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enable full resize opts in hires

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This commit is contained in:
Vladimir Mandic
2024-09-08 12:32:45 -04:00
parent 51e8567eec
commit a92e1ce664
19 changed files with 88 additions and 60 deletions
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modules/control/run.py
+3 -1
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@@ -66,7 +66,7 @@ def control_run(units: List[unit.Unit] = [], inputs: List[Image.Image] = [], ini
resize_mode_after: int = 0, resize_name_after: str = 'None', resize_context_after: str = 'None', width_after: int = 0, height_after: int = 0, scale_by_after: float = 1.0, selected_scale_tab_after: int = 0,
resize_mode_mask: int = 0, resize_name_mask: str = 'None', resize_context_mask: str = 'None', width_mask: int = 0, height_mask: int = 0, scale_by_mask: float = 1.0, selected_scale_tab_mask: int = 0,
denoising_strength: float = 0, batch_count: int = 1, batch_size: int = 1,
enable_hr: bool = False, hr_sampler_index: int = None, hr_denoising_strength: float = 0.3, hr_upscaler: str = None, hr_force: bool = False, hr_second_pass_steps: int = 20,
enable_hr: bool = False, hr_sampler_index: int = None, hr_denoising_strength: float = 0.3, hr_resize_mode: int = 0, hr_resize_context: str = 'None', hr_upscaler: str = None, hr_force: bool = False, hr_second_pass_steps: int = 20,
hr_scale: float = 1.0, hr_resize_x: int = 0, hr_resize_y: int = 0, refiner_steps: int = 5, refiner_start: float = 0.0, refiner_prompt: str = '', refiner_negative: str = '',
video_skip_frames: int = 0, video_type: str = 'None', video_duration: float = 2.0, video_loop: bool = False, video_pad: int = 0, video_interpolate: int = 0,
*input_script_args
@@ -180,6 +180,8 @@ def control_run(units: List[unit.Unit] = [], inputs: List[Image.Image] = [], ini
p.enable_hr = enable_hr
p.hr_sampler_name = processing.get_sampler_name(hr_sampler_index)
p.hr_denoising_strength = hr_denoising_strength
p.hr_resize_mode = hr_resize_mode
p.hr_resize_context = hr_resize_context
p.hr_upscaler = hr_upscaler
p.hr_force = hr_force
p.hr_second_pass_steps = hr_second_pass_steps
modules/images.py
+1 -1
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@@ -338,7 +338,7 @@ def resize_image(resize_mode, im, width, height, upscaler_name=None, output_type
res = im.copy()
shared.log.error(f'Invalid resize mode: {resize_mode}')
t1 = time.time()
shared.log.debug(f'Image resize: input={im} width={width} height={height} mode="{shared.resize_modes[resize_mode]}" upscaler="{upscaler_name}" context="{context}" type={output_type} time={t1-t0:.2f} fn={sys._getframe(1).f_code.co_name}') # pylint: disable=protected-access
shared.log.debug(f'Image resize: input={im} width={width} height={height} mode="{shared.resize_modes[resize_mode]}" upscaler="{upscaler_name}" context="{context}" type={output_type} result={res} time={t1-t0:.2f} fn={sys._getframe(1).f_code.co_name}') # pylint: disable=protected-access
return np.array(res) if output_type == 'np' else res
modules/processing.py
+9 -9
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@@ -36,21 +36,21 @@ images_tensor_to_samples = processing_helpers.images_tensor_to_samples
class Processed:
def __init__(self, p: StableDiffusionProcessing, images_list, seed=-1, info="", subseed=None, all_prompts=None, all_negative_prompts=None, all_seeds=None, all_subseeds=None, index_of_first_image=0, infotexts=None, comments=""):
self.images = images_list
self.prompt = p.prompt
self.negative_prompt = p.negative_prompt
self.prompt = p.prompt or ''
self.negative_prompt = p.negative_prompt or ''
self.seed = seed if seed != -1 else p.seed
self.subseed = subseed
self.subseed_strength = p.subseed_strength
self.info = info
self.comments = comments
self.comments = comments or ''
self.width = p.width if hasattr(p, 'width') else (self.images[0].width if len(self.images) > 0 else 0)
self.height = p.height if hasattr(p, 'height') else (self.images[0].height if len(self.images) > 0 else 0)
self.sampler_name = p.sampler_name
self.cfg_scale = p.cfg_scale
self.image_cfg_scale = p.image_cfg_scale
self.steps = p.steps
self.batch_size = p.batch_size
self.restore_faces = p.restore_faces
self.sampler_name = p.sampler_name or ''
self.cfg_scale = p.cfg_scale or 0
self.image_cfg_scale = p.image_cfg_scale or 0
self.steps = p.steps or 0
self.batch_size = max(1, p.batch_size)
self.restore_faces = p.restore_faces or False
self.face_restoration_model = shared.opts.face_restoration_model if p.restore_faces else None
self.sd_model_hash = getattr(shared.sd_model, 'sd_model_hash', '') if model_data.sd_model is not None else ''
self.seed_resize_from_w = p.seed_resize_from_w
modules/processing_callbacks.py
+1 -1
View File
@@ -84,7 +84,7 @@ def diffusers_callback(pipe, step: int, timestep: int, kwargs: dict):
kwargs[key] = kwargs[key].chunk(2)[-1]
try:
if hasattr(pipe, "_unpack_latents") and hasattr(pipe, "vae_scale_factor"): # FLUX
if p.hr_upscaler is not None and p.hr_upscaler != 'None':
if p.hr_resize_mode > 0 and (p.hr_upscaler != 'None' or p.hr_resize_mode == 5):
width = max(getattr(p, 'width', 0), getattr(p, 'hr_upscale_to_x', 0))
height = max(getattr(p, 'height', 0), getattr(p, 'hr_upscale_to_y', 0))
else:
modules/processing_class.py
+8 -1
View File
@@ -81,6 +81,8 @@ class StableDiffusionProcessing:
self.enable_hr = None
self.hr_scale = None
self.hr_upscaler = None
self.hr_resize_mode = 0
self.hr_resize_context = 'None'
self.hr_resize_x = 0
self.hr_resize_y = 0
self.hr_upscale_to_x = 0
@@ -187,13 +189,15 @@ class StableDiffusionProcessing:
class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
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_force: bool = False, hr_upscaler: str = None, hr_second_pass_steps: int = 0, hr_resize_x: int = 0, hr_resize_y: int = 0, refiner_steps: int = 5, refiner_start: float = 0, refiner_prompt: str = '', refiner_negative: str = '', **kwargs):
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_force: bool = False, hr_resize_mode: int = 0, hr_resize_context: str = 'None', hr_upscaler: str = None, hr_second_pass_steps: int = 0, hr_resize_x: int = 0, hr_resize_y: int = 0, refiner_steps: int = 5, refiner_start: float = 0, refiner_prompt: str = '', refiner_negative: str = '', **kwargs):
super().__init__(**kwargs)
self.enable_hr = enable_hr
self.denoising_strength = denoising_strength
self.hr_scale = hr_scale
self.hr_upscaler = hr_upscaler
self.hr_resize_mode = hr_resize_mode
self.hr_resize_context = hr_resize_context
self.hr_force = hr_force
self.hr_second_pass_steps = hr_second_pass_steps
self.hr_resize_x = hr_resize_x
@@ -241,6 +245,9 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
elif self.hr_resize_x == 0:
self.hr_upscale_to_x = self.hr_resize_y * self.width // self.height
self.hr_upscale_to_y = self.hr_resize_y
elif self.hr_resize_x > 0 and self.hr_resize_y > 0 and shared.native:
self.hr_upscale_to_x = self.hr_resize_x
self.hr_upscale_to_y = self.hr_resize_y
else:
target_w = self.hr_resize_x
target_h = self.hr_resize_y
modules/processing_diffusers.py
+3 -3
View File
@@ -153,8 +153,8 @@ def process_diffusers(p: processing.StableDiffusionProcessing):
shared.sd_model.restore_pipeline()
# upscale
if hasattr(p, 'height') and hasattr(p, 'width') and p.hr_upscaler is not None and p.hr_upscaler != 'None':
shared.log.info(f'Upscale: upscaler="{p.hr_upscaler}" resize={p.hr_resize_x}x{p.hr_resize_y} upscale={p.hr_upscale_to_x}x{p.hr_upscale_to_y}')
if hasattr(p, 'height') and hasattr(p, 'width') and p.hr_resize_mode >0 and (p.hr_upscaler != 'None' or p.hr_resize_mode == 5):
shared.log.info(f'Upscale: mode={p.hr_resize_mode} upscaler="{p.hr_upscaler}" context="{p.hr_resize_context}" resize={p.hr_resize_x}x{p.hr_resize_y} upscale={p.hr_upscale_to_x}x{p.hr_upscale_to_y}')
p.ops.append('upscale')
if shared.opts.save and not p.do_not_save_samples and shared.opts.save_images_before_highres_fix and hasattr(shared.sd_model, 'vae'):
save_intermediate(p, latents=output.images, suffix="-before-hires")
@@ -304,7 +304,7 @@ def process_diffusers(p: processing.StableDiffusionProcessing):
shared.log.debug(f'Generated: frames={len(output.frames[0])}')
output.images = output.frames[0]
if hasattr(shared.sd_model, "vae") and output.images is not None and len(output.images) > 0:
if p.hr_upscaler is not None and p.hr_upscaler != 'None':
if p.hr_resize_mode > 0 and (p.hr_upscaler != 'None' or p.hr_resize_mode == 5):
width = max(getattr(p, 'width', 0), getattr(p, 'hr_upscale_to_x', 0))
height = max(getattr(p, 'height', 0), getattr(p, 'hr_upscale_to_y', 0))
else:
modules/processing_helpers.py
+1 -1
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@@ -396,7 +396,7 @@ def resize_hires(p, latents): # input=latents output=pil if not latent_upscaler
resized_images = []
for img in first_pass_images:
if latent_upscaler is None:
resized_image = images.resize_image(1, img, p.hr_upscale_to_x, p.hr_upscale_to_y, upscaler_name=p.hr_upscaler)
resized_image = images.resize_image(p.hr_resize_mode, img, p.hr_upscale_to_x, p.hr_upscale_to_y, upscaler_name=p.hr_upscaler, context=p.hr_resize_context)
else:
resized_image = img
resized_images.append(resized_image)
modules/processing_info.py
+7 -4
View File
@@ -71,13 +71,16 @@ def create_infotext(p: StableDiffusionProcessing, all_prompts=None, all_seeds=No
args["Variation seed"] = all_subseeds[index] if p.subseed_strength > 0 else None
args["Variation strength"] = p.subseed_strength if p.subseed_strength > 0 else None
if 'hires' in p.ops or 'upscale' in p.ops:
is_resize = p.hr_resize_mode > 0 and (p.hr_upscaler != 'None' or p.hr_resize_mode == 5)
args["Second pass"] = p.enable_hr
args["Hires force"] = p.hr_force
args["Hires steps"] = p.hr_second_pass_steps
args["Hires upscaler"] = p.hr_upscaler if p.hr_upscaler is not None and p.hr_upscaler != 'None' else None
args["Hires upscale"] = p.hr_scale if p.hr_upscaler is not None and p.hr_upscaler != 'None' else None
args["Hires resize"] = f"{p.hr_resize_x}x{p.hr_resize_y}" if p.hr_upscaler is not None and p.hr_upscaler != 'None' else None
args["Hires size"] = f"{p.hr_upscale_to_x}x{p.hr_upscale_to_y}" if p.hr_upscaler is not None and p.hr_upscaler != 'None' else None
args["HiRes resize mode"] = p.hr_resize_mode if is_resize else None
args["HiRes resize context"] = p.hr_resize_context if p.hr_resize_mode == 5 else None
args["Hires upscaler"] = p.hr_upscaler if is_resize else None
args["Hires scale"] = p.hr_scale if is_resize else None
args["Hires resize"] = f"{p.hr_resize_x}x{p.hr_resize_y}" if is_resize else None
args["Hires size"] = f"{p.hr_upscale_to_x}x{p.hr_upscale_to_y}" if is_resize else None
args["Denoising strength"] = p.denoising_strength
args["Hires sampler"] = p.hr_sampler_name
args["Image CFG scale"] = p.image_cfg_scale
modules/shared.py
+1
View File
@@ -843,6 +843,7 @@ options_templates.update(options_section(('extra_networks', "Networks"), {
"extra_networks_sidebar_width": OptionInfo(35, "UI sidebar width (%)", gr.Slider, {"minimum": 10, "maximum": 80, "step": 1}),
"extra_networks_card_size": OptionInfo(160, "UI card size (px)", gr.Slider, {"minimum": 20, "maximum": 2000, "step": 1}),
"extra_networks_card_square": OptionInfo(True, "UI disable variable aspect ratio"),
"extra_networks_fetch": OptionInfo(True, "UI fetch network info on mouse-over"),
"extra_networks_card_fit": OptionInfo("cover", "UI image contain method", gr.Radio, {"choices": ["contain", "cover", "fill"], "visible": False}),
"extra_networks_sep2": OptionInfo("<h2>Extra networks general</h2>", "", gr.HTML),
"extra_network_reference": OptionInfo(False, "Use reference values when available", gr.Checkbox),
modules/txt2img.py
+4 -2
View File
@@ -18,13 +18,13 @@ def txt2img(id_task,
seed, subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w,
height, width,
enable_hr, denoising_strength,
hr_scale, hr_upscaler, hr_force, hr_second_pass_steps, hr_resize_x, hr_resize_y,
hr_scale, hr_resize_mode, hr_resize_context, hr_upscaler, hr_force, hr_second_pass_steps, hr_resize_x, hr_resize_y,
refiner_steps, refiner_start, refiner_prompt, refiner_negative,
hdr_mode, hdr_brightness, hdr_color, hdr_sharpen, hdr_clamp, hdr_boundary, hdr_threshold, hdr_maximize, hdr_max_center, hdr_max_boundry, hdr_color_picker, hdr_tint_ratio,
override_settings_texts,
*args):
debug(f'txt2img: id_task={id_task}|prompt={prompt}|negative={negative_prompt}|styles={prompt_styles}|steps={steps}|sampler_index={sampler_index}|hr_sampler_index={hr_sampler_index}|full_quality={full_quality}|restore_faces={restore_faces}|tiling={tiling}|hidiffusion={hidiffusion}|batch_count={n_iter}|batch_size={batch_size}|cfg_scale={cfg_scale}|clip_skip={clip_skip}|seed={seed}|subseed={subseed}|subseed_strength={subseed_strength}|seed_resize_from_h={seed_resize_from_h}|seed_resize_from_w={seed_resize_from_w}|height={height}|width={width}|enable_hr={enable_hr}|denoising_strength={denoising_strength}|hr_scale={hr_scale}|hr_upscaler={hr_upscaler}|hr_force={hr_force}|hr_second_pass_steps={hr_second_pass_steps}|hr_resize_x={hr_resize_x}|hr_resize_y={hr_resize_y}|image_cfg_scale={image_cfg_scale}|diffusers_guidance_rescale={diffusers_guidance_rescale}|refiner_steps={refiner_steps}|refiner_start={refiner_start}|refiner_prompt={refiner_prompt}|refiner_negative={refiner_negative}|override_settings={override_settings_texts}')
debug(f'txt2img: id_task={id_task}|prompt={prompt}|negative={negative_prompt}|styles={prompt_styles}|steps={steps}|sampler_index={sampler_index}|hr_sampler_index={hr_sampler_index}|full_quality={full_quality}|restore_faces={restore_faces}|tiling={tiling}|hidiffusion={hidiffusion}|batch_count={n_iter}|batch_size={batch_size}|cfg_scale={cfg_scale}|clip_skip={clip_skip}|seed={seed}|subseed={subseed}|subseed_strength={subseed_strength}|seed_resize_from_h={seed_resize_from_h}|seed_resize_from_w={seed_resize_from_w}|height={height}|width={width}|enable_hr={enable_hr}|denoising_strength={denoising_strength}|hr_resize_mode={hr_resize_mode}|hr_resize_context={hr_resize_context}|hr_scale={hr_scale}|hr_upscaler={hr_upscaler}|hr_force={hr_force}|hr_second_pass_steps={hr_second_pass_steps}|hr_resize_x={hr_resize_x}|hr_resize_y={hr_resize_y}|image_cfg_scale={image_cfg_scale}|diffusers_guidance_rescale={diffusers_guidance_rescale}|refiner_steps={refiner_steps}|refiner_start={refiner_start}|refiner_prompt={refiner_prompt}|refiner_negative={refiner_negative}|override_settings={override_settings_texts}')
if shared.sd_model is None:
shared.log.warning('Model not loaded')
@@ -71,6 +71,8 @@ def txt2img(id_task,
enable_hr=enable_hr,
denoising_strength=denoising_strength,
hr_scale=hr_scale,
hr_resize_mode=hr_resize_mode,
hr_resize_context=hr_resize_context,
hr_upscaler=hr_upscaler,
hr_force=hr_force,
hr_second_pass_steps=hr_second_pass_steps,
modules/ui_common.py
+1 -1
View File
@@ -42,7 +42,7 @@ def infotext_to_html(text):
negative = res.get('Negative prompt', '')
res.pop('Prompt', None)
res.pop('Negative prompt', None)
params = [f'{k}: {v}' for k, v in res.items() if v is not None]
params = [f'{k}: {v}' for k, v in res.items() if v is not None and 'size-' not in k.lower()]
params = '| '.join(params) if len(params) > 0 else ''
code = ''
if len(prompt) > 0:
modules/ui_control.py
+2 -2
View File
@@ -128,7 +128,7 @@ def create_ui(_blocks: gr.Blocks=None):
video_interpolate = gr.Slider(label='Interpolate frames', minimum=0, maximum=24, step=1, value=0, visible=False, elem_id="control_video_interpolate")
video_type.change(fn=helpers.video_type_change, inputs=[video_type], outputs=[video_duration, video_loop, video_pad, video_interpolate])
enable_hr, hr_sampler_index, hr_denoising_strength, hr_upscaler, hr_force, hr_second_pass_steps, hr_scale, hr_resize_x, hr_resize_y, refiner_steps, refiner_start, refiner_prompt, refiner_negative = ui_sections.create_hires_inputs('control')
enable_hr, hr_sampler_index, hr_denoising_strength, hr_resize_mode, hr_resize_context, hr_upscaler, hr_force, hr_second_pass_steps, hr_scale, hr_resize_x, hr_resize_y, refiner_steps, refiner_start, refiner_prompt, refiner_negative = ui_sections.create_hires_inputs('control')
with gr.Row():
override_settings = ui_common.create_override_inputs('control')
@@ -508,7 +508,7 @@ def create_ui(_blocks: gr.Blocks=None):
resize_mode_after, resize_name_after, resize_context_after, width_after, height_after, scale_by_after, selected_scale_tab_after,
resize_mode_mask, resize_name_mask, resize_context_mask, width_mask, height_mask, scale_by_mask, selected_scale_tab_mask,
denoising_strength, batch_count, batch_size,
enable_hr, hr_sampler_index, hr_denoising_strength, hr_upscaler, hr_force, hr_second_pass_steps, hr_scale, hr_resize_x, hr_resize_y, refiner_steps,
enable_hr, hr_sampler_index, hr_denoising_strength, hr_resize_mode, hr_resize_context, hr_upscaler, hr_force, hr_second_pass_steps, hr_scale, hr_resize_x, hr_resize_y, refiner_steps,
refiner_start, refiner_prompt, refiner_negative,
video_skip_frames, video_type, video_duration, video_loop, video_pad, video_interpolate,
]
modules/ui_sections.py
+16 -12
View File
@@ -280,12 +280,15 @@ def create_hires_inputs(tab):
with gr.Group():
with gr.Row(elem_id=f"{tab}_hires_row1"):
enable_hr = gr.Checkbox(label='Enable second pass', value=False, elem_id=f"{tab}_enable_hr")
"""
with gr.Row(elem_id=f"{tab}_hires_fix_row1", variant="compact"):
hr_upscaler = gr.Dropdown(label="Upscaler", elem_id=f"{tab}_hr_upscaler", choices=[*shared.latent_upscale_modes, *[x.name for x in shared.sd_upscalers]], value=shared.latent_upscale_default_mode)
hr_scale = gr.Slider(minimum=0.1, maximum=8.0, step=0.05, label="Rescale by", value=2.0, elem_id=f"{tab}_hr_scale")
with gr.Row(elem_id=f"{tab}_hires_fix_row3", variant="compact"):
hr_resize_x = gr.Slider(minimum=0, maximum=4096, step=8, label="Width resize", value=0, elem_id=f"{tab}_hr_resize_x")
hr_resize_y = gr.Slider(minimum=0, maximum=4096, step=8, label="Height resize", value=0, elem_id=f"{tab}_hr_resize_y")
"""
hr_resize_mode, hr_upscaler, hr_resize_context, hr_resize_x, hr_resize_y, hr_scale, _selected_scale_tab = create_resize_inputs(tab, None, accordion=False, latent=True, non_zero=False)
with gr.Row(elem_id=f"{tab}_hires_fix_row2", variant="compact"):
hr_force = gr.Checkbox(label='Force HiRes', value=False, elem_id=f"{tab}_hr_force")
hr_sampler_index = gr.Dropdown(label='Secondary sampler', elem_id=f"{tab}_sampling_alt", choices=[x.name for x in sd_samplers.samplers], value='Same as primary', type="index")
@@ -300,14 +303,14 @@ def create_hires_inputs(tab):
refiner_prompt = gr.Textbox(value='', label='Secondary prompt', elem_id=f"{tab}_refiner_prompt")
with gr.Row(elem_id="txt2img_refiner_row4", variant="compact"):
refiner_negative = gr.Textbox(value='', label='Secondary negative prompt', elem_id=f"{tab}_refiner_neg_prompt")
return enable_hr, hr_sampler_index, denoising_strength, hr_upscaler, hr_force, hr_second_pass_steps, hr_scale, hr_resize_x, hr_resize_y, refiner_steps, refiner_start, refiner_prompt, refiner_negative
return enable_hr, hr_sampler_index, denoising_strength, hr_resize_mode, hr_resize_context, hr_upscaler, hr_force, hr_second_pass_steps, hr_scale, hr_resize_x, hr_resize_y, refiner_steps, refiner_start, refiner_prompt, refiner_negative
def create_resize_inputs(tab, images, accordion=True, latent=False):
def create_resize_inputs(tab, images, accordion=True, latent=False, non_zero=True):
dummy_component = gr.Number(visible=False, value=0)
with gr.Accordion(open=False, label="Resize", elem_classes=["small-accordion"], elem_id=f"{tab}_resize_group") if accordion else gr.Group():
with gr.Row():
resize_mode = gr.Dropdown(label="Mode", elem_id=f"{tab}_resize_mode", choices=shared.resize_modes, type="index", value='Fixed')
resize_mode = gr.Dropdown(label="Mode" if non_zero else "Resize mode", elem_id=f"{tab}_resize_mode", choices=shared.resize_modes, type="index", value='Fixed')
resize_name = gr.Dropdown(label="Method", elem_id=f"{tab}_resize_name", choices=([] if not latent else list(shared.latent_upscale_modes)) + [x.name for x in shared.sd_upscalers], value=shared.latent_upscale_default_mode, visible=True)
resize_context_choices = ["Add with forward", "Remove with forward", "Add with backward", "Remove with backward"]
resize_context = gr.Dropdown(label="Context", elem_id=f"{tab}_resize_context", choices=resize_context_choices, value=resize_context_choices[0], visible=False)
@@ -316,19 +319,19 @@ def create_resize_inputs(tab, images, accordion=True, latent=False):
def resize_mode_change(mode):
if mode is None or mode == 0:
return gr.update(visible=False), gr.update(visible=False)
return gr.update(visible=(mode != 5)), gr.update(visible=(mode == 5))
return gr.update(visible=mode != 5), gr.update(visible=mode == 5)
resize_mode.change(fn=resize_mode_change, inputs=[resize_mode], outputs=[resize_name, resize_context])
with gr.Row(visible=True) as _resize_group:
with gr.Column(elem_id=f"{tab}_column_size"):
selected_scale_tab = gr.State(value=0) # pylint: disable=abstract-class-instantiated
with gr.Tabs(elem_id=f"{tab}_scale_tabs"):
with gr.Tab(label="Fixed", elem_id=f"{tab}_scale_tab_fixed") as tab_scale_to:
with gr.Tabs(elem_id=f"{tab}_scale_tabs", selected=0 if non_zero else 1):
with gr.Tab(label="Fixed", id=0, elem_id=f"{tab}_scale_tab_fixed") as tab_scale_to:
with gr.Row():
with gr.Column(elem_id=f"{tab}_column_size_fixed"):
with gr.Row():
width = gr.Slider(minimum=64, maximum=8192, step=8, label="Width", value=512, elem_id=f"{tab}_width")
height = gr.Slider(minimum=64, maximum=8192, step=8, label="Height", value=512, elem_id=f"{tab}_height")
width = gr.Slider(minimum=64 if non_zero else 0, maximum=8192, step=8, label="Width" if non_zero else "Resize width", value=1024 if non_zero else 0, elem_id=f"{tab}_width")
height = gr.Slider(minimum=64 if non_zero else 0, maximum=8192, step=8, label="Height" if non_zero else "Resize height", value=1024 if non_zero else 0, elem_id=f"{tab}_height")
ar_list = ['AR'] + [x.strip() for x in shared.opts.aspect_ratios.split(',') if x.strip() != '']
ar_dropdown = gr.Dropdown(show_label=False, interactive=True, choices=ar_list, value=ar_list[0], elem_id=f"{tab}_ar", elem_classes=["ar-dropdown"])
for c in [ar_dropdown, width, height]:
@@ -338,10 +341,11 @@ def create_resize_inputs(tab, images, accordion=True, latent=False):
detect_image_size_btn = ToolButton(value=ui_symbols.detect, elem_id=f"{tab}_detect_image_size_btn")
el = tab.split('_')[0]
detect_image_size_btn.click(fn=lambda w, h, _: (w or gr.update(), h or gr.update()), _js=f'currentImageResolution{el}', inputs=[dummy_component, dummy_component, dummy_component], outputs=[width, height], show_progress=False)
with gr.Tab(label="Scale", elem_id=f"{tab}_scale_tab_scale") as tab_scale_by:
scale_by = gr.Slider(minimum=0.05, maximum=8.0, step=0.05, label="Scale", value=1.0, elem_id=f"{tab}_scale")
for component in images:
component.change(fn=lambda: None, _js="updateImg2imgResizeToTextAfterChangingImage", inputs=[], outputs=[], show_progress=False)
with gr.Tab(label="Scale", id=1, elem_id=f"{tab}_scale_tab_scale") as tab_scale_by:
scale_by = gr.Slider(minimum=0.05, maximum=8.0, step=0.05, label="Scale" if non_zero else "Resize scale", value=1.0, elem_id=f"{tab}_scale")
if images is not None:
for component in images:
component.change(fn=lambda: None, _js="updateImg2imgResizeToTextAfterChangingImage", inputs=[], outputs=[], show_progress=False)
tab_scale_to.select(fn=lambda: 0, inputs=[], outputs=[selected_scale_tab])
tab_scale_by.select(fn=lambda: 1, inputs=[], outputs=[selected_scale_tab])
# resize_mode.change(fn=lambda x: gr.update(visible=x != 0), inputs=[resize_mode], outputs=[_resize_group])
modules/ui_txt2img.py
+8 -3
View File
@@ -48,7 +48,7 @@ def create_ui():
seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w = ui_sections.create_seed_inputs('txt2img')
_cfg_scale, clip_skip, image_cfg_scale, diffusers_guidance_rescale, pag_scale, pag_adaptive, _cfg_end = ui_sections.create_advanced_inputs('txt2img', base=False)
hdr_mode, hdr_brightness, hdr_color, hdr_sharpen, hdr_clamp, hdr_boundary, hdr_threshold, hdr_maximize, hdr_max_center, hdr_max_boundry, hdr_color_picker, hdr_tint_ratio = ui_sections.create_correction_inputs('txt2img')
enable_hr, hr_sampler_index, denoising_strength, hr_upscaler, hr_force, hr_second_pass_steps, hr_scale, hr_resize_x, hr_resize_y, refiner_steps, refiner_start, refiner_prompt, refiner_negative = ui_sections.create_hires_inputs('txt2img')
enable_hr, hr_sampler_index, denoising_strength, hr_resize_mode, hr_resize_context, hr_upscaler, hr_force, hr_second_pass_steps, hr_scale, hr_resize_x, hr_resize_y, refiner_steps, refiner_start, refiner_prompt, refiner_negative = ui_sections.create_hires_inputs('txt2img')
override_settings = ui_common.create_override_inputs('txt2img')
with gr.Group(elem_id="txt2img_script_container"):
@@ -70,7 +70,7 @@ def create_ui():
seed, subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w,
height, width,
enable_hr, denoising_strength,
hr_scale, hr_upscaler, hr_force, hr_second_pass_steps, hr_resize_x, hr_resize_y,
hr_scale, hr_resize_mode, hr_resize_context, hr_upscaler, hr_force, hr_second_pass_steps, hr_resize_x, hr_resize_y,
refiner_steps, refiner_start, refiner_prompt, refiner_negative,
hdr_mode, hdr_brightness, hdr_color, hdr_sharpen, hdr_clamp, hdr_boundary, hdr_threshold, hdr_maximize, hdr_max_center, hdr_max_boundry, hdr_color_picker, hdr_tint_ratio,
override_settings,
@@ -118,14 +118,19 @@ def create_ui():
(hidiffusion, "HiDiffusion"),
# second pass
(enable_hr, "Second pass"),
(hr_sampler_index, "Hires sampler"),
(denoising_strength, "Denoising strength"),
(hr_sampler_index, "Hires sampler"),
(hr_resize_mode, "Hires resize mode"),
(hr_resize_context, "Hires resize context"),
(hr_upscaler, "Hires upscaler"),
(hr_force, "Hires force"),
(hr_second_pass_steps, "Hires steps"),
(hr_scale, "Hires upscale"),
(hr_scale, "Hires scale"),
(hr_resize_x, "Hires resize-1"),
(hr_resize_y, "Hires resize-2"),
(hr_resize_x, "Hires size-1"),
(hr_resize_y, "Hires size-2"),
# refiner
(refiner_start, "Refiner start"),
(refiner_steps, "Refiner steps"),