Merge branch 'development' into development

ldm/dream/args.py

@@ -602,6 +602,16 @@ def metadata_dumps(opt,
     This is intended to be turned into JSON and stored in the 
     "sd
     '''
+
+    # top-level metadata minus `image` or `images`
+    metadata = {
+        'model'       : 'stable diffusion',
+        'model_id'    : opt.model,
+        'model_hash'  : model_hash,
+        'app_id'      : APP_ID,
+        'app_version' : APP_VERSION,
+    }
+
     # add some RFC266 fields that are generated internally, and not as
     # user args
     image_dict = opt.to_dict(
@@ -647,22 +657,22 @@ def metadata_dumps(opt,
     else:
         rfc_dict['type']  = 'txt2img'
 
-    images = []
     if len(seeds)==0 and opt.seed:
         seeds=[seed]
-        
-    for seed in seeds:
-        rfc_dict['seed'] = seed
-        images.append(copy.copy(rfc_dict))
 
-    return {
-        'model'       : 'stable diffusion',
-        'model_id'    : opt.model,
-        'model_hash'  : model_hash,
-        'app_id'      : APP_ID,
-        'app_version' : APP_VERSION,
-        'images'      : images,
-    }
+    if opt.grid:
+        images = []
+        for seed in seeds:
+            rfc_dict['seed'] = seed
+            images.append(copy.copy(rfc_dict))
+        metadata['images'] = images
+    else:
+        # there should only ever be a single seed if we did not generate a grid
+        assert len(seeds) == 1, 'Expected a single seed'
+        rfc_dict['seed'] = seeds[0]
+        metadata['image'] = rfc_dict
+
+    return metadata
 
 def metadata_loads(metadata):
     '''

ldm/dream/conditioning.py

@@ -38,14 +38,14 @@ def get_uc_and_c(prompt, model, log_tokens=False, skip_normalize=False):
         c = torch.zeros_like(uc)
         # normalize each "sub prompt" and add it
         for subprompt, weight in weighted_subprompts:
-            log_tokenization(subprompt, model, log_tokens)
+            log_tokenization(subprompt, model, log_tokens, weight)
             c = torch.add(
                 c,
                 model.get_learned_conditioning([subprompt]),
                 alpha=weight,
             )
     else:   # just standard 1 prompt
-        log_tokenization(prompt, model, log_tokens)
+        log_tokenization(prompt, model, log_tokens, 1)
         c = model.get_learned_conditioning([prompt])
         uc = model.get_learned_conditioning([unconditioned_words])
     return (uc, c)
@@ -86,7 +86,7 @@ def split_weighted_subprompts(text, skip_normalize=False)->list:
 # shows how the prompt is tokenized
 # usually tokens have '</w>' to indicate end-of-word,
 # but for readability it has been replaced with ' '
-def log_tokenization(text, model, log=False):
+def log_tokenization(text, model, log=False, weight=1):
     if not log:
         return
     tokens    = model.cond_stage_model.tokenizer._tokenize(text)
@@ -103,8 +103,8 @@ def log_tokenization(text, model, log=False):
             usedTokens += 1
         else:  # over max token length
             discarded = discarded + f"\x1b[0;3{s};40m{token}"
-        print(f"\n>> Tokens ({usedTokens}):\n{tokenized}\x1b[0m")
-        if discarded != "":
-            print(
-                f">> Tokens Discarded ({totalTokens-usedTokens}):\n{discarded}\x1b[0m"
-            )
+    print(f"\n>> Tokens ({usedTokens}), Weight ({weight:.2f}):\n{tokenized}\x1b[0m")
+    if discarded != "":
+        print(
+            f">> Tokens Discarded ({totalTokens-usedTokens}):\n{discarded}\x1b[0m"
+        )