Merge branch 'master' into master

This commit is contained in:
EyeDeck 2022-09-11 18:15:30 -04:00 committed by GitHub
commit e05e46aa3f
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
8 changed files with 62 additions and 35 deletions

View File

@ -14,3 +14,9 @@ def get_optimal_device():
return torch.device("mps")
return cpu
def torch_gc():
if torch.cuda.is_available():
torch.cuda.empty_cache()
torch.cuda.ipc_collect()

View File

@ -1,7 +1,7 @@
import numpy as np
from PIL import Image
from modules import processing, shared, images
from modules import processing, shared, images, devices
from modules.shared import opts
import modules.gfpgan_model
from modules.ui import plaintext_to_html
@ -11,7 +11,7 @@ cached_images = {}
def run_extras(image, gfpgan_visibility, codeformer_visibility, codeformer_weight, upscaling_resize, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility):
processing.torch_gc()
devices.torch_gc()
image = image.convert("RGB")
info = ""

View File

@ -243,16 +243,32 @@ def sanitize_filename_part(text):
return text.replace(' ', '_').translate({ord(x): '' for x in invalid_filename_chars})[:128]
def save_image(image, path, basename, seed=None, prompt=None, extension='png', info=None, short_filename=False, no_prompt=False, pnginfo_section_name='parameters'):
def save_image(image, path, basename, seed=None, prompt=None, extension='png', info=None, short_filename=False, no_prompt=False, pnginfo_section_name='parameters', process_info=None):
# would be better to add this as an argument in future, but will do for now
is_a_grid = basename != ""
if short_filename or prompt is None or seed is None:
file_decoration = ""
elif opts.save_to_dirs:
file_decoration = f"-{seed}"
file_decoration = opts.samples_filename_format or "[SEED]"
else:
file_decoration = f"-{seed}-{sanitize_filename_part(prompt)[:128]}"
file_decoration = opts.samples_filename_format or "[SEED]-[PROMPT]"
#file_decoration = f"-{seed}-{sanitize_filename_part(prompt)[:128]}"
#Add new filenames tags here
file_decoration = "-" + file_decoration
if seed is not None:
file_decoration = file_decoration.replace("[SEED]", str(seed))
if prompt is not None:
file_decoration = file_decoration.replace("[PROMPT]", sanitize_filename_part(prompt)[:128])
file_decoration = file_decoration.replace("[PROMPT_SPACES]", prompt.translate({ord(x): '' for x in invalid_filename_chars})[:128])
if process_info is not None:
file_decoration = file_decoration.replace("[STEPS]", str(process_info.steps))
file_decoration = file_decoration.replace("[CFG]", str(process_info.cfg_scale))
file_decoration = file_decoration.replace("[WIDTH]", str(process_info.width))
file_decoration = file_decoration.replace("[HEIGHT]", str(process_info.height))
file_decoration = file_decoration.replace("[SAMPLER]", str(process_info.sampler))
if extension == 'png' and opts.enable_pnginfo and info is not None:
pnginfo = PngImagePlugin.PngInfo()

View File

@ -3,6 +3,7 @@ import cv2
import numpy as np
from PIL import Image, ImageOps, ImageChops
from modules import devices
from modules.processing import Processed, StableDiffusionProcessingImg2Img, process_images
from modules.shared import opts, state
import modules.shared as shared
@ -131,7 +132,7 @@ def img2img(prompt: str, negative_prompt: str, prompt_style: str, init_img, init
upscaler = shared.sd_upscalers[upscaler_index]
img = upscaler.upscale(init_img, init_img.width * 2, init_img.height * 2)
processing.torch_gc()
devices.torch_gc()
grid = images.split_grid(img, tile_w=width, tile_h=height, overlap=upscale_overlap)
@ -179,7 +180,7 @@ def img2img(prompt: str, negative_prompt: str, prompt_style: str, init_img, init
result_images.append(combined_image)
if opts.samples_save:
images.save_image(combined_image, p.outpath_samples, "", start_seed, prompt, opts.grid_format, info=initial_info)
images.save_image(combined_image, p.outpath_samples, "", start_seed, prompt, opts.samples_format, info=initial_info)
processed = Processed(p, result_images, seed, initial_info)

View File

@ -1,3 +1,4 @@
import contextlib
import os
import sys
import traceback
@ -6,7 +7,6 @@ import re
import torch
from PIL import Image
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode
@ -26,6 +26,7 @@ class InterrogateModels:
clip_model = None
clip_preprocess = None
categories = None
dtype = None
def __init__(self, content_dir):
self.categories = []
@ -60,14 +61,20 @@ class InterrogateModels:
def load(self):
if self.blip_model is None:
self.blip_model = self.load_blip_model()
if not shared.cmd_opts.no_half:
self.blip_model = self.blip_model.half()
self.blip_model = self.blip_model.to(shared.device)
if self.clip_model is None:
self.clip_model, self.clip_preprocess = self.load_clip_model()
if not shared.cmd_opts.no_half:
self.clip_model = self.clip_model.half()
self.clip_model = self.clip_model.to(shared.device)
self.dtype = next(self.clip_model.parameters()).dtype
def unload(self):
if not shared.opts.interrogate_keep_models_in_memory:
if self.clip_model is not None:
@ -76,14 +83,14 @@ class InterrogateModels:
if self.blip_model is not None:
self.blip_model = self.blip_model.to(devices.cpu)
devices.torch_gc()
def rank(self, image_features, text_array, top_count=1):
import clip
top_count = min(top_count, len(text_array))
text_tokens = clip.tokenize([text for text in text_array]).cuda()
with torch.no_grad():
text_features = self.clip_model.encode_text(text_tokens).float()
text_tokens = clip.tokenize([text for text in text_array]).to(shared.device)
text_features = self.clip_model.encode_text(text_tokens).type(self.dtype)
text_features /= text_features.norm(dim=-1, keepdim=True)
similarity = torch.zeros((1, len(text_array))).to(shared.device)
@ -94,13 +101,12 @@ class InterrogateModels:
top_probs, top_labels = similarity.cpu().topk(top_count, dim=-1)
return [(text_array[top_labels[0][i].numpy()], (top_probs[0][i].numpy()*100)) for i in range(top_count)]
def generate_caption(self, pil_image):
gpu_image = transforms.Compose([
transforms.Resize((blip_image_eval_size, blip_image_eval_size), interpolation=InterpolationMode.BICUBIC),
transforms.ToTensor(),
transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711))
])(pil_image).unsqueeze(0).to(shared.device)
])(pil_image).unsqueeze(0).type(self.dtype).to(shared.device)
with torch.no_grad():
caption = self.blip_model.generate(gpu_image, sample=False, num_beams=shared.opts.interrogate_clip_num_beams, min_length=shared.opts.interrogate_clip_min_length, max_length=shared.opts.interrogate_clip_max_length)
@ -116,22 +122,23 @@ class InterrogateModels:
caption = self.generate_caption(pil_image)
res = caption
images = self.clip_preprocess(pil_image).unsqueeze(0).to(shared.device)
images = self.clip_preprocess(pil_image).unsqueeze(0).type(self.dtype).to(shared.device)
with torch.no_grad():
image_features = self.clip_model.encode_image(images).float()
precision_scope = torch.autocast if shared.cmd_opts.precision == "autocast" else contextlib.nullcontext
with torch.no_grad(), precision_scope("cuda"):
image_features = self.clip_model.encode_image(images).type(self.dtype)
image_features /= image_features.norm(dim=-1, keepdim=True)
image_features /= image_features.norm(dim=-1, keepdim=True)
if shared.opts.interrogate_use_builtin_artists:
artist = self.rank(image_features, ["by " + artist.name for artist in shared.artist_db.artists])[0]
if shared.opts.interrogate_use_builtin_artists:
artist = self.rank(image_features, ["by " + artist.name for artist in shared.artist_db.artists])[0]
res += ", " + artist[0]
res += ", " + artist[0]
for name, topn, items in self.categories:
matches = self.rank(image_features, items, top_count=topn)
for match, score in matches:
res += ", " + match
for name, topn, items in self.categories:
matches = self.rank(image_features, items, top_count=topn)
for match, score in matches:
res += ", " + match
except Exception:
print(f"Error interrogating", file=sys.stderr)

View File

@ -10,6 +10,7 @@ from PIL import Image, ImageFilter, ImageOps
import random
import modules.sd_hijack
from modules import devices
from modules.sd_hijack import model_hijack
from modules.sd_samplers import samplers, samplers_for_img2img
from modules.shared import opts, cmd_opts, state
@ -23,11 +24,6 @@ opt_C = 4
opt_f = 8
def torch_gc():
if torch.cuda.is_available():
torch.cuda.empty_cache()
torch.cuda.ipc_collect()
class StableDiffusionProcessing:
def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt="", prompt_style="None", seed=-1, subseed=-1, subseed_strength=0, seed_resize_from_h=-1, seed_resize_from_w=-1, sampler_index=0, batch_size=1, n_iter=1, steps=50, cfg_scale=7.0, width=512, height=512, restore_faces=False, tiling=False, do_not_save_samples=False, do_not_save_grid=False, extra_generation_params=None, overlay_images=None, negative_prompt=None):
@ -157,7 +153,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
"""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"""
assert p.prompt is not None
torch_gc()
devices.torch_gc()
fix_seed(p)
@ -258,7 +254,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
x_sample = x_sample.astype(np.uint8)
if p.restore_faces:
torch_gc()
devices.torch_gc()
x_sample = modules.face_restoration.restore_faces(x_sample)
@ -279,7 +275,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
image = image.convert('RGB')
if opts.samples_save and not p.do_not_save_samples:
images.save_image(image, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i))
images.save_image(image, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), process_info = Processed(p, output_images, all_seeds[0], infotext()))
output_images.append(image)
@ -297,7 +293,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
if opts.grid_save:
images.save_image(grid, p.outpath_grids, "grid", all_seeds[0], all_prompts[0], opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename)
torch_gc()
devices.torch_gc()
return Processed(p, output_images, all_seeds[0], infotext())

View File

@ -94,6 +94,7 @@ class Options:
data = None
hide_dirs = {"visible": False} if cmd_opts.hide_ui_dir_config else None
data_labels = {
"samples_filename_format": OptionInfo("", "Samples filename format using following tags: [STEPS],[CFG],[PROMPT],[PROMPT_SPACES],[WIDTH],[HEIGHT],[SAMPLER],[SEED]. Leave blank for default."),
"outdir_samples": OptionInfo("", "Output directory for images; if empty, defaults to two directories below", component_args=hide_dirs),
"outdir_txt2img_samples": OptionInfo("outputs/txt2img-images", 'Output directory for txt2img images', component_args=hide_dirs),
"outdir_img2img_samples": OptionInfo("outputs/img2img-images", 'Output directory for img2img images', component_args=hide_dirs),

View File

@ -4,7 +4,7 @@ import modules.scripts as scripts
import gradio as gr
from PIL import Image, ImageDraw
from modules import images, processing
from modules import images, processing, devices
from modules.processing import Processed, process_images
from modules.shared import opts, cmd_opts, state
@ -77,7 +77,7 @@ class Script(scripts.Script):
mask.height - down - (mask_blur//2 if down > 0 else 0)
), fill="black")
processing.torch_gc()
devices.torch_gc()
grid = images.split_grid(img, tile_w=p.width, tile_h=p.height, overlap=pixels)
grid_mask = images.split_grid(mask, tile_w=p.width, tile_h=p.height, overlap=pixels)