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"Which model to run inferences with. the dev model needs around 28 steps but the schnell model only needs around 4 steps."
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.bind_value(self, "flux_model")
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@@ -145,6 +178,9 @@ def setup_left_panel(self):
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.classes("w-full")
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.bind_value(self, "aspect_ratio")
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.tooltip(
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"Width of the generated image. Optional, only used when aspect_ratio=custom. Must be a multiple of 16 (if it's not, it will be rounded to nearest multiple of 16)"
"Width of the generated image. Optional, only used when aspect_ratio=custom. Must be a multiple of 16 (if it's not, it will be rounded to nearest multiple of 16)"
"Height of the generated image. Optional, only used when aspect_ratio=custom. Must be a multiple of 16 (if it's not, it will be rounded to nearest multiple of 16)"
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self.num_outputs_input= (
@@ -172,6 +214,7 @@ def setup_left_panel(self):
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)
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.classes("w-full")
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.bind_value(self, "num_outputs")
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.tooltip("Number of images to output.")
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)
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self.lora_scale_input= (
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ui.number(
@@ -182,6 +225,9 @@ def setup_left_panel(self):
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step=0.1,
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)
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.classes("w-full")
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.tooltip(
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"Determines how strongly the LoRA should be applied. Sane results between 0 and 1."
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)
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.bind_value(self, "lora_scale")
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)
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self.num_inference_steps_input= (
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max=50,
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)
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.classes("w-full")
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.tooltip("Number of Inference Steps")
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.bind_value(self, "num_inference_steps")
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)
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self.guidance_scale_input= (
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step=0.1,
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)
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.classes("w-full")
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.tooltip("Guidance Scale for the diffusion process")
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.bind_value(self, "guidance_scale")
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)
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self.seed_input= (
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value=self.settings.get("output_format", "webp"),
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)
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.classes("w-full")
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.tooltip("Format of the output images")
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.bind_value(self, "output_format")
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)
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self.output_quality_input= (
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max=100,
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)
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.classes("w-full")
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.tooltip(
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"Quality when saving the output images, from 0 to 100. 100 is best quality, 0 is lowest quality. Not relevant for .png outputs"
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