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Added a few features that were missed in initial 1.09 commit.
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Lincoln Stein 2022-08-24 15:35:10 -04:00 committed by GitHub
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@ -84,6 +84,10 @@ The --init_img (-I) option gives the path to the seed picture. --strength (-f) c
the original will be modified, ranging from 0.0 (keep the original intact), to 1.0 (ignore the original
completely). The default is 0.75, and ranges from 0.25-0.75 give interesting results.
You may also pass a -v<count> option to generate count variants on the original image. This is done by
passing the first generated image back into img2img the requested number of times. It generates interesting
variants.
## Weighted Prompts
You may weight different sections of the prompt to tell the sampler to attach different levels of
@ -128,10 +132,10 @@ samples, samples scaled for a sample of the prompt and one with the init word pr
On a RTX3090, the process for SD will take ~1h @1.6 iterations/sec.
Note: According to the associated paper, the optimal number of images
is 3-5 any more images than that and your model might not converge.
is 3-5. Your model may not converge if you use more images than that.
Training will run indefinately, but you may wish to stop it before the
heat death of the universe, when you fine a low loss epoch or around
heat death of the universe, when you find a low loss epoch or around
~5000 iterations.
Once the model is trained, specify the trained .pt file when starting
@ -169,6 +173,7 @@ repository and associated paper for details and limitations.
* v1.09 (24 August 2022)
* A new -v option allows you to generate multiple variants of an initial image
in img2img mode. (kudos to Oceanswave)
* Added ability to personalize text to image generation (kudos to nicolai256)
* v1.08 (24 August 2022)
* Escape single quotes on the dream> command before trying to parse. This avoids
@ -460,4 +465,4 @@ Original portions of the software are Copyright (c) 2020 Lincoln D. Stein (https
#Further Reading
Please see the original README for more information on this software
and underlying algorithm, located in the file README-CompViz.md.
and underlying algorithm, located in the file README-CompViz.md.