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Are those 2048 x 2048 images still sensible? SD 1.5 is best used at 512x512 and may produce sensible images upto 768. It generates monstrosities above that. Sim

Are those 2048 x 2048 images still sensible? SD 1.5 is best used at 512x512 and may produce sensible images upto 768. It generates monstrosities above that. Similarly SD XL is good upto 1024.

orbital-decay 3y ago
These are limitations of a single text-to-image gen, which is the least interesting way to use those models. When guided by a previous low-res generation, it won't fall apart at arbitrary resolutions, that's how all diffusion upscalers work. Just don't expect being able to fit every detail in one pass, use multiple ones (that's how detailers work).
dragonwriter 3y ago
> Are those 2048 x 2048 images still sensible? SD 1.5 is best used at 512x512 and may produce sensible images upto 768. It generates monstrosities above that. Similarly SD XL is good upto 1024. You can do significantly higher resolutions with various tricks like tiled diffusion, which is also a memory efficiency hack. (The stable-diffusion-webui tiled diffusion extension uses 2560×1280 direct [no upscale step] generation with an SD 1.5-based model as one of its examples.)
- smusamashah 3y ago
  Up scaling the image in chunk creates loads of semantic issues. For example, bottom of tree might look further in the mountains but it's top will be near you. You don't see problems like these in non scaled images.
  - dragonwriter 3y ago
    > Up scaling the image in chunk creates loads of semantic issues. No, tiled upscaling generally does not have that problem significantly (compared to direct generation at native model-supported size, which doesn't completely avoid that kind of issue), since the composition on that level is set before the upscale (direct tiled generation does, if you aren’t using something like controlnet to avoid it.) > You don’t see problems like these in non scaled images. You actually occasionally do, but its fairly rare.
  - orbital-decay 3y ago
    It's conditioned on the lowres input, so if it doesn't have semantic discontinuites it doesn't happen. It will eventually happen if you continue doing this indefinitely, but with reasonable size to tile ratio (say <6x) it works well. With manual or object detection-assisted tiling and proper conditioning (controlnets sidechannel, especially if it's a custom trained controlnet/t2i) it can be pushed further.
viraptor 3y ago
> Similarly SD XL is good upto 1024. I don't think that's right. SD xl is good starting from 1024. Anything lower generates a useless mess.
- dragonwriter 3y ago
  SDXL native trained resolution for 1:1 aspect ratio is 1024x1024 like SD 1.5’s is 512x512. Like SD 1.5, you can go a bit below or above that without too much problem; unlike SD 1.5, SDXL also has significant training in a fairly wide set of other resolutions (ranging from 2048x512 to 512x2048) with approximately 1 mebipixel resolution, and they can be treated as starting points as easily as 1024x1024 can. I think SDXL has a narrower (proportionate) range of viable resolutions around its starting points, but that’s offset but having more than one “starting point”.

Are those 2048 x 2048 images still sensible? SD 1.5 is best used at 512x512 and may produce sensible images upto 768. It generates monstrosities above that. Similarly SD XL is good upto 1024.

orbital-decay 3y ago

These are limitations of a single text-to-image gen, which is the least interesting way to use those models. When guided by a previous low-res generation, it won't fall apart at arbitrary resolutions, that's how all diffusion upscalers work. Just don't expect being able to fit every detail in one pass, use multiple ones (that's how detailers work).

dragonwriter 3y ago

> Are those 2048 x 2048 images still sensible? SD 1.5 is best used at 512x512 and may produce sensible images upto 768. It generates monstrosities above that. Similarly SD XL is good upto 1024. You can do significantly higher resolutions with various tricks like tiled diffusion, which is also a memory efficiency hack. (The stable-diffusion-webui tiled diffusion extension uses 2560×1280 direct [no upscale step] generation with an SD 1.5-based model as one of its examples.)

smusamashah 3y ago
Up scaling the image in chunk creates loads of semantic issues. For example, bottom of tree might look further in the mountains but it's top will be near you. You don't see problems like these in non scaled images.
- dragonwriter 3y ago
  > Up scaling the image in chunk creates loads of semantic issues. No, tiled upscaling generally does not have that problem significantly (compared to direct generation at native model-supported size, which doesn't completely avoid that kind of issue), since the composition on that level is set before the upscale (direct tiled generation does, if you aren’t using something like controlnet to avoid it.) > You don’t see problems like these in non scaled images. You actually occasionally do, but its fairly rare.
- orbital-decay 3y ago
  It's conditioned on the lowres input, so if it doesn't have semantic discontinuites it doesn't happen. It will eventually happen if you continue doing this indefinitely, but with reasonable size to tile ratio (say <6x) it works well. With manual or object detection-assisted tiling and proper conditioning (controlnets sidechannel, especially if it's a custom trained controlnet/t2i) it can be pushed further.

viraptor 3y ago

> Similarly SD XL is good upto 1024. I don't think that's right. SD xl is good starting from 1024. Anything lower generates a useless mess.

dragonwriter 3y ago
SDXL native trained resolution for 1:1 aspect ratio is 1024x1024 like SD 1.5’s is 512x512. Like SD 1.5, you can go a bit below or above that without too much problem; unlike SD 1.5, SDXL also has significant training in a fairly wide set of other resolutions (ranging from 2048x512 to 512x2048) with approximately 1 mebipixel resolution, and they can be treated as starting points as easily as 1024x1024 can. I think SDXL has a narrower (proportionate) range of viable resolutions around its starting points, but that’s offset but having more than one “starting point”.