From af081211ee93622473ee575de30fed2fd8263c09 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Tue, 11 Jul 2023 21:16:43 +0300
Subject: getting SD2.1 to run on SDXL repo

---
 modules/prompt_parser.py | 64 +++++++++++++++++++++++++++++++++++++++---------
 1 file changed, 52 insertions(+), 12 deletions(-)

(limited to 'modules/prompt_parser.py')

diff --git a/modules/prompt_parser.py b/modules/prompt_parser.py
index 0069d8b0..d7f9e9a9 100644
--- a/modules/prompt_parser.py
+++ b/modules/prompt_parser.py
@@ -144,7 +144,12 @@ def get_learned_conditioning(model, prompts, steps):
 
         cond_schedule = []
         for i, (end_at_step, _) in enumerate(prompt_schedule):
-            cond_schedule.append(ScheduledPromptConditioning(end_at_step, conds[i]))
+            if isinstance(conds, dict):
+                cond = {k: v[i] for k, v in conds.items()}
+            else:
+                cond = conds[i]
+
+            cond_schedule.append(ScheduledPromptConditioning(end_at_step, cond))
 
         cache[prompt] = cond_schedule
         res.append(cond_schedule)
@@ -214,20 +219,57 @@ def get_multicond_learned_conditioning(model, prompts, steps) -> MulticondLearne
     return MulticondLearnedConditioning(shape=(len(prompts),), batch=res)
 
 
+class DictWithShape(dict):
+    def __init__(self, x, shape):
+        super().__init__()
+        self.update(x)
+
+    @property
+    def shape(self):
+        return self["crossattn"].shape
+
+
 def reconstruct_cond_batch(c: List[List[ScheduledPromptConditioning]], current_step):
     param = c[0][0].cond
-    res = torch.zeros((len(c),) + param.shape, device=param.device, dtype=param.dtype)
+    is_dict = isinstance(param, dict)
+
+    if is_dict:
+        dict_cond = param
+        res = {k: torch.zeros((len(c),) + param.shape, device=param.device, dtype=param.dtype) for k, param in dict_cond.items()}
+        res = DictWithShape(res, (len(c),) + dict_cond['crossattn'].shape)
+    else:
+        res = torch.zeros((len(c),) + param.shape, device=param.device, dtype=param.dtype)
+
     for i, cond_schedule in enumerate(c):
         target_index = 0
         for current, entry in enumerate(cond_schedule):
             if current_step <= entry.end_at_step:
                 target_index = current
                 break
-        res[i] = cond_schedule[target_index].cond
+
+        if is_dict:
+            for k, param in cond_schedule[target_index].cond.items():
+                res[k][i] = param
+        else:
+            res[i] = cond_schedule[target_index].cond
 
     return res
 
 
+def stack_conds(tensors):
+    # if prompts have wildly different lengths above the limit we'll get tensors of different shapes
+    # and won't be able to torch.stack them. So this fixes that.
+    token_count = max([x.shape[0] for x in tensors])
+    for i in range(len(tensors)):
+        if tensors[i].shape[0] != token_count:
+            last_vector = tensors[i][-1:]
+            last_vector_repeated = last_vector.repeat([token_count - tensors[i].shape[0], 1])
+            tensors[i] = torch.vstack([tensors[i], last_vector_repeated])
+
+    return torch.stack(tensors)
+
+
+
 def reconstruct_multicond_batch(c: MulticondLearnedConditioning, current_step):
     param = c.batch[0][0].schedules[0].cond
 
@@ -249,16 +291,14 @@ def reconstruct_multicond_batch(c: MulticondLearnedConditioning, current_step):
 
         conds_list.append(conds_for_batch)
 
-    # if prompts have wildly different lengths above the limit we'll get tensors fo different shapes
-    # and won't be able to torch.stack them. So this fixes that.
-    token_count = max([x.shape[0] for x in tensors])
-    for i in range(len(tensors)):
-        if tensors[i].shape[0] != token_count:
-            last_vector = tensors[i][-1:]
-            last_vector_repeated = last_vector.repeat([token_count - tensors[i].shape[0], 1])
-            tensors[i] = torch.vstack([tensors[i], last_vector_repeated])
+    if isinstance(tensors[0], dict):
+        keys = list(tensors[0].keys())
+        stacked = {k: stack_conds([x[k] for x in tensors]) for k in keys}
+        stacked = DictWithShape(stacked, stacked['crossattn'].shape)
+    else:
+        stacked = stack_conds(tensors).to(device=param.device, dtype=param.dtype)
 
-    return conds_list, torch.stack(tensors).to(device=param.device, dtype=param.dtype)
+    return conds_list, stacked
 
 
 re_attention = re.compile(r"""
-- 
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