Remove T-GATE

app.py

@@ -180,6 +180,12 @@ with gr.Blocks(
             with gr.TabItem("🛠️ Advanced"):
                 with gr.Group():
                     with gr.Row():
+                        file_format = gr.Dropdown(
+                            choices=["png", "jpeg", "webp"],
+                            label="File Format",
+                            filterable=False,
+                            value="png",
+                        )
                         deepcache_interval = gr.Slider(
                             value=cfg.DEEPCACHE_INTERVAL,
                             label="DeepCache Interval",
@@ -187,21 +193,6 @@ with gr.Blocks(
                             maximum=4,
                             step=1,
                         )
-                        tgate_step = gr.Slider(
-                            maximum=cfg.INFERENCE_STEPS,
-                            value=cfg.TGATE_STEP,
-                            label="T-GATE Step",
-                            minimum=0,
-                            step=1,
-                        )
-
-                    with gr.Row():
-                        file_format = gr.Dropdown(
-                            choices=["png", "jpeg", "webp"],
-                            label="File Format",
-                            filterable=False,
-                            value="png",
-                        )
                         tome_ratio = gr.Slider(
                             value=cfg.TOME_RATIO,
                             label="ToMe Ratio",
@@ -227,7 +218,7 @@ with gr.Blocks(
                             elem_classes=["checkbox"],
                             label="Truncate prompts",
                             value=False,
-                            scale=3,
+                            scale=1,
                         )
 
             with gr.TabItem("ℹ️ Usage"):
@@ -288,12 +279,6 @@ with gr.Blocks(
         outputs=[output_images],
     )
 
-    inference_steps.change(
-        lambda max, step: gr.Slider(maximum=max, value=min(max, step)),
-        inputs=[inference_steps, tgate_step],
-        outputs=[tgate_step],
-    )
-
     gr.on(
         triggers=[generate_btn.click, prompt.submit],
         fn=handle_generate,
@@ -318,7 +303,6 @@ with gr.Blocks(
             truncate_prompts,
             increment_seed,
             deepcache_interval,
-            tgate_step,
             tome_ratio,
         ],
     )

cli.py

@@ -22,7 +22,6 @@ def main():
     parser.add_argument("-h", "--height", type=int, metavar="INT", default=cfg.HEIGHT)
     parser.add_argument("-m", "--model", type=str, metavar="STR", default=cfg.MODEL)
     parser.add_argument("-d", "--deepcache", type=int, metavar="INT", default=cfg.DEEPCACHE_INTERVAL)
-    parser.add_argument("-t", "--tgate", type=int, metavar="INT", default=cfg.TGATE_STEP)
     parser.add_argument("--style", type=str, metavar="STR", default=cfg.STYLE)
     parser.add_argument("--scheduler", type=str, metavar="STR", default=cfg.SCHEDULER)
     parser.add_argument("--guidance", type=float, metavar="FLOAT", default=cfg.GUIDANCE_SCALE)
@@ -54,7 +53,6 @@ def main():
         args.truncate,
         args.no_increment,
         args.deepcache,
-        args.tgate,
         args.tome,
     )
     save_images(images, args.filename)

config.py

@@ -49,6 +49,4 @@ INFERENCE_STEPS = 30
 
 DEEPCACHE_INTERVAL = 2
 
-TGATE_STEP = 0
-
 TOME_RATIO = 0.0

generate.py

@@ -25,8 +25,6 @@ from diffusers import (
     StableDiffusionPipeline,
 )
 from diffusers.models import AutoencoderKL, AutoencoderTiny
-from tgate.SD import tgate as tgate_sd
-from tgate.SD_DeepCache import tgate as tgate_sd_deepcache
 from torch._dynamo import OptimizedModule
 
 # some models use the deprecated CLIPFeatureExtractor class (should use CLIPImageProcessor)
@@ -77,17 +75,6 @@ class Loader:
         self.pipe.deepcache.enable()
         return self.pipe.deepcache
 
-    def _load_tgate(self):
-        has_tgate = hasattr(self.pipe, "tgate")
-        has_deepcache = hasattr(self.pipe, "deepcache")
-
-        if not has_tgate:
-            self.pipe.tgate = MethodType(
-                tgate_sd_deepcache if has_deepcache else tgate_sd,
-                self.pipe,
-            )
-        return self.pipe.tgate
-
     def _load_vae(self, model_name=None, taesd=False, dtype=None):
         vae_type = type(self.pipe.vae)
         is_kl = issubclass(vae_type, (AutoencoderKL, OptimizedModule))
@@ -172,7 +159,6 @@ class Loader:
 
                 self._load_vae(model_lower, taesd, dtype)
                 self._load_deepcache(interval=deepcache_interval)
-                self._load_tgate()
                 return self.pipe
             else:
                 print(f"Unloading {model_name.lower()}...")
@@ -189,13 +175,12 @@ class Loader:
 
         print(f"Loading {model_lower} with {'Tiny' if taesd else 'KL'} VAE...")
         self.pipe = StableDiffusionPipeline.from_pretrained(**pipe_kwargs).to(self.gpu)
-        self._load_vae(model_lower, taesd, dtype)
-        self._load_deepcache(interval=deepcache_interval)
-        self._load_tgate()
         self.pipe.load_textual_inversion(
             pretrained_model_name_or_path=list(EMBEDDINGS.keys()),
             tokens=list(EMBEDDINGS.values()),
         )
+        self._load_vae(model_lower, taesd, dtype)
+        self._load_deepcache(interval=deepcache_interval)
         return self.pipe
 
 
@@ -262,7 +247,6 @@ def generate(
     truncate_prompts=False,
     increment_seed=True,
     deepcache_interval=1,
-    tgate_step=0,
     tome_ratio=0,
     log: Callable[[str], None] = None,
     Error=Exception,
@@ -328,17 +312,11 @@ def generate(
                 raise Error("ParsingException: Invalid prompt")
 
             with token_merging(pipe, tome_ratio=tome_ratio):
-                # cap the tgate step
-                gate_step = min(
-                    tgate_step if tgate_step > 0 else inference_steps,
-                    inference_steps,
-                )
-                result = pipe.tgate(
+                result = pipe(
                     num_inference_steps=inference_steps,
                     negative_prompt_embeds=neg_embeds,
                     guidance_scale=guidance_scale,
                     prompt_embeds=pos_embeds,
-                    gate_step=gate_step,
                     generator=generator,
                     height=height,
                     width=width,

requirements.txt

@@ -7,7 +7,6 @@ gradio==4.39.0
 ruff
 scipy  # for LMS scheduler
 spaces
-tgate==0.1.2
 tomesd==0.1.3
 torch
 torchvision

usage.md

@@ -65,10 +65,6 @@ Optionally, the [Karras](https://arxiv.org/abs/2206.00364) noise schedule can be
 * `3`: balanced
 * `4`: more speed
 
-#### T-GATE
-
-[Temporal gating](https://github.com/HaozheLiu-ST/T-GATE) (Zhang et al. 2024) caches self and cross attention computations up to `Step`. Afterwards, attention is no longer computed and the cache is used, resulting in a noticeable speedup.
-
 #### ToMe
 
 [Token merging](https://arxiv.org/abs/2303.17604) (Bolya & Hoffman 2023) reduces the number of tokens processed by the model. Set `Ratio` to the desired reduction factor. ToMe's impact is more noticeable on larger images.