mirror of
https://github.com/nod-ai/AMD-SHARK-Studio.git
synced 2026-04-03 03:00:17 -04:00
[WebUi] txt2img_ui: Import png metadata (#1147)
This commit is contained in:
m68k-fr
@@ -9,9 +9,12 @@ from apps.stable_diffusion.src import args, clear_all
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from apps.stable_diffusion.web.utils.gradio_configs import (
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clear_gradio_tmp_imgs_folder,
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)
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from apps.stable_diffusion.web.ui.utils import get_custom_model_path
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# clear all gradio tmp images from the last session
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# Clear all gradio tmp images from the last session
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clear_gradio_tmp_imgs_folder()
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# Create the custom model folder if it doesn't already exist
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get_custom_model_path().mkdir(parents=True, exist_ok=True)
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if args.clear_all:
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clear_all()
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@@ -219,3 +219,7 @@ footer {
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pointer-events: none;
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}
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/* Import Png info box */
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#txt2img_prompt_image .fixed-height {
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height: var(--size-32);
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}
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@@ -1,6 +1,3 @@
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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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@@ -9,6 +6,10 @@ 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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get_custom_model_path,
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get_custom_model_files,
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scheduler_list,
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predefined_models,
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)
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@@ -27,32 +28,10 @@ with gr.Blocks(title="Image-to-Image") as img2img_web:
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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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label=f"Models (Custom Model path: {get_custom_model_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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"Linaqruf/anything-v3.0",
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"prompthero/openjourney",
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"wavymulder/Analog-Diffusion",
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"stabilityai/stable-diffusion-2-1",
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"stabilityai/stable-diffusion-2-1-base",
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"CompVis/stable-diffusion-v1-4",
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],
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choices=get_custom_model_files() + predefined_models,
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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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@@ -91,12 +70,7 @@ with gr.Blocks(title="Image-to-Image") as img2img_web:
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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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choices=scheduler_list,
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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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@@ -1,6 +1,3 @@
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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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@@ -9,6 +6,10 @@ 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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get_custom_model_path,
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get_custom_model_files,
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scheduler_list,
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predefined_paint_models,
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)
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@@ -27,28 +28,11 @@ with gr.Blocks(title="Inpainting") as inpaint_web:
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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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label=f"Models (Custom Model path: {get_custom_model_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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choices=get_custom_model_files()
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+ predefined_paint_models,
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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: ghunkins/stable-diffusion-liberty-inpainting",
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@@ -83,12 +67,7 @@ with gr.Blocks(title="Inpainting") as inpaint_web:
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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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choices=scheduler_list,
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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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@@ -1,6 +1,3 @@
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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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@@ -9,6 +6,10 @@ 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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get_custom_model_path,
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get_custom_model_files,
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scheduler_list,
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predefined_paint_models,
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)
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@@ -27,28 +28,11 @@ with gr.Blocks(title="Outpainting") as outpaint_web:
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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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label=f"Models (Custom Model path: {get_custom_model_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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choices=get_custom_model_files()
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+ predefined_paint_models,
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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: ghunkins/stable-diffusion-liberty-inpainting",
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@@ -80,12 +64,7 @@ with gr.Blocks(title="Outpainting") as outpaint_web:
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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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choices=scheduler_list,
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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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@@ -1,6 +1,3 @@
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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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@@ -9,9 +6,12 @@ from apps.stable_diffusion.src import prompt_examples, 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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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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)
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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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@@ -27,39 +27,30 @@ with gr.Blocks(title="Text-to-Image") as txt2img_web:
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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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"Linaqruf/anything-v3.0",
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"prompthero/openjourney",
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"wavymulder/Analog-Diffusion",
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"stabilityai/stable-diffusion-2-1",
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"stabilityai/stable-diffusion-2-1-base",
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"CompVis/stable-diffusion-v1-4",
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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.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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value=args.ckpt_loc
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if args.ckpt_loc
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else "None",
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choices=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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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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@@ -79,16 +70,7 @@ with gr.Blocks(title="Text-to-Image") as txt2img_web:
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scheduler = gr.Dropdown(
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label="Scheduler",
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value=args.scheduler,
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choices=[
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"DDIM",
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"PNDM",
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"LMSDiscrete",
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"KDPM2Discrete",
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"DPMSolverMultistep",
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"EulerDiscrete",
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"EulerAncestralDiscrete",
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"SharkEulerDiscrete",
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],
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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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@@ -234,3 +216,27 @@ with gr.Blocks(title="Text-to-Image") as txt2img_web:
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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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from apps.stable_diffusion.web.utils.png_metadata import (
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import_png_metadata,
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)
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png_info_img.change(
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fn=import_png_metadata,
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inputs=[
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png_info_img,
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],
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outputs=[
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png_info_img,
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prompt,
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negative_prompt,
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steps,
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scheduler,
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guidance_scale,
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seed,
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width,
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height,
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custom_model,
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hf_model_id,
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],
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)
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@@ -1,6 +1,44 @@
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import os
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import sys
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from apps.stable_diffusion.src import get_available_devices
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import glob
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from pathlib import Path
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from apps.stable_diffusion.src import args
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custom_model_filetypes = (
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"*.ckpt",
|
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"*.safetensors",
|
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) # the tuple of file types
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scheduler_list = [
|
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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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scheduler_list_txt2img = [
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"DDIM",
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"PNDM",
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"LMSDiscrete",
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"KDPM2Discrete",
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"DPMSolverMultistep",
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"EulerDiscrete",
|
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"EulerAncestralDiscrete",
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"SharkEulerDiscrete",
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]
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predefined_models = [
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"Linaqruf/anything-v3.0",
|
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"prompthero/openjourney",
|
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"wavymulder/Analog-Diffusion",
|
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"stabilityai/stable-diffusion-2-1",
|
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"stabilityai/stable-diffusion-2-1-base",
|
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"CompVis/stable-diffusion-v1-4",
|
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]
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predefined_paint_models = [
|
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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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def resource_path(relative_path):
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@@ -11,5 +49,17 @@ def resource_path(relative_path):
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return os.path.join(base_path, relative_path)
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def get_custom_model_path():
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return Path(args.ckpt_dir) if args.ckpt_dir else Path(Path.cwd(), "models")
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|
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|
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def get_custom_model_files():
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ckpt_files = ["None"]
|
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for extn in custom_model_filetypes:
|
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files = glob.glob(os.path.join(get_custom_model_path(), extn))
|
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ckpt_files.extend(files)
|
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return ckpt_files
|
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|
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|
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nodlogo_loc = resource_path("logos/nod-logo.png")
|
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available_devices = get_available_devices()
|
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|
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121
apps/stable_diffusion/web/utils/png_metadata.py
Normal file
121
apps/stable_diffusion/web/utils/png_metadata.py
Normal file
@@ -0,0 +1,121 @@
|
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import re
|
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import os
|
||||
from pathlib import Path
|
||||
from apps.stable_diffusion.web.ui.txt2img_ui import (
|
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png_info_img,
|
||||
prompt,
|
||||
negative_prompt,
|
||||
steps,
|
||||
scheduler,
|
||||
guidance_scale,
|
||||
seed,
|
||||
width,
|
||||
height,
|
||||
custom_model,
|
||||
hf_model_id,
|
||||
)
|
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from apps.stable_diffusion.web.ui.utils import (
|
||||
get_custom_model_path,
|
||||
scheduler_list_txt2img,
|
||||
predefined_models,
|
||||
)
|
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|
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re_param_code = r'\s*([\w ]+):\s*("(?:\\"[^,]|\\"|\\|[^\"])+"|[^,]*)(?:,|$)'
|
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re_param = re.compile(re_param_code)
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re_imagesize = re.compile(r"^(\d+)x(\d+)$")
|
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|
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|
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def parse_generation_parameters(x: str):
|
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res = {}
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prompt = ""
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negative_prompt = ""
|
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done_with_prompt = False
|
||||
|
||||
*lines, lastline = x.strip().split("\n")
|
||||
if len(re_param.findall(lastline)) < 3:
|
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lines.append(lastline)
|
||||
lastline = ""
|
||||
|
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for i, line in enumerate(lines):
|
||||
line = line.strip()
|
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if line.startswith("Negative prompt:"):
|
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done_with_prompt = True
|
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line = line[16:].strip()
|
||||
|
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if done_with_prompt:
|
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negative_prompt += ("" if negative_prompt == "" else "\n") + line
|
||||
else:
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||||
prompt += ("" if prompt == "" else "\n") + line
|
||||
|
||||
res["Prompt"] = prompt
|
||||
res["Negative prompt"] = negative_prompt
|
||||
|
||||
for k, v in re_param.findall(lastline):
|
||||
v = v[1:-1] if v[0] == '"' and v[-1] == '"' else v
|
||||
m = re_imagesize.match(v)
|
||||
if m is not None:
|
||||
res[k + "-1"] = m.group(1)
|
||||
res[k + "-2"] = m.group(2)
|
||||
else:
|
||||
res[k] = v
|
||||
|
||||
# Missing CLIP skip means it was set to 1 (the default)
|
||||
if "Clip skip" not in res:
|
||||
res["Clip skip"] = "1"
|
||||
|
||||
hypernet = res.get("Hypernet", None)
|
||||
if hypernet is not None:
|
||||
res[
|
||||
"Prompt"
|
||||
] += f"""<hypernet:{hypernet}:{res.get("Hypernet strength", "1.0")}>"""
|
||||
|
||||
if "Hires resize-1" not in res:
|
||||
res["Hires resize-1"] = 0
|
||||
res["Hires resize-2"] = 0
|
||||
|
||||
return res
|
||||
|
||||
|
||||
def import_png_metadata(pil_data):
|
||||
try:
|
||||
png_info = pil_data.info["parameters"]
|
||||
metadata = parse_generation_parameters(png_info)
|
||||
png_hf_model_id = ""
|
||||
|
||||
# Check for a model match with one of the local ckpt or safetensors files
|
||||
ckpt_path = get_custom_model_path()
|
||||
png_custom_model = os.path.join(ckpt_path, metadata["Model"])
|
||||
if not Path(png_custom_model).is_file():
|
||||
png_custom_model = "None"
|
||||
# Check for a model match with one of the default model list (ex: "Linaqruf/anything-v3.0")
|
||||
if metadata["Model"] in predefined_models:
|
||||
png_custom_model = metadata["Model"]
|
||||
# If nothing was found, fallback to hf model id
|
||||
if png_custom_model == "None":
|
||||
png_hf_model_id = metadata["Model"]
|
||||
|
||||
outputs = {
|
||||
png_info_img: None,
|
||||
negative_prompt: metadata["Negative prompt"],
|
||||
steps: int(metadata["Steps"]),
|
||||
guidance_scale: float(metadata["CFG scale"]),
|
||||
seed: int(metadata["Seed"]),
|
||||
width: float(metadata["Size-1"]),
|
||||
height: float(metadata["Size-2"]),
|
||||
custom_model: png_custom_model,
|
||||
hf_model_id: png_hf_model_id,
|
||||
}
|
||||
if metadata["Prompt"]:
|
||||
outputs[prompt] = metadata["Prompt"]
|
||||
if metadata["Sampler"] in scheduler_list_txt2img:
|
||||
outputs[scheduler] = metadata["Sampler"]
|
||||
return outputs
|
||||
|
||||
except Exception as ex:
|
||||
if pil_data and pil_data.info.get("parameters"):
|
||||
print("import_png_metadata failed with %s" % ex)
|
||||
pass
|
||||
|
||||
return {
|
||||
png_info_img: None,
|
||||
}
|
||||
Reference in New Issue
Block a user