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app.py
CHANGED
@@ -9,7 +9,7 @@ import copy
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import random
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import time
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from mod import (models, clear_cache, get_repo_safetensors, change_base_model,
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description_ui, num_loras, compose_lora_json, is_valid_lora, fuse_loras, get_trigger_word)
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from flux import (search_civitai_lora, select_civitai_lora, search_civitai_lora_json,
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download_my_lora, get_all_lora_tupled_list, apply_lora_prompt,
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update_loras)
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@@ -22,10 +22,6 @@ from tagger.fl2flux import predict_tags_fl2_flux
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with open('loras.json', 'r') as f:
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loras = json.load(f)
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# Initialize the base model
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base_model = models[0]
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pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=torch.bfloat16)
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MAX_SEED = 2**32-1
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class calculateDuration:
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@@ -228,7 +224,7 @@ with gr.Blocks(theme=gr.themes.Soft(), fill_width=True, css=css) as app:
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outputs=[result, seed]
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)
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model_name.change(change_base_model, [model_name], None)
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gr.on(
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triggers=[lora_search_civitai_submit.click, lora_search_civitai_query.submit],
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import random
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import time
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from mod import (models, clear_cache, get_repo_safetensors, change_base_model,
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description_ui, num_loras, compose_lora_json, is_valid_lora, fuse_loras, get_trigger_word, pipe)
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from flux import (search_civitai_lora, select_civitai_lora, search_civitai_lora_json,
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download_my_lora, get_all_lora_tupled_list, apply_lora_prompt,
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update_loras)
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with open('loras.json', 'r') as f:
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loras = json.load(f)
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MAX_SEED = 2**32-1
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class calculateDuration:
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outputs=[result, seed]
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)
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model_name.change(change_base_model, [model_name], None, queue=False)
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gr.on(
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triggers=[lora_search_civitai_submit.click, lora_search_civitai_query.submit],
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mod.py
CHANGED
@@ -68,12 +68,15 @@ def get_repo_safetensors(repo_id: str):
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else: return gr.update(value=files[0], choices=files)
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def change_base_model(repo_id: str):
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from huggingface_hub import HfApi
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global pipe
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api = HfApi()
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try:
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if
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clear_cache()
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pipe = DiffusionPipeline.from_pretrained(repo_id, torch_dtype=torch.bfloat16)
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except Exception as e:
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else: return gr.update(value=files[0], choices=files)
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# Initialize the base model
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base_model = models[0]
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pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=torch.bfloat16)
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def change_base_model(repo_id: str):
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global pipe
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try:
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if not is_repo_name(repo_id) or not is_repo_exists(repo_id): return
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clear_cache()
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pipe = DiffusionPipeline.from_pretrained(repo_id, torch_dtype=torch.bfloat16)
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except Exception as e:
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