mirror of
https://github.com/vladmandic/automatic
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+26
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# Change Log for SD.Next
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## Update for 2024-09-30
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## Update for 2024-10-03
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### Highlights for 2024-09-30
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### Highlights for 2024-10-03
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- **Reprocess**: New workflow options that allow you to generate at lower quality and then reprocess at higher quality for select images only, or generate without hires/refine and then reprocess with hires/refine
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- New fine-tuned [CLiP-ViT-L]((https://huggingface.co/zer0int/CLIP-GmP-ViT-L-14)) 1st stage **text-encoders** used by SD15, SDXL, Flux.1, etc. brings additional details to your images
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- Integration with [Ctrl+X](https://github.com/genforce/ctrl-x) which allows for control of **structure and appearance** without the need for extra models
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- Auto-detection of best available **device/dtype** settings for your platform and GPU reduces neeed for manual configuration
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- Full rewrite of **sampler options**, not far more streamlined with tons of new options to tweak scheduler behavior
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- Improved **LoRA** detection and handling for all supported models
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And other goodies like multiple XYZ grid improvements, additional Flux controlnets, additional interrogate models, better LoRA tags support, and more...
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### Details for 2024-09-30
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### Details for 2024-10-03
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- **reprocess**
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- new top-level button: reprocess your last generated image(s)
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@@ -30,6 +31,28 @@ And other goodies like multiple XYZ grid improvements, additional Flux controlne
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*note* sd/sdxl contain heavily distilled versions of reference models, so switching to reference model produces vastly different results
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- xyz grid support for text encoder
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- full prompt parser now correctly works with different prompts in batch
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- **sampler options**: full rewrite
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*notes*:
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- pick a sampler and then pick values, all values have "default" as a choice to make it simpler
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- a lot of options are new, some are old but moved around
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e.g. karras checkbox is replaced with a choice of different sigma methods
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- not every combination of settings is valid
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- some settings are specific to model types
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e.g. sd15/sdxl typically use epsilon prediction
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- quite a few well-known schedulers are just variations of settings, for example:
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- euler sgm is euler with trailing spacing and sample prediction type
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- dpm 2m or 3m are dpm 1s with orders of 2 or 3
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- dpm 2m sde is dpm++ 2m with sde as solver
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*options*:
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- sigma method: *default, karas, beta, exponential*
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- timesteps spacing: *default, linspace, leading, trailing*
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- beta schedule: *linear, scaled, cosine*
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- prediction type: *epsilon, sample, v-prediction*
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- timesteps presents: *none, ays-sd15, ays-sdxl*
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- timesteps override: <custom>
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- sampler order: *0=default, 1-5*
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- options: *dynamic, low order, rescale*
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- [Ctrl+X](https://github.com/genforce/ctrl-x):
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- control **structure** (*similar to controlnet*) and **appearance** (*similar to ipadapter*)
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without the need for extra models, all via code feed-forwards!
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@@ -173,7 +173,6 @@ class DiffusionSampler:
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self.config['beta_schedule'] = 'scaled_linear'
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elif shared.opts.schedulers_beta_schedule == 'cosine':
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self.config['beta_schedule'] = 'squaredcos_cap_v2'
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print('HERE', shared.opts.schedulers_beta_schedule, self.config['beta_schedule'])
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timesteps = re.split(',| ', shared.opts.schedulers_timesteps)
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timesteps = [int(x) for x in timesteps if x.isdigit()]
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