Mirrored_Repos/InvokeAI
1
0
Fork 0
mirror of https://github.com/invoke-ai/InvokeAI synced 2024-08-30 20:32:17 +00:00
Code Issues Packages Projects Releases Wiki Activity

Merge branch 'development' into fnformat

Browse Source
This commit is contained in:
Rainer Bernhardt 2022-10-07 15:39:41 +02:00 committed by GitHub
commit 173dc34194
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
2 changed files with 5 additions and 36 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

32
ldm/generate.py
View File

@ -34,23 +34,7 @@ from ldm.dream.image_util import InitImageResizer
from ldm.dream.devices import choose_torch_device, choose_precision from ldm.dream.devices import choose_torch_device, choose_precision
from ldm.dream.conditioning import get_uc_and_c from ldm.dream.conditioning import get_uc_and_c
def fix_func(orig):
if hasattr(torch.backends, 'mps') and torch.backends.mps.is_available():
def new_func(*args, **kw):
device = kw.get("device", "mps")
kw["device"]="cpu"
return orig(*args, **kw).to(device)
return new_func
return orig
torch.rand = fix_func(torch.rand)
torch.rand_like = fix_func(torch.rand_like)
torch.randn = fix_func(torch.randn)
torch.randn_like = fix_func(torch.randn_like)
torch.randint = fix_func(torch.randint)
torch.randint_like = fix_func(torch.randint_like)
torch.bernoulli = fix_func(torch.bernoulli)
torch.multinomial = fix_func(torch.multinomial)
def fix_func(orig): def fix_func(orig):
if hasattr(torch.backends, 'mps') and torch.backends.mps.is_available(): if hasattr(torch.backends, 'mps') and torch.backends.mps.is_available():
@ -70,23 +54,7 @@ torch.randint_like = fix_func(torch.randint_like)
torch.bernoulli = fix_func(torch.bernoulli) torch.bernoulli = fix_func(torch.bernoulli)
torch.multinomial = fix_func(torch.multinomial) torch.multinomial = fix_func(torch.multinomial)
def fix_func(orig):
if hasattr(torch.backends, 'mps') and torch.backends.mps.is_available():
def new_func(*args, **kw):
device = kw.get("device", "mps")
kw["device"]="cpu"
return orig(*args, **kw).to(device)
return new_func
return orig
torch.rand = fix_func(torch.rand)
torch.rand_like = fix_func(torch.rand_like)
torch.randn = fix_func(torch.randn)
torch.randn_like = fix_func(torch.randn_like)
torch.randint = fix_func(torch.randint)
torch.randint_like = fix_func(torch.randint_like)
torch.bernoulli = fix_func(torch.bernoulli)
torch.multinomial = fix_func(torch.multinomial)
"""Simplified text to image API for stable diffusion/latent diffusion """Simplified text to image API for stable diffusion/latent diffusion

9
ldm/models/diffusion/sampler.py
View File

@ -39,6 +39,7 @@ class Sampler(object):
ddim_eta=0.0, ddim_eta=0.0,
verbose=False, verbose=False,
): ):
self.total_steps = ddim_num_steps
self.ddim_timesteps = make_ddim_timesteps( self.ddim_timesteps = make_ddim_timesteps(
ddim_discr_method=ddim_discretize, ddim_discr_method=ddim_discretize,
num_ddim_timesteps=ddim_num_steps, num_ddim_timesteps=ddim_num_steps,
@ -211,6 +212,7 @@ class Sampler(object):
if ddim_use_original_steps if ddim_use_original_steps
else np.flip(timesteps) else np.flip(timesteps)
) )
total_steps=steps total_steps=steps
iterator = tqdm( iterator = tqdm(
@ -305,7 +307,7 @@ class Sampler(object):
time_range = np.flip(timesteps) time_range = np.flip(timesteps)
total_steps = timesteps.shape[0] total_steps = timesteps.shape[0]
print(f'>> Running {self.__class__.__name__} Sampling with {total_steps} timesteps') print(f'>> Running {self.__class__.__name__} sampling starting at step {self.total_steps - t_start} of {self.total_steps} ({total_steps} new sampling steps)')
iterator = tqdm(time_range, desc='Decoding image', total=total_steps) iterator = tqdm(time_range, desc='Decoding image', total=total_steps)
x_dec = x_latent x_dec = x_latent
@ -351,11 +353,10 @@ class Sampler(object):
return x_dec return x_dec
def get_initial_image(self,x_T,shape,timesteps=None): def get_initial_image(self,x_T,shape,timesteps=None):
x = torch.randn(shape, device=self.device)
if x_T is None: if x_T is None:
return x return torch.randn(shape, device=self.device)
else: else:
return x_T + x return x_T
def p_sample( def p_sample(
self, self,