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app.py
CHANGED
@@ -110,7 +110,9 @@ def run_lora(prompt, cfg_scale, steps, selected_index, randomize_seed, seed, wid
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image = generate_image(prompt, trigger_word, steps, seed, cfg_scale, width, height, lora_scale, progress)
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pipe.to("cpu")
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if selected_index is not None
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clear_cache()
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return image, seed
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@@ -211,7 +213,9 @@ with gr.Blocks(theme=gr.themes.Soft(), fill_width=True, css=css) as app:
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lora_search_civitai_json = gr.JSON(value={}, visible=False)
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lora_search_civitai_desc = gr.Markdown(value="", visible=False)
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lora_download_url = gr.Textbox(label="URL", placeholder="http://...my_lora_url.safetensors", lines=1)
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gallery.select(
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update_selection,
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@@ -247,8 +251,8 @@ with gr.Blocks(theme=gr.themes.Soft(), fill_width=True, css=css) as app:
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gr.on(
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triggers=[lora_download.click, lora_download_url.submit],
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fn=download_my_lora,
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inputs=[lora_download_url, lora_repo[
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outputs=[lora_repo[
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scroll_to_output=True,
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queue=True,
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show_api=False,
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image = generate_image(prompt, trigger_word, steps, seed, cfg_scale, width, height, lora_scale, progress)
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pipe.to("cpu")
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if selected_index is not None:
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pipe.unload_lora_weights()
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if is_valid_lora(lora_json): pipe.unfuse_lora()
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clear_cache()
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return image, seed
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lora_search_civitai_json = gr.JSON(value={}, visible=False)
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lora_search_civitai_desc = gr.Markdown(value="", visible=False)
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lora_download_url = gr.Textbox(label="URL", placeholder="http://...my_lora_url.safetensors", lines=1)
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with gr.Row():
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lora_slot = gr.Slider(label="Set to LoRA slot", minimum=1, maximum=num_loras+1, step=1, value=1)
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lora_download = gr.Button("Get and set LoRA")
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gallery.select(
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update_selection,
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gr.on(
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triggers=[lora_download.click, lora_download_url.submit],
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fn=download_my_lora,
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inputs=[lora_download_url, lora_repo[int(lora_slot.value - 1)]],
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outputs=[lora_repo[int(lora_slot.value - 1)]],
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scroll_to_output=True,
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queue=True,
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show_api=False,
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mod.py
CHANGED
@@ -134,7 +134,7 @@ def fuse_loras(pipe, lorajson: list[dict]):
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w_list.append(d["scale"])
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if not a_list: return
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pipe.set_adapters(a_list, adapter_weights=w_list)
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pipe.fuse_lora(adapter_names=a_list, lora_scale=1.0)
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#pipe.unload_lora_weights()
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w_list.append(d["scale"])
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if not a_list: return
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pipe.set_adapters(a_list, adapter_weights=w_list)
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#pipe.fuse_lora(adapter_names=a_list, lora_scale=1.0)
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#pipe.unload_lora_weights()
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