Commit 81490780 authored by AUTOMATIC's avatar AUTOMATIC
Browse files

added the option to specify target resolution with possibility of truncating... 

added the option to specify target resolution with possibility of truncating for hires fix; also sampling steps
parent 24d4a084

javascript/hints.js

+7 −4
Original line number Diff line number Diff line
@@ -81,9 +81,6 @@ titles = { 


    "vram": "Torch active: Peak amount of VRAM used by Torch during generation, excluding cached data.\nTorch reserved: Peak amount of VRAM allocated by Torch, including all active and cached data.\nSys VRAM: Peak amount of VRAM allocation across all applications / total GPU VRAM (peak utilization%).",



    "Highres. fix": "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition",

    "Scale latent": "Uscale the image in latent space. Alternative is to produce the full image from latent representation, upscale that, and then move it back to latent space.",



    "Eta noise seed delta": "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.",

    "Do not add watermark to images": "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.",
@@ -100,7 +97,13 @@ titles = { 
    "Clip skip": "Early stopping parameter for CLIP model; 1 is stop at last layer as usual, 2 is stop at penultimate layer, etc.",



    "Approx NN": "Cheap neural network approximation. Very fast compared to VAE, but produces pictures with 4 times smaller horizontal/vertical resoluton and lower quality.",

    "Approx cheap": "Very cheap approximation. Very fast compared to VAE, but produces pictures with 8 times smaller horizontal/vertical resoluton and extremely low quality."

    "Approx cheap": "Very cheap approximation. Very fast compared to VAE, but produces pictures with 8 times smaller horizontal/vertical resoluton and extremely low quality.",



    "Hires. fix": "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition",

    "Hires steps": "Number of sampling steps for upscaled picture. If 0, uses same as for original.",

    "Upscale by": "Adjusts the size of the image by multiplying the original width and height by the selected value. Ignored if either Resize width to or Resize height to are non-zero.",

    "Resize width to": "Resizes image to this width. If 0, width is inferred from either of two nearby sliders.",

    "Resize height to": "Resizes image to this height. If 0, height is inferred from either of two nearby sliders."

}

modules/generation_parameters_copypaste.py

+6 −3
Original line number Diff line number Diff line
@@ -212,11 +212,10 @@ def restore_old_hires_fix_params(res): 
        firstpass_width = math.ceil(scale * width / 64) * 64

        firstpass_height = math.ceil(scale * height / 64) * 64



    hr_scale = width / firstpass_width if firstpass_width > 0 else height / firstpass_height



    res['Size-1'] = firstpass_width

    res['Size-2'] = firstpass_height

    res['Hires upscale'] = hr_scale

    res['Hires resize-1'] = width

    res['Hires resize-2'] = height





def parse_generation_parameters(x: str):
@@ -276,6 +275,10 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model 
        hypernet_hash = res.get("Hypernet hash", None)

        res["Hypernet"] = find_hypernetwork_key(hypernet_name, hypernet_hash)



    if "Hires resize-1" not in res:

        res["Hires resize-1"] = 0

        res["Hires resize-2"] = 0



    restore_old_hires_fix_params(res)



    return res

modules/processing.py

+46 −5
Original line number Diff line number Diff line
@@ -662,12 +662,17 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: 
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, **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, **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_second_pass_steps = hr_second_pass_steps

        self.hr_resize_x = hr_resize_x

        self.hr_resize_y = hr_resize_y

        self.hr_upscale_to_x = hr_resize_x

        self.hr_upscale_to_y = hr_resize_y



        if firstphase_width != 0 or firstphase_height != 0:

            print("firstphase_width/firstphase_height no longer supported; use hr_scale", file=sys.stderr)
@@ -675,6 +680,9 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): 
            self.width = firstphase_width

            self.height = firstphase_height



        self.truncate_x = 0

        self.truncate_y = 0



    def init(self, all_prompts, all_seeds, all_subseeds):

        if self.enable_hr:

            if state.job_count == -1:
@@ -682,7 +690,38 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): 
            else:

                state.job_count = state.job_count * 2



            if self.hr_resize_x == 0 and self.hr_resize_y == 0:

                self.extra_generation_params["Hires upscale"] = self.hr_scale

                self.hr_upscale_to_x = int(self.width * self.hr_scale)

                self.hr_upscale_to_y = int(self.height * self.hr_scale)

            else:

                self.extra_generation_params["Hires resize"] = f"{self.hr_resize_x}x{self.hr_resize_y}"



                if self.hr_resize_y == 0:

                    self.hr_upscale_to_x = self.hr_resize_x

                    self.hr_upscale_to_y = self.hr_resize_x * self.height // self.width

                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

                else:

                    target_w = self.hr_resize_x

                    target_h = self.hr_resize_y

                    src_ratio = self.width / self.height

                    dst_ratio = self.hr_resize_x / self.hr_resize_y



                    if src_ratio < dst_ratio:

                        self.hr_upscale_to_x = self.hr_resize_x

                        self.hr_upscale_to_y = self.hr_resize_x * self.height // self.width

                    else:

                        self.hr_upscale_to_x = self.hr_resize_y * self.width // self.height

                        self.hr_upscale_to_y = self.hr_resize_y



                    self.truncate_x = (self.hr_upscale_to_x - target_w) // opt_f

                    self.truncate_y = (self.hr_upscale_to_y - target_h) // opt_f



            if self.hr_second_pass_steps:

                self.extra_generation_params["Hires steps"] = self.hr_second_pass_steps



            if self.hr_upscaler is not None:

                self.extra_generation_params["Hires upscaler"] = self.hr_upscaler
@@ -699,8 +738,8 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): 
        if not self.enable_hr:

            return samples



        target_width = int(self.width * self.hr_scale)

        target_height = int(self.height * self.hr_scale)

        target_width = self.hr_upscale_to_x

        target_height = self.hr_upscale_to_y



        def save_intermediate(image, index):

            """saves image before applying hires fix, if enabled in options; takes as an argument either an image or batch with latent space images"""
@@ -755,13 +794,15 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): 


        self.sampler = sd_samplers.create_sampler(self.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]



        noise = create_random_tensors(samples.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=subseed_strength, p=self)



        # GC now before running the next img2img to prevent running out of memory

        x = None

        devices.torch_gc()



        samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.steps, image_conditioning=image_conditioning)

        samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.hr_second_pass_steps or self.steps, image_conditioning=image_conditioning)



        return samples

modules/txt2img.py

+4 −1
Original line number Diff line number Diff line
@@ -8,7 +8,7 @@ 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, denoising_strength: float, hr_scale: float, hr_upscaler: str, *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, hr_scale: float, hr_upscaler: str, hr_second_pass_steps: int, hr_resize_x: int, hr_resize_y: int, *args):

    p = StableDiffusionProcessingTxt2Img(

        sd_model=shared.sd_model,

        outpath_samples=opts.outdir_samples or opts.outdir_txt2img_samples,
@@ -35,6 +35,9 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: 
        denoising_strength=denoising_strength if enable_hr else None,

        hr_scale=hr_scale,

        hr_upscaler=hr_upscaler,

        hr_second_pass_steps=hr_second_pass_steps,

        hr_resize_x=hr_resize_x,

        hr_resize_y=hr_resize_y,

    )



    p.scripts = modules.scripts.scripts_txt2img

modules/ui.py

+18 −6
Original line number Diff line number Diff line
@@ -637,10 +637,10 @@ def create_sampler_and_steps_selection(choices, tabname): 
        with FormRow(elem_id=f"sampler_selection_{tabname}"):

            sampler_index = gr.Dropdown(label='Sampling method', elem_id=f"{tabname}_sampling", choices=[x.name for x in choices], value=choices[0].name, type="index")

            sampler_index.save_to_config = True

            steps = gr.Slider(minimum=1, maximum=150, step=1, elem_id=f"{tabname}_steps", label="Sampling Steps", value=20)

            steps = gr.Slider(minimum=1, maximum=150, step=1, elem_id=f"{tabname}_steps", label="Sampling steps", value=20)

    else:

        with FormGroup(elem_id=f"sampler_selection_{tabname}"):

            steps = gr.Slider(minimum=1, maximum=150, step=1, elem_id=f"{tabname}_steps", label="Sampling Steps", value=20)

            steps = gr.Slider(minimum=1, maximum=150, step=1, elem_id=f"{tabname}_steps", label="Sampling steps", value=20)

            sampler_index = gr.Radio(label='Sampling method', elem_id=f"{tabname}_sampling", choices=[x.name for x in choices], value=choices[0].name, type="index")



    return steps, sampler_index
@@ -709,11 +709,17 @@ def create_ui(): 
                            enable_hr = gr.Checkbox(label='Hires. fix', value=False, elem_id="txt2img_enable_hr")



                    elif category == "hires_fix":

                        with FormRow(visible=False, elem_id="txt2img_hires_fix") as hr_options:

                        with FormGroup(visible=False, elem_id="txt2img_hires_fix") as hr_options:

                            with FormRow(elem_id="txt2img_hires_fix_row1"):

                                hr_upscaler = gr.Dropdown(label="Upscaler", elem_id="txt2img_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=1.0, maximum=4.0, step=0.05, label="Upscale by", value=2.0, elem_id="txt2img_hr_scale")

                                hr_second_pass_steps = gr.Slider(minimum=0, maximum=150, step=1, label='Hires steps', value=0, elem_id="txt2img_hires_steps")

                                denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7, elem_id="txt2img_denoising_strength")



                            with FormRow(elem_id="txt2img_hires_fix_row2"):

                                hr_scale = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label="Upscale by", value=2.0, elem_id="txt2img_hr_scale")

                                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")



                    elif category == "batch":

                        if not opts.dimensions_and_batch_together:

                            with FormRow(elem_id="txt2img_column_batch"):
@@ -753,6 +759,9 @@ def create_ui(): 
                    denoising_strength,

                    hr_scale,

                    hr_upscaler,

                    hr_second_pass_steps,

                    hr_resize_x,

                    hr_resize_y,

                ] + custom_inputs,



                outputs=[
@@ -804,6 +813,9 @@ def create_ui(): 
                (hr_options, lambda d: gr.Row.update(visible="Denoising strength" in d)),

                (hr_scale, "Hires upscale"),

                (hr_upscaler, "Hires upscaler"),

                (hr_second_pass_steps, "Hires steps"),

                (hr_resize_x, "Hires resize-1"),

                (hr_resize_y, "Hires resize-2"),

                *modules.scripts.scripts_txt2img.infotext_fields

            ]

            parameters_copypaste.add_paste_fields("txt2img", None, txt2img_paste_fields)

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