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https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
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remove scale latent and no-crop options from hires fix
support copy-pasting new parameters for hires fix
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AUTOMATIC
parent
b382de2d77
commit
e644b5a80b
@ -506,14 +506,12 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
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firstphase_width_truncated = 0
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firstphase_height_truncated = 0
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def __init__(self, enable_hr=False, scale_latent=True, denoising_strength=0.75, firstphase_width=512, firstphase_height=512, crop_scale=False, **kwargs):
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def __init__(self, enable_hr=False, denoising_strength=0.75, firstphase_width=512, firstphase_height=512, **kwargs):
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super().__init__(**kwargs)
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self.enable_hr = enable_hr
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self.scale_latent = scale_latent
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self.denoising_strength = denoising_strength
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self.firstphase_width = firstphase_width
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self.firstphase_height = firstphase_height
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self.crop_scale = crop_scale
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def init(self, all_prompts, all_seeds, all_subseeds):
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if self.enable_hr:
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@ -530,6 +528,8 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
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samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning)
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return samples
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self.extra_generation_params["First pass size"] = f"{self.firstphase_width}x{self.firstphase_height}"
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x = create_random_tensors([opt_C, self.firstphase_height // opt_f, self.firstphase_width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
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samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning)
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@ -538,46 +538,36 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
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width_ratio = self.width/self.firstphase_width
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height_ratio = self.height/self.firstphase_height
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if self.crop_scale:
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if width_ratio > height_ratio:
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#Crop to landscape
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truncate_y = int((self.width - self.firstphase_width) / width_ratio / height_ratio / opt_f)
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if width_ratio > height_ratio:
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truncate_y = int((self.width - self.firstphase_width) / width_ratio / height_ratio / opt_f)
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elif width_ratio < height_ratio:
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#Crop to portrait
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truncate_x = int((self.height - self.firstphase_height) / width_ratio / height_ratio / opt_f)
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elif width_ratio < height_ratio:
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truncate_x = int((self.height - self.firstphase_height) / width_ratio / height_ratio / opt_f)
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samples = samples[:, :, truncate_y//2:samples.shape[2]-truncate_y//2, truncate_x//2:samples.shape[3]-truncate_x//2]
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samples = samples[:, :, truncate_y//2:samples.shape[2]-truncate_y//2, truncate_x//2:samples.shape[3]-truncate_x//2]
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decoded_samples = decode_first_stage(self.sd_model, samples)
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if self.scale_latent:
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samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), mode="bilinear")
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if opts.upscaler_for_img2img is None or opts.upscaler_for_img2img == "None":
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decoded_samples = torch.nn.functional.interpolate(decoded_samples, size=(self.height, self.width), mode="bilinear")
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else:
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decoded_samples = decode_first_stage(self.sd_model, samples)
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lowres_samples = torch.clamp((decoded_samples + 1.0) / 2.0, min=0.0, max=1.0)
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if opts.upscaler_for_img2img is None or opts.upscaler_for_img2img == "None":
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decoded_samples = torch.nn.functional.interpolate(decoded_samples, size=(self.height, self.width), mode="bilinear")
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else:
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lowres_samples = torch.clamp((decoded_samples + 1.0) / 2.0, min=0.0, max=1.0)
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batch_images = []
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for i, x_sample in enumerate(lowres_samples):
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x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
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x_sample = x_sample.astype(np.uint8)
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image = Image.fromarray(x_sample)
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image = images.resize_image(0, image, self.width, self.height)
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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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batch_images.append(image)
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batch_images = []
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for i, x_sample in enumerate(lowres_samples):
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x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
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x_sample = x_sample.astype(np.uint8)
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image = Image.fromarray(x_sample)
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image = images.resize_image(0, image, self.width, self.height)
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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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batch_images.append(image)
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decoded_samples = torch.from_numpy(np.array(batch_images))
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decoded_samples = decoded_samples.to(shared.device)
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decoded_samples = 2. * decoded_samples - 1.
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decoded_samples = torch.from_numpy(np.array(batch_images))
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decoded_samples = decoded_samples.to(shared.device)
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decoded_samples = 2. * decoded_samples - 1.
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samples = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(decoded_samples))
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samples = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(decoded_samples))
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shared.state.nextjob()
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@ -6,7 +6,7 @@ import modules.processing as processing
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from modules.ui import plaintext_to_html
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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, scale_latent: bool, denoising_strength: float, firstphase_width: int, firstphase_height: int, crop_scale: bool, *args):
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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, firstphase_width: int, firstphase_height: int, *args):
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p = StableDiffusionProcessingTxt2Img(
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sd_model=shared.sd_model,
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outpath_samples=opts.outdir_samples or opts.outdir_txt2img_samples,
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@ -30,12 +30,9 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2:
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restore_faces=restore_faces,
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tiling=tiling,
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enable_hr=enable_hr,
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scale_latent=scale_latent if enable_hr else None,
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denoising_strength=denoising_strength if enable_hr else None,
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firstphase_width=firstphase_width if enable_hr else None,
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firstphase_height=firstphase_height if enable_hr else None,
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crop_scale=crop_scale if enable_hr else None,
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firstphase_width=firstphase_width if enable_hr else None,
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firstphase_height=firstphase_height if enable_hr else None,
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)
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if cmd_opts.enable_console_prompts:
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@ -540,16 +540,9 @@ def create_ui(wrap_gradio_gpu_call):
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enable_hr = gr.Checkbox(label='Highres. fix', value=False)
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with gr.Row(visible=False) as hr_options:
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with gr.Column(scale=1.0):
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firstphase_width = gr.Slider(minimum=64, maximum=1024, step=64, label="First pass width", value=512)
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firstphase_height = gr.Slider(minimum=64, maximum=1024, step=64, label="First pass height", value=512)
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with gr.Column(scale=1.0):
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with gr.Row():
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crop_scale = gr.Checkbox(label='Crop when scaling', value=False)
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scale_latent = gr.Checkbox(label='Scale latent', value=False)
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with gr.Row():
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denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7)
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firstphase_width = gr.Slider(minimum=64, maximum=1024, step=64, label="First pass width", value=512)
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firstphase_height = gr.Slider(minimum=64, maximum=1024, step=64, label="First pass height", value=512)
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denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7)
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with gr.Row(equal_height=True):
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batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1)
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@ -610,11 +603,9 @@ def create_ui(wrap_gradio_gpu_call):
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height,
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width,
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enable_hr,
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scale_latent,
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denoising_strength,
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firstphase_width,
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firstphase_height,
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crop_scale,
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] + custom_inputs,
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outputs=[
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txt2img_gallery,
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@ -679,8 +670,8 @@ def create_ui(wrap_gradio_gpu_call):
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(denoising_strength, "Denoising strength"),
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(enable_hr, lambda d: "Denoising strength" in d),
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(hr_options, lambda d: gr.Row.update(visible="Denoising strength" in d)),
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(firstphase_width, "First pass width"),
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(firstphase_height, "First pass height"),
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(firstphase_width, "First pass size-1"),
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(firstphase_height, "First pass size-2"),
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]
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modules.generation_parameters_copypaste.connect_paste(paste, txt2img_paste_fields, txt2img_prompt)
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token_button.click(fn=update_token_counter, inputs=[txt2img_prompt, steps], outputs=[token_counter])
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