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Update app.py
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app.py
CHANGED
@@ -36,7 +36,7 @@ controlnet_model = 'InstantX/FLUX.1-dev-Controlnet-Canny-alpha'
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# t5_slider_controlnet = T5SliderFlux(sd_pipe=pipe_controlnet,device=torch.device("cuda"))
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@spaces.GPU(duration=200)
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def generate(slider_x, prompt, seed, recalc_directions, iterations, steps, interm_steps, guidance_scale,
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x_concept_1, x_concept_2,
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avg_diff_x,
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img2img_type = None, img = None,
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@@ -55,8 +55,8 @@ def generate(slider_x, prompt, seed, recalc_directions, iterations, steps, inter
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x_concept_1, x_concept_2 = slider_x[0], slider_x[1]
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images = []
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high_scale =
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low_scale = -1 *
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for i in range(interm_steps):
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cur_scale = low_scale + (high_scale - low_scale) * i / (steps - 1)
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image = clip_slider.generate(prompt,
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@@ -71,7 +71,7 @@ def generate(slider_x, prompt, seed, recalc_directions, iterations, steps, inter
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avg_diff_x = avg_diff.cpu()
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return gr.update(label=comma_concepts_x, interactive=True, value=
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@spaces.GPU
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def update_scales(x,prompt,seed, steps, interm_steps, guidance_scale,
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@@ -174,7 +174,7 @@ with gr.Blocks(css=css) as demo:
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slider_x = gr.Dropdown(label="Slider concept range", allow_custom_value=True, multiselect=True, max_choices=2)
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#slider_y = gr.Dropdown(label="Slider Y concept range", allow_custom_value=True, multiselect=True, max_choices=2)
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prompt = gr.Textbox(label="Prompt")
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x = gr.Slider(minimum=0, value=1.25, step=0.1, maximum=2.5,
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submit = gr.Button("find directions")
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with gr.Column():
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with gr.Group(elem_id="group"):
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@@ -247,7 +247,7 @@ with gr.Blocks(css=css) as demo:
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# inputs=[slider_x, slider_y, prompt, seed, iterations, steps, guidance_scale, x_concept_1, x_concept_2, y_concept_1, y_concept_2, avg_diff_x, avg_diff_y],
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# outputs=[x, y, x_concept_1, x_concept_2, y_concept_1, y_concept_2, avg_diff_x, avg_diff_y, output_image])
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submit.click(fn=generate,
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inputs=[slider_x, prompt, seed, recalc_directions, iterations, steps, interm_steps, guidance_scale, x_concept_1, x_concept_2, avg_diff_x],
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outputs=[x, x_concept_1, x_concept_2, avg_diff_x, output_image, image_seq])
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iterations.change(fn=reset_recalc_directions, outputs=[recalc_directions])
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# t5_slider_controlnet = T5SliderFlux(sd_pipe=pipe_controlnet,device=torch.device("cuda"))
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@spaces.GPU(duration=200)
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def generate(slider_x, scale, prompt, seed, recalc_directions, iterations, steps, interm_steps, guidance_scale,
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x_concept_1, x_concept_2,
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avg_diff_x,
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img2img_type = None, img = None,
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x_concept_1, x_concept_2 = slider_x[0], slider_x[1]
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images = []
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high_scale = scale
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low_scale = -1 * scale
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for i in range(interm_steps):
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cur_scale = low_scale + (high_scale - low_scale) * i / (steps - 1)
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image = clip_slider.generate(prompt,
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avg_diff_x = avg_diff.cpu()
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return gr.update(label=comma_concepts_x, interactive=True, value=scale), x_concept_1, x_concept_2, avg_diff_x, export_to_gif(images, "clip.gif", fps=5), canvas
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@spaces.GPU
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def update_scales(x,prompt,seed, steps, interm_steps, guidance_scale,
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slider_x = gr.Dropdown(label="Slider concept range", allow_custom_value=True, multiselect=True, max_choices=2)
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#slider_y = gr.Dropdown(label="Slider Y concept range", allow_custom_value=True, multiselect=True, max_choices=2)
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prompt = gr.Textbox(label="Prompt")
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x = gr.Slider(minimum=0, value=1.25, step=0.1, maximum=2.5, info="the strength to scale in each direction")
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submit = gr.Button("find directions")
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with gr.Column():
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with gr.Group(elem_id="group"):
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# inputs=[slider_x, slider_y, prompt, seed, iterations, steps, guidance_scale, x_concept_1, x_concept_2, y_concept_1, y_concept_2, avg_diff_x, avg_diff_y],
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# outputs=[x, y, x_concept_1, x_concept_2, y_concept_1, y_concept_2, avg_diff_x, avg_diff_y, output_image])
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submit.click(fn=generate,
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inputs=[slider_x, x, prompt, seed, recalc_directions, iterations, steps, interm_steps, guidance_scale, x_concept_1, x_concept_2, avg_diff_x],
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outputs=[x, x_concept_1, x_concept_2, avg_diff_x, output_image, image_seq])
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iterations.change(fn=reset_recalc_directions, outputs=[recalc_directions])
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