Commit 5ec2c294 authored by AUTOMATIC's avatar AUTOMATIC
Browse files

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

parents 3885f8a6 f5e44364

modules/generation_parameters_copypaste.py

+5 −1
Original line number Diff line number Diff line
@@ -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"

modules/processing.py

+68 −5
Original line number Diff line number Diff line
@@ -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
@@ -699,7 +736,14 @@ 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():

                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))]
@@ -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())

modules/txt2img.py

+6 −2
Original line number Diff line number Diff line
@@ -6,7 +6,8 @@ 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):



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(
@@ -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,

    )

modules/ui.py

+18 −0
Original line number Diff line number Diff line
@@ -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)

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