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likelovewant
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README.md
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README.md
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## Sd-Forge-TeaCache: Speed up Your Diffusion Models
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**Introduction**
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Timestep Embedding Aware Cache (TeaCache) is a revolutionary training-free caching approach that leverages the
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fluctuating differences between model outputs across timesteps. This acceleration technique significantly boosts
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inference speed for various diffusion models, including Image, Video, and Audio.
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TeaCache's integration into SD Forge WebUI for Flux only. Installation is as
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straightforward as any other extension:
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* **Clone:** `git clone https://github.com/likelovewant/sd-forge-teacache.git`
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into extensions directory ,relauch the system .
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**Speed Up Your Diffusion Generation**
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TeaCache can accelerate FLUX inference by up to 2x with minimal visual quality degradation, all without requiring any training.
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Within the Forge WebUI, you can easily adjust the following settings:
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* **Relative L1 Threshold:** Controls the sensitivity of TeaCache's caching mechanism.
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* **Steps:** Matches the number of sampling steps used in TeaCache.
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**Performance Tuning**
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Based on [TeaCache4FLUX](https://github.com/ali-vilab/TeaCache/tree/main/TeaCache4FLUX), you can achieve different
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speedups:
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* 0.25 threshold for 1.5x speedup
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* 0.4 threshold for 1.8x speedup
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* 0.6 threshold for 2.0x speedup
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* 0.8 threshold for 2.25x speedup
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**Important Notes:**
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* **Maintain Consistency:** Keep the sampling steps in TeaCache aligned with the steps used in your Flux Sampling steps .Discrepancies can lead to lower quality outputs.
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* **LoRA Considerations:** When utilizing LoRAs, adjust the steps or scales based on your GPU's capabilities. A recommended starting point is 28 steps or more.
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To ensure smooth operation, remember to:
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1. **Clear Residual Cache (optional):** When changing image sizes or disabling the TeaCache extension, always click "Clear Residual Cache" within the Forge WebUI. This prevents potential conflicts and maintains optimal performance.
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2. **Disable TeaCache Properly:** Ensure disable the TeaCache extension if you don't need it in your Forge WebUI. If not proper `Clear Residual Cache`, you may encounter unexpected behavior and require a full relaunch.
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Several AI assistants has assisting with code generation and refinement for this extension based on the below resources.
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**Credits and Resources**
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This adaptation leverages [TeaCache4FLUX](https://github.com/ali-vilab/TeaCache/tree/main/TeaCache4FLUX) and
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builds upon the foundational work of the original TeaCache repository:
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[https://github.com/ali-vilab/TeaCache](https://github.com/ali-vilab/TeaCache).
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For additional information and other integrations, explore:
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* [ComfyUI-TeaCache](https://github.com/welltop-cn/ComfyUI-TeaCache)
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245
scripts/teacache.py
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scripts/teacache.py
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import torch
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import numpy as np
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from torch import Tensor
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import gradio as gr
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from modules import scripts
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from modules.ui_components import InputAccordion
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from backend.nn.flux import IntegratedFluxTransformer2DModel, timestep_embedding
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class TeaCache(scripts.Script):
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def __init__(self):
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super().__init__()
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self.enable_teacache = False
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self.rel_l1_thresh = 0.4
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self.steps = 25
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self.last_input_shape = None # Record the last input dimensions
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def title(self):
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return "TeaCache"
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def show(self, is_img2img):
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return scripts.AlwaysVisible
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def ui(self, is_img2img):
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with InputAccordion(False, label=self.title(), elem_id="extensions-teacache") as teacache_enabled:
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with gr.Row():
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enable_teacache_checkbox = gr.Checkbox(
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label="Enable TeaCache",
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value=self.enable_teacache,
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tooltip="Enable TeaCache to speed up inference by caching intermediate results."
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)
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with gr.Row():
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rel_l1_thresh_slider = gr.Slider(
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label="Relative L1 Threshold",
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minimum=0.0,
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maximum=1.0,
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step=0.01,
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value=self.rel_l1_thresh,
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tooltip="Threshold for caching intermediate results. Lower values cache more aggressively."
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)
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with gr.Row():
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steps_slider = gr.Slider(
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label="Steps",
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minimum=1,
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maximum=100,
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step=1,
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value=self.steps,
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tooltip="Number of steps to cache intermediate results."
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)
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with gr.Row():
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clear_cache_button = gr.Button("Clear Residual Cache", variant="secondary")
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with gr.Row():
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gr.Markdown("**Note**: Clear residual cache when changing image size or disabling TeaCache.")
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# Define the function to clear residual cache
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def clear_residual_cache_ui():
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self.clear_residual_cache()
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return "Residual cache cleared."
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# Bind the button click event
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clear_cache_button.click(
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fn=clear_residual_cache_ui,
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outputs=None
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)
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# Return UI components
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return [teacache_enabled, enable_teacache_checkbox, rel_l1_thresh_slider, steps_slider]
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def clear_residual_cache(self):
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"""Clear residual cache and free GPU memory."""
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if hasattr(IntegratedFluxTransformer2DModel, "previous_residual"):
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setattr(IntegratedFluxTransformer2DModel, "previous_residual", None)
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if hasattr(IntegratedFluxTransformer2DModel, "previous_modulated_input"):
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setattr(IntegratedFluxTransformer2DModel, "previous_modulated_input", None)
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if hasattr(IntegratedFluxTransformer2DModel, "cnt"):
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setattr(IntegratedFluxTransformer2DModel, "cnt", 0)
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if hasattr(IntegratedFluxTransformer2DModel, "accumulated_rel_l1_distance"):
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setattr(IntegratedFluxTransformer2DModel, "accumulated_rel_l1_distance", 0)
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# Free GPU memory
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torch.cuda.empty_cache()
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print("Residual cache cleared and GPU memory freed.")
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def process(self, p, teacache_enabled, enable_teacache_checkbox, rel_l1_thresh_slider, steps_slider):
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# Get the current input dimensions
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current_input_shape = (p.width, p.height)
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if self.last_input_shape is not None and current_input_shape != self.last_input_shape:
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# If input dimensions change, clear residual cache
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self.clear_residual_cache()
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self.last_input_shape = current_input_shape
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# Debug information
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print("TeaCache enabled:", teacache_enabled)
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print("Enable TeaCache checkbox:", enable_teacache_checkbox)
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print("Relative L1 Threshold:", rel_l1_thresh_slider)
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print("Steps:", steps_slider)
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# If TeaCache is enabled, add parameters to generation parameters
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if teacache_enabled:
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p.extra_generation_params.update({
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"enable_teacache": enable_teacache_checkbox,
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"rel_l1_thresh": rel_l1_thresh_slider,
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"steps": steps_slider,
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})
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# Dynamically modify class attributes of IntegratedFluxTransformer2DModel
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setattr(IntegratedFluxTransformer2DModel, "enable_teacache", enable_teacache_checkbox)
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setattr(IntegratedFluxTransformer2DModel, "cnt", 0)
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setattr(IntegratedFluxTransformer2DModel, "rel_l1_thresh", rel_l1_thresh_slider)
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setattr(IntegratedFluxTransformer2DModel, "steps", steps_slider)
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setattr(IntegratedFluxTransformer2DModel, "accumulated_rel_l1_distance", 0)
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setattr(IntegratedFluxTransformer2DModel, "previous_modulated_input", None)
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setattr(IntegratedFluxTransformer2DModel, "previous_residual", None)
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# Replace the original inner_forward method
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if hasattr(IntegratedFluxTransformer2DModel, "inner_forward"):
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# Save the original inner_forward method
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setattr(IntegratedFluxTransformer2DModel, "original_inner_forward", IntegratedFluxTransformer2DModel.inner_forward)
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# Replace with the new inner_forward method
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IntegratedFluxTransformer2DModel.inner_forward = patched_inner_forward
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# Replace the original forward method
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if not hasattr(IntegratedFluxTransformer2DModel, "original_forward"):
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IntegratedFluxTransformer2DModel.original_forward = IntegratedFluxTransformer2DModel.forward
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IntegratedFluxTransformer2DModel.forward = patched_forward
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else:
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# If TeaCache is disabled, restore the original inner_forward method
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if hasattr(IntegratedFluxTransformer2DModel, "original_inner_forward"):
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IntegratedFluxTransformer2DModel.inner_forward = IntegratedFluxTransformer2DModel.original_inner_forward
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# Clear residual cache and reset TeaCache attributes
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self.clear_residual_cache()
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setattr(IntegratedFluxTransformer2DModel, "enable_teacache", False)
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setattr(IntegratedFluxTransformer2DModel, "cnt", 0)
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setattr(IntegratedFluxTransformer2DModel, "rel_l1_thresh", 0.4)
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setattr(IntegratedFluxTransformer2DModel, "steps", 25)
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setattr(IntegratedFluxTransformer2DModel, "accumulated_rel_l1_distance", 0)
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setattr(IntegratedFluxTransformer2DModel, "previous_modulated_input", None)
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setattr(IntegratedFluxTransformer2DModel, "previous_residual", None)
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print("TeaCache fully disabled and cache cleared.")
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# Restore the original forward method
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if hasattr(IntegratedFluxTransformer2DModel, "original_forward"):
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IntegratedFluxTransformer2DModel.forward = IntegratedFluxTransformer2DModel.original_forward
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def patched_inner_forward(self, img, img_ids, txt, txt_ids, timesteps, y, guidance=None):
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# Print "TeaCache is enabled!" only once per generation
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if self.enable_teacache:
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if not hasattr(self, "_teacache_enabled_printed"): # Check if the message has been printed
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print("TeaCache is enabled!")
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self._teacache_enabled_printed = True # Set flag to avoid repeated printing
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# If TeaCache is not enabled, call the original method
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if not hasattr(self, "enable_teacache") or not self.enable_teacache:
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return self.original_inner_forward(img, img_ids, txt, txt_ids, timesteps, y, guidance)
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# Get parameters from UI
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rel_l1_thresh = getattr(self, "rel_l1_thresh", 0.4)
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steps = getattr(self, "steps", 25)
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# TeaCache logic
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if img.ndim != 3 or txt.ndim != 3:
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raise ValueError("Input img and txt tensors must have 3 dimensions.")
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# Image and text embedding
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img = self.img_in(img)
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vec = self.time_in(timestep_embedding(timesteps, 256).to(img.dtype))
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# If guidance_embed is enabled, add guidance information
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if self.guidance_embed:
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if guidance is None:
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raise ValueError("Didn't get guidance strength for guidance distilled model.")
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vec = vec + self.guidance_in(timestep_embedding(guidance, 256).to(img.dtype))
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vec = vec + self.vector_in(y)
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txt = self.txt_in(txt)
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# Merge image and text IDs
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ids = torch.cat((txt_ids, img_ids), dim=1)
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pe = self.pe_embedder(ids)
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# TeaCache logic
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inp = img.clone()
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vec_ = vec.clone()
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modulated_inp = img.clone() # Use img.clone() directly as modulated_inp
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# Check if previous_modulated_input exists and has the correct shape
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if hasattr(self, "previous_modulated_input") and self.previous_modulated_input is not None:
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if self.previous_modulated_input.shape != modulated_inp.shape:
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# Clear cache if shapes don't match
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self.previous_modulated_input = None
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self.previous_residual = None
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print(" Cleared cache due to shape mismatch.")
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if self.cnt == 0 or self.cnt == steps - 1:
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should_calc = True
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self.accumulated_rel_l1_distance = 0
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else:
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if hasattr(self, "previous_modulated_input") and self.previous_modulated_input is not None:
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coefficients = [4.98651651e+02, -2.83781631e+02, 5.58554382e+01, -3.82021401e+00, 2.64230861e-01]
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rescale_func = np.poly1d(coefficients)
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self.accumulated_rel_l1_distance += rescale_func(
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((modulated_inp - self.previous_modulated_input).abs().mean() / self.previous_modulated_input.abs().mean()).cpu().item()
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)
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if self.accumulated_rel_l1_distance < rel_l1_thresh:
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should_calc = False
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else:
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should_calc = True
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self.accumulated_rel_l1_distance = 0
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else:
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should_calc = True
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self.previous_modulated_input = modulated_inp
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self.cnt += 1
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if self.cnt == steps:
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self.cnt = 0
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if not should_calc:
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if hasattr(self, "previous_residual") and self.previous_residual is not None:
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img += self.previous_residual
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else:
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ori_img = img.clone()
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for block in self.double_blocks:
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img, txt = block(img=img, txt=txt, vec=vec, pe=pe)
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img = torch.cat((txt, img), 1)
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for block in self.single_blocks:
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img = block(img, vec=vec, pe=pe)
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img = img[:, txt.shape[1]:, ...]
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self.previous_residual = img - ori_img
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# Final output
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img = self.final_layer(img, vec)
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return img
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def patched_forward(self, x, timestep, context, y, guidance=None, **kwargs):
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# Set TeaCache parameters if provided
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if hasattr(self, "enable_teacache"):
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self.enable_teacache = kwargs.get("enable_teacache", self.enable_teacache)
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if hasattr(self, "rel_l1_thresh"):
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self.rel_l1_thresh = kwargs.get("rel_l1_thresh", self.rel_l1_thresh)
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if hasattr(self, "steps"):
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self.steps = kwargs.get("steps", self.steps)
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# Call the original forward method
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return self.original_forward(x, timestep, context, y, guidance, **kwargs)
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