (also great). Supertonic is OpenRAIL-M with commercial restrictions. Pocket TTS being MIT means you can do essentially whatever with it commercially.

Repo with raw CSV (180 rows), all 36 WAV samples, and the benchmark script is in comments below 👇

If anyone here has run Pocket TTS voice cloning with a real reference clip, would love to hear how it holds up on different voice types (accented English, non-English, singing, etc). That's the next thing I want to test but I need a clean dataset.

https://pastebin.com/cNsTjJCL

# Krea 2 Regional Multi-LoRA — Multi-Character + Bounding-Box Layout Control

Put multiple character LoRAs in a single Krea 2 image, each one locked to its own bounding box — no bleed, no merged faces, no averaging. And it's not just for LoRAs: draw and describe boxes for objects, props, backgrounds, and extra subjects too, exactly like Ideogram 4's bounding-box prompting.

## What it does

Normal LoRA loading applies everywhere, so two character LoRAs smear into each other. This node injects each LoRA's effect only into the image tokens inside its box, at forward time — outside the box the effect is multiplied by zero. It's a hard spatial guarantee, not an attention-bias nudge the model can ignore.

Pair it with an Ideogram 4-style prompt builder and every box does double duty:

- Every box places its described content via Krea 2's Qwen3-VL text encoder (a table, a neon sign, a dog on the left — Krea 2 honors the placement).

- LoRA boxes additionally lock in a specific trained identity on top of that placement.

Sketch the whole scene as boxes, describe each one, and drop LoRAs into the boxes that need a precise face. Objects and characters, all placed by the same boxes.

## Features

- Unlimited regions — 2 characters or 10, add a row per box

- Region rows auto-sync to the boxes you draw (draw a box, a row appears)

- Hard per-region LoRA masking (activation-delta injection)

- Bounding-box layout control for non-LoRA elements too

- fp8-safe — never touches quantized model weights

- Runs at Krea 2's native CFG 1

## Requirements

- ComfyUI with Krea 2 support (recent build)

- Models: krea2_turbo_bf16 (UNet), qwen3vl_4b_bf16 (CLIP, type krea2), qwen_image_vae (VAE)

- Custom node: ComfyUI-Krea2-Regional-MultiLoRA (this workflow's node)

- ComfyUI-KJNodes (for the box-drawing prompt builder)

- Character LoRAs trained against Krea 2 (e.g. via ai-toolkit)

## How to use

1. Write your scene prompt in the box builder (setting/lighting/camera — not the characters).
2. Draw one box per element, in order. Rows appear automatically in the LoRA node.
3. Assign a LoRA to each character box; leave object/scenery boxes as plain descriptions.
4. Sampler: euler / bong_tangent / 8–12 steps / CFG 1.
5. Queue.

## Tips

- Keep character boxes from overlapping to avoid bleed.

- If seams show, raise seam_feather a touch (0.12–0.15).

- Row order must match box order (row 1 = first box drawn).

- Character LoRAs must be Krea 2-trained — FLUX/SDXL LoRAs load but look wrong.

- Recommended LORA Strength is 1.2 - 1.6

## Node + workflow (GitHub)

Full source, install instructions, and the example workflow:

https://github.com/CliffNodes/Krea2-Multi-Character-Lora-Node-w-bounding-box-By-Fedor

Examples Images using 2 generic loras on Civit

Workflow Link - https://pastebin.com/67m8kBF2