fix normalized prompt when a variation is generated

- The seed printed needs to be the one generated prior to the
  initial noising operation. To do this, I added a new "first_seed"
  argument to the image callback in dream.py.
- Closes #641

ldm/dream/generator/base.py

@@ -51,6 +51,7 @@ def generate(self,prompt,init_image,width,height,iterations=1,seed=None,
 
         results             = []
         seed                = seed if seed else self.new_seed()
+        first_seed          = seed
         seed, initial_noise = self.generate_initial_noise(seed, width, height)
         with scope(self.model.device.type), self.model.ema_scope():
             for n in trange(iterations, desc='Generating'):
@@ -71,7 +72,7 @@ def generate(self,prompt,init_image,width,height,iterations=1,seed=None,
                 image = make_image(x_T)
                 results.append([image, seed])
                 if image_callback is not None:
-                    image_callback(image, seed)
+                    image_callback(image, seed, first_seed=first_seed)
                 seed = self.new_seed()
         return results
 

ldm/generate.py

@@ -765,10 +765,10 @@ def _load_model_from_config(self, config, weights):
         m, u  = model.load_state_dict(sd, strict=False)
 
         if self.precision == 'float16':
-            print('Using faster float16 precision')
+            print('>> Using faster float16 precision')
             model.to(torch.float16)
         else:
-            print('Using more accurate float32 precision')
+            print('>> Using more accurate float32 precision')
 
         model.to(self.device)
         model.eval()

scripts/dream.py

@@ -250,11 +250,12 @@ def main_loop(gen, opt, infile):
             results          = []  # list of filename, prompt pairs
             grid_images      = dict()  # seed -> Image, only used if `opt.grid`
             prior_variations = opt.with_variations or []
-            first_seed       = opt.seed
 
-            def image_writer(image, seed, upscaled=False):
+            def image_writer(image, seed, upscaled=False, first_seed=None):
+                # note the seed is the seed of the current image
+                # the first_seed is the original seed that noise is added to
+                # when the -v switch is used to generate variations
                 path = None
-                nonlocal first_seed
                 nonlocal prior_variations
                 if opt.grid:
                     grid_images[seed] = image