Merge branch 'master' into embed-embeddings-in-images
This commit is contained in:
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commit
ce2d7f7eac
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@ -0,0 +1,28 @@
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# Please read the [contributing wiki page](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Contributing) before submitting a pull request!
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If you have a large change, pay special attention to this paragraph:
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> Before making changes, if you think that your feature will result in more than 100 lines changing, find me and talk to me about the feature you are proposing. It pains me to reject the hard work someone else did, but I won't add everything to the repo, and it's better if the rejection happens before you have to waste time working on the feature.
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Otherwise, after making sure you're following the rules described in wiki page, remove this section and continue on.
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**Describe what this pull request is trying to achieve.**
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A clear and concise description of what you're trying to accomplish with this, so your intent doesn't have to be extracted from your code.
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**Additional notes and description of your changes**
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More technical discussion about your changes go here, plus anything that a maintainer might have to specifically take a look at, or be wary of.
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**Environment this was tested in**
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List the environment you have developed / tested this on. As per the contributing page, changes should be able to work on Windows out of the box.
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- OS: [e.g. Windows, Linux]
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- Browser [e.g. chrome, safari]
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- Graphics card [e.g. NVIDIA RTX 2080 8GB, AMD RX 6600 8GB]
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**Screenshots or videos of your changes**
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If applicable, screenshots or a video showing off your changes. If it edits an existing UI, it should ideally contain a comparison of what used to be there, before your changes were made.
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This is **required** for anything that touches the user interface.
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@ -36,6 +36,7 @@ errors.run(enable_tf32, "Enabling TF32")
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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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dtype_vae = torch.float16
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def randn(seed, shape):
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# Pytorch currently doesn't handle setting randomness correctly when the metal backend is used.
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@ -59,9 +60,12 @@ 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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def autocast(disable=False):
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from modules import shared
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if disable:
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return contextlib.nullcontext()
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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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@ -259,6 +259,13 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see
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return x
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def decode_first_stage(model, x):
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with devices.autocast(disable=x.dtype == devices.dtype_vae):
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x = model.decode_first_stage(x)
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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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@ -398,9 +405,8 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
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# use the image collected previously in sampler loop
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samples_ddim = shared.state.current_latent
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samples_ddim = samples_ddim.to(devices.dtype)
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x_samples_ddim = p.sd_model.decode_first_stage(samples_ddim)
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samples_ddim = samples_ddim.to(devices.dtype_vae)
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x_samples_ddim = decode_first_stage(p.sd_model, samples_ddim)
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x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
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del samples_ddim
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@ -533,7 +539,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
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if self.scale_latent:
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samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), mode="bilinear")
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else:
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decoded_samples = self.sd_model.decode_first_stage(samples)
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decoded_samples = decode_first_stage(self.sd_model, samples)
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if opts.upscaler_for_img2img is None or opts.upscaler_for_img2img == "None":
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decoded_samples = torch.nn.functional.interpolate(decoded_samples, size=(self.height, self.width), mode="bilinear")
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@ -12,6 +12,10 @@ import _codecs
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import zipfile
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# PyTorch 1.13 and later have _TypedStorage renamed to TypedStorage
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TypedStorage = torch.storage.TypedStorage if hasattr(torch.storage, 'TypedStorage') else torch.storage._TypedStorage
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def encode(*args):
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out = _codecs.encode(*args)
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return out
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@ -20,7 +24,7 @@ def encode(*args):
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class RestrictedUnpickler(pickle.Unpickler):
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def persistent_load(self, saved_id):
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assert saved_id[0] == 'storage'
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return torch.storage._TypedStorage()
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return TypedStorage()
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def find_class(self, module, name):
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if module == 'collections' and name == 'OrderedDict':
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@ -149,6 +149,7 @@ def load_model_weights(model, checkpoint_info):
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model.half()
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devices.dtype = torch.float32 if shared.cmd_opts.no_half else torch.float16
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devices.dtype_vae = torch.float32 if shared.cmd_opts.no_half or shared.cmd_opts.no_half_vae else torch.float16
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vae_file = os.path.splitext(checkpoint_file)[0] + ".vae.pt"
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if os.path.exists(vae_file):
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@ -158,6 +159,8 @@ def load_model_weights(model, checkpoint_info):
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model.first_stage_model.load_state_dict(vae_dict)
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model.first_stage_model.to(devices.dtype_vae)
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model.sd_model_hash = sd_model_hash
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model.sd_model_checkpoint = checkpoint_file
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model.sd_checkpoint_info = checkpoint_info
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@ -7,7 +7,7 @@ import inspect
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import k_diffusion.sampling
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import ldm.models.diffusion.ddim
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import ldm.models.diffusion.plms
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from modules import prompt_parser
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from modules import prompt_parser, devices, processing
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from modules.shared import opts, cmd_opts, state
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import modules.shared as shared
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@ -83,7 +83,7 @@ def setup_img2img_steps(p, steps=None):
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def sample_to_image(samples):
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x_sample = shared.sd_model.decode_first_stage(samples[0:1].type(shared.sd_model.dtype))[0]
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x_sample = processing.decode_first_stage(shared.sd_model, samples[0:1])[0]
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x_sample = torch.clamp((x_sample + 1.0) / 2.0, min=0.0, max=1.0)
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x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
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x_sample = x_sample.astype(np.uint8)
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@ -25,6 +25,7 @@ parser.add_argument("--ckpt-dir", type=str, default=None, help="Path to director
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parser.add_argument("--gfpgan-dir", type=str, help="GFPGAN directory", default=('./src/gfpgan' if os.path.exists('./src/gfpgan') else './GFPGAN'))
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parser.add_argument("--gfpgan-model", type=str, help="GFPGAN model file name", default=None)
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parser.add_argument("--no-half", action='store_true', help="do not switch the model to 16-bit floats")
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parser.add_argument("--no-half-vae", action='store_true', help="do not switch the VAE model to 16-bit floats")
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parser.add_argument("--no-progressbar-hiding", action='store_true', help="do not hide progressbar in gradio UI (we hide it because it slows down ML if you have hardware acceleration in browser)")
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parser.add_argument("--max-batch-count", type=int, default=16, help="maximum batch count value for the UI")
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parser.add_argument("--embeddings-dir", type=str, default=os.path.join(script_path, 'embeddings'), help="embeddings directory for textual inversion (default: embeddings)")
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@ -15,11 +15,10 @@ re_tag = re.compile(r"[a-zA-Z][_\w\d()]+")
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class PersonalizedBase(Dataset):
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def __init__(self, data_root, size=None, repeats=100, flip_p=0.5, placeholder_token="*", width=512, height=512, model=None, device=None, template_file=None):
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def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, device=None, template_file=None):
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self.placeholder_token = placeholder_token
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self.size = size
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self.width = width
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self.height = height
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self.flip = transforms.RandomHorizontalFlip(p=flip_p)
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@ -7,8 +7,9 @@ import tqdm
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from modules import shared, images
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def preprocess(process_src, process_dst, process_flip, process_split, process_caption):
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size = 512
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def preprocess(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption):
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width = process_width
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height = process_height
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src = os.path.abspath(process_src)
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dst = os.path.abspath(process_dst)
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@ -55,23 +56,23 @@ def preprocess(process_src, process_dst, process_flip, process_split, process_ca
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is_wide = ratio < 1 / 1.35
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if process_split and is_tall:
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img = img.resize((size, size * img.height // img.width))
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img = img.resize((width, height * img.height // img.width))
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top = img.crop((0, 0, size, size))
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top = img.crop((0, 0, width, height))
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save_pic(top, index)
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bot = img.crop((0, img.height - size, size, img.height))
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bot = img.crop((0, img.height - height, width, img.height))
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save_pic(bot, index)
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elif process_split and is_wide:
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img = img.resize((size * img.width // img.height, size))
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img = img.resize((width * img.width // img.height, height))
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left = img.crop((0, 0, size, size))
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left = img.crop((0, 0, width, height))
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save_pic(left, index)
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right = img.crop((img.width - size, 0, img.width, size))
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right = img.crop((img.width - width, 0, img.width, height))
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save_pic(right, index)
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else:
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img = images.resize_image(1, img, size, size)
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img = images.resize_image(1, img, width, height)
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save_pic(img, index)
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shared.state.nextjob()
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@ -190,7 +190,7 @@ def create_embedding(name, num_vectors_per_token, init_text='*'):
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return fn
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def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding):
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def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps, num_repeats, create_image_every, save_embedding_every, template_file):
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assert embedding_name, 'embedding not selected'
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shared.state.textinfo = "Initializing textual inversion training..."
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@ -222,7 +222,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps,
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shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
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with torch.autocast("cuda"):
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ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, size=512, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file)
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ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=num_repeats, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file)
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hijack = sd_hijack.model_hijack
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@ -240,6 +240,9 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps,
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if ititial_step > steps:
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return embedding, filename
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tr_img_len = len([os.path.join(data_root, file_path) for file_path in os.listdir(data_root)])
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epoch_len = (tr_img_len * num_repeats) + tr_img_len
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pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step)
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for i, (x, text) in pbar:
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embedding.step = i + ititial_step
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@ -263,7 +266,10 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps,
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loss.backward()
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optimizer.step()
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pbar.set_description(f"loss: {losses.mean():.7f}")
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epoch_num = embedding.step // epoch_len
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epoch_step = embedding.step - (epoch_num * epoch_len) + 1
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pbar.set_description(f"[Epoch {epoch_num}: {epoch_step}/{epoch_len}]loss: {losses.mean():.7f}")
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if embedding.step > 0 and embedding_dir is not None and embedding.step % save_embedding_every == 0:
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last_saved_file = os.path.join(embedding_dir, f'{embedding_name}-{embedding.step}.pt')
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@ -276,6 +282,8 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps,
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sd_model=shared.sd_model,
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prompt=text,
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steps=20,
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height=training_height,
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width=training_width,
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do_not_save_grid=True,
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do_not_save_samples=True,
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)
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@ -1029,6 +1029,8 @@ def create_ui(wrap_gradio_gpu_call):
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process_src = gr.Textbox(label='Source directory')
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process_dst = gr.Textbox(label='Destination directory')
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process_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
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process_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
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with gr.Row():
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process_flip = gr.Checkbox(label='Create flipped copies')
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@ -1043,13 +1045,16 @@ def create_ui(wrap_gradio_gpu_call):
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run_preprocess = gr.Button(value="Preprocess", variant='primary')
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with gr.Group():
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gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding; must specify a directory with a set of 512x512 images</p>")
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gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding; must specify a directory with a set of 1:1 ratio images</p>")
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train_embedding_name = gr.Dropdown(label='Embedding', choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
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learn_rate = gr.Number(label='Learning rate', value=5.0e-03)
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dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images")
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log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion")
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template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt"))
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training_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
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training_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
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steps = gr.Number(label='Max steps', value=100000, precision=0)
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num_repeats = gr.Number(label='Number of repeats for a single input image per epoch', value=100, precision=0)
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create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0)
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save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0)
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save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True)
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@ -1093,6 +1098,8 @@ def create_ui(wrap_gradio_gpu_call):
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inputs=[
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process_src,
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process_dst,
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process_width,
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process_height,
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process_flip,
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process_split,
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process_caption,
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@ -1111,7 +1118,10 @@ def create_ui(wrap_gradio_gpu_call):
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learn_rate,
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dataset_directory,
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log_directory,
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training_width,
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training_height,
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steps,
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num_repeats,
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create_image_every,
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save_embedding_every,
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template_file,
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