This commit is contained in:
lunar 2022-12-09 11:06:44 +00:00
parent b99d18f7b3
commit ab500dd288
41 changed files with 17 additions and 7 deletions

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@ -32,8 +32,8 @@ torch.set_grad_enabled(False)
#%% First let us spawn a stable diffusion holder #%% First let us spawn a stable diffusion holder
device = "cuda:0" device = "cuda:0"
fp_ckpt = "../stable_diffusion_models/ckpt/768-v-ema.ckpt" fp_ckpt = "../stable_diffusion_models/ckpt/v2-1_768-ema-pruned.ckpt"
fp_config = '../stablediffusion/configs/stable-diffusion/v2-inference-v.yaml' fp_config = 'configs/v2-inference-v.yaml'
sdh = StableDiffusionHolder(fp_ckpt, fp_config, device) sdh = StableDiffusionHolder(fp_ckpt, fp_config, device)

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ldm/ldm Symbolic link
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@ -0,0 +1 @@
ldm

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ldm/models/autoencoder.py Executable file → Normal file
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ldm/models/diffusion/__init__.py Executable file → Normal file
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ldm/models/diffusion/ddim.py Executable file → Normal file
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ldm/models/diffusion/ddpm.py Executable file → Normal file
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@ -17,8 +17,7 @@ from functools import partial
import itertools import itertools
from tqdm import tqdm from tqdm import tqdm
from torchvision.utils import make_grid from torchvision.utils import make_grid
from pytorch_lightning.utilities.rank_zero import rank_zero_only from pytorch_lightning.utilities.distributed import rank_zero_only
# from pytorch_lightning.utilities.distributed import rank_zero_only
from omegaconf import ListConfig from omegaconf import ListConfig
from ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config from ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config
@ -391,7 +390,7 @@ class DDPM(pl.LightningModule):
elif self.parameterization == "v": elif self.parameterization == "v":
target = self.get_v(x_start, noise, t) target = self.get_v(x_start, noise, t)
else: else:
raise NotImplementedError(f"Paramterization {self.parameterization} not yet supported") raise NotImplementedError(f"Parameterization {self.parameterization} not yet supported")
loss = self.get_loss(model_out, target, mean=False).mean(dim=[1, 2, 3]) loss = self.get_loss(model_out, target, mean=False).mean(dim=[1, 2, 3])

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ldm/models/diffusion/dpm_solver/__init__.py Executable file → Normal file
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ldm/models/diffusion/dpm_solver/dpm_solver.py Executable file → Normal file
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ldm/models/diffusion/dpm_solver/sampler.py Executable file → Normal file
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ldm/models/diffusion/sampling_util.py Executable file → Normal file
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ldm/modules/attention.py Executable file → Normal file
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@ -16,6 +16,9 @@ try:
except: except:
XFORMERS_IS_AVAILBLE = False XFORMERS_IS_AVAILBLE = False
# CrossAttn precision handling
import os
_ATTN_PRECISION = os.environ.get("ATTN_PRECISION", "fp32")
def exists(val): def exists(val):
return val is not None return val is not None
@ -167,7 +170,14 @@ class CrossAttention(nn.Module):
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v)) q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
sim = einsum('b i d, b j d -> b i j', q, k) * self.scale # force cast to fp32 to avoid overflowing
if _ATTN_PRECISION =="fp32":
with torch.autocast(enabled=False, device_type = 'cuda'):
q, k = q.float(), k.float()
sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
else:
sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
del q, k del q, k
if exists(mask): if exists(mask):

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