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Fix preview for textual inversion training
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@ -386,7 +386,7 @@ def validate_train_inputs(model_name, learn_rate, batch_size, gradient_step, dat
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assert log_directory, "Log directory is empty"
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def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, varsize, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, use_weight, create_image_every, save_embedding_every, template_filename, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
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def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, varsize, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, use_weight, create_image_every, save_embedding_every, template_filename, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_name, preview_cfg_scale, preview_seed, preview_width, preview_height):
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from modules import processing
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save_embedding_every = save_embedding_every or 0
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@ -590,7 +590,7 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st
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p.prompt = preview_prompt
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p.negative_prompt = preview_negative_prompt
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p.steps = preview_steps
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p.sampler_name = sd_samplers.samplers[preview_sampler_index].name
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p.sampler_name = sd_samplers.samplers_map[preview_sampler_name.lower()]
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p.cfg_scale = preview_cfg_scale
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p.seed = preview_seed
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p.width = preview_width
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