Merge remote-tracking branch 'upstream/master' into ModelLoader
This commit is contained in:
d8ahazard
commit
d73741794d
20 changed files with 560 additions and 149 deletions
2
.gitignore
vendored
2
.gitignore
vendored
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@ -8,6 +8,8 @@ __pycache__
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/tmp
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/model.ckpt
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/models/**/*
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/GFPGANv1.3.pth
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/gfpgan/weights/*.pth
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/ui-config.json
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/outputs
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/config.json
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@ -15,6 +15,7 @@ titles = {
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"\u267b\ufe0f": "Reuse seed from last generation, mostly useful if it was randomed",
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"\u{1f3a8}": "Add a random artist to the prompt.",
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"\u2199\ufe0f": "Read generation parameters from prompt into user interface.",
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"\uD83D\uDCC2": "Open images output directory",
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"Inpaint a part of image": "Draw a mask over an image, and the script will regenerate the masked area with content according to prompt",
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"SD upscale": "Upscale image normally, split result into tiles, improve each tile using img2img, merge whole image back",
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@ -182,4 +182,23 @@ onUiUpdate(function(){
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});
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json_elem.parentElement.style.display="none"
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if (!txt2img_textarea) {
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txt2img_textarea = gradioApp().querySelector("#txt2img_prompt > label > textarea");
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txt2img_textarea?.addEventListener("input", () => update_token_counter("txt2img_token_button"));
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}
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if (!img2img_textarea) {
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img2img_textarea = gradioApp().querySelector("#img2img_prompt > label > textarea");
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img2img_textarea?.addEventListener("input", () => update_token_counter("img2img_token_button"));
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}
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})
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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_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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11
launch.py
11
launch.py
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@ -15,11 +15,11 @@ torch_command = os.environ.get('TORCH_COMMAND', "pip install torch==1.12.1+cu113
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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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k_diffusion_package = os.environ.get('K_DIFFUSION_PACKAGE', "git+https://github.com/crowsonkb/k-diffusion.git@1a0703dfb7d24d8806267c3e7ccc4caf67fd1331")
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gfpgan_package = os.environ.get('GFPGAN_PACKAGE', "git+https://github.com/TencentARC/GFPGAN.git@8d2447a2d918f8eba5a4a01463fd48e45126a379")
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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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k_diffusion_commit_hash = os.environ.get('K_DIFFUSION_COMMIT_HASH', "a7ec1974d4ccb394c2dca275f42cd97490618924")
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codeformer_commit_hash = os.environ.get('CODEFORMER_COMMIT_HASH', "c5b4593074ba6214284d6acd5f1719b6c5d739af")
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blip_commit_hash = os.environ.get('BLIP_COMMIT_HASH', "48211a1594f1321b00f14c9f7a5b4813144b2fb9")
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@ -107,10 +107,7 @@ if not is_installed("torch") or not is_installed("torchvision"):
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run(f'"{python}" -m {torch_command}', "Installing torch and torchvision", "Couldn't install torch")
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if not skip_torch_cuda_test:
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run_python("import torch; assert torch.cuda.is_available(), 'Torch is not able to use GPU; add --skip-torch-cuda-test to COMMANDINE_ARGS variable to disable this check'")
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if not is_installed("k_diffusion.sampling"):
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run_pip(f"install {k_diffusion_package}", "k-diffusion")
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run_python("import torch; assert torch.cuda.is_available(), 'Torch is not able to use GPU; add --skip-torch-cuda-test to COMMANDLINE_ARGS variable to disable this check'")
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if not is_installed("gfpgan"):
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run_pip(f"install {gfpgan_package}", "gfpgan")
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@ -119,6 +116,7 @@ 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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git_clone("https://github.com/CompVis/taming-transformers.git", repo_dir('taming-transformers'), "Taming Transformers", taming_transformers_commit_hash)
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git_clone("https://github.com/crowsonkb/k-diffusion.git", repo_dir('k-diffusion'), "K-diffusion", k_diffusion_commit_hash)
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git_clone("https://github.com/sczhou/CodeFormer.git", repo_dir('CodeFormer'), "CodeFormer", codeformer_commit_hash)
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git_clone("https://github.com/salesforce/BLIP.git", repo_dir('BLIP'), "BLIP", blip_commit_hash)
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if os.path.isdir(repo_dir('latent-diffusion')):
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@ -133,6 +131,9 @@ run_pip(f"install -r {requirements_file}", "requirements for Web UI")
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sys.argv += args
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if "--exit" in args:
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print("Exiting because of --exit argument")
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exit(0)
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def start_webui():
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print(f"Launching Web UI with arguments: {' '.join(sys.argv[1:])}")
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@ -6,13 +6,14 @@ from PIL import Image
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import torch
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import tqdm
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from modules import processing, shared, images, devices
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from modules import processing, shared, images, devices, sd_models
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from modules.shared import opts
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import modules.gfpgan_model
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from modules.ui import plaintext_to_html
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import modules.codeformer_model
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import piexif
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import piexif.helper
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import gradio as gr
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cached_images = {}
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@ -141,7 +142,7 @@ def run_pnginfo(image):
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return '', geninfo, info
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def run_modelmerger(modelname_0, modelname_1, interp_method, interp_amount):
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def run_modelmerger(primary_model_name, secondary_model_name, interp_method, interp_amount, save_as_half, custom_name):
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# Linear interpolation (https://en.wikipedia.org/wiki/Linear_interpolation)
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def weighted_sum(theta0, theta1, alpha):
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return ((1 - alpha) * theta0) + (alpha * theta1)
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@ -151,45 +152,52 @@ def run_modelmerger(modelname_0, modelname_1, interp_method, interp_amount):
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alpha = alpha * alpha * (3 - (2 * alpha))
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return theta0 + ((theta1 - theta0) * alpha)
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if os.path.exists(modelname_0):
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model0_filename = modelname_0
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modelname_0 = os.path.splitext(os.path.basename(modelname_0))[0]
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else:
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model0_filename = 'models/' + modelname_0 + '.ckpt'
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# Inverse Smoothstep (https://en.wikipedia.org/wiki/Smoothstep)
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def inv_sigmoid(theta0, theta1, alpha):
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import math
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alpha = 0.5 - math.sin(math.asin(1.0 - 2.0 * alpha) / 3.0)
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return theta0 + ((theta1 - theta0) * alpha)
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if os.path.exists(modelname_1):
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model1_filename = modelname_1
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modelname_1 = os.path.splitext(os.path.basename(modelname_1))[0]
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else:
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model1_filename = 'models/' + modelname_1 + '.ckpt'
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primary_model_info = sd_models.checkpoints_list[primary_model_name]
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secondary_model_info = sd_models.checkpoints_list[secondary_model_name]
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print(f"Loading {model0_filename}...")
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model_0 = torch.load(model0_filename, map_location='cpu')
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print(f"Loading {primary_model_info.filename}...")
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primary_model = torch.load(primary_model_info.filename, map_location='cpu')
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print(f"Loading {model1_filename}...")
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model_1 = torch.load(model1_filename, map_location='cpu')
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theta_0 = model_0['state_dict']
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theta_1 = model_1['state_dict']
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print(f"Loading {secondary_model_info.filename}...")
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secondary_model = torch.load(secondary_model_info.filename, map_location='cpu')
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theta_0 = primary_model['state_dict']
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theta_1 = secondary_model['state_dict']
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theta_funcs = {
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"Weighted Sum": weighted_sum,
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"Sigmoid": sigmoid,
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"Inverse Sigmoid": inv_sigmoid,
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}
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theta_func = theta_funcs[interp_method]
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print(f"Merging...")
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for key in tqdm.tqdm(theta_0.keys()):
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if 'model' in key and key in theta_1:
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theta_0[key] = theta_func(theta_0[key], theta_1[key], interp_amount)
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theta_0[key] = theta_func(theta_0[key], theta_1[key], (float(1.0) - interp_amount)) # Need to reverse the interp_amount to match the desired mix ration in the merged checkpoint
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if save_as_half:
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theta_0[key] = theta_0[key].half()
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for key in theta_1.keys():
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if 'model' in key and key not in theta_0:
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theta_0[key] = theta_1[key]
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if save_as_half:
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theta_0[key] = theta_0[key].half()
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filename = primary_model_info.model_name + '_' + str(round(interp_amount, 2)) + '-' + secondary_model_info.model_name + '_' + str(round((float(1.0) - interp_amount), 2)) + '-' + interp_method.replace(" ", "_") + '-merged.ckpt'
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filename = filename if custom_name == '' else (custom_name + '.ckpt')
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output_modelname = os.path.join(shared.cmd_opts.ckpt_dir, filename)
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output_modelname = 'models/' + modelname_0 + '-' + modelname_1 + '-merged.ckpt'
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print(f"Saving to {output_modelname}...")
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torch.save(model_0, output_modelname)
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torch.save(primary_model, output_modelname)
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sd_models.list_models()
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print(f"Checkpoint saved.")
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return "Checkpoint saved to " + output_modelname
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return ["Checkpoint saved to " + output_modelname] + [gr.Dropdown.update(choices=sd_models.checkpoint_tiles()) for _ in range(3)]
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@ -124,4 +124,4 @@ def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, pro
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if opts.samples_log_stdout:
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print(generation_info_js)
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return processed.images, generation_info_js, plaintext_to_html(processed.info)
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return processed.images, generation_info_js, plaintext_to_html(processed.info)
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@ -21,6 +21,7 @@ path_dirs = [
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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'),
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(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'),
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]
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paths = {}
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@ -49,7 +49,7 @@ def apply_color_correction(correction, image):
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class StableDiffusionProcessing:
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def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt="", styles=None, seed=-1, subseed=-1, subseed_strength=0, seed_resize_from_h=-1, seed_resize_from_w=-1, seed_enable_extras=True, sampler_index=0, batch_size=1, n_iter=1, steps=50, cfg_scale=7.0, width=512, height=512, restore_faces=False, tiling=False, do_not_save_samples=False, do_not_save_grid=False, extra_generation_params=None, overlay_images=None, negative_prompt=None):
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def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt="", styles=None, seed=-1, subseed=-1, subseed_strength=0, seed_resize_from_h=-1, seed_resize_from_w=-1, seed_enable_extras=True, sampler_index=0, batch_size=1, n_iter=1, steps=50, cfg_scale=7.0, width=512, height=512, restore_faces=False, tiling=False, do_not_save_samples=False, do_not_save_grid=False, extra_generation_params=None, overlay_images=None, negative_prompt=None, eta=None):
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self.sd_model = sd_model
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self.outpath_samples: str = outpath_samples
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self.outpath_grids: str = outpath_grids
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@ -75,15 +75,15 @@ class StableDiffusionProcessing:
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self.do_not_save_grid: bool = do_not_save_grid
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self.extra_generation_params: dict = extra_generation_params or {}
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self.overlay_images = overlay_images
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self.eta = eta
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self.paste_to = None
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self.color_corrections = None
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self.denoising_strength: float = 0
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self.ddim_eta = opts.ddim_eta
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self.ddim_discretize = opts.ddim_discretize
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self.s_churn = opts.s_churn
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self.s_tmin = opts.s_tmin
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self.s_tmax = float('inf') # not representable as a standard ui option
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self.s_tmax = float('inf') # not representable as a standard ui option
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self.s_noise = opts.s_noise
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if not seed_enable_extras:
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@ -100,7 +100,7 @@ class StableDiffusionProcessing:
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class Processed:
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def __init__(self, p: StableDiffusionProcessing, images_list, seed=-1, info="", subseed=None, all_prompts=None, all_seeds=None, all_subseeds=None, index_of_first_image=0):
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def __init__(self, p: StableDiffusionProcessing, images_list, seed=-1, info="", subseed=None, all_prompts=None, all_seeds=None, all_subseeds=None, index_of_first_image=0, infotexts=None):
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self.images = images_list
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self.prompt = p.prompt
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self.negative_prompt = p.negative_prompt
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@ -124,7 +124,7 @@ class Processed:
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self.extra_generation_params = p.extra_generation_params
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self.index_of_first_image = index_of_first_image
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self.ddim_eta = p.ddim_eta
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self.eta = p.eta
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self.ddim_discretize = p.ddim_discretize
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self.s_churn = p.s_churn
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self.s_tmin = p.s_tmin
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@ -139,6 +139,7 @@ class Processed:
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self.all_prompts = all_prompts or [self.prompt]
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self.all_seeds = all_seeds or [self.seed]
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self.all_subseeds = all_subseeds or [self.subseed]
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self.infotexts = infotexts or [info]
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def js(self):
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obj = {
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@ -165,6 +166,7 @@ class Processed:
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"denoising_strength": self.denoising_strength,
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"extra_generation_params": self.extra_generation_params,
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"index_of_first_image": self.index_of_first_image,
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"infotexts": self.infotexts,
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}
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return json.dumps(obj)
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@ -269,6 +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),
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"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),
|
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"Eta": (None if p.sampler.eta == p.sampler.default_eta else p.sampler.eta),
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}
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generation_params.update(p.extra_generation_params)
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|
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@ -277,7 +280,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
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negative_prompt_text = "\nNegative prompt: " + p.negative_prompt if p.negative_prompt else ""
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|
||||
return f"{all_prompts[index]}{negative_prompt_text}\n{generation_params_text}".strip() + "".join(["\n\n" + x for x in comments])
|
||||
return f"{all_prompts[index]}{negative_prompt_text}\n{generation_params_text}".strip()
|
||||
|
||||
|
||||
def process_images(p: StableDiffusionProcessing) -> Processed:
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||||
|
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@ -322,6 +325,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
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|||
if os.path.exists(cmd_opts.embeddings_dir):
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||||
model_hijack.load_textual_inversion_embeddings(cmd_opts.embeddings_dir, p.sd_model)
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|
||||
infotexts = []
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output_images = []
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precision_scope = torch.autocast if cmd_opts.precision == "autocast" else contextlib.nullcontext
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||||
ema_scope = (contextlib.nullcontext if cmd_opts.lowvram else p.sd_model.ema_scope)
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||||
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|
@ -404,6 +408,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
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|||
if opts.samples_save and not p.do_not_save_samples:
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||||
images.save_image(image, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p)
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||||
infotexts.append(infotext(n, i))
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output_images.append(image)
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||||
state.nextjob()
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|
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@ -416,6 +421,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
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|||
grid = images.image_grid(output_images, p.batch_size)
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||||
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||||
if opts.return_grid:
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||||
infotexts.insert(0, infotext())
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||||
output_images.insert(0, grid)
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||||
index_of_first_image = 1
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||||
|
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@ -423,7 +429,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
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images.save_image(grid, p.outpath_grids, "grid", all_seeds[0], all_prompts[0], opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename, p=p, grid=True)
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||||
|
||||
devices.torch_gc()
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||||
return Processed(p, output_images, all_seeds[0], infotext(), subseed=all_subseeds[0], all_prompts=all_prompts, all_seeds=all_seeds, all_subseeds=all_subseeds, index_of_first_image=index_of_first_image)
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||||
return Processed(p, output_images, all_seeds[0], infotext() + "".join(["\n\n" + x for x in comments]), subseed=all_subseeds[0], all_prompts=all_prompts, all_seeds=all_seeds, all_subseeds=all_subseeds, index_of_first_image=index_of_first_image, infotexts=infotexts)
|
||||
|
||||
|
||||
class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
|
||||
|
|
|
|||
|
|
@ -126,5 +126,93 @@ def reconstruct_cond_batch(c: ScheduledPromptBatch, current_step):
|
|||
return res
|
||||
|
||||
|
||||
re_attention = re.compile(r"""
|
||||
\\\(|
|
||||
\\\)|
|
||||
\\\[|
|
||||
\\]|
|
||||
\\\\|
|
||||
\\|
|
||||
\(|
|
||||
\[|
|
||||
:([+-]?[.\d]+)\)|
|
||||
\)|
|
||||
]|
|
||||
[^\\()\[\]:]+|
|
||||
:
|
||||
""", re.X)
|
||||
|
||||
#get_learned_conditioning_prompt_schedules(["fantasy landscape with a [mountain:lake:0.25] and [an oak:a christmas tree:0.75][ in foreground::0.6][ in background:0.25] [shoddy:masterful:0.5]"], 100)
|
||||
|
||||
def parse_prompt_attention(text):
|
||||
"""
|
||||
Parses a string with attention tokens and returns a list of pairs: text and its assoicated weight.
|
||||
Accepted tokens are:
|
||||
(abc) - increases attention to abc by a multiplier of 1.1
|
||||
(abc:3.12) - increases attention to abc by a multiplier of 3.12
|
||||
[abc] - decreases attention to abc by a multiplier of 1.1
|
||||
\( - literal character '('
|
||||
\[ - literal character '['
|
||||
\) - literal character ')'
|
||||
\] - literal character ']'
|
||||
\\ - literal character '\'
|
||||
anything else - just text
|
||||
|
||||
Example:
|
||||
|
||||
'a (((house:1.3)) [on] a (hill:0.5), sun, (((sky))).'
|
||||
|
||||
produces:
|
||||
|
||||
[
|
||||
['a ', 1.0],
|
||||
['house', 1.5730000000000004],
|
||||
[' ', 1.1],
|
||||
['on', 1.0],
|
||||
[' a ', 1.1],
|
||||
['hill', 0.55],
|
||||
[', sun, ', 1.1],
|
||||
['sky', 1.4641000000000006],
|
||||
['.', 1.1]
|
||||
]
|
||||
"""
|
||||
|
||||
res = []
|
||||
round_brackets = []
|
||||
square_brackets = []
|
||||
|
||||
round_bracket_multiplier = 1.1
|
||||
square_bracket_multiplier = 1 / 1.1
|
||||
|
||||
def multiply_range(start_position, multiplier):
|
||||
for p in range(start_position, len(res)):
|
||||
res[p][1] *= multiplier
|
||||
|
||||
for m in re_attention.finditer(text):
|
||||
text = m.group(0)
|
||||
weight = m.group(1)
|
||||
|
||||
if text.startswith('\\'):
|
||||
res.append([text[1:], 1.0])
|
||||
elif text == '(':
|
||||
round_brackets.append(len(res))
|
||||
elif text == '[':
|
||||
square_brackets.append(len(res))
|
||||
elif weight is not None and len(round_brackets) > 0:
|
||||
multiply_range(round_brackets.pop(), float(weight))
|
||||
elif text == ')' and len(round_brackets) > 0:
|
||||
multiply_range(round_brackets.pop(), round_bracket_multiplier)
|
||||
elif text == ']' and len(square_brackets) > 0:
|
||||
multiply_range(square_brackets.pop(), square_bracket_multiplier)
|
||||
else:
|
||||
res.append([text, 1.0])
|
||||
|
||||
for pos in round_brackets:
|
||||
multiply_range(pos, round_bracket_multiplier)
|
||||
|
||||
for pos in square_brackets:
|
||||
multiply_range(pos, square_bracket_multiplier)
|
||||
|
||||
if len(res) == 0:
|
||||
res = [["", 1.0]]
|
||||
|
||||
return res
|
||||
|
|
|
|||
|
|
@ -55,7 +55,7 @@ def load_scripts(basedir):
|
|||
if not os.path.exists(basedir):
|
||||
return
|
||||
|
||||
for filename in os.listdir(basedir):
|
||||
for filename in sorted(os.listdir(basedir)):
|
||||
path = os.path.join(basedir, filename)
|
||||
|
||||
if not os.path.isfile(path):
|
||||
|
|
|
|||
|
|
@ -6,6 +6,7 @@ import torch
|
|||
import numpy as np
|
||||
from torch import einsum
|
||||
|
||||
from modules import prompt_parser
|
||||
from modules.shared import opts, device, cmd_opts
|
||||
|
||||
from ldm.util import default
|
||||
|
|
@ -180,6 +181,7 @@ class StableDiffusionModelHijack:
|
|||
dir_mtime = None
|
||||
layers = None
|
||||
circular_enabled = False
|
||||
clip = None
|
||||
|
||||
def load_textual_inversion_embeddings(self, dirname, model):
|
||||
mt = os.path.getmtime(dirname)
|
||||
|
|
@ -210,6 +212,7 @@ class StableDiffusionModelHijack:
|
|||
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'
|
||||
|
||||
|
|
@ -235,7 +238,7 @@ class StableDiffusionModelHijack:
|
|||
print(traceback.format_exc(), file=sys.stderr)
|
||||
continue
|
||||
|
||||
print(f"Loaded a total of {len(self.word_embeddings)} text inversion embeddings.")
|
||||
print(f"Loaded a total of {len(self.word_embeddings)} textual inversion embeddings.")
|
||||
|
||||
def hijack(self, m):
|
||||
model_embeddings = m.cond_stage_model.transformer.text_model.embeddings
|
||||
|
|
@ -243,6 +246,8 @@ class StableDiffusionModelHijack:
|
|||
model_embeddings.token_embedding = EmbeddingsWithFixes(model_embeddings.token_embedding, self)
|
||||
m.cond_stage_model = FrozenCLIPEmbedderWithCustomWords(m.cond_stage_model, self)
|
||||
|
||||
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()):
|
||||
|
|
@ -259,6 +264,14 @@ class StableDiffusionModelHijack:
|
|||
|
||||
self.layers = flatten(m)
|
||||
|
||||
def undo_hijack(self, m):
|
||||
if type(m.cond_stage_model) == FrozenCLIPEmbedderWithCustomWords:
|
||||
m.cond_stage_model = m.cond_stage_model.wrapped
|
||||
|
||||
model_embeddings = m.cond_stage_model.transformer.text_model.embeddings
|
||||
if type(model_embeddings.token_embedding) == EmbeddingsWithFixes:
|
||||
model_embeddings.token_embedding = model_embeddings.token_embedding.wrapped
|
||||
|
||||
def apply_circular(self, enable):
|
||||
if self.circular_enabled == enable:
|
||||
return
|
||||
|
|
@ -268,6 +281,11 @@ class StableDiffusionModelHijack:
|
|||
for layer in [layer for layer in self.layers if type(layer) == torch.nn.Conv2d]:
|
||||
layer.padding_mode = 'circular' if enable else 'zeros'
|
||||
|
||||
def tokenize(self, text):
|
||||
max_length = self.clip.max_length - 2
|
||||
_, remade_batch_tokens, _, _, _, token_count = self.clip.process_text([text])
|
||||
return remade_batch_tokens[0], token_count, max_length
|
||||
|
||||
|
||||
class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
|
||||
def __init__(self, wrapped, hijack):
|
||||
|
|
@ -294,14 +312,101 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
|
|||
if mult != 1.0:
|
||||
self.token_mults[ident] = mult
|
||||
|
||||
def forward(self, text):
|
||||
self.hijack.fixes = []
|
||||
self.hijack.comments = []
|
||||
|
||||
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
|
||||
maxlen = self.wrapped.max_length
|
||||
|
||||
if opts.enable_emphasis:
|
||||
parsed = prompt_parser.parse_prompt_attention(line)
|
||||
else:
|
||||
parsed = [[line, 1.0]]
|
||||
|
||||
tokenized = self.wrapped.tokenizer([text for text, _ in parsed], truncation=False, add_special_tokens=False)["input_ids"]
|
||||
|
||||
fixes = []
|
||||
remade_tokens = []
|
||||
multipliers = []
|
||||
|
||||
for tokens, (text, weight) in zip(tokenized, parsed):
|
||||
i = 0
|
||||
while i < len(tokens):
|
||||
token = tokens[i]
|
||||
|
||||
possible_matches = self.hijack.ids_lookup.get(token, None)
|
||||
|
||||
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:
|
||||
remade_tokens.append(token)
|
||||
multipliers.append(weight)
|
||||
i += 1
|
||||
|
||||
if len(remade_tokens) > maxlen - 2:
|
||||
vocab = {v: k for k, v in self.wrapped.tokenizer.get_vocab().items()}
|
||||
ovf = remade_tokens[maxlen - 2:]
|
||||
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]
|
||||
|
||||
multipliers = multipliers + [1.0] * (maxlen - 2 - len(multipliers))
|
||||
multipliers = [1.0] + multipliers[0:maxlen - 2] + [1.0]
|
||||
|
||||
return remade_tokens, fixes, multipliers, token_count
|
||||
|
||||
def process_text(self, texts):
|
||||
used_custom_terms = []
|
||||
remade_batch_tokens = []
|
||||
hijack_comments = []
|
||||
hijack_fixes = []
|
||||
token_count = 0
|
||||
|
||||
cache = {}
|
||||
batch_multipliers = []
|
||||
for line in texts:
|
||||
if line in cache:
|
||||
remade_tokens, fixes, multipliers = cache[line]
|
||||
else:
|
||||
remade_tokens, fixes, multipliers, token_count = self.tokenize_line(line, used_custom_terms, hijack_comments)
|
||||
|
||||
cache[line] = (remade_tokens, fixes, multipliers)
|
||||
|
||||
remade_batch_tokens.append(remade_tokens)
|
||||
hijack_fixes.append(fixes)
|
||||
batch_multipliers.append(multipliers)
|
||||
|
||||
return batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count
|
||||
|
||||
|
||||
def process_text_old(self, text):
|
||||
id_start = self.wrapped.tokenizer.bos_token_id
|
||||
id_end = self.wrapped.tokenizer.eos_token_id
|
||||
maxlen = self.wrapped.max_length
|
||||
used_custom_terms = []
|
||||
remade_batch_tokens = []
|
||||
overflowing_words = []
|
||||
hijack_comments = []
|
||||
hijack_fixes = []
|
||||
token_count = 0
|
||||
|
||||
cache = {}
|
||||
batch_tokens = self.wrapped.tokenizer(text, truncation=False, add_special_tokens=False)["input_ids"]
|
||||
|
|
@ -353,9 +458,8 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
|
|||
ovf = remade_tokens[maxlen - 2:]
|
||||
overflowing_words = [vocab.get(int(x), "") for x in ovf]
|
||||
overflowing_text = self.wrapped.tokenizer.convert_tokens_to_string(''.join(overflowing_words))
|
||||
|
||||
self.hijack.comments.append(f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n")
|
||||
|
||||
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]
|
||||
cache[tuple_tokens] = (remade_tokens, fixes, multipliers)
|
||||
|
|
@ -364,11 +468,23 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
|
|||
multipliers = [1.0] + multipliers[0:maxlen - 2] + [1.0]
|
||||
|
||||
remade_batch_tokens.append(remade_tokens)
|
||||
self.hijack.fixes.append(fixes)
|
||||
hijack_fixes.append(fixes)
|
||||
batch_multipliers.append(multipliers)
|
||||
return batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count
|
||||
|
||||
def forward(self, text):
|
||||
|
||||
if opts.use_old_emphasis_implementation:
|
||||
batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text_old(text)
|
||||
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
|
||||
|
||||
if len(used_custom_terms) > 0:
|
||||
self.hijack.comments.append("Used custom terms: " + ", ".join([f'{word} [{checksum}]' for word, checksum in used_custom_terms]))
|
||||
self.hijack.comments.append("Used embeddings: " + ", ".join([f'{word} [{checksum}]' for word, checksum in used_custom_terms]))
|
||||
|
||||
tokens = torch.asarray(remade_batch_tokens).to(device)
|
||||
outputs = self.wrapped.transformer(input_ids=tokens)
|
||||
|
|
|
|||
|
|
@ -15,8 +15,9 @@ model_dir = "Stable-diffusion"
|
|||
model_path = 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'])
|
||||
CheckpointInfo = namedtuple("CheckpointInfo", ['filename', 'title', 'hash', 'model_name'])
|
||||
checkpoints_list = {}
|
||||
|
||||
try:
|
||||
|
|
@ -47,23 +48,56 @@ def setup_model(dirname):
|
|||
global model_path
|
||||
global model_name
|
||||
global model_url
|
||||
global user_dir
|
||||
global model_list
|
||||
user_dir = dirname
|
||||
if not os.path.exists(model_path):
|
||||
os.makedirs(model_path)
|
||||
checkpoints_list.clear()
|
||||
model_list = modelloader.load_models(model_path=model_path, model_url=model_url, command_path=dirname, download_name=model_name, ext_filter=".ckpt")
|
||||
list_models()
|
||||
|
||||
|
||||
def checkpoint_tiles():
|
||||
return sorted([x.title for x in checkpoints_list.values()])
|
||||
|
||||
|
||||
def list_models():
|
||||
global model_path
|
||||
global model_url
|
||||
global model_name
|
||||
global user_dir
|
||||
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)
|
||||
print(f"Model list: {model_list}")
|
||||
model_dir = os.path.abspath(model_path)
|
||||
|
||||
def modeltitle(path, h):
|
||||
abspath = os.path.abspath(path)
|
||||
|
||||
if abspath.startswith(model_dir):
|
||||
name = abspath.replace(model_dir, '')
|
||||
else:
|
||||
name = os.path.basename(path)
|
||||
|
||||
if name.startswith("\\") or name.startswith("/"):
|
||||
name = name[1:]
|
||||
|
||||
shortname = os.path.splitext(name.replace("/", "_").replace("\\", "_"))[0]
|
||||
|
||||
return f'{name} [{h}]', shortname
|
||||
|
||||
cmd_ckpt = shared.cmd_opts.ckpt
|
||||
if os.path.exists(cmd_ckpt):
|
||||
h = model_hash(cmd_ckpt)
|
||||
title = modeltitle(cmd_ckpt, h)
|
||||
checkpoints_list[title] = CheckpointInfo(cmd_ckpt, title, h)
|
||||
title, model_name = modeltitle(cmd_ckpt, h)
|
||||
checkpoints_list[title] = CheckpointInfo(cmd_ckpt, title, h, model_name)
|
||||
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:
|
||||
h = model_hash(filename)
|
||||
title = modeltitle(filename, h)
|
||||
checkpoints_list[title] = CheckpointInfo(filename, title, h)
|
||||
checkpoints_list[title] = CheckpointInfo(filename, title, h, model_name)
|
||||
|
||||
|
||||
def model_hash(filename):
|
||||
|
|
@ -89,7 +123,7 @@ def select_checkpoint():
|
|||
if len(checkpoints_list) == 0:
|
||||
print(f"No checkpoints found. When searching for checkpoints, looked at:", file=sys.stderr)
|
||||
print(f" - file {os.path.abspath(shared.cmd_opts.ckpt)}", file=sys.stderr)
|
||||
print(f" - directory {os.path.abspath(shared.cmd_opts.ckpt_dir)}", file=sys.stderr)
|
||||
print(f" - directory {os.path.abspath(shared.cmd_opts.stablediffusion_models_path)}", 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)
|
||||
|
||||
|
|
@ -142,7 +176,7 @@ def load_model():
|
|||
|
||||
|
||||
def reload_model_weights(sd_model, info=None):
|
||||
from modules import lowvram, devices
|
||||
from modules import lowvram, devices, sd_hijack
|
||||
checkpoint_info = info or select_checkpoint()
|
||||
|
||||
if sd_model.sd_model_checkpint == checkpoint_info.filename:
|
||||
|
|
@ -153,8 +187,12 @@ def reload_model_weights(sd_model, info=None):
|
|||
else:
|
||||
sd_model.to(devices.cpu)
|
||||
|
||||
sd_hijack.model_hijack.undo_hijack(sd_model)
|
||||
|
||||
load_model_weights(sd_model, checkpoint_info.filename, checkpoint_info.hash)
|
||||
|
||||
sd_hijack.model_hijack.hijack(sd_model)
|
||||
|
||||
if not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram:
|
||||
sd_model.to(devices.device)
|
||||
|
||||
|
|
|
|||
|
|
@ -3,6 +3,7 @@ import numpy as np
|
|||
import torch
|
||||
import tqdm
|
||||
from PIL import Image
|
||||
import inspect
|
||||
|
||||
import k_diffusion.sampling
|
||||
import ldm.models.diffusion.ddim
|
||||
|
|
@ -22,6 +23,8 @@ samplers_k_diffusion = [
|
|||
('Heun', 'sample_heun', ['k_heun']),
|
||||
('DPM2', 'sample_dpm_2', ['k_dpm_2']),
|
||||
('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a']),
|
||||
('DPM fast', 'sample_dpm_fast', ['k_dpm_fast']),
|
||||
('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad']),
|
||||
]
|
||||
|
||||
samplers_data_k_diffusion = [
|
||||
|
|
@ -35,12 +38,12 @@ samplers = [
|
|||
SamplerData('DDIM', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.ddim.DDIMSampler, model), []),
|
||||
SamplerData('PLMS', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.plms.PLMSSampler, model), []),
|
||||
]
|
||||
samplers_for_img2img = [x for x in samplers if x.name != 'PLMS']
|
||||
samplers_for_img2img = [x for x in samplers if x.name not in ['PLMS', 'DPM fast', 'DPM adaptive']]
|
||||
|
||||
sampler_extra_params = {
|
||||
'sample_euler':['s_churn','s_tmin','s_tmax','s_noise'],
|
||||
'sample_heun' :['s_churn','s_tmin','s_tmax','s_noise'],
|
||||
'sample_dpm_2':['s_churn','s_tmin','s_tmax','s_noise'],
|
||||
'sample_euler': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
|
||||
'sample_heun': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
|
||||
'sample_dpm_2': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
|
||||
}
|
||||
|
||||
def setup_img2img_steps(p, steps=None):
|
||||
|
|
@ -98,6 +101,8 @@ class VanillaStableDiffusionSampler:
|
|||
self.init_latent = None
|
||||
self.sampler_noises = None
|
||||
self.step = 0
|
||||
self.eta = None
|
||||
self.default_eta = 0.0
|
||||
|
||||
def number_of_needed_noises(self, p):
|
||||
return 0
|
||||
|
|
@ -120,20 +125,29 @@ class VanillaStableDiffusionSampler:
|
|||
self.step += 1
|
||||
return res
|
||||
|
||||
def initialize(self, p):
|
||||
self.eta = p.eta or opts.eta_ddim
|
||||
|
||||
for fieldname in ['p_sample_ddim', 'p_sample_plms']:
|
||||
if hasattr(self.sampler, fieldname):
|
||||
setattr(self.sampler, fieldname, self.p_sample_ddim_hook)
|
||||
|
||||
self.mask = p.mask if hasattr(p, 'mask') else None
|
||||
self.nmask = p.nmask if hasattr(p, 'nmask') else None
|
||||
|
||||
def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None):
|
||||
steps, t_enc = setup_img2img_steps(p, steps)
|
||||
|
||||
self.initialize(p)
|
||||
|
||||
# existing code fails with cetain step counts, like 9
|
||||
try:
|
||||
self.sampler.make_schedule(ddim_num_steps=steps, ddim_eta=p.ddim_eta, ddim_discretize=p.ddim_discretize, verbose=False)
|
||||
self.sampler.make_schedule(ddim_num_steps=steps, ddim_eta=self.eta, ddim_discretize=p.ddim_discretize, verbose=False)
|
||||
except Exception:
|
||||
self.sampler.make_schedule(ddim_num_steps=steps+1,ddim_eta=p.ddim_eta, ddim_discretize=p.ddim_discretize, verbose=False)
|
||||
self.sampler.make_schedule(ddim_num_steps=steps+1, ddim_eta=self.eta, ddim_discretize=p.ddim_discretize, verbose=False)
|
||||
|
||||
x1 = self.sampler.stochastic_encode(x, torch.tensor([t_enc] * int(x.shape[0])).to(shared.device), noise=noise)
|
||||
|
||||
self.sampler.p_sample_ddim = self.p_sample_ddim_hook
|
||||
self.mask = p.mask if hasattr(p, 'mask') else None
|
||||
self.nmask = p.nmask if hasattr(p, 'nmask') else None
|
||||
self.init_latent = x
|
||||
self.step = 0
|
||||
|
||||
|
|
@ -142,11 +156,8 @@ class VanillaStableDiffusionSampler:
|
|||
return samples
|
||||
|
||||
def sample(self, p, x, conditioning, unconditional_conditioning, steps=None):
|
||||
for fieldname in ['p_sample_ddim', 'p_sample_plms']:
|
||||
if hasattr(self.sampler, fieldname):
|
||||
setattr(self.sampler, fieldname, self.p_sample_ddim_hook)
|
||||
self.mask = None
|
||||
self.nmask = None
|
||||
self.initialize(p)
|
||||
|
||||
self.init_latent = None
|
||||
self.step = 0
|
||||
|
||||
|
|
@ -154,9 +165,9 @@ class VanillaStableDiffusionSampler:
|
|||
|
||||
# existing code fails with cetin step counts, like 9
|
||||
try:
|
||||
samples_ddim, _ = self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_t=x, eta=p.ddim_eta)
|
||||
samples_ddim, _ = self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)
|
||||
except Exception:
|
||||
samples_ddim, _ = self.sampler.sample(S=steps+1, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_t=x, eta=p.ddim_eta)
|
||||
samples_ddim, _ = self.sampler.sample(S=steps+1, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)
|
||||
|
||||
return samples_ddim
|
||||
|
||||
|
|
@ -229,11 +240,13 @@ class KDiffusionSampler:
|
|||
self.model_wrap = k_diffusion.external.CompVisDenoiser(sd_model, quantize=shared.opts.enable_quantization)
|
||||
self.funcname = funcname
|
||||
self.func = getattr(k_diffusion.sampling, self.funcname)
|
||||
self.extra_params = sampler_extra_params.get(funcname,[])
|
||||
self.extra_params = sampler_extra_params.get(funcname, [])
|
||||
self.model_wrap_cfg = CFGDenoiser(self.model_wrap)
|
||||
self.sampler_noises = None
|
||||
self.sampler_noise_index = 0
|
||||
self.stop_at = None
|
||||
self.eta = None
|
||||
self.default_eta = 1.0
|
||||
|
||||
def callback_state(self, d):
|
||||
store_latent(d["denoised"])
|
||||
|
|
@ -252,22 +265,12 @@ class KDiffusionSampler:
|
|||
self.sampler_noise_index += 1
|
||||
return res
|
||||
|
||||
def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None):
|
||||
steps, t_enc = setup_img2img_steps(p, steps)
|
||||
|
||||
sigmas = self.model_wrap.get_sigmas(steps)
|
||||
|
||||
noise = noise * sigmas[steps - t_enc - 1]
|
||||
|
||||
xi = x + noise
|
||||
|
||||
sigma_sched = sigmas[steps - t_enc - 1:]
|
||||
|
||||
def initialize(self, p):
|
||||
self.model_wrap_cfg.mask = p.mask if hasattr(p, 'mask') else None
|
||||
self.model_wrap_cfg.nmask = p.nmask if hasattr(p, 'nmask') else None
|
||||
self.model_wrap_cfg.init_latent = x
|
||||
self.model_wrap.step = 0
|
||||
self.sampler_noise_index = 0
|
||||
self.eta = p.eta or opts.eta_ancestral
|
||||
|
||||
if hasattr(k_diffusion.sampling, 'trange'):
|
||||
k_diffusion.sampling.trange = lambda *args, **kwargs: extended_trange(self, *args, **kwargs)
|
||||
|
|
@ -276,9 +279,28 @@ class KDiffusionSampler:
|
|||
k_diffusion.sampling.torch = TorchHijack(self)
|
||||
|
||||
extra_params_kwargs = {}
|
||||
for val in self.extra_params:
|
||||
if hasattr(p,val):
|
||||
extra_params_kwargs[val] = getattr(p,val)
|
||||
for param_name in self.extra_params:
|
||||
if hasattr(p, param_name) and param_name in inspect.signature(self.func).parameters:
|
||||
extra_params_kwargs[param_name] = getattr(p, param_name)
|
||||
|
||||
if 'eta' in inspect.signature(self.func).parameters:
|
||||
extra_params_kwargs['eta'] = self.eta
|
||||
|
||||
return extra_params_kwargs
|
||||
|
||||
def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None):
|
||||
steps, t_enc = setup_img2img_steps(p, steps)
|
||||
|
||||
sigmas = self.model_wrap.get_sigmas(steps)
|
||||
|
||||
noise = noise * sigmas[steps - t_enc - 1]
|
||||
xi = x + noise
|
||||
|
||||
extra_params_kwargs = self.initialize(p)
|
||||
|
||||
sigma_sched = sigmas[steps - t_enc - 1:]
|
||||
|
||||
self.model_wrap_cfg.init_latent = x
|
||||
|
||||
return self.func(self.model_wrap_cfg, xi, sigma_sched, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs)
|
||||
|
||||
|
|
@ -288,21 +310,14 @@ class KDiffusionSampler:
|
|||
sigmas = self.model_wrap.get_sigmas(steps)
|
||||
x = x * sigmas[0]
|
||||
|
||||
self.model_wrap_cfg.step = 0
|
||||
self.sampler_noise_index = 0
|
||||
|
||||
if hasattr(k_diffusion.sampling, 'trange'):
|
||||
k_diffusion.sampling.trange = lambda *args, **kwargs: extended_trange(self, *args, **kwargs)
|
||||
|
||||
if self.sampler_noises is not None:
|
||||
k_diffusion.sampling.torch = TorchHijack(self)
|
||||
|
||||
extra_params_kwargs = {}
|
||||
for val in self.extra_params:
|
||||
if hasattr(p,val):
|
||||
extra_params_kwargs[val] = getattr(p,val)
|
||||
|
||||
samples = self.func(self.model_wrap_cfg, x, sigmas, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs)
|
||||
|
||||
extra_params_kwargs = self.initialize(p)
|
||||
if 'sigma_min' in inspect.signature(self.func).parameters:
|
||||
extra_params_kwargs['sigma_min'] = self.model_wrap.sigmas[0].item()
|
||||
extra_params_kwargs['sigma_max'] = self.model_wrap.sigmas[-1].item()
|
||||
if 'n' in inspect.signature(self.func).parameters:
|
||||
extra_params_kwargs['n'] = steps
|
||||
else:
|
||||
extra_params_kwargs['sigmas'] = sigmas
|
||||
samples = self.func(self.model_wrap_cfg, x, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs)
|
||||
return samples
|
||||
|
||||
|
|
|
|||
|
|
@ -155,6 +155,7 @@ options_templates.update(options_section(('saving-images', "Saving images/grids"
|
|||
"export_for_4chan": OptionInfo(True, "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG"),
|
||||
|
||||
"use_original_name_batch": OptionInfo(False, "Use original name for output filename during batch process in extras tab"),
|
||||
"save_selected_only": OptionInfo(True, "When using 'Save' button, only save a single selected image"),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('saving-paths', "Paths for saving"), {
|
||||
|
|
@ -182,7 +183,6 @@ options_templates.update(options_section(('upscaling', "Upscaling"), {
|
|||
"SWIN_tile": OptionInfo(192, "Tile size for all SwinIR.", gr.Slider, {"minimum": 16, "maximum": 512, "step": 16}),
|
||||
"SWIN_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for SwinIR. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}),
|
||||
"ldsr_steps": OptionInfo(100, "LDSR processing steps. Lower = faster", gr.Slider, {"minimum": 1, "maximum": 200, "step": 1}),
|
||||
"ldsr_pre_down": OptionInfo(1, "LDSR Pre-process downssample scale. 1 = no down-sampling, 4 = 1/4 scale.", gr.Slider, {"minimum": 1, "maximum": 4, "step": 1}),
|
||||
"upscaler_for_img2img": OptionInfo(None, "Upscaler for img2img", gr.Radio, lambda: {"choices": [x.name for x in sd_upscalers]}),
|
||||
}))
|
||||
|
||||
|
|
@ -190,7 +190,6 @@ options_templates.update(options_section(('face-restoration', "Face restoration"
|
|||
"face_restoration_model": OptionInfo(None, "Face restoration model", gr.Radio, lambda: {"choices": [x.name() for x in face_restorers]}),
|
||||
"code_former_weight": OptionInfo(0.5, "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
|
||||
"face_restoration_unload": OptionInfo(False, "Move face restoration model from VRAM into RAM after processing"),
|
||||
"save_selected_only": OptionInfo(False, "When using 'Save' button, only save a single selected image"),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('system', "System"), {
|
||||
|
|
@ -200,12 +199,13 @@ options_templates.update(options_section(('system', "System"), {
|
|||
}))
|
||||
|
||||
options_templates.update(options_section(('sd', "Stable Diffusion"), {
|
||||
"sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Radio, lambda: {"choices": [x.title for x in modules.sd_models.checkpoints_list.values()]}),
|
||||
"sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Radio, lambda: {"choices": modules.sd_models.checkpoint_tiles()}),
|
||||
"img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
|
||||
"save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"),
|
||||
"img2img_fix_steps": OptionInfo(False, "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising)."),
|
||||
"enable_quantization": OptionInfo(False, "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply."),
|
||||
"enable_emphasis": OptionInfo(True, "Use (text) to make model pay more attention to text and [text] to make it pay less attention"),
|
||||
"enable_emphasis": OptionInfo(True, "Eemphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention"),
|
||||
"use_old_emphasis_implementation": OptionInfo(False, "Use old emphasis implementation. Can be useful to reproduce old seeds."),
|
||||
"enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"),
|
||||
"filter_nsfw": OptionInfo(False, "Filter NSFW content"),
|
||||
"random_artist_categories": OptionInfo([], "Allowed categories for random artists selection when using the Roll button", gr.CheckboxGroup, {"choices": artist_db.categories()}),
|
||||
|
|
@ -231,8 +231,9 @@ options_templates.update(options_section(('ui', "User interface"), {
|
|||
}))
|
||||
|
||||
options_templates.update(options_section(('sampler-params', "Sampler parameters"), {
|
||||
"ddim_eta": OptionInfo(0.0, "DDIM eta", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
||||
"ddim_discretize": OptionInfo('uniform', "img2img DDIM discretize", gr.Radio, {"choices": ['uniform','quad']}),
|
||||
"eta_ddim": OptionInfo(0.0, "eta (noise multiplier) for DDIM", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
||||
"eta_ancestral": OptionInfo(1.0, "eta (noise multiplier) for ancestral samplers", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
||||
"ddim_discretize": OptionInfo('uniform', "img2img DDIM discretize", gr.Radio, {"choices": ['uniform', 'quad']}),
|
||||
's_churn': OptionInfo(0.0, "sigma churn", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
||||
's_tmin': OptionInfo(0.0, "sigma tmin", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
||||
's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
||||
|
|
|
|||
133
modules/ui.py
133
modules/ui.py
|
|
@ -9,10 +9,13 @@ import random
|
|||
import sys
|
||||
import time
|
||||
import traceback
|
||||
import platform
|
||||
import subprocess as sp
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
from PIL import Image
|
||||
from PIL import Image, PngImagePlugin
|
||||
import piexif
|
||||
|
||||
import gradio as gr
|
||||
import gradio.utils
|
||||
|
|
@ -22,6 +25,7 @@ from modules.paths import script_path
|
|||
from modules.shared import opts, cmd_opts
|
||||
import modules.shared as shared
|
||||
from modules.sd_samplers import samplers, samplers_for_img2img
|
||||
from modules.sd_hijack import model_hijack
|
||||
import modules.ldsr_model
|
||||
import modules.scripts
|
||||
import modules.gfpgan_model
|
||||
|
|
@ -60,7 +64,7 @@ random_symbol = '\U0001f3b2\ufe0f' # 🎲️
|
|||
reuse_symbol = '\u267b\ufe0f' # ♻️
|
||||
art_symbol = '\U0001f3a8' # 🎨
|
||||
paste_symbol = '\u2199\ufe0f' # ↙
|
||||
|
||||
folder_symbol = '\uD83D\uDCC2'
|
||||
|
||||
def plaintext_to_html(text):
|
||||
text = "<p>" + "<br>\n".join([f"{html.escape(x)}" for x in text.split('\n')]) + "</p>"
|
||||
|
|
@ -97,10 +101,11 @@ def save_files(js_data, images, index):
|
|||
filenames = []
|
||||
|
||||
data = json.loads(js_data)
|
||||
|
||||
if index > -1 and opts.save_selected_only and (index > 0 or not opts.return_grid): # ensures we are looking at a specific non-grid picture, and we have save_selected_only
|
||||
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]]
|
||||
data["seed"] += (index - 1 if opts.return_grid else index)
|
||||
infotexts = [data["infotexts"][index]]
|
||||
else:
|
||||
infotexts = data["infotexts"]
|
||||
|
||||
with open(os.path.join(opts.outdir_save, "log.csv"), "a", encoding="utf8", newline='') as file:
|
||||
at_start = file.tell() == 0
|
||||
|
|
@ -109,15 +114,26 @@ def save_files(js_data, images, index):
|
|||
writer.writerow(["prompt", "seed", "width", "height", "sampler", "cfgs", "steps", "filename", "negative_prompt"])
|
||||
|
||||
filename_base = str(int(time.time() * 1000))
|
||||
extension = opts.samples_format.lower()
|
||||
for i, filedata in enumerate(images):
|
||||
filename = filename_base + ("" if len(images) == 1 else "-" + str(i + 1)) + ".png"
|
||||
filename = filename_base + ("" if len(images) == 1 else "-" + str(i + 1)) + f".{extension}"
|
||||
filepath = os.path.join(opts.outdir_save, filename)
|
||||
|
||||
if filedata.startswith("data:image/png;base64,"):
|
||||
filedata = filedata[len("data:image/png;base64,"):]
|
||||
|
||||
with open(filepath, "wb") as imgfile:
|
||||
imgfile.write(base64.decodebytes(filedata.encode('utf-8')))
|
||||
image = Image.open(io.BytesIO(base64.decodebytes(filedata.encode('utf-8'))))
|
||||
if opts.enable_pnginfo and extension == 'png':
|
||||
pnginfo = PngImagePlugin.PngInfo()
|
||||
pnginfo.add_text('parameters', infotexts[i])
|
||||
image.save(filepath, pnginfo=pnginfo)
|
||||
else:
|
||||
image.save(filepath, quality=opts.jpeg_quality)
|
||||
|
||||
if opts.enable_pnginfo and extension in ("jpg", "jpeg", "webp"):
|
||||
piexif.insert(piexif.dump({"Exif": {
|
||||
piexif.ExifIFD.UserComment: piexif.helper.UserComment.dump(infotexts[i], encoding="unicode")
|
||||
}}), filepath)
|
||||
|
||||
filenames.append(filename)
|
||||
|
||||
|
|
@ -329,6 +345,10 @@ 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)
|
||||
style_class = ' class="red"' if (token_count > max_length) else ""
|
||||
return f"<span {style_class}>{token_count}/{max_length}</span>"
|
||||
|
||||
def create_toprow(is_img2img):
|
||||
id_part = "img2img" if is_img2img else "txt2img"
|
||||
|
|
@ -338,11 +358,14 @@ def create_toprow(is_img2img):
|
|||
with gr.Row():
|
||||
with gr.Column(scale=80):
|
||||
with gr.Row():
|
||||
prompt = gr.Textbox(label="Prompt", elem_id="prompt", show_label=False, placeholder="Prompt", lines=2)
|
||||
prompt = gr.Textbox(label="Prompt", elem_id=f"{id_part}_prompt", show_label=False, placeholder="Prompt", lines=2)
|
||||
|
||||
with gr.Column(scale=1, elem_id="roll_col"):
|
||||
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])
|
||||
|
||||
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)
|
||||
|
|
@ -449,6 +472,8 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
|
|||
send_to_img2img = gr.Button('Send to img2img')
|
||||
send_to_inpaint = gr.Button('Send to inpaint')
|
||||
send_to_extras = gr.Button('Send to extras')
|
||||
button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else 'open_folder'
|
||||
open_txt2img_folder = gr.Button(folder_symbol, elem_id=button_id)
|
||||
|
||||
with gr.Group():
|
||||
html_info = gr.HTML()
|
||||
|
|
@ -625,6 +650,8 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
|
|||
img2img_send_to_img2img = gr.Button('Send to img2img')
|
||||
img2img_send_to_inpaint = gr.Button('Send to inpaint')
|
||||
img2img_send_to_extras = gr.Button('Send to extras')
|
||||
button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else 'open_folder'
|
||||
open_img2img_folder = gr.Button(folder_symbol, elem_id=button_id)
|
||||
|
||||
with gr.Group():
|
||||
html_info = gr.HTML()
|
||||
|
|
@ -797,6 +824,8 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
|
|||
html_info = gr.HTML()
|
||||
extras_send_to_img2img = gr.Button('Send to img2img')
|
||||
extras_send_to_inpaint = gr.Button('Send to inpaint')
|
||||
button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else ''
|
||||
open_extras_folder = gr.Button('Open output directory', elem_id=button_id)
|
||||
|
||||
submit.click(
|
||||
fn=run_extras,
|
||||
|
|
@ -857,30 +886,20 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
|
|||
with gr.Blocks() as modelmerger_interface:
|
||||
with gr.Row().style(equal_height=False):
|
||||
with gr.Column(variant='panel'):
|
||||
gr.HTML(value="<p>A merger of the two checkpoints will be generated in your <b>/models</b> directory.</p>")
|
||||
gr.HTML(value="<p>A merger of the two checkpoints will be generated in your <b>checkpoint</b> directory.</p>")
|
||||
|
||||
modelname_0 = gr.Textbox(elem_id="modelmerger_modelname_0", label="Model Name (to)")
|
||||
modelname_1 = gr.Textbox(elem_id="modelmerger_modelname_1", label="Model Name (from)")
|
||||
interp_method = gr.Radio(choices=["Weighted Sum", "Sigmoid"], value="Weighted Sum", label="Interpolation Method")
|
||||
with gr.Row():
|
||||
primary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_primary_model_name", label="Primary Model Name")
|
||||
secondary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_secondary_model_name", label="Secondary Model Name")
|
||||
custom_name = gr.Textbox(label="Custom Name (Optional)")
|
||||
interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Interpolation Amount', value=0.3)
|
||||
submit = gr.Button(elem_id="modelmerger_merge", label="Merge", variant='primary')
|
||||
interp_method = gr.Radio(choices=["Weighted Sum", "Sigmoid", "Inverse Sigmoid"], value="Weighted Sum", label="Interpolation Method")
|
||||
save_as_half = gr.Checkbox(value=False, label="Safe as float16")
|
||||
modelmerger_merge = gr.Button(elem_id="modelmerger_merge", label="Merge", variant='primary')
|
||||
|
||||
with gr.Column(variant='panel'):
|
||||
submit_result = gr.Textbox(elem_id="modelmerger_result", show_label=False)
|
||||
|
||||
submit.click(
|
||||
fn=run_modelmerger,
|
||||
inputs=[
|
||||
modelname_0,
|
||||
modelname_1,
|
||||
interp_method,
|
||||
interp_amount
|
||||
],
|
||||
outputs=[
|
||||
submit_result,
|
||||
]
|
||||
)
|
||||
|
||||
def create_setting_component(key):
|
||||
def fun():
|
||||
return opts.data[key] if key in opts.data else opts.data_labels[key].default
|
||||
|
|
@ -904,6 +923,17 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
|
|||
return comp(label=info.label, value=fun, **(args or {}))
|
||||
|
||||
components = []
|
||||
component_dict = {}
|
||||
|
||||
def open_folder(f):
|
||||
if not shared.cmd_opts.hide_ui_dir_config:
|
||||
path = os.path.normpath(f)
|
||||
if platform.system() == "Windows":
|
||||
os.startfile(path)
|
||||
elif platform.system() == "Darwin":
|
||||
sp.Popen(["open", path])
|
||||
else:
|
||||
sp.Popen(["xdg-open", path])
|
||||
|
||||
def run_settings(*args):
|
||||
changed = 0
|
||||
|
|
@ -959,7 +989,9 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
|
|||
|
||||
gr.HTML(elem_id="settings_header_text_{}".format(item.section[0]), value='<h1 class="gr-button-lg">{}</h1>'.format(item.section[1]))
|
||||
|
||||
components.append(create_setting_component(k))
|
||||
component = create_setting_component(k)
|
||||
component_dict[k] = component
|
||||
components.append(component)
|
||||
items_displayed += 1
|
||||
|
||||
request_notifications = gr.Button(value='Request browser notifications', elem_id="request_notifications")
|
||||
|
|
@ -1009,7 +1041,34 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
|
|||
inputs=components,
|
||||
outputs=[result, text_settings],
|
||||
)
|
||||
|
||||
def modelmerger(*args):
|
||||
try:
|
||||
results = 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
|
||||
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
|
||||
|
||||
modelmerger_merge.click(
|
||||
fn=modelmerger,
|
||||
inputs=[
|
||||
primary_model_name,
|
||||
secondary_model_name,
|
||||
interp_method,
|
||||
interp_amount,
|
||||
save_as_half,
|
||||
custom_name,
|
||||
],
|
||||
outputs=[
|
||||
submit_result,
|
||||
primary_model_name,
|
||||
secondary_model_name,
|
||||
component_dict['sd_model_checkpoint'],
|
||||
]
|
||||
)
|
||||
paste_field_names = ['Prompt', 'Negative prompt', 'Steps', 'Face restoration', 'Seed', 'Size-1', 'Size-2']
|
||||
txt2img_fields = [field for field,name in txt2img_paste_fields if name in paste_field_names]
|
||||
img2img_fields = [field for field,name in img2img_paste_fields if name in paste_field_names]
|
||||
|
|
@ -1048,6 +1107,24 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
|
|||
outputs=[extras_image],
|
||||
)
|
||||
|
||||
open_txt2img_folder.click(
|
||||
fn=lambda: open_folder(opts.outdir_samples or opts.outdir_txt2img_samples),
|
||||
inputs=[],
|
||||
outputs=[],
|
||||
)
|
||||
|
||||
open_img2img_folder.click(
|
||||
fn=lambda: open_folder(opts.outdir_samples or opts.outdir_img2img_samples),
|
||||
inputs=[],
|
||||
outputs=[],
|
||||
)
|
||||
|
||||
open_extras_folder.click(
|
||||
fn=lambda: open_folder(opts.outdir_samples or opts.outdir_extras_samples),
|
||||
inputs=[],
|
||||
outputs=[],
|
||||
)
|
||||
|
||||
img2img_send_to_extras.click(
|
||||
fn=lambda x: image_from_url_text(x),
|
||||
_js="extract_image_from_gallery_extras",
|
||||
|
|
|
|||
|
|
@ -4,9 +4,8 @@ fairscale==0.4.4
|
|||
fonts
|
||||
font-roboto
|
||||
gfpgan
|
||||
gradio
|
||||
gradio==3.4b3
|
||||
invisible-watermark
|
||||
git+https://github.com/crowsonkb/k-diffusion.git
|
||||
numpy
|
||||
omegaconf
|
||||
piexif
|
||||
|
|
@ -16,5 +15,12 @@ realesrgan
|
|||
scikit-image>=0.19
|
||||
git+https://github.com/TencentARC/GFPGAN.git@8d2447a2d918f8eba5a4a01463fd48e45126a379
|
||||
timm==0.4.12
|
||||
transformers
|
||||
transformers==4.19.2
|
||||
torch
|
||||
einops
|
||||
jsonmerge
|
||||
clean-fid
|
||||
git+https://github.com/openai/CLIP@d50d76daa670286dd6cacf3bcd80b5e4823fc8e1
|
||||
resize-right
|
||||
torchdiffeq
|
||||
kornia
|
||||
|
|
|
|||
|
|
@ -14,4 +14,11 @@ fonts
|
|||
font-roboto
|
||||
timm==0.6.7
|
||||
fairscale==0.4.9
|
||||
piexif==1.1.3
|
||||
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
|
||||
|
|
|
|||
|
|
@ -87,12 +87,12 @@ axis_options = [
|
|||
AxisOption("Prompt S/R", str, apply_prompt, format_value),
|
||||
AxisOption("Sampler", str, apply_sampler, format_value),
|
||||
AxisOption("Checkpoint name", str, apply_checkpoint, format_value),
|
||||
AxisOption("Sigma Churn", float, apply_field("s_churn"), format_value_add_label),
|
||||
AxisOption("Sigma min", float, apply_field("s_tmin"), format_value_add_label),
|
||||
AxisOption("Sigma max", float, apply_field("s_tmax"), format_value_add_label),
|
||||
AxisOption("Sigma noise", float, apply_field("s_noise"), format_value_add_label),
|
||||
AxisOption("DDIM Eta", float, apply_field("ddim_eta"), format_value_add_label),
|
||||
AxisOptionImg2Img("Denoising", float, apply_field("denoising_strength"), format_value_add_label),# as it is now all AxisOptionImg2Img items must go after AxisOption ones
|
||||
AxisOption("Sigma Churn", float, apply_field("s_churn"), format_value_add_label),
|
||||
AxisOption("Sigma min", float, apply_field("s_tmin"), format_value_add_label),
|
||||
AxisOption("Sigma max", float, apply_field("s_tmax"), format_value_add_label),
|
||||
AxisOption("Sigma noise", float, apply_field("s_noise"), format_value_add_label),
|
||||
AxisOption("Eta", float, apply_field("eta"), format_value_add_label),
|
||||
AxisOptionImg2Img("Denoising", float, apply_field("denoising_strength"), format_value_add_label), # as it is now all AxisOptionImg2Img items must go after AxisOption ones
|
||||
]
|
||||
|
||||
|
||||
|
|
@ -159,6 +159,9 @@ class Script(scripts.Script):
|
|||
p.batch_size = 1
|
||||
|
||||
def process_axis(opt, vals):
|
||||
if opt.label == 'Nothing':
|
||||
return [0]
|
||||
|
||||
valslist = [x.strip() for x in vals.split(",")]
|
||||
|
||||
if opt.type == int:
|
||||
|
|
|
|||
16
style.css
16
style.css
|
|
@ -1,5 +1,11 @@
|
|||
.output-html p {margin: 0 0.5em;}
|
||||
|
||||
.row > *,
|
||||
.row > .gr-form > * {
|
||||
min-width: min(120px, 100%);
|
||||
flex: 1 1 0%;
|
||||
}
|
||||
|
||||
.performance {
|
||||
font-size: 0.85em;
|
||||
color: #444;
|
||||
|
|
@ -43,13 +49,17 @@
|
|||
margin-right: auto;
|
||||
}
|
||||
|
||||
#random_seed, #random_subseed, #reuse_seed, #reuse_subseed{
|
||||
#random_seed, #random_subseed, #reuse_seed, #reuse_subseed, #open_folder{
|
||||
min-width: auto;
|
||||
flex-grow: 0;
|
||||
padding-left: 0.25em;
|
||||
padding-right: 0.25em;
|
||||
}
|
||||
|
||||
#hidden_element{
|
||||
display: none;
|
||||
}
|
||||
|
||||
#seed_row, #subseed_row{
|
||||
gap: 0.5rem;
|
||||
}
|
||||
|
|
@ -389,3 +399,7 @@ input[type="range"]{
|
|||
border-radius: 8px;
|
||||
display: none;
|
||||
}
|
||||
|
||||
.red {
|
||||
color: red;
|
||||
}
|
||||
|
|
|
|||
8
webui.py
8
webui.py
|
|
@ -1,4 +1,8 @@
|
|||
import os
|
||||
import threading
|
||||
|
||||
from modules import devices
|
||||
from modules.paths import script_path
|
||||
import signal
|
||||
import threading
|
||||
import modules.paths
|
||||
|
|
@ -44,6 +48,8 @@ def wrap_queued_call(func):
|
|||
|
||||
def wrap_gradio_gpu_call(func):
|
||||
def f(*args, **kwargs):
|
||||
devices.torch_gc()
|
||||
|
||||
shared.state.sampling_step = 0
|
||||
shared.state.job_count = -1
|
||||
shared.state.job_no = 0
|
||||
|
|
@ -59,6 +65,8 @@ def wrap_gradio_gpu_call(func):
|
|||
shared.state.job = ""
|
||||
shared.state.job_count = 0
|
||||
|
||||
devices.torch_gc()
|
||||
|
||||
return res
|
||||
|
||||
return modules.ui.wrap_gradio_call(f)
|
||||
|
|
|
|||
Loading…
Reference in a new issue