Hr separate prompt test
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@ -516,6 +516,23 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
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else:
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p.all_negative_prompts = p.batch_size * p.n_iter * [shared.prompt_styles.apply_negative_styles_to_prompt(p.negative_prompt, p.styles)]
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if type(p) == StableDiffusionProcessingTxt2Img:
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if p.hr_enabled and p.is_hr_pass:
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if p.hr_prompt:
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if type(p.prompt) == list:
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p.all_prompts = [shared.prompt_styles.apply_styles_to_prompt(x, p.styles) for x in p.hr_prompt]
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else:
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p.all_prompts = p.batch_size * p.n_iter * [
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shared.prompt_styles.apply_styles_to_prompt(p.hr_prompt, p.styles)]
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if p.hr_negative_prompt:
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if type(p.negative_prompt) == list:
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p.all_negative_prompts = [shared.prompt_styles.apply_negative_styles_to_prompt(x, p.styles) for x in
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p.hr_negative_prompt]
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else:
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p.all_negative_prompts = p.batch_size * p.n_iter * [
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shared.prompt_styles.apply_negative_styles_to_prompt(p.hr_negative_prompt, p.styles)]
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if type(seed) == list:
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p.all_seeds = seed
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else:
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@ -710,7 +727,7 @@ def old_hires_fix_first_pass_dimensions(width, height):
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class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
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sampler = None
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def __init__(self, enable_hr: bool = False, denoising_strength: float = 0.75, firstphase_width: int = 0, firstphase_height: int = 0, hr_scale: float = 2.0, hr_upscaler: str = None, hr_second_pass_steps: int = 0, hr_resize_x: int = 0, hr_resize_y: int = 0, hr_sampler: str = '---', **kwargs):
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def __init__(self, enable_hr: bool = False, denoising_strength: float = 0.75, firstphase_width: int = 0, firstphase_height: int = 0, hr_scale: float = 2.0, hr_upscaler: str = None, hr_second_pass_steps: int = 0, hr_resize_x: int = 0, hr_resize_y: int = 0, hr_sampler: str = '---', hr_prompt: str = '', hr_negative_prompt: str = '', **kwargs):
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super().__init__(**kwargs)
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self.enable_hr = enable_hr
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self.denoising_strength = denoising_strength
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@ -722,6 +739,9 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
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self.hr_upscale_to_x = hr_resize_x
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self.hr_upscale_to_y = hr_resize_y
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self.hr_sampler = hr_sampler
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self.hr_prompt = hr_prompt if hr_prompt != '' else self.prompt
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self.hr_negative_prompt = hr_negative_prompt if hr_negative_prompt != '' else self.negative_prompt
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self.is_hr_pass = False
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if firstphase_width != 0 or firstphase_height != 0:
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self.hr_upscale_to_x = self.width
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@ -808,6 +828,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
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if not self.enable_hr:
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return samples
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self.is_hr_pass = True
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target_width = self.hr_upscale_to_x
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target_height = self.hr_upscale_to_y
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@ -8,7 +8,7 @@ import modules.processing as processing
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from modules.ui import plaintext_to_html
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def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_styles, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, denoising_strength: float, hr_scale: float, hr_upscaler: str, hr_second_pass_steps: int, hr_resize_x: int, hr_resize_y: int, hr_sampler_index: int, *args):
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def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_styles, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, denoising_strength: float, hr_scale: float, hr_upscaler: str, hr_second_pass_steps: int, hr_resize_x: int, hr_resize_y: int, hr_sampler_index: int, hr_prompt: str, hr_negative_prompt, *args):
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p = StableDiffusionProcessingTxt2Img(
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sd_model=shared.sd_model,
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outpath_samples=opts.outdir_samples or opts.outdir_txt2img_samples,
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@ -38,7 +38,9 @@ def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_styles, step
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hr_second_pass_steps=hr_second_pass_steps,
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hr_resize_x=hr_resize_x,
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hr_resize_y=hr_resize_y,
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hr_sampler=sd_samplers.samplers_for_img2img[hr_sampler_index - 1].name if hr_sampler_index != 0 else '---'
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hr_sampler=sd_samplers.samplers_for_img2img[hr_sampler_index - 1].name if hr_sampler_index != 0 else '---',
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hr_prompt=hr_prompt,
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hr_negative_prompt=hr_negative_prompt
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)
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p.scripts = modules.scripts.scripts_txt2img
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@ -675,6 +675,14 @@ def create_ui():
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with FormRow(elem_id="txt2img_hires_fix_row3", variant="compact"):
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hr_sampler_index = gr.Dropdown(label='Hires sampling method', elem_id=f"hr_sampler", choices=["---"] + [x.name for x in samplers_for_img2img], value="---", type="index")
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with FormRow(elem_id="txt2img_hires_fix_row4", variant="compact"):
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with gr.Column(scale=80):
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with gr.Row():
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hr_prompt = gr.Textbox(label="Prompt", elem_id=f"hires_prompt", show_label=False, lines=3, placeholder="Prompt that will be used for hires fix pass")
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with gr.Column(scale=80):
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with gr.Row():
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hr_negative_prompt = gr.Textbox(label="Negative prompt", elem_id=f"hires_neg_prompt", show_label=False, lines=3, placeholder="Negative prompt that will be used for hires fix pass")
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elif category == "batch":
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if not opts.dimensions_and_batch_together:
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with FormRow(elem_id="txt2img_column_batch"):
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@ -734,6 +742,8 @@ def create_ui():
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hr_resize_x,
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hr_resize_y,
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hr_sampler_index,
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hr_prompt,
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hr_negative_prompt,
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] + custom_inputs,
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outputs=[
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