[Docs] Add TGATE in section optimization

docs/source/en/_toctree.yml

@@ -172,6 +172,8 @@
       title: Token merging
     - local: optimization/deepcache
       title: DeepCache
+    - local: optimization/tgate
+      title: TGATE
     title: General optimizations
   - sections:
     - local: using-diffusers/stable_diffusion_jax_how_to

docs/source/en/optimization/tgate.md

@@ -0,0 +1,179 @@
+# T-GATE
+
+[T-GATE](https://github.com/HaozheLiu-ST/T-GATE/tree/main) accelerates inference for [Stable Diffusion](../api/pipelines/stable_diffusion/overview), [PixArt](../api/pipelines/pixart), and [Latency Consistency Model](../api/pipelines/latent_consistency_models.md) pipelines by skipping the cross-attention calculation once it converges. This method doesn't require any additional training and it can speed up inference from 10-50%. T-GATE is also compatible with other optimization methods like [DeepCache](./deepcache).
+
+Before you begin, make sure you install T-GATE.
+
+```bash
+pip install tgate
+pip install -U pytorch diffusers transformers accelerate DeepCache
+```
+
+
+To use T-GATE with a pipeline, you need to use its corresponding loader.
+
+| Pipeline | T-GATE Loader |
+|---|---|
+| PixArt | TgatePixArtLoader |
+| Stable Diffusion XL | TgateSDXLLoader |
+| Stable Diffusion XL + DeepCache | TgateSDXLDeepCacheLoader |
+| Stable Diffusion | TgateSDLoader |
+| Stable Diffusion + DeepCache | TgateSDDeepCacheLoader |
+
+Next, create a `TgateLoader` with a pipeline, the gate step (the time step to stop calculating the cross attention), and the number of inference steps. Then call the `tgate` method on the pipeline with a prompt, gate step, and the number of inference steps.
+
+Let's see how to enable this for several different pipelines.
+
+<hfoptions id="pipelines">
+<hfoption id="PixArt">
+
+Accelerate `PixArtAlphaPipeline` with T-GATE:
+
+```py
+import torch
+from diffusers import PixArtAlphaPipeline
+from tgate import TgatePixArtLoader
+
+pipe = PixArtAlphaPipeline.from_pretrained("PixArt-alpha/PixArt-XL-2-1024-MS", torch_dtype=torch.float16)
+pipe = TgatePixArtLoader(
+       pipe,
+       gate_step=8,
+       num_inference_steps=25,
+).to("cuda")
+
+image = pipe.tgate(
+       "An alpaca made of colorful building blocks, cyberpunk.",
+        gate_step=gate_step,
+       num_inference_steps=inference_step,
+).images[0]
+```
+</hfoption>
+<hfoption id="Stable Diffusion XL"> 
+
+Accelerate `StableDiffusionXLPipeline` with T-GATE:
+
+```py
+import torch
+from diffusers import StableDiffusionXLPipeline
+from diffusers import DPMSolverMultistepScheduler
+
+pipe = StableDiffusionXLPipeline.from_pretrained(
+            "stabilityai/stable-diffusion-xl-base-1.0",
+            torch_dtype=torch.float16,
+            variant="fp16",
+            use_safetensors=True,
+)
+pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
+
+from tgate import TgateSDXLLoader
+gate_step = 10
+inference_step = 25
+pipe = TgateSDXLLoader(
+       pipe,
+       gate_step=gate_step,
+       num_inference_steps=inference_step,
+).to("cuda")
+
+image = pipe.tgate(
+        "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k.",
+        gate_step=gate_step,
+        num_inference_steps=inference_step
+).images[0]
+```
+</hfoption>
+<hfoption id="StableDiffusionXL with DeepCache">
+
+Accelerate `StableDiffusionXLPipeline` with [DeepCache](https://github.com/horseee/DeepCache) and T-GATE:
+
+```py
+import torch
+from diffusers import StableDiffusionXLPipeline
+from diffusers import DPMSolverMultistepScheduler
+
+pipe = StableDiffusionXLPipeline.from_pretrained(
+            "stabilityai/stable-diffusion-xl-base-1.0",
+            torch_dtype=torch.float16,
+            variant="fp16",
+            use_safetensors=True,
+)
+pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
+
+from tgate import TgateSDXLDeepCacheLoader
+gate_step = 10
+inference_step = 25
+pipe = TgateSDXLDeepCacheLoader(
+       pipe,
+       cache_interval=3,
+       cache_branch_id=0,
+).to("cuda")
+
+image = pipe.tgate(
+        "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k.",
+        gate_step=gate_step,
+        num_inference_steps=inference_step
+).images[0]
+```
+</hfoption>
+<hfoption id="Latent Consistency Model">
+
+Accelerate `latent-consistency/lcm-sdxl` with T-GATE:
+
+```py
+import torch
+from diffusers import StableDiffusionXLPipeline
+from diffusers import UNet2DConditionModel, LCMScheduler
+from diffusers import DPMSolverMultistepScheduler
+
+unet = UNet2DConditionModel.from_pretrained(
+    "latent-consistency/lcm-sdxl",
+    torch_dtype=torch.float16,
+    variant="fp16",
+)
+pipe = StableDiffusionXLPipeline.from_pretrained(
+    "stabilityai/stable-diffusion-xl-base-1.0",
+    unet=unet,
+    torch_dtype=torch.float16,
+    variant="fp16",
+)
+pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
+
+from tgate import TgateSDXLLoader
+gate_step = 1
+inference_step = 4
+pipe = TgateSDXLLoader(
+       pipe,
+       gate_step=gate_step,
+       num_inference_steps=inference_step,
+       lcm=True
+).to("cuda")
+
+image = pipe.tgate(
+        "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k.",
+        gate_step=gate_step,
+        num_inference_steps=inference_step
+).images[0]
+```
+</hfoption>
+</hfoptions>
+
+T-GATE also supports [`StableDiffusionPipeline`] and [PixArt-alpha/PixArt-LCM-XL-2-1024-MS](https://hf.co/PixArt-alpha/PixArt-LCM-XL-2-1024-MS).
+
+## Benchmarks
+| Model                 | MACs     | Param     | Latency | Zero-shot 10K-FID on MS-COCO |
+|-----------------------|----------|-----------|---------|---------------------------|
+| SD-1.5                | 16.938T  | 859.520M  | 7.032s  | 23.927                    |
+| SD-1.5 w/ T-GATE       | 9.875T   | 815.557M  | 4.313s  | 20.789                    |
+| SD-2.1                | 38.041T  | 865.785M  | 16.121s | 22.609                    |
+| SD-2.1 w/ T-GATE       | 22.208T  | 815.433 M | 9.878s  | 19.940                    |
+| SD-XL                 | 149.438T | 2.570B    | 53.187s | 24.628                    |
+| SD-XL w/ T-GATE        | 84.438T  | 2.024B    | 27.932s | 22.738                    |
+| Pixart-Alpha          | 107.031T | 611.350M  | 61.502s | 38.669                    |
+| Pixart-Alpha w/ T-GATE | 65.318T  | 462.585M  | 37.867s | 35.825                    |
+| DeepCache (SD-XL)     | 57.888T  | -         | 19.931s | 23.755                    |
+| DeepCache w/ T-GATE    | 43.868T  | -         | 14.666s | 23.999                    |
+| LCM (SD-XL)           | 11.955T  | 2.570B    | 3.805s  | 25.044                    |
+| LCM w/ T-GATE          | 11.171T  | 2.024B    | 3.533s  | 25.028                    |
+| LCM (Pixart-Alpha)    | 8.563T   | 611.350M  | 4.733s  | 36.086                    |
+| LCM w/ T-GATE          | 7.623T   | 462.585M  | 4.543s  | 37.048                    |
+
+The latency is tested on an NVIDIA 1080TI, MACs and Params are calculated with [calflops](https://github.com/MrYxJ/calculate-flops.pytorch), and the FID is calculated with [PytorchFID](https://github.com/mseitzer/pytorch-fid).