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https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
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gfpgan is not kept in memory
font selection setting
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AUTOMATIC
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2b0ac45966
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3402461055
@ -252,7 +252,7 @@ display(processed.images, processed.seed, processed.info)
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### `--lowvram`
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Optimizations for GPUs with low VRAM. This should make it possible to generate 512x512 images on videocards with 4GB memory.
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The original idea of those ideas is by basujindal: https://github.com/basujindal/stable-diffusion. Model is separated into modules,
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The original idea of those optimizations is by basujindal: https://github.com/basujindal/stable-diffusion. Model is separated into modules,
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and only one module is kept in GPU memory; when another module needs to run, the previous is removed from GPU memory.
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It should be obvious but the nature of those optimizations makes the processing run slower -- about 10 times slower
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79
webui.py
79
webui.py
@ -53,11 +53,15 @@ parser.add_argument("--no-progressbar-hiding", action='store_true', help="do not
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parser.add_argument("--max-batch-count", type=int, default=16, help="maximum batch count value for the UI")
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parser.add_argument("--embeddings-dir", type=str, default='embeddings', help="embeddings dirtectory for textual inversion (default: embeddings)")
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parser.add_argument("--allow-code", action='store_true', help="allow custom script execution from webui")
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parser.add_argument("--lowvram", action='store_true', help="enamble optimizations for low vram")
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parser.add_argument("--lowvram", action='store_true', help="enamble stable diffusion model optimizations for low vram")
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parser.add_argument("--precision", type=str, help="evaluate at this precision", choices=["full", "autocast"], default="autocast")
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cmd_opts = parser.parse_args()
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cpu = torch.device("cpu")
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gpu = torch.device("cuda")
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device = gpu if torch.cuda.is_available() else cpu
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css_hide_progressbar = """
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.wrap .m-12 svg { display:none!important; }
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.wrap .m-12::before { content:"Loading..." }
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@ -106,7 +110,7 @@ try:
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]
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have_realesrgan = True
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except Exception:
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print("Error loading Real-ESRGAN:", file=sys.stderr)
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print("Error importing Real-ESRGAN:", file=sys.stderr)
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print(traceback.format_exc(), file=sys.stderr)
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realesrgan_models = [RealesrganModelInfo('None', '', 0, None)]
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@ -119,6 +123,27 @@ sd_upscalers = {
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}
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have_gfpgan = False
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if os.path.exists(cmd_opts.gfpgan_dir):
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try:
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sys.path.append(os.path.abspath(cmd_opts.gfpgan_dir))
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from gfpgan import GFPGANer
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have_gfpgan = True
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except:
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print("Error importing GFPGAN:", file=sys.stderr)
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print(traceback.format_exc(), file=sys.stderr)
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def gfpgan():
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model_name = 'GFPGANv1.3'
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model_path = os.path.join(cmd_opts.gfpgan_dir, 'experiments/pretrained_models', model_name + '.pth')
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if not os.path.isfile(model_path):
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raise Exception("GFPGAN model not found at path "+model_path)
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return GFPGANer(model_path=model_path, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None)
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class Options:
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class OptionInfo:
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def __init__(self, default=None, label="", component=None, component_args=None):
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@ -140,6 +165,7 @@ class Options:
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"jpeg_quality": OptionInfo(80, "Quality for saved jpeg images", gr.Slider, {"minimum": 1, "maximum": 100, "step": 1}),
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"export_for_4chan": OptionInfo(True, "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG"),
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"enable_pnginfo": OptionInfo(True, "Save text information about generation parameters as chunks to png files"),
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"font": OptionInfo("arial.ttf", "Font for image grids that have text"),
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"prompt_matrix_add_to_start": OptionInfo(True, "In prompt matrix, add the variable combination of text to the start of the prompt, rather than the end"),
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"sd_upscale_upscaler_index": OptionInfo("RealESRGAN", "Upscaler to use for SD upscale", gr.Radio, {"choices": list(sd_upscalers.keys())}),
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"sd_upscale_overlap": OptionInfo(64, "Overlap for tiles for SD upscale. The smaller it is, the less smooth transition from one tile to another", gr.Slider, {"minimum": 0, "maximum": 256, "step": 16}),
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@ -319,19 +345,6 @@ def plaintext_to_html(text):
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text = "".join([f"<p>{html.escape(x)}</p>\n" for x in text.split('\n')])
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return text
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def load_gfpgan():
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model_name = 'GFPGANv1.3'
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model_path = os.path.join(cmd_opts.gfpgan_dir, 'experiments/pretrained_models', model_name + '.pth')
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if not os.path.isfile(model_path):
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raise Exception("GFPGAN model not found at path "+model_path)
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sys.path.append(os.path.abspath(cmd_opts.gfpgan_dir))
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from gfpgan import GFPGANer
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return GFPGANer(model_path=model_path, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None)
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def image_grid(imgs, batch_size=1, rows=None):
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if rows is None:
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if opts.n_rows > 0:
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@ -449,7 +462,7 @@ def draw_grid_annotations(im, width, height, hor_texts, ver_texts):
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fontsize = (width + height) // 25
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line_spacing = fontsize // 2
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fnt = ImageFont.truetype("arial.ttf", fontsize)
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fnt = ImageFont.truetype(opts.font, fontsize)
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color_active = (0, 0, 0)
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color_inactive = (153, 153, 153)
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@ -581,16 +594,6 @@ def wrap_gradio_call(func):
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return f
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GFPGAN = None
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if os.path.exists(cmd_opts.gfpgan_dir):
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try:
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GFPGAN = load_gfpgan()
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print("Loaded GFPGAN")
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except Exception:
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print("Error loading GFPGAN:", file=sys.stderr)
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print(traceback.format_exc(), file=sys.stderr)
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class StableDiffusionModelHijack:
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ids_lookup = {}
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word_embeddings = {}
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@ -894,7 +897,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
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"Sampler": samplers[p.sampler_index].name,
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"CFG scale": p.cfg_scale,
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"Seed": seed,
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"GFPGAN": ("GFPGAN" if p.use_GFPGAN and GFPGAN is not None else None)
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"GFPGAN": ("GFPGAN" if p.use_GFPGAN else None)
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}
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if p.extra_generation_params is not None:
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@ -937,9 +940,11 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
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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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if p.use_GFPGAN and GFPGAN is not None:
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if p.use_GFPGAN:
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torch_gc()
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cropped_faces, restored_faces, restored_img = GFPGAN.enhance(x_sample, has_aligned=False, only_center_face=False, paste_back=True)
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gfpgan_model = gfpgan()
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cropped_faces, restored_faces, restored_img = gfpgan_model.enhance(x_sample, has_aligned=False, only_center_face=False, paste_back=True)
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x_sample = restored_img
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image = Image.fromarray(x_sample)
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@ -1073,7 +1078,7 @@ txt2img_interface = gr.Interface(
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gr.Textbox(label="Prompt", placeholder="A corgi wearing a top hat as an oil painting.", lines=1),
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gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=50),
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gr.Radio(label='Sampling method', choices=[x.name for x in samplers], value=samplers[0].name, type="index"),
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gr.Checkbox(label='Fix faces using GFPGAN', value=False, visible=GFPGAN is not None),
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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.Slider(minimum=1, maximum=cmd_opts.max_batch_count, step=1, label='Batch count (how many batches of images to generate)', value=1),
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gr.Slider(minimum=1, maximum=8, step=1, label='Batch size (how many images are in a batch; memory-hungry)', value=1),
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@ -1260,7 +1265,7 @@ img2img_interface = gr.Interface(
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gr.Image(value=sample_img2img, source="upload", interactive=True, type="pil"),
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gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=50),
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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.Checkbox(label='Fix faces using GFPGAN', value=False, visible=GFPGAN is not None),
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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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gr.Checkbox(label='Stable Diffusion upscale', value=False),
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@ -1306,8 +1311,9 @@ def run_extras(image, GFPGAN_strength, RealESRGAN_upscaling, RealESRGAN_model_in
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outpath = opts.outdir or "outputs/extras-samples"
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if GFPGAN is not None and GFPGAN_strength > 0:
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cropped_faces, restored_faces, restored_img = GFPGAN.enhance(np.array(image, dtype=np.uint8), has_aligned=False, only_center_face=False, paste_back=True)
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if have_gfpgan is not None and GFPGAN_strength > 0:
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gfpgan_model = gfpgan()
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cropped_faces, restored_faces, restored_img = gfpgan_model.enhance(np.array(image, dtype=np.uint8), has_aligned=False, only_center_face=False, paste_back=True)
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res = Image.fromarray(restored_img)
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if GFPGAN_strength < 1.0:
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@ -1328,7 +1334,7 @@ extras_interface = gr.Interface(
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wrap_gradio_call(run_extras),
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inputs=[
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gr.Image(label="Source", source="upload", interactive=True, type="pil"),
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gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="GFPGAN strength", value=1, interactive=GFPGAN is not None),
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gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="GFPGAN strength", value=1, interactive=have_gfpgan),
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gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label="Real-ESRGAN upscaling", value=2, interactive=have_realesrgan),
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gr.Radio(label='Real-ESRGAN model', choices=[x.name for x in realesrgan_models], value=realesrgan_models[0].name, type="index", interactive=have_realesrgan),
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],
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@ -1399,11 +1405,6 @@ interfaces = [
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sd_config = OmegaConf.load(cmd_opts.config)
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sd_model = load_model_from_config(sd_config, cmd_opts.ckpt)
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cpu = torch.device("cpu")
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gpu = torch.device("cuda")
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device = gpu if torch.cuda.is_available() else cpu
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sd_model = (sd_model if cmd_opts.no_half else sd_model.half())
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if not cmd_opts.lowvram:
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