It's. The workflow also has TXT2IMG, IMG2IMG, up to 3x IP Adapter, 2x Revision, predefined (and editable) styles, optional up-scaling, Control Net Canny, Control Net Depth, Lora, selection of recommended SDXL resolutions, adjusting input images to the closest SDXL resolution, etc. 0 is its ability to create complex and aesthetically pleasing images with just a few words as input. Hello, I am trying to get similar results from my local SD using sdXL_v10VAEFix model as images from online demos. 5 however takes much longer to get a good initial image. We generated each image at 1216 x 896 resolution, using the base model for 20 steps, and the refiner model for 15 steps. We present SDXL, a latent diffusion model for text-to-image synthesis. 0 base model. . With 3. Il se distingue par sa capacité à générer des images plus réalistes, des textes lisibles, des visages photoréalistes, une meilleure composition d'image et une meilleure. 5 model we'd sometimes generate images of heads/feet cropped out because of the autocropping to 512x512 used in training images. I’ll create images at 1024 size and then will want to upscale them. A successor that we will never get. Sdxl Lora training on RTX 3060. Yes the model is nice, and has some improvements over 1. Generate. For me what I found is best is to generate at 1024x576, and then upscale 2x to get 2048x1152 (both 16:9 resolutions) which is larger than my monitor resolution (1920x1080). However, different aspect ratios may be used. With Stable Diffusion XL 1. A text-guided inpainting model, finetuned from SD 2. (I’ll see myself out. some stupid scripting workaround to fix the buggy implementation and to make sure it redirects you to the actual full resolution original images (which are PNGs in this case), otherwise it. It is a more flexible and accurate way to control the image generation process. 9 models in ComfyUI and Vlad's SDnext. To learn how to use SDXL for various tasks, how to optimize performance, and other usage examples, take a look at the Stable Diffusion XL guide. 0, allowing users to specialize the generation to specific people or products using as few as five images. ¡No te lo pierdas! Hoy hablaremos de SDXL, un modelo de difusión latente que ha revolucionado la calidad de imágenes generadas en alta resolución. Compared to other leading models, SDXL shows a notable bump up in quality overall. Official list of SDXL resolutions (as defined in SDXL paper). Here are some facts about SDXL from SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis. json as a template). I have identical config for sampler, steps, resolution and even seed. . SDXL 1. (And they both use GPL license. Le Communiqué de presse sur SDXL 1. Developed by Stability AI, SDXL 1. It’ll be faster than 12GB VRAM, and if you generate in batches, it’ll be even better. 5 model which was trained on 512×512 size images, the new SDXL 1. py script shows how to implement the training procedure and adapt it for Stable Diffusion XL. 0 base model as of yesterday. SDXL is now available and so is the latest version of one of the best Stable Diffusion models. Not OP, but you can train LoRAs with kohya scripts (sdxl branch). . The chart above evaluates user preference for SDXL (with and without refinement) over SDXL 0. Le Code Source d’Automatic1111; SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis -. The fine-tuning can be done with 24GB GPU memory with the batch size of 1. If the training images exceed the resolution. Support for custom resolutions - you can just type it now in Resolution field, like "1280x640". Best Settings for SDXL 1. 1. Enlarged 128x128 latent space (vs SD1. Docker image for Stable Diffusion WebUI with ControlNet, After Detailer, Dreambooth, Deforum and roop extensions, as well as Kohya_ss and ComfyUI. Results. 9vae. Compact resolution and style selection (thx to runew0lf for hints). Disclaimer: Even though train_instruct_pix2pix_sdxl. SDXL for A1111 Extension - with BASE and REFINER Model support!!! This Extension is super easy to install and use. 5. 1 latent. Here are some native SD 2. ago. 9: The weights of SDXL-0. or maybe you are using many high weights,like (perfect face:1. 1's 860M parameters. json as a template). However, you can still change the aspect ratio of your images. The SDXL base model performs significantly better than the previous variants, and the model combined with the refinement module achieves the best overall performance. May need to test if including it improves finer details. However, a game-changing solution has emerged in the form of Deep-image. My full args for A1111 SDXL are --xformers --autolaunch --medvram --no-half. x and SDXL LoRAs. For example, the default value for HED is 512 and for depth 384, if I increase the value from 512 to 550, I see that the image becomes a bit more accurate. Stability AI has released the latest version of its text-to-image algorithm, SDXL 1. It's certainly good enough for my production work. Results – 60,600 Images for $79 Stable diffusion XL (SDXL) benchmark results on SaladCloudThis example demonstrates how to use the latent consistency distillation to distill SDXL for less timestep inference. Fantasy Architecture Prompt. Fwiw, SDXL took sizes of the image into consideration (as part of conditions pass into the model), this, you should be able to use it for upscaling, downscaling, tile-based inpainting etc if the model is properly trained. Stability AI has released the latest version of Stable Diffusion that adds image-to-image generation and other. 1. Support for custom resolutions - you can just type it now in Resolution field, like "1280x640". Stability AI published a couple of images alongside the announcement, and the improvement can be seen between outcomes (Image Credit)Stable Diffusion XL. The default resolution of SDXL is 1024x1024. SDXL is a new Stable Diffusion model that - as the name implies - is bigger than other Stable Diffusion models. The purpose of DreamShaper has always been to make "a better Stable Diffusion", a model capable of doing everything on its own, to weave dreams. The beta version of Stability AI’s latest model, SDXL, is now available for preview (Stable Diffusion XL Beta). Comfyui is more optimized though. I find the results interesting for comparison; hopefully others will too. While both videos involve inpainting resolutions of 768 or higher, the same 'trick' works perfectly for me on my laptop's 4GB GTX 1650 at 576x576 or 512x512. 512x256 2:1. yeah, upscaling to a higher resolution will so bring out more detail with highres fix, or with img2img. So I won't really know how terrible it is till it's done and I can test it the way SDXL prefers to generate images. Stability AI published a couple of images alongside the announcement, and the improvement can be seen between outcomes (Image Credit) arXiv. 0: A Leap Forward in AI Image Generation. The. I added it as a note in my comfy workflow, and IMO it would be nice to have a list of preset resolutions in A1111. Prompt:A wolf in Yosemite National Park, chilly nature documentary film photography. Unlike the previous SD 1. SDXL is ready to turn heads. 5)This capability, once restricted to high-end graphics studios, is now accessible to artists, designers, and enthusiasts alike. ; Updated Comfy. We present SDXL, a latent diffusion model for text-to-image synthesis. 0 repousse les limites de ce qui est possible en matière de génération d'images par IA. By reading this article, you will learn to generate high-resolution images using the new Stable Diffusion XL 0. From SDXL 1. in 0. 14:41 Base image vs high resolution fix applied image. Imaginez pouvoir décrire une scène, un objet ou même une idée abstraite, et voir cette description se transformer en une image claire et détaillée. While you can generate at 512 x 512, the results will be low quality and have distortions. Steps: 30 (the last image was 50 steps because SDXL does best at 50+ steps) Sampler: DPM++ 2M SDE Karras CFG set to 7 for all, resolution set to 1152x896 for all SDXL refiner used for both SDXL images (2nd and last image) at 10 steps Realistic vision took 30 seconds on my 3060 TI and used 5gb vram SDXL took 10 minutes per image and used. However, you can still change the aspect ratio of your images. Since I typically use this for redoing heads, I just need to make sure I never upscale the image to the point that any of the pieces I would want to inpaint are going to be bigge r than. This week Stability AI has announced the launch of its SDXL 1. Stable Diffusion XL (SDXL 1. Official list of SDXL resolutions (as defined in SDXL paper). 0, the flagship image model developed by Stability AI, stands as the pinnacle of open models for image generation. When creating images with Stable Diffusion, one important consideration is the image size or resolution. See the help message for the usage. Last month, Stability AI released Stable Diffusion XL 1. Start Training. For the kind of work I do, SDXL 1. panchovix. 0 with some of the current available custom models on civitai. We follow the original repository and provide basic inference scripts to sample from the models. 9 in terms of how nicely it does complex gens involving people. Use the following size settings to generate the initial image. Here’s a comparison created by Twitter user @amli_art using the prompt below:. Pretraining of the base model is carried out on an internal dataset, and training continues on higher resolution images, eventually incorporating multi-aspect training to handle various aspect ratios of ∼1024×1024 pixel. 5, SDXL is flexing some serious muscle—generating images nearly 50% larger in resolution vs its predecessor without breaking a sweat. x have a base resolution of 512x215 and achieve best results at that resolution, but can work at other resolutions like 256x256. 1 (768x768): SDXL Resolution Cheat Sheet and SDXL Multi-Aspect Training. Notes . 9 in terms of how nicely it does complex gens involving people. A simple script to calculate the recommended initial latent size for SDXL image generation and its Upscale Factor based on the desired Final Resolution output. That model architecture is big and heavy enough to accomplish that the. Some models aditionally have versions that require smaller memory footprints, which make them more suitable to be. Here are some examples of what I mean:Negative prompt: 3d render, smooth, plastic, blurry, grainy, low-resolution, anime. How are people upscaling SDXL? I’m looking to upscale to 4k and probably 8k even. Stable Diffusion XL (SDXL) is a powerful text-to-image generation model that iterates on the previous Stable Diffusion models in three key ways: the UNet is 3x larger and SDXL combines a second text encoder (OpenCLIP ViT-bigG/14) with the original text encoder to significantly increase the number of parameters. A successor to the Stable Diffusion 1. Image generated with SDXL 0. 1's 860M parameters. Stable Diffusion 2. 5 is version 1. Compact resolution and style selection (thx to runew0lf for hints). 9 Research License. 0 particularly excels in vibrant and accurate color rendition, boasting improvements in contrast, lighting, and shadows compared to its predecessor, all in a 1024x1024 resolution. Highly doubt training on 6gb is possible without massive offload to RAM. 0 model to your device. 0 is particularly well-tuned for vibrant and accurate colors, with better contrast, lighting, and shadows than its predecessor, all in native 1024×1024 resolution,” the company said in its announcement. 9 the refiner worked better. 9. [1] Following the research-only release of SDXL 0. safetensors in general since the 1. Set classifier free guidance (CFG) to zero after 8 steps. Compared to previous versions of Stable Diffusion, SDXL leverages a three times larger UNet backbone: The increase of model parameters is mainly due to more attention blocks and. They'll surely answer all your questions about the model :) For me, it's clear that RD's model. Negative prompt: 3d render, smooth, plastic, blurry, grainy, low-resolution, anime. Model type: Diffusion-based text-to-image generative model. They can compliment one another even. 5 so SDXL could be seen as SD 3. 0 enhancements include native 1024-pixel image generation at a variety of aspect ratios. If you would like to access these models for your research, please apply using one of the following links: SDXL. The SDXL base model performs significantly better than the previous variants, and the model combined with the refinement module achieves the best overall performance. The refiner adds more accurate. New AnimateDiff on ComfyUI supports Unlimited Context Length - Vid2Vid will never be the same!!! SDXL offers negative_original_size, negative_crops_coords_top_left, and negative_target_size to negatively condition the model on image resolution and cropping parameters. Now, let’s take a closer look at how some of these additions compare to previous stable diffusion models. For instance, SDXL produces high-quality images, displays better photorealism, and provides more Vram usage. 30 steps can take 40-45 seconds for 1024x1024. What Step. Model Description: This is a model that can be used to generate and modify images based on text prompts. Guidelines for SDXL Finetuning ; Set the Max resolution to at least 1024x1024, as this is the standard resolution for SDXL. 1’s 768×768. SDXL 1. SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis Aside from ~3x more training parameters than previous SD models, SDXL runs on two CLIP models, including the largest OpenCLIP model trained to-date (OpenCLIP ViT-G/14), and has a far higher native resolution of 1024×1024 , in contrast to SD 1. They are not intentionally misleading. We present SDXL, a latent diffusion model for text-to-image synthesis. Height and Width: These parameters set the resolution of the image. They could have provided us with more information on the model, but anyone who wants to may try it out. Also when I use it to generate a 1024x1416 image it takes up all 24GB of the vram on my 4090 and takes be over 5 minutes to make an image. Supporting nearly 3x the parameters of Stable Diffusion v1. ) MoonRide Edition is based on the original Fooocus. I'm super excited for the upcoming weeks and months on what the wider community will come up with in terms of additional fine tuned models. g. 1152 x 896 - 9:7. I highly recommend it. I'd actually like to completely get rid of the upper line (I also don't know why I have duplicate icons), but I didn't take the time to explore it further as of now. 11:55 Amazing details of hires fix generated image with SDXL. Also memory requirements—especially for model training—are disastrous for owners of older cards with less VRAM (this issue will disappear soon as better cards will resurface on second hand. It will work. ; Added ability to stop image generation. For your information, SDXL is a new pre-released latent diffusion model…SDXL model is an upgrade to the celebrated v1. The number 1152 must be exactly 1152, not 1152-1, not 1152+1, not 1152-8, not 1152+8. 0 is an open-source diffusion model, the long waited upgrade to Stable Diffusion v2. 6B parameters vs SD1. SDXL 1. (2) Even if you are able to train at this setting, you have to notice that SDXL is 1024x1024 model, and train it with 512 images leads to worse results. It is primarily used to generate detailed images conditioned on text descriptions, though it can also be applied to other tasks such as inpainting, outpainting, and generating image-to-image translations guided by a text prompt. SDXL 1. But I also had to use --medvram (on A1111) as I was getting out of memory errors (only on SDXL, not 1. Resolutions: Standard SDXL resolution💻 How to prompt with reality check xl. 9. "AI image generation is as good as done," CEO Mostaque said in a Q&A on the official Discord server shortly after SDXL's. 0 (SDXL) and open-sourced it without requiring any special permissions to access it. 5. r/StableDiffusion. Static engines use the least amount of VRAM. 0, which is more advanced than its predecessor, 0. They are not intentionally misleading. But still looks better than previous base models. e. 9 architecture. SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis Explained（GPTにて要約） Summary SDXL（Stable Diffusion XL）は高解像度画像合成のための潜在的拡散モデルの改良版であり、オープンソースである。モデルは効果的で、アーキテクチャに多くの変更が加えられており、データの変更だけでなく. Control Nets are compatible with SDXL, but you’ll have to download the SDXL-specific models. json as a template). ago. The SDXL uses Positional Encoding. Model type: Diffusion-based text-to-image generative model. 0 offers better design capabilities as compared to V1. 43 MRE ; Added support for Control-LoRA: Depth. 4 best) to remove artifacts. Support for multiple native resolutions instead of just one for SD1. Ouverture de la beta de Stable Diffusion XL. Run SDXL refiners to increase the quality of output with high resolution images. (Cmd BAT / SH + PY on GitHub) r/StableDiffusion •Very excited about the projects and companies involved. SDXL 1. SD1. 9 to create realistic imagery with greater depth and a higher resolution of 1024x1024. I know that SDXL is trained on 1024x1024 images, so this is the recommended resolution for square pictures. 1990s anime low resolution screengrab couple walking away in street at night. For the best results, it is. A new fine-tuning beta feature is also being introduced that uses a small set of images to fine-tune SDXL 1. 5 for now as well. Stability AI claims that the new model is “a leap. ago. Stabilty. Has anyone here trained a lora on a 3060, if so what what you total steps and basic settings used and your training time. We present SDXL, a latent diffusion model for text-to-image synthesis. Make sure to load the Lora. Detailed Explanation about SDXL sizes and where to use each size. One of the common challenges faced in the world of AI-generated images is the inherent limitation of low resolution. ; The fine-tuning can be done with 24GB GPU memory with the batch size of 1. 004/image: SDXL with Custom Asset (Fine-tuned) 30: 1024x1024: DDIM (and any not listed below as premium) $. To try the dev branch open a terminal in your A1111 folder and type: git checkout dev. 🟠 the community gathered around the creators of Midjourney. 5 in sd_resolution_set. Important As opposed to regular SD which was used with a resolution of 512x512, SDXL should be used at 1024x1024. Tap into a larger ecosystem of custom models, LoRAs and ControlNet features to better target the. That model architecture is big and heavy enough to accomplish that the. If you would like to access these models for your research, please apply using one of the following links: SDXL. 0 version. to do img2img, you essentially do the exact same setup as text to image, but have the first KSampler's latent output into the second KSampler's latent_image input. Dhanshree Shripad Shenwai. json - use resolutions-example. For SDXL, try to have around 1 million pixels (1024 x 1024 = 1,048,576) with both width and height divisible by 8. 9 and Stable Diffusion 1. SDXL Base model and Refiner. Its three times larger UNet backbone, innovative conditioning schemes, and multi-aspect training capabilities have. Dynamic engines generally offer slightly. Description: SDXL is a latent diffusion model for text-to-image synthesis. How to use the Prompts for Refine, Base, and General with the new SDXL Model. A non-overtrained model should work at CFG 7 just fine. SDXL now works best with 1024 x 1024 resolutions. A very nice feature is defining presets. 5 such as the better resolution and different prompt interpertation. Stable Diffusion XL. The point is that it didn't have to be this way. Here are some facts about SDXL from the StablityAI paper: SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis A new architecture with 2. 0 ComfyUI workflow with a few changes, here's the sample json file for the workflow I was using to generate these images:. 1 (768x768): SDXL Resolution Cheat Sheet and SDXL Multi-Aspect Training. The basic steps are: Select the SDXL 1. We present SDXL, a latent diffusion model for text-to-image synthesis. 0 is released. SDXL performance does seem sluggish for SD 1. Anyway, at SDXL resolutions faces can fill a smaller part of the image and not be a mess. 🧨 DiffusersSD XL. Here's a simple script ( also a Custom Node in ComfyUI thanks to u/CapsAdmin ), to calculate and automatically set the recommended initial latent size for SDXL image. Now we have better optimizaciones like X-formers or --opt-channelslast. Compared to previous versions of Stable Diffusion, SDXL leverages a three times larger UNet backbone: The increase of model parameters is mainly due to more attention blocks and a larger cross-attention context as SDXL uses a second text encoder. Then, we employ a multi-scale strategy for fine. Well, its old-known (if somebody miss) about models are trained at 512x512, and going much bigger just make repeatings. For comparison, Juggernaut is at 600k. However, in the new version, we have implemented a more effective two-stage training strategy. You can go higher if your card can. Unfortunately, using version 1. SDXL can generate images in different styles just by picking a parameter. SD generations used 20 sampling steps while SDXL used 50 sampling steps. ; Added support for generate forever mode (ported from SD web UI). In the AI world, we can expect it to be better. You should either use exactly 1024x1024 res or multiples of it. The default value is 512 but you should set it to 1024 since it is the resolution used for SDXL training. mo pixels, mo problems — Stability AI releases Stable Diffusion XL, its next-gen image synthesis model New SDXL 1. You generate the normal way, then you send the image to imgtoimg and use the sdxl refiner model to enhance it. This is a really cool feature of the model, because it could lead to people training on high resolution crispy detailed images with many smaller cropped sections. Stable Diffusion XL (SDXL) is one of the latest and most powerful AI image generation models, capable of creating high-resolution and photorealistic images. Skip buckets that are bigger than the image in any dimension unless bucket upscaling is enabled. (Interesting side note - I can render 4k images on 16GB VRAM. Added support for custom resolutions and custom resolutions list. The chart above evaluates user preference for SDXL (with and without refinement) over SDXL 0. 9, trained at a base resolution of 1024 x 1024, produces massively improved image and composition detail over its predecessor. 9, produces visuals that are more realistic than its predecessor. 7gb without generating anything. With native 1024×1024 resolution, the generated images are detailed and visually stunning. txt is updated to support SDXL training. Este modelo no solo supera a las versiones. 5 with Base or Custom Asset (Fine-tuned) 30: 512x512: DDIM (and any not listed. I haven't seen anything that makes the case. 0 outputs. fit_aspect_to_bucket adjusts your aspect ratio after determining the bucketed resolution to match that resolution so that crop_w and crop_h should end up either 0 or very nearly 0. (As a sample, we have prepared a resolution set for SD1. To prevent this from happening, SDXL accepts cropping and target resolution values that allow us to control how much (if any) cropping we want to apply to the generated images, and the level of. The Stable Diffusion XL (SDXL) model is the official upgrade to the v1. I extract that aspect ratio full list from SDXL technical report below. The Stability AI team takes great pride in introducing SDXL 1. Many models use images of this size, so it is safe to use images of this size when learning LoRA. 9) The SDXL series also offers various. The model is capable of generating images with complex concepts in various art styles, including photorealism, at quality levels that exceed the best image models available today. Keep in mind the default resolution for SDXL is supposed to be 1024x1024, but people are using the refiner to generate images competently at 680x680, so maybe someone should try training smaller images on the refiner instead?SDXL 1. Stable Diffusion XL. 5 method. The sdxl_resolution_set. Thankfully, some people have made this much easier by publishing their own workflow and sharing them # SeargeSDXL. Several models are available, from different providers, e. The training is based on image-caption pairs datasets using SDXL 1. json - use resolutions-example. The input images are shrunk to 768x to save VRAM, and SDXL handles that with grace (it's trained to support dynamic resolutions!). Initiate the download: Click on the download button or link provided to start downloading the SDXL 1. The speed difference between this and SD 1. 9)" Enhancing the Resolution of AI-Generated Images. 0, a new text-to-image model by Stability AI, by exploring the guidance scale, number of steps, scheduler and refiner settings. SDXL shows significant improvements in synthesized image quality, prompt adherence, and composition. If you choose to use a lower resolution, such as <code> (256, 256)</code>, the model still generates 1024x1024 images, but they'll look like the low resolution images (simpler patterns, blurring) in the dataset. SDXL or Stable Diffusion XL is an advanced model developed by Stability AI that allows high-resolution AI image synthesis and enables local machine execution. SDXL 1. To learn how to use SDXL for various tasks, how to optimize performance, and other usage examples, take a look at the Stable Diffusion XL guide. The model’s visual quality—trained at 1024x1024 resolution compared to version 1. DreamStudio offers a limited free trial quota, after which the account must be recharged. 5 as the original set of ControlNet models were trained from it. 0 enhancements include native 1024-pixel image generation at a variety of aspect ratios. According to SDXL paper references (Page 17), it's advised to avoid arbitrary resolutions and stick to. 0, renowned as the best open model for photorealistic image generation, offers vibrant, accurate colors, superior contrast, and detailed shadows at a native resolution of…VRAM consumption is surprisingly okay even at the resolution which is above 1024x1024 default. Sped up SDXL generation from 4 mins to 25 seconds! r/StableDiffusion • Massive SDNext update. You can't just pipe the latent from SD1. huggingface. 9 is run on two CLIP models, including one of the largest CLIP models trained to date (CLIP ViT-g/14), which beefs up 0. SDXL Report (official) Summary: The document discusses the advancements and limitations of the Stable Diffusion (SDXL) model for text-to-image synthesis. •. Reduce the batch size to prevent Out-of. It features significant improvements and enhancements over its predecessor. Sampling sharpness is developed by Fooocus as a final solution to the problem that SDXL sometimes generates overly smooth images or images with plastic appearance. I had a really hard time remembering all the "correct" resolutions for SDXL, so I bolted together a super-simple utility node, with all the officially supported resolutions and aspect ratios. 5 on AUTO is manageable and not as bad as I would have thought considering the higher resolutions. Stable Diffusion XL SDXL 1. Official list of SDXL resolutions (as defined in SDXL paper). I had a similar experience when playing with the leaked SDXL 0. ai Discord server to generate SDXL images, visit one of the #bot-1 – #bot-10 channels. These include image-to-image prompting (inputting one image to get variations of that image), inpainting (reconstructing. 0: Guidance, Schedulers, and. In addition, SDXL can generate concepts that are notoriously difficult for image models to render, such as hands and text or spatially arranged compositions (e. Just like its predecessors, SDXL has the ability to generate image variations using image-to-image prompting, inpainting (reimagining. 0. 1, not the 1. 9 are available and subject to a research license. SDXL offers negative_original_size, negative_crops_coords_top_left, and negative_target_size to negatively condition the model on image resolution and. -. Furthermore, I will test the speed of Automatic1111 with SDXL on a cheap RunPod RTX 3090 GPU. A custom node for Stable Diffusion ComfyUI to enable easy selection of image resolutions for SDXL SD15 SD21. 1. I have tried putting the base safetensors file in the regular models/Stable-diffusion folder. 0 and updating could break your Civitai lora's which has happened to lora's updating to SD 2. It is a Latent Diffusion Model that uses two fixed, pretrained text encoders ( OpenCLIP-ViT/G and CLIP-ViT/L ). 5 (512x512) and SD2. For the record I can run SDXL fine on my 3060ti 8gb card by adding those arguments. maybe you need to check your negative prompt, add everything you don't want to like "stains, cartoon". Prompt: a painting by the artist of the dream world, in the style of hybrid creature compositions, intricate psychedelic landscapes, hyper. Please see Additional Notes for a list of aspect ratios the base Hotshot-XL model was trained with. I hope you enjoy it! MASSIVE SDXL ARTIST COMPARISON: I tried out 208 different artist names with the same subject prompt for SDXL. I'm not trying to mix models (yet) apart from sd_xl_base and sd_xl_refiner latents.