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Research#llm📝 BlogAnalyzed: Jan 4, 2026 05:54

Blurry Results with Bigasp Model

Published:Jan 4, 2026 05:00
1 min read
r/StableDiffusion

Analysis

The article describes a user's problem with generating images using the Bigasp model in Stable Diffusion, resulting in blurry outputs. The user is seeking help with settings or potential errors in their workflow. The provided information includes the model used (bigASP v2.5), a LoRA (Hyper-SDXL-8steps-CFG-lora.safetensors), and a VAE (sdxl_vae.safetensors). The article is a forum post from r/StableDiffusion.
Reference

“I am working on building my first workflow following gemini prompts but i only end up with very blurry results. Can anyone help with the settings or anything i did wrong?”

Permalinkr/StableDiffusion
Paper#Diffusion Models, Image Generation, AI🔬 ResearchAnalyzed: Jan 3, 2026 15:49

Internal Guidance for Diffusion Transformers

Published:Dec 30, 2025 12:16
1 min read
ArXiv

Analysis

This paper introduces a novel guidance strategy, Internal Guidance (IG), for diffusion models to improve image generation quality. It addresses the limitations of existing guidance methods like Classifier-Free Guidance (CFG) and methods relying on degraded versions of the model. The proposed IG method uses auxiliary supervision during training and extrapolates intermediate layer outputs during sampling. The results show significant improvements in both training efficiency and generation quality, achieving state-of-the-art FID scores on ImageNet 256x256, especially when combined with CFG. The simplicity and effectiveness of IG make it a valuable contribution to the field.
Reference

“LightningDiT-XL/1+IG achieves FID=1.34 which achieves a large margin between all of these methods. Combined with CFG, LightningDiT-XL/1+IG achieves the current state-of-the-art FID of 1.19.”

PermalinkArXiv
Research Paper#Diffusion Models, AI, Machine Learning🔬 ResearchAnalyzed: Jan 3, 2026 16:19

Guided Path Sampling Improves Diffusion Model Refinement

Published:Dec 28, 2025 11:12
1 min read
ArXiv

Analysis

This paper addresses a key limitation in iterative refinement methods for diffusion models, specifically the instability caused by Classifier-Free Guidance (CFG). The authors identify that CFG's extrapolation pushes the sampling path off the data manifold, leading to error divergence. They propose Guided Path Sampling (GPS) as a solution, which uses manifold-constrained interpolation to maintain path stability. This is a significant contribution because it provides a more robust and effective approach to improving the quality and control of diffusion models, particularly in complex scenarios.
Reference

“GPS replaces unstable extrapolation with a principled, manifold-constrained interpolation, ensuring the sampling path remains on the data manifold.”

PermalinkArXiv
Research#llm🔬 ResearchAnalyzed: Jan 4, 2026 07:31

Two CFG Nahuatl for automatic corpora expansion

Published:Dec 16, 2025 09:49
1 min read
ArXiv

Analysis

The article likely presents research on using Context-Free Grammars (CFGs) for expanding Nahuatl language corpora. This suggests a focus on computational linguistics and natural language processing, specifically for a low-resource language. The use of CFGs implies a formal approach to modeling the language's structure for automated generation or analysis of text.
Reference

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PermalinkArXiv
Research#llm🔬 ResearchAnalyzed: Jan 4, 2026 08:11

LLMCFG-TGen: Using LLM-Generated Control Flow Graphs to Automatically Create Test Cases from Use Cases

Published:Dec 6, 2025 11:19
1 min read
ArXiv

Analysis

This article describes a research paper that leverages Large Language Models (LLMs) to automate test case generation. The core idea is to use LLMs to create Control Flow Graphs (CFGs) from use cases, which are then used to derive test cases. This approach aims to improve the efficiency and coverage of software testing by automating a traditionally manual process. The use of LLMs for this task is novel and potentially impactful.
Reference

“The paper likely details the specific LLM used, the process of CFG generation, and the methods for deriving test cases from the CFGs. It would also likely include evaluation metrics to assess the effectiveness of the approach.”

PermalinkArXiv