Merge branch 'master' into image_info_tab
This commit is contained in:
AUTOMATIC1111
GitHub
commit
0d7fdb1791
@ -51,7 +51,7 @@ Alternatively, use [Google Colab](https://colab.research.google.com/drive/1Iy-xW
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1. Install [Python 3.10.6](https://www.python.org/downloads/windows/), checking "Add Python to PATH"
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2. Install [git](https://git-scm.com/download/win).
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3. Download the stable-diffusion-webui repository, for example by running `git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git`.
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4. Place `model.ckpt` in the base directory, alongside `webui.py`.
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4. Place `model.ckpt` in the `models` directory.
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5. _*(Optional)*_ Place `GFPGANv1.3.pth` in the base directory, alongside `webui.py`.
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6. Run `webui-user.bat` from Windows Explorer as normal, non-administrate, user.
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@ -81,6 +81,7 @@ The documentation was moved from this README over to the project's [wiki](https:
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- Ideas for optimizations - https://github.com/basujindal/stable-diffusion
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- Doggettx - Cross Attention layer optimization - https://github.com/Doggettx/stable-diffusion, original idea for prompt editing.
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- Idea for SD upscale - https://github.com/jquesnelle/txt2imghd
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- Noise generation for outpainting mk2 - https://github.com/parlance-zz/g-diffuser-bot
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- CLIP interrogator idea and borrowing some code - https://github.com/pharmapsychotic/clip-interrogator
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- Initial Gradio script - posted on 4chan by an Anonymous user. Thank you Anonymous user.
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- (You)
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0
models/Put Stable Diffusion checkpoints here.txt
Normal file
0
models/Put Stable Diffusion checkpoints here.txt
Normal file
@ -274,7 +274,7 @@ def apply_filename_pattern(x, p, seed, prompt):
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x = x.replace("[height]", str(p.height))
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x = x.replace("[sampler]", sd_samplers.samplers[p.sampler_index].name)
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x = x.replace("[model_hash]", shared.sd_model_hash)
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x = x.replace("[model_hash]", shared.sd_model.sd_model_hash)
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x = x.replace("[date]", datetime.date.today().isoformat())
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if cmd_opts.hide_ui_dir_config:
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@ -353,13 +353,12 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
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})
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if extension.lower() in ("jpg", "jpeg", "webp"):
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image.save(fullfn, quality=opts.jpeg_quality, exif_bytes=exif_bytes())
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image.save(fullfn, quality=opts.jpeg_quality)
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if opts.enable_pnginfo and info is not None:
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piexif.insert(exif_bytes(), fullfn)
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else:
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image.save(fullfn, quality=opts.jpeg_quality, pnginfo=pnginfo)
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if extension.lower() == "webp":
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piexif.insert(exif_bytes, fullfn)
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target_side_length = 4000
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oversize = image.width > target_side_length or image.height > target_side_length
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if opts.export_for_4chan and (oversize or os.stat(fullfn).st_size > 4 * 1024 * 1024):
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@ -370,7 +369,9 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
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elif oversize:
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image = image.resize((image.width * target_side_length // image.height, target_side_length), LANCZOS)
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image.save(fullfn_without_extension + ".jpg", quality=opts.jpeg_quality, exif_bytes=exif_bytes())
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image.save(fullfn_without_extension + ".jpg", quality=opts.jpeg_quality)
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if opts.enable_pnginfo and info is not None:
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piexif.insert(exif_bytes(), fullfn)
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if opts.save_txt and info is not None:
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with open(f"{fullfn_without_extension}.txt", "w", encoding="utf8") as file:
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77
modules/memmon.py
Normal file
77
modules/memmon.py
Normal file
@ -0,0 +1,77 @@
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import threading
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import time
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from collections import defaultdict
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import torch
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class MemUsageMonitor(threading.Thread):
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run_flag = None
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device = None
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disabled = False
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opts = None
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data = None
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def __init__(self, name, device, opts):
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threading.Thread.__init__(self)
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self.name = name
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self.device = device
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self.opts = opts
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self.daemon = True
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self.run_flag = threading.Event()
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self.data = defaultdict(int)
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def run(self):
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if self.disabled:
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return
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while True:
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self.run_flag.wait()
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torch.cuda.reset_peak_memory_stats()
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self.data.clear()
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if self.opts.memmon_poll_rate <= 0:
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self.run_flag.clear()
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continue
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self.data["min_free"] = torch.cuda.mem_get_info()[0]
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while self.run_flag.is_set():
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free, total = torch.cuda.mem_get_info() # calling with self.device errors, torch bug?
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self.data["min_free"] = min(self.data["min_free"], free)
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time.sleep(1 / self.opts.memmon_poll_rate)
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def dump_debug(self):
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print(self, 'recorded data:')
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for k, v in self.read().items():
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print(k, -(v // -(1024 ** 2)))
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print(self, 'raw torch memory stats:')
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tm = torch.cuda.memory_stats(self.device)
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for k, v in tm.items():
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if 'bytes' not in k:
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continue
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print('\t' if 'peak' in k else '', k, -(v // -(1024 ** 2)))
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print(torch.cuda.memory_summary())
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def monitor(self):
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self.run_flag.set()
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def read(self):
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free, total = torch.cuda.mem_get_info()
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self.data["total"] = total
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torch_stats = torch.cuda.memory_stats(self.device)
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self.data["active_peak"] = torch_stats["active_bytes.all.peak"]
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self.data["reserved_peak"] = torch_stats["reserved_bytes.all.peak"]
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self.data["system_peak"] = total - self.data["min_free"]
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return self.data
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def stop(self):
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self.run_flag.clear()
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return self.read()
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@ -188,7 +188,11 @@ def fix_seed(p):
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def process_images(p: StableDiffusionProcessing) -> Processed:
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"""this is the main loop that both txt2img and img2img use; it calls func_init once inside all the scopes and func_sample once per batch"""
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assert p.prompt is not None
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if type(p.prompt) == list:
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assert(len(p.prompt) > 0)
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else:
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assert p.prompt is not None
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devices.torch_gc()
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fix_seed(p)
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@ -227,7 +231,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
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"Seed": all_seeds[index],
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"Face restoration": (opts.face_restoration_model if p.restore_faces else None),
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"Size": f"{p.width}x{p.height}",
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"Model hash": (None if not opts.add_model_hash_to_info or not shared.sd_model_hash else shared.sd_model_hash),
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"Model hash": (None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash),
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"Batch size": (None if p.batch_size < 2 else p.batch_size),
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"Batch pos": (None if p.batch_size < 2 else position_in_batch),
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"Variation seed": (None if p.subseed_strength == 0 else all_subseeds[index]),
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@ -265,6 +269,9 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
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seeds = all_seeds[n * p.batch_size:(n + 1) * p.batch_size]
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subseeds = all_subseeds[n * p.batch_size:(n + 1) * p.batch_size]
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if (len(prompts) == 0):
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break
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#uc = p.sd_model.get_learned_conditioning(len(prompts) * [p.negative_prompt])
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#c = p.sd_model.get_learned_conditioning(prompts)
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uc = prompt_parser.get_learned_conditioning(len(prompts) * [p.negative_prompt], p.steps)
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148
modules/sd_models.py
Normal file
148
modules/sd_models.py
Normal file
@ -0,0 +1,148 @@
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import glob
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import os.path
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import sys
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from collections import namedtuple
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import torch
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from omegaconf import OmegaConf
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from ldm.util import instantiate_from_config
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from modules import shared
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CheckpointInfo = namedtuple("CheckpointInfo", ['filename', 'title', 'hash'])
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checkpoints_list = {}
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try:
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# this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
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from transformers import logging
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logging.set_verbosity_error()
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except Exception:
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pass
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def list_models():
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checkpoints_list.clear()
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model_dir = os.path.abspath(shared.cmd_opts.ckpt_dir)
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def modeltitle(path, h):
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abspath = os.path.abspath(path)
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if abspath.startswith(model_dir):
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name = abspath.replace(model_dir, '')
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else:
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name = os.path.basename(path)
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if name.startswith("\\") or name.startswith("/"):
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name = name[1:]
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return f'{name} [{h}]'
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cmd_ckpt = shared.cmd_opts.ckpt
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if os.path.exists(cmd_ckpt):
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h = model_hash(cmd_ckpt)
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title = modeltitle(cmd_ckpt, h)
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checkpoints_list[title] = CheckpointInfo(cmd_ckpt, title, h)
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elif cmd_ckpt is not None and cmd_ckpt != shared.default_sd_model_file:
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print(f"Checkpoint in --ckpt argument not found: {cmd_ckpt}", file=sys.stderr)
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if os.path.exists(model_dir):
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for filename in glob.glob(model_dir + '/**/*.ckpt', recursive=True):
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h = model_hash(filename)
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title = modeltitle(filename, h)
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checkpoints_list[title] = CheckpointInfo(filename, title, h)
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def model_hash(filename):
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try:
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with open(filename, "rb") as file:
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import hashlib
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m = hashlib.sha256()
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file.seek(0x100000)
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m.update(file.read(0x10000))
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return m.hexdigest()[0:8]
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except FileNotFoundError:
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return 'NOFILE'
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def select_checkpoint():
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model_checkpoint = shared.opts.sd_model_checkpoint
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checkpoint_info = checkpoints_list.get(model_checkpoint, None)
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if checkpoint_info is not None:
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return checkpoint_info
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if len(checkpoints_list) == 0:
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print(f"Checkpoint {model_checkpoint} not found and no other checkpoints found", file=sys.stderr)
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return None
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checkpoint_info = next(iter(checkpoints_list.values()))
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if model_checkpoint is not None:
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print(f"Checkpoint {model_checkpoint} not found; loading fallback {checkpoint_info.title}", file=sys.stderr)
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return checkpoint_info
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def load_model_weights(model, checkpoint_file, sd_model_hash):
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print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}")
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pl_sd = torch.load(checkpoint_file, map_location="cpu")
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if "global_step" in pl_sd:
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print(f"Global Step: {pl_sd['global_step']}")
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sd = pl_sd["state_dict"]
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model.load_state_dict(sd, strict=False)
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if shared.cmd_opts.opt_channelslast:
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model.to(memory_format=torch.channels_last)
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if not shared.cmd_opts.no_half:
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model.half()
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model.sd_model_hash = sd_model_hash
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model.sd_model_checkpint = checkpoint_file
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def load_model():
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from modules import lowvram, sd_hijack
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checkpoint_info = select_checkpoint()
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sd_config = OmegaConf.load(shared.cmd_opts.config)
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sd_model = instantiate_from_config(sd_config.model)
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load_model_weights(sd_model, checkpoint_info.filename, checkpoint_info.hash)
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if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
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lowvram.setup_for_low_vram(sd_model, shared.cmd_opts.medvram)
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else:
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sd_model.to(shared.device)
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sd_hijack.model_hijack.hijack(sd_model)
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sd_model.eval()
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print(f"Model loaded.")
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return sd_model
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def reload_model_weights(sd_model, info=None):
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from modules import lowvram, devices
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checkpoint_info = info or select_checkpoint()
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if sd_model.sd_model_checkpint == checkpoint_info.filename:
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return
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if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
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lowvram.send_everything_to_cpu()
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else:
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sd_model.to(devices.cpu)
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load_model_weights(sd_model, checkpoint_info.filename, checkpoint_info.hash)
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if not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram:
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sd_model.to(devices.device)
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print(f"Weights loaded.")
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return sd_model
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@ -12,14 +12,16 @@ from modules.paths import script_path, sd_path
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from modules.devices import get_optimal_device
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import modules.styles
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import modules.interrogate
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import modules.memmon
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import modules.sd_models
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sd_model_file = os.path.join(script_path, 'model.ckpt')
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if not os.path.exists(sd_model_file):
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sd_model_file = "models/ldm/stable-diffusion-v1/model.ckpt"
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default_sd_model_file = sd_model_file
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parser = argparse.ArgumentParser()
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parser.add_argument("--config", type=str, default=os.path.join(sd_path, "configs/stable-diffusion/v1-inference.yaml"), help="path to config which constructs model",)
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parser.add_argument("--ckpt", type=str, default=os.path.join(sd_path, sd_model_file), help="path to checkpoint of model",)
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parser.add_argument("--ckpt", type=str, default=sd_model_file, help="path to checkpoint of stable diffusion model; this checkpoint will be added to the list of checkpoints and loaded by default if you don't have a checkpoint selected in settings",)
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parser.add_argument("--ckpt-dir", type=str, default=os.path.join(script_path, 'models'), help="path to directory with stable diffusion checkpoints",)
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parser.add_argument("--gfpgan-dir", type=str, help="GFPGAN directory", default=('./src/gfpgan' if os.path.exists('./src/gfpgan') else './GFPGAN'))
|
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parser.add_argument("--gfpgan-model", type=str, help="GFPGAN model file name", default='GFPGANv1.3.pth')
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parser.add_argument("--no-half", action='store_true', help="do not switch the model to 16-bit floats")
|
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@ -87,13 +89,17 @@ interrogator = modules.interrogate.InterrogateModels("interrogate")
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||||
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face_restorers = []
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|
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modules.sd_models.list_models()
|
||||
|
||||
|
||||
class Options:
|
||||
class OptionInfo:
|
||||
def __init__(self, default=None, label="", component=None, component_args=None):
|
||||
def __init__(self, default=None, label="", component=None, component_args=None, onchange=None):
|
||||
self.default = default
|
||||
self.label = label
|
||||
self.component = component
|
||||
self.component_args = component_args
|
||||
self.onchange = onchange
|
||||
|
||||
data = None
|
||||
hide_dirs = {"visible": False} if cmd_opts.hide_ui_dir_config else None
|
||||
@ -138,6 +144,7 @@ class Options:
|
||||
"show_progressbar": OptionInfo(True, "Show progressbar"),
|
||||
"show_progress_every_n_steps": OptionInfo(0, "Show show image creation progress every N sampling steps. Set 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 32, "step": 1}),
|
||||
"multiple_tqdm": OptionInfo(True, "Add a second progress bar to the console that shows progress for an entire job. Broken in PyCharm console."),
|
||||
"memmon_poll_rate": OptionInfo(8, "VRAM usage polls per second during generation. Set to 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 40, "step":1}),
|
||||
"face_restoration_model": OptionInfo(None, "Face restoration model", gr.Radio, lambda: {"choices": [x.name() for x in face_restorers]}),
|
||||
"code_former_weight": OptionInfo(0.5, "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
|
||||
"save_images_before_face_restoration": OptionInfo(False, "Save a copy of image before doing face restoration."),
|
||||
@ -148,6 +155,7 @@ class Options:
|
||||
"interrogate_clip_min_length": OptionInfo(24, "Interrogate: minimum description length (excluding artists, etc..)", gr.Slider, {"minimum": 1, "maximum": 128, "step": 1}),
|
||||
"interrogate_clip_max_length": OptionInfo(48, "Interrogate: maximum description length", gr.Slider, {"minimum": 1, "maximum": 256, "step": 1}),
|
||||
"interrogate_clip_dict_limit": OptionInfo(1500, "Interrogate: maximum number of lines in text file (0 = No limit)"),
|
||||
"sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Radio, lambda: {"choices": [x.title for x in modules.sd_models.checkpoints_list.values()]}),
|
||||
}
|
||||
|
||||
def __init__(self):
|
||||
@ -178,6 +186,10 @@ class Options:
|
||||
with open(filename, "r", encoding="utf8") as file:
|
||||
self.data = json.load(file)
|
||||
|
||||
def onchange(self, key, func):
|
||||
item = self.data_labels.get(key)
|
||||
item.onchange = func
|
||||
|
||||
|
||||
opts = Options()
|
||||
if os.path.exists(config_filename):
|
||||
@ -186,7 +198,6 @@ if os.path.exists(config_filename):
|
||||
sd_upscalers = []
|
||||
|
||||
sd_model = None
|
||||
sd_model_hash = ''
|
||||
|
||||
progress_print_out = sys.stdout
|
||||
|
||||
@ -217,3 +228,6 @@ class TotalTQDM:
|
||||
|
||||
|
||||
total_tqdm = TotalTQDM()
|
||||
|
||||
mem_mon = modules.memmon.MemUsageMonitor("MemMon", device, opts)
|
||||
mem_mon.start()
|
||||
|
||||
@ -119,6 +119,7 @@ def save_files(js_data, images, index):
|
||||
|
||||
def wrap_gradio_call(func):
|
||||
def f(*args, **kwargs):
|
||||
shared.mem_mon.monitor()
|
||||
t = time.perf_counter()
|
||||
|
||||
try:
|
||||
@ -135,8 +136,20 @@ def wrap_gradio_call(func):
|
||||
|
||||
elapsed = time.perf_counter() - t
|
||||
|
||||
mem_stats = {k: -(v//-(1024*1024)) for k,v in shared.mem_mon.stop().items()}
|
||||
active_peak = mem_stats['active_peak']
|
||||
reserved_peak = mem_stats['reserved_peak']
|
||||
sys_peak = '?' if opts.memmon_poll_rate <= 0 else mem_stats['system_peak']
|
||||
sys_total = mem_stats['total']
|
||||
sys_pct = '?' if opts.memmon_poll_rate <= 0 else round(sys_peak/sys_total * 100, 2)
|
||||
vram_tooltip = "Torch active: Peak amount of VRAM used by Torch during generation, excluding cached data.
" \
|
||||
"Torch reserved: Peak amount of VRAM allocated by Torch, including all active and cached data.
" \
|
||||
"Sys VRAM: Peak amount of VRAM allocation across all applications / total GPU VRAM (peak utilization%)."
|
||||
|
||||
vram_html = '' if opts.memmon_poll_rate == 0 else f"<p class='vram' title='{vram_tooltip}'>Torch active/reserved: {active_peak}/{reserved_peak} MiB, <wbr>Sys VRAM: {sys_peak}/{sys_total} MiB ({sys_pct}%)</p>"
|
||||
|
||||
# last item is always HTML
|
||||
res[-1] = res[-1] + f"<p class='performance'>Time taken: {elapsed:.2f}s</p>"
|
||||
res[-1] += f"<div class='performance'><p class='time'>Time taken: <wbr>{elapsed:.2f}s</p>{vram_html}</div>"
|
||||
|
||||
shared.state.interrupted = False
|
||||
|
||||
@ -324,6 +337,8 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
|
||||
custom_inputs = modules.scripts.scripts_txt2img.setup_ui(is_img2img=False)
|
||||
|
||||
with gr.Column(variant='panel'):
|
||||
progressbar = gr.HTML(elem_id="progressbar")
|
||||
|
||||
with gr.Group():
|
||||
txt2img_preview = gr.Image(elem_id='txt2img_preview', visible=False)
|
||||
txt2img_gallery = gr.Gallery(label='Output', elem_id='txt2img_gallery').style(grid=4)
|
||||
@ -336,8 +351,6 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
|
||||
send_to_extras = gr.Button('Send to extras')
|
||||
interrupt = gr.Button('Interrupt')
|
||||
|
||||
progressbar = gr.HTML(elem_id="progressbar")
|
||||
|
||||
with gr.Group():
|
||||
html_info = gr.HTML()
|
||||
generation_info = gr.Textbox(visible=False)
|
||||
@ -461,6 +474,8 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
|
||||
custom_inputs = modules.scripts.scripts_img2img.setup_ui(is_img2img=True)
|
||||
|
||||
with gr.Column(variant='panel'):
|
||||
progressbar = gr.HTML(elem_id="progressbar")
|
||||
|
||||
with gr.Group():
|
||||
img2img_preview = gr.Image(elem_id='img2img_preview', visible=False)
|
||||
img2img_gallery = gr.Gallery(label='Output', elem_id='img2img_gallery').style(grid=4)
|
||||
@ -474,7 +489,6 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
|
||||
interrupt = gr.Button('Interrupt')
|
||||
img2img_save_style = gr.Button('Save prompt as style')
|
||||
|
||||
progressbar = gr.HTML(elem_id="progressbar")
|
||||
|
||||
with gr.Group():
|
||||
html_info = gr.HTML()
|
||||
@ -745,7 +759,12 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
|
||||
if comp_args and isinstance(comp_args, dict) and comp_args.get('visible') is False:
|
||||
continue
|
||||
|
||||
oldval = opts.data.get(key, None)
|
||||
opts.data[key] = value
|
||||
|
||||
if oldval != value and opts.data_labels[key].onchange is not None:
|
||||
opts.data_labels[key].onchange()
|
||||
|
||||
up.append(comp.update(value=value))
|
||||
|
||||
opts.save(shared.config_filename)
|
||||
|
||||
76
script.js
76
script.js
@ -66,6 +66,8 @@ titles = {
|
||||
"Style 2": "Style to apply; styles have components for both positive and negative prompts and apply to both",
|
||||
"Apply style": "Insert selected styles into prompt fields",
|
||||
"Create style": "Save current prompts as a style. If you add the token {prompt} to the text, the style use that as placeholder for your prompt when you use the style in the future.",
|
||||
|
||||
"Checkpoint name": "Loads weights from checkpoint before making images. You can either use hash or a part of filename (as seen in settings) for checkpoint name. Recommended to use with Y axis for less switching.",
|
||||
}
|
||||
|
||||
function gradioApp(){
|
||||
@ -74,6 +76,41 @@ function gradioApp(){
|
||||
|
||||
global_progressbar = null
|
||||
|
||||
function closeModal() {
|
||||
gradioApp().getElementById("lightboxModal").style.display = "none";
|
||||
}
|
||||
|
||||
function showModal(elem) {
|
||||
gradioApp().getElementById("modalImage").src = elem.src
|
||||
gradioApp().getElementById("lightboxModal").style.display = "block";
|
||||
}
|
||||
|
||||
function showGalleryImage(){
|
||||
setTimeout(function() {
|
||||
fullImg_preview = gradioApp().querySelectorAll('img.w-full.object-contain')
|
||||
|
||||
if(fullImg_preview != null){
|
||||
fullImg_preview.forEach(function function_name(e) {
|
||||
if(e && e.parentElement.tagName == 'DIV'){
|
||||
e.style.cursor='pointer'
|
||||
|
||||
elemfunc = function(elem){
|
||||
elem.onclick = function(){showModal(elem)};
|
||||
}
|
||||
elemfunc(e)
|
||||
}
|
||||
});
|
||||
}
|
||||
|
||||
}, 100);
|
||||
}
|
||||
|
||||
function galleryImageHandler(e){
|
||||
if(e && e.parentElement.tagName == 'BUTTON'){
|
||||
e.onclick = showGalleryImage;
|
||||
}
|
||||
}
|
||||
|
||||
function addTitles(root){
|
||||
root.querySelectorAll('span, button, select').forEach(function(span){
|
||||
tooltip = titles[span.textContent];
|
||||
@ -115,13 +152,18 @@ function addTitles(root){
|
||||
img2img_preview.style.width = img2img_gallery.clientWidth + "px"
|
||||
img2img_preview.style.height = img2img_gallery.clientHeight + "px"
|
||||
}
|
||||
|
||||
|
||||
|
||||
window.setTimeout(requestProgress, 500)
|
||||
});
|
||||
mutationObserver.observe( progressbar, { childList:true, subtree:true })
|
||||
}
|
||||
|
||||
fullImg_preview = gradioApp().querySelectorAll('img.w-full')
|
||||
|
||||
if(fullImg_preview != null){
|
||||
fullImg_preview.forEach(galleryImageHandler);
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
document.addEventListener("DOMContentLoaded", function() {
|
||||
@ -129,6 +171,27 @@ document.addEventListener("DOMContentLoaded", function() {
|
||||
addTitles(gradioApp());
|
||||
});
|
||||
mutationObserver.observe( gradioApp(), { childList:true, subtree:true })
|
||||
|
||||
const modalFragment = document.createDocumentFragment();
|
||||
const modal = document.createElement('div')
|
||||
modal.onclick = closeModal;
|
||||
|
||||
const modalClose = document.createElement('span')
|
||||
modalClose.className = 'modalClose cursor';
|
||||
modalClose.innerHTML = '×'
|
||||
modalClose.onclick = closeModal;
|
||||
modal.id = "lightboxModal";
|
||||
modal.appendChild(modalClose)
|
||||
|
||||
const modalImage = document.createElement('img')
|
||||
modalImage.id = 'modalImage';
|
||||
modalImage.onclick = closeModal;
|
||||
modal.appendChild(modalImage)
|
||||
|
||||
gradioApp().getRootNode().appendChild(modal)
|
||||
|
||||
document.body.appendChild(modalFragment);
|
||||
|
||||
});
|
||||
|
||||
function selected_gallery_index(){
|
||||
@ -180,6 +243,15 @@ function submit(){
|
||||
for(var i=0;i<arguments.length;i++){
|
||||
res.push(arguments[i])
|
||||
}
|
||||
|
||||
// As it is currently, txt2img and img2img send back the previous output args (txt2img_gallery, generation_info, html_info) whenever you generate a new image.
|
||||
// This can lead to uploading a huge gallery of previously generated images, which leads to an unnecessary delay between submitting and beginning to generate.
|
||||
// I don't know why gradio is seding outputs along with inputs, but we can prevent sending the image gallery here, which seems to be an issue for some.
|
||||
// If gradio at some point stops sending outputs, this may break something
|
||||
if(Array.isArray(res[res.length - 3])){
|
||||
res[res.length - 3] = null
|
||||
}
|
||||
|
||||
return res
|
||||
}
|
||||
|
||||
|
||||
@ -59,7 +59,7 @@ def find_noise_for_image(p, cond, uncond, cfg_scale, steps):
|
||||
return x / x.std()
|
||||
|
||||
|
||||
Cached = namedtuple("Cached", ["noise", "cfg_scale", "steps", "latent", "original_prompt"])
|
||||
Cached = namedtuple("Cached", ["noise", "cfg_scale", "steps", "latent", "original_prompt", "original_negative_prompt"])
|
||||
|
||||
|
||||
class Script(scripts.Script):
|
||||
@ -74,19 +74,20 @@ class Script(scripts.Script):
|
||||
|
||||
def ui(self, is_img2img):
|
||||
original_prompt = gr.Textbox(label="Original prompt", lines=1)
|
||||
original_negative_prompt = gr.Textbox(label="Original negative prompt", lines=1)
|
||||
cfg = gr.Slider(label="Decode CFG scale", minimum=0.0, maximum=15.0, step=0.1, value=1.0)
|
||||
st = gr.Slider(label="Decode steps", minimum=1, maximum=150, step=1, value=50)
|
||||
randomness = gr.Slider(label="randomness", minimum=0.0, maximum=1.0, step=0.01, value=0.0)
|
||||
return [original_prompt, cfg, st, randomness]
|
||||
return [original_prompt, original_negative_prompt, cfg, st, randomness]
|
||||
|
||||
def run(self, p, original_prompt, cfg, st, randomness):
|
||||
def run(self, p, original_prompt, original_negative_prompt, cfg, st, randomness):
|
||||
p.batch_size = 1
|
||||
p.batch_count = 1
|
||||
|
||||
def sample_extra(x, conditioning, unconditional_conditioning):
|
||||
lat = (p.init_latent.cpu().numpy() * 10).astype(int)
|
||||
|
||||
same_params = self.cache is not None and self.cache.cfg_scale == cfg and self.cache.steps == st and self.cache.original_prompt == original_prompt
|
||||
same_params = self.cache is not None and self.cache.cfg_scale == cfg and self.cache.steps == st and self.cache.original_prompt == original_prompt and self.cache.original_negative_prompt == original_negative_prompt
|
||||
same_everything = same_params and self.cache.latent.shape == lat.shape and np.abs(self.cache.latent-lat).sum() < 100
|
||||
|
||||
if same_everything:
|
||||
@ -94,9 +95,9 @@ class Script(scripts.Script):
|
||||
else:
|
||||
shared.state.job_count += 1
|
||||
cond = p.sd_model.get_learned_conditioning(p.batch_size * [original_prompt])
|
||||
uncond = p.sd_model.get_learned_conditioning(p.batch_size * [""])
|
||||
uncond = p.sd_model.get_learned_conditioning(p.batch_size * [original_negative_prompt])
|
||||
rec_noise = find_noise_for_image(p, cond, uncond, cfg, st)
|
||||
self.cache = Cached(rec_noise, cfg, st, lat, original_prompt)
|
||||
self.cache = Cached(rec_noise, cfg, st, lat, original_prompt, original_negative_prompt)
|
||||
|
||||
rand_noise = processing.create_random_tensors(p.init_latent.shape[1:], [p.seed + x + 1 for x in range(p.init_latent.shape[0])])
|
||||
|
||||
|
||||
290
scripts/outpainting_mk_2.py
Normal file
290
scripts/outpainting_mk_2.py
Normal file
@ -0,0 +1,290 @@
|
||||
import math
|
||||
|
||||
import numpy as np
|
||||
import skimage
|
||||
|
||||
import modules.scripts as scripts
|
||||
import gradio as gr
|
||||
from PIL import Image, ImageDraw
|
||||
|
||||
from modules import images, processing, devices
|
||||
from modules.processing import Processed, process_images
|
||||
from modules.shared import opts, cmd_opts, state
|
||||
|
||||
|
||||
def expand(x, dir, amount, power=0.75):
|
||||
is_left = dir == 3
|
||||
is_right = dir == 1
|
||||
is_up = dir == 0
|
||||
is_down = dir == 2
|
||||
|
||||
if is_left or is_right:
|
||||
noise = np.zeros((x.shape[0], amount, 3), dtype=float)
|
||||
indexes = np.random.random((x.shape[0], amount)) ** power * (1 - np.arange(amount) / amount)
|
||||
if is_right:
|
||||
indexes = 1 - indexes
|
||||
indexes = (indexes * (x.shape[1] - 1)).astype(int)
|
||||
|
||||
for row in range(x.shape[0]):
|
||||
if is_left:
|
||||
noise[row] = x[row][indexes[row]]
|
||||
else:
|
||||
noise[row] = np.flip(x[row][indexes[row]], axis=0)
|
||||
|
||||
x = np.concatenate([noise, x] if is_left else [x, noise], axis=1)
|
||||
return x
|
||||
|
||||
if is_up or is_down:
|
||||
noise = np.zeros((amount, x.shape[1], 3), dtype=float)
|
||||
indexes = np.random.random((x.shape[1], amount)) ** power * (1 - np.arange(amount) / amount)
|
||||
if is_down:
|
||||
indexes = 1 - indexes
|
||||
indexes = (indexes * x.shape[0] - 1).astype(int)
|
||||
|
||||
for row in range(x.shape[1]):
|
||||
if is_up:
|
||||
noise[:, row] = x[:, row][indexes[row]]
|
||||
else:
|
||||
noise[:, row] = np.flip(x[:, row][indexes[row]], axis=0)
|
||||
|
||||
x = np.concatenate([noise, x] if is_up else [x, noise], axis=0)
|
||||
return x
|
||||
|
||||
|
||||
def get_matched_noise(_np_src_image, np_mask_rgb, noise_q=1, color_variation=0.05):
|
||||
# helper fft routines that keep ortho normalization and auto-shift before and after fft
|
||||
def _fft2(data):
|
||||
if data.ndim > 2: # has channels
|
||||
out_fft = np.zeros((data.shape[0], data.shape[1], data.shape[2]), dtype=np.complex128)
|
||||
for c in range(data.shape[2]):
|
||||
c_data = data[:, :, c]
|
||||
out_fft[:, :, c] = np.fft.fft2(np.fft.fftshift(c_data), norm="ortho")
|
||||
out_fft[:, :, c] = np.fft.ifftshift(out_fft[:, :, c])
|
||||
else: # one channel
|
||||
out_fft = np.zeros((data.shape[0], data.shape[1]), dtype=np.complex128)
|
||||
out_fft[:, :] = np.fft.fft2(np.fft.fftshift(data), norm="ortho")
|
||||
out_fft[:, :] = np.fft.ifftshift(out_fft[:, :])
|
||||
|
||||
return out_fft
|
||||
|
||||
def _ifft2(data):
|
||||
if data.ndim > 2: # has channels
|
||||
out_ifft = np.zeros((data.shape[0], data.shape[1], data.shape[2]), dtype=np.complex128)
|
||||
for c in range(data.shape[2]):
|
||||
c_data = data[:, :, c]
|
||||
out_ifft[:, :, c] = np.fft.ifft2(np.fft.fftshift(c_data), norm="ortho")
|
||||
out_ifft[:, :, c] = np.fft.ifftshift(out_ifft[:, :, c])
|
||||
else: # one channel
|
||||
out_ifft = np.zeros((data.shape[0], data.shape[1]), dtype=np.complex128)
|
||||
out_ifft[:, :] = np.fft.ifft2(np.fft.fftshift(data), norm="ortho")
|
||||
out_ifft[:, :] = np.fft.ifftshift(out_ifft[:, :])
|
||||
|
||||
return out_ifft
|
||||
|
||||
def _get_gaussian_window(width, height, std=3.14, mode=0):
|
||||
window_scale_x = float(width / min(width, height))
|
||||
window_scale_y = float(height / min(width, height))
|
||||
|
||||
window = np.zeros((width, height))
|
||||
x = (np.arange(width) / width * 2. - 1.) * window_scale_x
|
||||
for y in range(height):
|
||||
fy = (y / height * 2. - 1.) * window_scale_y
|
||||
if mode == 0:
|
||||
window[:, y] = np.exp(-(x ** 2 + fy ** 2) * std)
|
||||
else:
|
||||
window[:, y] = (1 / ((x ** 2 + 1.) * (fy ** 2 + 1.))) ** (std / 3.14) # hey wait a minute that's not gaussian
|
||||
|
||||
return window
|
||||
|
||||
def _get_masked_window_rgb(np_mask_grey, hardness=1.):
|
||||
np_mask_rgb = np.zeros((np_mask_grey.shape[0], np_mask_grey.shape[1], 3))
|
||||
if hardness != 1.:
|
||||
hardened = np_mask_grey[:] ** hardness
|
||||
else:
|
||||
hardened = np_mask_grey[:]
|
||||
for c in range(3):
|
||||
np_mask_rgb[:, :, c] = hardened[:]
|
||||
return np_mask_rgb
|
||||
|
||||
width = _np_src_image.shape[0]
|
||||
height = _np_src_image.shape[1]
|
||||
num_channels = _np_src_image.shape[2]
|
||||
|
||||
np_src_image = _np_src_image[:] * (1. - np_mask_rgb)
|
||||
np_mask_grey = (np.sum(np_mask_rgb, axis=2) / 3.)
|
||||
img_mask = np_mask_grey > 1e-6
|
||||
ref_mask = np_mask_grey < 1e-3
|
||||
|
||||
windowed_image = _np_src_image * (1. - _get_masked_window_rgb(np_mask_grey))
|
||||
windowed_image /= np.max(windowed_image)
|
||||
windowed_image += np.average(_np_src_image) * np_mask_rgb # / (1.-np.average(np_mask_rgb)) # rather than leave the masked area black, we get better results from fft by filling the average unmasked color
|
||||
|
||||
src_fft = _fft2(windowed_image) # get feature statistics from masked src img
|
||||
src_dist = np.absolute(src_fft)
|
||||
src_phase = src_fft / src_dist
|
||||
|
||||
noise_window = _get_gaussian_window(width, height, mode=1) # start with simple gaussian noise
|
||||
noise_rgb = np.random.random_sample((width, height, num_channels))
|
||||
noise_grey = (np.sum(noise_rgb, axis=2) / 3.)
|
||||
noise_rgb *= color_variation # the colorfulness of the starting noise is blended to greyscale with a parameter
|
||||
for c in range(num_channels):
|
||||
noise_rgb[:, :, c] += (1. - color_variation) * noise_grey
|
||||
|
||||
noise_fft = _fft2(noise_rgb)
|
||||
for c in range(num_channels):
|
||||
noise_fft[:, :, c] *= noise_window
|
||||
noise_rgb = np.real(_ifft2(noise_fft))
|
||||
shaped_noise_fft = _fft2(noise_rgb)
|
||||
shaped_noise_fft[:, :, :] = np.absolute(shaped_noise_fft[:, :, :]) ** 2 * (src_dist ** noise_q) * src_phase # perform the actual shaping
|
||||
|
||||
brightness_variation = 0. # color_variation # todo: temporarily tieing brightness variation to color variation for now
|
||||
contrast_adjusted_np_src = _np_src_image[:] * (brightness_variation + 1.) - brightness_variation * 2.
|
||||
|
||||
# scikit-image is used for histogram matching, very convenient!
|
||||
shaped_noise = np.real(_ifft2(shaped_noise_fft))
|
||||
shaped_noise -= np.min(shaped_noise)
|
||||
shaped_noise /= np.max(shaped_noise)
|
||||
shaped_noise[img_mask, :] = skimage.exposure.match_histograms(shaped_noise[img_mask, :] ** 1., contrast_adjusted_np_src[ref_mask, :], channel_axis=1)
|
||||
shaped_noise = _np_src_image[:] * (1. - np_mask_rgb) + shaped_noise * np_mask_rgb
|
||||
|
||||
matched_noise = shaped_noise[:]
|
||||
|
||||
return np.clip(matched_noise, 0., 1.)
|
||||
|
||||
|
||||
|
||||
class Script(scripts.Script):
|
||||
def title(self):
|
||||
return "Outpainting mk2"
|
||||
|
||||
def show(self, is_img2img):
|
||||
return is_img2img
|
||||
|
||||
def ui(self, is_img2img):
|
||||
if not is_img2img:
|
||||
return None
|
||||
|
||||
info = gr.HTML("<p style=\"margin-bottom:0.75em\">Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8</p>")
|
||||
|
||||
pixels = gr.Slider(label="Pixels to expand", minimum=8, maximum=256, step=8, value=128)
|
||||
mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=8, visible=False)
|
||||
direction = gr.CheckboxGroup(label="Outpainting direction", choices=['left', 'right', 'up', 'down'], value=['left', 'right', 'up', 'down'])
|
||||
noise_q = gr.Slider(label="Fall-off exponent (lower=higher detail)", minimum=0.0, maximum=4.0, step=0.01, value=1.0)
|
||||
color_variation = gr.Slider(label="Color variation", minimum=0.0, maximum=1.0, step=0.01, value=0.05)
|
||||
|
||||
return [info, pixels, mask_blur, direction, noise_q, color_variation]
|
||||
|
||||
def run(self, p, _, pixels, mask_blur, direction, noise_q, color_variation):
|
||||
initial_seed_and_info = [None, None]
|
||||
|
||||
process_width = p.width
|
||||
process_height = p.height
|
||||
|
||||
p.mask_blur = mask_blur*4
|
||||
p.inpaint_full_res = False
|
||||
p.inpainting_fill = 1
|
||||
p.do_not_save_samples = True
|
||||
p.do_not_save_grid = True
|
||||
|
||||
left = pixels if "left" in direction else 0
|
||||
right = pixels if "right" in direction else 0
|
||||
up = pixels if "up" in direction else 0
|
||||
down = pixels if "down" in direction else 0
|
||||
|
||||
init_img = p.init_images[0]
|
||||
target_w = math.ceil((init_img.width + left + right) / 64) * 64
|
||||
target_h = math.ceil((init_img.height + up + down) / 64) * 64
|
||||
|
||||
if left > 0:
|
||||
left = left * (target_w - init_img.width) // (left + right)
|
||||
if right > 0:
|
||||
right = target_w - init_img.width - left
|
||||
|
||||
if up > 0:
|
||||
up = up * (target_h - init_img.height) // (up + down)
|
||||
|
||||
if down > 0:
|
||||
down = target_h - init_img.height - up
|
||||
|
||||
init_image = p.init_images[0]
|
||||
|
||||
state.job_count = (1 if left > 0 else 0) + (1 if right > 0 else 0)+ (1 if up > 0 else 0)+ (1 if down > 0 else 0)
|
||||
|
||||
def expand(init, expand_pixels, is_left=False, is_right=False, is_top=False, is_bottom=False):
|
||||
is_horiz = is_left or is_right
|
||||
is_vert = is_top or is_bottom
|
||||
pixels_horiz = expand_pixels if is_horiz else 0
|
||||
pixels_vert = expand_pixels if is_vert else 0
|
||||
|
||||
img = Image.new("RGB", (init.width + pixels_horiz, init.height + pixels_vert))
|
||||
img.paste(init, (pixels_horiz if is_left else 0, pixels_vert if is_top else 0))
|
||||
mask = Image.new("RGB", (init.width + pixels_horiz, init.height + pixels_vert), "white")
|
||||
draw = ImageDraw.Draw(mask)
|
||||
draw.rectangle((
|
||||
expand_pixels + mask_blur if is_left else 0,
|
||||
expand_pixels + mask_blur if is_top else 0,
|
||||
mask.width - expand_pixels - mask_blur if is_right else mask.width,
|
||||
mask.height - expand_pixels - mask_blur if is_bottom else mask.height,
|
||||
), fill="black")
|
||||
|
||||
np_image = (np.asarray(img) / 255.0).astype(np.float64)
|
||||
np_mask = (np.asarray(mask) / 255.0).astype(np.float64)
|
||||
noised = get_matched_noise(np_image, np_mask, noise_q, color_variation)
|
||||
out = Image.fromarray(np.clip(noised * 255., 0., 255.).astype(np.uint8), mode="RGB")
|
||||
|
||||
target_width = min(process_width, init.width + pixels_horiz) if is_horiz else img.width
|
||||
target_height = min(process_height, init.height + pixels_vert) if is_vert else img.height
|
||||
|
||||
crop_region = (
|
||||
0 if is_left else out.width - target_width,
|
||||
0 if is_top else out.height - target_height,
|
||||
target_width if is_left else out.width,
|
||||
target_height if is_top else out.height,
|
||||
)
|
||||
|
||||
image_to_process = out.crop(crop_region)
|
||||
mask = mask.crop(crop_region)
|
||||
|
||||
p.width = target_width if is_horiz else img.width
|
||||
p.height = target_height if is_vert else img.height
|
||||
p.init_images = [image_to_process]
|
||||
p.image_mask = mask
|
||||
|
||||
latent_mask = Image.new("RGB", (p.width, p.height), "white")
|
||||
draw = ImageDraw.Draw(latent_mask)
|
||||
draw.rectangle((
|
||||
expand_pixels + mask_blur * 2 if is_left else 0,
|
||||
expand_pixels + mask_blur * 2 if is_top else 0,
|
||||
mask.width - expand_pixels - mask_blur * 2 if is_right else mask.width,
|
||||
mask.height - expand_pixels - mask_blur * 2 if is_bottom else mask.height,
|
||||
), fill="black")
|
||||
p.latent_mask = latent_mask
|
||||
|
||||
proc = process_images(p)
|
||||
proc_img = proc.images[0]
|
||||
|
||||
if initial_seed_and_info[0] is None:
|
||||
initial_seed_and_info[0] = proc.seed
|
||||
initial_seed_and_info[1] = proc.info
|
||||
|
||||
out.paste(proc_img, (0 if is_left else out.width - proc_img.width, 0 if is_top else out.height - proc_img.height))
|
||||
return out
|
||||
|
||||
img = init_image
|
||||
|
||||
if left > 0:
|
||||
img = expand(img, left, is_left=True)
|
||||
if right > 0:
|
||||
img = expand(img, right, is_right=True)
|
||||
if up > 0:
|
||||
img = expand(img, up, is_top=True)
|
||||
if down > 0:
|
||||
img = expand(img, down, is_bottom=True)
|
||||
|
||||
res = Processed(p, [img], initial_seed_and_info[0], initial_seed_and_info[1])
|
||||
|
||||
if opts.samples_save:
|
||||
images.save_image(img, p.outpath_samples, "", res.seed, p.prompt, opts.grid_format, info=res.info, p=p)
|
||||
|
||||
return res
|
||||
|
||||
@ -13,28 +13,42 @@ from modules.shared import opts, cmd_opts, state
|
||||
|
||||
class Script(scripts.Script):
|
||||
def title(self):
|
||||
return "Prompts from file"
|
||||
return "Prompts from file or textbox"
|
||||
|
||||
def ui(self, is_img2img):
|
||||
# This checkbox would look nicer as two tabs, but there are two problems:
|
||||
# 1) There is a bug in Gradio 3.3 that prevents visibility from working on Tabs
|
||||
# 2) Even with Gradio 3.3.1, returning a control (like Tabs) that can't be used as input
|
||||
# causes a AttributeError: 'Tabs' object has no attribute 'preprocess' assert,
|
||||
# due to the way Script assumes all controls returned can be used as inputs.
|
||||
# Therefore, there's no good way to use grouping components right now,
|
||||
# so we will use a checkbox! :)
|
||||
checkbox_txt = gr.Checkbox(label="Show Textbox", value=False)
|
||||
file = gr.File(label="File with inputs", type='bytes')
|
||||
prompt_txt = gr.TextArea(label="Prompts")
|
||||
checkbox_txt.change(fn=lambda x: [gr.File.update(visible = not x), gr.TextArea.update(visible = x)], inputs=[checkbox_txt], outputs=[file, prompt_txt])
|
||||
return [checkbox_txt, file, prompt_txt]
|
||||
|
||||
return [file]
|
||||
|
||||
def run(self, p, data: bytes):
|
||||
lines = [x.strip() for x in data.decode('utf8', errors='ignore').split("\n")]
|
||||
def run(self, p, checkbox_txt, data: bytes, prompt_txt: str):
|
||||
if (checkbox_txt):
|
||||
lines = [x.strip() for x in prompt_txt.splitlines()]
|
||||
else:
|
||||
lines = [x.strip() for x in data.decode('utf8', errors='ignore').split("\n")]
|
||||
lines = [x for x in lines if len(x) > 0]
|
||||
|
||||
batch_count = math.ceil(len(lines) / p.batch_size)
|
||||
print(f"Will process {len(lines) * p.n_iter} images in {batch_count * p.n_iter} batches.")
|
||||
img_count = len(lines) * p.n_iter
|
||||
batch_count = math.ceil(img_count / p.batch_size)
|
||||
loop_count = math.ceil(batch_count / p.n_iter)
|
||||
print(f"Will process {img_count} images in {batch_count} batches.")
|
||||
|
||||
p.do_not_save_grid = True
|
||||
|
||||
state.job_count = batch_count
|
||||
|
||||
images = []
|
||||
for batch_no in range(batch_count):
|
||||
state.job = f"{batch_no + 1} out of {batch_count * p.n_iter}"
|
||||
p.prompt = lines[batch_no*p.batch_size:(batch_no+1)*p.batch_size] * p.n_iter
|
||||
for loop_no in range(loop_count):
|
||||
state.job = f"{loop_no + 1} out of {loop_count}"
|
||||
p.prompt = lines[loop_no*p.batch_size:(loop_no+1)*p.batch_size] * p.n_iter
|
||||
proc = process_images(p)
|
||||
images += proc.images
|
||||
|
||||
|
||||
@ -10,7 +10,9 @@ import gradio as gr
|
||||
from modules import images
|
||||
from modules.processing import process_images, Processed
|
||||
from modules.shared import opts, cmd_opts, state
|
||||
import modules.shared as shared
|
||||
import modules.sd_samplers
|
||||
import modules.sd_models
|
||||
import re
|
||||
|
||||
|
||||
@ -41,6 +43,15 @@ def apply_sampler(p, x, xs):
|
||||
p.sampler_index = sampler_index
|
||||
|
||||
|
||||
def apply_checkpoint(p, x, xs):
|
||||
applicable = [info for info in modules.sd_models.checkpoints_list.values() if x in info.title]
|
||||
assert len(applicable) > 0, f'Checkpoint {x} for found'
|
||||
|
||||
info = applicable[0]
|
||||
|
||||
modules.sd_models.reload_model_weights(shared.sd_model, info)
|
||||
|
||||
|
||||
def format_value_add_label(p, opt, x):
|
||||
if type(x) == float:
|
||||
x = round(x, 8)
|
||||
@ -74,15 +85,16 @@ axis_options = [
|
||||
AxisOption("CFG Scale", float, apply_field("cfg_scale"), format_value_add_label),
|
||||
AxisOption("Prompt S/R", str, apply_prompt, format_value),
|
||||
AxisOption("Sampler", str, apply_sampler, format_value),
|
||||
AxisOption("Checkpoint name", str, apply_checkpoint, format_value),
|
||||
AxisOptionImg2Img("Denoising", float, apply_field("denoising_strength"), format_value_add_label), # as it is now all AxisOptionImg2Img items must go after AxisOption ones
|
||||
]
|
||||
|
||||
|
||||
def draw_xy_grid(p, xs, ys, x_label, y_label, cell, draw_legend):
|
||||
def draw_xy_grid(p, xs, ys, x_labels, y_labels, cell, draw_legend):
|
||||
res = []
|
||||
|
||||
ver_texts = [[images.GridAnnotation(y_label(y))] for y in ys]
|
||||
hor_texts = [[images.GridAnnotation(x_label(x))] for x in xs]
|
||||
ver_texts = [[images.GridAnnotation(y)] for y in y_labels]
|
||||
hor_texts = [[images.GridAnnotation(x)] for x in x_labels]
|
||||
|
||||
first_pocessed = None
|
||||
|
||||
@ -206,8 +218,8 @@ class Script(scripts.Script):
|
||||
p,
|
||||
xs=xs,
|
||||
ys=ys,
|
||||
x_label=lambda x: x_opt.format_value(p, x_opt, x),
|
||||
y_label=lambda y: y_opt.format_value(p, y_opt, y),
|
||||
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
|
||||
)
|
||||
@ -215,4 +227,7 @@ class Script(scripts.Script):
|
||||
if opts.grid_save:
|
||||
images.save_image(processed.images[0], p.outpath_grids, "xy_grid", prompt=p.prompt, seed=processed.seed, grid=True, p=p)
|
||||
|
||||
# restore checkpoint in case it was changed by axes
|
||||
modules.sd_models.reload_model_weights(shared.sd_model)
|
||||
|
||||
return processed
|
||||
|
||||
61
style.css
61
style.css
@ -1,5 +1,21 @@
|
||||
.output-html p {margin: 0 0.5em;}
|
||||
.performance { font-size: 0.85em; color: #444; }
|
||||
|
||||
.performance {
|
||||
font-size: 0.85em;
|
||||
color: #444;
|
||||
display: flex;
|
||||
justify-content: space-between;
|
||||
white-space: nowrap;
|
||||
}
|
||||
|
||||
.performance .time {
|
||||
margin-right: 0;
|
||||
}
|
||||
|
||||
.performance .vram {
|
||||
margin-left: 0;
|
||||
text-align: right;
|
||||
}
|
||||
|
||||
#generate{
|
||||
min-height: 4.5em;
|
||||
@ -151,6 +167,12 @@ input[type="range"]{
|
||||
#txt2img_negative_prompt, #img2img_negative_prompt{
|
||||
}
|
||||
|
||||
#progressbar{
|
||||
position: absolute;
|
||||
z-index: 1000;
|
||||
right: 0;
|
||||
}
|
||||
|
||||
.progressDiv{
|
||||
width: 100%;
|
||||
height: 30px;
|
||||
@ -174,3 +196,40 @@ input[type="range"]{
|
||||
border-radius: 8px;
|
||||
}
|
||||
|
||||
#lightboxModal{
|
||||
display: none;
|
||||
position: fixed;
|
||||
z-index: 900;
|
||||
padding-top: 100px;
|
||||
left: 0;
|
||||
top: 0;
|
||||
width: 100%;
|
||||
height: 100%;
|
||||
overflow: auto;
|
||||
background-color: rgba(20, 20, 20, 0.95);
|
||||
}
|
||||
|
||||
.modalClose {
|
||||
color: white;
|
||||
position: absolute;
|
||||
top: 10px;
|
||||
right: 25px;
|
||||
font-size: 35px;
|
||||
font-weight: bold;
|
||||
}
|
||||
|
||||
.modalClose:hover,
|
||||
.modalClose:focus {
|
||||
color: #999;
|
||||
text-decoration: none;
|
||||
cursor: pointer;
|
||||
}
|
||||
|
||||
#modalImage {
|
||||
display: block;
|
||||
margin-left: auto;
|
||||
margin-right: auto;
|
||||
margin-top: auto;
|
||||
width: auto;
|
||||
}
|
||||
|
||||
|
||||
66
webui.py
66
webui.py
@ -3,12 +3,8 @@ import threading
|
||||
|
||||
from modules.paths import script_path
|
||||
|
||||
import torch
|
||||
from omegaconf import OmegaConf
|
||||
|
||||
import signal
|
||||
|
||||
from ldm.util import instantiate_from_config
|
||||
from modules.shared import opts, cmd_opts, state
|
||||
import modules.shared as shared
|
||||
import modules.ui
|
||||
@ -23,6 +19,7 @@ import modules.extras
|
||||
import modules.lowvram
|
||||
import modules.txt2img
|
||||
import modules.img2img
|
||||
import modules.sd_models
|
||||
|
||||
|
||||
modules.codeformer_model.setup_codeformer()
|
||||
@ -32,31 +29,19 @@ shared.face_restorers.append(modules.face_restoration.FaceRestoration())
|
||||
esrgan.load_models(cmd_opts.esrgan_models_path)
|
||||
realesrgan.setup_realesrgan()
|
||||
|
||||
|
||||
def load_model_from_config(config, ckpt, verbose=False):
|
||||
print(f"Loading model [{shared.sd_model_hash}] from {ckpt}")
|
||||
pl_sd = torch.load(ckpt, map_location="cpu")
|
||||
if "global_step" in pl_sd:
|
||||
print(f"Global Step: {pl_sd['global_step']}")
|
||||
sd = pl_sd["state_dict"]
|
||||
|
||||
model = instantiate_from_config(config.model)
|
||||
m, u = model.load_state_dict(sd, strict=False)
|
||||
if len(m) > 0 and verbose:
|
||||
print("missing keys:")
|
||||
print(m)
|
||||
if len(u) > 0 and verbose:
|
||||
print("unexpected keys:")
|
||||
print(u)
|
||||
if cmd_opts.opt_channelslast:
|
||||
model = model.to(memory_format=torch.channels_last)
|
||||
model.eval()
|
||||
return model
|
||||
|
||||
|
||||
queue_lock = threading.Lock()
|
||||
|
||||
|
||||
def wrap_queued_call(func):
|
||||
def f(*args, **kwargs):
|
||||
with queue_lock:
|
||||
res = func(*args, **kwargs)
|
||||
|
||||
return res
|
||||
|
||||
return f
|
||||
|
||||
|
||||
def wrap_gradio_gpu_call(func):
|
||||
def f(*args, **kwargs):
|
||||
shared.state.sampling_step = 0
|
||||
@ -79,33 +64,8 @@ def wrap_gradio_gpu_call(func):
|
||||
|
||||
modules.scripts.load_scripts(os.path.join(script_path, "scripts"))
|
||||
|
||||
try:
|
||||
# this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
|
||||
|
||||
from transformers import logging
|
||||
|
||||
logging.set_verbosity_error()
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
with open(cmd_opts.ckpt, "rb") as file:
|
||||
import hashlib
|
||||
m = hashlib.sha256()
|
||||
|
||||
file.seek(0x100000)
|
||||
m.update(file.read(0x10000))
|
||||
shared.sd_model_hash = m.hexdigest()[0:8]
|
||||
|
||||
sd_config = OmegaConf.load(cmd_opts.config)
|
||||
shared.sd_model = load_model_from_config(sd_config, cmd_opts.ckpt)
|
||||
shared.sd_model = (shared.sd_model if cmd_opts.no_half else shared.sd_model.half())
|
||||
|
||||
if cmd_opts.lowvram or cmd_opts.medvram:
|
||||
modules.lowvram.setup_for_low_vram(shared.sd_model, cmd_opts.medvram)
|
||||
else:
|
||||
shared.sd_model = shared.sd_model.to(shared.device)
|
||||
|
||||
modules.sd_hijack.model_hijack.hijack(shared.sd_model)
|
||||
shared.sd_model = modules.sd_models.load_model()
|
||||
shared.opts.onchange("sd_model_checkpoint", wrap_queued_call(lambda: modules.sd_models.reload_model_weights(shared.sd_model)))
|
||||
|
||||
|
||||
def webui():
|
||||
|
||||
Loading…
Reference in New Issue
Block a user