From ff98e09d726fae5c87aab8c1316150865edf89b9 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Tue, 30 Aug 2022 14:04:49 +0300 Subject: [PATCH] UI options for mask blur and inpainting fill --- webui.py | 43 ++++++++++++++++++++++++++++++++++--------- 1 file changed, 34 insertions(+), 9 deletions(-) diff --git a/webui.py b/webui.py index 6998464c..27838325 100644 --- a/webui.py +++ b/webui.py @@ -789,7 +789,7 @@ class EmbeddingsWithFixes(nn.Module): class StableDiffusionProcessing: - def __init__(self, outpath=None, prompt="", seed=-1, sampler_index=0, batch_size=1, n_iter=1, steps=50, cfg_scale=7.0, width=512, height=512, prompt_matrix=False, use_GFPGAN=False, do_not_save_samples=False, do_not_save_grid=False, extra_generation_params=None): + def __init__(self, outpath=None, prompt="", seed=-1, sampler_index=0, batch_size=1, n_iter=1, steps=50, cfg_scale=7.0, width=512, height=512, prompt_matrix=False, use_GFPGAN=False, do_not_save_samples=False, do_not_save_grid=False, extra_generation_params=None, overlay_images=None): self.outpath: str = outpath self.prompt: str = prompt self.seed: int = seed @@ -805,6 +805,7 @@ class StableDiffusionProcessing: self.do_not_save_samples: bool = do_not_save_samples self.do_not_save_grid: bool = do_not_save_grid self.extra_generation_params: dict = extra_generation_params + self.overlay_images = overlay_images def init(self): pass @@ -950,6 +951,11 @@ def process_images(p: StableDiffusionProcessing) -> Processed: image = Image.fromarray(x_sample) + if p.overlay_images is not None and i < len(p.overlay_images): + image = image.convert('RGBA') + image.alpha_composite(p.overlay_images[i]) + image = image.convert('RGB') + if not p.do_not_save_samples: save_image(image, sample_path, f"{base_count:05}", seeds[i], prompts[i], opts.samples_format, info=infotext()) @@ -1122,7 +1128,7 @@ def fill(image, mask): class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): sampler = None - def __init__(self, init_images=None, resize_mode=0, denoising_strength=0.75, mask=None, mask_blur=4, **kwargs): + def __init__(self, init_images=None, resize_mode=0, denoising_strength=0.75, mask=None, mask_blur=4, inpainting_fill=0, **kwargs): super().__init__(**kwargs) self.init_images = init_images @@ -1131,6 +1137,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): self.init_latent = None self.original_mask = mask self.mask_blur = mask_blur + self.inpainting_fill = inpainting_fill self.mask = None self.nmask = None @@ -1149,14 +1156,22 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): self.mask = torch.asarray(1.0 - latmask).to(device).type(sd_model.dtype) self.nmask = torch.asarray(latmask).to(device).type(sd_model.dtype) + self.overlay_images = [] + imgs = [] for img in self.init_images: image = img.convert("RGB") image = resize_image(self.resize_mode, image, self.width, self.height) - if self.original_mask is not None - image = fill(image, self.original_mask) + if self.original_mask is not None: + if self.inpainting_fill == 0: + image = fill(image, self.original_mask) + + image_masked = Image.new('RGBa', (image.width, image.height)) + image_masked.paste(image.convert("RGBA").convert("RGBa"), mask=ImageOps.invert(self.original_mask.convert('L'))) + + self.overlay_images.append(image_masked.convert('RGBA')) image = np.array(image).astype(np.float32) / 255.0 image = np.moveaxis(image, 2, 0) @@ -1165,6 +1180,8 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): if len(imgs) == 1: batch_images = np.expand_dims(imgs[0], axis=0).repeat(self.batch_size, axis=0) + if self.overlay_images is not None: + self.overlay_images = self.overlay_images * self.batch_size elif len(imgs) <= self.batch_size: self.batch_size = len(imgs) batch_images = np.array(imgs) @@ -1178,15 +1195,19 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): self.init_latent = sd_model.get_first_stage_encoding(sd_model.encode_first_stage(image)) def sample(self, x, conditioning, unconditional_conditioning): - t_enc = int(self.denoising_strength * self.steps) + t_enc = int(min(self.denoising_strength, 0.999) * self.steps) sigmas = self.sampler.model_wrap.get_sigmas(self.steps) noise = x * sigmas[self.steps - t_enc - 1] xi = self.init_latent + noise - sigma_sched = sigmas[self.steps - t_enc - 1:] - #if self.mask is not None: - # xi = xi * self.mask + noise * self.nmask + if self.mask is not None: + if self.inpainting_fill == 2: + xi = xi * self.mask + noise * self.nmask + elif self.inpainting_fill == 3: + xi = xi * self.mask + + sigma_sched = sigmas[self.steps - t_enc - 1:] def mask_cb(v): v["denoised"][:] = v["denoised"][:] * self.nmask + self.init_latent * self.mask @@ -1199,7 +1220,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): return samples_ddim -def img2img(prompt: str, init_img, init_img_with_mask, ddim_steps: int, sampler_index: int, use_GFPGAN: bool, prompt_matrix, loopback: bool, sd_upscale: bool, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, height: int, width: int, resize_mode: int): +def img2img(prompt: str, init_img, init_img_with_mask, ddim_steps: int, sampler_index: int, mask_blur: int, inpainting_fill: int, use_GFPGAN: bool, prompt_matrix, loopback: bool, sd_upscale: bool, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, height: int, width: int, resize_mode: int): outpath = opts.outdir or "outputs/img2img-samples" if init_img_with_mask is not None: @@ -1226,6 +1247,8 @@ def img2img(prompt: str, init_img, init_img_with_mask, ddim_steps: int, sampler_ use_GFPGAN=use_GFPGAN, init_images=[image], mask=mask, + mask_blur=mask_blur, + inpainting_fill=inpainting_fill, resize_mode=resize_mode, denoising_strength=denoising_strength, extra_generation_params={"Denoising Strength": denoising_strength} @@ -1327,6 +1350,8 @@ img2img_interface = gr.Interface( gr.Image(label="Image for inpainting with mask", source="upload", interactive=True, type="pil", tool="sketch"), gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=20), gr.Radio(label='Sampling method', choices=[x.name for x in samplers_for_img2img], value=samplers_for_img2img[0].name, type="index"), + gr.Slider(label='Inpainting: mask blur', minimum=0, maximum=64, step=1, value=4), + gr.Radio(label='Inpainting: masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='fill', type="index"), gr.Checkbox(label='Fix faces using GFPGAN', value=False, visible=have_gfpgan), gr.Checkbox(label='Create prompt matrix (separate multiple prompts using |, and get all combinations of them)', value=False), gr.Checkbox(label='Loopback (use images from previous batch when creating next batch)', value=False),