Add upscaler to img2img
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space-nuko
parent
68999d0b15
commit
7ea5d395c4
@ -282,8 +282,8 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
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res["Hires resize-1"] = 0
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res["Hires resize-2"] = 0
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if "Img2Img Upscale" not in res:
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res["Img2Img Upscale"] = 1
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if "Img2Img upscale" not in res:
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res["Img2Img upscale"] = 1
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restore_old_hires_fix_params(res)
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@ -78,7 +78,7 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args):
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processed_image.save(os.path.join(output_dir, filename))
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def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_styles, init_img, sketch, init_img_with_mask, inpaint_color_sketch, inpaint_color_sketch_orig, init_img_inpaint, init_mask_inpaint, steps: int, sampler_index: int, mask_blur: int, mask_alpha: float, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, image_cfg_scale: float, denoising_strength: 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, scale: float, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, img2img_batch_inpaint_mask_dir: str, override_settings_texts, *args):
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def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_styles, init_img, sketch, init_img_with_mask, inpaint_color_sketch, inpaint_color_sketch_orig, init_img_inpaint, init_mask_inpaint, steps: int, sampler_index: int, mask_blur: int, mask_alpha: float, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, image_cfg_scale: float, denoising_strength: 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, scale: float, upscaler: str, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, img2img_batch_inpaint_mask_dir: str, override_settings_texts, *args):
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override_settings = create_override_settings_dict(override_settings_texts)
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is_batch = mode == 5
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@ -150,6 +150,7 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s
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inpainting_mask_invert=inpainting_mask_invert,
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override_settings=override_settings,
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scale=scale,
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upscaler=upscaler,
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)
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p.scripts = modules.scripts.scripts_txt2img
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@ -929,7 +929,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
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class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
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sampler = None
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def __init__(self, init_images: Optional[list] = None, resize_mode: int = 0, denoising_strength: float = 0.75, image_cfg_scale: Optional[float] = None, mask: Any = None, mask_blur: int = 4, inpainting_fill: int = 0, inpaint_full_res: bool = True, inpaint_full_res_padding: int = 0, inpainting_mask_invert: int = 0, initial_noise_multiplier: Optional[float] = None, scale: float = 0, **kwargs):
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def __init__(self, init_images: Optional[list] = None, resize_mode: int = 0, denoising_strength: float = 0.75, image_cfg_scale: Optional[float] = None, mask: Any = None, mask_blur: int = 4, inpainting_fill: int = 0, inpaint_full_res: bool = True, inpaint_full_res_padding: int = 0, inpainting_mask_invert: int = 0, initial_noise_multiplier: Optional[float] = None, scale: float = 0, upscaler: Optional[str] = None, **kwargs):
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super().__init__(**kwargs)
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self.init_images = init_images
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@ -950,6 +950,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
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self.nmask = None
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self.image_conditioning = None
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self.scale = scale
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self.upscaler = upscaler
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def get_final_size(self):
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if self.scale > 1:
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@ -966,7 +967,16 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
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crop_region = None
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if self.scale > 1:
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self.extra_generation_params["Img2Img Upscale"] = self.scale
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self.extra_generation_params["Img2Img upscale"] = self.scale
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# Non-latent upscalers are run before sampling
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# Latent upscalers are run during sampling
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init_upscaler = None
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if self.upscaler is not None:
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self.extra_generation_params["Img2Img upscaler"] = self.upscaler
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if self.upscaler not in shared.latent_upscale_modes:
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assert len([x for x in shared.sd_upscalers if x.name == self.upscaler]) > 0, f"could not find upscaler named {self.upscaler}"
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init_upscaler = self.upscaler
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self.width, self.height = self.get_final_size()
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@ -992,7 +1002,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
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image_mask = images.resize_image(2, mask, self.width, self.height)
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self.paste_to = (x1, y1, x2-x1, y2-y1)
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else:
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image_mask = images.resize_image(self.resize_mode, image_mask, self.width, self.height)
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image_mask = images.resize_image(self.resize_mode, image_mask, self.width, self.height, init_upscaler)
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np_mask = np.array(image_mask)
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np_mask = np.clip((np_mask.astype(np.float32)) * 2, 0, 255).astype(np.uint8)
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self.mask_for_overlay = Image.fromarray(np_mask)
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@ -1009,7 +1019,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
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image = images.flatten(img, opts.img2img_background_color)
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if crop_region is None and self.resize_mode != 3:
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image = images.resize_image(self.resize_mode, image, self.width, self.height)
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image = images.resize_image(self.resize_mode, image, self.width, self.height, init_upscaler)
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if image_mask is not None:
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image_masked = Image.new('RGBa', (image.width, image.height))
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@ -1054,8 +1064,9 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
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self.init_latent = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image))
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if self.resize_mode == 3:
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self.init_latent = torch.nn.functional.interpolate(self.init_latent, size=(self.height // opt_f, self.width // opt_f), mode="bilinear")
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latent_scale_mode = shared.latent_upscale_modes.get(self.upscaler, None) if self.upscaler is not None else shared.latent_upscale_modes.get(shared.latent_upscale_default_mode, "nearest")
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if latent_scale_mode is not None:
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self.init_latent = torch.nn.functional.interpolate(self.init_latent, size=(self.height // opt_f, self.width // opt_f), mode=latent_scale_mode["mode"], antialias=latent_scale_mode["antialias"])
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if image_mask is not None:
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init_mask = latent_mask
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@ -767,7 +767,7 @@ def create_ui():
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)
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with FormRow():
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resize_mode = gr.Radio(label="Resize mode", elem_id="resize_mode", choices=["Just resize", "Crop and resize", "Resize and fill", "Just resize (latent upscale)"], type="index", value="Just resize")
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resize_mode = gr.Radio(label="Resize mode", elem_id="resize_mode", choices=["Just resize", "Crop and resize", "Resize and fill"], type="index", value="Just resize")
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for category in ordered_ui_categories():
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if category == "sampler":
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@ -797,6 +797,8 @@ def create_ui():
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with FormRow():
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cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0, elem_id="img2img_cfg_scale")
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image_cfg_scale = gr.Slider(minimum=0, maximum=3.0, step=0.05, label='Image CFG Scale', value=1.5, elem_id="img2img_image_cfg_scale", visible=shared.sd_model and shared.sd_model.cond_stage_key == "edit")
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with FormRow():
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upscaler = gr.Dropdown(label="Upscaler", elem_id="img2img_upscaler", choices=[*shared.latent_upscale_modes, *[x.name for x in shared.sd_upscalers]], value=shared.latent_upscale_default_mode)
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denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.75, elem_id="img2img_denoising_strength")
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elif category == "seed":
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@ -934,6 +936,7 @@ def create_ui():
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height,
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width,
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scale,
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upscaler,
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resize_mode,
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inpaint_full_res,
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inpaint_full_res_padding,
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@ -1019,7 +1022,8 @@ def create_ui():
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(seed, "Seed"),
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(width, "Size-1"),
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(height, "Size-2"),
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(scale, "Img2Img Upscale"),
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(scale, "Img2Img upscale"),
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(upscaler, "Img2Img upscaler"),
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(batch_size, "Batch size"),
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(subseed, "Variation seed"),
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(subseed_strength, "Variation seed strength"),
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@ -220,6 +220,7 @@ axis_options = [
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AxisOption("Clip skip", int, apply_clip_skip),
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AxisOption("Denoising", float, apply_field("denoising_strength")),
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AxisOptionTxt2Img("Hires upscaler", str, apply_field("hr_upscaler"), choices=lambda: [*shared.latent_upscale_modes, *[x.name for x in shared.sd_upscalers]]),
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AxisOptionImg2Img("Upscaler", str, apply_field("upscaler"), choices=lambda: [*shared.latent_upscale_modes, *[x.name for x in shared.sd_upscalers]]),
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AxisOptionImg2Img("Cond. Image Mask Weight", float, apply_field("inpainting_mask_weight")),
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AxisOption("VAE", str, apply_vae, cost=0.7, choices=lambda: list(sd_vae.vae_dict)),
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AxisOption("Styles", str, apply_styles, choices=lambda: list(shared.prompt_styles.styles)),
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