Summary: A Natural father will even beg the so called male visitors in joining him/ to fight the savages and deviants off and to protect his...

Perdana Menteri Mongolia Nyam-Osor Uchral pada hari Senin (27/10) waktu setempat, menggambarkan kebakaran besar di padang rumput Mongolia timur, yang diduga ...

OSOR Report, here.

Το Open Source Observatory (OSOR) της Ευρωπαϊκής Επιτροπής δημοσίευσε την έκδοση του 2026 της έκθεσης «Progress and Trends in the National Open Source Policies and Legal Frameworks». Η έκθεση αποτυπών...

A mining payout puts savings and public investment in competition Mongolia is asking citizens how to use an expected 3 trillion tugrik in Oyu Tolgoi dividends in 2027, with Prime Minister Uchral Nyam-Osor proposing to save one third and spend…

Pagamento de mineradora coloca poupança e investimento público em disputa A Mongólia está perguntando aos cidadãos como usar os 3 trilhões de tugriks esperados em dividendos da Oyu Tolgoi em 2027. O primeiro-ministro Uchral Nyam-Osor propõe poupar um terço e…

Bergbaudividenden: Sparen und öffentliche Investitionen im Wettbewerb Mongolei fragt ihre Bürger, wie erwartete Oyu-Tolgoi-Dividenden in Höhe von 3 Billionen Tugrik im Jahr 2027 verwendet werden sollen. Ministerpräsident Uchral Nyam-Osor schlägt vor, ein Drittel zu sparen und den Rest für Stromversorgung,…

El pago de una minera enfrenta el ahorro a la inversión pública Mongolia pregunta a la ciudadanía cómo utilizar los 3 billones de tugriks que espera recibir en 2027 en dividendos de Oyu Tolgoi. El primer ministro, Uchral Nyam-Osor, propone…

The Chile international wasn’t even on the bench...

YouTube video by Yurttaş TV

The Crystal Palace No.24 is now starting to come alive this season...

On September 15, Mongolia’s parliament, the State Great Khural, began its autumn session – and it started full throttle. The incumbent government headed by Prime Minister Uchral Nyam-Osor has an ambitious agenda that saw it propose one of the largest state budgets. Among its goals: establishing a sovereign wealth fund, securing energy from China, and building data centers. Parliament will likely scrutinize many of these big initiatives. 

catalpa [incl. C. speciosa Teas] · família: Bignoniaceae · tàxon introduït [casual]: Amèrica del Nord · forma biològica: macrofaneròfit caducifoli · hàbitat: 0 · rang altitudinal: 60 m Sectors Altitud (quadrícules UTM 2km) Observacions pròpies (quadrícules UTM 2km) Citacions bibliogràfiques (veure referències bibliogràfiques): Aymerich (2025) [Catalpa sp.] – Osor: riera d’Osor més amunt del pont […]

Hi John, Thank you for the detailed breakdown and the suggestions of LaMa, RORem, and OSOR. I’ve spent the last few days benchmarking these against my specific use case, and I wanted to share the results, as they highlight a significant gap in performance for high-end virtual staging. **The Current Workflow:** I am using a fine-tuned **RF-DETR Seg Large** model to generate the initial furniture masks. To ensure we capture shadows and contact edges, I apply a **20px dilation** before feeding the mask to the inpainting models. **The Results:** 1. **LaMa:** As you noted, it is a great “cheap” eraser, but for “defurnishing” (removing large items like beds/sofas), it fails semantically. It essentially “smudges” the surrounding colors into the hole. It creates a blurry, greyish-red “cloud” where the bed was, rather than reconstructing the hidden floor grain or wall texture. It lacks the “imagination” required for architectural reconstruction. 2. **RORem:** While RORem is built on SDXL and is much faster than Qwen, the results have been disappointing in terms of **Texture Integrity and Blending**. I am seeing significant “texture melting” (as seen in the attached output). The floor patterns become warped/liquid, and the transition between the original pixels and the AI-generated pixels is very jarring, even with feathered masks. I used: pipe = AutoPipelineForInpainting.from_pretrained( “LetsThink/RORem”, torch_dtype=torch.float16, variant=None).to(“cuda”) 3. **The Qwen Comparison:** Honestly, **Qwen-Image-Edit** remains the gold standard in my tests. Even though it is VRAM-heavy and slow, it is the only model that actually “understands” the instruction to reveal the architectural shell. It reconstructs the hardwood floor lines and wall textures with a level of logic that the others haven’t matched. **Visual Evidence (Attached):** * **Image 1:** Original Photo (Furnished) * **Image 2:** My RF-DETR + Dilated Mask (The input provided to models) * **Image 3:** RORem Output (Showing the “melting” floor and poor blending) **My Concern:** We are aiming for a commercial-grade product where we need to meet strict metrics (SSIM ≥≥ 0.95 and EdgeMAD ≤≤ 2px). While Qwen hits the visual quality, the latency is the blocker. However, RORem and LaMa seem to fail the visual “realism” test entirely for large-scale removal. Do you have any suggestions on how to stabilize the geometry in RORem (perhaps via ControlNet-MLSD or Depth)? Or is there a lighter “instruction-based” model that behaves more like Qwen but with the speed of SDXL? Looking forward to your thoughts! * * *