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Update app.py
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app.py
CHANGED
@@ -104,7 +104,7 @@ def generate(slider_x, slider_y, prompt, seed, iterations, steps, guidance_scale
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elif img2img_type=="ip adapter" and img is not None:
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image = clip_slider.generate(prompt, guidance_scale=guidance_scale, ip_adapter_image=img, scale=0, scale_2nd=0, seed=seed, num_inference_steps=steps, avg_diff=(avg_diff_0,avg_diff_1), avg_diff_2nd=(avg_diff_2nd_0,avg_diff_2nd_1))
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elif img2img_type=="inversion":
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image = clip_slider.generate(prompt, guidance_scale=guidance_scale,
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else: # text to image
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image = clip_slider.generate(prompt, guidance_scale=guidance_scale, scale=0, scale_2nd=0, seed=seed, num_inference_steps=steps, avg_diff=(avg_diff_0,avg_diff_1), avg_diff_2nd=(avg_diff_2nd_0,avg_diff_2nd_1))
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@@ -136,7 +136,7 @@ def update_scales(x,y,prompt,seed, steps, guidance_scale,
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elif img2img_type=="ip adapter" and img is not None:
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image = clip_slider.generate(prompt, guidance_scale=guidance_scale, ip_adapter_image=img, scale=x, scale_2nd=y, seed=seed, num_inference_steps=steps, avg_diff=avg_diff,avg_diff_2nd=avg_diff_2nd)
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elif img2img_type=="inversion":
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image = clip_slider.generate(prompt, guidance_scale=guidance_scale,
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else:
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image = clip_slider.generate(prompt, guidance_scale=guidance_scale, scale=x, scale_2nd=y, seed=seed, num_inference_steps=steps, avg_diff=avg_diff,avg_diff_2nd=avg_diff_2nd)
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return image
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elif img2img_type=="ip adapter" and img is not None:
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image = clip_slider.generate(prompt, guidance_scale=guidance_scale, ip_adapter_image=img, scale=0, scale_2nd=0, seed=seed, num_inference_steps=steps, avg_diff=(avg_diff_0,avg_diff_1), avg_diff_2nd=(avg_diff_2nd_0,avg_diff_2nd_1))
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elif img2img_type=="inversion":
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image = clip_slider.generate(prompt, guidance_scale=guidance_scale, scale=0, scale_2nd=0, seed=seed, num_inference_steps=steps, avg_diff=(avg_diff_0,avg_diff_1), avg_diff_2nd=(avg_diff_2nd_0,avg_diff_2nd_1), init_latents = init_latents, zs=zs, edit_threshold=edit_threshold, edit_guidance_scale = edit_guidance_scale)
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else: # text to image
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image = clip_slider.generate(prompt, guidance_scale=guidance_scale, scale=0, scale_2nd=0, seed=seed, num_inference_steps=steps, avg_diff=(avg_diff_0,avg_diff_1), avg_diff_2nd=(avg_diff_2nd_0,avg_diff_2nd_1))
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elif img2img_type=="ip adapter" and img is not None:
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image = clip_slider.generate(prompt, guidance_scale=guidance_scale, ip_adapter_image=img, scale=x, scale_2nd=y, seed=seed, num_inference_steps=steps, avg_diff=avg_diff,avg_diff_2nd=avg_diff_2nd)
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elif img2img_type=="inversion":
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image = clip_slider.generate(prompt, guidance_scale=guidance_scale, scale=x, scale_2nd=y, seed=seed, num_inference_steps=steps, avg_diff=(avg_diff_0,avg_diff_1), avg_diff_2nd=(avg_diff_2nd_0,avg_diff_2nd_1), edit_threshold=edit_threshold, edit_guidance_scale = edit_guidance_scale, init_latents = init_latents, zs=zs)
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else:
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image = clip_slider.generate(prompt, guidance_scale=guidance_scale, scale=x, scale_2nd=y, seed=seed, num_inference_steps=steps, avg_diff=avg_diff,avg_diff_2nd=avg_diff_2nd)
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return image
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