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Update app.py
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app.py
CHANGED
@@ -5,7 +5,6 @@ import timm
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import time
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from huggingface_hub import hf_hub_download
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from safetensors.torch import load_file
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from share_btn import community_icon_html, loading_icon_html, share_js
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from cog_sdxl_dataset_and_utils import TokenEmbeddingsHandler
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import lora
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@@ -148,13 +147,7 @@ pipe.to(device)
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last_lora = ""
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last_fused = False
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var button = document.getElementById('button');
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// Add a click event listener to the button
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button.addEventListener('click', function() {
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element.classList.add('selected');
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});
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'''
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def update_selection(selected_state: gr.SelectData, sdxl_loras, face_strength, image_strength, weight, depth_control_scale, negative, is_new=False):
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lora_repo = sdxl_loras[selected_state.index]["repo"]
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new_placeholder = "Type a prompt to use your selected LoRA"
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@@ -349,7 +342,7 @@ def run_lora(face_image, prompt, negative, lora_scale, selected_state, face_stre
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st = time.time()
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image = generate_image(prompt, negative, face_emb, face_image, face_kps, image_strength, guidance_scale, face_strength, depth_control_scale, repo_name, full_path_lora, lora_scale, sdxl_loras, selected_state_index, st)
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return image
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def shuffle_gallery(sdxl_loras):
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random.shuffle(sdxl_loras)
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@@ -368,7 +361,7 @@ def swap_gallery(order, sdxl_loras):
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def deselect():
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return gr.Gallery(selected_index=None)
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with gr.Blocks(
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gr_sdxl_loras = gr.State(value=sdxl_loras_raw)
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title = gr.HTML(
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"""<h1><img src="https://i.imgur.com/DVoGw04.png">
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@@ -409,10 +402,7 @@ with gr.Blocks(css="custom.css") as demo:
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result = gr.Image(
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interactive=False, label="Generated Image", elem_id="result-image"
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)
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community_icon = gr.HTML(community_icon_html)
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loading_icon = gr.HTML(loading_icon_html)
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share_button = gr.Button("Share to community", elem_id="share-btn")
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with gr.Accordion("Advanced options", open=False):
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negative = gr.Textbox(label="Negative Prompt")
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@@ -463,7 +453,7 @@ with gr.Blocks(css="custom.css") as demo:
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).success(
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fn=run_lora,
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inputs=[photo, prompt, negative, weight, selected_state, face_strength, image_strength, guidance_scale, depth_control_scale, gr_sdxl_loras],
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outputs=[result
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)
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button.click(
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fn=check_selected,
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@@ -473,9 +463,9 @@ with gr.Blocks(css="custom.css") as demo:
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).success(
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fn=run_lora,
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inputs=[photo, prompt, negative, weight, selected_state, face_strength, image_strength, guidance_scale, depth_control_scale, gr_sdxl_loras],
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outputs=[result
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)
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demo.load(fn=classify_gallery, inputs=[gr_sdxl_loras], outputs=[gallery, gr_sdxl_loras], queue=False
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demo.queue(max_size=20)
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demo.launch(
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import time
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from huggingface_hub import hf_hub_download
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from safetensors.torch import load_file
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from cog_sdxl_dataset_and_utils import TokenEmbeddingsHandler
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import lora
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last_lora = ""
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last_fused = False
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def update_selection(selected_state: gr.SelectData, sdxl_loras, face_strength, image_strength, weight, depth_control_scale, negative, is_new=False):
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lora_repo = sdxl_loras[selected_state.index]["repo"]
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new_placeholder = "Type a prompt to use your selected LoRA"
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st = time.time()
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image = generate_image(prompt, negative, face_emb, face_image, face_kps, image_strength, guidance_scale, face_strength, depth_control_scale, repo_name, full_path_lora, lora_scale, sdxl_loras, selected_state_index, st)
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return image
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def shuffle_gallery(sdxl_loras):
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random.shuffle(sdxl_loras)
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def deselect():
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return gr.Gallery(selected_index=None)
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with gr.Blocks() as demo:
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gr_sdxl_loras = gr.State(value=sdxl_loras_raw)
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title = gr.HTML(
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"""<h1><img src="https://i.imgur.com/DVoGw04.png">
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result = gr.Image(
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interactive=False, label="Generated Image", elem_id="result-image"
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)
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with gr.Accordion("Advanced options", open=False):
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negative = gr.Textbox(label="Negative Prompt")
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).success(
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fn=run_lora,
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inputs=[photo, prompt, negative, weight, selected_state, face_strength, image_strength, guidance_scale, depth_control_scale, gr_sdxl_loras],
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outputs=[result],
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)
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button.click(
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fn=check_selected,
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).success(
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fn=run_lora,
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inputs=[photo, prompt, negative, weight, selected_state, face_strength, image_strength, guidance_scale, depth_control_scale, gr_sdxl_loras],
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outputs=[result],
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)
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demo.load(fn=classify_gallery, inputs=[gr_sdxl_loras], outputs=[gallery, gr_sdxl_loras], queue=False)
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demo.queue(max_size=20)
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demo.launch()
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