Is there any simple tutorials for regional promping , for only 2 characters (regions ) that will have different loras ? I am using the Comfyui

Abstract

Recent advances in 3D generative models have rapidly improved image-to-3D synthesis quality, enabling higher-resolution geometry and more realistic appearance. Yet fidelity, which measures pixel-level faithfulness of the generated 3D asset to the input image, still remains a central bottleneck. We argue this stems from an implicit 2D-3D correspondence issue: most 3D-native generators synthesize shape in canonical space and inject image cues via attention, leaving pixel-to-3D associations ambiguous. To tackle this issue, we draw inspiration from 3D reconstruction and propose Pixal3D, a pixel-aligned 3D generation paradigm for high-fidelity 3D asset creation from images. Instead of generating in a canonical pose, Pixal3D directly generates 3D in a pixel-aligned way, consistent with the input view. To enable this, we introduce a pixel back-projection conditioning scheme that explicitly lifts multi-scale image features into a 3D feature volume, establishing direct pixel-to-3D correspondence without ambiguity. We show that Pixal3D is not only scalable and capable of producing high-quality 3D assets, but also substantially improves fidelity, approaching the fidelity level of reconstruction. Furthermore, Pixal3D naturally extends to multi-view generation by aggregating back-projected feature volumes across views. Finally, we show pixel-aligned generation benefits scene synthesis, and present a modular pipeline that produces high-fidelity, object-separated 3D scenes from images. Pixal3D for the first time demonstrates 3D-native pixel-aligned generation at scale, and provides a new inspiring way towards high-fidelity 3D generation of object or scene from single or multi-view images. Project page: this https URL

Paper: https://arxiv.org/abs/2605.10922

Code: https://github.com/TencentARC/Pixal3D

Model: https://huggingface.co/TencentARC/Pixal3D

Project Page: https://ldyang694.github.io/projects/pixal3d/

Abstract

Flow-based generation in high-dimensional spaces is difficult because velocity prediction requires modeling high-dimensional noise, even when data has strong low-rank structure. We present Asymmetric Flow Modeling (AsymFlow), a rank-asymmetric velocity parameterization that restricts noise prediction to a low-rank subspace while keeping data prediction full-dimensional. From this asymmetric prediction, AsymFlow analytically recovers the full-dimensional velocity without changing the network architecture or training/sampling procedures. On ImageNet 256 256, AsymFlow achieves a leading 1.57 FID, outperforming prior DiT/JiT-like pixel diffusion models by a large margin. AsymFlow also provides the first-ever route for finetuning pretrained latent flow models into pixel-space models: aligning the low-rank pixel subspace to the latent space gives a seamless initialization that preserves the latent model's high-level semantics and structure, so finetuning mainly improves low-level mismatches rather than relearning pixel generation. We show that the pixel AsymFlow model finetuned from FLUX.2 klein 9B establishes a new state of the art for pixel-space text-to-image generation, beating its latent base on HPSv3, DPG-Bench, and GenEval while qualitatively showing substantially improved visual realism.

Paper: https://arxiv.org/abs/2605.12964

Code: https://github.com/Lakonik/LakonLab

Demo: https://huggingface.co/spaces/Lakonik/AsymFLUX.2-klein

Abstract

A LoRA that reproduces its training images perfectly is not a well-trained model. It is a memorization device. This article argues that generalization within the trained concept is the right quality criterion for small-dataset LoRA fine-tuning, and proposes a five-tell diagnostic framework — base capability degradation, concept narrowing, caption rigidity, entanglement leak, and visual signature reproduction — for identifying when a LoRA has crossed from learning into memorization. We then describe a chained training schedule we use in our own work, which rotates through dataset subsets before reintroducing the full combined dataset for a final consolidation phase. This methodology has roots in early Stable Diffusion 1.5-era practitioner trainers, where dataset switching was first-class in the UI; in modern trainers it must be reconstructed manually. We report a paired A/B run on Qwen-Image at two scales — a 244-image illustration style LoRA and a 27-image character LoRA — comparing chained training against a monotonic straight-baseline twin under matched conditions. Both runs produce competent results that pass the five-tell diagnostic without obvious failure on either side, and the hyperparameter recipe we use proves itself viable for Qwen-Image at these scales. We observe specific signals of chained-side flexibility advantages, but the gap is not dramatic enough at these dataset sizes to claim a general methodological advantage from this experiment alone. The natural next test is at smaller and harder dataset sizes where overfitting is more likely to surface; we identify that as the experimental program this position paper opens onto rather than closes.

Tutorial: https://youtu.be/fZgtkRcu4_k

HF Model: https://huggingface.co/ResembleAI/Dramabox

HF Space: https://huggingface.co/spaces/ResembleAI/Dramabox

Abstract

Wepresent Alice v1, a 14-billion parameter open-source video generation model that achieves state-of-the-art quality through consistency distillation with score regularization (rCM). Contrary to conventional distillation-which trades quality for speed-we demonstrate that rCM-based distillation can exceed teacher model quality. We attribute this to three mechanisms: (1) the score regularization term acts as a mode-seeking objective that concentrates probability mass on high-quality outputs rather than covering the full teacher distribution, (2) our targeted synthetic data pipeline with hard example mining provides training signal specifically for failure modes (physics, hands, faces) that the teacher handles inconsistently, and (3) consistency enforcement acts as implicit regularization, eliminating "lucky path" dependence on specific noise samples. Alice v1 generates 5-second 720p videos at 24fps in 4 denoising steps (~8 seconds on H100), a 7x speedup over the 50-step teacher while improving VBench score from 84.0 (Wan2.2) to 91.2. This surpasses both the teacher and closed-source systems including Veo3 (~90) and Sora2 (~88) on automated benchmarks, with competitive results in human preference studies. We release all model weights, training code, synthetic data pipelines, and evaluation scripts to advance open research in video generation.

Paper: https://arxiv.org/abs/2605.08115

Code: https://github.com/mirage-video/Alice

Model: https://huggingface.co/gomirageai/Alice-T2V-14B-MoE

Twitter: https://xcancel.com/gomirageai

HiDream-O1-Image is a natively unified image generative foundation model built on a Pixel-level Unified Transformer (UiT) without external VAEs or disjoint text encoders, which natively encodes raw pixels, text, and task-specific conditions in a single shared token space — supporting text-to-image, image editing, and subject-driven personalization at up to 2,048 × 2,048.

Technical Report: https://github.com/HiDream-ai/HiDream-O1-Image/blob/main/assets/HiDream-O1-Image.pdf

Code: https://github.com/HiDream-ai/HiDream-O1-Image

Blog:

HiDream-O1-Image-Dev 28 steps: https://huggingface.co/HiDream-ai/HiDream-O1-Image-Dev

HiDream-O1-Image 50 steps: https://huggingface.co/HiDream-ai/HiDream-O1-Image