mirror of
https://github.com/nod-ai/AMD-SHARK-Studio.git
synced 2026-04-03 03:00:17 -04:00
Fix webui circular import issue (#1271)
This commit is contained in:
jinchen62
@@ -45,7 +45,12 @@ if __name__ == "__main__":
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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 (
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get_txt2img_web,
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txt2img_web,
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txt2img_gallery,
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txt2img_sendto_img2img,
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txt2img_sendto_inpaint,
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txt2img_sendto_outpaint,
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txt2img_sendto_upscaler,
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img2img_web,
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img2img_gallery,
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img2img_init_image,
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@@ -91,14 +96,6 @@ if __name__ == "__main__":
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) as sd_web:
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with gr.Tabs() as tabs:
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with gr.TabItem(label="Text-to-Image", id=0):
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(
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txt2img_web,
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txt2img_gallery,
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txt2img_sendto_img2img,
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txt2img_sendto_inpaint,
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txt2img_sendto_outpaint,
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txt2img_sendto_upscaler,
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) = get_txt2img_web()
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txt2img_web.render()
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with gr.TabItem(label="Image-to-Image", id=1):
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img2img_web.render()
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@@ -1,6 +1,11 @@
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from apps.stable_diffusion.web.ui.txt2img_ui import (
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txt2img_inf,
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get_txt2img_web,
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txt2img_web,
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txt2img_gallery,
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txt2img_sendto_img2img,
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txt2img_sendto_inpaint,
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txt2img_sendto_outpaint,
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txt2img_sendto_upscaler,
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)
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from apps.stable_diffusion.web.ui.img2img_ui import (
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img2img_web,
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@@ -4,6 +4,15 @@ import torch
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import time
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import gradio as gr
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from PIL import Image
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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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get_custom_model_path,
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get_custom_model_files,
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scheduler_list_txt2img,
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predefined_models,
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cancel_sd,
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)
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from apps.stable_diffusion.src import (
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args,
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Text2ImagePipeline,
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@@ -184,306 +193,280 @@ def txt2img_inf(
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return generated_imgs, text_output
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def get_txt2img_web():
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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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get_custom_model_path,
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get_custom_model_files,
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scheduler_list_txt2img,
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predefined_models,
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cancel_sd,
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)
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with gr.Blocks(title="Text-to-Image") as txt2img_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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with gr.Column(scale=10):
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with gr.Row():
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custom_model = gr.Dropdown(
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label=f"Models (Custom Model path: {get_custom_model_path()})",
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elem_id="custom_model",
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value=os.path.basename(args.ckpt_loc)
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if args.ckpt_loc
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else "None",
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choices=["None"]
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+ get_custom_model_files()
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+ predefined_models,
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)
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hf_model_id = gr.Textbox(
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elem_id="hf_model_id",
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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.Column(scale=1, min_width=170):
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png_info_img = gr.Image(
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label="Import PNG info",
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elem_id="txt2img_prompt_image",
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type="pil",
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tool="None",
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visible=True,
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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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with gr.Accordion(label="LoRA Options", open=False):
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with gr.Blocks(title="Text-to-Image") as txt2img_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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with gr.Column(scale=10):
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with gr.Row():
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lora_weights = gr.Dropdown(
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label=f"Standlone LoRA weights (Path: {get_custom_model_path('lora')})",
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elem_id="lora_weights",
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value="None",
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custom_model = gr.Dropdown(
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label=f"Models (Custom Model path: {get_custom_model_path()})",
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elem_id="custom_model",
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value=os.path.basename(args.ckpt_loc)
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if args.ckpt_loc
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else "None",
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choices=["None"]
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+ get_custom_model_files("lora"),
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+ get_custom_model_files()
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+ predefined_models,
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)
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lora_hf_id = gr.Textbox(
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elem_id="lora_hf_id",
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placeholder="Select 'None' in the Standlone LoRA weights dropdown on the left if you want to use a standalone HuggingFace model ID for LoRA here e.g: sayakpaul/sd-model-finetuned-lora-t4",
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hf_model_id = gr.Textbox(
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elem_id="hf_model_id",
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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.Accordion(label="Advanced Options", open=False):
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with gr.Row():
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scheduler = gr.Dropdown(
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elem_id="scheduler",
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label="Scheduler",
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value=args.scheduler,
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choices=scheduler_list_txt2img,
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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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height = gr.Slider(
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384,
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768,
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value=args.height,
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step=8,
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label="Height",
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)
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width = gr.Slider(
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384,
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768,
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value=args.width,
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step=8,
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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=args.steps, step=1, label="Steps"
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)
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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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with gr.Row():
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with gr.Column(scale=3):
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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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with gr.Column(scale=3):
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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=True,
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)
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stop_batch = gr.Button("Stop Batch")
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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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elem_id="device",
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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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with gr.Column(scale=2):
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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="() => -1",
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)
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with gr.Column(scale=6):
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stable_diffusion = gr.Button("Generate Image(s)")
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with gr.Accordion(label="Prompt Examples!", open=False):
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ex = gr.Examples(
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examples=prompt_examples,
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inputs=prompt,
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cache_examples=False,
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elem_id="prompt_examples",
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with gr.Column(scale=1, min_width=170):
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png_info_img = gr.Image(
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label="Import PNG info",
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elem_id="txt2img_prompt_image",
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type="pil",
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tool="None",
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visible=True,
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)
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with gr.Column(scale=1, min_width=600):
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with gr.Group():
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txt2img_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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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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with gr.Accordion(label="LoRA Options", open=False):
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with gr.Row():
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lora_weights = gr.Dropdown(
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label=f"Standlone LoRA weights (Path: {get_custom_model_path('lora')})",
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elem_id="lora_weights",
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value="None",
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choices=["None"] + get_custom_model_files("lora"),
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)
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lora_hf_id = gr.Textbox(
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elem_id="lora_hf_id",
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placeholder="Select 'None' in the Standlone LoRA weights dropdown on the left if you want to use a standalone HuggingFace model ID for LoRA here e.g: sayakpaul/sd-model-finetuned-lora-t4",
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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.Accordion(label="Advanced Options", open=False):
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with gr.Row():
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scheduler = gr.Dropdown(
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elem_id="scheduler",
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label="Scheduler",
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value=args.scheduler,
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choices=scheduler_list_txt2img,
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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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height = gr.Slider(
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384,
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768,
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value=args.height,
|
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step=8,
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label="Height",
|
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)
|
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width = gr.Slider(
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384,
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768,
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value=args.width,
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step=8,
|
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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=args.steps, step=1, label="Steps"
|
||||
)
|
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guidance_scale = gr.Slider(
|
||||
0,
|
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50,
|
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value=args.guidance_scale,
|
||||
step=0.1,
|
||||
label="CFG Scale",
|
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)
|
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ondemand = gr.Checkbox(
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value=args.ondemand,
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label="Low VRAM",
|
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interactive=True,
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)
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output_dir = (
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args.output_dir if args.output_dir else Path.cwd()
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)
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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,
|
||||
interactive=False,
|
||||
)
|
||||
with gr.Row():
|
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txt2img_sendto_img2img = gr.Button(
|
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value="SendTo Img2Img"
|
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)
|
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txt2img_sendto_inpaint = gr.Button(
|
||||
value="SendTo Inpaint"
|
||||
)
|
||||
txt2img_sendto_outpaint = gr.Button(
|
||||
value="SendTo Outpaint"
|
||||
)
|
||||
txt2img_sendto_upscaler = gr.Button(
|
||||
value="SendTo Upscaler"
|
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with gr.Column(scale=3):
|
||||
batch_count = gr.Slider(
|
||||
1,
|
||||
100,
|
||||
value=args.batch_count,
|
||||
step=1,
|
||||
label="Batch Count",
|
||||
interactive=True,
|
||||
)
|
||||
with gr.Column(scale=3):
|
||||
batch_size = gr.Slider(
|
||||
1,
|
||||
4,
|
||||
value=args.batch_size,
|
||||
step=1,
|
||||
label="Batch Size",
|
||||
interactive=True,
|
||||
)
|
||||
stop_batch = gr.Button("Stop Batch")
|
||||
with gr.Row():
|
||||
seed = gr.Number(
|
||||
value=args.seed, precision=0, label="Seed"
|
||||
)
|
||||
device = gr.Dropdown(
|
||||
elem_id="device",
|
||||
label="Device",
|
||||
value=available_devices[0],
|
||||
choices=available_devices,
|
||||
)
|
||||
with gr.Row():
|
||||
with gr.Column(scale=2):
|
||||
random_seed = gr.Button("Randomize Seed")
|
||||
random_seed.click(
|
||||
None,
|
||||
inputs=[],
|
||||
outputs=[seed],
|
||||
_js="() => -1",
|
||||
)
|
||||
with gr.Column(scale=6):
|
||||
stable_diffusion = gr.Button("Generate Image(s)")
|
||||
|
||||
kwargs = dict(
|
||||
fn=txt2img_inf,
|
||||
inputs=[
|
||||
prompt,
|
||||
negative_prompt,
|
||||
height,
|
||||
width,
|
||||
steps,
|
||||
guidance_scale,
|
||||
seed,
|
||||
batch_count,
|
||||
batch_size,
|
||||
scheduler,
|
||||
custom_model,
|
||||
hf_model_id,
|
||||
precision,
|
||||
device,
|
||||
max_length,
|
||||
save_metadata_to_json,
|
||||
save_metadata_to_png,
|
||||
lora_weights,
|
||||
lora_hf_id,
|
||||
ondemand,
|
||||
],
|
||||
outputs=[txt2img_gallery, std_output],
|
||||
show_progress=args.progress_bar,
|
||||
)
|
||||
with gr.Accordion(label="Prompt Examples!", open=False):
|
||||
ex = gr.Examples(
|
||||
examples=prompt_examples,
|
||||
inputs=prompt,
|
||||
cache_examples=False,
|
||||
elem_id="prompt_examples",
|
||||
)
|
||||
|
||||
prompt_submit = prompt.submit(**kwargs)
|
||||
neg_prompt_submit = negative_prompt.submit(**kwargs)
|
||||
generate_click = stable_diffusion.click(**kwargs)
|
||||
stop_batch.click(
|
||||
fn=cancel_sd,
|
||||
cancels=[prompt_submit, neg_prompt_submit, generate_click],
|
||||
)
|
||||
with gr.Column(scale=1, min_width=600):
|
||||
with gr.Group():
|
||||
txt2img_gallery = gr.Gallery(
|
||||
label="Generated images",
|
||||
show_label=False,
|
||||
elem_id="gallery",
|
||||
).style(grid=[2])
|
||||
std_output = gr.Textbox(
|
||||
value="Nothing to show.",
|
||||
lines=1,
|
||||
show_label=False,
|
||||
)
|
||||
output_dir = args.output_dir if args.output_dir else Path.cwd()
|
||||
output_dir = Path(output_dir, "generated_imgs")
|
||||
output_loc = gr.Textbox(
|
||||
label="Saving Images at",
|
||||
value=output_dir,
|
||||
interactive=False,
|
||||
)
|
||||
with gr.Row():
|
||||
txt2img_sendto_img2img = gr.Button(value="SendTo Img2Img")
|
||||
txt2img_sendto_inpaint = gr.Button(value="SendTo Inpaint")
|
||||
txt2img_sendto_outpaint = gr.Button(
|
||||
value="SendTo Outpaint"
|
||||
)
|
||||
txt2img_sendto_upscaler = gr.Button(
|
||||
value="SendTo Upscaler"
|
||||
)
|
||||
|
||||
from apps.stable_diffusion.web.utils.png_metadata import (
|
||||
import_png_metadata,
|
||||
)
|
||||
kwargs = dict(
|
||||
fn=txt2img_inf,
|
||||
inputs=[
|
||||
prompt,
|
||||
negative_prompt,
|
||||
height,
|
||||
width,
|
||||
steps,
|
||||
guidance_scale,
|
||||
seed,
|
||||
batch_count,
|
||||
batch_size,
|
||||
scheduler,
|
||||
custom_model,
|
||||
hf_model_id,
|
||||
precision,
|
||||
device,
|
||||
max_length,
|
||||
save_metadata_to_json,
|
||||
save_metadata_to_png,
|
||||
lora_weights,
|
||||
lora_hf_id,
|
||||
ondemand,
|
||||
],
|
||||
outputs=[txt2img_gallery, std_output],
|
||||
show_progress=args.progress_bar,
|
||||
)
|
||||
|
||||
png_info_img.change(
|
||||
fn=import_png_metadata,
|
||||
inputs=[
|
||||
png_info_img,
|
||||
],
|
||||
outputs=[
|
||||
png_info_img,
|
||||
prompt,
|
||||
negative_prompt,
|
||||
steps,
|
||||
scheduler,
|
||||
guidance_scale,
|
||||
seed,
|
||||
width,
|
||||
height,
|
||||
custom_model,
|
||||
hf_model_id,
|
||||
],
|
||||
)
|
||||
return (
|
||||
txt2img_web,
|
||||
txt2img_gallery,
|
||||
txt2img_sendto_img2img,
|
||||
txt2img_sendto_inpaint,
|
||||
txt2img_sendto_outpaint,
|
||||
txt2img_sendto_upscaler,
|
||||
)
|
||||
prompt_submit = prompt.submit(**kwargs)
|
||||
neg_prompt_submit = negative_prompt.submit(**kwargs)
|
||||
generate_click = stable_diffusion.click(**kwargs)
|
||||
stop_batch.click(
|
||||
fn=cancel_sd,
|
||||
cancels=[prompt_submit, neg_prompt_submit, generate_click],
|
||||
)
|
||||
|
||||
from apps.stable_diffusion.web.utils.png_metadata import (
|
||||
import_png_metadata,
|
||||
)
|
||||
|
||||
png_info_img.change(
|
||||
fn=import_png_metadata,
|
||||
inputs=[
|
||||
png_info_img,
|
||||
],
|
||||
outputs=[
|
||||
png_info_img,
|
||||
prompt,
|
||||
negative_prompt,
|
||||
steps,
|
||||
scheduler,
|
||||
guidance_scale,
|
||||
seed,
|
||||
width,
|
||||
height,
|
||||
custom_model,
|
||||
hf_model_id,
|
||||
],
|
||||
)
|
||||
|
||||
Reference in New Issue
Block a user