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  1. README.md +1 -1
  2. app.py +1 -0
  3. modules/Attention/AttentionMethods.py +2 -2
README.md CHANGED
@@ -8,6 +8,7 @@ sdk_version: 5.14.0
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  # Say hi to LightDiffusion-Next 👋
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  **LightDiffusion-Next** is the fastest AI-powered image generation GUI/CLI, combining speed, precision, and flexibility in one cohesive tool.
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  </br>
@@ -19,7 +20,6 @@ sdk_version: 5.14.0
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  </br>
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  </div>
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-
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  As a refactored and improved version of the original [LightDiffusion repository](https://github.com/Aatrick/LightDiffusion), this project enhances usability, maintainability, and functionality while introducing a host of new features to streamline your creative workflows.
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  ## Motivation:
 
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  # Say hi to LightDiffusion-Next 👋
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+ [![demo platform](https://img.shields.io/badge/Play%20with%20LightDiffusion%21-LightDiffusion%20demo%20platform-lightblue)](https://huggingface.co/spaces/Aatricks/LightDiffusion-Next)&nbsp;
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  **LightDiffusion-Next** is the fastest AI-powered image generation GUI/CLI, combining speed, precision, and flexibility in one cohesive tool.
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  </br>
 
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  </br>
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  </div>
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  As a refactored and improved version of the original [LightDiffusion repository](https://github.com/Aatrick/LightDiffusion), this project enhances usability, maintainability, and functionality while introducing a host of new features to streamline your creative workflows.
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  ## Motivation:
app.py CHANGED
@@ -91,6 +91,7 @@ def generate_images(
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  with gr.Blocks(title="LightDiffusion Web UI") as demo:
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  gr.Markdown("# LightDiffusion Web UI")
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  gr.Markdown("Generate AI images using LightDiffusion")
 
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  with gr.Row():
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  with gr.Column():
 
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  with gr.Blocks(title="LightDiffusion Web UI") as demo:
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  gr.Markdown("# LightDiffusion Web UI")
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  gr.Markdown("Generate AI images using LightDiffusion")
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+ gr.Markdown("")
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  with gr.Row():
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  with gr.Column():
modules/Attention/AttentionMethods.py CHANGED
@@ -6,7 +6,7 @@ import torch
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  def attention_xformers(
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- q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, heads: int, mask=None
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  ) -> torch.Tensor:
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  """#### Make an attention call using xformers. Fastest attention implementation.
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@@ -44,7 +44,7 @@ def attention_xformers(
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  def attention_pytorch(
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- q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, heads: int, mask=None
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  ) -> torch.Tensor:
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  """#### Make an attention call using PyTorch.
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  def attention_xformers(
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+ q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, heads: int, mask=None, skip_reshape=False
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  ) -> torch.Tensor:
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  """#### Make an attention call using xformers. Fastest attention implementation.
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  def attention_pytorch(
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+ q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, heads: int, mask=None, skip_reshape=False
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  ) -> torch.Tensor:
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  """#### Make an attention call using PyTorch.
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