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remove scale latent and no-crop options from hires fix

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support copy-pasting new parameters for hires fix
This commit is contained in:
AUTOMATIC 2022-10-14 17:03:03 +03:00
parent b382de2d77
commit e644b5a80b
3 changed files with 33 additions and 55 deletions
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16
modules/processing.py
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@ -506,14 +506,12 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
firstphase_width_truncated = 0
firstphase_height_truncated = 0
def __init__(self, enable_hr=False, scale_latent=True, denoising_strength=0.75, firstphase_width=512, firstphase_height=512, crop_scale=False, **kwargs):
def __init__(self, enable_hr=False, denoising_strength=0.75, firstphase_width=512, firstphase_height=512, **kwargs):
super().__init__(**kwargs)
self.enable_hr = enable_hr
self.scale_latent = scale_latent
self.denoising_strength = denoising_strength
self.firstphase_width = firstphase_width
self.firstphase_height = firstphase_height
self.crop_scale = crop_scale
def init(self, all_prompts, all_seeds, all_subseeds):
if self.enable_hr:
@ -530,6 +528,8 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning)
return samples
self.extra_generation_params["First pass size"] = f"{self.firstphase_width}x{self.firstphase_height}"
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)
samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning)
@ -538,24 +538,14 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
width_ratio = self.width/self.firstphase_width
height_ratio = self.height/self.firstphase_height
if self.crop_scale:
if width_ratio > height_ratio:
#Crop to landscape
truncate_y = int((self.width - self.firstphase_width) / width_ratio / height_ratio / opt_f)
elif width_ratio < height_ratio:
#Crop to portrait
truncate_x = int((self.height - self.firstphase_height) / width_ratio / height_ratio / opt_f)
samples = samples[:, :, truncate_y//2:samples.shape[2]-truncate_y//2, truncate_x//2:samples.shape[3]-truncate_x//2]
if self.scale_latent:
samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), mode="bilinear")
else:
decoded_samples = decode_first_stage(self.sd_model, samples)
if opts.upscaler_for_img2img is None or opts.upscaler_for_img2img == "None":

5
modules/txt2img.py
View File

@ -6,7 +6,7 @@ import modules.processing as processing
from modules.ui import plaintext_to_html
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):
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, denoising_strength: float, firstphase_width: int, firstphase_height: int, *args):
p = StableDiffusionProcessingTxt2Img(
sd_model=shared.sd_model,
outpath_samples=opts.outdir_samples or opts.outdir_txt2img_samples,
@ -30,12 +30,9 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2:
restore_faces=restore_faces,
tiling=tiling,
enable_hr=enable_hr,
scale_latent=scale_latent if enable_hr else None,
denoising_strength=denoising_strength if enable_hr else None,
firstphase_width=firstphase_width if enable_hr else None,
firstphase_height=firstphase_height if enable_hr else None,
crop_scale=crop_scale if enable_hr else None,
)
if cmd_opts.enable_console_prompts:

13
modules/ui.py
View File

@ -540,15 +540,8 @@ def create_ui(wrap_gradio_gpu_call):
enable_hr = gr.Checkbox(label='Highres. fix', value=False)
with gr.Row(visible=False) as hr_options:
with gr.Column(scale=1.0):
firstphase_width = gr.Slider(minimum=64, maximum=1024, step=64, label="First pass width", value=512)
firstphase_height = gr.Slider(minimum=64, maximum=1024, step=64, label="First pass height", value=512)
with gr.Column(scale=1.0):
with gr.Row():
crop_scale = gr.Checkbox(label='Crop when scaling', value=False)
scale_latent = gr.Checkbox(label='Scale latent', value=False)
with gr.Row():
denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7)
with gr.Row(equal_height=True):
@ -610,11 +603,9 @@ def create_ui(wrap_gradio_gpu_call):
height,
width,
enable_hr,
scale_latent,
denoising_strength,
firstphase_width,
firstphase_height,
crop_scale,
] + custom_inputs,
outputs=[
txt2img_gallery,
@ -679,8 +670,8 @@ def create_ui(wrap_gradio_gpu_call):
(denoising_strength, "Denoising strength"),
(enable_hr, lambda d: "Denoising strength" in d),
(hr_options, lambda d: gr.Row.update(visible="Denoising strength" in d)),
(firstphase_width, "First pass width"),
(firstphase_height, "First pass height"),
(firstphase_width, "First pass size-1"),
(firstphase_height, "First pass size-2"),
]
modules.generation_parameters_copypaste.connect_paste(paste, txt2img_paste_fields, txt2img_prompt)
token_button.click(fn=update_token_counter, inputs=[txt2img_prompt, steps], outputs=[token_counter])