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

Blurry Results with Bigasp Model

Published:Jan 4, 2026 05:00

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1 min read

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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.

Key Takeaways

•User is experiencing blurry image generation with the Bigasp model.
•The user is using a specific LoRA and VAE.
•The issue is related to a Stable Diffusion workflow.

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?”

Permalink r/StableDiffusion

Research #llm 📝 BlogAnalyzed: Dec 28, 2025 21:57

WAN2.1 SCAIL Pose Transfer Test

Published:Dec 28, 2025 11:20

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1 min read

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r/StableDiffusion

Analysis

This news snippet reports on a test of the SCAIL model from WAN for pose control, likely within the context of Stable Diffusion. The information is concise, mentioning the model's name, its function (pose control), and the source (WAN). It also indicates the availability of a workflow (WF) by Kijai on GitHub, providing a practical element for users interested in replicating or experimenting with the model. The submission source is also provided, giving context to the origin of the information.

Key Takeaways

•The article highlights a test of the SCAIL model for pose control.
•The model originates from WAN.
•A workflow is available on Kijai's GitHub for users to access and experiment with.

Reference

“testing the SCAIL model from WAN for pose control, WF available by Kijai on his GitHub repo.”

Permalink r/StableDiffusion

Research #Image Generation 📝 BlogAnalyzed: Dec 29, 2025 01:43

Just Image Transformer: Flow Matching Model Predicting Real Images in Pixel Space

Published:Dec 14, 2025 07:17

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1 min read

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Zenn DL

Analysis

The article introduces the Just Image Transformer (JiT), a flow-matching model designed to predict real images directly within the pixel space, bypassing the use of Variational Autoencoders (VAEs). The core innovation lies in predicting the real image (x-pred) instead of the velocity (v), achieving superior performance. The loss function, however, is calculated using the velocity (v-loss) derived from the real image (x) and a noisy image (z). The article highlights the shift from U-Net-based models, prevalent in diffusion-based image generation like Stable Diffusion, and hints at further developments.

Key Takeaways

•JiT is a flow-matching model that operates directly in pixel space.
•It predicts real images (x-pred) for better performance.
•The loss function is calculated using velocity derived from real and noisy images.

Reference

“JiT (Just image Transformer) does not use VAE and performs flow-matching in pixel space. The model performs better by predicting the real image x (x-pred) rather than the velocity v.”

Permalink Zenn DL

Research #llm 📝 BlogAnalyzed: Dec 24, 2025 20:26

Exploring Img2Img Settings Reveals Possibilities Before Changing Models

Published:Dec 12, 2025 15:00

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1 min read

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Zenn SD

Analysis

This article highlights a common pitfall in Stable Diffusion image generation: focusing solely on model and LoRA changes while neglecting fundamental Img2Img settings. The author shares their experience of struggling to create a specific image format (a wide banner from a chibi character) and realizing that adjusting Img2Img parameters offered more control and better results than simply swapping models. This emphasizes the importance of understanding and experimenting with these settings to optimize image generation before resorting to drastic model changes. It's a valuable reminder to explore the full potential of existing tools before seeking external solutions.

Key Takeaways

•Don't overlook fundamental Img2Img settings.
•Experiment with Img2Img parameters before changing models.
•Optimize existing tools before seeking external solutions.

Reference

“"I was spending time only on changing models, changing LoRAs, and tweaking prompts."”

Permalink Zenn SD

Research #llm 🔬 ResearchAnalyzed: Jan 4, 2026 07:16

Zero-shot Adaptation of Stable Diffusion via Plug-in Hierarchical Degradation Representation for Real-World Super-Resolution

Published:Dec 11, 2025 06:45

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1 min read

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ArXiv

Analysis

This article presents a novel approach to real-world super-resolution using Stable Diffusion. The core innovation lies in the zero-shot adaptation, meaning the model can perform super-resolution without prior training on specific datasets. The use of a plug-in hierarchical degradation representation is key to this adaptation. The paper likely details the technical aspects of this representation and how it allows for effective super-resolution. The source being ArXiv suggests this is a research paper, likely detailing the methodology, experiments, and results.

Key Takeaways

•Presents a zero-shot adaptation method for Stable Diffusion.
•Utilizes a plug-in hierarchical degradation representation.
•Focuses on real-world super-resolution.
•Published on ArXiv, indicating a research paper.

Reference

“The article likely discusses the technical details of the plug-in hierarchical degradation representation and its effectiveness in achieving zero-shot adaptation for real-world super-resolution.”

Permalink ArXiv

Research #llm 🔬 ResearchAnalyzed: Jan 4, 2026 07:44

Color encoding in Latent Space of Stable Diffusion Models

Published:Dec 10, 2025 09:54

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1 min read

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ArXiv

Analysis

This article likely explores how color information is represented and manipulated within the latent space of Stable Diffusion models. The focus is on understanding the internal workings of these models concerning color, which is crucial for image generation and editing tasks. The research could involve analyzing how color is encoded, how it interacts with other image features, and how it can be controlled or modified.

Key Takeaways

Reference

“”

Permalink ArXiv

Research #AI/ML 👥 CommunityAnalyzed: Jan 3, 2026 06:50

Stable Diffusion 3.5 Reimplementation

Published:Jun 14, 2025 13:56

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1 min read

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Hacker News

Analysis

The article highlights a significant technical achievement: a complete reimplementation of Stable Diffusion 3.5 using only PyTorch. This suggests a deep understanding of the model and its underlying mechanisms. It could lead to optimizations, better control, or a deeper understanding of the model's behavior. The use of 'pure PyTorch' is noteworthy, as it implies no reliance on pre-built libraries or frameworks beyond the core PyTorch library, potentially allowing for greater flexibility and customization.

Key Takeaways

•Reimplementation of Stable Diffusion 3.5 in pure PyTorch.
•Potential for optimization and deeper understanding of the model.
•Implies a strong understanding of the model's architecture and PyTorch.
•Could lead to greater flexibility and customization.

Reference

“N/A”

Blurry Results with Bigasp Model

Analysis

Key Takeaways

WAN2.1 SCAIL Pose Transfer Test

Analysis

Key Takeaways

Just Image Transformer: Flow Matching Model Predicting Real Images in Pixel Space

Analysis

Key Takeaways

Exploring Img2Img Settings Reveals Possibilities Before Changing Models

Analysis

Key Takeaways

Zero-shot Adaptation of Stable Diffusion via Plug-in Hierarchical Degradation Representation for Real-World Super-Resolution

Analysis

Key Takeaways

Color encoding in Latent Space of Stable Diffusion Models

Analysis

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Stable Diffusion 3.5 Reimplementation

Analysis

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Stable Diffusion 3.5 Large Available on Azure AI Foundry

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AuraFlow v0.1: Open Source Alternative to Stable Diffusion 3

Analysis

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Generating Realistic People in Stable Diffusion

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Announcing the Open Release of Stable Diffusion 3 Medium

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Stable Diffusion 3 API Now Available

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Easy Stable Diffusion XL in your device, offline

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Stable Diffusion: Real-time prompting with SDXL Turbo and ComfyUI running locally

Analysis

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Segmind Stable Diffusion – A smaller version of Stable Diffusion XL

Analysis

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Running Stable Diffusion XL 1.0 in 298MB of RAM

Analysis

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AMD Ryzen APU turned into a 16GB VRAM GPU and it can run Stable Diffusion

Analysis

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Opendream: A layer-based UI for Stable Diffusion

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The company behind Stable Diffusion appears to be crumbling into chaos

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Stable Diffusion and ControlNet: "Hidden" Text (see thumbnail vs. full image)

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Redditor creates working anime QR codes using Stable Diffusion

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Stable Diffusion Renders QR Readable Images

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Stability AI Releases StableLM: A New Open-Source LLM

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Art Portrait of Dog Created with Stable Diffusion and Dreambooth

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AI Porn Industry Emergence

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Illusion Diffusion: Optical Illusions Using Stable Diffusion

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Img2Prompt – Get prompts from stable diffusion generated images

Analysis