Commit 64311faa authored by AUTOMATIC1111's avatar AUTOMATIC1111
Browse files

put refiner into main UI, into the new accordions section 

add VAE from main model into infotext, not from refiner model
option to make scripts UI without gr.Group
fix inconsistencies with refiner when usings samplers that do more denoising than steps
parent 26c92f05

modules/processing.py

+15 −7
Original line number Diff line number Diff line
@@ -373,9 +373,10 @@ class StableDiffusionProcessing: 
        negative_prompts = prompt_parser.SdConditioning(self.negative_prompts, width=self.width, height=self.height, is_negative_prompt=True)



        sampler_config = sd_samplers.find_sampler_config(self.sampler_name)

        self.step_multiplier = 2 if sampler_config and sampler_config.options.get("second_order", False) else 1

        self.uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, self.steps * self.step_multiplier, [self.cached_uc], self.extra_network_data)

        self.c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, self.steps * self.step_multiplier, [self.cached_c], self.extra_network_data)

        total_steps = sampler_config.total_steps(self.steps) if sampler_config else self.steps

        self.step_multiplier = total_steps // self.steps

        self.uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, total_steps, [self.cached_uc], self.extra_network_data)

        self.c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, total_steps, [self.cached_c], self.extra_network_data)



    def get_conds(self):

        return self.c, self.uc
@@ -579,8 +580,8 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter 
        "Size": f"{p.width}x{p.height}",

        "Model hash": getattr(p, 'sd_model_hash', None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash),

        "Model": (None if not opts.add_model_name_to_info else shared.sd_model.sd_checkpoint_info.name_for_extra),

        "VAE hash": sd_vae.get_loaded_vae_hash() if opts.add_model_hash_to_info else None,

        "VAE": sd_vae.get_loaded_vae_name() if opts.add_model_name_to_info else None,

        "VAE hash": p.loaded_vae_hash if opts.add_model_hash_to_info else None,

        "VAE": p.loaded_vae_name if opts.add_model_name_to_info else None,

        "Variation seed": (None if p.subseed_strength == 0 else (p.all_subseeds[0] if use_main_prompt else all_subseeds[index])),

        "Variation seed strength": (None if p.subseed_strength == 0 else p.subseed_strength),

        "Seed resize from": (None if p.seed_resize_from_w <= 0 or p.seed_resize_from_h <= 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"),
@@ -669,6 +670,9 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: 
    if p.tiling is None:

        p.tiling = opts.tiling



    p.loaded_vae_name = sd_vae.get_loaded_vae_name()

    p.loaded_vae_hash = sd_vae.get_loaded_vae_hash()



    modules.sd_hijack.model_hijack.apply_circular(p.tiling)

    modules.sd_hijack.model_hijack.clear_comments()
@@ -1188,8 +1192,12 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): 
        hr_prompts = prompt_parser.SdConditioning(self.hr_prompts, width=self.hr_upscale_to_x, height=self.hr_upscale_to_y)

        hr_negative_prompts = prompt_parser.SdConditioning(self.hr_negative_prompts, width=self.hr_upscale_to_x, height=self.hr_upscale_to_y, is_negative_prompt=True)



        self.hr_uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, hr_negative_prompts, self.steps * self.step_multiplier, [self.cached_hr_uc, self.cached_uc], self.hr_extra_network_data)

        self.hr_c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, hr_prompts, self.steps * self.step_multiplier, [self.cached_hr_c, self.cached_c], self.hr_extra_network_data)

        sampler_config = sd_samplers.find_sampler_config(self.hr_sampler_name or self.sampler_name)

        steps = self.hr_second_pass_steps or self.steps

        total_steps = sampler_config.total_steps(steps) if sampler_config else steps



        self.hr_uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, hr_negative_prompts, total_steps, [self.cached_hr_uc, self.cached_uc], self.hr_extra_network_data)

        self.hr_c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, hr_prompts, total_steps, [self.cached_hr_c, self.cached_c], self.hr_extra_network_data)



    def setup_conds(self):

        super().setup_conds()

modules/processing_scripts/refiner.py

0 → 100644
+55 −0
Original line number Diff line number Diff line 
import gradio as gr



from modules import scripts, sd_models

from modules.ui_common import create_refresh_button

from modules.ui_components import InputAccordion





class ScriptRefiner(scripts.Script):

    section = "accordions"

    create_group = False



    def __init__(self):

        pass



    def title(self):

        return "Refiner"



    def show(self, is_img2img):

        return scripts.AlwaysVisible



    def ui(self, is_img2img):

        with InputAccordion(False, label="Refiner", elem_id=self.elem_id("enable")) as enable_refiner:

            with gr.Row():

                refiner_checkpoint = gr.Dropdown(label='Checkpoint', elem_id=self.elem_id("checkpoint"), choices=sd_models.checkpoint_tiles(), value='', tooltip="switch to another model in the middle of generation")

                create_refresh_button(refiner_checkpoint, sd_models.list_models, lambda: {"choices": sd_models.checkpoint_tiles()}, self.elem_id("checkpoint_refresh"))



                refiner_switch_at = gr.Slider(value=0.8, label="Switch at", minimum=0.01, maximum=1.0, step=0.01, elem_id=self.elem_id("switch_at"), tooltip="fraction of sampling steps when the swtch to refiner model should happen; 1=never, 0.5=switch in the middle of generation")



        def lookup_checkpoint(title):

            info = sd_models.get_closet_checkpoint_match(title)

            return None if info is None else info.title



        self.infotext_fields = [

            (enable_refiner, lambda d: 'Refiner' in d),

            (refiner_checkpoint, lambda d: lookup_checkpoint(d.get('Refiner'))),

            (refiner_switch_at, 'Refiner switch at'),

        ]



        return enable_refiner, refiner_checkpoint, refiner_switch_at



    def before_process(self, p, enable_refiner, refiner_checkpoint, refiner_switch_at):

        # the actual implementation is in sd_samplers_common.py, apply_refiner



        p.refiner_checkpoint_info = None

        p.refiner_switch_at = None



        if not enable_refiner or refiner_checkpoint in (None, "", "None"):

            return



        refiner_checkpoint_info = sd_models.get_closet_checkpoint_match(refiner_checkpoint)

        if refiner_checkpoint_info is None:

            raise Exception(f'Could not find checkpoint with name {refiner_checkpoint}')



        p.refiner_checkpoint_info = refiner_checkpoint_info

        p.refiner_switch_at = refiner_switch_at

modules/scripts.py

+16 −8
Original line number Diff line number Diff line
@@ -37,7 +37,10 @@ class Script: 
    is_img2img = False



    group = None

    """A gr.Group component that has all script's UI inside it"""

    """A gr.Group component that has all script's UI inside it."""



    create_group = True

    """If False, for alwayson scripts, a group component will not be created."""



    infotext_fields = None

    """if set in ui(), this is a list of pairs of gradio component + text; the text will be used when
@@ -232,6 +235,7 @@ class Script: 
        """

        pass





current_basedir = paths.script_path
@@ -250,7 +254,7 @@ postprocessing_scripts_data = [] 
ScriptClassData = namedtuple("ScriptClassData", ["script_class", "path", "basedir", "module"])





def list_scripts(scriptdirname, extension):

def list_scripts(scriptdirname, extension, *, include_extensions=True):

    scripts_list = []



    basedir = os.path.join(paths.script_path, scriptdirname)
@@ -258,6 +262,7 @@ def list_scripts(scriptdirname, extension): 
        for filename in sorted(os.listdir(basedir)):

            scripts_list.append(ScriptFile(paths.script_path, filename, os.path.join(basedir, filename)))



    if include_extensions:

        for ext in extensions.active():

            scripts_list += ext.list_files(scriptdirname, extension)
@@ -288,7 +293,7 @@ def load_scripts(): 
    postprocessing_scripts_data.clear()

    script_callbacks.clear_callbacks()



    scripts_list = list_scripts("scripts", ".py")

    scripts_list = list_scripts("scripts", ".py") + list_scripts("modules/processing_scripts", ".py", include_extensions=False)



    syspath = sys.path
@@ -429,10 +434,13 @@ class ScriptRunner: 
            if script.alwayson and script.section != section:

                continue



            if script.create_group:

                with gr.Group(visible=script.alwayson) as group:

                    self.create_script_ui(script)



                script.group = group

            else:

                self.create_script_ui(script)



    def prepare_ui(self):

        self.inputs = [None]

modules/sd_models.py

+3 −0
Original line number Diff line number Diff line
@@ -147,6 +147,9 @@ re_strip_checksum = re.compile(r"\s*\[[^]]+]\s*$") 




def get_closet_checkpoint_match(search_string):

    if not search_string:

        return None



    checkpoint_info = checkpoint_aliases.get(search_string, None)

    if checkpoint_info is not None:

        return checkpoint_info

modules/sd_samplers_cfg_denoiser.py

+5 −1
Original line number Diff line number Diff line
@@ -45,6 +45,11 @@ class CFGDenoiser(torch.nn.Module): 
        self.nmask = None

        self.init_latent = None

        self.steps = None

        """number of steps as specified by user in UI"""



        self.total_steps = None

        """expected number of calls to denoiser calculated from self.steps and specifics of the selected sampler"""



        self.step = 0

        self.image_cfg_scale = None

        self.padded_cond_uncond = False
@@ -56,7 +61,6 @@ class CFGDenoiser(torch.nn.Module): 
    def inner_model(self):

        raise NotImplementedError()





    def combine_denoised(self, x_out, conds_list, uncond, cond_scale):

        denoised_uncond = x_out[-uncond.shape[0]:]

        denoised = torch.clone(denoised_uncond)

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