mirror of
https://github.com/openvinotoolkit/stable-diffusion-webui.git
synced 2024-12-15 07:03:06 +03:00
Merge branch 'master' into fix-vram
This commit is contained in:
Jairo Correa
commit
1f50971fb8
@ -15,7 +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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"\u{1f4c2}": "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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@ -47,6 +47,7 @@ titles = {
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"Custom code": "Run Python code. Advanced user only. Must run program with --allow-code for this to work",
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"Prompt S/R": "Separate a list of words with commas, and the first word will be used as a keyword: script will search for this word in the prompt, and replace it with others",
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"Prompt order": "Separate a list of words with commas, and the script will make a variation of prompt with those words for their every possible order",
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"Tiling": "Produce an image that can be tiled.",
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"Tile overlap": "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.",
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@ -8,7 +8,7 @@ import torch
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from basicsr.utils.download_util import load_file_from_url
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import modules.upscaler
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from modules import shared, modelloader
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from modules import devices, modelloader
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from modules.bsrgan_model_arch import RRDBNet
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from modules.paths import models_path
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@ -44,13 +44,13 @@ class UpscalerBSRGAN(modules.upscaler.Upscaler):
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model = self.load_model(selected_file)
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if model is None:
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return img
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model.to(shared.device)
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model.to(devices.device_bsrgan)
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torch.cuda.empty_cache()
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img = np.array(img)
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img = img[:, :, ::-1]
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img = np.moveaxis(img, 2, 0) / 255
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img = torch.from_numpy(img).float()
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img = img.unsqueeze(0).to(shared.device)
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img = img.unsqueeze(0).to(devices.device_bsrgan)
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with torch.no_grad():
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output = model(img)
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output = output.squeeze().float().cpu().clamp_(0, 1).numpy()
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@ -69,10 +69,14 @@ def setup_model(dirname):
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self.net = net
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self.face_helper = face_helper
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self.net.to(devices.device_codeformer)
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return net, face_helper
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def send_model_to(self, device):
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self.net.to(device)
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self.face_helper.face_det.to(device)
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self.face_helper.face_parse.to(device)
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def restore(self, np_image, w=None):
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np_image = np_image[:, :, ::-1]
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@ -82,6 +86,8 @@ def setup_model(dirname):
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if self.net is None or self.face_helper is None:
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return np_image
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self.send_model_to(devices.device_codeformer)
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self.face_helper.clean_all()
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self.face_helper.read_image(np_image)
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self.face_helper.get_face_landmarks_5(only_center_face=False, resize=640, eye_dist_threshold=5)
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@ -97,7 +103,7 @@ def setup_model(dirname):
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output = self.net(cropped_face_t, w=w if w is not None else shared.opts.code_former_weight, adain=True)[0]
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restored_face = tensor2img(output, rgb2bgr=True, min_max=(-1, 1))
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del output
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devices.torch_gc()
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torch.cuda.empty_cache()
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except Exception as error:
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print(f'\tFailed inference for CodeFormer: {error}', file=sys.stderr)
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restored_face = tensor2img(cropped_face_t, rgb2bgr=True, min_max=(-1, 1))
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@ -113,10 +119,10 @@ def setup_model(dirname):
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if original_resolution != restored_img.shape[0:2]:
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restored_img = cv2.resize(restored_img, (0, 0), fx=original_resolution[1]/restored_img.shape[1], fy=original_resolution[0]/restored_img.shape[0], interpolation=cv2.INTER_LINEAR)
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self.face_helper.clean_all()
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if shared.opts.face_restoration_unload:
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self.net = None
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self.face_helper = None
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devices.torch_gc()
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self.send_model_to(devices.cpu)
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return restored_img
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@ -1,9 +1,11 @@
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import contextlib
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import torch
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import gc
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# has_mps is only available in nightly pytorch (for now), `getattr` for compatibility
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from modules import errors
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# has_mps is only available in nightly pytorch (for now), `getattr` for compatibility
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has_mps = getattr(torch, 'has_mps', False)
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cpu = torch.device("cpu")
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@ -33,8 +35,7 @@ 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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device = device_gfpgan = device_bsrgan = device_esrgan = device_scunet = device_codeformer = get_optimal_device()
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dtype = torch.float16
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def randn(seed, shape):
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@ -58,3 +59,11 @@ def randn_without_seed(shape):
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return torch.randn(shape, device=device)
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def autocast():
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from modules import shared
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if dtype == torch.float32 or shared.cmd_opts.precision == "full":
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return contextlib.nullcontext()
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return torch.autocast("cuda")
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@ -6,8 +6,7 @@ from PIL import Image
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from basicsr.utils.download_util import load_file_from_url
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import modules.esrgam_model_arch as arch
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from modules import shared, modelloader, images
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from modules.devices import has_mps
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from modules import shared, modelloader, images, devices
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from modules.paths import models_path
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from modules.upscaler import Upscaler, UpscalerData
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from modules.shared import opts
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@ -97,7 +96,7 @@ class UpscalerESRGAN(Upscaler):
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model = self.load_model(selected_model)
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if model is None:
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return img
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model.to(shared.device)
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model.to(devices.device_esrgan)
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img = esrgan_upscale(model, img)
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return img
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@ -112,7 +111,7 @@ class UpscalerESRGAN(Upscaler):
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print("Unable to load %s from %s" % (self.model_path, filename))
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return None
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pretrained_net = torch.load(filename, map_location='cpu' if has_mps else None)
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pretrained_net = torch.load(filename, map_location='cpu' if shared.device.type == 'mps' else None)
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crt_model = arch.RRDBNet(3, 3, 64, 23, gc=32)
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pretrained_net = fix_model_layers(crt_model, pretrained_net)
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@ -127,7 +126,7 @@ def upscale_without_tiling(model, img):
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img = img[:, :, ::-1]
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img = np.moveaxis(img, 2, 0) / 255
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img = torch.from_numpy(img).float()
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img = img.unsqueeze(0).to(shared.device)
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img = img.unsqueeze(0).to(devices.device_esrgan)
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with torch.no_grad():
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output = model(img)
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output = output.squeeze().float().cpu().clamp_(0, 1).numpy()
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@ -21,7 +21,7 @@ def gfpgann():
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global loaded_gfpgan_model
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global model_path
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if loaded_gfpgan_model is not None:
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loaded_gfpgan_model.gfpgan.to(shared.device)
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loaded_gfpgan_model.gfpgan.to(devices.device_gfpgan)
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return loaded_gfpgan_model
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if gfpgan_constructor is None:
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@ -37,25 +37,32 @@ def gfpgann():
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print("Unable to load gfpgan model!")
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return None
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model = gfpgan_constructor(model_path=model_file, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None)
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model.gfpgan.to(shared.device)
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loaded_gfpgan_model = model
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return model
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def send_model_to(model, device):
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model.gfpgan.to(device)
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model.face_helper.face_det.to(device)
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model.face_helper.face_parse.to(device)
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def gfpgan_fix_faces(np_image):
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global loaded_gfpgan_model
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model = gfpgann()
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if model is None:
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return np_image
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send_model_to(model, devices.device_gfpgan)
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np_image_bgr = np_image[:, :, ::-1]
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cropped_faces, restored_faces, gfpgan_output_bgr = model.enhance(np_image_bgr, has_aligned=False, only_center_face=False, paste_back=True)
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np_image = gfpgan_output_bgr[:, :, ::-1]
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model.face_helper.clean_all()
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if shared.opts.face_restoration_unload:
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del model
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loaded_gfpgan_model = None
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devices.torch_gc()
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send_model_to(model, devices.cpu)
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return np_image
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@ -287,6 +287,25 @@ def apply_filename_pattern(x, p, seed, prompt):
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if seed is not None:
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x = x.replace("[seed]", str(seed))
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if p is not None:
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x = x.replace("[steps]", str(p.steps))
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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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#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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x = x.replace("[date]", datetime.date.today().isoformat())
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x = x.replace("[datetime]", datetime.datetime.now().strftime("%Y%m%d%H%M%S"))
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x = x.replace("[job_timestamp]", shared.state.job_timestamp)
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# Apply [prompt] at last. Because it may contain any replacement word.^M
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if prompt is not None:
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x = x.replace("[prompt]", sanitize_filename_part(prompt))
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if "[prompt_no_styles]" in x:
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@ -306,24 +325,6 @@ def apply_filename_pattern(x, p, seed, prompt):
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words = ["empty"]
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x = x.replace("[prompt_words]", sanitize_filename_part(" ".join(words[0:max_prompt_words]), replace_spaces=False))
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if p is not None:
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x = x.replace("[steps]", str(p.steps))
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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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#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"]), 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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x = x.replace("[date]", datetime.date.today().isoformat())
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x = x.replace("[datetime]", datetime.datetime.now().strftime("%Y%m%d%H%M%S"))
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x = x.replace("[job_timestamp]", shared.state.job_timestamp)
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if cmd_opts.hide_ui_dir_config:
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x = re.sub(r'^[\\/]+|\.{2,}[\\/]+|[\\/]+\.{2,}', '', x)
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@ -379,7 +380,7 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
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save_to_dirs = (grid and opts.grid_save_to_dirs) or (not grid and opts.save_to_dirs and not no_prompt)
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if save_to_dirs:
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dirname = apply_filename_pattern(opts.directories_filename_pattern or "[prompt_words]", p, seed, prompt)
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dirname = apply_filename_pattern(opts.directories_filename_pattern or "[prompt_words]", p, seed, prompt).strip('\\ /')
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path = os.path.join(path, dirname)
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os.makedirs(path, exist_ok=True)
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@ -23,8 +23,10 @@ def process_batch(p, input_dir, output_dir, args):
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print(f"Will process {len(images)} images, creating {p.n_iter * p.batch_size} new images for each.")
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save_normally = output_dir == ''
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p.do_not_save_grid = True
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p.do_not_save_samples = True
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p.do_not_save_samples = not save_normally
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state.job_count = len(images) * p.n_iter
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@ -48,7 +50,8 @@ def process_batch(p, input_dir, output_dir, args):
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left, right = os.path.splitext(filename)
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filename = f"{left}-{n}{right}"
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processed_image.save(os.path.join(output_dir, filename))
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if not save_normally:
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processed_image.save(os.path.join(output_dir, filename))
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def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, init_img, init_img_with_mask, init_img_inpaint, init_mask_inpaint, mask_mode, steps: int, sampler_index: int, mask_blur: int, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, *args):
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@ -126,4 +129,7 @@ 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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if opts.do_not_show_images:
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processed.images = []
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return processed.images, generation_info_js, plaintext_to_html(processed.info)
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@ -1,4 +1,3 @@
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import contextlib
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import json
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import math
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import os
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@ -249,9 +248,16 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see
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return x
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def get_fixed_seed(seed):
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if seed is None or seed == '' or seed == -1:
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return int(random.randrange(4294967294))
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|
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return seed
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|
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|
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def fix_seed(p):
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p.seed = int(random.randrange(4294967294)) if p.seed is None or p.seed == '' or p.seed == -1 else p.seed
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p.subseed = int(random.randrange(4294967294)) if p.subseed is None or p.subseed == '' or p.subseed == -1 else p.subseed
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p.seed = get_fixed_seed(p.seed)
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p.subseed = get_fixed_seed(p.subseed)
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|
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|
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def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration=0, position_in_batch=0):
|
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@ -293,7 +299,8 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
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devices.torch_gc()
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fix_seed(p)
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seed = get_fixed_seed(p.seed)
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subseed = get_fixed_seed(p.subseed)
|
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|
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if p.outpath_samples is not None:
|
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os.makedirs(p.outpath_samples, exist_ok=True)
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@ -312,15 +319,15 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
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else:
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all_prompts = p.batch_size * p.n_iter * [p.prompt]
|
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|
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if type(p.seed) == list:
|
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all_seeds = p.seed
|
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if type(seed) == list:
|
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all_seeds = seed
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else:
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all_seeds = [int(p.seed) + (x if p.subseed_strength == 0 else 0) for x in range(len(all_prompts))]
|
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all_seeds = [int(seed) + (x if p.subseed_strength == 0 else 0) for x in range(len(all_prompts))]
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|
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if type(p.subseed) == list:
|
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all_subseeds = p.subseed
|
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if type(subseed) == list:
|
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all_subseeds = subseed
|
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else:
|
||||
all_subseeds = [int(p.subseed) + x for x in range(len(all_prompts))]
|
||||
all_subseeds = [int(subseed) + x for x in range(len(all_prompts))]
|
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|
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def infotext(iteration=0, position_in_batch=0):
|
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return create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration, position_in_batch)
|
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@ -330,10 +337,10 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
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|
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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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with torch.no_grad(), precision_scope("cuda"), ema_scope():
|
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p.init(all_prompts, all_seeds, all_subseeds)
|
||||
|
||||
with torch.no_grad():
|
||||
with devices.autocast():
|
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p.init(all_prompts, all_seeds, all_subseeds)
|
||||
|
||||
if state.job_count == -1:
|
||||
state.job_count = p.n_iter
|
||||
@ -352,8 +359,9 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
|
||||
|
||||
#uc = p.sd_model.get_learned_conditioning(len(prompts) * [p.negative_prompt])
|
||||
#c = p.sd_model.get_learned_conditioning(prompts)
|
||||
uc = prompt_parser.get_learned_conditioning(len(prompts) * [p.negative_prompt], p.steps)
|
||||
c = prompt_parser.get_learned_conditioning(prompts, p.steps)
|
||||
with devices.autocast():
|
||||
uc = prompt_parser.get_learned_conditioning(shared.sd_model, len(prompts) * [p.negative_prompt], p.steps)
|
||||
c = prompt_parser.get_learned_conditioning(shared.sd_model, prompts, p.steps)
|
||||
|
||||
if len(model_hijack.comments) > 0:
|
||||
for comment in model_hijack.comments:
|
||||
@ -362,13 +370,17 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
|
||||
if p.n_iter > 1:
|
||||
shared.state.job = f"Batch {n+1} out of {p.n_iter}"
|
||||
|
||||
samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength)
|
||||
with devices.autocast():
|
||||
samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength)
|
||||
|
||||
if state.interrupted:
|
||||
|
||||
# if we are interruped, sample returns just noise
|
||||
# use the image collected previously in sampler loop
|
||||
samples_ddim = shared.state.current_latent
|
||||
|
||||
samples_ddim = samples_ddim.to(devices.dtype)
|
||||
|
||||
x_samples_ddim = p.sd_model.decode_first_stage(samples_ddim)
|
||||
x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
|
||||
|
||||
@ -394,6 +406,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
|
||||
images.save_image(Image.fromarray(x_sample), p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-before-face-restoration")
|
||||
|
||||
x_sample = modules.face_restoration.restore_faces(x_sample)
|
||||
devices.torch_gc()
|
||||
|
||||
devices.torch_gc()
|
||||
|
||||
|
||||
@ -1,19 +1,7 @@
|
||||
import re
|
||||
from collections import namedtuple
|
||||
import torch
|
||||
|
||||
import modules.shared as shared
|
||||
|
||||
re_prompt = re.compile(r'''
|
||||
(.*?)
|
||||
\[
|
||||
([^]:]+):
|
||||
(?:([^]:]*):)?
|
||||
([0-9]*\.?[0-9]+)
|
||||
]
|
||||
|
|
||||
(.+)
|
||||
''', re.X)
|
||||
import lark
|
||||
|
||||
# a prompt like this: "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]"
|
||||
# will be represented with prompt_schedule like this (assuming steps=100):
|
||||
@ -23,71 +11,96 @@ re_prompt = re.compile(r'''
|
||||
# [75, 'fantasy landscape with a lake and an oak in background masterful']
|
||||
# [100, 'fantasy landscape with a lake and a christmas tree in background masterful']
|
||||
|
||||
schedule_parser = lark.Lark(r"""
|
||||
!start: (prompt | /[][():]/+)*
|
||||
prompt: (emphasized | scheduled | plain | WHITESPACE)*
|
||||
!emphasized: "(" prompt ")"
|
||||
| "(" prompt ":" prompt ")"
|
||||
| "[" prompt "]"
|
||||
scheduled: "[" [prompt ":"] prompt ":" [WHITESPACE] NUMBER "]"
|
||||
WHITESPACE: /\s+/
|
||||
plain: /([^\\\[\]():]|\\.)+/
|
||||
%import common.SIGNED_NUMBER -> NUMBER
|
||||
""")
|
||||
|
||||
def get_learned_conditioning_prompt_schedules(prompts, steps):
|
||||
res = []
|
||||
cache = {}
|
||||
"""
|
||||
>>> g = lambda p: get_learned_conditioning_prompt_schedules([p], 10)[0]
|
||||
>>> g("test")
|
||||
[[10, 'test']]
|
||||
>>> g("a [b:3]")
|
||||
[[3, 'a '], [10, 'a b']]
|
||||
>>> g("a [b: 3]")
|
||||
[[3, 'a '], [10, 'a b']]
|
||||
>>> g("a [[[b]]:2]")
|
||||
[[2, 'a '], [10, 'a [[b]]']]
|
||||
>>> g("[(a:2):3]")
|
||||
[[3, ''], [10, '(a:2)']]
|
||||
>>> g("a [b : c : 1] d")
|
||||
[[1, 'a b d'], [10, 'a c d']]
|
||||
>>> g("a[b:[c:d:2]:1]e")
|
||||
[[1, 'abe'], [2, 'ace'], [10, 'ade']]
|
||||
>>> g("a [unbalanced")
|
||||
[[10, 'a [unbalanced']]
|
||||
>>> g("a [b:.5] c")
|
||||
[[5, 'a c'], [10, 'a b c']]
|
||||
>>> g("a [{b|d{:.5] c") # not handling this right now
|
||||
[[5, 'a c'], [10, 'a {b|d{ c']]
|
||||
>>> g("((a][:b:c [d:3]")
|
||||
[[3, '((a][:b:c '], [10, '((a][:b:c d']]
|
||||
"""
|
||||
|
||||
for prompt in prompts:
|
||||
prompt_schedule: list[list[str | int]] = [[steps, ""]]
|
||||
def collect_steps(steps, tree):
|
||||
l = [steps]
|
||||
class CollectSteps(lark.Visitor):
|
||||
def scheduled(self, tree):
|
||||
tree.children[-1] = float(tree.children[-1])
|
||||
if tree.children[-1] < 1:
|
||||
tree.children[-1] *= steps
|
||||
tree.children[-1] = min(steps, int(tree.children[-1]))
|
||||
l.append(tree.children[-1])
|
||||
CollectSteps().visit(tree)
|
||||
return sorted(set(l))
|
||||
|
||||
cached = cache.get(prompt, None)
|
||||
if cached is not None:
|
||||
res.append(cached)
|
||||
continue
|
||||
def at_step(step, tree):
|
||||
class AtStep(lark.Transformer):
|
||||
def scheduled(self, args):
|
||||
before, after, _, when = args
|
||||
yield before or () if step <= when else after
|
||||
def start(self, args):
|
||||
def flatten(x):
|
||||
if type(x) == str:
|
||||
yield x
|
||||
else:
|
||||
for gen in x:
|
||||
yield from flatten(gen)
|
||||
return ''.join(flatten(args))
|
||||
def plain(self, args):
|
||||
yield args[0].value
|
||||
def __default__(self, data, children, meta):
|
||||
for child in children:
|
||||
yield from child
|
||||
return AtStep().transform(tree)
|
||||
|
||||
for m in re_prompt.finditer(prompt):
|
||||
plaintext = m.group(1) if m.group(5) is None else m.group(5)
|
||||
concept_from = m.group(2)
|
||||
concept_to = m.group(3)
|
||||
if concept_to is None:
|
||||
concept_to = concept_from
|
||||
concept_from = ""
|
||||
swap_position = float(m.group(4)) if m.group(4) is not None else None
|
||||
def get_schedule(prompt):
|
||||
try:
|
||||
tree = schedule_parser.parse(prompt)
|
||||
except lark.exceptions.LarkError as e:
|
||||
if 0:
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
return [[steps, prompt]]
|
||||
return [[t, at_step(t, tree)] for t in collect_steps(steps, tree)]
|
||||
|
||||
if swap_position is not None:
|
||||
if swap_position < 1:
|
||||
swap_position = swap_position * steps
|
||||
swap_position = int(min(swap_position, steps))
|
||||
|
||||
swap_index = None
|
||||
found_exact_index = False
|
||||
for i in range(len(prompt_schedule)):
|
||||
end_step = prompt_schedule[i][0]
|
||||
prompt_schedule[i][1] += plaintext
|
||||
|
||||
if swap_position is not None and swap_index is None:
|
||||
if swap_position == end_step:
|
||||
swap_index = i
|
||||
found_exact_index = True
|
||||
|
||||
if swap_position < end_step:
|
||||
swap_index = i
|
||||
|
||||
if swap_index is not None:
|
||||
if not found_exact_index:
|
||||
prompt_schedule.insert(swap_index, [swap_position, prompt_schedule[swap_index][1]])
|
||||
|
||||
for i in range(len(prompt_schedule)):
|
||||
end_step = prompt_schedule[i][0]
|
||||
must_replace = swap_position < end_step
|
||||
|
||||
prompt_schedule[i][1] += concept_to if must_replace else concept_from
|
||||
|
||||
res.append(prompt_schedule)
|
||||
cache[prompt] = prompt_schedule
|
||||
#for t in prompt_schedule:
|
||||
# print(t)
|
||||
|
||||
return res
|
||||
promptdict = {prompt: get_schedule(prompt) for prompt in set(prompts)}
|
||||
return [promptdict[prompt] for prompt in prompts]
|
||||
|
||||
|
||||
ScheduledPromptConditioning = namedtuple("ScheduledPromptConditioning", ["end_at_step", "cond"])
|
||||
ScheduledPromptBatch = namedtuple("ScheduledPromptBatch", ["shape", "schedules"])
|
||||
|
||||
|
||||
def get_learned_conditioning(prompts, steps):
|
||||
|
||||
def get_learned_conditioning(model, prompts, steps):
|
||||
res = []
|
||||
|
||||
prompt_schedules = get_learned_conditioning_prompt_schedules(prompts, steps)
|
||||
@ -101,7 +114,7 @@ def get_learned_conditioning(prompts, steps):
|
||||
continue
|
||||
|
||||
texts = [x[1] for x in prompt_schedule]
|
||||
conds = shared.sd_model.get_learned_conditioning(texts)
|
||||
conds = model.get_learned_conditioning(texts)
|
||||
|
||||
cond_schedule = []
|
||||
for i, (end_at_step, text) in enumerate(prompt_schedule):
|
||||
@ -114,12 +127,13 @@ def get_learned_conditioning(prompts, steps):
|
||||
|
||||
|
||||
def reconstruct_cond_batch(c: ScheduledPromptBatch, current_step):
|
||||
res = torch.zeros(c.shape, device=shared.device, dtype=next(shared.sd_model.parameters()).dtype)
|
||||
param = c.schedules[0][0].cond
|
||||
res = torch.zeros(c.shape, device=param.device, dtype=param.dtype)
|
||||
for i, cond_schedule in enumerate(c.schedules):
|
||||
target_index = 0
|
||||
for curret_index, (end_at, cond) in enumerate(cond_schedule):
|
||||
for current, (end_at, cond) in enumerate(cond_schedule):
|
||||
if current_step <= end_at:
|
||||
target_index = curret_index
|
||||
target_index = current
|
||||
break
|
||||
res[i] = cond_schedule[target_index].cond
|
||||
|
||||
@ -157,23 +171,26 @@ def parse_prompt_attention(text):
|
||||
\\ - 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]
|
||||
]
|
||||
>>> parse_prompt_attention('normal text')
|
||||
[['normal text', 1.0]]
|
||||
>>> parse_prompt_attention('an (important) word')
|
||||
[['an ', 1.0], ['important', 1.1], [' word', 1.0]]
|
||||
>>> parse_prompt_attention('(unbalanced')
|
||||
[['unbalanced', 1.1]]
|
||||
>>> parse_prompt_attention('\(literal\]')
|
||||
[['(literal]', 1.0]]
|
||||
>>> parse_prompt_attention('(unnecessary)(parens)')
|
||||
[['unnecessaryparens', 1.1]]
|
||||
>>> parse_prompt_attention('a (((house:1.3)) [on] a (hill:0.5), sun, (((sky))).')
|
||||
[['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 = []
|
||||
@ -215,4 +232,19 @@ def parse_prompt_attention(text):
|
||||
if len(res) == 0:
|
||||
res = [["", 1.0]]
|
||||
|
||||
# merge runs of identical weights
|
||||
i = 0
|
||||
while i + 1 < len(res):
|
||||
if res[i][1] == res[i + 1][1]:
|
||||
res[i][0] += res[i + 1][0]
|
||||
res.pop(i + 1)
|
||||
else:
|
||||
i += 1
|
||||
|
||||
return res
|
||||
|
||||
if __name__ == "__main__":
|
||||
import doctest
|
||||
doctest.testmod(optionflags=doctest.NORMALIZE_WHITESPACE)
|
||||
else:
|
||||
import torch # doctest faster
|
||||
|
||||
@ -8,7 +8,7 @@ import torch
|
||||
from basicsr.utils.download_util import load_file_from_url
|
||||
|
||||
import modules.upscaler
|
||||
from modules import shared, modelloader
|
||||
from modules import devices, modelloader
|
||||
from modules.paths import models_path
|
||||
from modules.scunet_model_arch import SCUNet as net
|
||||
|
||||
@ -51,12 +51,12 @@ class UpscalerScuNET(modules.upscaler.Upscaler):
|
||||
if model is None:
|
||||
return img
|
||||
|
||||
device = shared.device
|
||||
device = devices.device_scunet
|
||||
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.unsqueeze(0).to(device)
|
||||
|
||||
img = img.to(device)
|
||||
with torch.no_grad():
|
||||
@ -69,7 +69,7 @@ class UpscalerScuNET(modules.upscaler.Upscaler):
|
||||
return PIL.Image.fromarray(output, 'RGB')
|
||||
|
||||
def load_model(self, path: str):
|
||||
device = shared.device
|
||||
device = devices.device_scunet
|
||||
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)
|
||||
|
||||
@ -127,7 +127,7 @@ class VanillaStableDiffusionSampler:
|
||||
return res
|
||||
|
||||
def initialize(self, p):
|
||||
self.eta = p.eta or opts.eta_ddim
|
||||
self.eta = p.eta if p.eta is not None else opts.eta_ddim
|
||||
|
||||
for fieldname in ['p_sample_ddim', 'p_sample_plms']:
|
||||
if hasattr(self.sampler, fieldname):
|
||||
|
||||
@ -12,7 +12,7 @@ import modules.interrogate
|
||||
import modules.memmon
|
||||
import modules.sd_models
|
||||
import modules.styles
|
||||
from modules.devices import get_optimal_device
|
||||
import modules.devices as devices
|
||||
from modules.paths import script_path, sd_path
|
||||
|
||||
sd_model_file = os.path.join(script_path, 'model.ckpt')
|
||||
@ -46,6 +46,7 @@ parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with
|
||||
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.")
|
||||
parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization")
|
||||
parser.add_argument("--opt-split-attention-v1", action='store_true', help="enable older version of split attention optimization that does not consume all the VRAM it can find")
|
||||
parser.add_argument("--use-cpu", nargs='+',choices=['SD', 'GFPGAN', 'BSRGAN', 'ESRGAN', 'SCUNet', 'CodeFormer'], help="use CPU as torch device for specified modules", default=[])
|
||||
parser.add_argument("--listen", action='store_true', help="launch gradio with 0.0.0.0 as server name, allowing to respond to network requests")
|
||||
parser.add_argument("--port", type=int, help="launch gradio with given server port, you need root/admin rights for ports < 1024, defaults to 7860 if available", default=None)
|
||||
parser.add_argument("--show-negative-prompt", action='store_true', help="does not do anything", default=False)
|
||||
@ -54,6 +55,7 @@ parser.add_argument("--hide-ui-dir-config", action='store_true', help="hide dire
|
||||
parser.add_argument("--ui-settings-file", type=str, help="filename to use for ui settings", default=os.path.join(script_path, 'config.json'))
|
||||
parser.add_argument("--gradio-debug", action='store_true', help="launch gradio with --debug option")
|
||||
parser.add_argument("--gradio-auth", type=str, help='set gradio authentication like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3"', default=None)
|
||||
parser.add_argument("--gradio-img2img-tool", type=str, help='gradio image uploader tool: can be either editor for ctopping, or color-sketch for drawing', choices=["color-sketch", "editor"], default="color-sketch")
|
||||
parser.add_argument("--opt-channelslast", action='store_true', help="change memory type for stable diffusion to channels last")
|
||||
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)
|
||||
@ -63,7 +65,11 @@ parser.add_argument("--enable-console-prompts", action='store_true', help="print
|
||||
|
||||
|
||||
cmd_opts = parser.parse_args()
|
||||
device = get_optimal_device()
|
||||
|
||||
devices.device, devices.device_gfpgan, devices.device_bsrgan, devices.device_esrgan, devices.device_scunet, devices.device_codeformer = \
|
||||
(devices.cpu if x in cmd_opts.use_cpu else devices.get_optimal_device() for x in ['SD', 'GFPGAN', 'BSRGAN', 'ESRGAN', 'SCUNet', 'CodeFormer'])
|
||||
|
||||
device = devices.device
|
||||
|
||||
batch_cond_uncond = cmd_opts.always_batch_cond_uncond or not (cmd_opts.lowvram or cmd_opts.medvram)
|
||||
parallel_processing_allowed = not cmd_opts.lowvram and not cmd_opts.medvram
|
||||
@ -183,7 +189,7 @@ 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}),
|
||||
"upscaler_for_img2img": OptionInfo(None, "Upscaler for img2img", gr.Radio, lambda: {"choices": [x.name for x in sd_upscalers]}),
|
||||
"upscaler_for_img2img": OptionInfo(None, "Upscaler for img2img", gr.Dropdown, lambda: {"choices": [x.name for x in sd_upscalers]}),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('face-restoration', "Face restoration"), {
|
||||
@ -195,7 +201,7 @@ options_templates.update(options_section(('face-restoration', "Face restoration"
|
||||
options_templates.update(options_section(('system', "System"), {
|
||||
"memmon_poll_rate": OptionInfo(8, "VRAM usage polls per second during generation. Set to 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 40, "step": 1}),
|
||||
"samples_log_stdout": OptionInfo(False, "Always print all generation info to standard output"),
|
||||
"multiple_tqdm": OptionInfo(True, "Add a second progress bar to the console that shows progress for an entire job. Broken in PyCharm console."),
|
||||
"multiple_tqdm": OptionInfo(True, "Add a second progress bar to the console that shows progress for an entire job."),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('sd', "Stable Diffusion"), {
|
||||
@ -204,7 +210,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
|
||||
"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, "Eemphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention"),
|
||||
"enable_emphasis": OptionInfo(True, "Emphasis: 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"),
|
||||
@ -224,6 +230,7 @@ options_templates.update(options_section(('ui', "User interface"), {
|
||||
"show_progressbar": OptionInfo(True, "Show progressbar"),
|
||||
"show_progress_every_n_steps": OptionInfo(0, "Show show image creation progress every N sampling steps. Set 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 32, "step": 1}),
|
||||
"return_grid": OptionInfo(True, "Show grid in results for web"),
|
||||
"do_not_show_images": OptionInfo(False, "Do not show any images in results for web"),
|
||||
"add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"),
|
||||
"font": OptionInfo("", "Font for image grids that have text"),
|
||||
"js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"),
|
||||
|
||||
@ -9,6 +9,9 @@ from torchvision import transforms
|
||||
import random
|
||||
import tqdm
|
||||
from modules import devices
|
||||
import re
|
||||
|
||||
re_tag = re.compile(r"[a-zA-Z][_\w\d()]+")
|
||||
|
||||
|
||||
class PersonalizedBase(Dataset):
|
||||
@ -38,8 +41,8 @@ class PersonalizedBase(Dataset):
|
||||
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()]
|
||||
filename_tokens = os.path.splitext(filename)[0]
|
||||
filename_tokens = re_tag.findall(filename_tokens)
|
||||
|
||||
npimage = np.array(image).astype(np.uint8)
|
||||
npimage = (npimage / 127.5 - 1.0).astype(np.float32)
|
||||
|
||||
@ -26,7 +26,9 @@ def preprocess(process_src, process_dst, process_flip, process_split, process_ca
|
||||
if process_caption:
|
||||
caption = "-" + shared.interrogator.generate_caption(image)
|
||||
else:
|
||||
caption = ""
|
||||
caption = filename
|
||||
caption = os.path.splitext(caption)[0]
|
||||
caption = os.path.basename(caption)
|
||||
|
||||
image.save(os.path.join(dst, f"{index:05}-{subindex[0]}{caption}.png"))
|
||||
subindex[0] += 1
|
||||
|
||||
@ -164,7 +164,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, 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)
|
||||
log_directory = os.path.join(log_directory, datetime.datetime.now().strftime("%Y-%m-%d"), embedding_name)
|
||||
|
||||
if save_embedding_every > 0:
|
||||
embedding_dir = os.path.join(log_directory, "embeddings")
|
||||
|
||||
@ -48,5 +48,8 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2:
|
||||
if opts.samples_log_stdout:
|
||||
print(generation_info_js)
|
||||
|
||||
if opts.do_not_show_images:
|
||||
processed.images = []
|
||||
|
||||
return processed.images, generation_info_js, plaintext_to_html(processed.info)
|
||||
|
||||
|
||||
@ -69,7 +69,7 @@ random_symbol = '\U0001f3b2\ufe0f' # 🎲️
|
||||
reuse_symbol = '\u267b\ufe0f' # ♻️
|
||||
art_symbol = '\U0001f3a8' # 🎨
|
||||
paste_symbol = '\u2199\ufe0f' # ↙
|
||||
folder_symbol = '\uD83D\uDCC2'
|
||||
folder_symbol = '\U0001f4c2' # 📂
|
||||
|
||||
def plaintext_to_html(text):
|
||||
text = "<p>" + "<br>\n".join([f"{html.escape(x)}" for x in text.split('\n')]) + "</p>"
|
||||
@ -196,6 +196,11 @@ def wrap_gradio_call(func, extra_outputs=None):
|
||||
res = extra_outputs_array + [f"<div class='error'>{plaintext_to_html(type(e).__name__+': '+str(e))}</div>"]
|
||||
|
||||
elapsed = time.perf_counter() - t
|
||||
elapsed_m = int(elapsed // 60)
|
||||
elapsed_s = elapsed % 60
|
||||
elapsed_text = f"{elapsed_s:.2f}s"
|
||||
if (elapsed_m > 0):
|
||||
elapsed_text = f"{elapsed_m}m "+elapsed_text
|
||||
|
||||
if run_memmon:
|
||||
mem_stats = {k: -(v//-(1024*1024)) for k, v in shared.mem_mon.stop().items()}
|
||||
@ -210,7 +215,7 @@ def wrap_gradio_call(func, extra_outputs=None):
|
||||
vram_html = ''
|
||||
|
||||
# last item is always HTML
|
||||
res[-1] += f"<div class='performance'><p class='time'>Time taken: <wbr>{elapsed:.2f}s</p>{vram_html}</div>"
|
||||
res[-1] += f"<div class='performance'><p class='time'>Time taken: <wbr>{elapsed_text}</p>{vram_html}</div>"
|
||||
|
||||
shared.state.interrupted = False
|
||||
shared.state.job_count = 0
|
||||
@ -386,14 +391,22 @@ def connect_reuse_seed(seed: gr.Number, reuse_seed: gr.Button, generation_info:
|
||||
outputs=[seed, dummy_component]
|
||||
)
|
||||
|
||||
|
||||
def update_token_counter(text, steps):
|
||||
prompt_schedules = get_learned_conditioning_prompt_schedules([text], steps)
|
||||
try:
|
||||
prompt_schedules = get_learned_conditioning_prompt_schedules([text], steps)
|
||||
except Exception:
|
||||
# a parsing error can happen here during typing, and we don't want to bother the user with
|
||||
# messages related to it in console
|
||||
prompt_schedules = [[[steps, text]]]
|
||||
|
||||
flat_prompts = reduce(lambda list1, list2: list1+list2, prompt_schedules)
|
||||
prompts = [prompt_text for step,prompt_text in flat_prompts]
|
||||
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>"
|
||||
|
||||
|
||||
def create_toprow(is_img2img):
|
||||
id_part = "img2img" if is_img2img else "txt2img"
|
||||
|
||||
@ -636,7 +649,7 @@ def create_ui(wrap_gradio_gpu_call):
|
||||
|
||||
with gr.Tabs(elem_id="mode_img2img") as tabs_img2img_mode:
|
||||
with gr.TabItem('img2img', id='img2img'):
|
||||
init_img = gr.Image(label="Image for img2img", show_label=False, source="upload", interactive=True, type="pil")
|
||||
init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", tool=cmd_opts.gradio_img2img_tool)
|
||||
|
||||
with gr.TabItem('Inpaint', id='inpaint'):
|
||||
init_img_with_mask = gr.Image(label="Image for inpainting with mask", show_label=False, elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool="sketch", image_mode="RGBA")
|
||||
@ -658,7 +671,7 @@ def create_ui(wrap_gradio_gpu_call):
|
||||
|
||||
with gr.TabItem('Batch img2img', id='batch'):
|
||||
hidden = '<br>Disabled when launched with --hide-ui-dir-config.' if shared.cmd_opts.hide_ui_dir_config else ''
|
||||
gr.HTML(f"<p class=\"text-gray-500\">Process images in a directory on the same machine where the server is running.{hidden}</p>")
|
||||
gr.HTML(f"<p class=\"text-gray-500\">Process images in a directory on the same machine where the server is running.<br>Use an empty output directory to save pictures normally instead of writing to the output directory.{hidden}</p>")
|
||||
img2img_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs)
|
||||
img2img_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs)
|
||||
|
||||
|
||||
@ -22,3 +22,4 @@ clean-fid
|
||||
resize-right
|
||||
torchdiffeq
|
||||
kornia
|
||||
lark
|
||||
|
||||
@ -21,3 +21,4 @@ clean-fid==0.1.29
|
||||
resize-right==0.0.2
|
||||
torchdiffeq==0.2.3
|
||||
kornia==0.6.7
|
||||
lark==1.1.2
|
||||
|
||||
@ -1,5 +1,6 @@
|
||||
from collections import namedtuple
|
||||
from copy import copy
|
||||
from itertools import permutations
|
||||
import random
|
||||
|
||||
from PIL import Image
|
||||
@ -29,6 +30,31 @@ def apply_prompt(p, x, xs):
|
||||
p.negative_prompt = p.negative_prompt.replace(xs[0], x)
|
||||
|
||||
|
||||
def apply_order(p, x, xs):
|
||||
token_order = []
|
||||
|
||||
# Initally grab the tokens from the prompt, so they can be replaced in order of earliest seen
|
||||
for token in x:
|
||||
token_order.append((p.prompt.find(token), token))
|
||||
|
||||
token_order.sort(key=lambda t: t[0])
|
||||
|
||||
prompt_parts = []
|
||||
|
||||
# Split the prompt up, taking out the tokens
|
||||
for _, token in token_order:
|
||||
n = p.prompt.find(token)
|
||||
prompt_parts.append(p.prompt[0:n])
|
||||
p.prompt = p.prompt[n + len(token):]
|
||||
|
||||
# Rebuild the prompt with the tokens in the order we want
|
||||
prompt_tmp = ""
|
||||
for idx, part in enumerate(prompt_parts):
|
||||
prompt_tmp += part
|
||||
prompt_tmp += x[idx]
|
||||
p.prompt = prompt_tmp + p.prompt
|
||||
|
||||
|
||||
samplers_dict = {}
|
||||
for i, sampler in enumerate(modules.sd_samplers.samplers):
|
||||
samplers_dict[sampler.name.lower()] = i
|
||||
@ -60,16 +86,26 @@ def format_value_add_label(p, opt, x):
|
||||
def format_value(p, opt, x):
|
||||
if type(x) == float:
|
||||
x = round(x, 8)
|
||||
|
||||
return x
|
||||
|
||||
|
||||
def format_value_join_list(p, opt, x):
|
||||
return ", ".join(x)
|
||||
|
||||
|
||||
def do_nothing(p, x, xs):
|
||||
pass
|
||||
|
||||
|
||||
def format_nothing(p, opt, x):
|
||||
return ""
|
||||
|
||||
|
||||
def str_permutations(x):
|
||||
"""dummy function for specifying it in AxisOption's type when you want to get a list of permutations"""
|
||||
return x
|
||||
|
||||
|
||||
AxisOption = namedtuple("AxisOption", ["label", "type", "apply", "format_value"])
|
||||
AxisOptionImg2Img = namedtuple("AxisOptionImg2Img", ["label", "type", "apply", "format_value"])
|
||||
|
||||
@ -82,6 +118,7 @@ axis_options = [
|
||||
AxisOption("Steps", int, apply_field("steps"), format_value_add_label),
|
||||
AxisOption("CFG Scale", float, apply_field("cfg_scale"), format_value_add_label),
|
||||
AxisOption("Prompt S/R", str, apply_prompt, format_value),
|
||||
AxisOption("Prompt order", str_permutations, apply_order, format_value_join_list),
|
||||
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),
|
||||
@ -131,6 +168,7 @@ re_range_float = re.compile(r"\s*([+-]?\s*\d+(?:.\d*)?)\s*-\s*([+-]?\s*\d+(?:.\d
|
||||
re_range_count = re.compile(r"\s*([+-]?\s*\d+)\s*-\s*([+-]?\s*\d+)(?:\s*\[(\d+)\s*\])?\s*")
|
||||
re_range_count_float = re.compile(r"\s*([+-]?\s*\d+(?:.\d*)?)\s*-\s*([+-]?\s*\d+(?:.\d*)?)(?:\s*\[(\d+(?:.\d*)?)\s*\])?\s*")
|
||||
|
||||
|
||||
class Script(scripts.Script):
|
||||
def title(self):
|
||||
return "X/Y plot"
|
||||
@ -206,6 +244,8 @@ class Script(scripts.Script):
|
||||
valslist_ext.append(val)
|
||||
|
||||
valslist = valslist_ext
|
||||
elif opt.type == str_permutations:
|
||||
valslist = list(permutations(valslist))
|
||||
|
||||
valslist = [opt.type(x) for x in valslist]
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user