Sdxl Turbo Automatic1111<\/h2>\n\n\n\n

sdxl turbo automatic1111ek<\/strong> variation hai Stable Diffusion XL (SDXL<\/strong>) model ka jo high-quality images ko produce karte hue ek step mein focus karta hai.<\/p>\n\n\n\n

Adversarial Diffusion Distillation<\/strong> (ADD) ka use karke develop kiya gaya, SDXL Turbo standard SDXL model par improve karta hai ek GAN discriminator ko introduce karke jo image sharpness ko enhance karta hai<\/strong>.<\/p>\n\n\n\n

Jabki SDXL Turbo<\/strong> significant speed improvements provide karta hai, isme kuch limitations hai, jaise text ko render karne mein difficulties aur human faces generate karne mein inconsistent performance.<\/p>\n\n\n\n

SDXL Turbo ek<\/strong> fine-tuned version hai of Stable Diffusion XL (SDXL) jo 512×512<\/strong> images generate kar sakta<\/strong> hai sirf 1-4 sampling steps mein instead of typical 50 steps jo diffusion models require karte hai.<\/p>\n\n\n\n

It achieves this speed ek novel training technique called Adversarial Diffusion Distillation<\/strong> (ADD) ke through jo full SDXL model <\/strong>ka knowledge ko distill karta hai into ek fast 1-step model jo SDXL ke output quality ko mimic karte hue try karta hai.<\/p>\n\n\n\n

See ALso: What is Caktus AI & How To Use Caktus AI<\/a><\/p>\n\n\n\n

Key Features<\/h2>\n\n\n\n

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• Generates images<\/strong> 0.1-0.2 seconds mein on high-end GPUs<\/a> jaise Nvidia A100<\/li>\n\n\n\n
• Near real-time text-to-image generation achieve karta hai<\/li>\n\n\n\n
• Image quality SDXL ke close hai lekin details lack kar sakta hai ya inaccuracies ho sakte hai<\/li>\n\n\n\n
• Diffusion models aur GANs ke strengths ko leverage karta hai<\/li>\n\n\n\n
• Ek non-commercial research license ke under release kiya gaya hai<\/li>\n<\/ul>\n\n\n\n

  Running SDXL Turbo on AUTOMATIC1111<\/strong><\/h2>\n\n\n\n

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  • Halki AUTOMATIC1111 officially SDXL Turbo ko support<\/strong> nahi karta hai, ise chalaya ja sakta hai following key settings use karke:<\/li>\n<\/ul>\n\n\n
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    <\/figure><\/div>\n\n\n

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    • Sampling method: DDPM++ SDE Karras<\/li>\n\n\n\n
    • Sampling steps: 1-4<\/li>\n\n\n\n
    • CFG scale: 1<\/li>\n\n\n\n
    • Euler a sampler<\/li>\n\n\n\n
    • Yeh SDXL Turbo ke saath align hai jiske liye ek step mein image generate karne ke liye train kiya gaya tha.<\/li>\n\n\n\n
    • Kuch launch arguments jaise –no-half-vae<\/strong> compatibility ke liye specify karna pad sakta hai.<\/li>\n<\/ul>\n\n\n\n

      See Also: How To Use Spicychat AI: What Is It?<\/a><\/p>\n\n\n\n

      Image Quality And Limitations<\/h2>\n\n\n\n

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      • Image quality SDXL<\/strong> ke close hai lekin kabhi kabhi inaccuracies ya duplicates dikh sakti hai. Negative prompts nahi chalte jo capabilities ko limit karta hai.<\/li>\n\n\n\n
      • Sirf 512×512 images generate karta hai jabki SDXL<\/strong> higher resolutions kar sakta hai.<\/li>\n\n\n\n
      • Ab kai fine-tuned versions hai SDXL<\/strong> Turbo ke jo quality improve karne pe focus karte hai.<\/li>\n<\/ul>\n\n\n\n

        SDXL Turbo ek fast 1-step model mein SDXL<\/strong> ko distill karke GPUs par real-time image generation ki potential ko unleash karta hai. Sahi settings ke saath, isko AUTOMATIC1111<\/strong> par lightning speed stable diffusion ke liye chalaya ja sakta hai.<\/p>\n\n\n\n

        How To Use SDXL Turbo With AUTOMATIC1111?<\/h2>\n\n\n\n

        Yahan hai ek summary SDXL Turbo ko Automatic1111<\/strong> ke saath kaise use karna hai search results ke basis par:<\/p>\n\n\n\n

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        1. SDXL Turbo model<\/strong> file (sd_xl_turbo_1.0_fp16<\/strong>) prapt karein aur ise Automatic1111<\/strong> ke andar models\/Stable-diffusion folder mein rakhein.<\/li>\n\n\n\n
        2. txt2img section mein, checkpoint dropdown menu se “sd_xl_turbo_1.0_fp16<\/strong>” model choose karein.<\/li>\n\n\n\n
        3. Sampling method set karein “DPS++ SDE Karras<\/strong>” jo 1-4 sampling steps ke saath best kaam karta hai. Jyada steps detail ko increase karte hai lekin speed ko kam karte hai.<\/li>\n\n\n\n
        4. CFG scale ko 1-2.5 ke beech adjust karein artistic style ko control karne ke liye. Lower values behtar kaam karte hai.<\/li>\n\n\n\n
        5. Images generate karein Generate button dabane se. SDXL Turbo detailed 512×512 images create kar sakta hai sirf 0.6 seconds mein ek RTX 3090 GPU par bass 1 step ke saath.<\/strong><\/li>\n<\/ol>\n\n\n\n

           SDXL Turbo<\/strong>, adversarial training ka use karke, SDXL<\/strong> ki image quality ko sirf 1 step mein match karta hai. Sahi settings ke saath Automatic1111<\/strong> mein, aap real-time image generation achieve kar sakte hai.<\/p>\n\n\n\n

           \nhttps:\/\/twitter.com\/P_Galbraith\/status\/1730531413777367078\n<\/div><\/figure>\n\n\n\n

           See Also: IBM Watson Machine Learning Unleash Ai Power<\/a><\/p>\n\n\n\n

           Does SDXL Work With AUTOMATIC1111?<\/h2>\n\n\n\n

           Haan, SDXL AUTOMATIC1111<\/strong> ke saath kaam karta hai lekin kuch additional setup ki zarurat hoti hai:<\/p>\n\n\n\n

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           • Poora SDXL support<\/strong> ke liye aapko AUTOMATIC1111<\/strong> ko latest version (v1.5.1 ya upar<\/strong>) par upgrade karna padega.<\/li>\n\n\n\n
           • SDXL<\/strong> base aur refiner models download karein aur unhe apne AUTOMATIC1111<\/strong> models folder mein daalein. 12GB se kam VRAM ke saath fp16 model versions ka use karein memory bachane ke liye.<\/li>\n\n\n\n
           • –medvram<\/strong> argument shuru karte waqt add karein agar aapke paas 12GB se kam VRAM hai. Yeh performance ko \u0437\u043d\u0430\u0447\u0438\u0442\u0435\u043b\u044c\u043d\u043e improve karega.<\/li>\n\n\n\n
           • SDXL <\/strong>pehle ke Stable Diffusion models se jyada time leti hai images generate karne mein, khas kar ke kam VRAM wale GPUs<\/strong> ke saath. Modern GPUs par generation time expect karein 30 seconds se lekar kuch minutes tak.<\/li>\n\n\n\n
           • SDXL refiner image quality aur enhance kar sakta hai lekin usse bhi jyada time lagta hai generate karne mein. Ise kam VRAM<\/strong> wale GPUs<\/strong> par bahut kam use karein.<\/li>\n\n\n\n
           • Kuch features jaise ControlNet abhi theek se SDXL<\/strong> ke saath nahi chal rahe hain lekin support improve hona chahiye time ke saath.<\/li>\n<\/ul>\n\n\n\n

             SDXL latest AUTOMATIC1111<\/strong> mein supported hai lekin apne GPU VRAM<\/strong> ke basis par optimal performance ke liye thodi tweaking ki zarurat hoti hai. Jaisa support mature hota jaega, experience smooth hona chahiye.<\/strong><\/p>\n\n\n\n

             See Also: What is Kupid AI: How To Use & Alternative<\/a><\/p>\n\n\n\n

             Sdxl Turbo Automatic1111 Tutorial<\/h2>\n\n\n\n

             Yahan hain steps SDXL Turbo ko AUTOMATIC1111<\/strong> ke saath use karne ke liye search results ke basis par:<\/p>\n\n\n

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             <\/figure><\/div>\n\n\n

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             1. SDXL Turbo model download<\/strong> karein Civitai ya Stable Diffusion Art se.<\/li>\n\n\n\n
             2. AUTOMATIC1111<\/strong> kholein aur txt2img settings mein jaayein.<\/li>\n\n\n\n
             3. Latent image width aur height ko 512 par set karein.<\/li>\n\n\n\n
             4. Steps ki number ko 1-5 par set karein.<\/li>\n\n\n\n
             5. CFG ko 1-2.5 par set karein.<\/li>\n\n\n\n
             6. Sampler ko Euler a ya LCM par set karein.<\/li>\n\n\n\n
             7. Image banane ke liye generate button dabayein.<\/li>\n<\/ol>\n\n\n\n

                Dhyan rakhein ki AUTOMATIC1111<\/strong> ke paas SDXL Turbo<\/strong> ke liye official support<\/strong> nahi hai, lekin sahi settings ke saath chalaaya ja sakta hai.<\/p>\n\n\n\n

                Iske alaava, YouTube<\/strong> jaise platforms par SDXL Turbo<\/strong> ko aapke apne system par chalane ke liye tutorials uplabdh hain.<\/p>\n\n\n\n

                Read Also: AI Jadrolinija Toto Video Original<\/a><\/p>\n\n\n\n

                Sdxl Turbo Automatic1111 Download<\/h2>\n\n\n\n

                SDXL Turbo ko AUTOMATIC1111<\/strong> par chalane ke liye:<\/p>\n\n\n

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                <\/figure><\/div>\n\n\n

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                • Civitai jaise sites se ek SDXL Turbo model<\/strong> file download karein aur usko apne AUTOMATIC1111<\/strong> model folder mein add karein.<\/li>\n\n\n\n
                • AUTOMATIC1111<\/strong> launch karein –no-half-vae –disable-safe-unpickle jaise command line arguments ke saath compatibility ensure karne ke liye.<\/li>\n\n\n\n
                • Ek aise sampling method ka use karein jo 1 step ke liye design kiya gaya hai jaise DPM++ SDE<\/strong> Karras. Steps ko 1-4 par set karein.<\/li>\n\n\n\n
                • CFG scale try karein around 1-2.5. Yeh artistic interpretation ko control karta hai.<\/li>\n\n\n\n
                • 4x-UltraSharp jaise upscaling method ka use karein image quality ko enhance karne ke liye.<\/li>\n<\/ul>\n\n\n\n

                  Kuch specific setup ke saath, SDXL Turbo<\/strong> real-time image generation speed achieve kar sakta hai AUTOMATIC1111<\/strong> par, halki official support abhi tak nahi hai. Key cheezen hain ek 1-step compatible sampling method ka use aur compatibility ensure karne ke liye launch arguments.<\/p>\n\n\n\n

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                  Est-ce qu'on peut utiliser SDXL Turbo dans Automatic1111 ?
                  
                  La r\u00e9ponse est OUI
                  
                  Et ce n'est pas tr\u00e8s compliqu\u00e9 :
                  – Checkpoint : sd_xl_turbo_1.0_fp16
                  – Sampling method: Euler a
                  – Sampling steps: 1
                  – Size: 512 x 512
                  – CFG Scale: 1
                  
                  Plus de d\u00e9tails \u2192 https:\/\/t.co\/UbdzLWwqdk<\/a> pic.twitter.com\/onQtylul3t<\/a><\/p>— Creative Diffusion FR (@StableTom) November 30, 2023<\/a><\/blockquote>