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Merge remote-tracking branch 'InvincibleDude/improved-hr-conflict-test' into hires-fix-ext

This commit is contained in:
AUTOMATIC 2023-05-18 17:57:16 +03:00
parent 3885f8a63e f5e4436453
commit 5ec2c294ee
4 changed files with 97 additions and 8 deletions
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6
modules/generation_parameters_copypaste.py
View File

@ -252,7 +252,6 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
if line.startswith("Negative prompt:"):
done_with_prompt = True
line = line[16:].strip()
if done_with_prompt:
negative_prompt += ("" if negative_prompt == "" else "\n") + line
else:
@ -270,6 +269,11 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
else:
res[k] = v
if k.startswith("Hires prompt"):
res["Hires prompt"] = v[1:][:-1].replace(';', ',')
elif k.startswith("Hires negative prompt"):
res["Hires negative prompt"] = v[1:][:-1].replace(';', ',')
# Missing CLIP skip means it was set to 1 (the default)
if "Clip skip" not in res:
res["Clip skip"] = "1"

73
modules/processing.py
View File

@ -271,7 +271,7 @@ class StableDiffusionProcessing:
def init(self, all_prompts, all_seeds, all_subseeds):
pass
def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts, hr_conditioning=None, hr_unconditional_conditioning=None):
raise NotImplementedError()
def close(self):
@ -592,6 +592,20 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
else:
p.all_negative_prompts = p.batch_size * p.n_iter * [shared.prompt_styles.apply_negative_styles_to_prompt(p.negative_prompt, p.styles)]
if type(p) == StableDiffusionProcessingTxt2Img:
if p.enable_hr and p.hr_prompt == '':
p.all_hr_prompts, p.all_hr_negative_prompts = p.all_prompts, p.all_negative_prompts
elif p.enable_hr and p.hr_prompt != '':
if type(p.prompt) == list:
p.all_hr_prompts = [shared.prompt_styles.apply_styles_to_prompt(x, p.styles) for x in p.hr_prompt]
else:
p.all_hr_prompts = p.batch_size * p.n_iter * [shared.prompt_styles.apply_styles_to_prompt(p.hr_prompt, p.styles)]
if type(p.negative_prompt) == list:
p.all_hr_negative_prompts = [shared.prompt_styles.apply_negative_styles_to_prompt(x, p.styles) for x in p.hr_negative_prompt]
else:
p.all_hr_negative_prompts = p.batch_size * p.n_iter * [shared.prompt_styles.apply_negative_styles_to_prompt(p.hr_negative_prompt, p.styles)]
if type(seed) == list:
p.all_seeds = seed
else:
@ -660,6 +674,15 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
prompts = p.all_prompts[n * p.batch_size:(n + 1) * p.batch_size]
negative_prompts = p.all_negative_prompts[n * p.batch_size:(n + 1) * p.batch_size]
if type(p) == StableDiffusionProcessingTxt2Img:
if p.enable_hr:
if p.hr_prompt == '':
hr_prompts, hr_negative_prompts = prompts, negative_prompts
else:
hr_prompts = p.all_hr_prompts[n * p.batch_size:(n + 1) * p.batch_size]
hr_negative_prompts = p.all_hr_negative_prompts[n * p.batch_size:(n + 1) * p.batch_size]
seeds = p.all_seeds[n * p.batch_size:(n + 1) * p.batch_size]
subseeds = p.all_subseeds[n * p.batch_size:(n + 1) * p.batch_size]
@ -671,6 +694,12 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
prompts, extra_network_data = extra_networks.parse_prompts(prompts)
if type(p) == StableDiffusionProcessingTxt2Img:
if p.enable_hr and hr_prompts != prompts:
_, hr_extra_network_data = extra_networks.parse_prompts(hr_prompts)
extra_network_data.update(hr_extra_network_data)
if not p.disable_extra_networks:
with devices.autocast():
extra_networks.activate(p, extra_network_data)
@ -692,6 +721,14 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
uc = get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, p.steps * step_multiplier, cached_uc)
c = get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, p.steps * step_multiplier, cached_c)
if type(p) == StableDiffusionProcessingTxt2Img:
if p.enable_hr:
if prompts != hr_prompts:
hr_uc = get_conds_with_caching(prompt_parser.get_learned_conditioning, hr_negative_prompts, p.steps, cached_uc)
hr_c = get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, hr_prompts, p.steps, cached_c)
else:
hr_uc, hr_c = uc, c
if len(model_hijack.comments) > 0:
for comment in model_hijack.comments:
comments[comment] = 1
@ -699,8 +736,15 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
if p.n_iter > 1:
shared.state.job = f"Batch {n+1} out of {p.n_iter}"
with devices.without_autocast() if devices.unet_needs_upcast else devices.autocast():
samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, prompts=prompts)
if type(p) == StableDiffusionProcessingTxt2Img:
if p.enable_hr:
samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, hr_conditioning=hr_c, hr_unconditional_conditioning=hr_uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, prompts=prompts)
else:
samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, prompts=prompts)
else:
samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, prompts=prompts)
x_samples_ddim = [decode_first_stage(p.sd_model, samples_ddim[i:i+1].to(dtype=devices.dtype_vae))[0].cpu() for i in range(samples_ddim.size(0))]
for x in x_samples_ddim:
@ -835,7 +879,7 @@ def old_hires_fix_first_pass_dimensions(width, height):
class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
sampler = None
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, **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_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):
super().__init__(**kwargs)
self.enable_hr = enable_hr
self.denoising_strength = denoising_strength
@ -846,6 +890,11 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
self.hr_resize_y = hr_resize_y
self.hr_upscale_to_x = hr_resize_x
self.hr_upscale_to_y = hr_resize_y
self.hr_sampler = hr_sampler
self.hr_prompt = hr_prompt if hr_prompt != '' else ''
self.hr_negative_prompt = hr_negative_prompt if hr_negative_prompt != '' else ''
self.all_hr_prompts = None
self.all_hr_negative_prompts = None
if firstphase_width != 0 or firstphase_height != 0:
self.hr_upscale_to_x = self.width
@ -859,6 +908,13 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
def init(self, all_prompts, all_seeds, all_subseeds):
if self.enable_hr:
if self.hr_sampler != '---':
self.extra_generation_params["Hires sampler"] = self.hr_sampler
if self.hr_prompt != '':
self.extra_generation_params["Hires prompt"] = f'({self.hr_prompt.replace(",", ";")})'
self.extra_generation_params["Hires negative prompt"] = f'({self.hr_negative_prompt.replace(",", ";")})'
if opts.use_old_hires_fix_width_height and self.applied_old_hires_behavior_to != (self.width, self.height):
self.hr_resize_x = self.width
self.hr_resize_y = self.height
@ -919,7 +975,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
if self.hr_upscaler is not None:
self.extra_generation_params["Hires upscaler"] = self.hr_upscaler
def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts, hr_conditioning=None, hr_unconditional_conditioning=None):
self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model)
latent_scale_mode = shared.latent_upscale_modes.get(self.hr_upscaler, None) if self.hr_upscaler is not None else shared.latent_upscale_modes.get(shared.latent_upscale_default_mode, "nearest")
@ -989,8 +1045,15 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
shared.state.nextjob()
img2img_sampler_name = self.sampler_name
if self.sampler_name in ['PLMS', 'UniPC']: # PLMS/UniPC do not support img2img so we just silently switch to DDIM
img2img_sampler_name = 'DDIM'
if self.hr_sampler == '---':
pass
else:
img2img_sampler_name = self.hr_sampler
self.sampler = sd_samplers.create_sampler(img2img_sampler_name, self.sd_model)
samples = samples[:, :, self.truncate_y//2:samples.shape[2]-(self.truncate_y+1)//2, self.truncate_x//2:samples.shape[3]-(self.truncate_x+1)//2]
@ -1003,7 +1066,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
sd_models.apply_token_merging(self.sd_model, self.get_token_merging_ratio(for_hr=True))
samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.hr_second_pass_steps or self.steps, image_conditioning=image_conditioning)
samples = self.sampler.sample_img2img(self, samples, noise, hr_conditioning, hr_unconditional_conditioning, steps=self.hr_second_pass_steps or self.steps, image_conditioning=image_conditioning)
sd_models.apply_token_merging(self.sd_model, self.get_token_merging_ratio())

8
modules/txt2img.py
View File

@ -6,9 +6,10 @@ import modules.shared as shared
from modules.ui import plaintext_to_html
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, override_settings_texts, *args):
override_settings = create_override_settings_dict(override_settings_texts)
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, override_settings_texts, *args):
override_settings = create_override_settings_dict(override_settings_texts)
p = processing.StableDiffusionProcessingTxt2Img(
sd_model=shared.sd_model,
outpath_samples=opts.outdir_samples or opts.outdir_txt2img_samples,
@ -38,6 +39,9 @@ def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_styles, step
hr_second_pass_steps=hr_second_pass_steps,
hr_resize_x=hr_resize_x,
hr_resize_y=hr_resize_y,
hr_sampler=sd_samplers.samplers_for_img2img[hr_sampler_index - 1].name if hr_sampler_index != 0 else '---',
hr_prompt=hr_prompt,
hr_negative_prompt=hr_negative_prompt,
override_settings=override_settings,
)

18
modules/ui.py
View File

@ -499,6 +499,17 @@ def create_ui():
hr_resize_x = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize width to", value=0, elem_id="txt2img_hr_resize_x")
hr_resize_y = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize height to", value=0, elem_id="txt2img_hr_resize_y")
with FormRow(elem_id="txt2img_hires_fix_row3", variant="compact"):
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")
with FormRow(elem_id="txt2img_hires_fix_row4", variant="compact"):
with gr.Column(scale=80):
with gr.Row():
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 (leave it blank to use the same prompt as in initial txt2img gen)")
with gr.Column(scale=80):
with gr.Row():
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 (leave it blank to use the same prompt as in initial txt2img gen)")
elif category == "batch":
if not opts.dimensions_and_batch_together:
with FormRow(elem_id="txt2img_column_batch"):
@ -560,7 +571,11 @@ def create_ui():
hr_second_pass_steps,
hr_resize_x,
hr_resize_y,
hr_sampler_index,
hr_prompt,
hr_negative_prompt,
override_settings,
] + custom_inputs,
outputs=[
@ -631,6 +646,9 @@ def create_ui():
(hr_second_pass_steps, "Hires steps"),
(hr_resize_x, "Hires resize-1"),
(hr_resize_y, "Hires resize-2"),
(hr_sampler_index, "Hires sampling method"),
(hr_prompt, "Hires prompt"),
(hr_negative_prompt, "Hires negative prompt"),
*modules.scripts.scripts_txt2img.infotext_fields
]
parameters_copypaste.add_paste_fields("txt2img", None, txt2img_paste_fields, override_settings)