@@ -85,6 +85,7 @@ def save_model_card(
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images = None ,
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base_model : str = None ,
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instance_prompt = None ,
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+ system_prompt = None ,
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validation_prompt = None ,
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repo_folder = None ,
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):
@@ -112,6 +113,8 @@ def save_model_card(
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You should use `{ instance_prompt } ` to trigger the image generation.
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+ The following `system_prompt` was also used used during training (ignore if `None`): { system_prompt } .
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+
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## Download model
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[Download the *.safetensors LoRA]({ repo_id } /tree/main) in the Files & versions tab.
@@ -1373,8 +1376,8 @@ def get_sigmas(timesteps, n_dim=4, dtype=torch.float32):
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noisy_model_input = (1.0 - sigmas ) * noise + sigmas * model_input
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# Predict the noise residual
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- # reverse the timestep since Lumina uses t=0 as the noise and t=1 as the image
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- timesteps = 1 - timesteps / noise_scheduler .config .num_train_timesteps
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+ # scale the timesteps (reversal not needed as we used a reverse lerp above already)
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+ timesteps = timesteps / noise_scheduler .config .num_train_timesteps
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model_pred = transformer (
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hidden_states = noisy_model_input ,
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encoder_hidden_states = prompt_embeds ,
@@ -1532,6 +1535,7 @@ def get_sigmas(timesteps, n_dim=4, dtype=torch.float32):
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images = images ,
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base_model = args .pretrained_model_name_or_path ,
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instance_prompt = args .instance_prompt ,
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+ system_prompt = args .system_prompt ,
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validation_prompt = args .validation_prompt ,
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repo_folder = args .output_dir ,
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)
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