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refactor lora load/unload

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Vladimir Mandic
2023-10-08 12:00:51 -04:00
parent c0e4605dfb
commit f2fc41cfc2
10 changed files with 344 additions and 297 deletions
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modules/processing.py
+8 -15
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@@ -22,6 +22,7 @@ import modules.lowvram
import modules.masking
import modules.paths
import modules.scripts
import modules.script_callbacks
import modules.prompt_parser
import modules.extra_networks
import modules.face_restoration
@@ -364,8 +365,7 @@ class Processed:
return self.token_merging_ratio_hr if for_hr else self.token_merging_ratio
# from https://discuss.pytorch.org/t/help-regarding-slerp-function-for-generative-model-sampling/32475/3
def slerp(val, low, high):
def slerp(val, low, high): # from https://discuss.pytorch.org/t/help-regarding-slerp-function-for-generative-model-sampling/32475/3
low_norm = low/torch.norm(low, dim=1, keepdim=True)
high_norm = high/torch.norm(high, dim=1, keepdim=True)
dot = (low_norm*high_norm).sum(1)
@@ -382,7 +382,6 @@ def slerp(val, low, high):
def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, seed_resize_from_h=0, seed_resize_from_w=0, p=None):
eta_noise_seed_delta = shared.opts.eta_noise_seed_delta or 0
xs = []
# if we have multiple seeds, this means we are working with batch size>1; this then
# enables the generation of additional tensors with noise that the sampler will use during its processing.
# Using those pre-generated tensors instead of simple torch.randn allows a batch with seeds [100, 101] to
@@ -391,24 +390,19 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see
sampler_noises = [[] for _ in range(p.sampler.number_of_needed_noises(p))]
else:
sampler_noises = None
for i, seed in enumerate(seeds):
noise_shape = shape if seed_resize_from_h <= 0 or seed_resize_from_w <= 0 else (shape[0], seed_resize_from_h//8, seed_resize_from_w//8)
subnoise = None
if subseeds is not None:
subseed = 0 if i >= len(subseeds) else subseeds[i]
subnoise = devices.randn(subseed, noise_shape)
# randn results depend on device; gpu and cpu get different results for same seed;
# the way I see it, it's better to do this on CPU, so that everyone gets same result;
# but the original script had it like this, so I do not dare change it for now because
# it will break everyone's seeds.
noise = devices.randn(seed, noise_shape)
if subnoise is not None:
noise = slerp(subseed_strength, noise, subnoise)
if noise_shape != shape:
x = devices.randn(seed, shape)
dx = (shape[2] - noise_shape[2]) // 2
@@ -421,19 +415,15 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see
dy = max(-dy, 0)
x[:, ty:ty+h, tx:tx+w] = noise[:, dy:dy+h, dx:dx+w]
noise = x
if sampler_noises is not None:
cnt = p.sampler.number_of_needed_noises(p)
if eta_noise_seed_delta > 0:
torch.manual_seed(seed + eta_noise_seed_delta)
for j in range(cnt):
sampler_noises[j].append(devices.randn_without_seed(tuple(noise_shape)))
xs.append(noise)
if sampler_noises is not None:
p.sampler.sampler_noises = [torch.stack(n).to(shared.device) for n in sampler_noises]
x = torch.stack(xs).to(shared.device)
return x
@@ -532,10 +522,9 @@ def create_infotext(p: StableDiffusionProcessing, all_prompts, all_seeds, all_su
args["Face restoration"] = shared.opts.face_restoration_model
if 'color' in p.ops:
args["Color correction"] = True
# embeddings
if hasattr(modules.sd_hijack.model_hijack, 'embedding_db') and len(modules.sd_hijack.model_hijack.embedding_db.embeddings_used) > 0: # this is for original hijaacked models only, diffusers are handled separately
args["Embeddings"] = ', '.join(modules.sd_hijack.model_hijack.embedding_db.embeddings_used)
# samplers
args["Sampler ENSD"] = shared.opts.eta_noise_seed_delta if shared.opts.eta_noise_seed_delta != 0 and modules.sd_samplers_common.is_sampler_using_eta_noise_seed_delta(p) else None
args["Sampler ENSM"] = p.initial_noise_multiplier if getattr(p, 'initial_noise_multiplier', 1.0) != 1.0 else None
@@ -645,6 +634,8 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
modules.sd_models.apply_token_merging(p.sd_model, p.get_token_merging_ratio())
modules.sd_hijack_freeu.apply_freeu(p, shared.backend == shared.Backend.ORIGINAL)
modules.script_callbacks.before_process_callback(p)
if shared.cmd_opts.profile:
"""
import torch.profiler # pylint: disable=redefined-outer-name
@@ -656,7 +647,8 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
import cProfile
pr = cProfile.Profile()
pr.enable()
res = process_images_inner(p)
with context_hypertile_vae(p), context_hypertile_unet(p):
res = process_images_inner(p)
print_profile(pr, 'Torch')
else:
with context_hypertile_vae(p), context_hypertile_unet(p):
@@ -664,6 +656,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
finally:
if not shared.opts.cuda_compile:
modules.sd_models.apply_token_merging(p.sd_model, 0)
modules.script_callbacks.after_process_callback(p)
if p.override_settings_restore_afterwards: # restore opts to original state
for k, v in stored_opts.items():
setattr(shared.opts, k, v)