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@@ -19,6 +19,8 @@ Our generative model has `Next-DiT` as the backbone, the text encoder is the `Ge
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  ## πŸ“° News
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  - [2024-5-16] ❗❗❗ We have converted the `.pth` weights to `.safetensors` weights. Please pull the latest code to use `demo.py` for inference.
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  - [2024-5-12] πŸš€πŸš€πŸš€ We release the next version of `Lumina-T2I`, called `Lumina-Next-T2I` for faster and lower memory usage image generation model.
@@ -164,13 +166,6 @@ Update your own personal inference settings to generate different styles of imag
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  reverse: false # option: true or false
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  likelihood: false # option: true or false
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- sde:
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- sampling_method: "Euler" # option: ["Euler", "Heun"]
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- diffusion_form: "sigma" # option: ["constant", "SBDM", "sigma", "linear", "decreasing", "increasing-decreasing"]
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- diffusion_norm: 1.0 # range: 0-1
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- last_step: Mean # option: [None, "Mean", "Tweedie", "Euler"]
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- last_step_size: 0.04
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-
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  infer:
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  resolution: "1024x1024" # option: ["1024x1024", "512x2048", "2048x512", "(Extrapolation) 1664x1664", "(Extrapolation) 1024x2048", "(Extrapolation) 2048x1024"]
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  num_sampling_steps: 60 # range: 1-1000
@@ -197,12 +192,6 @@ Update your own personal inference settings to generate different styles of imag
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  - `rtol`: Relative tolerance for the ODE solver. (option: ["velocity", "score", "noise"])
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  - `reverse`: run the ODE solver in reverse. (option: [None, "velocity", "likelihood"])
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  - `likelihood`: Enable calculation of likelihood during the ODE solving process.
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- - sde
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- - `sampling-method`: the numerical method used for sampling the stochastic differential equation: 'Euler' for simplicity or 'Heun' for improved accuracy.
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- - `diffusion-form`: form of diffusion coefficient in the SDE
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- - `diffusion-norm`: Normalizes the diffusion coefficient, affecting the scale of the stochastic component.
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- - `last-step`: form of last step taken in the SDE
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- - `last-step-size`: size of the last step taken
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  - infer
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  - `resolution`: generated image resolution.
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  - `num_sampling_steps`: sampling step for generating image.
 
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  ## πŸ“° News
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+ - [2024-5-28] πŸš€πŸš€πŸš€ We updated the `Lumina-Next-T2I` model for supporting 2K Resolution image generation.
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+
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  - [2024-5-16] ❗❗❗ We have converted the `.pth` weights to `.safetensors` weights. Please pull the latest code to use `demo.py` for inference.
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  - [2024-5-12] πŸš€πŸš€πŸš€ We release the next version of `Lumina-T2I`, called `Lumina-Next-T2I` for faster and lower memory usage image generation model.
 
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  reverse: false # option: true or false
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  likelihood: false # option: true or false
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  infer:
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  resolution: "1024x1024" # option: ["1024x1024", "512x2048", "2048x512", "(Extrapolation) 1664x1664", "(Extrapolation) 1024x2048", "(Extrapolation) 2048x1024"]
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  num_sampling_steps: 60 # range: 1-1000
 
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  - `rtol`: Relative tolerance for the ODE solver. (option: ["velocity", "score", "noise"])
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  - `reverse`: run the ODE solver in reverse. (option: [None, "velocity", "likelihood"])
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  - `likelihood`: Enable calculation of likelihood during the ODE solving process.
 
 
 
 
 
 
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  - infer
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  - `resolution`: generated image resolution.
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  - `num_sampling_steps`: sampling step for generating image.