Merge branch 'master' into #1484_fix_empty_styles_pattern
This commit is contained in:
AUTOMATIC1111
GitHub
commit
3fac3764b3
1
.gitignore
vendored
1
.gitignore
vendored
|
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@ -25,3 +25,4 @@ __pycache__
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/.idea
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notification.mp3
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/SwinIR
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/textual_inversion
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|
|
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17
README.md
17
README.md
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@ -11,12 +11,12 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web
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- One click install and run script (but you still must install python and git)
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- Outpainting
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- Inpainting
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- Prompt
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- Stable Diffusion upscale
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- Prompt Matrix
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- Stable Diffusion Upscale
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- Attention, specify parts of text that the model should pay more attention to
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- a man in a ((txuedo)) - will pay more attentinoto tuxedo
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- a man in a (txuedo:1.21) - alternative syntax
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- Loopback, run img2img procvessing multiple times
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- a man in a ((tuxedo)) - will pay more attention to tuxedo
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- a man in a (tuxedo:1.21) - alternative syntax
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- Loopback, run img2img processing multiple times
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- X/Y plot, a way to draw a 2 dimensional plot of images with different parameters
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- Textual Inversion
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- have as many embeddings as you want and use any names you like for them
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@ -35,15 +35,15 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web
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- 4GB video card support (also reports of 2GB working)
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- Correct seeds for batches
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- Prompt length validation
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- get length of prompt in tokensas you type
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- get a warning after geenration if some text was truncated
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- get length of prompt in tokens as you type
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- get a warning after generation if some text was truncated
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- Generation parameters
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- parameters you used to generate images are saved with that image
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- in PNG chunks for PNG, in EXIF for JPEG
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- can drag the image to PNG info tab to restore generation parameters and automatically copy them into UI
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- can be disabled in settings
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- Settings page
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- Running arbitrary python code from UI (must run with commandline flag to enable)
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- Running arbitrary python code from UI (must run with --allow-code to enable)
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- Mouseover hints for most UI elements
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- Possible to change defaults/mix/max/step values for UI elements via text config
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- Random artist button
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@ -113,6 +113,7 @@ The documentation was moved from this README over to the project's [wiki](https:
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- LDSR - https://github.com/Hafiidz/latent-diffusion
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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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- Rinon Gal - Textual Inversion - https://github.com/rinongal/textual_inversion (we're not using his code, but we are using his ideas).
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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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@ -30,6 +30,7 @@ function check_progressbar(id_part, id_progressbar, id_progressbar_span, id_inte
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onUiUpdate(function(){
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check_progressbar('txt2img', 'txt2img_progressbar', 'txt2img_progress_span', 'txt2img_interrupt', 'txt2img_preview', 'txt2img_gallery')
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check_progressbar('img2img', 'img2img_progressbar', 'img2img_progress_span', 'img2img_interrupt', 'img2img_preview', 'img2img_gallery')
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check_progressbar('ti', 'ti_progressbar', 'ti_progress_span', 'ti_interrupt', 'ti_preview', 'ti_gallery')
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})
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function requestMoreProgress(id_part, id_progressbar_span, id_interrupt){
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8
javascript/textualInversion.js
Normal file
8
javascript/textualInversion.js
Normal file
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@ -0,0 +1,8 @@
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function start_training_textual_inversion(){
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requestProgress('ti')
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gradioApp().querySelector('#ti_error').innerHTML=''
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return args_to_array(arguments)
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}
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@ -199,6 +199,26 @@ let txt2img_textarea, img2img_textarea = undefined;
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let wait_time = 800
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let token_timeout;
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function update_txt2img_tokens(...args) {
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update_token_counter("txt2img_token_button")
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if (args.length == 2)
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return args[0]
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return args;
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}
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function update_img2img_tokens(...args) {
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update_token_counter("img2img_token_button")
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if (args.length == 2)
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return args[0]
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return args;
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}
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function update_token_counter(button_id) {
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if (token_timeout)
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clearTimeout(token_timeout);
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token_timeout = setTimeout(() => gradioApp().getElementById(button_id)?.click(), wait_time);
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}
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function submit_prompt(event, generate_button_id) {
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if (event.altKey && event.keyCode === 13) {
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event.preventDefault();
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@ -207,8 +227,7 @@ function submit_prompt(event, generate_button_id) {
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}
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}
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function update_token_counter(button_id) {
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if (token_timeout)
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clearTimeout(token_timeout);
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token_timeout = setTimeout(() => gradioApp().getElementById(button_id)?.click(), wait_time);
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function restart_reload(){
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document.body.innerHTML='<h1 style="font-family:monospace;margin-top:20%;color:lightgray;text-align:center;">Reloading...</h1>';
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setTimeout(function(){location.reload()},2000)
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}
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@ -15,6 +15,7 @@ requirements_file = os.environ.get('REQS_FILE', "requirements_versions.txt")
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commandline_args = os.environ.get('COMMANDLINE_ARGS', "")
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gfpgan_package = os.environ.get('GFPGAN_PACKAGE', "git+https://github.com/TencentARC/GFPGAN.git@8d2447a2d918f8eba5a4a01463fd48e45126a379")
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clip_package = os.environ.get('CLIP_PACKAGE', "git+https://github.com/openai/CLIP.git@d50d76daa670286dd6cacf3bcd80b5e4823fc8e1")
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stable_diffusion_commit_hash = os.environ.get('STABLE_DIFFUSION_COMMIT_HASH', "69ae4b35e0a0f6ee1af8bb9a5d0016ccb27e36dc")
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taming_transformers_commit_hash = os.environ.get('TAMING_TRANSFORMERS_COMMIT_HASH', "24268930bf1dce879235a7fddd0b2355b84d7ea6")
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@ -111,6 +112,9 @@ if not skip_torch_cuda_test:
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if not is_installed("gfpgan"):
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run_pip(f"install {gfpgan_package}", "gfpgan")
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if not is_installed("clip"):
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run_pip(f"install {clip_package}", "clip")
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os.makedirs(dir_repos, exist_ok=True)
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git_clone("https://github.com/CompVis/stable-diffusion.git", repo_dir('stable-diffusion'), "Stable Diffusion", stable_diffusion_commit_hash)
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@ -32,10 +32,9 @@ def enable_tf32():
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errors.run(enable_tf32, "Enabling TF32")
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device = get_optimal_device()
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device_codeformer = cpu if has_mps else device
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dtype = torch.float16
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def randn(seed, shape):
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# Pytorch currently doesn't handle setting randomness correctly when the metal backend is used.
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@ -73,8 +73,8 @@ def fix_model_layers(crt_model, pretrained_net):
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class UpscalerESRGAN(Upscaler):
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def __init__(self, dirname):
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self.name = "ESRGAN"
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self.model_url = "https://drive.google.com/u/0/uc?id=1TPrz5QKd8DHHt1k8SRtm6tMiPjz_Qene&export=download"
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self.model_name = "ESRGAN 4x"
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self.model_url = "https://github.com/cszn/KAIR/releases/download/v1.0/ESRGAN.pth"
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self.model_name = "ESRGAN_4x"
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self.scalers = []
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self.user_path = dirname
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self.model_path = os.path.join(models_path, self.name)
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@ -311,7 +311,12 @@ def apply_filename_pattern(x, p, seed, prompt):
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x = x.replace("[cfg]", str(p.cfg_scale))
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x = x.replace("[width]", str(p.width))
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x = x.replace("[height]", str(p.height))
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x = x.replace("[styles]", sanitize_filename_part(", ".join([x for x in p.styles if not x == "None"]) or "No styles", replace_spaces=False))
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#currently disabled if using the save button, will work otherwise
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# if enabled it will cause a bug because styles is not included in the save_files data dictionary
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if hasattr(p, "styles"):
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x = x.replace("[styles]", sanitize_filename_part(", ".join([x for x in p.styles if not x == "None"] or "None"), replace_spaces=False))
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x = x.replace("[sampler]", sanitize_filename_part(sd_samplers.samplers[p.sampler_index].name, replace_spaces=False))
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x = x.replace("[model_hash]", shared.sd_model.sd_model_hash)
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@ -103,6 +103,8 @@ def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, pro
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inpaint_full_res_padding=inpaint_full_res_padding,
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inpainting_mask_invert=inpainting_mask_invert,
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)
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if shared.cmd_opts.enable_console_prompts:
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print(f"\nimg2img: {prompt}", file=shared.progress_print_out)
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p.extra_generation_params["Mask blur"] = mask_blur
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@ -21,6 +21,7 @@ Category = namedtuple("Category", ["name", "topn", "items"])
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re_topn = re.compile(r"\.top(\d+)\.")
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class InterrogateModels:
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blip_model = None
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clip_model = None
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@ -5,7 +5,6 @@ import importlib
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from urllib.parse import urlparse
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from basicsr.utils.download_util import load_file_from_url
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from modules import shared
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from modules.upscaler import Upscaler
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from modules.paths import script_path, models_path
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@ -43,7 +42,7 @@ def load_models(model_path: str, model_url: str = None, command_path: str = None
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for place in places:
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if os.path.exists(place):
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for file in glob.iglob(place + '**/**', recursive=True):
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full_path = os.path.join(place, file)
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full_path = file
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if os.path.isdir(full_path):
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continue
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if len(ext_filter) != 0:
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@ -121,16 +120,30 @@ def move_files(src_path: str, dest_path: str, ext_filter: str = None):
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def load_upscalers():
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sd = shared.script_path
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# We can only do this 'magic' method to dynamically load upscalers if they are referenced,
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# so we'll try to import any _model.py files before looking in __subclasses__
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modules_dir = os.path.join(sd, "modules")
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for file in os.listdir(modules_dir):
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if "_model.py" in file:
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model_name = file.replace("_model.py", "")
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full_model = f"modules.{model_name}_model"
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try:
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importlib.import_module(full_model)
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except:
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pass
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datas = []
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c_o = vars(shared.cmd_opts)
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for cls in Upscaler.__subclasses__():
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name = cls.__name__
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module_name = cls.__module__
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module = importlib.import_module(module_name)
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class_ = getattr(module, name)
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cmd_name = f"{name.lower().replace('upscaler', '')}-models-path"
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cmd_name = f"{name.lower().replace('upscaler', '')}_models_path"
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opt_string = None
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try:
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opt_string = shared.opts.__getattr__(cmd_name)
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if cmd_name in c_o:
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opt_string = c_o[cmd_name]
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except:
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pass
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scaler = class_(opt_string)
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|
|
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@ -20,7 +20,6 @@ path_dirs = [
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(os.path.join(sd_path, '../taming-transformers'), 'taming', 'Taming Transformers', []),
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(os.path.join(sd_path, '../CodeFormer'), 'inference_codeformer.py', 'CodeFormer', []),
|
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(os.path.join(sd_path, '../BLIP'), 'models/blip.py', 'BLIP', []),
|
||||
(os.path.join(sd_path, '../latent-diffusion'), 'LDSR.py', 'LDSR', []),
|
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(os.path.join(sd_path, '../k-diffusion'), 'k_diffusion/sampling.py', 'k_diffusion', ["atstart"]),
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]
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|
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|
|
|
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|
@ -56,7 +56,7 @@ class StableDiffusionProcessing:
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self.prompt: str = prompt
|
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self.prompt_for_display: str = None
|
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self.negative_prompt: str = (negative_prompt or "")
|
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self.styles: str = styles
|
||||
self.styles: list = styles or []
|
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self.seed: int = seed
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self.subseed: int = subseed
|
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self.subseed_strength: float = subseed_strength
|
||||
|
|
@ -271,7 +271,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
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"Variation seed strength": (None if p.subseed_strength == 0 else p.subseed_strength),
|
||||
"Seed resize from": (None if p.seed_resize_from_w == 0 or p.seed_resize_from_h == 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"),
|
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"Denoising strength": getattr(p, 'denoising_strength', None),
|
||||
"Eta": (None if p.sampler.eta == p.sampler.default_eta else p.sampler.eta),
|
||||
"Eta": (None if p.sampler is None or p.sampler.eta == p.sampler.default_eta else p.sampler.eta),
|
||||
}
|
||||
|
||||
generation_params.update(p.extra_generation_params)
|
||||
|
|
@ -295,7 +295,10 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
|
|||
|
||||
fix_seed(p)
|
||||
|
||||
if p.outpath_samples is not None:
|
||||
os.makedirs(p.outpath_samples, exist_ok=True)
|
||||
|
||||
if p.outpath_grids is not None:
|
||||
os.makedirs(p.outpath_grids, exist_ok=True)
|
||||
|
||||
modules.sd_hijack.model_hijack.apply_circular(p.tiling)
|
||||
|
|
@ -323,7 +326,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
|
|||
return create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration, position_in_batch)
|
||||
|
||||
if os.path.exists(cmd_opts.embeddings_dir):
|
||||
model_hijack.load_textual_inversion_embeddings(cmd_opts.embeddings_dir, p.sd_model)
|
||||
model_hijack.embedding_db.load_textual_inversion_embeddings()
|
||||
|
||||
infotexts = []
|
||||
output_images = []
|
||||
|
|
|
|||
|
|
@ -162,6 +162,40 @@ class ScriptRunner:
|
|||
|
||||
return processed
|
||||
|
||||
def reload_sources(self):
|
||||
for si, script in list(enumerate(self.scripts)):
|
||||
with open(script.filename, "r", encoding="utf8") as file:
|
||||
args_from = script.args_from
|
||||
args_to = script.args_to
|
||||
filename = script.filename
|
||||
text = file.read()
|
||||
|
||||
from types import ModuleType
|
||||
|
||||
compiled = compile(text, filename, 'exec')
|
||||
module = ModuleType(script.filename)
|
||||
exec(compiled, module.__dict__)
|
||||
|
||||
for key, script_class in module.__dict__.items():
|
||||
if type(script_class) == type and issubclass(script_class, Script):
|
||||
self.scripts[si] = script_class()
|
||||
self.scripts[si].filename = filename
|
||||
self.scripts[si].args_from = args_from
|
||||
self.scripts[si].args_to = args_to
|
||||
|
||||
scripts_txt2img = ScriptRunner()
|
||||
scripts_img2img = ScriptRunner()
|
||||
|
||||
def reload_script_body_only():
|
||||
scripts_txt2img.reload_sources()
|
||||
scripts_img2img.reload_sources()
|
||||
|
||||
|
||||
def reload_scripts(basedir):
|
||||
global scripts_txt2img, scripts_img2img
|
||||
|
||||
scripts_data.clear()
|
||||
load_scripts(basedir)
|
||||
|
||||
scripts_txt2img = ScriptRunner()
|
||||
scripts_img2img = ScriptRunner()
|
||||
|
|
|
|||
90
modules/scunet_model.py
Normal file
90
modules/scunet_model.py
Normal file
|
|
@ -0,0 +1,90 @@
|
|||
import os.path
|
||||
import sys
|
||||
import traceback
|
||||
|
||||
import PIL.Image
|
||||
import numpy as np
|
||||
import torch
|
||||
from basicsr.utils.download_util import load_file_from_url
|
||||
|
||||
import modules.upscaler
|
||||
from modules import shared, modelloader
|
||||
from modules.paths import models_path
|
||||
from modules.scunet_model_arch import SCUNet as net
|
||||
|
||||
|
||||
class UpscalerScuNET(modules.upscaler.Upscaler):
|
||||
def __init__(self, dirname):
|
||||
self.name = "ScuNET"
|
||||
self.model_path = os.path.join(models_path, self.name)
|
||||
self.model_name = "ScuNET GAN"
|
||||
self.model_name2 = "ScuNET PSNR"
|
||||
self.model_url = "https://github.com/cszn/KAIR/releases/download/v1.0/scunet_color_real_gan.pth"
|
||||
self.model_url2 = "https://github.com/cszn/KAIR/releases/download/v1.0/scunet_color_real_psnr.pth"
|
||||
self.user_path = dirname
|
||||
super().__init__()
|
||||
model_paths = self.find_models(ext_filter=[".pth"])
|
||||
scalers = []
|
||||
add_model2 = True
|
||||
for file in model_paths:
|
||||
if "http" in file:
|
||||
name = self.model_name
|
||||
else:
|
||||
name = modelloader.friendly_name(file)
|
||||
if name == self.model_name2 or file == self.model_url2:
|
||||
add_model2 = False
|
||||
try:
|
||||
scaler_data = modules.upscaler.UpscalerData(name, file, self, 4)
|
||||
scalers.append(scaler_data)
|
||||
except Exception:
|
||||
print(f"Error loading ScuNET model: {file}", file=sys.stderr)
|
||||
print(traceback.format_exc(), file=sys.stderr)
|
||||
if add_model2:
|
||||
scaler_data2 = modules.upscaler.UpscalerData(self.model_name2, self.model_url2, self)
|
||||
scalers.append(scaler_data2)
|
||||
self.scalers = scalers
|
||||
|
||||
def do_upscale(self, img: PIL.Image, selected_file):
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
model = self.load_model(selected_file)
|
||||
if model is None:
|
||||
return img
|
||||
|
||||
device = shared.device
|
||||
img = np.array(img)
|
||||
img = img[:, :, ::-1]
|
||||
img = np.moveaxis(img, 2, 0) / 255
|
||||
img = torch.from_numpy(img).float()
|
||||
img = img.unsqueeze(0).to(shared.device)
|
||||
|
||||
img = img.to(device)
|
||||
with torch.no_grad():
|
||||
output = model(img)
|
||||
output = output.squeeze().float().cpu().clamp_(0, 1).numpy()
|
||||
output = 255. * np.moveaxis(output, 0, 2)
|
||||
output = output.astype(np.uint8)
|
||||
output = output[:, :, ::-1]
|
||||
torch.cuda.empty_cache()
|
||||
return PIL.Image.fromarray(output, 'RGB')
|
||||
|
||||
def load_model(self, path: str):
|
||||
device = shared.device
|
||||
if "http" in path:
|
||||
filename = load_file_from_url(url=self.model_url, model_dir=self.model_path, file_name="%s.pth" % self.name,
|
||||
progress=True)
|
||||
else:
|
||||
filename = path
|
||||
if not os.path.exists(os.path.join(self.model_path, filename)) or filename is None:
|
||||
print(f"ScuNET: Unable to load model from {filename}", file=sys.stderr)
|
||||
return None
|
||||
|
||||
model = net(in_nc=3, config=[4, 4, 4, 4, 4, 4, 4], dim=64)
|
||||
model.load_state_dict(torch.load(filename), strict=True)
|
||||
model.eval()
|
||||
for k, v in model.named_parameters():
|
||||
v.requires_grad = False
|
||||
model = model.to(device)
|
||||
|
||||
return model
|
||||
|
||||
265
modules/scunet_model_arch.py
Normal file
265
modules/scunet_model_arch.py
Normal file
|
|
@ -0,0 +1,265 @@
|
|||
# -*- coding: utf-8 -*-
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from einops import rearrange
|
||||
from einops.layers.torch import Rearrange
|
||||
from timm.models.layers import trunc_normal_, DropPath
|
||||
|
||||
|
||||
class WMSA(nn.Module):
|
||||
""" Self-attention module in Swin Transformer
|
||||
"""
|
||||
|
||||
def __init__(self, input_dim, output_dim, head_dim, window_size, type):
|
||||
super(WMSA, self).__init__()
|
||||
self.input_dim = input_dim
|
||||
self.output_dim = output_dim
|
||||
self.head_dim = head_dim
|
||||
self.scale = self.head_dim ** -0.5
|
||||
self.n_heads = input_dim // head_dim
|
||||
self.window_size = window_size
|
||||
self.type = type
|
||||
self.embedding_layer = nn.Linear(self.input_dim, 3 * self.input_dim, bias=True)
|
||||
|
||||
self.relative_position_params = nn.Parameter(
|
||||
torch.zeros((2 * window_size - 1) * (2 * window_size - 1), self.n_heads))
|
||||
|
||||
self.linear = nn.Linear(self.input_dim, self.output_dim)
|
||||
|
||||
trunc_normal_(self.relative_position_params, std=.02)
|
||||
self.relative_position_params = torch.nn.Parameter(
|
||||
self.relative_position_params.view(2 * window_size - 1, 2 * window_size - 1, self.n_heads).transpose(1,
|
||||
2).transpose(
|
||||
0, 1))
|
||||
|
||||
def generate_mask(self, h, w, p, shift):
|
||||
""" generating the mask of SW-MSA
|
||||
Args:
|
||||
shift: shift parameters in CyclicShift.
|
||||
Returns:
|
||||
attn_mask: should be (1 1 w p p),
|
||||
"""
|
||||
# supporting sqaure.
|
||||
attn_mask = torch.zeros(h, w, p, p, p, p, dtype=torch.bool, device=self.relative_position_params.device)
|
||||
if self.type == 'W':
|
||||
return attn_mask
|
||||
|
||||
s = p - shift
|
||||
attn_mask[-1, :, :s, :, s:, :] = True
|
||||
attn_mask[-1, :, s:, :, :s, :] = True
|
||||
attn_mask[:, -1, :, :s, :, s:] = True
|
||||
attn_mask[:, -1, :, s:, :, :s] = True
|
||||
attn_mask = rearrange(attn_mask, 'w1 w2 p1 p2 p3 p4 -> 1 1 (w1 w2) (p1 p2) (p3 p4)')
|
||||
return attn_mask
|
||||
|
||||
def forward(self, x):
|
||||
""" Forward pass of Window Multi-head Self-attention module.
|
||||
Args:
|
||||
x: input tensor with shape of [b h w c];
|
||||
attn_mask: attention mask, fill -inf where the value is True;
|
||||
Returns:
|
||||
output: tensor shape [b h w c]
|
||||
"""
|
||||
if self.type != 'W': x = torch.roll(x, shifts=(-(self.window_size // 2), -(self.window_size // 2)), dims=(1, 2))
|
||||
x = rearrange(x, 'b (w1 p1) (w2 p2) c -> b w1 w2 p1 p2 c', p1=self.window_size, p2=self.window_size)
|
||||
h_windows = x.size(1)
|
||||
w_windows = x.size(2)
|
||||
# sqaure validation
|
||||
# assert h_windows == w_windows
|
||||
|
||||
x = rearrange(x, 'b w1 w2 p1 p2 c -> b (w1 w2) (p1 p2) c', p1=self.window_size, p2=self.window_size)
|
||||
qkv = self.embedding_layer(x)
|
||||
q, k, v = rearrange(qkv, 'b nw np (threeh c) -> threeh b nw np c', c=self.head_dim).chunk(3, dim=0)
|
||||
sim = torch.einsum('hbwpc,hbwqc->hbwpq', q, k) * self.scale
|
||||
# Adding learnable relative embedding
|
||||
sim = sim + rearrange(self.relative_embedding(), 'h p q -> h 1 1 p q')
|
||||
# Using Attn Mask to distinguish different subwindows.
|
||||
if self.type != 'W':
|
||||
attn_mask = self.generate_mask(h_windows, w_windows, self.window_size, shift=self.window_size // 2)
|
||||
sim = sim.masked_fill_(attn_mask, float("-inf"))
|
||||
|
||||
probs = nn.functional.softmax(sim, dim=-1)
|
||||
output = torch.einsum('hbwij,hbwjc->hbwic', probs, v)
|
||||
output = rearrange(output, 'h b w p c -> b w p (h c)')
|
||||
output = self.linear(output)
|
||||
output = rearrange(output, 'b (w1 w2) (p1 p2) c -> b (w1 p1) (w2 p2) c', w1=h_windows, p1=self.window_size)
|
||||
|
||||
if self.type != 'W': output = torch.roll(output, shifts=(self.window_size // 2, self.window_size // 2),
|
||||
dims=(1, 2))
|
||||
return output
|
||||
|
||||
def relative_embedding(self):
|
||||
cord = torch.tensor(np.array([[i, j] for i in range(self.window_size) for j in range(self.window_size)]))
|
||||
relation = cord[:, None, :] - cord[None, :, :] + self.window_size - 1
|
||||
# negative is allowed
|
||||
return self.relative_position_params[:, relation[:, :, 0].long(), relation[:, :, 1].long()]
|
||||
|
||||
|
||||
class Block(nn.Module):
|
||||
def __init__(self, input_dim, output_dim, head_dim, window_size, drop_path, type='W', input_resolution=None):
|
||||
""" SwinTransformer Block
|
||||
"""
|
||||
super(Block, self).__init__()
|
||||
self.input_dim = input_dim
|
||||
self.output_dim = output_dim
|
||||
assert type in ['W', 'SW']
|
||||
self.type = type
|
||||
if input_resolution <= window_size:
|
||||
self.type = 'W'
|
||||
|
||||
self.ln1 = nn.LayerNorm(input_dim)
|
||||
self.msa = WMSA(input_dim, input_dim, head_dim, window_size, self.type)
|
||||
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
|
||||
self.ln2 = nn.LayerNorm(input_dim)
|
||||
self.mlp = nn.Sequential(
|
||||
nn.Linear(input_dim, 4 * input_dim),
|
||||
nn.GELU(),
|
||||
nn.Linear(4 * input_dim, output_dim),
|
||||
)
|
||||
|
||||
def forward(self, x):
|
||||
x = x + self.drop_path(self.msa(self.ln1(x)))
|
||||
x = x + self.drop_path(self.mlp(self.ln2(x)))
|
||||
return x
|
||||
|
||||
|
||||
class ConvTransBlock(nn.Module):
|
||||
def __init__(self, conv_dim, trans_dim, head_dim, window_size, drop_path, type='W', input_resolution=None):
|
||||
""" SwinTransformer and Conv Block
|
||||
"""
|
||||
super(ConvTransBlock, self).__init__()
|
||||
self.conv_dim = conv_dim
|
||||
self.trans_dim = trans_dim
|
||||
self.head_dim = head_dim
|
||||
self.window_size = window_size
|
||||
self.drop_path = drop_path
|
||||
self.type = type
|
||||
self.input_resolution = input_resolution
|
||||
|
||||
assert self.type in ['W', 'SW']
|
||||
if self.input_resolution <= self.window_size:
|
||||
self.type = 'W'
|
||||
|
||||
self.trans_block = Block(self.trans_dim, self.trans_dim, self.head_dim, self.window_size, self.drop_path,
|
||||
self.type, self.input_resolution)
|
||||
self.conv1_1 = nn.Conv2d(self.conv_dim + self.trans_dim, self.conv_dim + self.trans_dim, 1, 1, 0, bias=True)
|
||||
self.conv1_2 = nn.Conv2d(self.conv_dim + self.trans_dim, self.conv_dim + self.trans_dim, 1, 1, 0, bias=True)
|
||||
|
||||
self.conv_block = nn.Sequential(
|
||||
nn.Conv2d(self.conv_dim, self.conv_dim, 3, 1, 1, bias=False),
|
||||
nn.ReLU(True),
|
||||
nn.Conv2d(self.conv_dim, self.conv_dim, 3, 1, 1, bias=False)
|
||||
)
|
||||
|
||||
def forward(self, x):
|
||||
conv_x, trans_x = torch.split(self.conv1_1(x), (self.conv_dim, self.trans_dim), dim=1)
|
||||
conv_x = self.conv_block(conv_x) + conv_x
|
||||
trans_x = Rearrange('b c h w -> b h w c')(trans_x)
|
||||
trans_x = self.trans_block(trans_x)
|
||||
trans_x = Rearrange('b h w c -> b c h w')(trans_x)
|
||||
res = self.conv1_2(torch.cat((conv_x, trans_x), dim=1))
|
||||
x = x + res
|
||||
|
||||
return x
|
||||
|
||||
|
||||
class SCUNet(nn.Module):
|
||||
# def __init__(self, in_nc=3, config=[2, 2, 2, 2, 2, 2, 2], dim=64, drop_path_rate=0.0, input_resolution=256):
|
||||
def __init__(self, in_nc=3, config=None, dim=64, drop_path_rate=0.0, input_resolution=256):
|
||||
super(SCUNet, self).__init__()
|
||||
if config is None:
|
||||
config = [2, 2, 2, 2, 2, 2, 2]
|
||||
self.config = config
|
||||
self.dim = dim
|
||||
self.head_dim = 32
|
||||
self.window_size = 8
|
||||
|
||||
# drop path rate for each layer
|
||||
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(config))]
|
||||
|
||||
self.m_head = [nn.Conv2d(in_nc, dim, 3, 1, 1, bias=False)]
|
||||
|
||||
begin = 0
|
||||
self.m_down1 = [ConvTransBlock(dim // 2, dim // 2, self.head_dim, self.window_size, dpr[i + begin],
|
||||
'W' if not i % 2 else 'SW', input_resolution)
|
||||
for i in range(config[0])] + \
|
||||
[nn.Conv2d(dim, 2 * dim, 2, 2, 0, bias=False)]
|
||||
|
||||
begin += config[0]
|
||||
self.m_down2 = [ConvTransBlock(dim, dim, self.head_dim, self.window_size, dpr[i + begin],
|
||||
'W' if not i % 2 else 'SW', input_resolution // 2)
|
||||
for i in range(config[1])] + \
|
||||
[nn.Conv2d(2 * dim, 4 * dim, 2, 2, 0, bias=False)]
|
||||
|
||||
begin += config[1]
|
||||
self.m_down3 = [ConvTransBlock(2 * dim, 2 * dim, self.head_dim, self.window_size, dpr[i + begin],
|
||||
'W' if not i % 2 else 'SW', input_resolution // 4)
|
||||
for i in range(config[2])] + \
|
||||
[nn.Conv2d(4 * dim, 8 * dim, 2, 2, 0, bias=False)]
|
||||
|
||||
begin += config[2]
|
||||
self.m_body = [ConvTransBlock(4 * dim, 4 * dim, self.head_dim, self.window_size, dpr[i + begin],
|
||||
'W' if not i % 2 else 'SW', input_resolution // 8)
|
||||
for i in range(config[3])]
|
||||
|
||||
begin += config[3]
|
||||
self.m_up3 = [nn.ConvTranspose2d(8 * dim, 4 * dim, 2, 2, 0, bias=False), ] + \
|
||||
[ConvTransBlock(2 * dim, 2 * dim, self.head_dim, self.window_size, dpr[i + begin],
|
||||
'W' if not i % 2 else 'SW', input_resolution // 4)
|
||||
for i in range(config[4])]
|
||||
|
||||
begin += config[4]
|
||||
self.m_up2 = [nn.ConvTranspose2d(4 * dim, 2 * dim, 2, 2, 0, bias=False), ] + \
|
||||
[ConvTransBlock(dim, dim, self.head_dim, self.window_size, dpr[i + begin],
|
||||
'W' if not i % 2 else 'SW', input_resolution // 2)
|
||||
for i in range(config[5])]
|
||||
|
||||
begin += config[5]
|
||||
self.m_up1 = [nn.ConvTranspose2d(2 * dim, dim, 2, 2, 0, bias=False), ] + \
|
||||
[ConvTransBlock(dim // 2, dim // 2, self.head_dim, self.window_size, dpr[i + begin],
|
||||
'W' if not i % 2 else 'SW', input_resolution)
|
||||
for i in range(config[6])]
|
||||
|
||||
self.m_tail = [nn.Conv2d(dim, in_nc, 3, 1, 1, bias=False)]
|
||||
|
||||
self.m_head = nn.Sequential(*self.m_head)
|
||||
self.m_down1 = nn.Sequential(*self.m_down1)
|
||||
self.m_down2 = nn.Sequential(*self.m_down2)
|
||||
self.m_down3 = nn.Sequential(*self.m_down3)
|
||||
self.m_body = nn.Sequential(*self.m_body)
|
||||
self.m_up3 = nn.Sequential(*self.m_up3)
|
||||
self.m_up2 = nn.Sequential(*self.m_up2)
|
||||
self.m_up1 = nn.Sequential(*self.m_up1)
|
||||
self.m_tail = nn.Sequential(*self.m_tail)
|
||||
# self.apply(self._init_weights)
|
||||
|
||||
def forward(self, x0):
|
||||
|
||||
h, w = x0.size()[-2:]
|
||||
paddingBottom = int(np.ceil(h / 64) * 64 - h)
|
||||
paddingRight = int(np.ceil(w / 64) * 64 - w)
|
||||
x0 = nn.ReplicationPad2d((0, paddingRight, 0, paddingBottom))(x0)
|
||||
|
||||
x1 = self.m_head(x0)
|
||||
x2 = self.m_down1(x1)
|
||||
x3 = self.m_down2(x2)
|
||||
x4 = self.m_down3(x3)
|
||||
x = self.m_body(x4)
|
||||
x = self.m_up3(x + x4)
|
||||
x = self.m_up2(x + x3)
|
||||
x = self.m_up1(x + x2)
|
||||
x = self.m_tail(x + x1)
|
||||
|
||||
x = x[..., :h, :w]
|
||||
|
||||
return x
|
||||
|
||||
def _init_weights(self, m):
|
||||
if isinstance(m, nn.Linear):
|
||||
trunc_normal_(m.weight, std=.02)
|
||||
if m.bias is not None:
|
||||
nn.init.constant_(m.bias, 0)
|
||||
elif isinstance(m, nn.LayerNorm):
|
||||
nn.init.constant_(m.bias, 0)
|
||||
nn.init.constant_(m.weight, 1.0)
|
||||
|
|
@ -6,244 +6,41 @@ import torch
|
|||
import numpy as np
|
||||
from torch import einsum
|
||||
|
||||
from modules import prompt_parser
|
||||
import modules.textual_inversion.textual_inversion
|
||||
from modules import prompt_parser, devices, sd_hijack_optimizations, shared
|
||||
from modules.shared import opts, device, cmd_opts
|
||||
|
||||
from ldm.util import default
|
||||
from einops import rearrange
|
||||
import ldm.modules.attention
|
||||
import ldm.modules.diffusionmodules.model
|
||||
|
||||
|
||||
# see https://github.com/basujindal/stable-diffusion/pull/117 for discussion
|
||||
def split_cross_attention_forward_v1(self, x, context=None, mask=None):
|
||||
h = self.heads
|
||||
|
||||
q = self.to_q(x)
|
||||
context = default(context, x)
|
||||
k = self.to_k(context)
|
||||
v = self.to_v(context)
|
||||
del context, x
|
||||
|
||||
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
|
||||
|
||||
r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device)
|
||||
for i in range(0, q.shape[0], 2):
|
||||
end = i + 2
|
||||
s1 = einsum('b i d, b j d -> b i j', q[i:end], k[i:end])
|
||||
s1 *= self.scale
|
||||
|
||||
s2 = s1.softmax(dim=-1)
|
||||
del s1
|
||||
|
||||
r1[i:end] = einsum('b i j, b j d -> b i d', s2, v[i:end])
|
||||
del s2
|
||||
|
||||
r2 = rearrange(r1, '(b h) n d -> b n (h d)', h=h)
|
||||
del r1
|
||||
|
||||
return self.to_out(r2)
|
||||
attention_CrossAttention_forward = ldm.modules.attention.CrossAttention.forward
|
||||
diffusionmodules_model_nonlinearity = ldm.modules.diffusionmodules.model.nonlinearity
|
||||
diffusionmodules_model_AttnBlock_forward = ldm.modules.diffusionmodules.model.AttnBlock.forward
|
||||
|
||||
|
||||
# taken from https://github.com/Doggettx/stable-diffusion
|
||||
def split_cross_attention_forward(self, x, context=None, mask=None):
|
||||
h = self.heads
|
||||
def apply_optimizations():
|
||||
if cmd_opts.opt_split_attention_v1:
|
||||
ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1
|
||||
elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention or torch.cuda.is_available()):
|
||||
ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward
|
||||
ldm.modules.diffusionmodules.model.nonlinearity = sd_hijack_optimizations.nonlinearity_hijack
|
||||
ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward
|
||||
|
||||
q_in = self.to_q(x)
|
||||
context = default(context, x)
|
||||
k_in = self.to_k(context) * self.scale
|
||||
v_in = self.to_v(context)
|
||||
del context, x
|
||||
|
||||
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in))
|
||||
del q_in, k_in, v_in
|
||||
def undo_optimizations():
|
||||
ldm.modules.attention.CrossAttention.forward = attention_CrossAttention_forward
|
||||
ldm.modules.diffusionmodules.model.nonlinearity = diffusionmodules_model_nonlinearity
|
||||
ldm.modules.diffusionmodules.model.AttnBlock.forward = diffusionmodules_model_AttnBlock_forward
|
||||
|
||||
r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
|
||||
|
||||
stats = torch.cuda.memory_stats(q.device)
|
||||
mem_active = stats['active_bytes.all.current']
|
||||
mem_reserved = stats['reserved_bytes.all.current']
|
||||
mem_free_cuda, _ = torch.cuda.mem_get_info(torch.cuda.current_device())
|
||||
mem_free_torch = mem_reserved - mem_active
|
||||
mem_free_total = mem_free_cuda + mem_free_torch
|
||||
|
||||
gb = 1024 ** 3
|
||||
tensor_size = q.shape[0] * q.shape[1] * k.shape[1] * q.element_size()
|
||||
modifier = 3 if q.element_size() == 2 else 2.5
|
||||
mem_required = tensor_size * modifier
|
||||
steps = 1
|
||||
|
||||
if mem_required > mem_free_total:
|
||||
steps = 2 ** (math.ceil(math.log(mem_required / mem_free_total, 2)))
|
||||
# print(f"Expected tensor size:{tensor_size/gb:0.1f}GB, cuda free:{mem_free_cuda/gb:0.1f}GB "
|
||||
# f"torch free:{mem_free_torch/gb:0.1f} total:{mem_free_total/gb:0.1f} steps:{steps}")
|
||||
|
||||
if steps > 64:
|
||||
max_res = math.floor(math.sqrt(math.sqrt(mem_free_total / 2.5)) / 8) * 64
|
||||
raise RuntimeError(f'Not enough memory, use lower resolution (max approx. {max_res}x{max_res}). '
|
||||
f'Need: {mem_required / 64 / gb:0.1f}GB free, Have:{mem_free_total / gb:0.1f}GB free')
|
||||
|
||||
slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
|
||||
for i in range(0, q.shape[1], slice_size):
|
||||
end = i + slice_size
|
||||
s1 = einsum('b i d, b j d -> b i j', q[:, i:end], k)
|
||||
|
||||
s2 = s1.softmax(dim=-1, dtype=q.dtype)
|
||||
del s1
|
||||
|
||||
r1[:, i:end] = einsum('b i j, b j d -> b i d', s2, v)
|
||||
del s2
|
||||
|
||||
del q, k, v
|
||||
|
||||
r2 = rearrange(r1, '(b h) n d -> b n (h d)', h=h)
|
||||
del r1
|
||||
|
||||
return self.to_out(r2)
|
||||
|
||||
def nonlinearity_hijack(x):
|
||||
# swish
|
||||
t = torch.sigmoid(x)
|
||||
x *= t
|
||||
del t
|
||||
|
||||
return x
|
||||
|
||||
def cross_attention_attnblock_forward(self, x):
|
||||
h_ = x
|
||||
h_ = self.norm(h_)
|
||||
q1 = self.q(h_)
|
||||
k1 = self.k(h_)
|
||||
v = self.v(h_)
|
||||
|
||||
# compute attention
|
||||
b, c, h, w = q1.shape
|
||||
|
||||
q2 = q1.reshape(b, c, h*w)
|
||||
del q1
|
||||
|
||||
q = q2.permute(0, 2, 1) # b,hw,c
|
||||
del q2
|
||||
|
||||
k = k1.reshape(b, c, h*w) # b,c,hw
|
||||
del k1
|
||||
|
||||
h_ = torch.zeros_like(k, device=q.device)
|
||||
|
||||
stats = torch.cuda.memory_stats(q.device)
|
||||
mem_active = stats['active_bytes.all.current']
|
||||
mem_reserved = stats['reserved_bytes.all.current']
|
||||
mem_free_cuda, _ = torch.cuda.mem_get_info(torch.cuda.current_device())
|
||||
mem_free_torch = mem_reserved - mem_active
|
||||
mem_free_total = mem_free_cuda + mem_free_torch
|
||||
|
||||
tensor_size = q.shape[0] * q.shape[1] * k.shape[2] * q.element_size()
|
||||
mem_required = tensor_size * 2.5
|
||||
steps = 1
|
||||
|
||||
if mem_required > mem_free_total:
|
||||
steps = 2**(math.ceil(math.log(mem_required / mem_free_total, 2)))
|
||||
|
||||
slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
|
||||
for i in range(0, q.shape[1], slice_size):
|
||||
end = i + slice_size
|
||||
|
||||
w1 = torch.bmm(q[:, i:end], k) # b,hw,hw w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
|
||||
w2 = w1 * (int(c)**(-0.5))
|
||||
del w1
|
||||
w3 = torch.nn.functional.softmax(w2, dim=2, dtype=q.dtype)
|
||||
del w2
|
||||
|
||||
# attend to values
|
||||
v1 = v.reshape(b, c, h*w)
|
||||
w4 = w3.permute(0, 2, 1) # b,hw,hw (first hw of k, second of q)
|
||||
del w3
|
||||
|
||||
h_[:, :, i:end] = torch.bmm(v1, w4) # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
|
||||
del v1, w4
|
||||
|
||||
h2 = h_.reshape(b, c, h, w)
|
||||
del h_
|
||||
|
||||
h3 = self.proj_out(h2)
|
||||
del h2
|
||||
|
||||
h3 += x
|
||||
|
||||
return h3
|
||||
|
||||
class StableDiffusionModelHijack:
|
||||
ids_lookup = {}
|
||||
word_embeddings = {}
|
||||
word_embeddings_checksums = {}
|
||||
fixes = None
|
||||
comments = []
|
||||
dir_mtime = None
|
||||
layers = None
|
||||
circular_enabled = False
|
||||
clip = None
|
||||
|
||||
def load_textual_inversion_embeddings(self, dirname, model):
|
||||
mt = os.path.getmtime(dirname)
|
||||
if self.dir_mtime is not None and mt <= self.dir_mtime:
|
||||
return
|
||||
|
||||
self.dir_mtime = mt
|
||||
self.ids_lookup.clear()
|
||||
self.word_embeddings.clear()
|
||||
|
||||
tokenizer = model.cond_stage_model.tokenizer
|
||||
|
||||
def const_hash(a):
|
||||
r = 0
|
||||
for v in a:
|
||||
r = (r * 281 ^ int(v) * 997) & 0xFFFFFFFF
|
||||
return r
|
||||
|
||||
def process_file(path, filename):
|
||||
name = os.path.splitext(filename)[0]
|
||||
|
||||
data = torch.load(path, map_location="cpu")
|
||||
|
||||
# textual inversion embeddings
|
||||
if 'string_to_param' in data:
|
||||
param_dict = data['string_to_param']
|
||||
if hasattr(param_dict, '_parameters'):
|
||||
param_dict = getattr(param_dict, '_parameters') # fix for torch 1.12.1 loading saved file from torch 1.11
|
||||
assert len(param_dict) == 1, 'embedding file has multiple terms in it'
|
||||
emb = next(iter(param_dict.items()))[1]
|
||||
# diffuser concepts
|
||||
elif type(data) == dict and type(next(iter(data.values()))) == torch.Tensor:
|
||||
assert len(data.keys()) == 1, 'embedding file has multiple terms in it'
|
||||
|
||||
emb = next(iter(data.values()))
|
||||
if len(emb.shape) == 1:
|
||||
emb = emb.unsqueeze(0)
|
||||
|
||||
self.word_embeddings[name] = emb.detach().to(device)
|
||||
self.word_embeddings_checksums[name] = f'{const_hash(emb.reshape(-1)*100)&0xffff:04x}'
|
||||
|
||||
ids = tokenizer([name], add_special_tokens=False)['input_ids'][0]
|
||||
|
||||
first_id = ids[0]
|
||||
if first_id not in self.ids_lookup:
|
||||
self.ids_lookup[first_id] = []
|
||||
self.ids_lookup[first_id].append((ids, name))
|
||||
|
||||
for fn in os.listdir(dirname):
|
||||
try:
|
||||
fullfn = os.path.join(dirname, fn)
|
||||
|
||||
if os.stat(fullfn).st_size == 0:
|
||||
continue
|
||||
|
||||
process_file(fullfn, fn)
|
||||
except Exception:
|
||||
print(f"Error loading emedding {fn}:", file=sys.stderr)
|
||||
print(traceback.format_exc(), file=sys.stderr)
|
||||
continue
|
||||
|
||||
print(f"Loaded a total of {len(self.word_embeddings)} textual inversion embeddings.")
|
||||
embedding_db = modules.textual_inversion.textual_inversion.EmbeddingDatabase(cmd_opts.embeddings_dir)
|
||||
|
||||
def hijack(self, m):
|
||||
model_embeddings = m.cond_stage_model.transformer.text_model.embeddings
|
||||
|
|
@ -253,12 +50,7 @@ class StableDiffusionModelHijack:
|
|||
|
||||
self.clip = m.cond_stage_model
|
||||
|
||||
if cmd_opts.opt_split_attention_v1:
|
||||
ldm.modules.attention.CrossAttention.forward = split_cross_attention_forward_v1
|
||||
elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention or torch.cuda.is_available()):
|
||||
ldm.modules.attention.CrossAttention.forward = split_cross_attention_forward
|
||||
ldm.modules.diffusionmodules.model.nonlinearity = nonlinearity_hijack
|
||||
ldm.modules.diffusionmodules.model.AttnBlock.forward = cross_attention_attnblock_forward
|
||||
apply_optimizations()
|
||||
|
||||
def flatten(el):
|
||||
flattened = [flatten(children) for children in el.children()]
|
||||
|
|
@ -296,7 +88,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
|
|||
def __init__(self, wrapped, hijack):
|
||||
super().__init__()
|
||||
self.wrapped = wrapped
|
||||
self.hijack = hijack
|
||||
self.hijack: StableDiffusionModelHijack = hijack
|
||||
self.tokenizer = wrapped.tokenizer
|
||||
self.max_length = wrapped.max_length
|
||||
self.token_mults = {}
|
||||
|
|
@ -317,7 +109,6 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
|
|||
if mult != 1.0:
|
||||
self.token_mults[ident] = mult
|
||||
|
||||
|
||||
def tokenize_line(self, line, used_custom_terms, hijack_comments):
|
||||
id_start = self.wrapped.tokenizer.bos_token_id
|
||||
id_end = self.wrapped.tokenizer.eos_token_id
|
||||
|
|
@ -339,28 +130,19 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
|
|||
while i < len(tokens):
|
||||
token = tokens[i]
|
||||
|
||||
possible_matches = self.hijack.ids_lookup.get(token, None)
|
||||
embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, i)
|
||||
|
||||
if possible_matches is None:
|
||||
remade_tokens.append(token)
|
||||
multipliers.append(weight)
|
||||
else:
|
||||
found = False
|
||||
for ids, word in possible_matches:
|
||||
if tokens[i:i + len(ids)] == ids:
|
||||
emb_len = int(self.hijack.word_embeddings[word].shape[0])
|
||||
fixes.append((len(remade_tokens), word))
|
||||
remade_tokens += [0] * emb_len
|
||||
multipliers += [weight] * emb_len
|
||||
i += len(ids) - 1
|
||||
found = True
|
||||
used_custom_terms.append((word, self.hijack.word_embeddings_checksums[word]))
|
||||
break
|
||||
|
||||
if not found:
|
||||
if embedding is None:
|
||||
remade_tokens.append(token)
|
||||
multipliers.append(weight)
|
||||
i += 1
|
||||
else:
|
||||
emb_len = int(embedding.vec.shape[0])
|
||||
fixes.append((len(remade_tokens), embedding))
|
||||
remade_tokens += [0] * emb_len
|
||||
multipliers += [weight] * emb_len
|
||||
used_custom_terms.append((embedding.name, embedding.checksum()))
|
||||
i += embedding_length_in_tokens
|
||||
|
||||
if len(remade_tokens) > maxlen - 2:
|
||||
vocab = {v: k for k, v in self.wrapped.tokenizer.get_vocab().items()}
|
||||
|
|
@ -431,32 +213,23 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
|
|||
while i < len(tokens):
|
||||
token = tokens[i]
|
||||
|
||||
possible_matches = self.hijack.ids_lookup.get(token, None)
|
||||
embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, i)
|
||||
|
||||
mult_change = self.token_mults.get(token) if opts.enable_emphasis else None
|
||||
if mult_change is not None:
|
||||
mult *= mult_change
|
||||
elif possible_matches is None:
|
||||
i += 1
|
||||
elif embedding is None:
|
||||
remade_tokens.append(token)
|
||||
multipliers.append(mult)
|
||||
i += 1
|
||||
else:
|
||||
found = False
|
||||
for ids, word in possible_matches:
|
||||
if tokens[i:i+len(ids)] == ids:
|
||||
emb_len = int(self.hijack.word_embeddings[word].shape[0])
|
||||
fixes.append((len(remade_tokens), word))
|
||||
emb_len = int(embedding.vec.shape[0])
|
||||
fixes.append((len(remade_tokens), embedding))
|
||||
remade_tokens += [0] * emb_len
|
||||
multipliers += [mult] * emb_len
|
||||
i += len(ids) - 1
|
||||
found = True
|
||||
used_custom_terms.append((word, self.hijack.word_embeddings_checksums[word]))
|
||||
break
|
||||
|
||||
if not found:
|
||||
remade_tokens.append(token)
|
||||
multipliers.append(mult)
|
||||
|
||||
i += 1
|
||||
used_custom_terms.append((embedding.name, embedding.checksum()))
|
||||
i += embedding_length_in_tokens
|
||||
|
||||
if len(remade_tokens) > maxlen - 2:
|
||||
vocab = {v: k for k, v in self.wrapped.tokenizer.get_vocab().items()}
|
||||
|
|
@ -464,6 +237,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
|
|||
overflowing_words = [vocab.get(int(x), "") for x in ovf]
|
||||
overflowing_text = self.wrapped.tokenizer.convert_tokens_to_string(''.join(overflowing_words))
|
||||
hijack_comments.append(f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n")
|
||||
|
||||
token_count = len(remade_tokens)
|
||||
remade_tokens = remade_tokens + [id_end] * (maxlen - 2 - len(remade_tokens))
|
||||
remade_tokens = [id_start] + remade_tokens[0:maxlen-2] + [id_end]
|
||||
|
|
@ -484,7 +258,6 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
|
|||
else:
|
||||
batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text(text)
|
||||
|
||||
|
||||
self.hijack.fixes = hijack_fixes
|
||||
self.hijack.comments = hijack_comments
|
||||
|
||||
|
|
@ -517,15 +290,20 @@ class EmbeddingsWithFixes(torch.nn.Module):
|
|||
|
||||
inputs_embeds = self.wrapped(input_ids)
|
||||
|
||||
if batch_fixes is not None:
|
||||
for fixes, tensor in zip(batch_fixes, inputs_embeds):
|
||||
for offset, word in fixes:
|
||||
emb = self.embeddings.word_embeddings[word]
|
||||
emb_len = min(tensor.shape[0]-offset-1, emb.shape[0])
|
||||
tensor[offset+1:offset+1+emb_len] = self.embeddings.word_embeddings[word][0:emb_len]
|
||||
|
||||
if batch_fixes is None or len(batch_fixes) == 0 or max([len(x) for x in batch_fixes]) == 0:
|
||||
return inputs_embeds
|
||||
|
||||
vecs = []
|
||||
for fixes, tensor in zip(batch_fixes, inputs_embeds):
|
||||
for offset, embedding in fixes:
|
||||
emb = embedding.vec
|
||||
emb_len = min(tensor.shape[0]-offset-1, emb.shape[0])
|
||||
tensor = torch.cat([tensor[0:offset+1], emb[0:emb_len], tensor[offset+1+emb_len:]])
|
||||
|
||||
vecs.append(tensor)
|
||||
|
||||
return torch.stack(vecs)
|
||||
|
||||
|
||||
def add_circular_option_to_conv_2d():
|
||||
conv2d_constructor = torch.nn.Conv2d.__init__
|
||||
|
|
|
|||
164
modules/sd_hijack_optimizations.py
Normal file
164
modules/sd_hijack_optimizations.py
Normal file
|
|
@ -0,0 +1,164 @@
|
|||
import math
|
||||
import torch
|
||||
from torch import einsum
|
||||
|
||||
from ldm.util import default
|
||||
from einops import rearrange
|
||||
|
||||
|
||||
# see https://github.com/basujindal/stable-diffusion/pull/117 for discussion
|
||||
def split_cross_attention_forward_v1(self, x, context=None, mask=None):
|
||||
h = self.heads
|
||||
|
||||
q = self.to_q(x)
|
||||
context = default(context, x)
|
||||
k = self.to_k(context)
|
||||
v = self.to_v(context)
|
||||
del context, x
|
||||
|
||||
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
|
||||
|
||||
r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device)
|
||||
for i in range(0, q.shape[0], 2):
|
||||
end = i + 2
|
||||
s1 = einsum('b i d, b j d -> b i j', q[i:end], k[i:end])
|
||||
s1 *= self.scale
|
||||
|
||||
s2 = s1.softmax(dim=-1)
|
||||
del s1
|
||||
|
||||
r1[i:end] = einsum('b i j, b j d -> b i d', s2, v[i:end])
|
||||
del s2
|
||||
|
||||
r2 = rearrange(r1, '(b h) n d -> b n (h d)', h=h)
|
||||
del r1
|
||||
|
||||
return self.to_out(r2)
|
||||
|
||||
|
||||
# taken from https://github.com/Doggettx/stable-diffusion
|
||||
def split_cross_attention_forward(self, x, context=None, mask=None):
|
||||
h = self.heads
|
||||
|
||||
q_in = self.to_q(x)
|
||||
context = default(context, x)
|
||||
k_in = self.to_k(context) * self.scale
|
||||
v_in = self.to_v(context)
|
||||
del context, x
|
||||
|
||||
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in))
|
||||
del q_in, k_in, v_in
|
||||
|
||||
r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
|
||||
|
||||
stats = torch.cuda.memory_stats(q.device)
|
||||
mem_active = stats['active_bytes.all.current']
|
||||
mem_reserved = stats['reserved_bytes.all.current']
|
||||
mem_free_cuda, _ = torch.cuda.mem_get_info(torch.cuda.current_device())
|
||||
mem_free_torch = mem_reserved - mem_active
|
||||
mem_free_total = mem_free_cuda + mem_free_torch
|
||||
|
||||
gb = 1024 ** 3
|
||||
tensor_size = q.shape[0] * q.shape[1] * k.shape[1] * q.element_size()
|
||||
modifier = 3 if q.element_size() == 2 else 2.5
|
||||
mem_required = tensor_size * modifier
|
||||
steps = 1
|
||||
|
||||
if mem_required > mem_free_total:
|
||||
steps = 2 ** (math.ceil(math.log(mem_required / mem_free_total, 2)))
|
||||
# print(f"Expected tensor size:{tensor_size/gb:0.1f}GB, cuda free:{mem_free_cuda/gb:0.1f}GB "
|
||||
# f"torch free:{mem_free_torch/gb:0.1f} total:{mem_free_total/gb:0.1f} steps:{steps}")
|
||||
|
||||
if steps > 64:
|
||||
max_res = math.floor(math.sqrt(math.sqrt(mem_free_total / 2.5)) / 8) * 64
|
||||
raise RuntimeError(f'Not enough memory, use lower resolution (max approx. {max_res}x{max_res}). '
|
||||
f'Need: {mem_required / 64 / gb:0.1f}GB free, Have:{mem_free_total / gb:0.1f}GB free')
|
||||
|
||||
slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
|
||||
for i in range(0, q.shape[1], slice_size):
|
||||
end = i + slice_size
|
||||
s1 = einsum('b i d, b j d -> b i j', q[:, i:end], k)
|
||||
|
||||
s2 = s1.softmax(dim=-1, dtype=q.dtype)
|
||||
del s1
|
||||
|
||||
r1[:, i:end] = einsum('b i j, b j d -> b i d', s2, v)
|
||||
del s2
|
||||
|
||||
del q, k, v
|
||||
|
||||
r2 = rearrange(r1, '(b h) n d -> b n (h d)', h=h)
|
||||
del r1
|
||||
|
||||
return self.to_out(r2)
|
||||
|
||||
def nonlinearity_hijack(x):
|
||||
# swish
|
||||
t = torch.sigmoid(x)
|
||||
x *= t
|
||||
del t
|
||||
|
||||
return x
|
||||
|
||||
def cross_attention_attnblock_forward(self, x):
|
||||
h_ = x
|
||||
h_ = self.norm(h_)
|
||||
q1 = self.q(h_)
|
||||
k1 = self.k(h_)
|
||||
v = self.v(h_)
|
||||
|
||||
# compute attention
|
||||
b, c, h, w = q1.shape
|
||||
|
||||
q2 = q1.reshape(b, c, h*w)
|
||||
del q1
|
||||
|
||||
q = q2.permute(0, 2, 1) # b,hw,c
|
||||
del q2
|
||||
|
||||
k = k1.reshape(b, c, h*w) # b,c,hw
|
||||
del k1
|
||||
|
||||
h_ = torch.zeros_like(k, device=q.device)
|
||||
|
||||
stats = torch.cuda.memory_stats(q.device)
|
||||
mem_active = stats['active_bytes.all.current']
|
||||
mem_reserved = stats['reserved_bytes.all.current']
|
||||
mem_free_cuda, _ = torch.cuda.mem_get_info(torch.cuda.current_device())
|
||||
mem_free_torch = mem_reserved - mem_active
|
||||
mem_free_total = mem_free_cuda + mem_free_torch
|
||||
|
||||
tensor_size = q.shape[0] * q.shape[1] * k.shape[2] * q.element_size()
|
||||
mem_required = tensor_size * 2.5
|
||||
steps = 1
|
||||
|
||||
if mem_required > mem_free_total:
|
||||
steps = 2**(math.ceil(math.log(mem_required / mem_free_total, 2)))
|
||||
|
||||
slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
|
||||
for i in range(0, q.shape[1], slice_size):
|
||||
end = i + slice_size
|
||||
|
||||
w1 = torch.bmm(q[:, i:end], k) # b,hw,hw w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
|
||||
w2 = w1 * (int(c)**(-0.5))
|
||||
del w1
|
||||
w3 = torch.nn.functional.softmax(w2, dim=2, dtype=q.dtype)
|
||||
del w2
|
||||
|
||||
# attend to values
|
||||
v1 = v.reshape(b, c, h*w)
|
||||
w4 = w3.permute(0, 2, 1) # b,hw,hw (first hw of k, second of q)
|
||||
del w3
|
||||
|
||||
h_[:, :, i:end] = torch.bmm(v1, w4) # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
|
||||
del v1, w4
|
||||
|
||||
h2 = h_.reshape(b, c, h, w)
|
||||
del h_
|
||||
|
||||
h3 = self.proj_out(h2)
|
||||
del h2
|
||||
|
||||
h3 += x
|
||||
|
||||
return h3
|
||||
|
|
@ -8,14 +8,11 @@ from omegaconf import OmegaConf
|
|||
|
||||
from ldm.util import instantiate_from_config
|
||||
|
||||
from modules import shared, modelloader
|
||||
from modules import shared, modelloader, devices
|
||||
from modules.paths import models_path
|
||||
|
||||
model_dir = "Stable-diffusion"
|
||||
model_path = os.path.abspath(os.path.join(models_path, model_dir))
|
||||
model_name = "sd-v1-4.ckpt"
|
||||
model_url = "https://drive.yerf.org/wl/?id=EBfTrmcCCUAGaQBXVIj5lJmEhjoP1tgl&mode=grid&download=1"
|
||||
user_dir = None
|
||||
|
||||
CheckpointInfo = namedtuple("CheckpointInfo", ['filename', 'title', 'hash', 'model_name'])
|
||||
checkpoints_list = {}
|
||||
|
|
@ -30,12 +27,10 @@ except Exception:
|
|||
pass
|
||||
|
||||
|
||||
def setup_model(dirname):
|
||||
global user_dir
|
||||
user_dir = dirname
|
||||
def setup_model():
|
||||
if not os.path.exists(model_path):
|
||||
os.makedirs(model_path)
|
||||
checkpoints_list.clear()
|
||||
|
||||
list_models()
|
||||
|
||||
|
||||
|
|
@ -45,13 +40,13 @@ def checkpoint_tiles():
|
|||
|
||||
def list_models():
|
||||
checkpoints_list.clear()
|
||||
model_list = modelloader.load_models(model_path=model_path, model_url=model_url, command_path=user_dir, ext_filter=[".ckpt"], download_name=model_name)
|
||||
model_list = modelloader.load_models(model_path=model_path, command_path=shared.cmd_opts.ckpt_dir, ext_filter=[".ckpt"])
|
||||
|
||||
def modeltitle(path, shorthash):
|
||||
abspath = os.path.abspath(path)
|
||||
|
||||
if user_dir is not None and abspath.startswith(user_dir):
|
||||
name = abspath.replace(user_dir, '')
|
||||
if shared.cmd_opts.ckpt_dir is not None and abspath.startswith(shared.cmd_opts.ckpt_dir):
|
||||
name = abspath.replace(shared.cmd_opts.ckpt_dir, '')
|
||||
elif abspath.startswith(model_path):
|
||||
name = abspath.replace(model_path, '')
|
||||
else:
|
||||
|
|
@ -69,7 +64,7 @@ def list_models():
|
|||
h = model_hash(cmd_ckpt)
|
||||
title, short_model_name = modeltitle(cmd_ckpt, h)
|
||||
checkpoints_list[title] = CheckpointInfo(cmd_ckpt, title, h, short_model_name)
|
||||
shared.opts.sd_model_checkpoint = title
|
||||
shared.opts.data['sd_model_checkpoint'] = title
|
||||
elif cmd_ckpt is not None and cmd_ckpt != shared.default_sd_model_file:
|
||||
print(f"Checkpoint in --ckpt argument not found (Possible it was moved to {model_path}: {cmd_ckpt}", file=sys.stderr)
|
||||
for filename in model_list:
|
||||
|
|
@ -106,7 +101,10 @@ def select_checkpoint():
|
|||
|
||||
if len(checkpoints_list) == 0:
|
||||
print(f"No checkpoints found. When searching for checkpoints, looked at:", file=sys.stderr)
|
||||
if shared.cmd_opts.ckpt is not None:
|
||||
print(f" - file {os.path.abspath(shared.cmd_opts.ckpt)}", file=sys.stderr)
|
||||
print(f" - directory {model_path}", file=sys.stderr)
|
||||
if shared.cmd_opts.ckpt_dir is not None:
|
||||
print(f" - directory {os.path.abspath(shared.cmd_opts.ckpt_dir)}", file=sys.stderr)
|
||||
print(f"Can't run without a checkpoint. Find and place a .ckpt file into any of those locations. The program will exit.", file=sys.stderr)
|
||||
exit(1)
|
||||
|
|
@ -134,6 +132,8 @@ def load_model_weights(model, checkpoint_file, sd_model_hash):
|
|||
if not shared.cmd_opts.no_half:
|
||||
model.half()
|
||||
|
||||
devices.dtype = torch.float32 if shared.cmd_opts.no_half else torch.float16
|
||||
|
||||
model.sd_model_hash = sd_model_hash
|
||||
model.sd_model_checkpint = checkpoint_file
|
||||
|
||||
|
|
|
|||
|
|
@ -77,7 +77,9 @@ def extended_tdqm(sequence, *args, desc=None, **kwargs):
|
|||
state.sampling_steps = len(sequence)
|
||||
state.sampling_step = 0
|
||||
|
||||
for x in tqdm.tqdm(sequence, *args, desc=state.job, file=shared.progress_print_out, **kwargs):
|
||||
seq = sequence if cmd_opts.disable_console_progressbars else tqdm.tqdm(sequence, *args, desc=state.job, file=shared.progress_print_out, **kwargs)
|
||||
|
||||
for x in seq:
|
||||
if state.interrupted:
|
||||
break
|
||||
|
||||
|
|
@ -207,7 +209,9 @@ def extended_trange(sampler, count, *args, **kwargs):
|
|||
state.sampling_steps = count
|
||||
state.sampling_step = 0
|
||||
|
||||
for x in tqdm.trange(count, *args, desc=state.job, file=shared.progress_print_out, **kwargs):
|
||||
seq = range(count) if cmd_opts.disable_console_progressbars else tqdm.trange(count, *args, desc=state.job, file=shared.progress_print_out, **kwargs)
|
||||
|
||||
for x in seq:
|
||||
if state.interrupted:
|
||||
break
|
||||
|
||||
|
|
|
|||
|
|
@ -40,6 +40,7 @@ parser.add_argument("--gfpgan-models-path", type=str, help="Path to directory wi
|
|||
parser.add_argument("--esrgan-models-path", type=str, help="Path to directory with ESRGAN model file(s).", default=os.path.join(model_path, 'ESRGAN'))
|
||||
parser.add_argument("--bsrgan-models-path", type=str, help="Path to directory with BSRGAN model file(s).", default=os.path.join(model_path, 'BSRGAN'))
|
||||
parser.add_argument("--realesrgan-models-path", type=str, help="Path to directory with RealESRGAN model file(s).", default=os.path.join(model_path, 'RealESRGAN'))
|
||||
parser.add_argument("--scunet-models-path", type=str, help="Path to directory with ScuNET model file(s).", default=os.path.join(model_path, 'ScuNET'))
|
||||
parser.add_argument("--swinir-models-path", type=str, help="Path to directory with SwinIR model file(s).", default=os.path.join(model_path, 'SwinIR'))
|
||||
parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with LDSR model file(s).", default=os.path.join(model_path, 'LDSR'))
|
||||
parser.add_argument("--opt-split-attention", action='store_true', help="force-enables cross-attention layer optimization. By default, it's on for torch.cuda and off for other torch devices.")
|
||||
|
|
@ -57,6 +58,9 @@ parser.add_argument("--opt-channelslast", action='store_true', help="change memo
|
|||
parser.add_argument("--styles-file", type=str, help="filename to use for styles", default=os.path.join(script_path, 'styles.csv'))
|
||||
parser.add_argument("--autolaunch", action='store_true', help="open the webui URL in the system's default browser upon launch", default=False)
|
||||
parser.add_argument("--use-textbox-seed", action='store_true', help="use textbox for seeds in UI (no up/down, but possible to input long seeds)", default=False)
|
||||
parser.add_argument("--disable-console-progressbars", action='store_true', help="do not output progressbars to console", default=False)
|
||||
parser.add_argument("--enable-console-prompts", action='store_true', help="print prompts to console when generating with txt2img and img2img", default=False)
|
||||
|
||||
|
||||
cmd_opts = parser.parse_args()
|
||||
device = get_optimal_device()
|
||||
|
|
@ -78,6 +82,7 @@ class State:
|
|||
current_latent = None
|
||||
current_image = None
|
||||
current_image_sampling_step = 0
|
||||
textinfo = None
|
||||
|
||||
def interrupt(self):
|
||||
self.interrupted = True
|
||||
|
|
@ -88,7 +93,7 @@ class State:
|
|||
self.current_image_sampling_step = 0
|
||||
|
||||
def get_job_timestamp(self):
|
||||
return datetime.datetime.now().strftime("%Y%m%d%H%M%S")
|
||||
return datetime.datetime.now().strftime("%Y%m%d%H%M%S") # shouldn't this return job_timestamp?
|
||||
|
||||
|
||||
state = State()
|
||||
|
|
@ -165,9 +170,10 @@ options_templates.update(options_section(('saving-paths', "Paths for saving"), {
|
|||
|
||||
options_templates.update(options_section(('saving-to-dirs', "Saving to a directory"), {
|
||||
"save_to_dirs": OptionInfo(False, "Save images to a subdirectory"),
|
||||
"grid_save_to_dirs": OptionInfo(False, "Save grids to subdirectory"),
|
||||
"grid_save_to_dirs": OptionInfo(False, "Save grids to a subdirectory"),
|
||||
"use_save_to_dirs_for_ui": OptionInfo(False, "When using \"Save\" button, save images to a subdirectory"),
|
||||
"directories_filename_pattern": OptionInfo("", "Directory name pattern"),
|
||||
"directories_max_prompt_words": OptionInfo(8, "Max prompt words", gr.Slider, {"minimum": 1, "maximum": 20, "step": 1}),
|
||||
"directories_max_prompt_words": OptionInfo(8, "Max prompt words for [prompt_words] pattern", gr.Slider, {"minimum": 1, "maximum": 20, "step": 1}),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('upscaling', "Upscaling"), {
|
||||
|
|
@ -318,14 +324,14 @@ class TotalTQDM:
|
|||
)
|
||||
|
||||
def update(self):
|
||||
if not opts.multiple_tqdm:
|
||||
if not opts.multiple_tqdm or cmd_opts.disable_console_progressbars:
|
||||
return
|
||||
if self._tqdm is None:
|
||||
self.reset()
|
||||
self._tqdm.update()
|
||||
|
||||
def updateTotal(self, new_total):
|
||||
if not opts.multiple_tqdm:
|
||||
if not opts.multiple_tqdm or cmd_opts.disable_console_progressbars:
|
||||
return
|
||||
if self._tqdm is None:
|
||||
self.reset()
|
||||
|
|
|
|||
|
|
@ -5,6 +5,7 @@ import numpy as np
|
|||
import torch
|
||||
from PIL import Image
|
||||
from basicsr.utils.download_util import load_file_from_url
|
||||
from tqdm import tqdm
|
||||
|
||||
from modules import modelloader
|
||||
from modules.paths import models_path
|
||||
|
|
@ -122,6 +123,7 @@ def inference(img, model, tile, tile_overlap, window_size, scale):
|
|||
E = torch.zeros(b, c, h * sf, w * sf, dtype=torch.half, device=device).type_as(img)
|
||||
W = torch.zeros_like(E, dtype=torch.half, device=device)
|
||||
|
||||
with tqdm(total=len(h_idx_list) * len(w_idx_list), desc="SwinIR tiles") as pbar:
|
||||
for h_idx in h_idx_list:
|
||||
for w_idx in w_idx_list:
|
||||
in_patch = img[..., h_idx: h_idx + tile, w_idx: w_idx + tile]
|
||||
|
|
@ -134,6 +136,7 @@ def inference(img, model, tile, tile_overlap, window_size, scale):
|
|||
W[
|
||||
..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
|
||||
].add_(out_patch_mask)
|
||||
pbar.update(1)
|
||||
output = E.div_(W)
|
||||
|
||||
return output
|
||||
|
|
|
|||
78
modules/textual_inversion/dataset.py
Normal file
78
modules/textual_inversion/dataset.py
Normal file
|
|
@ -0,0 +1,78 @@
|
|||
import os
|
||||
import numpy as np
|
||||
import PIL
|
||||
import torch
|
||||
from PIL import Image
|
||||
from torch.utils.data import Dataset
|
||||
from torchvision import transforms
|
||||
|
||||
import random
|
||||
import tqdm
|
||||
from modules import devices
|
||||
|
||||
|
||||
class PersonalizedBase(Dataset):
|
||||
def __init__(self, data_root, size=None, repeats=100, flip_p=0.5, placeholder_token="*", width=512, height=512, model=None, device=None, template_file=None):
|
||||
|
||||
self.placeholder_token = placeholder_token
|
||||
|
||||
self.size = size
|
||||
self.width = width
|
||||
self.height = height
|
||||
self.flip = transforms.RandomHorizontalFlip(p=flip_p)
|
||||
|
||||
self.dataset = []
|
||||
|
||||
with open(template_file, "r") as file:
|
||||
lines = [x.strip() for x in file.readlines()]
|
||||
|
||||
self.lines = lines
|
||||
|
||||
assert data_root, 'dataset directory not specified'
|
||||
|
||||
self.image_paths = [os.path.join(data_root, file_path) for file_path in os.listdir(data_root)]
|
||||
print("Preparing dataset...")
|
||||
for path in tqdm.tqdm(self.image_paths):
|
||||
image = Image.open(path)
|
||||
image = image.convert('RGB')
|
||||
image = image.resize((self.width, self.height), PIL.Image.BICUBIC)
|
||||
|
||||
filename = os.path.basename(path)
|
||||
filename_tokens = os.path.splitext(filename)[0].replace('_', '-').replace(' ', '-').split('-')
|
||||
filename_tokens = [token for token in filename_tokens if token.isalpha()]
|
||||
|
||||
npimage = np.array(image).astype(np.uint8)
|
||||
npimage = (npimage / 127.5 - 1.0).astype(np.float32)
|
||||
|
||||
torchdata = torch.from_numpy(npimage).to(device=device, dtype=torch.float32)
|
||||
torchdata = torch.moveaxis(torchdata, 2, 0)
|
||||
|
||||
init_latent = model.get_first_stage_encoding(model.encode_first_stage(torchdata.unsqueeze(dim=0))).squeeze()
|
||||
init_latent = init_latent.to(devices.cpu)
|
||||
|
||||
self.dataset.append((init_latent, filename_tokens))
|
||||
|
||||
self.length = len(self.dataset) * repeats
|
||||
|
||||
self.initial_indexes = np.arange(self.length) % len(self.dataset)
|
||||
self.indexes = None
|
||||
self.shuffle()
|
||||
|
||||
def shuffle(self):
|
||||
self.indexes = self.initial_indexes[torch.randperm(self.initial_indexes.shape[0])]
|
||||
|
||||
def __len__(self):
|
||||
return self.length
|
||||
|
||||
def __getitem__(self, i):
|
||||
if i % len(self.dataset) == 0:
|
||||
self.shuffle()
|
||||
|
||||
index = self.indexes[i % len(self.indexes)]
|
||||
x, filename_tokens = self.dataset[index]
|
||||
|
||||
text = random.choice(self.lines)
|
||||
text = text.replace("[name]", self.placeholder_token)
|
||||
text = text.replace("[filewords]", ' '.join(filename_tokens))
|
||||
|
||||
return x, text
|
||||
75
modules/textual_inversion/preprocess.py
Normal file
75
modules/textual_inversion/preprocess.py
Normal file
|
|
@ -0,0 +1,75 @@
|
|||
import os
|
||||
from PIL import Image, ImageOps
|
||||
import tqdm
|
||||
|
||||
from modules import shared, images
|
||||
|
||||
|
||||
def preprocess(process_src, process_dst, process_flip, process_split, process_caption):
|
||||
size = 512
|
||||
src = os.path.abspath(process_src)
|
||||
dst = os.path.abspath(process_dst)
|
||||
|
||||
assert src != dst, 'same directory specified as source and desitnation'
|
||||
|
||||
os.makedirs(dst, exist_ok=True)
|
||||
|
||||
files = os.listdir(src)
|
||||
|
||||
shared.state.textinfo = "Preprocessing..."
|
||||
shared.state.job_count = len(files)
|
||||
|
||||
if process_caption:
|
||||
shared.interrogator.load()
|
||||
|
||||
def save_pic_with_caption(image, index):
|
||||
if process_caption:
|
||||
caption = "-" + shared.interrogator.generate_caption(image)
|
||||
else:
|
||||
caption = ""
|
||||
|
||||
image.save(os.path.join(dst, f"{index:05}-{subindex[0]}{caption}.png"))
|
||||
subindex[0] += 1
|
||||
|
||||
def save_pic(image, index):
|
||||
save_pic_with_caption(image, index)
|
||||
|
||||
if process_flip:
|
||||
save_pic_with_caption(ImageOps.mirror(image), index)
|
||||
|
||||
for index, imagefile in enumerate(tqdm.tqdm(files)):
|
||||
subindex = [0]
|
||||
filename = os.path.join(src, imagefile)
|
||||
img = Image.open(filename).convert("RGB")
|
||||
|
||||
if shared.state.interrupted:
|
||||
break
|
||||
|
||||
ratio = img.height / img.width
|
||||
is_tall = ratio > 1.35
|
||||
is_wide = ratio < 1 / 1.35
|
||||
|
||||
if process_split and is_tall:
|
||||
img = img.resize((size, size * img.height // img.width))
|
||||
|
||||
top = img.crop((0, 0, size, size))
|
||||
save_pic(top, index)
|
||||
|
||||
bot = img.crop((0, img.height - size, size, img.height))
|
||||
save_pic(bot, index)
|
||||
elif process_split and is_wide:
|
||||
img = img.resize((size * img.width // img.height, size))
|
||||
|
||||
left = img.crop((0, 0, size, size))
|
||||
save_pic(left, index)
|
||||
|
||||
right = img.crop((img.width - size, 0, img.width, size))
|
||||
save_pic(right, index)
|
||||
else:
|
||||
img = images.resize_image(1, img, size, size)
|
||||
save_pic(img, index)
|
||||
|
||||
shared.state.nextjob()
|
||||
|
||||
if process_caption:
|
||||
shared.interrogator.send_blip_to_ram()
|
||||
271
modules/textual_inversion/textual_inversion.py
Normal file
271
modules/textual_inversion/textual_inversion.py
Normal file
|
|
@ -0,0 +1,271 @@
|
|||
import os
|
||||
import sys
|
||||
import traceback
|
||||
|
||||
import torch
|
||||
import tqdm
|
||||
import html
|
||||
import datetime
|
||||
|
||||
|
||||
from modules import shared, devices, sd_hijack, processing, sd_models
|
||||
import modules.textual_inversion.dataset
|
||||
|
||||
|
||||
class Embedding:
|
||||
def __init__(self, vec, name, step=None):
|
||||
self.vec = vec
|
||||
self.name = name
|
||||
self.step = step
|
||||
self.cached_checksum = None
|
||||
self.sd_checkpoint = None
|
||||
self.sd_checkpoint_name = None
|
||||
|
||||
def save(self, filename):
|
||||
embedding_data = {
|
||||
"string_to_token": {"*": 265},
|
||||
"string_to_param": {"*": self.vec},
|
||||
"name": self.name,
|
||||
"step": self.step,
|
||||
"sd_checkpoint": self.sd_checkpoint,
|
||||
"sd_checkpoint_name": self.sd_checkpoint_name,
|
||||
}
|
||||
|
||||
torch.save(embedding_data, filename)
|
||||
|
||||
def checksum(self):
|
||||
if self.cached_checksum is not None:
|
||||
return self.cached_checksum
|
||||
|
||||
def const_hash(a):
|
||||
r = 0
|
||||
for v in a:
|
||||
r = (r * 281 ^ int(v) * 997) & 0xFFFFFFFF
|
||||
return r
|
||||
|
||||
self.cached_checksum = f'{const_hash(self.vec.reshape(-1) * 100) & 0xffff:04x}'
|
||||
return self.cached_checksum
|
||||
|
||||
|
||||
class EmbeddingDatabase:
|
||||
def __init__(self, embeddings_dir):
|
||||
self.ids_lookup = {}
|
||||
self.word_embeddings = {}
|
||||
self.dir_mtime = None
|
||||
self.embeddings_dir = embeddings_dir
|
||||
|
||||
def register_embedding(self, embedding, model):
|
||||
|
||||
self.word_embeddings[embedding.name] = embedding
|
||||
|
||||
ids = model.cond_stage_model.tokenizer([embedding.name], add_special_tokens=False)['input_ids'][0]
|
||||
|
||||
first_id = ids[0]
|
||||
if first_id not in self.ids_lookup:
|
||||
self.ids_lookup[first_id] = []
|
||||
|
||||
self.ids_lookup[first_id] = sorted(self.ids_lookup[first_id] + [(ids, embedding)], key=lambda x: len(x[0]), reverse=True)
|
||||
|
||||
return embedding
|
||||
|
||||
def load_textual_inversion_embeddings(self):
|
||||
mt = os.path.getmtime(self.embeddings_dir)
|
||||
if self.dir_mtime is not None and mt <= self.dir_mtime:
|
||||
return
|
||||
|
||||
self.dir_mtime = mt
|
||||
self.ids_lookup.clear()
|
||||
self.word_embeddings.clear()
|
||||
|
||||
def process_file(path, filename):
|
||||
name = os.path.splitext(filename)[0]
|
||||
|
||||
data = torch.load(path, map_location="cpu")
|
||||
|
||||
# textual inversion embeddings
|
||||
if 'string_to_param' in data:
|
||||
param_dict = data['string_to_param']
|
||||
if hasattr(param_dict, '_parameters'):
|
||||
param_dict = getattr(param_dict, '_parameters') # fix for torch 1.12.1 loading saved file from torch 1.11
|
||||
assert len(param_dict) == 1, 'embedding file has multiple terms in it'
|
||||
emb = next(iter(param_dict.items()))[1]
|
||||
# diffuser concepts
|
||||
elif type(data) == dict and type(next(iter(data.values()))) == torch.Tensor:
|
||||
assert len(data.keys()) == 1, 'embedding file has multiple terms in it'
|
||||
|
||||
emb = next(iter(data.values()))
|
||||
if len(emb.shape) == 1:
|
||||
emb = emb.unsqueeze(0)
|
||||
else:
|
||||
raise Exception(f"Couldn't identify {filename} as neither textual inversion embedding nor diffuser concept.")
|
||||
|
||||
vec = emb.detach().to(devices.device, dtype=torch.float32)
|
||||
embedding = Embedding(vec, name)
|
||||
embedding.step = data.get('step', None)
|
||||
embedding.sd_checkpoint = data.get('hash', None)
|
||||
embedding.sd_checkpoint_name = data.get('sd_checkpoint_name', None)
|
||||
self.register_embedding(embedding, shared.sd_model)
|
||||
|
||||
for fn in os.listdir(self.embeddings_dir):
|
||||
try:
|
||||
fullfn = os.path.join(self.embeddings_dir, fn)
|
||||
|
||||
if os.stat(fullfn).st_size == 0:
|
||||
continue
|
||||
|
||||
process_file(fullfn, fn)
|
||||
except Exception:
|
||||
print(f"Error loading emedding {fn}:", file=sys.stderr)
|
||||
print(traceback.format_exc(), file=sys.stderr)
|
||||
continue
|
||||
|
||||
print(f"Loaded a total of {len(self.word_embeddings)} textual inversion embeddings.")
|
||||
|
||||
def find_embedding_at_position(self, tokens, offset):
|
||||
token = tokens[offset]
|
||||
possible_matches = self.ids_lookup.get(token, None)
|
||||
|
||||
if possible_matches is None:
|
||||
return None, None
|
||||
|
||||
for ids, embedding in possible_matches:
|
||||
if tokens[offset:offset + len(ids)] == ids:
|
||||
return embedding, len(ids)
|
||||
|
||||
return None, None
|
||||
|
||||
|
||||
def create_embedding(name, num_vectors_per_token, init_text='*'):
|
||||
cond_model = shared.sd_model.cond_stage_model
|
||||
embedding_layer = cond_model.wrapped.transformer.text_model.embeddings
|
||||
|
||||
ids = cond_model.tokenizer(init_text, max_length=num_vectors_per_token, return_tensors="pt", add_special_tokens=False)["input_ids"]
|
||||
embedded = embedding_layer.token_embedding.wrapped(ids.to(devices.device)).squeeze(0)
|
||||
vec = torch.zeros((num_vectors_per_token, embedded.shape[1]), device=devices.device)
|
||||
|
||||
for i in range(num_vectors_per_token):
|
||||
vec[i] = embedded[i * int(embedded.shape[0]) // num_vectors_per_token]
|
||||
|
||||
fn = os.path.join(shared.cmd_opts.embeddings_dir, f"{name}.pt")
|
||||
assert not os.path.exists(fn), f"file {fn} already exists"
|
||||
|
||||
embedding = Embedding(vec, name)
|
||||
embedding.step = 0
|
||||
embedding.save(fn)
|
||||
|
||||
return fn
|
||||
|
||||
|
||||
def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps, create_image_every, save_embedding_every, template_file):
|
||||
assert embedding_name, 'embedding not selected'
|
||||
|
||||
shared.state.textinfo = "Initializing textual inversion training..."
|
||||
shared.state.job_count = steps
|
||||
|
||||
filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding_name}.pt')
|
||||
|
||||
log_directory = os.path.join(log_directory, datetime.datetime.now().strftime("%Y-%d-%m"), embedding_name)
|
||||
|
||||
if save_embedding_every > 0:
|
||||
embedding_dir = os.path.join(log_directory, "embeddings")
|
||||
os.makedirs(embedding_dir, exist_ok=True)
|
||||
else:
|
||||
embedding_dir = None
|
||||
|
||||
if create_image_every > 0:
|
||||
images_dir = os.path.join(log_directory, "images")
|
||||
os.makedirs(images_dir, exist_ok=True)
|
||||
else:
|
||||
images_dir = None
|
||||
|
||||
cond_model = shared.sd_model.cond_stage_model
|
||||
|
||||
shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
|
||||
with torch.autocast("cuda"):
|
||||
ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, size=512, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file)
|
||||
|
||||
hijack = sd_hijack.model_hijack
|
||||
|
||||
embedding = hijack.embedding_db.word_embeddings[embedding_name]
|
||||
embedding.vec.requires_grad = True
|
||||
|
||||
optimizer = torch.optim.AdamW([embedding.vec], lr=learn_rate)
|
||||
|
||||
losses = torch.zeros((32,))
|
||||
|
||||
last_saved_file = "<none>"
|
||||
last_saved_image = "<none>"
|
||||
|
||||
ititial_step = embedding.step or 0
|
||||
if ititial_step > steps:
|
||||
return embedding, filename
|
||||
|
||||
pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step)
|
||||
for i, (x, text) in pbar:
|
||||
embedding.step = i + ititial_step
|
||||
|
||||
if embedding.step > steps:
|
||||
break
|
||||
|
||||
if shared.state.interrupted:
|
||||
break
|
||||
|
||||
with torch.autocast("cuda"):
|
||||
c = cond_model([text])
|
||||
|
||||
x = x.to(devices.device)
|
||||
loss = shared.sd_model(x.unsqueeze(0), c)[0]
|
||||
del x
|
||||
|
||||
losses[embedding.step % losses.shape[0]] = loss.item()
|
||||
|
||||
optimizer.zero_grad()
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
|
||||
pbar.set_description(f"loss: {losses.mean():.7f}")
|
||||
|
||||
if embedding.step > 0 and embedding_dir is not None and embedding.step % save_embedding_every == 0:
|
||||
last_saved_file = os.path.join(embedding_dir, f'{embedding_name}-{embedding.step}.pt')
|
||||
embedding.save(last_saved_file)
|
||||
|
||||
if embedding.step > 0 and images_dir is not None and embedding.step % create_image_every == 0:
|
||||
last_saved_image = os.path.join(images_dir, f'{embedding_name}-{embedding.step}.png')
|
||||
|
||||
p = processing.StableDiffusionProcessingTxt2Img(
|
||||
sd_model=shared.sd_model,
|
||||
prompt=text,
|
||||
steps=20,
|
||||
do_not_save_grid=True,
|
||||
do_not_save_samples=True,
|
||||
)
|
||||
|
||||
processed = processing.process_images(p)
|
||||
image = processed.images[0]
|
||||
|
||||
shared.state.current_image = image
|
||||
image.save(last_saved_image)
|
||||
|
||||
last_saved_image += f", prompt: {text}"
|
||||
|
||||
shared.state.job_no = embedding.step
|
||||
|
||||
shared.state.textinfo = f"""
|
||||
<p>
|
||||
Loss: {losses.mean():.7f}<br/>
|
||||
Step: {embedding.step}<br/>
|
||||
Last prompt: {html.escape(text)}<br/>
|
||||
Last saved embedding: {html.escape(last_saved_file)}<br/>
|
||||
Last saved image: {html.escape(last_saved_image)}<br/>
|
||||
</p>
|
||||
"""
|
||||
|
||||
checkpoint = sd_models.select_checkpoint()
|
||||
|
||||
embedding.sd_checkpoint = checkpoint.hash
|
||||
embedding.sd_checkpoint_name = checkpoint.model_name
|
||||
embedding.cached_checksum = None
|
||||
embedding.save(filename)
|
||||
|
||||
return embedding, filename
|
||||
|
||||
40
modules/textual_inversion/ui.py
Normal file
40
modules/textual_inversion/ui.py
Normal file
|
|
@ -0,0 +1,40 @@
|
|||
import html
|
||||
|
||||
import gradio as gr
|
||||
|
||||
import modules.textual_inversion.textual_inversion
|
||||
import modules.textual_inversion.preprocess
|
||||
from modules import sd_hijack, shared
|
||||
|
||||
|
||||
def create_embedding(name, initialization_text, nvpt):
|
||||
filename = modules.textual_inversion.textual_inversion.create_embedding(name, nvpt, init_text=initialization_text)
|
||||
|
||||
sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings()
|
||||
|
||||
return gr.Dropdown.update(choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())), f"Created: {filename}", ""
|
||||
|
||||
|
||||
def preprocess(*args):
|
||||
modules.textual_inversion.preprocess.preprocess(*args)
|
||||
|
||||
return "Preprocessing finished.", ""
|
||||
|
||||
|
||||
def train_embedding(*args):
|
||||
|
||||
try:
|
||||
sd_hijack.undo_optimizations()
|
||||
|
||||
embedding, filename = modules.textual_inversion.textual_inversion.train_embedding(*args)
|
||||
|
||||
res = f"""
|
||||
Training {'interrupted' if shared.state.interrupted else 'finished'} at {embedding.step} steps.
|
||||
Embedding saved to {html.escape(filename)}
|
||||
"""
|
||||
return res, ""
|
||||
except Exception:
|
||||
raise
|
||||
finally:
|
||||
sd_hijack.apply_optimizations()
|
||||
|
||||
|
|
@ -34,7 +34,9 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2:
|
|||
denoising_strength=denoising_strength if enable_hr else None,
|
||||
)
|
||||
|
||||
if cmd_opts.enable_console_prompts:
|
||||
print(f"\ntxt2img: {prompt}", file=shared.progress_print_out)
|
||||
|
||||
processed = modules.scripts.scripts_txt2img.run(p, *args)
|
||||
|
||||
if processed is None:
|
||||
|
|
|
|||
273
modules/ui.py
273
modules/ui.py
|
|
@ -11,6 +11,7 @@ import time
|
|||
import traceback
|
||||
import platform
|
||||
import subprocess as sp
|
||||
from functools import reduce
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
|
|
@ -21,6 +22,7 @@ import gradio as gr
|
|||
import gradio.utils
|
||||
import gradio.routes
|
||||
|
||||
from modules import sd_hijack
|
||||
from modules.paths import script_path
|
||||
from modules.shared import opts, cmd_opts
|
||||
import modules.shared as shared
|
||||
|
|
@ -32,6 +34,9 @@ import modules.gfpgan_model
|
|||
import modules.codeformer_model
|
||||
import modules.styles
|
||||
import modules.generation_parameters_copypaste
|
||||
from modules.prompt_parser import get_learned_conditioning_prompt_schedules
|
||||
from modules.images import apply_filename_pattern, get_next_sequence_number
|
||||
import modules.textual_inversion.ui
|
||||
|
||||
# this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the bowser will not show any UI
|
||||
mimetypes.init()
|
||||
|
|
@ -95,13 +100,30 @@ def send_gradio_gallery_to_image(x):
|
|||
|
||||
def save_files(js_data, images, index):
|
||||
import csv
|
||||
|
||||
os.makedirs(opts.outdir_save, exist_ok=True)
|
||||
|
||||
filenames = []
|
||||
|
||||
#quick dictionary to class object conversion. Its neccesary due apply_filename_pattern requiring it
|
||||
class MyObject:
|
||||
def __init__(self, d=None):
|
||||
if d is not None:
|
||||
for key, value in d.items():
|
||||
setattr(self, key, value)
|
||||
|
||||
data = json.loads(js_data)
|
||||
|
||||
p = MyObject(data)
|
||||
path = opts.outdir_save
|
||||
save_to_dirs = opts.use_save_to_dirs_for_ui
|
||||
|
||||
if save_to_dirs:
|
||||
dirname = apply_filename_pattern(opts.directories_filename_pattern or "[prompt_words]", p, p.seed, p.prompt)
|
||||
path = os.path.join(opts.outdir_save, dirname)
|
||||
|
||||
os.makedirs(path, exist_ok=True)
|
||||
|
||||
|
||||
if index > -1 and opts.save_selected_only and (index >= data["index_of_first_image"]): # ensures we are looking at a specific non-grid picture, and we have save_selected_only
|
||||
|
||||
images = [images[index]]
|
||||
infotexts = [data["infotexts"][index]]
|
||||
else:
|
||||
|
|
@ -113,11 +135,20 @@ def save_files(js_data, images, index):
|
|||
if at_start:
|
||||
writer.writerow(["prompt", "seed", "width", "height", "sampler", "cfgs", "steps", "filename", "negative_prompt"])
|
||||
|
||||
filename_base = str(int(time.time() * 1000))
|
||||
file_decoration = opts.samples_filename_pattern or "[seed]-[prompt_spaces]"
|
||||
if file_decoration != "":
|
||||
file_decoration = "-" + file_decoration.lower()
|
||||
file_decoration = apply_filename_pattern(file_decoration, p, p.seed, p.prompt)
|
||||
truncated = (file_decoration[:240] + '..') if len(file_decoration) > 240 else file_decoration
|
||||
filename_base = truncated
|
||||
extension = opts.samples_format.lower()
|
||||
|
||||
basecount = get_next_sequence_number(path, "")
|
||||
for i, filedata in enumerate(images):
|
||||
filename = filename_base + ("" if len(images) == 1 else "-" + str(i + 1)) + f".{extension}"
|
||||
filepath = os.path.join(opts.outdir_save, filename)
|
||||
file_number = f"{basecount+i:05}"
|
||||
filename = file_number + filename_base + f".{extension}"
|
||||
filepath = os.path.join(path, filename)
|
||||
|
||||
|
||||
if filedata.startswith("data:image/png;base64,"):
|
||||
filedata = filedata[len("data:image/png;base64,"):]
|
||||
|
|
@ -142,8 +173,8 @@ def save_files(js_data, images, index):
|
|||
return '', '', plaintext_to_html(f"Saved: {filenames[0]}")
|
||||
|
||||
|
||||
def wrap_gradio_call(func):
|
||||
def f(*args, **kwargs):
|
||||
def wrap_gradio_call(func, extra_outputs=None):
|
||||
def f(*args, extra_outputs_array=extra_outputs, **kwargs):
|
||||
run_memmon = opts.memmon_poll_rate > 0 and not shared.mem_mon.disabled
|
||||
if run_memmon:
|
||||
shared.mem_mon.monitor()
|
||||
|
|
@ -159,7 +190,10 @@ def wrap_gradio_call(func):
|
|||
shared.state.job = ""
|
||||
shared.state.job_count = 0
|
||||
|
||||
res = [None, '', f"<div class='error'>{plaintext_to_html(type(e).__name__+': '+str(e))}</div>"]
|
||||
if extra_outputs_array is None:
|
||||
extra_outputs_array = [None, '']
|
||||
|
||||
res = extra_outputs_array + [f"<div class='error'>{plaintext_to_html(type(e).__name__+': '+str(e))}</div>"]
|
||||
|
||||
elapsed = time.perf_counter() - t
|
||||
|
||||
|
|
@ -179,6 +213,7 @@ def wrap_gradio_call(func):
|
|||
res[-1] += f"<div class='performance'><p class='time'>Time taken: <wbr>{elapsed:.2f}s</p>{vram_html}</div>"
|
||||
|
||||
shared.state.interrupted = False
|
||||
shared.state.job_count = 0
|
||||
|
||||
return tuple(res)
|
||||
|
||||
|
|
@ -187,7 +222,7 @@ def wrap_gradio_call(func):
|
|||
|
||||
def check_progress_call(id_part):
|
||||
if shared.state.job_count == 0:
|
||||
return "", gr_show(False), gr_show(False)
|
||||
return "", gr_show(False), gr_show(False), gr_show(False)
|
||||
|
||||
progress = 0
|
||||
|
||||
|
|
@ -219,13 +254,19 @@ def check_progress_call(id_part):
|
|||
else:
|
||||
preview_visibility = gr_show(True)
|
||||
|
||||
return f"<span id='{id_part}_progress_span' style='display: none'>{time.time()}</span><p>{progressbar}</p>", preview_visibility, image
|
||||
if shared.state.textinfo is not None:
|
||||
textinfo_result = gr.HTML.update(value=shared.state.textinfo, visible=True)
|
||||
else:
|
||||
textinfo_result = gr_show(False)
|
||||
|
||||
return f"<span id='{id_part}_progress_span' style='display: none'>{time.time()}</span><p>{progressbar}</p>", preview_visibility, image, textinfo_result
|
||||
|
||||
|
||||
def check_progress_call_initial(id_part):
|
||||
shared.state.job_count = -1
|
||||
shared.state.current_latent = None
|
||||
shared.state.current_image = None
|
||||
shared.state.textinfo = None
|
||||
|
||||
return check_progress_call(id_part)
|
||||
|
||||
|
|
@ -345,8 +386,11 @@ def connect_reuse_seed(seed: gr.Number, reuse_seed: gr.Button, generation_info:
|
|||
outputs=[seed, dummy_component]
|
||||
)
|
||||
|
||||
def update_token_counter(text):
|
||||
tokens, token_count, max_length = model_hijack.tokenize(text)
|
||||
def update_token_counter(text, steps):
|
||||
prompt_schedules = get_learned_conditioning_prompt_schedules([text], steps)
|
||||
flat_prompts = reduce(lambda list1, list2: list1+list2, prompt_schedules)
|
||||
prompts = [prompt_text for step,prompt_text in flat_prompts]
|
||||
tokens, token_count, max_length = max([model_hijack.tokenize(prompt) for prompt in prompts], key=lambda args: args[1])
|
||||
style_class = ' class="red"' if (token_count > max_length) else ""
|
||||
return f"<span {style_class}>{token_count}/{max_length}</span>"
|
||||
|
||||
|
|
@ -364,8 +408,7 @@ def create_toprow(is_img2img):
|
|||
roll = gr.Button(value=art_symbol, elem_id="roll", visible=len(shared.artist_db.artists) > 0)
|
||||
paste = gr.Button(value=paste_symbol, elem_id="paste")
|
||||
token_counter = gr.HTML(value="<span></span>", elem_id=f"{id_part}_token_counter")
|
||||
hidden_button = gr.Button(visible=False, elem_id=f"{id_part}_token_button")
|
||||
hidden_button.click(fn=update_token_counter, inputs=[prompt], outputs=[token_counter])
|
||||
token_button = gr.Button(visible=False, elem_id=f"{id_part}_token_button")
|
||||
|
||||
with gr.Column(scale=10, elem_id="style_pos_col"):
|
||||
prompt_style = gr.Dropdown(label="Style 1", elem_id=f"{id_part}_style_index", choices=[k for k, v in shared.prompt_styles.styles.items()], value=next(iter(shared.prompt_styles.styles.keys())), visible=len(shared.prompt_styles.styles) > 1)
|
||||
|
|
@ -396,16 +439,19 @@ def create_toprow(is_img2img):
|
|||
prompt_style_apply = gr.Button('Apply style', elem_id="style_apply")
|
||||
save_style = gr.Button('Create style', elem_id="style_create")
|
||||
|
||||
return prompt, roll, prompt_style, negative_prompt, prompt_style2, submit, interrogate, prompt_style_apply, save_style, paste
|
||||
return prompt, roll, prompt_style, negative_prompt, prompt_style2, submit, interrogate, prompt_style_apply, save_style, paste, token_counter, token_button
|
||||
|
||||
|
||||
def setup_progressbar(progressbar, preview, id_part):
|
||||
def setup_progressbar(progressbar, preview, id_part, textinfo=None):
|
||||
if textinfo is None:
|
||||
textinfo = gr.HTML(visible=False)
|
||||
|
||||
check_progress = gr.Button('Check progress', elem_id=f"{id_part}_check_progress", visible=False)
|
||||
check_progress.click(
|
||||
fn=lambda: check_progress_call(id_part),
|
||||
show_progress=False,
|
||||
inputs=[],
|
||||
outputs=[progressbar, preview, preview],
|
||||
outputs=[progressbar, preview, preview, textinfo],
|
||||
)
|
||||
|
||||
check_progress_initial = gr.Button('Check progress (first)', elem_id=f"{id_part}_check_progress_initial", visible=False)
|
||||
|
|
@ -413,13 +459,16 @@ def setup_progressbar(progressbar, preview, id_part):
|
|||
fn=lambda: check_progress_call_initial(id_part),
|
||||
show_progress=False,
|
||||
inputs=[],
|
||||
outputs=[progressbar, preview, preview],
|
||||
outputs=[progressbar, preview, preview, textinfo],
|
||||
)
|
||||
|
||||
|
||||
def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
|
||||
def create_ui(wrap_gradio_gpu_call):
|
||||
import modules.img2img
|
||||
import modules.txt2img
|
||||
|
||||
with gr.Blocks(analytics_enabled=False) as txt2img_interface:
|
||||
txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, txt2img_prompt_style_apply, txt2img_save_style, paste = create_toprow(is_img2img=False)
|
||||
txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, txt2img_prompt_style_apply, txt2img_save_style, paste, token_counter, token_button = create_toprow(is_img2img=False)
|
||||
dummy_component = gr.Label(visible=False)
|
||||
|
||||
with gr.Row(elem_id='txt2img_progress_row'):
|
||||
|
|
@ -483,7 +532,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
|
|||
connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
|
||||
|
||||
txt2img_args = dict(
|
||||
fn=txt2img,
|
||||
fn=wrap_gradio_gpu_call(modules.txt2img.txt2img),
|
||||
_js="submit",
|
||||
inputs=[
|
||||
txt2img_prompt,
|
||||
|
|
@ -539,6 +588,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
|
|||
|
||||
roll.click(
|
||||
fn=roll_artist,
|
||||
_js="update_txt2img_tokens",
|
||||
inputs=[
|
||||
txt2img_prompt,
|
||||
],
|
||||
|
|
@ -567,9 +617,10 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
|
|||
(hr_options, lambda d: gr.Row.update(visible="Denoising strength" in d)),
|
||||
]
|
||||
modules.generation_parameters_copypaste.connect_paste(paste, txt2img_paste_fields, txt2img_prompt)
|
||||
token_button.click(fn=update_token_counter, inputs=[txt2img_prompt, steps], outputs=[token_counter])
|
||||
|
||||
with gr.Blocks(analytics_enabled=False) as img2img_interface:
|
||||
img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_prompt_style_apply, img2img_save_style, paste = create_toprow(is_img2img=True)
|
||||
img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_prompt_style_apply, img2img_save_style, paste, token_counter, token_button = create_toprow(is_img2img=True)
|
||||
|
||||
with gr.Row(elem_id='img2img_progress_row'):
|
||||
with gr.Column(scale=1):
|
||||
|
|
@ -675,7 +726,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
|
|||
)
|
||||
|
||||
img2img_args = dict(
|
||||
fn=img2img,
|
||||
fn=wrap_gradio_gpu_call(modules.img2img.img2img),
|
||||
_js="submit_img2img",
|
||||
inputs=[
|
||||
dummy_component,
|
||||
|
|
@ -743,6 +794,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
|
|||
|
||||
roll.click(
|
||||
fn=roll_artist,
|
||||
_js="update_img2img_tokens",
|
||||
inputs=[
|
||||
img2img_prompt,
|
||||
],
|
||||
|
|
@ -753,6 +805,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
|
|||
|
||||
prompts = [(txt2img_prompt, txt2img_negative_prompt), (img2img_prompt, img2img_negative_prompt)]
|
||||
style_dropdowns = [(txt2img_prompt_style, txt2img_prompt_style2), (img2img_prompt_style, img2img_prompt_style2)]
|
||||
style_js_funcs = ["update_txt2img_tokens", "update_img2img_tokens"]
|
||||
|
||||
for button, (prompt, negative_prompt) in zip([txt2img_save_style, img2img_save_style], prompts):
|
||||
button.click(
|
||||
|
|
@ -764,9 +817,10 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
|
|||
outputs=[txt2img_prompt_style, img2img_prompt_style, txt2img_prompt_style2, img2img_prompt_style2],
|
||||
)
|
||||
|
||||
for button, (prompt, negative_prompt), (style1, style2) in zip([txt2img_prompt_style_apply, img2img_prompt_style_apply], prompts, style_dropdowns):
|
||||
for button, (prompt, negative_prompt), (style1, style2), js_func in zip([txt2img_prompt_style_apply, img2img_prompt_style_apply], prompts, style_dropdowns, style_js_funcs):
|
||||
button.click(
|
||||
fn=apply_styles,
|
||||
_js=js_func,
|
||||
inputs=[prompt, negative_prompt, style1, style2],
|
||||
outputs=[prompt, negative_prompt, style1, style2],
|
||||
)
|
||||
|
|
@ -789,6 +843,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
|
|||
(denoising_strength, "Denoising strength"),
|
||||
]
|
||||
modules.generation_parameters_copypaste.connect_paste(paste, img2img_paste_fields, img2img_prompt)
|
||||
token_button.click(fn=update_token_counter, inputs=[img2img_prompt, steps], outputs=[token_counter])
|
||||
|
||||
with gr.Blocks(analytics_enabled=False) as extras_interface:
|
||||
with gr.Row().style(equal_height=False):
|
||||
|
|
@ -828,7 +883,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
|
|||
open_extras_folder = gr.Button('Open output directory', elem_id=button_id)
|
||||
|
||||
submit.click(
|
||||
fn=run_extras,
|
||||
fn=wrap_gradio_gpu_call(modules.extras.run_extras),
|
||||
_js="get_extras_tab_index",
|
||||
inputs=[
|
||||
dummy_component,
|
||||
|
|
@ -878,7 +933,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
|
|||
pnginfo_send_to_img2img = gr.Button('Send to img2img')
|
||||
|
||||
image.change(
|
||||
fn=wrap_gradio_call(run_pnginfo),
|
||||
fn=wrap_gradio_call(modules.extras.run_pnginfo),
|
||||
inputs=[image],
|
||||
outputs=[html, generation_info, html2],
|
||||
)
|
||||
|
|
@ -900,6 +955,130 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
|
|||
with gr.Column(variant='panel'):
|
||||
submit_result = gr.Textbox(elem_id="modelmerger_result", show_label=False)
|
||||
|
||||
sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings()
|
||||
|
||||
with gr.Blocks() as textual_inversion_interface:
|
||||
with gr.Row().style(equal_height=False):
|
||||
with gr.Column():
|
||||
with gr.Group():
|
||||
gr.HTML(value="<p style='margin-bottom: 0.7em'>See <b><a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\">wiki</a></b> for detailed explanation.</p>")
|
||||
|
||||
gr.HTML(value="<p style='margin-bottom: 0.7em'>Create a new embedding</p>")
|
||||
|
||||
new_embedding_name = gr.Textbox(label="Name")
|
||||
initialization_text = gr.Textbox(label="Initialization text", value="*")
|
||||
nvpt = gr.Slider(label="Number of vectors per token", minimum=1, maximum=75, step=1, value=1)
|
||||
|
||||
with gr.Row():
|
||||
with gr.Column(scale=3):
|
||||
gr.HTML(value="")
|
||||
|
||||
with gr.Column():
|
||||
create_embedding = gr.Button(value="Create", variant='primary')
|
||||
|
||||
with gr.Group():
|
||||
gr.HTML(value="<p style='margin-bottom: 0.7em'>Preprocess images</p>")
|
||||
|
||||
process_src = gr.Textbox(label='Source directory')
|
||||
process_dst = gr.Textbox(label='Destination directory')
|
||||
|
||||
with gr.Row():
|
||||
process_flip = gr.Checkbox(label='Flip')
|
||||
process_split = gr.Checkbox(label='Split into two')
|
||||
process_caption = gr.Checkbox(label='Add caption')
|
||||
|
||||
with gr.Row():
|
||||
with gr.Column(scale=3):
|
||||
gr.HTML(value="")
|
||||
|
||||
with gr.Column():
|
||||
run_preprocess = gr.Button(value="Preprocess", variant='primary')
|
||||
|
||||
with gr.Group():
|
||||
gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding; must specify a directory with a set of 512x512 images</p>")
|
||||
train_embedding_name = gr.Dropdown(label='Embedding', choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
|
||||
learn_rate = gr.Number(label='Learning rate', value=5.0e-03)
|
||||
dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images")
|
||||
log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion")
|
||||
template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt"))
|
||||
steps = gr.Number(label='Max steps', value=100000, precision=0)
|
||||
create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0)
|
||||
save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0)
|
||||
|
||||
with gr.Row():
|
||||
with gr.Column(scale=2):
|
||||
gr.HTML(value="")
|
||||
|
||||
with gr.Column():
|
||||
with gr.Row():
|
||||
interrupt_training = gr.Button(value="Interrupt")
|
||||
train_embedding = gr.Button(value="Train", variant='primary')
|
||||
|
||||
with gr.Column():
|
||||
progressbar = gr.HTML(elem_id="ti_progressbar")
|
||||
ti_output = gr.Text(elem_id="ti_output", value="", show_label=False)
|
||||
|
||||
ti_gallery = gr.Gallery(label='Output', show_label=False, elem_id='ti_gallery').style(grid=4)
|
||||
ti_preview = gr.Image(elem_id='ti_preview', visible=False)
|
||||
ti_progress = gr.HTML(elem_id="ti_progress", value="")
|
||||
ti_outcome = gr.HTML(elem_id="ti_error", value="")
|
||||
setup_progressbar(progressbar, ti_preview, 'ti', textinfo=ti_progress)
|
||||
|
||||
create_embedding.click(
|
||||
fn=modules.textual_inversion.ui.create_embedding,
|
||||
inputs=[
|
||||
new_embedding_name,
|
||||
initialization_text,
|
||||
nvpt,
|
||||
],
|
||||
outputs=[
|
||||
train_embedding_name,
|
||||
ti_output,
|
||||
ti_outcome,
|
||||
]
|
||||
)
|
||||
|
||||
run_preprocess.click(
|
||||
fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.preprocess, extra_outputs=[gr.update()]),
|
||||
_js="start_training_textual_inversion",
|
||||
inputs=[
|
||||
process_src,
|
||||
process_dst,
|
||||
process_flip,
|
||||
process_split,
|
||||
process_caption,
|
||||
],
|
||||
outputs=[
|
||||
ti_output,
|
||||
ti_outcome,
|
||||
],
|
||||
)
|
||||
|
||||
train_embedding.click(
|
||||
fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.train_embedding, extra_outputs=[gr.update()]),
|
||||
_js="start_training_textual_inversion",
|
||||
inputs=[
|
||||
train_embedding_name,
|
||||
learn_rate,
|
||||
dataset_directory,
|
||||
log_directory,
|
||||
steps,
|
||||
create_image_every,
|
||||
save_embedding_every,
|
||||
template_file,
|
||||
],
|
||||
outputs=[
|
||||
ti_output,
|
||||
ti_outcome,
|
||||
]
|
||||
)
|
||||
|
||||
interrupt_training.click(
|
||||
fn=lambda: shared.state.interrupt(),
|
||||
inputs=[],
|
||||
outputs=[],
|
||||
)
|
||||
|
||||
def create_setting_component(key):
|
||||
def fun():
|
||||
return opts.data[key] if key in opts.data else opts.data_labels[key].default
|
||||
|
|
@ -1002,6 +1181,31 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
|
|||
_js='function(){}'
|
||||
)
|
||||
|
||||
with gr.Row():
|
||||
reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary')
|
||||
restart_gradio = gr.Button(value='Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)', variant='primary')
|
||||
|
||||
|
||||
def reload_scripts():
|
||||
modules.scripts.reload_script_body_only()
|
||||
|
||||
reload_script_bodies.click(
|
||||
fn=reload_scripts,
|
||||
inputs=[],
|
||||
outputs=[],
|
||||
_js='function(){}'
|
||||
)
|
||||
|
||||
def request_restart():
|
||||
settings_interface.gradio_ref.do_restart = True
|
||||
|
||||
restart_gradio.click(
|
||||
fn=request_restart,
|
||||
inputs=[],
|
||||
outputs=[],
|
||||
_js='function(){restart_reload()}'
|
||||
)
|
||||
|
||||
if column is not None:
|
||||
column.__exit__()
|
||||
|
||||
|
|
@ -1011,6 +1215,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
|
|||
(extras_interface, "Extras", "extras"),
|
||||
(pnginfo_interface, "PNG Info", "pnginfo"),
|
||||
(modelmerger_interface, "Checkpoint Merger", "modelmerger"),
|
||||
(textual_inversion_interface, "Textual inversion", "ti"),
|
||||
(settings_interface, "Settings", "settings"),
|
||||
]
|
||||
|
||||
|
|
@ -1027,6 +1232,8 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
|
|||
|
||||
with gr.Blocks(css=css, analytics_enabled=False, title="Stable Diffusion") as demo:
|
||||
|
||||
settings_interface.gradio_ref = demo
|
||||
|
||||
with gr.Tabs() as tabs:
|
||||
for interface, label, ifid in interfaces:
|
||||
with gr.TabItem(label, id=ifid):
|
||||
|
|
@ -1044,11 +1251,11 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
|
|||
|
||||
def modelmerger(*args):
|
||||
try:
|
||||
results = run_modelmerger(*args)
|
||||
results = modules.extras.run_modelmerger(*args)
|
||||
except Exception as e:
|
||||
print("Error loading/saving model file:", file=sys.stderr)
|
||||
print(traceback.format_exc(), file=sys.stderr)
|
||||
modules.sd_models.list_models() #To remove the potentially missing models from the list
|
||||
modules.sd_models.list_models() # to remove the potentially missing models from the list
|
||||
return ["Error loading/saving model file. It doesn't exist or the name contains illegal characters"] + [gr.Dropdown.update(choices=modules.sd_models.checkpoint_tiles()) for _ in range(3)]
|
||||
return results
|
||||
|
||||
|
|
@ -1206,12 +1413,12 @@ for filename in sorted(os.listdir(jsdir)):
|
|||
javascript += f"\n<script>{jsfile.read()}</script>"
|
||||
|
||||
|
||||
def template_response(*args, **kwargs):
|
||||
if 'gradio_routes_templates_response' not in globals():
|
||||
def template_response(*args, **kwargs):
|
||||
res = gradio_routes_templates_response(*args, **kwargs)
|
||||
res.body = res.body.replace(b'</head>', f'{javascript}</head>'.encode("utf8"))
|
||||
res.init_headers()
|
||||
return res
|
||||
|
||||
|
||||
gradio_routes_templates_response = gradio.routes.templates.TemplateResponse
|
||||
gradio.routes.templates.TemplateResponse = template_response
|
||||
gradio_routes_templates_response = gradio.routes.templates.TemplateResponse
|
||||
gradio.routes.templates.TemplateResponse = template_response
|
||||
|
|
|
|||
|
|
@ -13,14 +13,12 @@ Pillow
|
|||
pytorch_lightning
|
||||
realesrgan
|
||||
scikit-image>=0.19
|
||||
git+https://github.com/TencentARC/GFPGAN.git@8d2447a2d918f8eba5a4a01463fd48e45126a379
|
||||
timm==0.4.12
|
||||
transformers==4.19.2
|
||||
torch
|
||||
einops
|
||||
jsonmerge
|
||||
clean-fid
|
||||
git+https://github.com/openai/CLIP@d50d76daa670286dd6cacf3bcd80b5e4823fc8e1
|
||||
resize-right
|
||||
torchdiffeq
|
||||
kornia
|
||||
|
|
|
|||
|
|
@ -18,7 +18,6 @@ piexif==1.1.3
|
|||
einops==0.4.1
|
||||
jsonmerge==1.8.0
|
||||
clean-fid==0.1.29
|
||||
git+https://github.com/openai/CLIP@d50d76daa670286dd6cacf3bcd80b5e4823fc8e1
|
||||
resize-right==0.0.2
|
||||
torchdiffeq==0.2.3
|
||||
kornia==0.6.7
|
||||
|
|
|
|||
|
|
@ -34,7 +34,11 @@ class Script(scripts.Script):
|
|||
seed = p.seed
|
||||
|
||||
init_img = p.init_images[0]
|
||||
|
||||
if(upscaler.name != "None"):
|
||||
img = upscaler.scaler.upscale(init_img, 2, upscaler.data_path)
|
||||
else:
|
||||
img = init_img
|
||||
|
||||
devices.torch_gc()
|
||||
|
||||
|
|
|
|||
10
style.css
10
style.css
|
|
@ -157,7 +157,7 @@ button{
|
|||
max-width: 10em;
|
||||
}
|
||||
|
||||
#txt2img_preview, #img2img_preview{
|
||||
#txt2img_preview, #img2img_preview, #ti_preview{
|
||||
position: absolute;
|
||||
width: 320px;
|
||||
left: 0;
|
||||
|
|
@ -172,18 +172,18 @@ button{
|
|||
}
|
||||
|
||||
@media screen and (min-width: 768px) {
|
||||
#txt2img_preview, #img2img_preview {
|
||||
#txt2img_preview, #img2img_preview, #ti_preview {
|
||||
position: absolute;
|
||||
}
|
||||
}
|
||||
|
||||
@media screen and (max-width: 767px) {
|
||||
#txt2img_preview, #img2img_preview {
|
||||
#txt2img_preview, #img2img_preview, #ti_preview {
|
||||
position: relative;
|
||||
}
|
||||
}
|
||||
|
||||
#txt2img_preview div.left-0.top-0, #img2img_preview div.left-0.top-0{
|
||||
#txt2img_preview div.left-0.top-0, #img2img_preview div.left-0.top-0, #ti_preview div.left-0.top-0{
|
||||
display: none;
|
||||
}
|
||||
|
||||
|
|
@ -247,7 +247,7 @@ input[type="range"]{
|
|||
#txt2img_negative_prompt, #img2img_negative_prompt{
|
||||
}
|
||||
|
||||
#txt2img_progressbar, #img2img_progressbar{
|
||||
#txt2img_progressbar, #img2img_progressbar, #ti_progressbar{
|
||||
position: absolute;
|
||||
z-index: 1000;
|
||||
right: 0;
|
||||
|
|
|
|||
19
textual_inversion_templates/style.txt
Normal file
19
textual_inversion_templates/style.txt
Normal file
|
|
@ -0,0 +1,19 @@
|
|||
a painting, art by [name]
|
||||
a rendering, art by [name]
|
||||
a cropped painting, art by [name]
|
||||
the painting, art by [name]
|
||||
a clean painting, art by [name]
|
||||
a dirty painting, art by [name]
|
||||
a dark painting, art by [name]
|
||||
a picture, art by [name]
|
||||
a cool painting, art by [name]
|
||||
a close-up painting, art by [name]
|
||||
a bright painting, art by [name]
|
||||
a cropped painting, art by [name]
|
||||
a good painting, art by [name]
|
||||
a close-up painting, art by [name]
|
||||
a rendition, art by [name]
|
||||
a nice painting, art by [name]
|
||||
a small painting, art by [name]
|
||||
a weird painting, art by [name]
|
||||
a large painting, art by [name]
|
||||
19
textual_inversion_templates/style_filewords.txt
Normal file
19
textual_inversion_templates/style_filewords.txt
Normal file
|
|
@ -0,0 +1,19 @@
|
|||
a painting of [filewords], art by [name]
|
||||
a rendering of [filewords], art by [name]
|
||||
a cropped painting of [filewords], art by [name]
|
||||
the painting of [filewords], art by [name]
|
||||
a clean painting of [filewords], art by [name]
|
||||
a dirty painting of [filewords], art by [name]
|
||||
a dark painting of [filewords], art by [name]
|
||||
a picture of [filewords], art by [name]
|
||||
a cool painting of [filewords], art by [name]
|
||||
a close-up painting of [filewords], art by [name]
|
||||
a bright painting of [filewords], art by [name]
|
||||
a cropped painting of [filewords], art by [name]
|
||||
a good painting of [filewords], art by [name]
|
||||
a close-up painting of [filewords], art by [name]
|
||||
a rendition of [filewords], art by [name]
|
||||
a nice painting of [filewords], art by [name]
|
||||
a small painting of [filewords], art by [name]
|
||||
a weird painting of [filewords], art by [name]
|
||||
a large painting of [filewords], art by [name]
|
||||
27
textual_inversion_templates/subject.txt
Normal file
27
textual_inversion_templates/subject.txt
Normal file
|
|
@ -0,0 +1,27 @@
|
|||
a photo of a [name]
|
||||
a rendering of a [name]
|
||||
a cropped photo of the [name]
|
||||
the photo of a [name]
|
||||
a photo of a clean [name]
|
||||
a photo of a dirty [name]
|
||||
a dark photo of the [name]
|
||||
a photo of my [name]
|
||||
a photo of the cool [name]
|
||||
a close-up photo of a [name]
|
||||
a bright photo of the [name]
|
||||
a cropped photo of a [name]
|
||||
a photo of the [name]
|
||||
a good photo of the [name]
|
||||
a photo of one [name]
|
||||
a close-up photo of the [name]
|
||||
a rendition of the [name]
|
||||
a photo of the clean [name]
|
||||
a rendition of a [name]
|
||||
a photo of a nice [name]
|
||||
a good photo of a [name]
|
||||
a photo of the nice [name]
|
||||
a photo of the small [name]
|
||||
a photo of the weird [name]
|
||||
a photo of the large [name]
|
||||
a photo of a cool [name]
|
||||
a photo of a small [name]
|
||||
27
textual_inversion_templates/subject_filewords.txt
Normal file
27
textual_inversion_templates/subject_filewords.txt
Normal file
|
|
@ -0,0 +1,27 @@
|
|||
a photo of a [name], [filewords]
|
||||
a rendering of a [name], [filewords]
|
||||
a cropped photo of the [name], [filewords]
|
||||
the photo of a [name], [filewords]
|
||||
a photo of a clean [name], [filewords]
|
||||
a photo of a dirty [name], [filewords]
|
||||
a dark photo of the [name], [filewords]
|
||||
a photo of my [name], [filewords]
|
||||
a photo of the cool [name], [filewords]
|
||||
a close-up photo of a [name], [filewords]
|
||||
a bright photo of the [name], [filewords]
|
||||
a cropped photo of a [name], [filewords]
|
||||
a photo of the [name], [filewords]
|
||||
a good photo of the [name], [filewords]
|
||||
a photo of one [name], [filewords]
|
||||
a close-up photo of the [name], [filewords]
|
||||
a rendition of the [name], [filewords]
|
||||
a photo of the clean [name], [filewords]
|
||||
a rendition of a [name], [filewords]
|
||||
a photo of a nice [name], [filewords]
|
||||
a good photo of a [name], [filewords]
|
||||
a photo of the nice [name], [filewords]
|
||||
a photo of the small [name], [filewords]
|
||||
a photo of the weird [name], [filewords]
|
||||
a photo of the large [name], [filewords]
|
||||
a photo of a cool [name], [filewords]
|
||||
a photo of a small [name], [filewords]
|
||||
47
webui.py
47
webui.py
|
|
@ -1,34 +1,34 @@
|
|||
import os
|
||||
import threading
|
||||
|
||||
import time
|
||||
import importlib
|
||||
from modules import devices
|
||||
from modules.paths import script_path
|
||||
import signal
|
||||
import threading
|
||||
import modules.paths
|
||||
|
||||
import modules.codeformer_model as codeformer
|
||||
import modules.esrgan_model as esrgan
|
||||
import modules.bsrgan_model as bsrgan
|
||||
import modules.extras
|
||||
import modules.face_restoration
|
||||
import modules.gfpgan_model as gfpgan
|
||||
import modules.img2img
|
||||
import modules.ldsr_model as ldsr
|
||||
|
||||
import modules.lowvram
|
||||
import modules.realesrgan_model as realesrgan
|
||||
import modules.paths
|
||||
import modules.scripts
|
||||
import modules.sd_hijack
|
||||
import modules.sd_models
|
||||
import modules.shared as shared
|
||||
import modules.swinir_model as swinir
|
||||
import modules.txt2img
|
||||
|
||||
import modules.ui
|
||||
from modules import devices
|
||||
from modules import modelloader
|
||||
from modules.paths import script_path
|
||||
from modules.shared import cmd_opts
|
||||
|
||||
modelloader.cleanup_models()
|
||||
modules.sd_models.setup_model(cmd_opts.ckpt_dir)
|
||||
modules.sd_models.setup_model()
|
||||
codeformer.setup_model(cmd_opts.codeformer_models_path)
|
||||
gfpgan.setup_model(cmd_opts.gfpgan_models_path)
|
||||
shared.face_restorers.append(modules.face_restoration.FaceRestoration())
|
||||
|
|
@ -46,7 +46,7 @@ def wrap_queued_call(func):
|
|||
return f
|
||||
|
||||
|
||||
def wrap_gradio_gpu_call(func):
|
||||
def wrap_gradio_gpu_call(func, extra_outputs=None):
|
||||
def f(*args, **kwargs):
|
||||
devices.torch_gc()
|
||||
|
||||
|
|
@ -58,6 +58,7 @@ def wrap_gradio_gpu_call(func):
|
|||
shared.state.current_image = None
|
||||
shared.state.current_image_sampling_step = 0
|
||||
shared.state.interrupted = False
|
||||
shared.state.textinfo = None
|
||||
|
||||
with queue_lock:
|
||||
res = func(*args, **kwargs)
|
||||
|
|
@ -69,7 +70,7 @@ def wrap_gradio_gpu_call(func):
|
|||
|
||||
return res
|
||||
|
||||
return modules.ui.wrap_gradio_call(f)
|
||||
return modules.ui.wrap_gradio_call(f, extra_outputs=extra_outputs)
|
||||
|
||||
|
||||
modules.scripts.load_scripts(os.path.join(script_path, "scripts"))
|
||||
|
|
@ -86,13 +87,9 @@ def webui():
|
|||
|
||||
signal.signal(signal.SIGINT, sigint_handler)
|
||||
|
||||
demo = modules.ui.create_ui(
|
||||
txt2img=wrap_gradio_gpu_call(modules.txt2img.txt2img),
|
||||
img2img=wrap_gradio_gpu_call(modules.img2img.img2img),
|
||||
run_extras=wrap_gradio_gpu_call(modules.extras.run_extras),
|
||||
run_pnginfo=modules.extras.run_pnginfo,
|
||||
run_modelmerger=modules.extras.run_modelmerger
|
||||
)
|
||||
while 1:
|
||||
|
||||
demo = modules.ui.create_ui(wrap_gradio_gpu_call=wrap_gradio_gpu_call)
|
||||
|
||||
demo.launch(
|
||||
share=cmd_opts.share,
|
||||
|
|
@ -101,8 +98,24 @@ def webui():
|
|||
debug=cmd_opts.gradio_debug,
|
||||
auth=[tuple(cred.split(':')) for cred in cmd_opts.gradio_auth.strip('"').split(',')] if cmd_opts.gradio_auth else None,
|
||||
inbrowser=cmd_opts.autolaunch,
|
||||
prevent_thread_lock=True
|
||||
)
|
||||
|
||||
while 1:
|
||||
time.sleep(0.5)
|
||||
if getattr(demo, 'do_restart', False):
|
||||
time.sleep(0.5)
|
||||
demo.close()
|
||||
time.sleep(0.5)
|
||||
break
|
||||
|
||||
print('Reloading Custom Scripts')
|
||||
modules.scripts.reload_scripts(os.path.join(script_path, "scripts"))
|
||||
print('Reloading modules: modules.ui')
|
||||
importlib.reload(modules.ui)
|
||||
print('Restarting Gradio')
|
||||
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
webui()
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user