diff options
Diffstat (limited to 'scripts/xy_grid.py')
| -rw-r--r-- | scripts/xy_grid.py | 430 |
1 files changed, 0 insertions, 430 deletions
diff --git a/scripts/xy_grid.py b/scripts/xy_grid.py deleted file mode 100644 index 3e0b2805..00000000 --- a/scripts/xy_grid.py +++ /dev/null @@ -1,430 +0,0 @@ -from collections import namedtuple
-from copy import copy
-from itertools import permutations, chain
-import random
-import csv
-from io import StringIO
-from PIL import Image
-import numpy as np
-
-import modules.scripts as scripts
-import gradio as gr
-
-from modules import images, paths, sd_samplers
-from modules.hypernetworks import hypernetwork
-from modules.processing import process_images, Processed, StableDiffusionProcessingTxt2Img
-from modules.shared import opts, cmd_opts, state
-import modules.shared as shared
-import modules.sd_samplers
-import modules.sd_models
-import modules.sd_vae
-import glob
-import os
-import re
-
-
-def apply_field(field):
- def fun(p, x, xs):
- setattr(p, field, x)
-
- return fun
-
-
-def apply_prompt(p, x, xs):
- if xs[0] not in p.prompt and xs[0] not in p.negative_prompt:
- raise RuntimeError(f"Prompt S/R did not find {xs[0]} in prompt or negative prompt.")
-
- p.prompt = p.prompt.replace(xs[0], x)
- p.negative_prompt = p.negative_prompt.replace(xs[0], x)
-
-
-def apply_order(p, x, xs):
- token_order = []
-
- # Initally grab the tokens from the prompt, so they can be replaced in order of earliest seen
- for token in x:
- token_order.append((p.prompt.find(token), token))
-
- token_order.sort(key=lambda t: t[0])
-
- prompt_parts = []
-
- # Split the prompt up, taking out the tokens
- for _, token in token_order:
- n = p.prompt.find(token)
- prompt_parts.append(p.prompt[0:n])
- p.prompt = p.prompt[n + len(token):]
-
- # Rebuild the prompt with the tokens in the order we want
- prompt_tmp = ""
- for idx, part in enumerate(prompt_parts):
- prompt_tmp += part
- prompt_tmp += x[idx]
- p.prompt = prompt_tmp + p.prompt
-
-
-def apply_sampler(p, x, xs):
- sampler_name = sd_samplers.samplers_map.get(x.lower(), None)
- if sampler_name is None:
- raise RuntimeError(f"Unknown sampler: {x}")
-
- p.sampler_name = sampler_name
-
-
-def confirm_samplers(p, xs):
- for x in xs:
- if x.lower() not in sd_samplers.samplers_map:
- raise RuntimeError(f"Unknown sampler: {x}")
-
-
-def apply_checkpoint(p, x, xs):
- info = modules.sd_models.get_closet_checkpoint_match(x)
- if info is None:
- raise RuntimeError(f"Unknown checkpoint: {x}")
- modules.sd_models.reload_model_weights(shared.sd_model, info)
- p.sd_model = shared.sd_model
-
-
-def confirm_checkpoints(p, xs):
- for x in xs:
- if modules.sd_models.get_closet_checkpoint_match(x) is None:
- raise RuntimeError(f"Unknown checkpoint: {x}")
-
-
-def apply_hypernetwork(p, x, xs):
- if x.lower() in ["", "none"]:
- name = None
- else:
- name = hypernetwork.find_closest_hypernetwork_name(x)
- if not name:
- raise RuntimeError(f"Unknown hypernetwork: {x}")
- hypernetwork.load_hypernetwork(name)
-
-
-def apply_hypernetwork_strength(p, x, xs):
- hypernetwork.apply_strength(x)
-
-
-def confirm_hypernetworks(p, xs):
- for x in xs:
- if x.lower() in ["", "none"]:
- continue
- if not hypernetwork.find_closest_hypernetwork_name(x):
- raise RuntimeError(f"Unknown hypernetwork: {x}")
-
-
-def apply_clip_skip(p, x, xs):
- opts.data["CLIP_stop_at_last_layers"] = x
-
-
-def apply_upscale_latent_space(p, x, xs):
- if x.lower().strip() != '0':
- opts.data["use_scale_latent_for_hires_fix"] = True
- else:
- opts.data["use_scale_latent_for_hires_fix"] = False
-
-
-def find_vae(name: str):
- if name.lower() in ['auto', 'none']:
- return name
- else:
- vae_path = os.path.abspath(os.path.join(paths.models_path, 'VAE'))
- found = glob.glob(os.path.join(vae_path, f'**/{name}.*pt'), recursive=True)
- if found:
- return found[0]
- else:
- return 'auto'
-
-
-def apply_vae(p, x, xs):
- if x.lower().strip() == 'none':
- modules.sd_vae.reload_vae_weights(shared.sd_model, vae_file='None')
- else:
- found = find_vae(x)
- if found:
- v = modules.sd_vae.reload_vae_weights(shared.sd_model, vae_file=found)
-
-
-def apply_styles(p: StableDiffusionProcessingTxt2Img, x: str, _):
- p.styles = x.split(',')
-
-
-def format_value_add_label(p, opt, x):
- if type(x) == float:
- x = round(x, 8)
-
- return f"{opt.label}: {x}"
-
-
-def format_value(p, opt, x):
- if type(x) == float:
- x = round(x, 8)
- return x
-
-
-def format_value_join_list(p, opt, x):
- return ", ".join(x)
-
-
-def do_nothing(p, x, xs):
- pass
-
-
-def format_nothing(p, opt, x):
- return ""
-
-
-def str_permutations(x):
- """dummy function for specifying it in AxisOption's type when you want to get a list of permutations"""
- return x
-
-AxisOption = namedtuple("AxisOption", ["label", "type", "apply", "format_value", "confirm"])
-AxisOptionImg2Img = namedtuple("AxisOptionImg2Img", ["label", "type", "apply", "format_value", "confirm"])
-
-
-axis_options = [
- AxisOption("Nothing", str, do_nothing, format_nothing, None),
- AxisOption("Seed", int, apply_field("seed"), format_value_add_label, None),
- AxisOption("Var. seed", int, apply_field("subseed"), format_value_add_label, None),
- AxisOption("Var. strength", float, apply_field("subseed_strength"), format_value_add_label, None),
- AxisOption("Steps", int, apply_field("steps"), format_value_add_label, None),
- AxisOption("CFG Scale", float, apply_field("cfg_scale"), format_value_add_label, None),
- AxisOption("Prompt S/R", str, apply_prompt, format_value, None),
- AxisOption("Prompt order", str_permutations, apply_order, format_value_join_list, None),
- AxisOption("Sampler", str, apply_sampler, format_value, confirm_samplers),
- AxisOption("Checkpoint name", str, apply_checkpoint, format_value, confirm_checkpoints),
- AxisOption("Hypernetwork", str, apply_hypernetwork, format_value, confirm_hypernetworks),
- AxisOption("Hypernet str.", float, apply_hypernetwork_strength, format_value_add_label, None),
- AxisOption("Sigma Churn", float, apply_field("s_churn"), format_value_add_label, None),
- AxisOption("Sigma min", float, apply_field("s_tmin"), format_value_add_label, None),
- AxisOption("Sigma max", float, apply_field("s_tmax"), format_value_add_label, None),
- AxisOption("Sigma noise", float, apply_field("s_noise"), format_value_add_label, None),
- AxisOption("Eta", float, apply_field("eta"), format_value_add_label, None),
- AxisOption("Clip skip", int, apply_clip_skip, format_value_add_label, None),
- AxisOption("Denoising", float, apply_field("denoising_strength"), format_value_add_label, None),
- AxisOption("Upscale latent space for hires.", str, apply_upscale_latent_space, format_value_add_label, None),
- AxisOption("Cond. Image Mask Weight", float, apply_field("inpainting_mask_weight"), format_value_add_label, None),
- AxisOption("VAE", str, apply_vae, format_value_add_label, None),
- AxisOption("Styles", str, apply_styles, format_value_add_label, None),
-]
-
-
-def draw_xy_grid(p, xs, ys, x_labels, y_labels, cell, draw_legend, include_lone_images):
- ver_texts = [[images.GridAnnotation(y)] for y in y_labels]
- hor_texts = [[images.GridAnnotation(x)] for x in x_labels]
-
- # Temporary list of all the images that are generated to be populated into the grid.
- # Will be filled with empty images for any individual step that fails to process properly
- image_cache = []
-
- processed_result = None
- cell_mode = "P"
- cell_size = (1,1)
-
- state.job_count = len(xs) * len(ys) * p.n_iter
-
- for iy, y in enumerate(ys):
- for ix, x in enumerate(xs):
- state.job = f"{ix + iy * len(xs) + 1} out of {len(xs) * len(ys)}"
-
- processed:Processed = cell(x, y)
- try:
- # this dereference will throw an exception if the image was not processed
- # (this happens in cases such as if the user stops the process from the UI)
- processed_image = processed.images[0]
-
- if processed_result is None:
- # Use our first valid processed result as a template container to hold our full results
- processed_result = copy(processed)
- cell_mode = processed_image.mode
- cell_size = processed_image.size
- processed_result.images = [Image.new(cell_mode, cell_size)]
-
- image_cache.append(processed_image)
- if include_lone_images:
- processed_result.images.append(processed_image)
- processed_result.all_prompts.append(processed.prompt)
- processed_result.all_seeds.append(processed.seed)
- processed_result.infotexts.append(processed.infotexts[0])
- except:
- image_cache.append(Image.new(cell_mode, cell_size))
-
- if not processed_result:
- print("Unexpected error: draw_xy_grid failed to return even a single processed image")
- return Processed()
-
- grid = images.image_grid(image_cache, rows=len(ys))
- if draw_legend:
- grid = images.draw_grid_annotations(grid, cell_size[0], cell_size[1], hor_texts, ver_texts)
-
- processed_result.images[0] = grid
-
- return processed_result
-
-
-class SharedSettingsStackHelper(object):
- def __enter__(self):
- self.CLIP_stop_at_last_layers = opts.CLIP_stop_at_last_layers
- self.hypernetwork = opts.sd_hypernetwork
- self.model = shared.sd_model
- self.use_scale_latent_for_hires_fix = opts.use_scale_latent_for_hires_fix
- self.vae = opts.sd_vae
-
- def __exit__(self, exc_type, exc_value, tb):
- modules.sd_models.reload_model_weights(self.model)
- modules.sd_vae.reload_vae_weights(self.model, vae_file=find_vae(self.vae))
-
- hypernetwork.load_hypernetwork(self.hypernetwork)
- hypernetwork.apply_strength()
-
- opts.data["CLIP_stop_at_last_layers"] = self.CLIP_stop_at_last_layers
- opts.data["use_scale_latent_for_hires_fix"] = self.use_scale_latent_for_hires_fix
-
-
-re_range = re.compile(r"\s*([+-]?\s*\d+)\s*-\s*([+-]?\s*\d+)(?:\s*\(([+-]\d+)\s*\))?\s*")
-re_range_float = re.compile(r"\s*([+-]?\s*\d+(?:.\d*)?)\s*-\s*([+-]?\s*\d+(?:.\d*)?)(?:\s*\(([+-]\d+(?:.\d*)?)\s*\))?\s*")
-
-re_range_count = re.compile(r"\s*([+-]?\s*\d+)\s*-\s*([+-]?\s*\d+)(?:\s*\[(\d+)\s*\])?\s*")
-re_range_count_float = re.compile(r"\s*([+-]?\s*\d+(?:.\d*)?)\s*-\s*([+-]?\s*\d+(?:.\d*)?)(?:\s*\[(\d+(?:.\d*)?)\s*\])?\s*")
-
-class Script(scripts.Script):
- def title(self):
- return "X/Y plot"
-
- def ui(self, is_img2img):
- current_axis_options = [x for x in axis_options if type(x) == AxisOption or type(x) == AxisOptionImg2Img and is_img2img]
-
- with gr.Row():
- x_type = gr.Dropdown(label="X type", choices=[x.label for x in current_axis_options], value=current_axis_options[1].label, type="index", elem_id="x_type")
- x_values = gr.Textbox(label="X values", lines=1)
-
- with gr.Row():
- y_type = gr.Dropdown(label="Y type", choices=[x.label for x in current_axis_options], value=current_axis_options[0].label, type="index", elem_id="y_type")
- y_values = gr.Textbox(label="Y values", lines=1)
-
- draw_legend = gr.Checkbox(label='Draw legend', value=True)
- include_lone_images = gr.Checkbox(label='Include Separate Images', value=False)
- no_fixed_seeds = gr.Checkbox(label='Keep -1 for seeds', value=False)
-
- return [x_type, x_values, y_type, y_values, draw_legend, include_lone_images, no_fixed_seeds]
-
- def run(self, p, x_type, x_values, y_type, y_values, draw_legend, include_lone_images, no_fixed_seeds):
- if not no_fixed_seeds:
- modules.processing.fix_seed(p)
-
- if not opts.return_grid:
- p.batch_size = 1
-
- def process_axis(opt, vals):
- if opt.label == 'Nothing':
- return [0]
-
- valslist = [x.strip() for x in chain.from_iterable(csv.reader(StringIO(vals)))]
-
- if opt.type == int:
- valslist_ext = []
-
- for val in valslist:
- m = re_range.fullmatch(val)
- mc = re_range_count.fullmatch(val)
- if m is not None:
- start = int(m.group(1))
- end = int(m.group(2))+1
- step = int(m.group(3)) if m.group(3) is not None else 1
-
- valslist_ext += list(range(start, end, step))
- elif mc is not None:
- start = int(mc.group(1))
- end = int(mc.group(2))
- num = int(mc.group(3)) if mc.group(3) is not None else 1
-
- valslist_ext += [int(x) for x in np.linspace(start=start, stop=end, num=num).tolist()]
- else:
- valslist_ext.append(val)
-
- valslist = valslist_ext
- elif opt.type == float:
- valslist_ext = []
-
- for val in valslist:
- m = re_range_float.fullmatch(val)
- mc = re_range_count_float.fullmatch(val)
- if m is not None:
- start = float(m.group(1))
- end = float(m.group(2))
- step = float(m.group(3)) if m.group(3) is not None else 1
-
- valslist_ext += np.arange(start, end + step, step).tolist()
- elif mc is not None:
- start = float(mc.group(1))
- end = float(mc.group(2))
- num = int(mc.group(3)) if mc.group(3) is not None else 1
-
- valslist_ext += np.linspace(start=start, stop=end, num=num).tolist()
- else:
- valslist_ext.append(val)
-
- valslist = valslist_ext
- elif opt.type == str_permutations:
- valslist = list(permutations(valslist))
-
- valslist = [opt.type(x) for x in valslist]
-
- # Confirm options are valid before starting
- if opt.confirm:
- opt.confirm(p, valslist)
-
- return valslist
-
- x_opt = axis_options[x_type]
- xs = process_axis(x_opt, x_values)
-
- y_opt = axis_options[y_type]
- ys = process_axis(y_opt, y_values)
-
- def fix_axis_seeds(axis_opt, axis_list):
- if axis_opt.label in ['Seed','Var. seed']:
- return [int(random.randrange(4294967294)) if val is None or val == '' or val == -1 else val for val in axis_list]
- else:
- return axis_list
-
- if not no_fixed_seeds:
- xs = fix_axis_seeds(x_opt, xs)
- ys = fix_axis_seeds(y_opt, ys)
-
- if x_opt.label == 'Steps':
- total_steps = sum(xs) * len(ys)
- elif y_opt.label == 'Steps':
- total_steps = sum(ys) * len(xs)
- else:
- total_steps = p.steps * len(xs) * len(ys)
-
- if isinstance(p, StableDiffusionProcessingTxt2Img) and p.enable_hr:
- total_steps *= 2
-
- print(f"X/Y plot will create {len(xs) * len(ys) * p.n_iter} images on a {len(xs)}x{len(ys)} grid. (Total steps to process: {total_steps * p.n_iter})")
- shared.total_tqdm.updateTotal(total_steps * p.n_iter)
-
- def cell(x, y):
- pc = copy(p)
- x_opt.apply(pc, x, xs)
- y_opt.apply(pc, y, ys)
-
- return process_images(pc)
-
- with SharedSettingsStackHelper():
- processed = draw_xy_grid(
- p,
- xs=xs,
- ys=ys,
- x_labels=[x_opt.format_value(p, x_opt, x) for x in xs],
- y_labels=[y_opt.format_value(p, y_opt, y) for y in ys],
- cell=cell,
- draw_legend=draw_legend,
- include_lone_images=include_lone_images
- )
-
- if opts.grid_save:
- images.save_image(processed.images[0], p.outpath_grids, "xy_grid", extension=opts.grid_format, prompt=p.prompt, seed=processed.seed, grid=True, p=p)
-
- return processed
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