UI options for mask blur and inpainting fill
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AUTOMATIC
parent
54f74d4472
commit
ff98e09d72
43
webui.py
43
webui.py
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@ -789,7 +789,7 @@ class EmbeddingsWithFixes(nn.Module):
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class StableDiffusionProcessing:
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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):
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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):
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self.outpath: str = outpath
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self.prompt: str = prompt
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self.seed: int = seed
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@ -805,6 +805,7 @@ class StableDiffusionProcessing:
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self.do_not_save_samples: bool = do_not_save_samples
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self.do_not_save_grid: bool = do_not_save_grid
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self.extra_generation_params: dict = extra_generation_params
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self.overlay_images = overlay_images
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def init(self):
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pass
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@ -950,6 +951,11 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
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image = Image.fromarray(x_sample)
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if p.overlay_images is not None and i < len(p.overlay_images):
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image = image.convert('RGBA')
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image.alpha_composite(p.overlay_images[i])
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image = image.convert('RGB')
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if not p.do_not_save_samples:
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save_image(image, sample_path, f"{base_count:05}", seeds[i], prompts[i], opts.samples_format, info=infotext())
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@ -1122,7 +1128,7 @@ def fill(image, mask):
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class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
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sampler = None
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def __init__(self, init_images=None, resize_mode=0, denoising_strength=0.75, mask=None, mask_blur=4, **kwargs):
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def __init__(self, init_images=None, resize_mode=0, denoising_strength=0.75, mask=None, mask_blur=4, inpainting_fill=0, **kwargs):
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super().__init__(**kwargs)
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self.init_images = init_images
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@ -1131,6 +1137,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
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self.init_latent = None
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self.original_mask = mask
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self.mask_blur = mask_blur
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self.inpainting_fill = inpainting_fill
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self.mask = None
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self.nmask = None
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@ -1149,14 +1156,22 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
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self.mask = torch.asarray(1.0 - latmask).to(device).type(sd_model.dtype)
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self.nmask = torch.asarray(latmask).to(device).type(sd_model.dtype)
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self.overlay_images = []
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imgs = []
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for img in self.init_images:
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image = img.convert("RGB")
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image = resize_image(self.resize_mode, image, self.width, self.height)
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if self.original_mask is not None
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image = fill(image, self.original_mask)
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if self.original_mask is not None:
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if self.inpainting_fill == 0:
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image = fill(image, self.original_mask)
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image_masked = Image.new('RGBa', (image.width, image.height))
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image_masked.paste(image.convert("RGBA").convert("RGBa"), mask=ImageOps.invert(self.original_mask.convert('L')))
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self.overlay_images.append(image_masked.convert('RGBA'))
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image = np.array(image).astype(np.float32) / 255.0
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image = np.moveaxis(image, 2, 0)
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@ -1165,6 +1180,8 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
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if len(imgs) == 1:
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batch_images = np.expand_dims(imgs[0], axis=0).repeat(self.batch_size, axis=0)
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if self.overlay_images is not None:
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self.overlay_images = self.overlay_images * self.batch_size
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elif len(imgs) <= self.batch_size:
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self.batch_size = len(imgs)
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batch_images = np.array(imgs)
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@ -1178,15 +1195,19 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
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self.init_latent = sd_model.get_first_stage_encoding(sd_model.encode_first_stage(image))
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def sample(self, x, conditioning, unconditional_conditioning):
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t_enc = int(self.denoising_strength * self.steps)
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t_enc = int(min(self.denoising_strength, 0.999) * self.steps)
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sigmas = self.sampler.model_wrap.get_sigmas(self.steps)
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noise = x * sigmas[self.steps - t_enc - 1]
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xi = self.init_latent + noise
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sigma_sched = sigmas[self.steps - t_enc - 1:]
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#if self.mask is not None:
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# xi = xi * self.mask + noise * self.nmask
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if self.mask is not None:
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if self.inpainting_fill == 2:
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xi = xi * self.mask + noise * self.nmask
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elif self.inpainting_fill == 3:
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xi = xi * self.mask
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sigma_sched = sigmas[self.steps - t_enc - 1:]
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def mask_cb(v):
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v["denoised"][:] = v["denoised"][:] * self.nmask + self.init_latent * self.mask
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@ -1199,7 +1220,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
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return samples_ddim
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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):
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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):
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outpath = opts.outdir or "outputs/img2img-samples"
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if init_img_with_mask is not None:
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@ -1226,6 +1247,8 @@ def img2img(prompt: str, init_img, init_img_with_mask, ddim_steps: int, sampler_
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use_GFPGAN=use_GFPGAN,
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init_images=[image],
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mask=mask,
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mask_blur=mask_blur,
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inpainting_fill=inpainting_fill,
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resize_mode=resize_mode,
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denoising_strength=denoising_strength,
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extra_generation_params={"Denoising Strength": denoising_strength}
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@ -1327,6 +1350,8 @@ img2img_interface = gr.Interface(
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gr.Image(label="Image for inpainting with mask", source="upload", interactive=True, type="pil", tool="sketch"),
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gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=20),
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gr.Radio(label='Sampling method', choices=[x.name for x in samplers_for_img2img], value=samplers_for_img2img[0].name, type="index"),
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gr.Slider(label='Inpainting: mask blur', minimum=0, maximum=64, step=1, value=4),
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gr.Radio(label='Inpainting: masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='fill', type="index"),
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gr.Checkbox(label='Fix faces using GFPGAN', value=False, visible=have_gfpgan),
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gr.Checkbox(label='Create prompt matrix (separate multiple prompts using |, and get all combinations of them)', value=False),
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gr.Checkbox(label='Loopback (use images from previous batch when creating next batch)', value=False),
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