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Update app.py
Browse files
app.py
CHANGED
@@ -33,6 +33,7 @@ def invert(x0, prompt_src, num_diffusion_steps, cfg_scale_src): # , ldm_stable)
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return zs, wts
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def sample(zs, wts, steps, prompt_tar, tstart, cfg_scale_tar): # , ldm_stable):
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# reverse process (via Zs and wT)
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tstart = torch.tensor(tstart, dtype=torch.int)
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@@ -57,6 +58,14 @@ def sample(zs, wts, steps, prompt_tar, tstart, cfg_scale_tar): # , ldm_stable):
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return f.name
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def edit(input_audio,
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model_id: str,
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@@ -92,6 +101,9 @@ def edit(input_audio,
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zs = gr.State(value=zs_tensor)
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saved_inv_model = model_id
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do_inversion = False
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output = sample(zs.value, wts.value, steps, prompt_tar=target_prompt, tstart=t_start,
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cfg_scale_tar=cfg_scale_tar)
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@@ -221,21 +233,14 @@ with gr.Blocks(css='style.css') as demo:
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label="Source Guidance Scale", interactive=True, scale=1)
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cfg_scale_tar = gr.Number(value=12, minimum=0.5, maximum=25, precision=None,
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label="Target Guidance Scale", interactive=True, scale=1)
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steps = gr.Number(value=200,
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label="Num Diffusion Steps", interactive=True, scale=1)
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with gr.Row():
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seed = gr.Number(value=0, precision=0, label="Seed", interactive=True)
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randomize_seed = gr.Checkbox(label='Randomize seed', value=False)
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length = gr.Number(label="Length", interactive=False, visible=False)
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-
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t_start.maximum = int(160/200 * steps)
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t_start.minimum = int(30/200 * steps)
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if t_start.value > t_start.maximum:
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t_start.value = t_start.maximum
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if t_start.value < t_start.minimum:
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t_start.value = t_start.minimum
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return t_start
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submit.click(
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fn=randomize_seed_fn,
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@@ -262,7 +267,7 @@ with gr.Blocks(css='style.css') as demo:
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input_audio.change(fn=reset_do_inversion, outputs=[do_inversion])
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src_prompt.change(fn=reset_do_inversion, outputs=[do_inversion])
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model_id.change(fn=reset_do_inversion, outputs=[do_inversion])
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steps.change(fn=change_tstart_range, inputs=[steps], outputs=[t_start])
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gr.Examples(
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label="Examples",
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return zs, wts
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+
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def sample(zs, wts, steps, prompt_tar, tstart, cfg_scale_tar): # , ldm_stable):
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# reverse process (via Zs and wT)
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tstart = torch.tensor(tstart, dtype=torch.int)
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return f.name
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def change_tstart_range(t_start, steps):
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maximum = int(0.8 * steps)
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minimum = int(0.15 * steps)
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if t_start > maximum:
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t_start = maximum
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elif t_start < minimum:
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t_start = minimum
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return t_start
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def edit(input_audio,
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model_id: str,
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zs = gr.State(value=zs_tensor)
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saved_inv_model = model_id
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do_inversion = False
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# make sure t_start is in the right limit
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t_start = change_tstart_range(t_start, steps)
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output = sample(zs.value, wts.value, steps, prompt_tar=target_prompt, tstart=t_start,
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cfg_scale_tar=cfg_scale_tar)
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label="Source Guidance Scale", interactive=True, scale=1)
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cfg_scale_tar = gr.Number(value=12, minimum=0.5, maximum=25, precision=None,
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label="Target Guidance Scale", interactive=True, scale=1)
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steps = gr.Number(value=200, step=1, minimum=20, maximum=1000,
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label="Num Diffusion Steps", interactive=True, scale=1)
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with gr.Row():
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seed = gr.Number(value=0, precision=0, label="Seed", interactive=True)
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randomize_seed = gr.Checkbox(label='Randomize seed', value=False)
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length = gr.Number(label="Length", interactive=False, visible=False)
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submit.click(
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fn=randomize_seed_fn,
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input_audio.change(fn=reset_do_inversion, outputs=[do_inversion])
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src_prompt.change(fn=reset_do_inversion, outputs=[do_inversion])
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model_id.change(fn=reset_do_inversion, outputs=[do_inversion])
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# steps.change(fn=change_tstart_range, inputs=[steps], outputs=[t_start])
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gr.Examples(
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label="Examples",
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