Merge branch 'master' into master
This commit is contained in:
commit
e05e46aa3f
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@ -14,3 +14,9 @@ def get_optimal_device():
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return torch.device("mps")
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return cpu
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def torch_gc():
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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torch.cuda.ipc_collect()
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@ -1,7 +1,7 @@
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import numpy as np
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from PIL import Image
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from modules import processing, shared, images
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from modules import processing, shared, images, devices
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from modules.shared import opts
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import modules.gfpgan_model
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from modules.ui import plaintext_to_html
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@ -11,7 +11,7 @@ cached_images = {}
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def run_extras(image, gfpgan_visibility, codeformer_visibility, codeformer_weight, upscaling_resize, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility):
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processing.torch_gc()
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devices.torch_gc()
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image = image.convert("RGB")
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info = ""
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@ -243,16 +243,32 @@ def sanitize_filename_part(text):
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return text.replace(' ', '_').translate({ord(x): '' for x in invalid_filename_chars})[:128]
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def save_image(image, path, basename, seed=None, prompt=None, extension='png', info=None, short_filename=False, no_prompt=False, pnginfo_section_name='parameters'):
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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):
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# would be better to add this as an argument in future, but will do for now
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is_a_grid = basename != ""
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if short_filename or prompt is None or seed is None:
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file_decoration = ""
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elif opts.save_to_dirs:
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file_decoration = f"-{seed}"
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file_decoration = opts.samples_filename_format or "[SEED]"
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else:
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file_decoration = f"-{seed}-{sanitize_filename_part(prompt)[:128]}"
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file_decoration = opts.samples_filename_format or "[SEED]-[PROMPT]"
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#file_decoration = f"-{seed}-{sanitize_filename_part(prompt)[:128]}"
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#Add new filenames tags here
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file_decoration = "-" + file_decoration
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if seed is not None:
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file_decoration = file_decoration.replace("[SEED]", str(seed))
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if prompt is not None:
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file_decoration = file_decoration.replace("[PROMPT]", sanitize_filename_part(prompt)[:128])
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file_decoration = file_decoration.replace("[PROMPT_SPACES]", prompt.translate({ord(x): '' for x in invalid_filename_chars})[:128])
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if process_info is not None:
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file_decoration = file_decoration.replace("[STEPS]", str(process_info.steps))
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file_decoration = file_decoration.replace("[CFG]", str(process_info.cfg_scale))
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file_decoration = file_decoration.replace("[WIDTH]", str(process_info.width))
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file_decoration = file_decoration.replace("[HEIGHT]", str(process_info.height))
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file_decoration = file_decoration.replace("[SAMPLER]", str(process_info.sampler))
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if extension == 'png' and opts.enable_pnginfo and info is not None:
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pnginfo = PngImagePlugin.PngInfo()
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@ -3,6 +3,7 @@ import cv2
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import numpy as np
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from PIL import Image, ImageOps, ImageChops
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from modules import devices
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from modules.processing import Processed, StableDiffusionProcessingImg2Img, process_images
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from modules.shared import opts, state
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import modules.shared as shared
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@ -131,7 +132,7 @@ def img2img(prompt: str, negative_prompt: str, prompt_style: str, init_img, init
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upscaler = shared.sd_upscalers[upscaler_index]
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img = upscaler.upscale(init_img, init_img.width * 2, init_img.height * 2)
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processing.torch_gc()
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devices.torch_gc()
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grid = images.split_grid(img, tile_w=width, tile_h=height, overlap=upscale_overlap)
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@ -179,7 +180,7 @@ def img2img(prompt: str, negative_prompt: str, prompt_style: str, init_img, init
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result_images.append(combined_image)
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if opts.samples_save:
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images.save_image(combined_image, p.outpath_samples, "", start_seed, prompt, opts.grid_format, info=initial_info)
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images.save_image(combined_image, p.outpath_samples, "", start_seed, prompt, opts.samples_format, info=initial_info)
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processed = Processed(p, result_images, seed, initial_info)
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@ -1,3 +1,4 @@
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import contextlib
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import os
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import sys
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import traceback
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@ -6,7 +7,6 @@ import re
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import torch
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from PIL import Image
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from torchvision import transforms
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from torchvision.transforms.functional import InterpolationMode
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@ -26,6 +26,7 @@ class InterrogateModels:
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clip_model = None
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clip_preprocess = None
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categories = None
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dtype = None
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def __init__(self, content_dir):
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self.categories = []
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@ -60,14 +61,20 @@ class InterrogateModels:
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def load(self):
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if self.blip_model is None:
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self.blip_model = self.load_blip_model()
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if not shared.cmd_opts.no_half:
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self.blip_model = self.blip_model.half()
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self.blip_model = self.blip_model.to(shared.device)
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if self.clip_model is None:
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self.clip_model, self.clip_preprocess = self.load_clip_model()
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if not shared.cmd_opts.no_half:
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self.clip_model = self.clip_model.half()
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self.clip_model = self.clip_model.to(shared.device)
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self.dtype = next(self.clip_model.parameters()).dtype
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def unload(self):
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if not shared.opts.interrogate_keep_models_in_memory:
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if self.clip_model is not None:
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@ -76,14 +83,14 @@ class InterrogateModels:
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if self.blip_model is not None:
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self.blip_model = self.blip_model.to(devices.cpu)
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devices.torch_gc()
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def rank(self, image_features, text_array, top_count=1):
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import clip
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top_count = min(top_count, len(text_array))
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text_tokens = clip.tokenize([text for text in text_array]).cuda()
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with torch.no_grad():
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text_features = self.clip_model.encode_text(text_tokens).float()
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text_tokens = clip.tokenize([text for text in text_array]).to(shared.device)
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text_features = self.clip_model.encode_text(text_tokens).type(self.dtype)
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text_features /= text_features.norm(dim=-1, keepdim=True)
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similarity = torch.zeros((1, len(text_array))).to(shared.device)
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@ -94,13 +101,12 @@ class InterrogateModels:
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top_probs, top_labels = similarity.cpu().topk(top_count, dim=-1)
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return [(text_array[top_labels[0][i].numpy()], (top_probs[0][i].numpy()*100)) for i in range(top_count)]
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def generate_caption(self, pil_image):
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gpu_image = transforms.Compose([
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transforms.Resize((blip_image_eval_size, blip_image_eval_size), interpolation=InterpolationMode.BICUBIC),
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transforms.ToTensor(),
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transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711))
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])(pil_image).unsqueeze(0).to(shared.device)
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])(pil_image).unsqueeze(0).type(self.dtype).to(shared.device)
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with torch.no_grad():
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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)
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@ -116,22 +122,23 @@ class InterrogateModels:
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caption = self.generate_caption(pil_image)
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res = caption
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images = self.clip_preprocess(pil_image).unsqueeze(0).to(shared.device)
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images = self.clip_preprocess(pil_image).unsqueeze(0).type(self.dtype).to(shared.device)
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with torch.no_grad():
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image_features = self.clip_model.encode_image(images).float()
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precision_scope = torch.autocast if shared.cmd_opts.precision == "autocast" else contextlib.nullcontext
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with torch.no_grad(), precision_scope("cuda"):
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image_features = self.clip_model.encode_image(images).type(self.dtype)
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image_features /= image_features.norm(dim=-1, keepdim=True)
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image_features /= image_features.norm(dim=-1, keepdim=True)
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if shared.opts.interrogate_use_builtin_artists:
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artist = self.rank(image_features, ["by " + artist.name for artist in shared.artist_db.artists])[0]
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if shared.opts.interrogate_use_builtin_artists:
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artist = self.rank(image_features, ["by " + artist.name for artist in shared.artist_db.artists])[0]
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res += ", " + artist[0]
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res += ", " + artist[0]
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for name, topn, items in self.categories:
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matches = self.rank(image_features, items, top_count=topn)
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for match, score in matches:
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res += ", " + match
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for name, topn, items in self.categories:
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matches = self.rank(image_features, items, top_count=topn)
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for match, score in matches:
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res += ", " + match
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except Exception:
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print(f"Error interrogating", file=sys.stderr)
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@ -10,6 +10,7 @@ from PIL import Image, ImageFilter, ImageOps
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import random
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import modules.sd_hijack
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from modules import devices
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from modules.sd_hijack import model_hijack
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from modules.sd_samplers import samplers, samplers_for_img2img
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from modules.shared import opts, cmd_opts, state
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@ -23,11 +24,6 @@ opt_C = 4
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opt_f = 8
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def torch_gc():
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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torch.cuda.ipc_collect()
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class StableDiffusionProcessing:
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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):
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@ -157,7 +153,7 @@ 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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torch_gc()
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devices.torch_gc()
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fix_seed(p)
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@ -258,7 +254,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
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x_sample = x_sample.astype(np.uint8)
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if p.restore_faces:
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torch_gc()
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devices.torch_gc()
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x_sample = modules.face_restoration.restore_faces(x_sample)
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@ -279,7 +275,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
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image = image.convert('RGB')
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if opts.samples_save and not p.do_not_save_samples:
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images.save_image(image, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i))
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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()))
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output_images.append(image)
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@ -297,7 +293,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
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if opts.grid_save:
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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)
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torch_gc()
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devices.torch_gc()
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return Processed(p, output_images, all_seeds[0], infotext())
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@ -94,6 +94,7 @@ class Options:
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data = None
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hide_dirs = {"visible": False} if cmd_opts.hide_ui_dir_config else None
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data_labels = {
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"samples_filename_format": OptionInfo("", "Samples filename format using following tags: [STEPS],[CFG],[PROMPT],[PROMPT_SPACES],[WIDTH],[HEIGHT],[SAMPLER],[SEED]. Leave blank for default."),
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"outdir_samples": OptionInfo("", "Output directory for images; if empty, defaults to two directories below", component_args=hide_dirs),
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"outdir_txt2img_samples": OptionInfo("outputs/txt2img-images", 'Output directory for txt2img images', component_args=hide_dirs),
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"outdir_img2img_samples": OptionInfo("outputs/img2img-images", 'Output directory for img2img images', component_args=hide_dirs),
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@ -4,7 +4,7 @@ import modules.scripts as scripts
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import gradio as gr
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from PIL import Image, ImageDraw
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from modules import images, processing
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from modules import images, processing, devices
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from modules.processing import Processed, process_images
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from modules.shared import opts, cmd_opts, state
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@ -77,7 +77,7 @@ class Script(scripts.Script):
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mask.height - down - (mask_blur//2 if down > 0 else 0)
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), fill="black")
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processing.torch_gc()
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devices.torch_gc()
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grid = images.split_grid(img, tile_w=p.width, tile_h=p.height, overlap=pixels)
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grid_mask = images.split_grid(mask, tile_w=p.width, tile_h=p.height, overlap=pixels)
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