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readme extras for VRAM for
added missing packages to requirements for #74 add support for negative numbers in X/Y plot (plus ranges) #73 changed progressbar to work properly with custom modes
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README.md
26
README.md
@ -149,9 +149,9 @@ Open the URL in browser, and you are good to go.
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### What options to use for low VRAM videocards?
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- If you have 4GB VRAM and want to make 512x512 (or maybe up to 640x640) images, use `--medvram`.
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- If you have 4GB VRAM and want to make 512x512 images, but you get an out of memory error with `--medvram`, use `--medvram --opt-split-attention` instead.
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- If you have 4GB VRAM and want to make 512x512 images, and you still get an out of memory error, use `--lowvram --always-batch-cond-uncond` instead.
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- If you have 4GB VRAM and want to make images larger than you can with `--medvram`, use `--lowvram`.
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- If you have more VRAM and want to make larger images than you can usually make, use `--medvram`. You can use `--lowvram`
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- If you have 4GB VRAM and want to make 512x512 images, and you still get an out of memory error, use `--lowvram --always-batch-cond-uncond --opt-split-attention` instead.
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- If you have 4GB VRAM and want to make images larger than you can with `--medvram`, use `--lowvram --opt-split-attention`.
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- If you have more VRAM and want to make larger images than you can usually make, use `--medvram --opt-split-attention`. You can use `--lowvram`
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also but the effect will likely be barely noticeable.
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- Otherwise, do not use any of those.
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@ -159,6 +159,26 @@ Extra: if you get a green screen instead of generated pictures, you have a card
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precision floating point numbers. You must use `--precision full --no-half` in addition to other flags,
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and the model will take much more space in VRAM.
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### How to change UI defaults?
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After running once, a `ui-config.json` file appears in webui directory:
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```json
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{
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"txt2img/Sampling Steps/value": 20,
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"txt2img/Sampling Steps/minimum": 1,
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"txt2img/Sampling Steps/maximum": 150,
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"txt2img/Sampling Steps/step": 1,
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"txt2img/Batch count/value": 1,
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"txt2img/Batch count/minimum": 1,
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"txt2img/Batch count/maximum": 32,
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"txt2img/Batch count/step": 1,
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"txt2img/Batch size/value": 1,
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"txt2img/Batch size/minimum": 1,
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```
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Edit values to your liking and the next time you launch the program they will be applied.
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## Credits
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- Stable Diffusion - https://github.com/CompVis/stable-diffusion, https://github.com/CompVis/taming-transformers
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- k-diffusion - https://github.com/crowsonkb/k-diffusion.git
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@ -153,7 +153,8 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
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with torch.no_grad(), precision_scope("cuda"), ema_scope():
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p.init()
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state.job_count = p.n_iter
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if state.job_count == -1:
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state.job_count = p.n_iter
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for n in range(p.n_iter):
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if state.interrupted:
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@ -54,6 +54,7 @@ class State:
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self.job_no += 1
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self.sampling_step = 0
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state = State()
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artist_db = modules.artists.ArtistsDatabase(os.path.join(script_path, 'artists.csv'))
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@ -140,7 +140,10 @@ def check_progress_call():
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if shared.state.job_count == 0:
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return ""
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progress = shared.state.job_no / shared.state.job_count
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progress = 0
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if shared.state.job_count > 0:
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progress += shared.state.job_no / shared.state.job_count
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if shared.state.sampling_steps > 0:
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progress += 1 / shared.state.job_count * shared.state.sampling_step / shared.state.sampling_steps
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@ -8,3 +8,7 @@ torch
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transformers
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omegaconf
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pytorch_lightning
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diffusers
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invisible-watermark
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git+https://github.com/crowsonkb/k-diffusion.git
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git+https://github.com/TencentARC/GFPGAN.git
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@ -9,6 +9,7 @@ from modules import images
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from modules.processing import process_images, Processed
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from modules.shared import opts, cmd_opts, state
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import modules.sd_samplers
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import re
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def apply_field(field):
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@ -89,6 +90,8 @@ def draw_xy_grid(xs, ys, x_label, y_label, cell):
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return first_pocessed
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re_range = re.compile(r"\s*([+-]?\s*\d+)\s*-\s*([+-]?\s*\d+)(?:\s*\(([+-]\d+)\s*\))?\s*")
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class Script(scripts.Script):
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def title(self):
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return "X/Y plot"
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@ -118,11 +121,13 @@ class Script(scripts.Script):
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valslist_ext = []
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for val in valslist:
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if "-" in val:
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s = val.split("-")
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start = int(s[0])
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end = int(s[1])+1
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step = 1 if len(s) < 3 else int(s[2])
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m = re_range.fullmatch(val)
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if m is not None:
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start = int(m.group(1))
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end = int(m.group(2))+1
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step = int(m.group(3)) if m.group(3) is not None else 1
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valslist_ext += list(range(start, end, step))
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else:
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valslist_ext.append(val)
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