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output directory options

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AUTOMATIC 2022-09-01 17:11:47 +03:00
parent 2d5689a051
commit d28c94b8c3
1 changed files with 49 additions and 29 deletions
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webui.py
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@ -36,6 +36,7 @@ from collections import namedtuple
from contextlib import nullcontext
import signal
import tqdm
import re
import k_diffusion.sampling
from ldm.util import instantiate_from_config
@ -187,7 +188,14 @@ class Options:
data = None
data_labels = {
"outdir": OptionInfo("", "Output dictectory; if empty, defaults to 'outputs/*'"),
"outdir_samples": OptionInfo("", "Output dictectory for images; if empty, defaults to two directories below"),
"outdir_txt2img_samples": OptionInfo("outputs/txt2img-images", 'Output dictectory for txt2img images'),
"outdir_img2img_samples": OptionInfo("outputs/img2img-images", 'Output dictectory for img2img images'),
"outdir_grids": OptionInfo("", "Output dictectory for grids; if empty, defaults to two directories below"),
"outdir_txt2img_grids": OptionInfo("outputs/txt2img-grids", 'Output dictectory for txt2img grids'),
"outdir_img2img_grids": OptionInfo("outputs/img2img-grids", 'Output dictectory for img2img grids'),
"save_to_dirs": OptionInfo(False, "When writing images/grids, create a directory with name derived from the prompt"),
"save_to_dirs_prompt_len": OptionInfo(10, "When using above, how many words from prompt to put into directory name", gr.Slider, {"minimum": 1, "maximum": 32, "step": 1}),
"samples_save": OptionInfo(True, "Save indiviual samples"),
"samples_format": OptionInfo('png', 'File format for indiviual samples'),
"grid_save": OptionInfo(True, "Save image grids"),
@ -342,11 +350,12 @@ def torch_gc():
def save_image(image, path, basename, seed=None, prompt=None, extension='png', info=None, short_filename=False):
if short_filename or prompt is None or seed is None:
filename = f"{basename}"
file_decoration = ""
elif opts.save_to_dirs:
file_decoration = f"-{seed}"
else:
filename = f"{basename}-{seed}-{sanitize_filename_part(prompt)[:128]}"
file_decoration = f"-{seed}-{sanitize_filename_part(prompt)[:128]}"
if extension == 'png' and opts.enable_pnginfo and info is not None:
pnginfo = PngImagePlugin.PngInfo()
@ -354,8 +363,26 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
else:
pnginfo = None
if opts.save_to_dirs:
words = re.findall(r'\w+', prompt or "")
if len(words) == 0:
words = ["empty"]
dirname = " ".join(words[0:opts.save_to_dirs_prompt_len])
path = os.path.join(path, dirname)
os.makedirs(path, exist_ok=True)
fullfn = os.path.join(path, f"{filename}.{extension}")
filecount = len(os.listdir(path))
fullfn = "a.png"
fullfn_without_extension = "a"
for i in range(100):
fn = f"{filecount:05}" if basename == '' else f"{basename}-{filecount:04}"
fullfn = os.path.join(path, f"{fn}{file_decoration}.{extension}")
fullfn_without_extension = os.path.join(path, f"{fn}{file_decoration}")
if not os.path.exists(fullfn):
break
image.save(fullfn, quality=opts.jpeg_quality, pnginfo=pnginfo)
target_side_length = 4000
@ -368,7 +395,7 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
elif oversize:
image = image.resize((image.width * target_side_length // image.height, target_side_length), LANCZOS)
image.save(os.path.join(path, f"{filename}.jpg"), quality=opts.jpeg_quality, pnginfo=pnginfo)
image.save(os.path.join(path, f"{fullfn_without_extension}.jpg"), quality=opts.jpeg_quality, pnginfo=pnginfo)
@ -824,8 +851,9 @@ class EmbeddingsWithFixes(nn.Module):
class StableDiffusionProcessing:
def __init__(self, outpath=None, prompt="", seed=-1, sampler_index=0, batch_size=1, n_iter=1, steps=50, cfg_scale=7.0, width=512, height=512, prompt_matrix=False, use_GFPGAN=False, do_not_save_samples=False, do_not_save_grid=False, extra_generation_params=None, overlay_images=None, negative_prompt=None):
self.outpath: str = outpath
def __init__(self, outpath_samples=None, outpath_grids=None, prompt="", seed=-1, sampler_index=0, batch_size=1, n_iter=1, steps=50, cfg_scale=7.0, width=512, height=512, prompt_matrix=False, use_GFPGAN=False, do_not_save_samples=False, do_not_save_grid=False, extra_generation_params=None, overlay_images=None, negative_prompt=None):
self.outpath_samples: str = outpath_samples
self.outpath_grids: str = outpath_grids
self.prompt: str = prompt
self.negative_prompt: str = (negative_prompt or "")
self.seed: int = seed
@ -969,10 +997,8 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
seed = int(random.randrange(4294967294) if p.seed == -1 else p.seed)
sample_path = os.path.join(p.outpath, "samples")
os.makedirs(sample_path, exist_ok=True)
base_count = len(os.listdir(sample_path))
grid_count = len(os.listdir(p.outpath)) - 1
os.makedirs(p.outpath_samples, exist_ok=True)
os.makedirs(p.outpath_grids, exist_ok=True)
comments = []
@ -1071,10 +1097,9 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
image = image.convert('RGB')
if not p.do_not_save_samples:
save_image(image, sample_path, f"{base_count:05}", seeds[i], prompts[i], opts.samples_format, info=infotext())
save_image(image, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext())
output_images.append(image)
base_count += 1
unwanted_grid_because_of_img_count = len(output_images) < 2 and opts.grid_only_if_multiple
if (p.prompt_matrix or opts.grid_save) and not p.do_not_save_grid and not unwanted_grid_because_of_img_count:
@ -1097,8 +1122,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
if return_grid:
output_images.insert(0, grid)
save_image(grid, p.outpath, f"grid-{grid_count:04}", seed, prompt, opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename)
grid_count += 1
save_image(grid, p.outpath_grids, "grid", seed, prompt, opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename)
torch_gc()
return Processed(output_images, seed, infotext())
@ -1114,11 +1138,11 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
samples_ddim = self.sampler.sample(self, x, conditioning, unconditional_conditioning)
return samples_ddim
def txt2img(prompt: str, negative_prompt: str, steps: int, sampler_index: int, use_GFPGAN: bool, prompt_matrix: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, height: int, width: int, code: str):
outpath = opts.outdir or "outputs/txt2img-samples"
def txt2img(prompt: str, negative_prompt: str, steps: int, sampler_index: int, use_GFPGAN: bool, prompt_matrix: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, height: int, width: int, code: str):
p = StableDiffusionProcessingTxt2Img(
outpath=outpath,
outpath_samples=opts.outdir_samples or opts.outdir_txt2img_samples,
outpath_grids=opts.outdir_grids or opts.outdir_txt2img_grids,
prompt=prompt,
negative_prompt=negative_prompt,
seed=seed,
@ -1138,6 +1162,7 @@ def txt2img(prompt: str, negative_prompt: str, steps: int, sampler_index: int, u
p.do_not_save_samples = True
display_result_data = [[], -1, ""]
def display(imgs, s=display_result_data[1], i=display_result_data[2]):
display_result_data[0] = imgs
display_result_data[1] = s
@ -1422,8 +1447,6 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
def img2img(prompt: str, init_img, init_img_with_mask, steps: int, sampler_index: int, mask_blur: int, inpainting_fill: int, use_GFPGAN: bool, prompt_matrix, mode: int, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, height: int, width: int, resize_mode: int, upscaler_name: str, upscale_overlap: int, inpaint_full_res: bool):
outpath = opts.outdir or "outputs/img2img-samples"
is_classic = mode == 0
is_inpaint = mode == 1
is_loopback = mode == 2
@ -1439,7 +1462,8 @@ def img2img(prompt: str, init_img, init_img_with_mask, steps: int, sampler_index
assert 0. <= denoising_strength <= 1., 'can only work with strength in [0.0, 1.0]'
p = StableDiffusionProcessingImg2Img(
outpath=outpath,
outpath_samples=opts.outdir_samples or opts.outdir_img2img_samples,
outpath_grids=opts.outdir_grids or opts.outdir_img2img_grids,
prompt=prompt,
seed=seed,
sampler_index=sampler_index,
@ -1483,10 +1507,9 @@ def img2img(prompt: str, init_img, init_img_with_mask, steps: int, sampler_index
p.denoising_strength = max(p.denoising_strength * 0.95, 0.1)
history.append(processed.images[0])
grid_count = len(os.listdir(outpath)) - 1
grid = image_grid(history, batch_size, rows=1)
save_image(grid, outpath, f"grid-{grid_count:04}", initial_seed, prompt, opts.grid_format, info=info, short_filename=not opts.grid_extended_filename)
save_image(grid, p.outpath_grids, "grid", initial_seed, prompt, opts.grid_format, info=info, short_filename=not opts.grid_extended_filename)
processed = Processed(history, initial_seed, initial_info)
@ -1535,8 +1558,7 @@ def img2img(prompt: str, init_img, init_img_with_mask, steps: int, sampler_index
combined_image = combine_grid(grid)
grid_count = len(os.listdir(outpath)) - 1
save_image(combined_image, outpath, f"grid-{grid_count:04}", initial_seed, prompt, opts.grid_format, info=initial_info, short_filename=not opts.grid_extended_filename)
save_image(combined_image, p.outpath_grids, "grid", initial_seed, prompt, opts.grid_format, info=initial_info, short_filename=not opts.grid_extended_filename)
processed = Processed([combined_image], initial_seed, initial_info)
@ -1708,9 +1730,7 @@ def run_extras(image, GFPGAN_strength, RealESRGAN_upscaling, RealESRGAN_model_in
if have_realesrgan and RealESRGAN_upscaling != 1.0:
image = upscale_with_realesrgan(image, RealESRGAN_upscaling, RealESRGAN_model_index)
os.makedirs(outpath, exist_ok=True)
base_count = len(os.listdir(outpath))
save_image(image, outpath, f"{base_count:05}", None, '', opts.samples_format, short_filename=True)
save_image(image, outpath, "", None, '', opts.samples_format, short_filename=True)
return image, 0, ''