mirror of
https://github.com/nod-ai/AMD-SHARK-Studio.git
synced 2026-04-03 03:00:17 -04:00
be3cdec290
* [WIP] Add ControlNet to SD pipeline -- This commit adds ControlNet to SD pipeline. Signed-off-by: Abhishek Varma <abhishek@nod-labs.com> * [SD] Add ControlNet to img2img + fix bug for img2img scheduler -- This commit adds ControlNet execution to img2img. -- It restructures the addition of ControlNet variants. -- It also fixes scheduler selecting bug for img2img pipeline. Signed-off-by: Abhishek Varma <abhishek@nod-labs.com> * add shark models for stencilSD * Add Stencil controlled SD in img2img pipeline (#1106) * use shark stencil modules * adjust diffusers change * modify to use pipeline * remove control from unet * pump stencils through unet * complete integration in img2img * fix lint and comments * [SD] Add ControlNet pipeline + integrate with WebUI + add compiled flow execution -- This commit creates a dedicated SD pipeline for ControlNet. -- Integrates it with img2img WebUI. -- Integrates the compiled execution flow for ControlNet. Signed-off-by: Abhishek Varma <abhishek@nod-labs.com> * [SD] Stencil execution * Remove integration setup * [SD] Fix args.use_stencil overriding bug + vmfb caching issue -- This commit fixes args.use_stencil overriding issue which caused img2img pipeline to pick wrong set of modules. -- It also fixes vmfb caching issue to speed up the loading time and pick right set of modules based on a mask. Signed-off-by: Abhishek Varma <abhishek@nod-labs.com> --------- Signed-off-by: Abhishek Varma <abhishek@nod-labs.com> Co-authored-by: Abhishek Varma <abhishek@nod-labs.com> Co-authored-by: PhaneeshB <b.phaneesh@gmail.com>
242 lines
9.4 KiB
Python
242 lines
9.4 KiB
Python
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 img2img_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="Image-to-Image") as img2img_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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"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.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="pil").style(
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height=300
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)
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with gr.Accordion(label="Stencil Options", open=False):
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with gr.Row():
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use_stencil = gr.Dropdown(
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label="Stencil model",
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value="None",
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choices=["None", "canny"],
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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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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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height = gr.Slider(
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384, 768, value=args.height, step=8, label="Height"
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)
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width = gr.Slider(
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384, 768, 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=True,
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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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strength = gr.Slider(
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0,
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1,
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value=args.strength,
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step=0.01,
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label="Strength",
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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=img2img_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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height,
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width,
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steps,
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strength,
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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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use_stencil,
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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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