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Runtime error
guardiancc
commited on
Update app.py
Browse files
app.py
CHANGED
@@ -20,9 +20,10 @@ print("downloading models - 4/4")
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hf_hub_download("XLabs-AI/flux-controlnet-canny", "controlnet.safetensors")
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print("downloaded!")
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def process_image(lora_path, lora_name, number_of_images, image, prompt, steps, use_lora, use_controlnet, use_depth, use_hed, use_ip, lora_weight, negative_image, neg_prompt, true_gs, guidance, cfg):
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from src.flux.xflux_pipeline import XFluxPipeline
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def run_xflux_pipeline(
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prompt, image, repo_id, name, device,
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model_type, width, height, timestep_to_start_cfg, num_steps, true_gs, guidance,
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@@ -139,58 +140,7 @@ def process_image(lora_path, lora_name, number_of_images, image, prompt, steps,
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)
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body {
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overflow-y: hidden;
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}
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.gradio-container {
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margin: unset !important;
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padding: unset !important;
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max-width: unset !important;
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}
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.gradio-app {
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min-height: unset !important;
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}
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.app {
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height: 100%;
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}
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.app-container {
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height: 100vh;
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background: rgb(24, 24, 27);
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gap: unset;
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}
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.sidebar {
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overflow-y: auto;
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height: 100%;
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max-width: 500px;
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padding: 1rem;
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background: rgb(31, 31, 35);
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border-right: 1px solid rgb(41, 41, 41);
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}
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.grid-wrap {
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max-height: unset;
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overflow-y: unset;
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}
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.thumbnail-item img {
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object-fit: contain;
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}
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.side_items {
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height: auto;
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}
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.form {
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flex-wrap: unset;
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height: auto;
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}
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"""
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with gr.Blocks(css=custom_css) as demo:
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with gr.Row(elem_classes="app-container"):
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with gr.Column(scale=1, min_width=500, elem_classes="sidebar"):
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with gr.Column(elem_classes="side_items"):
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@@ -208,7 +158,6 @@ with gr.Blocks(css=custom_css) as demo:
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lora_name = gr.Textbox(label="Lora Name", value="realism_lora.safetensors")
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lora_weight = gr.Slider(step=0.1, minimum=0, maximum=1, value=0.7, label="Lora Weight")
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number_of_images = gr.Slider(step=1, minimum=0, maximum=4, value=2, label="Number of Images")
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true_gs = gr.Slider(step=0.1, minimum=0, maximum=10, value=3.5, label="TrueGs")
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guidance = gr.Slider(minimum=1, maximum=10, value=4, label="Guidance")
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cfg = gr.Slider(minimum=1, maximum=10, value=1, label="CFG")
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with gr.Column(scale=2, elem_classes="app"):
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output = gr.Gallery(label="Galery output", elem_classes="galery")
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submit_btn.click(process_image, inputs=[lora_path, lora_name,
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if __name__ == '__main__':
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demo.launch(share=True, debug=True)
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hf_hub_download("XLabs-AI/flux-controlnet-canny", "controlnet.safetensors")
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print("downloaded!")
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from src.flux.xflux_pipeline import XFluxPipeline
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@spaces.GPU(duration=200)
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def process_image(lora_path, lora_name, number_of_images, image, prompt, steps, use_lora, use_controlnet, use_depth, use_hed, use_ip, lora_weight, negative_image, neg_prompt, true_gs, guidance, cfg):
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def run_xflux_pipeline(
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prompt, image, repo_id, name, device,
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model_type, width, height, timestep_to_start_cfg, num_steps, true_gs, guidance,
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)
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with gr.Blocks() as demo:
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with gr.Row(elem_classes="app-container"):
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with gr.Column(scale=1, min_width=500, elem_classes="sidebar"):
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with gr.Column(elem_classes="side_items"):
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lora_name = gr.Textbox(label="Lora Name", value="realism_lora.safetensors")
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lora_weight = gr.Slider(step=0.1, minimum=0, maximum=1, value=0.7, label="Lora Weight")
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true_gs = gr.Slider(step=0.1, minimum=0, maximum=10, value=3.5, label="TrueGs")
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guidance = gr.Slider(minimum=1, maximum=10, value=4, label="Guidance")
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cfg = gr.Slider(minimum=1, maximum=10, value=1, label="CFG")
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with gr.Column(scale=2, elem_classes="app"):
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output = gr.Gallery(label="Galery output", elem_classes="galery")
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submit_btn.click(process_image, inputs=[lora_path, lora_name, 1, input_image, prompt, steps, use_lora, controlnet, use_depth, use_hed, use_ip, lora_weight, negative_image, neg_prompt, true_gs, guidance, cfg], outputs=output)
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if __name__ == '__main__':
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demo.launch(share=True, debug=True)
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