Merge branch 'master' of https://github.com/AUTOMATIC1111/stable-diffusion-webui into Base
This commit is contained in:
JashoBell
commit
d2c7ad2fec
19 changed files with 1013 additions and 117 deletions
1
.gitignore
vendored
1
.gitignore
vendored
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@ -16,3 +16,4 @@ __pycache__
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/webui-user.bat
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/webui-user.sh
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/interrogate
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/user.css
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|
|
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@ -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
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@ -48,3 +48,13 @@ def randn(seed, shape):
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torch.manual_seed(seed)
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return torch.randn(shape, device=device)
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def randn_without_seed(shape):
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# Pytorch currently doesn't handle setting randomness correctly when the metal backend is used.
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if device.type == 'mps':
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generator = torch.Generator(device=cpu)
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noise = torch.randn(shape, generator=generator, device=cpu).to(device)
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return noise
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return torch.randn(shape, device=device)
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@ -36,6 +36,7 @@ def run_extras(image, image_folder, gfpgan_visibility, codeformer_visibility, co
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outpath = opts.outdir_samples or opts.outdir_extras_samples
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outputs = []
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for image in imageArr:
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existing_pnginfo = image.info or {}
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@ -91,7 +92,9 @@ def run_extras(image, image_folder, gfpgan_visibility, codeformer_visibility, co
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images.save_image(image, path=outpath, basename="", seed=None, prompt=None, extension=opts.samples_format, info=info, short_filename=True, no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo)
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return imageArr, plaintext_to_html(info), ''
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outputs.append(image)
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return outputs, plaintext_to_html(info), ''
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def run_pnginfo(image):
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@ -108,8 +111,9 @@ def run_pnginfo(image):
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items['exif comment'] = exif_comment
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for field in ['jfif', 'jfif_version', 'jfif_unit', 'jfif_density', 'dpi', 'exif']:
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del items[field]
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for field in ['jfif', 'jfif_version', 'jfif_unit', 'jfif_density', 'dpi', 'exif',
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'loop', 'background', 'timestamp', 'duration']:
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items.pop(field, None)
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info = ''
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@ -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_without_extension + ".jpg")
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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
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@ -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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@ -119,8 +119,18 @@ def slerp(val, low, high):
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return res
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def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, seed_resize_from_h=0, seed_resize_from_w=0):
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def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, seed_resize_from_h=0, seed_resize_from_w=0, p=None):
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xs = []
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# if we have multiple seeds, this means we are working with batch size>1; this then
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# enables the generation of additional tensors with noise that the sampler will use during its processing.
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# Using those pre-genrated tensors instead of siimple torch.randn allows a batch with seeds [100, 101] to
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# produce the same images as with two batches [100], [101].
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if p is not None and p.sampler is not None and len(seeds) > 1 and opts.enable_batch_seeds:
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sampler_noises = [[] for _ in range(p.sampler.number_of_needed_noises(p))]
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else:
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sampler_noises = None
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for i, seed in enumerate(seeds):
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noise_shape = shape if seed_resize_from_h <= 0 or seed_resize_from_w <= 0 else (shape[0], seed_resize_from_h//8, seed_resize_from_w//8)
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@ -155,9 +165,17 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see
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x[:, ty:ty+h, tx:tx+w] = noise[:, dy:dy+h, dx:dx+w]
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noise = x
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if sampler_noises is not None:
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cnt = p.sampler.number_of_needed_noises(p)
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for j in range(cnt):
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sampler_noises[j].append(devices.randn_without_seed(tuple(noise_shape)))
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xs.append(noise)
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if sampler_noises is not None:
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p.sampler.sampler_noises = [torch.stack(n).to(shared.device) for n in sampler_noises]
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x = torch.stack(xs).to(shared.device)
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return x
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@ -170,7 +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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@ -209,7 +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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@ -247,6 +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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@ -257,7 +282,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
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comments[comment] = 1
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# we manually generate all input noises because each one should have a specific seed
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x = create_random_tensors([opt_C, p.height // opt_f, p.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, seed_resize_from_h=p.seed_resize_from_h, seed_resize_from_w=p.seed_resize_from_w)
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x = create_random_tensors([opt_C, p.height // opt_f, p.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, seed_resize_from_h=p.seed_resize_from_h, seed_resize_from_w=p.seed_resize_from_w, p=p)
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if p.n_iter > 1:
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shared.state.job = f"Batch {n+1} out of {p.n_iter}"
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|
|
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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'
|
||||
|
||||
|
||||
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:
|
||||
return checkpoint_info
|
||||
|
||||
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()))
|
||||
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
|
||||
|
||||
|
||||
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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|
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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)
|
||||
|
||||
if shared.cmd_opts.opt_channelslast:
|
||||
model.to(memory_format=torch.channels_last)
|
||||
|
||||
if not shared.cmd_opts.no_half:
|
||||
model.half()
|
||||
|
||||
model.sd_model_hash = sd_model_hash
|
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model.sd_model_checkpint = checkpoint_file
|
||||
|
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|
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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)
|
||||
else:
|
||||
sd_model.to(shared.device)
|
||||
|
||||
sd_hijack.model_hijack.hijack(sd_model)
|
||||
|
||||
sd_model.eval()
|
||||
|
||||
print(f"Model loaded.")
|
||||
return sd_model
|
||||
|
||||
|
||||
def reload_model_weights(sd_model, info=None):
|
||||
from modules import lowvram, devices
|
||||
checkpoint_info = info or select_checkpoint()
|
||||
|
||||
if sd_model.sd_model_checkpint == checkpoint_info.filename:
|
||||
return
|
||||
|
||||
if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
|
||||
lowvram.send_everything_to_cpu()
|
||||
else:
|
||||
sd_model.to(devices.cpu)
|
||||
|
||||
load_model_weights(sd_model, checkpoint_info.filename, checkpoint_info.hash)
|
||||
|
||||
if not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram:
|
||||
sd_model.to(devices.device)
|
||||
|
||||
print(f"Weights loaded.")
|
||||
return sd_model
|
||||
|
|
@ -38,6 +38,17 @@ samplers = [
|
|||
samplers_for_img2img = [x for x in samplers if x.name != 'PLMS']
|
||||
|
||||
|
||||
def setup_img2img_steps(p):
|
||||
if opts.img2img_fix_steps:
|
||||
steps = int(p.steps / min(p.denoising_strength, 0.999))
|
||||
t_enc = p.steps - 1
|
||||
else:
|
||||
steps = p.steps
|
||||
t_enc = int(min(p.denoising_strength, 0.999) * steps)
|
||||
|
||||
return steps, t_enc
|
||||
|
||||
|
||||
def sample_to_image(samples):
|
||||
x_sample = shared.sd_model.decode_first_stage(samples[0:1].type(shared.sd_model.dtype))[0]
|
||||
x_sample = torch.clamp((x_sample + 1.0) / 2.0, min=0.0, max=1.0)
|
||||
|
|
@ -80,8 +91,12 @@ class VanillaStableDiffusionSampler:
|
|||
self.mask = None
|
||||
self.nmask = None
|
||||
self.init_latent = None
|
||||
self.sampler_noises = None
|
||||
self.step = 0
|
||||
|
||||
def number_of_needed_noises(self, p):
|
||||
return 0
|
||||
|
||||
def p_sample_ddim_hook(self, x_dec, cond, ts, unconditional_conditioning, *args, **kwargs):
|
||||
cond = prompt_parser.reconstruct_cond_batch(cond, self.step)
|
||||
unconditional_conditioning = prompt_parser.reconstruct_cond_batch(unconditional_conditioning, self.step)
|
||||
|
|
@ -101,13 +116,13 @@ class VanillaStableDiffusionSampler:
|
|||
return res
|
||||
|
||||
def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning):
|
||||
t_enc = int(min(p.denoising_strength, 0.999) * p.steps)
|
||||
steps, t_enc = setup_img2img_steps(p)
|
||||
|
||||
# existing code fails with cetain step counts, like 9
|
||||
try:
|
||||
self.sampler.make_schedule(ddim_num_steps=p.steps, verbose=False)
|
||||
self.sampler.make_schedule(ddim_num_steps=steps, verbose=False)
|
||||
except Exception:
|
||||
self.sampler.make_schedule(ddim_num_steps=p.steps+1, verbose=False)
|
||||
self.sampler.make_schedule(ddim_num_steps=steps+1, verbose=False)
|
||||
|
||||
x1 = self.sampler.stochastic_encode(x, torch.tensor([t_enc] * int(x.shape[0])).to(shared.device), noise=noise)
|
||||
|
||||
|
|
@ -115,6 +130,7 @@ class VanillaStableDiffusionSampler:
|
|||
self.mask = p.mask
|
||||
self.nmask = p.nmask
|
||||
self.init_latent = p.init_latent
|
||||
self.step = 0
|
||||
|
||||
samples = self.sampler.decode(x1, conditioning, t_enc, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning)
|
||||
|
||||
|
|
@ -127,6 +143,7 @@ class VanillaStableDiffusionSampler:
|
|||
self.mask = None
|
||||
self.nmask = None
|
||||
self.init_latent = None
|
||||
self.step = 0
|
||||
|
||||
# existing code fails with cetin step counts, like 9
|
||||
try:
|
||||
|
|
@ -183,42 +200,82 @@ def extended_trange(count, *args, **kwargs):
|
|||
shared.total_tqdm.update()
|
||||
|
||||
|
||||
class TorchHijack:
|
||||
def __init__(self, kdiff_sampler):
|
||||
self.kdiff_sampler = kdiff_sampler
|
||||
|
||||
def __getattr__(self, item):
|
||||
if item == 'randn_like':
|
||||
return self.kdiff_sampler.randn_like
|
||||
|
||||
if hasattr(torch, item):
|
||||
return getattr(torch, item)
|
||||
|
||||
raise AttributeError("'{}' object has no attribute '{}'".format(type(self).__name__, item))
|
||||
|
||||
|
||||
class KDiffusionSampler:
|
||||
def __init__(self, funcname, sd_model):
|
||||
self.model_wrap = k_diffusion.external.CompVisDenoiser(sd_model, quantize=shared.opts.enable_quantization)
|
||||
self.funcname = funcname
|
||||
self.func = getattr(k_diffusion.sampling, self.funcname)
|
||||
self.model_wrap_cfg = CFGDenoiser(self.model_wrap)
|
||||
self.sampler_noises = None
|
||||
self.sampler_noise_index = 0
|
||||
|
||||
def callback_state(self, d):
|
||||
store_latent(d["denoised"])
|
||||
|
||||
def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning):
|
||||
t_enc = int(min(p.denoising_strength, 0.999) * p.steps)
|
||||
sigmas = self.model_wrap.get_sigmas(p.steps)
|
||||
def number_of_needed_noises(self, p):
|
||||
return p.steps
|
||||
|
||||
noise = noise * sigmas[p.steps - t_enc - 1]
|
||||
def randn_like(self, x):
|
||||
noise = self.sampler_noises[self.sampler_noise_index] if self.sampler_noises is not None and self.sampler_noise_index < len(self.sampler_noises) else None
|
||||
|
||||
if noise is not None and x.shape == noise.shape:
|
||||
res = noise
|
||||
else:
|
||||
res = torch.randn_like(x)
|
||||
|
||||
self.sampler_noise_index += 1
|
||||
return res
|
||||
|
||||
def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning):
|
||||
steps, t_enc = setup_img2img_steps(p)
|
||||
|
||||
sigmas = self.model_wrap.get_sigmas(steps)
|
||||
|
||||
noise = noise * sigmas[steps - t_enc - 1]
|
||||
|
||||
xi = x + noise
|
||||
|
||||
sigma_sched = sigmas[p.steps - t_enc - 1:]
|
||||
sigma_sched = sigmas[steps - t_enc - 1:]
|
||||
|
||||
self.model_wrap_cfg.mask = p.mask
|
||||
self.model_wrap_cfg.nmask = p.nmask
|
||||
self.model_wrap_cfg.init_latent = p.init_latent
|
||||
self.model_wrap.step = 0
|
||||
|
||||
if hasattr(k_diffusion.sampling, 'trange'):
|
||||
k_diffusion.sampling.trange = lambda *args, **kwargs: extended_trange(*args, **kwargs)
|
||||
|
||||
if self.sampler_noises is not None:
|
||||
k_diffusion.sampling.torch = TorchHijack(self)
|
||||
|
||||
return self.func(self.model_wrap_cfg, xi, sigma_sched, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state)
|
||||
|
||||
def sample(self, p, x, conditioning, unconditional_conditioning):
|
||||
sigmas = self.model_wrap.get_sigmas(p.steps)
|
||||
x = x * sigmas[0]
|
||||
|
||||
self.model_wrap_cfg.step = 0
|
||||
|
||||
if hasattr(k_diffusion.sampling, 'trange'):
|
||||
k_diffusion.sampling.trange = lambda *args, **kwargs: extended_trange(*args, **kwargs)
|
||||
|
||||
if self.sampler_noises is not None:
|
||||
k_diffusion.sampling.torch = TorchHijack(self)
|
||||
|
||||
samples_ddim = self.func(self.model_wrap_cfg, x, sigmas, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state)
|
||||
return samples_ddim
|
||||
|
||||
|
|
|
|||
|
|
@ -12,14 +12,16 @@ from modules.paths import script_path, sd_path
|
|||
from modules.devices import get_optimal_device
|
||||
import modules.styles
|
||||
import modules.interrogate
|
||||
import modules.memmon
|
||||
import modules.sd_models
|
||||
|
||||
sd_model_file = os.path.join(script_path, 'model.ckpt')
|
||||
if not os.path.exists(sd_model_file):
|
||||
sd_model_file = "models/ldm/stable-diffusion-v1/model.ckpt"
|
||||
default_sd_model_file = sd_model_file
|
||||
|
||||
parser = argparse.ArgumentParser()
|
||||
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",)
|
||||
parser.add_argument("--ckpt", type=str, default=os.path.join(sd_path, sd_model_file), help="path to checkpoint of model",)
|
||||
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",)
|
||||
parser.add_argument("--ckpt-dir", type=str, default=os.path.join(script_path, 'models'), help="path to directory with stable diffusion checkpoints",)
|
||||
parser.add_argument("--gfpgan-dir", type=str, help="GFPGAN directory", default=('./src/gfpgan' if os.path.exists('./src/gfpgan') else './GFPGAN'))
|
||||
parser.add_argument("--gfpgan-model", type=str, help="GFPGAN model file name", default='GFPGANv1.3.pth')
|
||||
parser.add_argument("--no-half", action='store_true', help="do not switch the model to 16-bit floats")
|
||||
|
|
@ -87,13 +89,17 @@ interrogator = modules.interrogate.InterrogateModels("interrogate")
|
|||
|
||||
face_restorers = []
|
||||
|
||||
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
|
||||
|
|
@ -125,9 +131,11 @@ class Options:
|
|||
"enable_pnginfo": OptionInfo(True, "Save text information about generation parameters as chunks to png files"),
|
||||
"add_model_hash_to_info": OptionInfo(False, "Add model hash to generation information"),
|
||||
"img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
|
||||
"img2img_fix_steps": OptionInfo(False, "With img2img, do exactly the amount of steps the slider specifies (normaly you'd do less with less denoising)."),
|
||||
"enable_quantization": OptionInfo(False, "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply."),
|
||||
"font": OptionInfo("", "Font for image grids that have text"),
|
||||
"enable_emphasis": OptionInfo(True, "Use (text) to make model pay more attention to text and [text] to make it pay less attention"),
|
||||
"enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"),
|
||||
"save_txt": OptionInfo(False, "Create a text file next to every image with generation parameters."),
|
||||
"ESRGAN_tile": OptionInfo(192, "Tile size for upscaling. 0 = no tiling.", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}),
|
||||
"ESRGAN_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for upscaling. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}),
|
||||
|
|
@ -136,6 +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."),
|
||||
|
|
@ -146,6 +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):
|
||||
|
|
@ -176,6 +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):
|
||||
|
|
@ -184,7 +198,6 @@ if os.path.exists(config_filename):
|
|||
sd_upscalers = []
|
||||
|
||||
sd_model = None
|
||||
sd_model_hash = ''
|
||||
|
||||
progress_print_out = sys.stdout
|
||||
|
||||
|
|
@ -215,3 +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()
|
||||
|
|
@ -649,7 +663,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
|
|||
image = gr.Image(label="Source", source="upload", interactive=True, type="pil")
|
||||
|
||||
with gr.TabItem('Batch Process'):
|
||||
image_batch = gr.File(label="Batch Process", file_count="multiple", source="upload", interactive=True, type="file")
|
||||
image_batch = gr.File(label="Batch Process", file_count="multiple", interactive=True, type="file")
|
||||
|
||||
upscaling_resize = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label="Resize", value=2)
|
||||
|
||||
|
|
@ -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)
|
||||
|
|
@ -782,6 +801,11 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
|
|||
with open(os.path.join(script_path, "style.css"), "r", encoding="utf8") as file:
|
||||
css = file.read()
|
||||
|
||||
if os.path.exists(os.path.join(script_path, "user.css")):
|
||||
with open(os.path.join(script_path, "user.css"), "r", encoding="utf8") as file:
|
||||
usercss = file.read()
|
||||
css += usercss
|
||||
|
||||
if not cmd_opts.no_progressbar_hiding:
|
||||
css += css_hide_progressbar
|
||||
|
||||
|
|
|
|||
147
script.js
147
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 +76,90 @@ function gradioApp(){
|
|||
|
||||
global_progressbar = null
|
||||
|
||||
function closeModal() {
|
||||
gradioApp().getElementById("lightboxModal").style.display = "none";
|
||||
}
|
||||
|
||||
function showModal(event) {
|
||||
var source = event.target || event.srcElement;
|
||||
gradioApp().getElementById("modalImage").src = source.src
|
||||
var lb = gradioApp().getElementById("lightboxModal")
|
||||
lb.style.display = "block";
|
||||
lb.focus()
|
||||
event.stopPropagation()
|
||||
}
|
||||
|
||||
function negmod(n, m) {
|
||||
return ((n % m) + m) % m;
|
||||
}
|
||||
|
||||
function modalImageSwitch(offset){
|
||||
var galleryButtons = gradioApp().querySelectorAll(".gallery-item.transition-all")
|
||||
|
||||
if(galleryButtons.length>1){
|
||||
var currentButton = gradioApp().querySelector(".gallery-item.transition-all.\\!ring-2")
|
||||
|
||||
var result = -1
|
||||
galleryButtons.forEach(function(v, i){ if(v==currentButton) { result = i } })
|
||||
|
||||
if(result != -1){
|
||||
nextButton = galleryButtons[negmod((result+offset),galleryButtons.length)]
|
||||
nextButton.click()
|
||||
gradioApp().getElementById("modalImage").src = nextButton.children[0].src
|
||||
setTimeout( function(){gradioApp().getElementById("lightboxModal").focus()},10)
|
||||
}
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
function modalNextImage(event){
|
||||
modalImageSwitch(1)
|
||||
event.stopPropagation()
|
||||
}
|
||||
|
||||
function modalPrevImage(event){
|
||||
modalImageSwitch(-1)
|
||||
event.stopPropagation()
|
||||
}
|
||||
|
||||
function modalKeyHandler(event){
|
||||
switch (event.key) {
|
||||
case "ArrowLeft":
|
||||
modalPrevImage(event)
|
||||
break;
|
||||
case "ArrowRight":
|
||||
modalNextImage(event)
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
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'
|
||||
|
||||
e.addEventListener('click', function (evt) {
|
||||
showModal(evt)
|
||||
|
||||
},true);
|
||||
}
|
||||
});
|
||||
}
|
||||
|
||||
}, 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 +201,18 @@ 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 +220,49 @@ 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.tabIndex=0
|
||||
modal.addEventListener('keydown', modalKeyHandler, true)
|
||||
modal.appendChild(modalClose)
|
||||
|
||||
const modalImage = document.createElement('img')
|
||||
modalImage.id = 'modalImage';
|
||||
modalImage.onclick = closeModal;
|
||||
modalImage.tabIndex=0
|
||||
modalImage.addEventListener('keydown', modalKeyHandler, true)
|
||||
modal.appendChild(modalImage)
|
||||
|
||||
const modalPrev = document.createElement('a')
|
||||
modalPrev.className = 'modalPrev';
|
||||
modalPrev.innerHTML = '❮'
|
||||
modalPrev.tabIndex=0
|
||||
modalPrev.addEventListener('click',modalPrevImage,true);
|
||||
modalPrev.addEventListener('keydown', modalKeyHandler, true)
|
||||
modal.appendChild(modalPrev)
|
||||
|
||||
const modalNext = document.createElement('a')
|
||||
modalNext.className = 'modalNext';
|
||||
modalNext.innerHTML = '❯'
|
||||
modalNext.tabIndex=0
|
||||
modalNext.addEventListener('click',modalNextImage,true);
|
||||
modalNext.addEventListener('keydown', modalKeyHandler, true)
|
||||
|
||||
modal.appendChild(modalNext)
|
||||
|
||||
|
||||
gradioApp().getRootNode().appendChild(modal)
|
||||
|
||||
document.body.appendChild(modalFragment);
|
||||
|
||||
});
|
||||
|
||||
function selected_gallery_index(){
|
||||
|
|
@ -180,6 +314,15 @@ 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):
|
||||
|
|
@ -74,34 +74,45 @@ 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, original_negative_prompt, cfg, st, randomness]
|
||||
|
||||
return [original_prompt, cfg, st]
|
||||
|
||||
def run(self, p, original_prompt, cfg, st):
|
||||
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:
|
||||
noise = self.cache.noise
|
||||
rec_noise = self.cache.noise
|
||||
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 * [""])
|
||||
noise = find_noise_for_image(p, cond, uncond, cfg, st)
|
||||
self.cache = Cached(noise, cfg, st, lat, original_prompt)
|
||||
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, 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])])
|
||||
|
||||
combined_noise = ((1 - randomness) * rec_noise + randomness * rand_noise) / ((randomness**2 + (1-randomness)**2) ** 0.5)
|
||||
|
||||
sampler = samplers[p.sampler_index].constructor(p.sd_model)
|
||||
|
||||
samples_ddim = sampler.sample(p, noise, conditioning, unconditional_conditioning)
|
||||
return samples_ddim
|
||||
sigmas = sampler.model_wrap.get_sigmas(p.steps)
|
||||
|
||||
noise_dt = combined_noise - ( p.init_latent / sigmas[0] )
|
||||
|
||||
p.seed = p.seed + 1
|
||||
|
||||
return sampler.sample_img2img(p, p.init_latent, noise_dt, conditioning, unconditional_conditioning)
|
||||
|
||||
|
||||
p.sample = sample_extra
|
||||
|
||||
|
|
|
|||
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
|
||||
|
|
|
|||
106
style.css
106
style.css
|
|
@ -1,17 +1,48 @@
|
|||
.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;
|
||||
}
|
||||
|
||||
#txt2img_gallery, #img2img_gallery{
|
||||
min-height: 768px;
|
||||
@media screen and (min-width: 2500px) {
|
||||
#txt2img_gallery, #img2img_gallery {
|
||||
min-height: 768px;
|
||||
}
|
||||
}
|
||||
|
||||
#txt2img_gallery img, #img2img_gallery img{
|
||||
object-fit: scale-down;
|
||||
}
|
||||
|
||||
.justify-center.overflow-x-scroll {
|
||||
justify-content: left;
|
||||
}
|
||||
|
||||
.justify-center.overflow-x-scroll button:first-of-type {
|
||||
margin-left: auto;
|
||||
}
|
||||
|
||||
.justify-center.overflow-x-scroll button:last-of-type {
|
||||
margin-right: auto;
|
||||
}
|
||||
|
||||
#subseed_show{
|
||||
min-width: 6em;
|
||||
max-width: 6em;
|
||||
|
|
@ -151,6 +182,12 @@ input[type="range"]{
|
|||
#txt2img_negative_prompt, #img2img_negative_prompt{
|
||||
}
|
||||
|
||||
#progressbar{
|
||||
position: absolute;
|
||||
z-index: 1000;
|
||||
right: 0;
|
||||
}
|
||||
|
||||
.progressDiv{
|
||||
width: 100%;
|
||||
height: 30px;
|
||||
|
|
@ -174,3 +211,66 @@ 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;
|
||||
}
|
||||
|
||||
.modalPrev,
|
||||
.modalNext {
|
||||
cursor: pointer;
|
||||
position: absolute;
|
||||
top: 50%;
|
||||
width: auto;
|
||||
padding: 16px;
|
||||
margin-top: -50px;
|
||||
color: white;
|
||||
font-weight: bold;
|
||||
font-size: 20px;
|
||||
transition: 0.6s ease;
|
||||
border-radius: 0 3px 3px 0;
|
||||
user-select: none;
|
||||
-webkit-user-select: none;
|
||||
}
|
||||
|
||||
.modalNext {
|
||||
right: 0;
|
||||
border-radius: 3px 0 0 3px;
|
||||
}
|
||||
|
||||
.modalPrev:hover,
|
||||
.modalNext:hover {
|
||||
background-color: rgba(0, 0, 0, 0.8);
|
||||
}
|
||||
|
|
|
|||
67
webui.py
67
webui.py
|
|
@ -3,13 +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
|
||||
|
|
@ -24,6 +19,7 @@ import modules.extras
|
|||
import modules.lowvram
|
||||
import modules.txt2img
|
||||
import modules.img2img
|
||||
import modules.sd_models
|
||||
|
||||
|
||||
modules.codeformer_model.setup_codeformer()
|
||||
|
|
@ -33,31 +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
|
||||
|
|
@ -80,33 +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 a new issue