mrq/stable-diffusion-webui
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Merge pull request #8 from dogewanwan/master

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Support for using the script with textual inversion repo, should be safe for use in normal repositories too.
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AUTOMATIC1111 2022-08-24 23:00:45 +03:00 committed by GitHub
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1 changed files with 20 additions and 6 deletions
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webui.py
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@ -58,6 +58,7 @@ parser.add_argument("--grid-extended-filename", action='store_true', help="save
parser.add_argument("--jpeg-quality", type=int, default=80, help="quality for saved jpeg images") parser.add_argument("--jpeg-quality", type=int, default=80, help="quality for saved jpeg images")
parser.add_argument("--disable-pnginfo", action='store_true', help="disable saving text information about generation parameters as chunks to png files") parser.add_argument("--disable-pnginfo", action='store_true', help="disable saving text information about generation parameters as chunks to png files")
parser.add_argument("--inversion", action='store_true', help="switch to stable inversion version; allows for uploading embeddings; this option should be used only with textual inversion repo")
opt = parser.parse_args() opt = parser.parse_args()
GFPGAN_dir = opt.gfpgan_dir GFPGAN_dir = opt.gfpgan_dir
@ -189,8 +190,8 @@ if os.path.exists(GFPGAN_dir):
print("Error loading GFPGAN:", file=sys.stderr) print("Error loading GFPGAN:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr) print(traceback.format_exc(), file=sys.stderr)
config = OmegaConf.load("configs/stable-diffusion/v1-inference.yaml") config = OmegaConf.load(opt.config)
model = load_model_from_config(config, "models/ldm/stable-diffusion-v1/model.ckpt") model = load_model_from_config(config, opt.ckpt)
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
model = (model if opt.no_half else model.half()).to(device) model = (model if opt.no_half else model.half()).to(device)
@ -467,9 +468,17 @@ def process_images(outpath, func_init, func_sample, prompt, seed, sampler_name,
return output_images, seed, info return output_images, seed, info
def txt2img(prompt: str, ddim_steps: int, sampler_name: str, use_GFPGAN: bool, prompt_matrix: bool, ddim_eta: float, n_iter: int, batch_size: int, cfg_scale: float, seed: int, height: int, width: int): def load_embeddings(fp):
if fp is not None and hasattr(model, "embedding_manager"):
# load the file
model.embedding_manager.load(fp.name)
def txt2img(prompt: str, ddim_steps: int, sampler_name: str, use_GFPGAN: bool, prompt_matrix: bool, ddim_eta: float, n_iter: int, batch_size: int, cfg_scale: float, seed: int, height: int, width: int, embeddings_fp):
outpath = opt.outdir or "outputs/txt2img-samples" outpath = opt.outdir or "outputs/txt2img-samples"
load_embeddings(embeddings_fp)
if sampler_name == 'PLMS': if sampler_name == 'PLMS':
sampler = PLMSSampler(model) sampler = PLMSSampler(model)
elif sampler_name == 'DDIM': elif sampler_name == 'DDIM':
@ -564,6 +573,7 @@ txt2img_interface = gr.Interface(
gr.Number(label='Seed', value=-1), gr.Number(label='Seed', value=-1),
gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512), gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512),
gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512), gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512),
gr.File(label = "Embeddings file for textual inversion", visible=opt.inversion)
], ],
outputs=[ outputs=[
gr.Gallery(label="Images"), gr.Gallery(label="Images"),
@ -576,9 +586,11 @@ txt2img_interface = gr.Interface(
) )
def img2img(prompt: str, init_img, ddim_steps: int, use_GFPGAN: bool, prompt_matrix, loopback: 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, ddim_steps: int, use_GFPGAN: bool, prompt_matrix, loopback: bool, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, height: int, width: int, resize_mode: int, embeddings_fp):
outpath = opt.outdir or "outputs/img2img-samples" outpath = opt.outdir or "outputs/img2img-samples"
load_embeddings(embeddings_fp)
sampler = KDiffusionSampler(model) sampler = KDiffusionSampler(model)
assert 0. <= denoising_strength <= 1., 'can only work with strength in [0.0, 1.0]' assert 0. <= denoising_strength <= 1., 'can only work with strength in [0.0, 1.0]'
@ -693,7 +705,8 @@ img2img_interface = gr.Interface(
gr.Number(label='Seed', value=-1), gr.Number(label='Seed', value=-1),
gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512), gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512),
gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512), gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512),
gr.Radio(label="Resize mode", choices=["Just resize", "Crop and resize", "Resize and fill"], type="index", value="Just resize") gr.Radio(label="Resize mode", choices=["Just resize", "Crop and resize", "Resize and fill"], type="index", value="Just resize"),
gr.File(label = "Embeddings file for textual inversion", visible=opt.inversion)
], ],
outputs=[ outputs=[
gr.Gallery(), gr.Gallery(),
@ -739,6 +752,7 @@ if GFPGAN is not None:
allow_flagging="never", allow_flagging="never",
), "GFPGAN")) ), "GFPGAN"))
demo = gr.TabbedInterface( demo = gr.TabbedInterface(
interface_list=[x[0] for x in interfaces], interface_list=[x[0] for x in interfaces],
tab_names=[x[1] for x in interfaces], tab_names=[x[1] for x in interfaces],
@ -748,4 +762,4 @@ demo = gr.TabbedInterface(
""" """
) )
demo.launch() demo.launch()