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https://github.com/nod-ai/AMD-SHARK-Studio.git
synced 2026-04-25 03:00:12 -04:00
Add outpainting web UI (#1083)
This commit is contained in:
jinchen62
@@ -1,3 +1,4 @@
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from apps.stable_diffusion.scripts.txt2img import txt2img_inf
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from apps.stable_diffusion.scripts.img2img import img2img_inf
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from apps.stable_diffusion.scripts.inpaint import inpaint_inf
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from apps.stable_diffusion.scripts.outpaint import outpaint_inf
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@@ -35,13 +35,12 @@ schedulers = None
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def outpaint_inf(
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prompt: str,
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negative_prompt: str,
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image: Image,
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init_image: str,
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pixels: int,
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mask_blur: int,
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left: bool,
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right: bool,
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top: bool,
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bottom: bool,
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directions: list,
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noise_q: float,
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color_variation: float,
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height: int,
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width: int,
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steps: int,
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@@ -67,6 +66,7 @@ def outpaint_inf(
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args.guidance_scale = guidance_scale
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args.steps = steps
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args.scheduler = scheduler
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args.img_path = init_image
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# set ckpt_loc and hf_model_id.
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types = (
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@@ -143,6 +143,13 @@ def outpaint_inf(
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generated_imgs = []
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seeds = []
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img_seed = utils.sanitize_seed(seed)
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image = Image.open(args.img_path)
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left = True if "left" in directions else False
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right = True if "right" in directions else False
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top = True if "up" in directions else False
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bottom = True if "down" in directions else False
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for i in range(batch_count):
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if i > 0:
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img_seed = utils.sanitize_seed(-1)
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@@ -152,10 +159,12 @@ def outpaint_inf(
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image,
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args.pixels,
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args.mask_blur,
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args.left,
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args.right,
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args.top,
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args.bottom,
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left,
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right,
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top,
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bottom,
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noise_q,
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color_variation,
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batch_size,
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height,
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width,
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@@ -235,6 +244,8 @@ if __name__ == "__main__":
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args.right,
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args.top,
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args.bottom,
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args.noise_q,
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args.color_variation,
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args.batch_size,
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args.height,
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args.width,
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@@ -341,6 +341,8 @@ class OutpaintPipeline(StableDiffusionPipeline):
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is_right,
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is_top,
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is_bottom,
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noise_q,
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color_variation,
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batch_size,
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height,
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width,
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@@ -445,7 +447,9 @@ class OutpaintPipeline(StableDiffusionPipeline):
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np_image = (np.asarray(img) / 255.0).astype(np.float64)
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np_mask = (np.asarray(msk) / 255.0).astype(np.float64)
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noised = self.get_matched_noise(np_image, np_mask)
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noised = self.get_matched_noise(
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np_image, np_mask, noise_q, color_variation
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)
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output_image = Image.fromarray(
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np.clip(noised * 255.0, 0.0, 255.0).astype(np.uint8),
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mode="RGB",
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@@ -146,6 +146,20 @@ p.add_argument(
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help="If expend bottom for outpainting",
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)
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p.add_argument(
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"--noise_q",
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type=float,
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default=1.0,
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help="Fall-off exponent for outpainting (lower=higher detail) (min=0.0, max=4.0)",
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)
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p.add_argument(
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"--color_variation",
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type=float,
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default=0.05,
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help="Color variation for outpainting (min=0.0, max=1.0)",
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)
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##############################################################################
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### Model Config and Usage Params
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##############################################################################
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@@ -27,11 +27,16 @@ def resource_path(relative_path):
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dark_theme = resource_path("ui/css/sd_dark_theme.css")
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from apps.stable_diffusion.web.ui import txt2img_web, img2img_web, inpaint_web
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from apps.stable_diffusion.web.ui import (
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txt2img_web,
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img2img_web,
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inpaint_web,
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outpaint_web,
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)
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sd_web = gr.TabbedInterface(
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[txt2img_web, img2img_web, inpaint_web],
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["Text-to-Image", "Image-to-Image", "Inpainting"],
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[txt2img_web, img2img_web, inpaint_web, outpaint_web],
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["Text-to-Image", "Image-to-Image", "Inpainting", "Outpainting"],
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css=dark_theme,
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)
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@@ -1,3 +1,4 @@
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from apps.stable_diffusion.web.ui.txt2img_ui import txt2img_web
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from apps.stable_diffusion.web.ui.img2img_ui import img2img_web
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from apps.stable_diffusion.web.ui.inpaint_ui import inpaint_web
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from apps.stable_diffusion.web.ui.outpaint_ui import outpaint_web
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260
apps/stable_diffusion/web/ui/outpaint_ui.py
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260
apps/stable_diffusion/web/ui/outpaint_ui.py
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@@ -0,0 +1,260 @@
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import os
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import sys
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import glob
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from pathlib import Path
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import gradio as gr
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from PIL import Image
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from apps.stable_diffusion.scripts import outpaint_inf
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from apps.stable_diffusion.src import args
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from apps.stable_diffusion.web.ui.utils import (
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available_devices,
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nodlogo_loc,
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)
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with gr.Blocks(title="Outpainting") as outpaint_web:
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with gr.Row(elem_id="ui_title"):
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nod_logo = Image.open(nodlogo_loc)
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with gr.Row():
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with gr.Column(scale=1, elem_id="demo_title_outer"):
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gr.Image(
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value=nod_logo,
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show_label=False,
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interactive=False,
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elem_id="top_logo",
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).style(width=150, height=50)
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with gr.Row(elem_id="ui_body"):
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with gr.Row():
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with gr.Column(scale=1, min_width=600):
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with gr.Row():
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ckpt_path = (
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Path(args.ckpt_dir)
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if args.ckpt_dir
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else Path(Path.cwd(), "models")
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)
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ckpt_path.mkdir(parents=True, exist_ok=True)
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types = (
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"*.ckpt",
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"*.safetensors",
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) # the tuple of file types
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ckpt_files = ["None"]
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for extn in types:
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files = glob.glob(os.path.join(ckpt_path, extn))
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ckpt_files.extend(files)
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custom_model = gr.Dropdown(
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label=f"Models (Custom Model path: {ckpt_path})",
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value=args.ckpt_loc if args.ckpt_loc else "None",
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choices=ckpt_files
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+ [
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"runwayml/stable-diffusion-inpainting",
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"stabilityai/stable-diffusion-2-inpainting",
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],
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)
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hf_model_id = gr.Textbox(
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placeholder="Select 'None' in the Models dropdown on the left and enter model ID here e.g: SG161222/Realistic_Vision_V1.3",
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value="",
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label="HuggingFace Model ID",
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lines=3,
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)
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with gr.Group(elem_id="prompt_box_outer"):
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prompt = gr.Textbox(
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label="Prompt",
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value=args.prompts[0],
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lines=1,
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elem_id="prompt_box",
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)
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negative_prompt = gr.Textbox(
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label="Negative Prompt",
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value=args.negative_prompts[0],
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lines=1,
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elem_id="negative_prompt_box",
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)
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init_image = gr.Image(label="Input Image", type="filepath")
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with gr.Accordion(label="Advanced Options", open=False):
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with gr.Row():
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scheduler = gr.Dropdown(
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label="Scheduler",
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value="PNDM",
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choices=[
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"DDIM",
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"PNDM",
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"DPMSolverMultistep",
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"EulerAncestralDiscrete",
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],
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)
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with gr.Group():
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save_metadata_to_png = gr.Checkbox(
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label="Save prompt information to PNG",
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value=args.write_metadata_to_png,
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interactive=True,
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)
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save_metadata_to_json = gr.Checkbox(
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label="Save prompt information to JSON file",
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value=args.save_metadata_to_json,
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interactive=True,
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)
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with gr.Row():
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pixels = gr.Slider(
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8,
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256,
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value=args.pixels,
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step=8,
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label="Pixels to expand",
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)
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mask_blur = gr.Slider(
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0,
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64,
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value=args.mask_blur,
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step=1,
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label="Mask blur",
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)
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with gr.Row():
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directions = gr.CheckboxGroup(
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label="Outpainting direction",
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choices=["left", "right", "up", "down"],
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value=["left", "right", "up", "down"],
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)
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with gr.Row():
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noise_q = gr.Slider(
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0.0,
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4.0,
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value=1.0,
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step=0.01,
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label="Fall-off exponent (lower=higher detail)",
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)
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color_variation = gr.Slider(
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0.0,
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1.0,
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value=0.05,
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step=0.01,
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label="Color variation",
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)
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with gr.Row():
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height = gr.Slider(
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384, 786, value=args.height, step=8, label="Height"
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)
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width = gr.Slider(
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384, 786, value=args.width, step=8, label="Width"
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)
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precision = gr.Radio(
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label="Precision",
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value=args.precision,
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choices=[
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"fp16",
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"fp32",
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],
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visible=False,
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)
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max_length = gr.Radio(
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label="Max Length",
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value=args.max_length,
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choices=[
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64,
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77,
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],
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visible=False,
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)
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with gr.Row():
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steps = gr.Slider(
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1, 100, value=20, step=1, label="Steps"
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)
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with gr.Row():
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guidance_scale = gr.Slider(
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0,
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50,
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value=args.guidance_scale,
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step=0.1,
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label="CFG Scale",
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)
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batch_count = gr.Slider(
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1,
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100,
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value=args.batch_count,
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step=1,
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label="Batch Count",
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interactive=True,
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)
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batch_size = gr.Slider(
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1,
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4,
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value=args.batch_size,
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step=1,
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label="Batch Size",
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interactive=False,
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visible=False,
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)
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with gr.Row():
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seed = gr.Number(
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value=args.seed, precision=0, label="Seed"
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)
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device = gr.Dropdown(
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label="Device",
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value=available_devices[0],
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choices=available_devices,
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)
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with gr.Row():
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random_seed = gr.Button("Randomize Seed")
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random_seed.click(
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None,
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inputs=[],
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outputs=[seed],
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_js="() => Math.floor(Math.random() * 4294967295)",
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)
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stable_diffusion = gr.Button("Generate Image(s)")
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with gr.Column(scale=1, min_width=600):
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with gr.Group():
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gallery = gr.Gallery(
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label="Generated images",
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show_label=False,
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elem_id="gallery",
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).style(grid=[2])
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std_output = gr.Textbox(
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value="Nothing to show.",
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lines=1,
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show_label=False,
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)
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output_dir = args.output_dir if args.output_dir else Path.cwd()
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output_dir = Path(output_dir, "generated_imgs")
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output_loc = gr.Textbox(
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label="Saving Images at",
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value=output_dir,
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interactive=False,
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)
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kwargs = dict(
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fn=outpaint_inf,
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inputs=[
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prompt,
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negative_prompt,
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init_image,
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pixels,
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mask_blur,
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directions,
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noise_q,
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color_variation,
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height,
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width,
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steps,
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guidance_scale,
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seed,
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batch_count,
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batch_size,
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scheduler,
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custom_model,
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hf_model_id,
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precision,
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device,
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max_length,
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save_metadata_to_json,
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save_metadata_to_png,
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],
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outputs=[gallery, std_output],
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show_progress=args.progress_bar,
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)
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prompt.submit(**kwargs)
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negative_prompt.submit(**kwargs)
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stable_diffusion.click(**kwargs)
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