mirror of
https://github.com/nod-ai/SHARK-Studio.git
synced 2026-01-08 21:38:04 -05:00
68e9281778
* Shark Studio SDXL support, HIP driver support, simpler device info, small fixes * Fixups to llm API/UI and ignore user config files. * Small fixes for unifying pipelines. * Update requirements.txt for iree-turbine (#2130) * Fix Llama2 on CPU (#2133) * Filesystem cleanup and custom model fixes (#2127) * Fix some formatting issues * Remove IREE pin (fixes exe issue) (#2126) * Update find links for IREE packages (#2136) * Shark Studio SDXL support, HIP driver support, simpler device info, small fixes * Abstract out SD pipelines from Studio Webui (WIP) * Switch from pin to minimum torch version and fix index url * Fix device parsing. * Fix linux setup * Fix custom weights. --------- Co-authored-by: saienduri <77521230+saienduri@users.noreply.github.com> Co-authored-by: gpetters-amd <159576198+gpetters-amd@users.noreply.github.com> Co-authored-by: gpetters94 <gpetters@protonmail.com>
768 lines
30 KiB
Python
768 lines
30 KiB
Python
import os
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import json
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import gradio as gr
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import numpy as np
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from inspect import signature
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from PIL import Image
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from pathlib import Path
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from datetime import datetime as dt
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from gradio.components.image_editor import (
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EditorValue,
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)
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from apps.shark_studio.web.utils.file_utils import (
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get_generated_imgs_path,
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get_checkpoints_path,
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get_checkpoints,
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get_configs_path,
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write_default_sd_config,
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)
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from apps.shark_studio.api.sd import (
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shark_sd_fn_dict_input,
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cancel_sd,
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)
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from apps.shark_studio.api.controlnet import (
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cnet_preview,
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)
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from apps.shark_studio.modules.schedulers import (
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scheduler_model_map,
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)
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from apps.shark_studio.modules.img_processing import (
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resampler_list,
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resize_stencil,
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)
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from apps.shark_studio.modules.shared_cmd_opts import cmd_opts
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from apps.shark_studio.web.ui.utils import (
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nodlogo_loc,
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none_to_str_none,
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str_none_to_none,
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)
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from apps.shark_studio.web.utils.state import (
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status_label,
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)
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from apps.shark_studio.web.ui.common_events import lora_changed
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from apps.shark_studio.modules import logger
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import apps.shark_studio.web.utils.globals as global_obj
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sd_default_models = [
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"runwayml/stable-diffusion-v1-5",
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"stabilityai/stable-diffusion-2-1-base",
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"stabilityai/stable-diffusion-2-1",
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"stabilityai/stable-diffusion-xl-base-1.0",
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"stabilityai/sdxl-turbo",
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]
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def view_json_file(file_path):
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content = ""
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with open(file_path, "r") as fopen:
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content = fopen.read()
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return content
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def submit_to_cnet_config(
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stencil: str,
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preprocessed_hint: str,
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cnet_strength: int,
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control_mode: str,
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curr_config: dict,
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):
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if any(i in [None, ""] for i in [stencil, preprocessed_hint]):
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return gr.update()
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if curr_config is not None:
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if "controlnets" in curr_config:
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curr_config["controlnets"]["control_mode"] = control_mode
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curr_config["controlnets"]["model"].append(stencil)
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curr_config["controlnets"]["hint"].append(preprocessed_hint)
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curr_config["controlnets"]["strength"].append(cnet_strength)
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return curr_config
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cnet_map = {}
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cnet_map["controlnets"] = {
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"control_mode": control_mode,
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"model": [stencil],
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"hint": [preprocessed_hint],
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"strength": [cnet_strength],
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}
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return cnet_map
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def update_embeddings_json(embedding):
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return {"embeddings": [embedding]}
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def submit_to_main_config(input_cfg: dict, main_cfg: dict):
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if main_cfg in [None, "", {}]:
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return input_cfg
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for base_key in input_cfg:
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main_cfg[base_key] = input_cfg[base_key]
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return main_cfg
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def pull_sd_configs(
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prompt,
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negative_prompt,
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sd_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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base_model_id,
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custom_weights,
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custom_vae,
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precision,
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device,
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ondemand,
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repeatable_seeds,
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resample_type,
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controlnets,
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embeddings,
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):
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sd_args = str_none_to_none(locals())
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sd_cfg = {}
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for arg in sd_args:
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if arg in [
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"prompt",
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"negative_prompt",
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"sd_init_image",
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]:
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sd_cfg[arg] = [sd_args[arg]]
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elif arg in ["controlnets", "embeddings"]:
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if isinstance(arg, dict):
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sd_cfg[arg] = json.loads(sd_args[arg])
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else:
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sd_cfg[arg] = {}
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else:
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sd_cfg[arg] = sd_args[arg]
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return json.dumps(sd_cfg)
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def load_sd_cfg(sd_json: dict, load_sd_config: str):
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new_sd_config = none_to_str_none(json.loads(view_json_file(load_sd_config)))
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if sd_json:
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for key in new_sd_config:
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sd_json[key] = new_sd_config[key]
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else:
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sd_json = new_sd_config
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for i in sd_json["sd_init_image"]:
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if i is not None:
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if os.path.isfile(i):
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sd_image = [Image.open(i, mode="r")]
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else:
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sd_image = None
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return [
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sd_json["prompt"][0],
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sd_json["negative_prompt"][0],
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sd_image,
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sd_json["height"],
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sd_json["width"],
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sd_json["steps"],
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sd_json["strength"],
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sd_json["guidance_scale"],
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sd_json["seed"],
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sd_json["batch_count"],
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sd_json["batch_size"],
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sd_json["scheduler"],
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sd_json["base_model_id"],
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sd_json["custom_weights"],
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sd_json["custom_vae"],
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sd_json["precision"],
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sd_json["device"],
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sd_json["ondemand"],
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sd_json["repeatable_seeds"],
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sd_json["resample_type"],
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sd_json["controlnets"],
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sd_json["embeddings"],
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sd_json,
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]
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def save_sd_cfg(config: dict, save_name: str):
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if os.path.exists(save_name):
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filepath = save_name
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elif cmd_opts.configs_path:
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filepath = os.path.join(cmd_opts.configs_path, save_name)
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else:
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filepath = os.path.join(get_configs_path(), save_name)
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if ".json" not in filepath:
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filepath += ".json"
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with open(filepath, mode="w") as f:
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f.write(json.dumps(config))
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return "..."
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def create_canvas(width, height):
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data = Image.fromarray(
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np.zeros(
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shape=(height, width, 3),
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dtype=np.uint8,
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)
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+ 255
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)
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img_dict = {
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"background": data,
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"layers": [],
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"composite": None,
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}
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return EditorValue(img_dict)
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def import_original(original_img, width, height):
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if original_img is None:
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resized_img = create_canvas(width, height)
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return resized_img
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else:
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resized_img, _, _ = resize_stencil(original_img, width, height)
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img_dict = {
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"background": resized_img,
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"layers": [],
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"composite": None,
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}
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return EditorValue(img_dict)
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def base_model_changed(base_model_id):
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new_choices = get_checkpoints(
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os.path.join("checkpoints", os.path.basename(str(base_model_id)))
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) + get_checkpoints(model_type="checkpoints")
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return gr.Dropdown(
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value=new_choices[0] if len(new_choices) > 0 else "None",
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choices=["None"] + new_choices,
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)
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with gr.Blocks(title="Stable Diffusion") as sd_element:
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with gr.Column(elem_id="ui_body"):
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with gr.Row():
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with gr.Column(scale=2, min_width=600):
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with gr.Accordion(
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label="\U0001F4D0\U0000FE0F Device Settings", open=False
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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=global_obj.get_device_list()[0],
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choices=global_obj.get_device_list(),
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allow_custom_value=False,
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)
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with gr.Row():
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ondemand = gr.Checkbox(
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value=cmd_opts.lowvram,
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label="Low VRAM",
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interactive=True,
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)
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precision = gr.Radio(
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label="Precision",
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value=cmd_opts.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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sd_model_info = f"Checkpoint Path: {str(get_checkpoints_path())}"
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base_model_id = gr.Dropdown(
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label="\U000026F0\U0000FE0F Base Model",
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info="Select or enter HF model ID",
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elem_id="custom_model",
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value="stabilityai/stable-diffusion-2-1-base",
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choices=sd_default_models,
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) # base_model_id
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with gr.Row():
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height = gr.Slider(
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384,
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1024,
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value=cmd_opts.height,
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step=8,
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label="\U00002195\U0000FE0F Height",
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)
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width = gr.Slider(
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384,
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1024,
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value=cmd_opts.width,
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step=8,
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label="\U00002194\U0000FE0F Width",
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)
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with gr.Accordion(
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label="\U00002696\U0000FE0F Model Weights", open=False
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):
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with gr.Column():
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custom_weights = gr.Dropdown(
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label="Checkpoint Weights",
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info="Select or enter HF model ID",
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elem_id="custom_model",
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value="None",
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allow_custom_value=True,
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choices=["None"]
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+ get_checkpoints(os.path.basename(str(base_model_id))),
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) # custom_weights
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base_model_id.change(
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fn=base_model_changed,
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inputs=[base_model_id],
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outputs=[custom_weights],
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)
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sd_vae_info = (str(get_checkpoints_path("vae"))).replace(
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"\\", "\n\\"
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)
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sd_vae_info = f"VAE Path: {sd_vae_info}"
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custom_vae = gr.Dropdown(
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label=f"VAE Model",
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info=sd_vae_info,
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elem_id="custom_model",
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value=(
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os.path.basename(cmd_opts.custom_vae)
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if cmd_opts.custom_vae
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else "None"
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),
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choices=["None"] + get_checkpoints("vae"),
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allow_custom_value=True,
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scale=1,
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)
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sd_lora_info = (str(get_checkpoints_path("loras"))).replace(
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"\\", "\n\\"
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)
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lora_opt = gr.Dropdown(
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allow_custom_value=True,
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label=f"Standalone LoRA Weights",
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info=sd_lora_info,
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elem_id="lora_weights",
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value=None,
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multiselect=True,
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choices=[] + get_checkpoints("lora"),
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scale=2,
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)
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lora_tags = gr.HTML(
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value="<div><i>No LoRA selected</i></div>",
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elem_classes="lora-tags",
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)
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embeddings_config = gr.JSON(
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label="Embeddings Options", min_width=50, scale=1
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)
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gr.on(
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triggers=[lora_opt.change],
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fn=lora_changed,
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inputs=[lora_opt],
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outputs=[lora_tags],
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queue=True,
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show_progress=False,
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).then(
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fn=update_embeddings_json,
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inputs=[lora_opt],
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outputs=[embeddings_config],
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show_progress=False,
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)
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with gr.Accordion(
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label="\U0001F9EA\U0000FE0F Input Image Processing", open=False
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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=cmd_opts.strength,
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step=0.01,
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label="Denoising Strength",
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)
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resample_type = gr.Dropdown(
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value=cmd_opts.resample_type,
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choices=resampler_list,
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label="Resample Type",
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allow_custom_value=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="\U00002795\U0000FE0F Prompt",
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value=cmd_opts.prompt[0],
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lines=2,
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elem_id="prompt_box",
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show_copy_button=True,
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)
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negative_prompt = gr.Textbox(
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label="\U00002796\U0000FE0F Negative Prompt",
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value=cmd_opts.negative_prompt[0],
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lines=2,
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elem_id="negative_prompt_box",
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show_copy_button=True,
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)
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with gr.Row(equal_height=True):
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seed = gr.Textbox(
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value=cmd_opts.seed,
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label="\U0001F331\U0000FE0F Seed",
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info="An integer or a JSON list of integers, -1 for random",
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show_copy_button=True,
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)
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scheduler = gr.Dropdown(
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elem_id="scheduler",
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label="\U0001F4C5\U0000FE0F Scheduler",
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info="\U000E0020", # forces same height as seed
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value="EulerDiscrete",
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choices=scheduler_model_map.keys(),
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allow_custom_value=False,
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)
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with gr.Row():
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steps = gr.Slider(
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1,
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100,
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value=cmd_opts.steps,
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step=1,
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label="\U0001F3C3\U0000FE0F 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=cmd_opts.guidance_scale,
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step=0.1,
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label="\U0001F5C3\U0000FE0F CFG Scale",
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)
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with gr.Accordion(
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label="Controlnet Options",
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open=False,
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visible=False,
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):
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preprocessed_hints = gr.State([])
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with gr.Column():
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sd_cnet_info = (
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str(get_checkpoints_path("controlnet"))
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).replace("\\", "\n\\")
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with gr.Row():
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cnet_config = gr.JSON()
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with gr.Column():
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clear_config = gr.ClearButton(
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value="Clear Controlnet Config",
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size="sm",
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components=cnet_config,
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)
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control_mode = gr.Radio(
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choices=["Prompt", "Balanced", "Controlnet"],
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value="Balanced",
|
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label="Control Mode",
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)
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with gr.Row():
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with gr.Column(scale=1):
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cnet_model = gr.Dropdown(
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allow_custom_value=True,
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label=f"Controlnet Model",
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info=sd_cnet_info,
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value="None",
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choices=[
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"None",
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"canny",
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"openpose",
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"scribble",
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"zoedepth",
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]
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+ get_checkpoints("controlnet"),
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)
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cnet_strength = gr.Slider(
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label="Controlnet Strength",
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minimum=0,
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maximum=100,
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value=50,
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step=1,
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)
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with gr.Row():
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canvas_width = gr.Slider(
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label="Canvas Width",
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minimum=256,
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maximum=1024,
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value=512,
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step=8,
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)
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canvas_height = gr.Slider(
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label="Canvas Height",
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minimum=256,
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maximum=1024,
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value=512,
|
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step=8,
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)
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make_canvas = gr.Button(
|
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value="Make Canvas!",
|
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)
|
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use_input_img = gr.Button(
|
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value="Use Original Image",
|
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size="sm",
|
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)
|
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cnet_input = gr.Image(
|
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value=None,
|
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type="pil",
|
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image_mode="RGB",
|
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interactive=True,
|
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)
|
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with gr.Column(scale=1):
|
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cnet_output = gr.Image(
|
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value=None,
|
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visible=True,
|
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label="Preprocessed Hint",
|
|
interactive=False,
|
|
show_label=True,
|
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)
|
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cnet_gen = gr.Button(
|
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value="Preprocess controlnet input",
|
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)
|
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use_result = gr.Button(
|
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"Submit",
|
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size="sm",
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)
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make_canvas.click(
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fn=create_canvas,
|
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inputs=[canvas_width, canvas_height],
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outputs=[cnet_input],
|
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queue=False,
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)
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cnet_gen.click(
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fn=cnet_preview,
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inputs=[
|
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cnet_model,
|
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cnet_input,
|
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],
|
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outputs=[
|
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cnet_output,
|
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preprocessed_hints,
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],
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)
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use_result.click(
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fn=submit_to_cnet_config,
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inputs=[
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cnet_model,
|
|
cnet_output,
|
|
cnet_strength,
|
|
control_mode,
|
|
cnet_config,
|
|
],
|
|
outputs=[
|
|
cnet_config,
|
|
],
|
|
queue=False,
|
|
)
|
|
with gr.Column(scale=3, min_width=600):
|
|
with gr.Tabs() as sd_tabs:
|
|
sd_element.load(
|
|
# Workaround for Gradio issue #7085
|
|
# TODO: revert to setting selected= in gr.Tabs declaration
|
|
# once this is resolved in Gradio
|
|
lambda: gr.Tabs(selected=101),
|
|
outputs=[sd_tabs],
|
|
)
|
|
with gr.Tab(label="Input Image", id=100) as sd_tab_init_image:
|
|
with gr.Column(elem_classes=["sd-right-panel"]):
|
|
with gr.Row(elem_classes=["fill"]):
|
|
# TODO: make this import image prompt info if it exists
|
|
sd_init_image = gr.Image(
|
|
type="pil",
|
|
interactive=True,
|
|
show_label=False,
|
|
)
|
|
use_input_img.click(
|
|
fn=import_original,
|
|
inputs=[
|
|
sd_init_image,
|
|
canvas_width,
|
|
canvas_height,
|
|
],
|
|
outputs=[cnet_input],
|
|
queue=False,
|
|
)
|
|
with gr.Tab(label="Generate Images", id=101) as sd_tab_gallery:
|
|
with gr.Column(elem_classes=["sd-right-panel"]):
|
|
with gr.Row(elem_classes=["fill"]):
|
|
sd_gallery = gr.Gallery(
|
|
label="Generated images",
|
|
show_label=False,
|
|
elem_id="gallery",
|
|
columns=2,
|
|
object_fit="fit",
|
|
preview=True,
|
|
)
|
|
with gr.Row():
|
|
std_output = gr.Textbox(
|
|
value=f"{sd_model_info}\n"
|
|
f"Images will be saved at "
|
|
f"{get_generated_imgs_path()}",
|
|
lines=2,
|
|
elem_id="std_output",
|
|
show_label=True,
|
|
label="Log",
|
|
show_copy_button=True,
|
|
)
|
|
sd_element.load(
|
|
logger.read_sd_logs, None, std_output, every=1
|
|
)
|
|
sd_status = gr.Textbox(visible=False)
|
|
with gr.Row():
|
|
batch_count = gr.Slider(
|
|
1,
|
|
100,
|
|
value=cmd_opts.batch_count,
|
|
step=1,
|
|
label="Batch Count",
|
|
interactive=True,
|
|
)
|
|
batch_size = gr.Slider(
|
|
1,
|
|
4,
|
|
value=cmd_opts.batch_size,
|
|
step=1,
|
|
label="Batch Size",
|
|
interactive=True,
|
|
visible=True,
|
|
)
|
|
repeatable_seeds = gr.Checkbox(
|
|
cmd_opts.repeatable_seeds,
|
|
label="Use Repeatable Seeds for Batches",
|
|
)
|
|
with gr.Row():
|
|
stable_diffusion = gr.Button("Start")
|
|
random_seed = gr.Button("Randomize Seed")
|
|
random_seed.click(
|
|
lambda: -1,
|
|
inputs=[],
|
|
outputs=[seed],
|
|
queue=False,
|
|
show_progress=False,
|
|
)
|
|
stop_batch = gr.Button("Stop")
|
|
with gr.Tab(label="Config", id=102) as sd_tab_config:
|
|
with gr.Column(elem_classes=["sd-right-panel"]):
|
|
with gr.Row(elem_classes=["fill"]):
|
|
Path(get_configs_path()).mkdir(
|
|
parents=True, exist_ok=True
|
|
)
|
|
default_config_file = os.path.join(
|
|
get_configs_path(),
|
|
"default_sd_config.json",
|
|
)
|
|
write_default_sd_config(default_config_file)
|
|
sd_json = gr.JSON(
|
|
elem_classes=["fill"],
|
|
value=view_json_file(default_config_file),
|
|
)
|
|
with gr.Row():
|
|
with gr.Column(scale=3):
|
|
load_sd_config = gr.FileExplorer(
|
|
label="Load Config",
|
|
file_count="single",
|
|
root_dir=(
|
|
cmd_opts.configs_path
|
|
if cmd_opts.configs_path
|
|
else get_configs_path()
|
|
),
|
|
height=75,
|
|
)
|
|
with gr.Column(scale=1):
|
|
save_sd_config = gr.Button(
|
|
value="Save Config", size="sm"
|
|
)
|
|
clear_sd_config = gr.ClearButton(
|
|
value="Clear Config",
|
|
size="sm",
|
|
components=sd_json,
|
|
)
|
|
with gr.Row():
|
|
sd_config_name = gr.Textbox(
|
|
value="Config Name",
|
|
info="Name of the file this config will be saved to.",
|
|
interactive=True,
|
|
show_label=False,
|
|
)
|
|
load_sd_config.change(
|
|
fn=load_sd_cfg,
|
|
inputs=[sd_json, load_sd_config],
|
|
outputs=[
|
|
prompt,
|
|
negative_prompt,
|
|
sd_init_image,
|
|
height,
|
|
width,
|
|
steps,
|
|
strength,
|
|
guidance_scale,
|
|
seed,
|
|
batch_count,
|
|
batch_size,
|
|
scheduler,
|
|
base_model_id,
|
|
custom_weights,
|
|
custom_vae,
|
|
precision,
|
|
device,
|
|
ondemand,
|
|
repeatable_seeds,
|
|
resample_type,
|
|
cnet_config,
|
|
embeddings_config,
|
|
sd_json,
|
|
],
|
|
)
|
|
save_sd_config.click(
|
|
fn=save_sd_cfg,
|
|
inputs=[sd_json, sd_config_name],
|
|
outputs=[sd_config_name],
|
|
)
|
|
save_sd_config.click(
|
|
fn=save_sd_cfg,
|
|
inputs=[sd_json, sd_config_name],
|
|
outputs=[sd_config_name],
|
|
)
|
|
|
|
pull_kwargs = dict(
|
|
fn=pull_sd_configs,
|
|
inputs=[
|
|
prompt,
|
|
negative_prompt,
|
|
sd_init_image,
|
|
height,
|
|
width,
|
|
steps,
|
|
strength,
|
|
guidance_scale,
|
|
seed,
|
|
batch_count,
|
|
batch_size,
|
|
scheduler,
|
|
base_model_id,
|
|
custom_weights,
|
|
custom_vae,
|
|
precision,
|
|
device,
|
|
ondemand,
|
|
repeatable_seeds,
|
|
resample_type,
|
|
cnet_config,
|
|
embeddings_config,
|
|
],
|
|
outputs=[
|
|
sd_json,
|
|
],
|
|
)
|
|
|
|
status_kwargs = dict(
|
|
fn=lambda bc, bs: status_label("Stable Diffusion", 0, bc, bs),
|
|
inputs=[batch_count, batch_size],
|
|
outputs=sd_status,
|
|
)
|
|
|
|
gen_kwargs = dict(
|
|
fn=shark_sd_fn_dict_input,
|
|
inputs=[sd_json],
|
|
outputs=[
|
|
sd_gallery,
|
|
sd_status,
|
|
],
|
|
)
|
|
|
|
prompt_submit = prompt.submit(**status_kwargs).then(**pull_kwargs)
|
|
neg_prompt_submit = negative_prompt.submit(**status_kwargs).then(**pull_kwargs)
|
|
generate_click = (
|
|
stable_diffusion.click(**status_kwargs).then(**pull_kwargs).then(**gen_kwargs)
|
|
)
|
|
stop_batch.click(
|
|
fn=cancel_sd,
|
|
cancels=[prompt_submit, neg_prompt_submit, generate_click],
|
|
)
|