Renamed GFPGAN to extras
Added Real-ESRGAN to extras tab
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AUTOMATIC
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README.md
15
README.md
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@ -62,14 +62,25 @@ Open the URL in browser, and you are good to go.
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The script creates a web UI for Stable Diffusion's txt2img and img2img scripts. Following are features added
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that are not in original script.
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### GFPGAN
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### Extras tab
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Additional neural network image improvement methods unrelated to stable diffusion.
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#### GFPGAN
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Lets you improve faces in pictures using the GFPGAN model. There is a checkbox in every tab to use GFPGAN at 100%, and
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also a separate tab that just allows you to use GFPGAN on any picture, with a slider that controls how strongthe effect is.
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#### Real-ESRGAN
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Image upscaler. You can choose from multiple models by original author, and specify by how much the image should be upscaled.
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Requires `realesrgan` librarty:
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```commandline
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pip install realesrgan
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```
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### Sampling method selection
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Pick out of three sampling methods for txt2img: DDIM, PLMS, k-diffusion:
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Pick out of multiple sampling methods for txt2img:
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97
webui.py
97
webui.py
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@ -76,6 +76,38 @@ samplers = [
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SamplerData('PLMS', lambda model: PLMSSampler(model)),
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]
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RealesrganModelInfo = namedtuple("RealesrganModelInfo", ["name", "location", "model", "netscale"])
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try:
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from basicsr.archs.rrdbnet_arch import RRDBNet
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from realesrgan import RealESRGANer
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from realesrgan.archs.srvgg_arch import SRVGGNetCompact
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realesrgan_models = [
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RealesrganModelInfo(
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name="Real-ESRGAN 2x plus",
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location="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth",
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netscale=2, model=lambda: RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2)
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),
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RealesrganModelInfo(
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name="Real-ESRGAN 4x plus",
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location="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth",
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netscale=4, model=lambda: RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
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),
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RealesrganModelInfo(
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name="Real-ESRGAN 4x plus anime 6B",
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location="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth",
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netscale=4, model=lambda: RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4)
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),
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]
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have_realesrgan = True
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except:
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print("Error loading 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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have_realesrgan = False
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class Options:
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data = None
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@ -196,10 +228,6 @@ def torch_gc():
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torch.cuda.ipc_collect()
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def sanitize_filename_part(text):
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return text.replace(' ', '_').translate({ord(x): '' for x in invalid_filename_chars})[:128]
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def save_image(image, path, basename, seed, prompt, extension, info=None, short_filename=False):
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prompt = sanitize_filename_part(prompt)
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@ -208,7 +236,7 @@ def save_image(image, path, basename, seed, prompt, extension, info=None, short_
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else:
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filename = f"{basename}-{seed}-{prompt[:128]}.{extension}"
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if extension == 'png' and opts.enable_pnginfo:
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if extension == 'png' and opts.enable_pnginfo and info is not None:
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pnginfo = PngImagePlugin.PngInfo()
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pnginfo.add_text("parameters", info)
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else:
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@ -217,6 +245,10 @@ def save_image(image, path, basename, seed, prompt, extension, info=None, short_
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image.save(os.path.join(path, filename), quality=opts.jpeg_quality, pnginfo=pnginfo)
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def sanitize_filename_part(text):
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return text.replace(' ', '_').translate({ord(x): '' for x in invalid_filename_chars})[:128]
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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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@ -835,7 +867,7 @@ def img2img(prompt: str, init_img, ddim_steps: int, use_GFPGAN: bool, prompt_mat
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prompt_matrix=prompt_matrix,
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use_GFPGAN=use_GFPGAN,
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do_not_save_grid=True,
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extra_generation_params = {"Denoising Strength": denoising_strength},
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extra_generation_params={"Denoising Strength": denoising_strength},
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)
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if initial_seed is None:
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@ -870,7 +902,7 @@ def img2img(prompt: str, init_img, ddim_steps: int, use_GFPGAN: bool, prompt_mat
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height=height,
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prompt_matrix=prompt_matrix,
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use_GFPGAN=use_GFPGAN,
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extra_generation_params = {"Denoising Strength": denoising_strength},
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extra_generation_params={"Denoising Strength": denoising_strength},
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)
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del sampler
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@ -908,30 +940,56 @@ img2img_interface = gr.Interface(
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)
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def run_GFPGAN(image, strength):
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def run_extras(image, GFPGAN_strength, RealESRGAN_upscaling, RealESRGAN_model_index):
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image = image.convert("RGB")
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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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res = Image.fromarray(restored_img)
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outpath = opts.outdir or "outputs/extras-samples"
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if strength < 1.0:
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res = Image.blend(image, res, strength)
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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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res = Image.fromarray(restored_img)
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return res, 0, ''
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if GFPGAN_strength < 1.0:
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res = Image.blend(image, res, GFPGAN_strength)
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image = res
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if have_realesrgan and RealESRGAN_upscaling != 1.0:
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info = realesrgan_models[RealESRGAN_model_index]
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model = info.model()
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upsampler = RealESRGANer(
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scale=info.netscale,
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model_path=info.location,
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model=model,
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half=True
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)
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upsampled = upsampler.enhance(np.array(image), outscale=RealESRGAN_upscaling)[0]
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image = Image.fromarray(upsampled)
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os.makedirs(outpath, exist_ok=True)
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base_count = len(os.listdir(outpath))
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save_image(image, outpath, f"{base_count:05}", None, '', opts.samples_format, short_filename=True)
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return image, 0, ''
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gfpgan_interface = gr.Interface(
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run_GFPGAN,
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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="Effect strength", value=100),
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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=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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outputs=[
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gr.Image(label="Result"),
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gr.Number(label='Seed', visible=False),
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gr.HTML(),
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],
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description="Fix faces on images",
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allow_flagging="never",
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)
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@ -989,7 +1047,7 @@ settings_interface = gr.Interface(
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interfaces = [
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(txt2img_interface, "txt2img"),
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(img2img_interface, "img2img"),
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(gfpgan_interface, "GFPGAN"),
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(extras_interface, "Extras"),
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(settings_interface, "Settings"),
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]
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@ -1003,9 +1061,6 @@ text_inversion_embeddings = TextInversionEmbeddings()
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if os.path.exists(cmd_opts.embeddings_dir):
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text_inversion_embeddings.hijack(model)
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if GFPGAN is None:
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interfaces = [x for x in interfaces if x[0] != gfpgan_interface]
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demo = gr.TabbedInterface(
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interface_list=[x[0] for x in interfaces],
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tab_names=[x[1] for x in interfaces],
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