Option between stretch and crop for Highres. fix
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Buckzor
AUTOMATIC1111
parent
b2261b53ae
commit
40d1c6e423
3 changed files with 42 additions and 24 deletions
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@ -506,13 +506,14 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
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firstphase_width_truncated = 0
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firstphase_height_truncated = 0
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def __init__(self, enable_hr=False, scale_latent=True, denoising_strength=0.75, first_pass_width=512, first_pass_height=512, **kwargs):
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def __init__(self, enable_hr=False, scale_latent=True, denoising_strength=0.75, firstphase_width=512, firstphase_height=512, crop_scale=False, **kwargs):
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super().__init__(**kwargs)
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self.enable_hr = enable_hr
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self.scale_latent = scale_latent
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self.denoising_strength = denoising_strength
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self.first_pass_width = first_pass_width
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self.first_pass_height = first_pass_height
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self.firstphase_width = firstphase_width
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self.firstphase_height = firstphase_height
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self.crop_scale = crop_scale
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def init(self, all_prompts, all_seeds, all_subseeds):
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if self.enable_hr:
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@ -521,14 +522,14 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
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else:
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state.job_count = state.job_count * 2
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desired_pixel_count = self.first_pass_width * self.first_pass_height
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actual_pixel_count = self.width * self.height
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scale = math.sqrt(desired_pixel_count / actual_pixel_count)
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#desired_pixel_count = self.firstphase_width * self.firstphase_height
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#actual_pixel_count = self.width * self.height
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#scale = math.sqrt(desired_pixel_count / actual_pixel_count)
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self.firstphase_width = math.ceil(scale * self.width / 64) * 64
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self.firstphase_height = math.ceil(scale * self.height / 64) * 64
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self.firstphase_width_truncated = int(scale * self.width)
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self.firstphase_height_truncated = int(scale * self.height)
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#self.firstphase_width = math.ceil(scale * self.width / 64) * 64
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#self.firstphase_height = math.ceil(scale * self.height / 64) * 64
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#self.firstphase_width_truncated = int(scale * self.width)
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#self.firstphase_height_truncated = int(scale * self.height)
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def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
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self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model)
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@ -541,8 +542,17 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
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x = create_random_tensors([opt_C, self.firstphase_height // opt_f, self.firstphase_width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
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samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning)
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truncate_x = (self.firstphase_width - self.firstphase_width_truncated) // opt_f
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truncate_y = (self.firstphase_height - self.firstphase_height_truncated) // opt_f
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truncate_x = 0
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truncate_y = 0
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if self.crop_scale:
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if self.width/self.firstphase_width > self.height/self.firstphase_height:
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#Crop to landscape
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truncate_y = (self.width - self.firstphase_width)//2 // opt_f
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elif self.width/self.firstphase_width < self.height/self.firstphase_height:
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#Crop to portrait
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truncate_x = (self.height - self.firstphase_height)//2 // opt_f
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samples = samples[:, :, truncate_y//2:samples.shape[2]-truncate_y//2, truncate_x//2:samples.shape[3]-truncate_x//2]
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@ -6,7 +6,7 @@ import modules.processing as processing
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from modules.ui import plaintext_to_html
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def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, 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, scale_latent: bool, denoising_strength: float, first_pass_width: int, first_pass_height: int, *args):
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def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, 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, scale_latent: bool, denoising_strength: float, firstphase_width: int, firstphase_height: int, crop_scale: bool, *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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@ -32,8 +32,9 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2:
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enable_hr=enable_hr,
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scale_latent=scale_latent if enable_hr else None,
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denoising_strength=denoising_strength if enable_hr else None,
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first_pass_width=first_pass_width if enable_hr else None,
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first_pass_height=first_pass_height if enable_hr else None,
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firstphase_width=firstphase_width if enable_hr else None,
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firstphase_height=firstphase_height if enable_hr else None,
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crop_scale=crop_scale if enable_hr else None,
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)
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@ -540,12 +540,18 @@ def create_ui(wrap_gradio_gpu_call):
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enable_hr = gr.Checkbox(label='Highres. fix', value=False)
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with gr.Row(visible=False) as hr_options:
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first_pass_width = gr.Slider(minimum=64, maximum=1024, step=64, label="First pass width", value=512)
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first_pass_height = gr.Slider(minimum=64, maximum=1024, step=64, label="First pass height", value=512)
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scale_latent = gr.Checkbox(label='Scale latent', value=False)
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denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7)
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with gr.Column(scale=1.0):
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firstphase_width = gr.Slider(minimum=64, maximum=1024, step=64, label="First pass width", value=512)
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firstphase_height = gr.Slider(minimum=64, maximum=1024, step=64, label="First pass height", value=512)
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with gr.Column(scale=1.0):
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with gr.Row():
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crop_scale = gr.Checkbox(label='Crop when scaling', value=False)
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scale_latent = gr.Checkbox(label='Scale latent', value=False)
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with gr.Row():
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denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7)
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with gr.Row():
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with gr.Row(equal_height=True):
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batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1)
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batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1)
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@ -606,8 +612,9 @@ def create_ui(wrap_gradio_gpu_call):
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enable_hr,
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scale_latent,
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denoising_strength,
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first_pass_width,
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first_pass_height,
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firstphase_width,
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firstphase_height,
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crop_scale,
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] + custom_inputs,
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outputs=[
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txt2img_gallery,
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@ -672,8 +679,8 @@ def create_ui(wrap_gradio_gpu_call):
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(denoising_strength, "Denoising strength"),
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(enable_hr, lambda d: "Denoising strength" in d),
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(hr_options, lambda d: gr.Row.update(visible="Denoising strength" in d)),
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(first_pass_width, "First pass width"),
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(first_pass_height, "First pass height"),
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(firstphase_width, "First pass width"),
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(firstphase_height, "First pass height"),
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]
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modules.generation_parameters_copypaste.connect_paste(paste, txt2img_paste_fields, txt2img_prompt)
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token_button.click(fn=update_token_counter, inputs=[txt2img_prompt, steps], outputs=[token_counter])
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