diff options
Diffstat (limited to 'modules')
| -rw-r--r-- | modules/devices.py | 6 | ||||
| -rw-r--r-- | modules/processing.py | 14 | ||||
| -rw-r--r-- | modules/safe.py | 6 | ||||
| -rw-r--r-- | modules/sd_models.py | 3 | ||||
| -rw-r--r-- | modules/sd_samplers.py | 4 | ||||
| -rw-r--r-- | modules/shared.py | 1 | ||||
| -rw-r--r-- | modules/textual_inversion/dataset.py | 3 | ||||
| -rw-r--r-- | modules/textual_inversion/preprocess.py | 19 | ||||
| -rw-r--r-- | modules/textual_inversion/textual_inversion.py | 14 | ||||
| -rw-r--r-- | modules/ui.py | 12 |
10 files changed, 59 insertions, 23 deletions
diff --git a/modules/devices.py b/modules/devices.py index 0158b11f..03ef58f1 100644 --- a/modules/devices.py +++ b/modules/devices.py @@ -36,6 +36,7 @@ errors.run(enable_tf32, "Enabling TF32") device = device_gfpgan = device_bsrgan = device_esrgan = device_scunet = device_codeformer = get_optimal_device() dtype = torch.float16 +dtype_vae = torch.float16 def randn(seed, shape): # Pytorch currently doesn't handle setting randomness correctly when the metal backend is used. @@ -59,9 +60,12 @@ def randn_without_seed(shape): return torch.randn(shape, device=device) -def autocast(): +def autocast(disable=False): from modules import shared + if disable: + return contextlib.nullcontext() + if dtype == torch.float32 or shared.cmd_opts.precision == "full": return contextlib.nullcontext() diff --git a/modules/processing.py b/modules/processing.py index 94d2dd62..50ba4fc5 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -259,6 +259,13 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see return x
+def decode_first_stage(model, x):
+ with devices.autocast(disable=x.dtype == devices.dtype_vae):
+ x = model.decode_first_stage(x)
+
+ return x
+
+
def get_fixed_seed(seed):
if seed is None or seed == '' or seed == -1:
return int(random.randrange(4294967294))
@@ -398,9 +405,8 @@ def process_images(p: StableDiffusionProcessing) -> Processed: # 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)
+ samples_ddim = samples_ddim.to(devices.dtype_vae)
+ x_samples_ddim = decode_first_stage(p.sd_model, samples_ddim)
x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
del samples_ddim
@@ -533,7 +539,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): if self.scale_latent:
samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), mode="bilinear")
else:
- decoded_samples = self.sd_model.decode_first_stage(samples)
+ decoded_samples = decode_first_stage(self.sd_model, samples)
if opts.upscaler_for_img2img is None or opts.upscaler_for_img2img == "None":
decoded_samples = torch.nn.functional.interpolate(decoded_samples, size=(self.height, self.width), mode="bilinear")
diff --git a/modules/safe.py b/modules/safe.py index 4d06f2a5..05917463 100644 --- a/modules/safe.py +++ b/modules/safe.py @@ -12,6 +12,10 @@ import _codecs import zipfile
+# PyTorch 1.13 and later have _TypedStorage renamed to TypedStorage
+TypedStorage = torch.storage.TypedStorage if hasattr(torch.storage, 'TypedStorage') else torch.storage._TypedStorage
+
+
def encode(*args):
out = _codecs.encode(*args)
return out
@@ -20,7 +24,7 @@ def encode(*args): class RestrictedUnpickler(pickle.Unpickler):
def persistent_load(self, saved_id):
assert saved_id[0] == 'storage'
- return torch.storage._TypedStorage()
+ return TypedStorage()
def find_class(self, module, name):
if module == 'collections' and name == 'OrderedDict':
diff --git a/modules/sd_models.py b/modules/sd_models.py index e63d3c29..2cdcd84f 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -149,6 +149,7 @@ def load_model_weights(model, checkpoint_info): model.half()
devices.dtype = torch.float32 if shared.cmd_opts.no_half else torch.float16
+ devices.dtype_vae = torch.float32 if shared.cmd_opts.no_half or shared.cmd_opts.no_half_vae else torch.float16
vae_file = os.path.splitext(checkpoint_file)[0] + ".vae.pt"
if os.path.exists(vae_file):
@@ -158,6 +159,8 @@ def load_model_weights(model, checkpoint_info): model.first_stage_model.load_state_dict(vae_dict)
+ model.first_stage_model.to(devices.dtype_vae)
+
model.sd_model_hash = sd_model_hash
model.sd_model_checkpoint = checkpoint_file
model.sd_checkpoint_info = checkpoint_info
diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index 6e743f7e..d168b938 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -7,7 +7,7 @@ import inspect import k_diffusion.sampling
import ldm.models.diffusion.ddim
import ldm.models.diffusion.plms
-from modules import prompt_parser
+from modules import prompt_parser, devices, processing
from modules.shared import opts, cmd_opts, state
import modules.shared as shared
@@ -83,7 +83,7 @@ def setup_img2img_steps(p, steps=None): def sample_to_image(samples):
- x_sample = shared.sd_model.decode_first_stage(samples[0:1].type(shared.sd_model.dtype))[0]
+ x_sample = processing.decode_first_stage(shared.sd_model, samples[0:1])[0]
x_sample = torch.clamp((x_sample + 1.0) / 2.0, min=0.0, max=1.0)
x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
x_sample = x_sample.astype(np.uint8)
diff --git a/modules/shared.py b/modules/shared.py index 1995a99a..5dfc344c 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -25,6 +25,7 @@ parser.add_argument("--ckpt-dir", type=str, default=None, help="Path to director parser.add_argument("--gfpgan-dir", type=str, help="GFPGAN directory", default=('./src/gfpgan' if os.path.exists('./src/gfpgan') else './GFPGAN'))
parser.add_argument("--gfpgan-model", type=str, help="GFPGAN model file name", default=None)
parser.add_argument("--no-half", action='store_true', help="do not switch the model to 16-bit floats")
+parser.add_argument("--no-half-vae", action='store_true', help="do not switch the VAE model to 16-bit floats")
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)")
parser.add_argument("--max-batch-count", type=int, default=16, help="maximum batch count value for the UI")
parser.add_argument("--embeddings-dir", type=str, default=os.path.join(script_path, 'embeddings'), help="embeddings directory for textual inversion (default: embeddings)")
diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py index 7c44ea5b..bcf772d2 100644 --- a/modules/textual_inversion/dataset.py +++ b/modules/textual_inversion/dataset.py @@ -15,11 +15,10 @@ re_tag = re.compile(r"[a-zA-Z][_\w\d()]+") class PersonalizedBase(Dataset):
- def __init__(self, data_root, size=None, repeats=100, flip_p=0.5, placeholder_token="*", width=512, height=512, model=None, device=None, template_file=None):
+ def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, device=None, template_file=None):
self.placeholder_token = placeholder_token
- self.size = size
self.width = width
self.height = height
self.flip = transforms.RandomHorizontalFlip(p=flip_p)
diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py index f1c002a2..d7efdef2 100644 --- a/modules/textual_inversion/preprocess.py +++ b/modules/textual_inversion/preprocess.py @@ -7,8 +7,9 @@ import tqdm from modules import shared, images
-def preprocess(process_src, process_dst, process_flip, process_split, process_caption):
- size = 512
+def preprocess(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption):
+ width = process_width
+ height = process_height
src = os.path.abspath(process_src)
dst = os.path.abspath(process_dst)
@@ -55,23 +56,23 @@ def preprocess(process_src, process_dst, process_flip, process_split, process_ca is_wide = ratio < 1 / 1.35
if process_split and is_tall:
- img = img.resize((size, size * img.height // img.width))
+ img = img.resize((width, height * img.height // img.width))
- top = img.crop((0, 0, size, size))
+ top = img.crop((0, 0, width, height))
save_pic(top, index)
- bot = img.crop((0, img.height - size, size, img.height))
+ bot = img.crop((0, img.height - height, width, img.height))
save_pic(bot, index)
elif process_split and is_wide:
- img = img.resize((size * img.width // img.height, size))
+ img = img.resize((width * img.width // img.height, height))
- left = img.crop((0, 0, size, size))
+ left = img.crop((0, 0, width, height))
save_pic(left, index)
- right = img.crop((img.width - size, 0, img.width, size))
+ right = img.crop((img.width - width, 0, img.width, height))
save_pic(right, index)
else:
- img = images.resize_image(1, img, size, size)
+ img = images.resize_image(1, img, width, height)
save_pic(img, index)
shared.state.nextjob()
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index 9a18ee5c..7a24192e 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -190,7 +190,7 @@ def create_embedding(name, num_vectors_per_token, init_text='*'): return fn
-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):
+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):
assert embedding_name, 'embedding not selected'
shared.state.textinfo = "Initializing textual inversion training..."
@@ -222,7 +222,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps, shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
with torch.autocast("cuda"):
- ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, size=512, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file)
+ 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)
hijack = sd_hijack.model_hijack
@@ -240,6 +240,9 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps, if ititial_step > steps:
return embedding, filename
+ tr_img_len = len([os.path.join(data_root, file_path) for file_path in os.listdir(data_root)])
+ epoch_len = (tr_img_len * num_repeats) + tr_img_len
+
pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step)
for i, (x, text) in pbar:
embedding.step = i + ititial_step
@@ -263,7 +266,10 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps, loss.backward()
optimizer.step()
- pbar.set_description(f"loss: {losses.mean():.7f}")
+ epoch_num = embedding.step // epoch_len
+ epoch_step = embedding.step - (epoch_num * epoch_len) + 1
+
+ pbar.set_description(f"[Epoch {epoch_num}: {epoch_step}/{epoch_len}]loss: {losses.mean():.7f}")
if embedding.step > 0 and embedding_dir is not None and embedding.step % save_embedding_every == 0:
last_saved_file = os.path.join(embedding_dir, f'{embedding_name}-{embedding.step}.pt')
@@ -276,6 +282,8 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps, sd_model=shared.sd_model,
prompt=text,
steps=20,
+ height=training_height,
+ width=training_width,
do_not_save_grid=True,
do_not_save_samples=True,
)
diff --git a/modules/ui.py b/modules/ui.py index 202c4866..0f6427a6 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1029,6 +1029,8 @@ def create_ui(wrap_gradio_gpu_call): process_src = gr.Textbox(label='Source directory')
process_dst = gr.Textbox(label='Destination directory')
+ process_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
+ process_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
with gr.Row():
process_flip = gr.Checkbox(label='Create flipped copies')
@@ -1043,13 +1045,16 @@ def create_ui(wrap_gradio_gpu_call): run_preprocess = gr.Button(value="Preprocess", variant='primary')
with gr.Group():
- gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding; must specify a directory with a set of 512x512 images</p>")
+ 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>")
train_embedding_name = gr.Dropdown(label='Embedding', choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
learn_rate = gr.Number(label='Learning rate', value=5.0e-03)
dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images")
log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion")
template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt"))
+ training_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
+ training_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
steps = gr.Number(label='Max steps', value=100000, precision=0)
+ num_repeats = gr.Number(label='Number of repeats for a single input image per epoch', value=100, precision=0)
create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0)
save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0)
save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True)
@@ -1093,6 +1098,8 @@ def create_ui(wrap_gradio_gpu_call): inputs=[
process_src,
process_dst,
+ process_width,
+ process_height,
process_flip,
process_split,
process_caption,
@@ -1111,7 +1118,10 @@ def create_ui(wrap_gradio_gpu_call): learn_rate,
dataset_directory,
log_directory,
+ training_width,
+ training_height,
steps,
+ num_repeats,
create_image_every,
save_embedding_every,
template_file,
|