Fixes issue with cuda/current mismatch

ldm/models/diffusion/ddim.py

@@ -10,13 +10,17 @@ from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, mak
 
 
 class DDIMSampler(object):
-    def __init__(self, model, schedule="linear", **kwargs):
+    def __init__(self, model, schedule="linear", device="cuda", **kwargs):
         super().__init__()
         self.model = model
         self.ddpm_num_timesteps = model.num_timesteps
         self.schedule = schedule
+        self.device = device
 
     def register_buffer(self, name, attr):
+        if type(attr) == torch.Tensor:
+            if attr.device != torch.device(self.device):
+                attr = attr.to(torch.device(self.device))
         setattr(self, name, attr)
 
     def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True):

ldm/models/diffusion/plms.py

@@ -9,13 +9,18 @@ from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, mak
 
 
 class PLMSSampler(object):
-    def __init__(self, model, schedule="linear", **kwargs):
+    def __init__(self, model, schedule="linear", device="cuda", **kwargs):
         super().__init__()
         self.model = model
         self.ddpm_num_timesteps = model.num_timesteps
         self.schedule = schedule
+        self.device = device
 
     def register_buffer(self, name, attr):
+        if type(attr) == torch.Tensor:
+            if attr.device != torch.device(self.device):
+                attr = attr.to(torch.device(self.device))
+
         setattr(self, name, attr)
 
     def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True):

ldm/simplet2i.py

@@ -58,7 +58,6 @@ import sys
 import os
 from omegaconf import OmegaConf
 from PIL import Image
-import PIL
 from tqdm import tqdm, trange
 from itertools import islice
 from einops import rearrange, repeat
@@ -286,7 +285,8 @@ The vast majority of these arguments default to reasonable values.
     @torch.no_grad()
     def img2img(self,prompt,outdir=None,init_img=None,batch_size=None,iterations=None,
                 steps=None,seed=None,grid=None,individual=None,width=None,height=None,
-                cfg_scale=None,ddim_eta=None,strength=None,skip_normalize=False):
+                cfg_scale=None,ddim_eta=None,strength=None,embedding_path=None,
+                skip_normalize=False,variants=None):
         """
         Generate an image from the prompt and the initial image, writing iteration images into the outdir
         The output is a list of lists in the format: [[filename1,seed1], [filename2,seed2],...]
@@ -324,7 +324,7 @@ The vast majority of these arguments default to reasonable values.
         # PLMS sampler not supported yet, so ignore previous sampler
         if self.sampler_name!='ddim':
             print(f"sampler '{self.sampler_name}' is not yet supported. Using DDM sampler")
-            sampler = DDIMSampler(model)
+            sampler = DDIMSampler(model, device=self.device)
         else:
             sampler = self.sampler
 
@@ -461,9 +461,9 @@ The vast majority of these arguments default to reasonable values.
 
             msg = f'setting sampler to {self.sampler_name}'
             if self.sampler_name=='plms':
-                self.sampler = PLMSSampler(self.model)
+                self.sampler = PLMSSampler(self.model, device=self.device)
             elif self.sampler_name == 'ddim':
-                self.sampler = DDIMSampler(self.model)
+                self.sampler = DDIMSampler(self.model, device=self.device)
             elif self.sampler_name == 'k_dpm_2_a':
                 self.sampler = KSampler(self.model,'dpm_2_ancestral')
             elif self.sampler_name == 'k_dpm_2':
@@ -478,7 +478,7 @@ The vast majority of these arguments default to reasonable values.
                 self.sampler = KSampler(self.model,'lms')
             else:
                 msg = f'unsupported sampler {self.sampler_name}, defaulting to plms'
-                self.sampler = PLMSSampler(self.model)
+                self.sampler = PLMSSampler(self.model, device=self.device)
 
             print(msg)
 
@@ -505,7 +505,7 @@ The vast majority of these arguments default to reasonable values.
         w, h = image.size
         print(f"loaded input image of size ({w}, {h}) from {path}")
         w, h = map(lambda x: x - x % 32, (w, h))  # resize to integer multiple of 32
-        image = image.resize((w, h), resample=PIL.Image.LANCZOS)
+        image = image.resize((w, h), resample=Image.Resampling.LANCZOS)
         image = np.array(image).astype(np.float32) / 255.0
         image = image[None].transpose(0, 3, 1, 2)
         image = torch.from_numpy(image)

scripts/dream.py

@@ -191,16 +191,16 @@ def main_loop(t2i,parser,log,infile):
                     print(f"{newopt.init_img}")
                     try:
                         variantResults = t2i.img2img(**vars(newopt))
+                        allVariantResults.append([newopt,variantResults])
                     except AssertionError as e:
                         print(e)
                         continue
-                    allVariantResults.append([newopt,variantResults])
             print(f"{opt.variants} Variants generated!")
 
         print("Outputs:")
         write_log_message(t2i,opt,results,log)
             
-        if len(allVariantResults)>0:
+        if allVariantResults:
             print("Variant outputs:")
             for vr in allVariantResults:
                 write_log_message(t2i,vr[0],vr[1],log)

src/k-diffusion

@@ -1 +1 @@
-Subproject commit db5799068749bf3a6d5845120ed32df16b7d883b
+Subproject commit ef1bf07627c9a10ba9137e68a0206b844544a7d9