sd 2.1

example1_standard.py

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

ldm/ldm

@@ -0,0 +1 @@
+ldm

ldm/models/diffusion/ddpm.py

@@ -17,8 +17,7 @@ from functools import partial
 import itertools
 from tqdm import tqdm
 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 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":
             target = self.get_v(x_start, noise, t)
         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])
 

ldm/modules/attention.py

@@ -16,6 +16,9 @@ try:
 except:
     XFORMERS_IS_AVAILBLE = False
 
+# CrossAttn precision handling
+import os
+_ATTN_PRECISION = os.environ.get("ATTN_PRECISION", "fp32")
 
 def exists(val):
     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))
 
+        # 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
     
         if exists(mask):