From ac8002d2a43080052289e4a16bd23cd50d9c367c Mon Sep 17 00:00:00 2001
From: lllyasviel <lyuminzhang@outlook.com>
Date: Sun, 12 Nov 2023 09:12:09 -0800
Subject: [PATCH] speed up lcm again

---
 fooocus_version.py          |  2 +-
 modules/async_worker.py     |  8 ++++++--
 modules/default_pipeline.py | 19 +++++++++++++++++++
 3 files changed, 26 insertions(+), 3 deletions(-)

diff --git a/fooocus_version.py b/fooocus_version.py
index 3981e67..393af6c 100644
--- a/fooocus_version.py
+++ b/fooocus_version.py
@@ -1 +1 @@
-version = '2.1.799'
+version = '2.1.800'
diff --git a/modules/async_worker.py b/modules/async_worker.py
index 4f4c65a..00d40d6 100644
--- a/modules/async_worker.py
+++ b/modules/async_worker.py
@@ -383,8 +383,12 @@ def worker():
                 t['c'] = pipeline.clip_encode(texts=t['positive'], pool_top_k=t['positive_top_k'])
 
             for i, t in enumerate(tasks):
-                progressbar(10, f'Encoding negative #{i + 1} ...')
-                t['uc'] = pipeline.clip_encode(texts=t['negative'], pool_top_k=t['negative_top_k'])
+                if abs(float(cfg_scale) - 1.0) < 1e-4:
+                    progressbar(10, f'Skipped negative #{i + 1} ...')
+                    t['uc'] = pipeline.clone_cond(t['c'])
+                else:
+                    progressbar(10, f'Encoding negative #{i + 1} ...')
+                    t['uc'] = pipeline.clip_encode(texts=t['negative'], pool_top_k=t['negative_top_k'])
 
         if len(goals) > 0:
             progressbar(13, 'Image processing ...')
diff --git a/modules/default_pipeline.py b/modules/default_pipeline.py
index 0026f12..c828644 100644
--- a/modules/default_pipeline.py
+++ b/modules/default_pipeline.py
@@ -132,6 +132,25 @@ def clip_encode_single(clip, text, verbose=False):
     return result
 
 
+@torch.no_grad()
+@torch.inference_mode()
+def clone_cond(conds):
+    results = []
+
+    for c, p in conds:
+        p = p["pooled_output"]
+
+        if isinstance(c, torch.Tensor):
+            c = c.clone()
+
+        if isinstance(p, torch.Tensor):
+            p = p.clone()
+
+        results.append([c, {"pooled_output": p}])
+
+    return results
+
+
 @torch.no_grad()
 @torch.inference_mode()
 def clip_encode(texts, pool_top_k=1):