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readded nserver backend modules
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Believethehype
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"""StableDiffusionXL Module
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"""
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import gc
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import sys
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import os
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sys.path.insert(0, os.path.dirname(__file__))
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from nova_utils.interfaces.server_module import Processor
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# Setting defaults
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_default_options = {"kind": "prompt", "mode": "fast" }
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# TODO: add log infos,
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class ImageInterrogator(Processor):
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self.options = _default_options | self.options
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self.device = None
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self.ds_iter = None
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self.current_session = None
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# IO shortcuts
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self.input = [x for x in self.model_io if x.io_type == "input"]
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self.output = [x for x in self.model_io if x.io_type == "output"]
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self.input = self.input[0]
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self.output = self.output[0]
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def process_data(self, ds_iter) -> dict:
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from PIL import Image as PILImage
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import torch
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self.device = "cuda" if torch.cuda.is_available() else "cpu"
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self.ds_iter = ds_iter
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current_session_name = self.ds_iter.session_names[0]
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self.current_session = self.ds_iter.sessions[current_session_name]['manager']
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#os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "max_split_size_mb:512"
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kind = self.options['kind'] #"prompt" #"analysis" #prompt
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mode = self.options['mode']
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#url = self.current_session.input_data['input_image_url'].data[0]
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#print(url)
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input_image = self.current_session.input_data['input_image'].data
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init_image = PILImage.fromarray(input_image)
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mwidth = 256
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mheight = 256
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w = mwidth
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h = mheight
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if init_image.width > init_image.height:
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scale = float(init_image.height / init_image.width)
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w = mwidth
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h = int(mheight * scale)
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elif init_image.width < init_image.height:
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scale = float(init_image.width / init_image.height)
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w = int(mwidth * scale)
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h = mheight
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else:
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w = mwidth
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h = mheight
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init_image = init_image.resize((w, h))
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from clip_interrogator import Config, Interrogator
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config = Config(clip_model_name="ViT-L-14/openai", device="cuda")
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if kind == "analysis":
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ci = Interrogator(config)
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image_features = ci.image_to_features(init_image)
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top_mediums = ci.mediums.rank(image_features, 5)
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top_artists = ci.artists.rank(image_features, 5)
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top_movements = ci.movements.rank(image_features, 5)
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top_trendings = ci.trendings.rank(image_features, 5)
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top_flavors = ci.flavors.rank(image_features, 5)
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medium_ranks = {medium: sim for medium, sim in zip(top_mediums, ci.similarities(image_features, top_mediums))}
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artist_ranks = {artist: sim for artist, sim in zip(top_artists, ci.similarities(image_features, top_artists))}
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movement_ranks = {movement: sim for movement, sim in
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zip(top_movements, ci.similarities(image_features, top_movements))}
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trending_ranks = {trending: sim for trending, sim in
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zip(top_trendings, ci.similarities(image_features, top_trendings))}
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flavor_ranks = {flavor: sim for flavor, sim in zip(top_flavors, ci.similarities(image_features, top_flavors))}
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result = "Medium Ranks:\n" + str(medium_ranks) + "\nArtist Ranks: " + str(artist_ranks) + "\nMovement Ranks:\n" + str(movement_ranks) + "\nTrending Ranks:\n" + str(trending_ranks) + "\nFlavor Ranks:\n" + str(flavor_ranks)
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print(result)
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return result
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else:
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ci = Interrogator(config)
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ci.config.blip_num_beams = 64
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ci.config.chunk_size = 2024
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ci.config.clip_offload = True
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ci.config.apply_low_vram_defaults()
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#MODELS = ['ViT-L (best for Stable Diffusion 1.*)']
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ci.config.flavor_intermediate_count = 2024 #if clip_model_name == MODELS[0] else 1024
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image = init_image
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if mode == 'best':
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prompt = ci.interrogate(image)
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elif mode == 'classic':
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prompt = ci.interrogate_classic(image)
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elif mode == 'fast':
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prompt = ci.interrogate_fast(image)
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elif mode == 'negative':
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prompt = ci.interrogate_negative(image)
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#print(str(prompt))
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return prompt
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# config = Config(clip_model_name=os.environ['TRANSFORMERS_CACHE'] + "ViT-L-14/openai", device="cuda")git
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# ci = Interrogator(config)
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# "ViT-L-14/openai"))
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# "ViT-g-14/laion2B-s34B-b88K"))
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def to_output(self, data: dict):
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import numpy as np
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self.current_session.output_data_templates['output'].data = np.array([data])
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return self.current_session.output_data_templates
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<?xml version="1.0" ?>
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<trainer ssi-v="5">
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<info trained="true" seed="1234"/>
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<meta backend="nova-server" category="ImageGeneration" description="Generates Prompt from Image" is_iterable="False">
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<io type="input" id="input_image" data="image" default_value=""/>
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<io type="output" id="output" data="text" default_value=""/>
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</meta>
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<model create="ImageInterrogator" script="image_interrogator.py" optstr="{kind:LIST:prompt,analysis};{mode:LIST:best,classic,fast,negative}"/>
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</trainer>
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11
backends/nova-server/modules/image_interrogator/readme.md
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11
backends/nova-server/modules/image_interrogator/readme.md
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#Clip Interogator
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This modules provides prompt generation based on images
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* https://huggingface.co/spaces/pharmapsychotic/CLIP-Interrogator
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## Options
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- `kind`: string, identifier of the kind of processing
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- `prompt`: Generates a prompt from image
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- `analysis`: Generates a categorical analysis
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hcai-nova-utils>=1.5.5
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--extra-index-url https://download.pytorch.org/whl/cu118
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torch==2.1.1
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clip_interrogator
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git+https://github.com/huggingface/diffusers.git
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12
backends/nova-server/modules/image_interrogator/version.py
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12
backends/nova-server/modules/image_interrogator/version.py
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""" Clip Interrorgator
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"""
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# We follow Semantic Versioning (https://semver.org/)
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_MAJOR_VERSION = '1'
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_MINOR_VERSION = '0'
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_PATCH_VERSION = '0'
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__version__ = '.'.join([
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_MAJOR_VERSION,
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_MINOR_VERSION,
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_PATCH_VERSION,
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])
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"""RealESRGan Module
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"""
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import os
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import glob
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import sys
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from nova_utils.interfaces.server_module import Processor
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from basicsr.archs.rrdbnet_arch import RRDBNet
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from basicsr.utils.download_util import load_file_from_url
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import numpy as np
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from realesrgan import RealESRGANer
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from realesrgan.archs.srvgg_arch import SRVGGNetCompact
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import cv2
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from PIL import Image as PILImage
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# Setting defaults
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_default_options = {"model": "RealESRGAN_x4plus", "outscale": 4, "denoise_strength": 0.5, "tile": 0,"tile_pad": 10,"pre_pad": 0, "compute_type": "fp32", "face_enhance": False }
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# TODO: add log infos,
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class RealESRGan(Processor):
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self.options = _default_options | self.options
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self.device = None
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self.ds_iter = None
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self.current_session = None
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self.model_path = None #Maybe need this later for manual path
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# IO shortcuts
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self.input = [x for x in self.model_io if x.io_type == "input"]
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self.output = [x for x in self.model_io if x.io_type == "output"]
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self.input = self.input[0]
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self.output = self.output[0]
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def process_data(self, ds_iter) -> dict:
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self.ds_iter = ds_iter
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current_session_name = self.ds_iter.session_names[0]
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self.current_session = self.ds_iter.sessions[current_session_name]['manager']
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input_image = self.current_session.input_data['input_image'].data
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try:
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model, netscale, file_url = self.manageModel(str(self.options['model']))
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if self.model_path is not None:
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model_path = self.model_path
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else:
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model_path = os.path.join('weights', self.options['model'] + '.pth')
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if not os.path.isfile(model_path):
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ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
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for url in file_url:
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# model_path will be updated
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model_path = load_file_from_url(
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url=url, model_dir=os.path.join(ROOT_DIR, 'weights'), progress=True, file_name=None)
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# use dni to control the denoise strength
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dni_weight = None
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if self.options['model'] == 'realesr-general-x4v3' and float(self.options['denoise_strength']) != 1:
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wdn_model_path = model_path.replace('realesr-general-x4v3', 'realesr-general-wdn-x4v3')
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model_path = [model_path, wdn_model_path]
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dni_weight = [float(self.options['denoise_strength']), 1 - float(self.options['denoise_strength'])]
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half = True
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if self.options["compute_type"] == "fp32":
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half=False
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upsampler = RealESRGANer(
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scale=netscale,
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model_path=model_path,
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dni_weight=dni_weight,
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model=model,
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tile= int(self.options['tile']),
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tile_pad=int(self.options['tile_pad']),
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pre_pad=int(self.options['pre_pad']),
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half=half,
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gpu_id=None) #Can be set if multiple gpus are available
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if bool(self.options['face_enhance']): # Use GFPGAN for face enhancement
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from gfpgan import GFPGANer
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face_enhancer = GFPGANer(
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model_path='https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth',
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upscale=int(self.options['outscale']),
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arch='clean',
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channel_multiplier=2,
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bg_upsampler=upsampler)
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pilimage = PILImage.fromarray(input_image)
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img = cv2.cvtColor(np.array(pilimage), cv2.COLOR_RGB2BGR)
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try:
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if bool(self.options['face_enhance']):
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_, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True)
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else:
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output, _ = upsampler.enhance(img, outscale=int(self.options['outscale']))
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except RuntimeError as error:
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print('Error', error)
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print('If you encounter CUDA out of memory, try to set --tile with a smaller number.')
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output = cv2.cvtColor(output, cv2.COLOR_BGR2RGB)
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return output
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except Exception as e:
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print(e)
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sys.stdout.flush()
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return "Error"
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def to_output(self, data: dict):
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self.current_session.output_data_templates['output_image'].data = data
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return self.current_session.output_data_templates
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def manageModel(self, model_name):
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if model_name == 'RealESRGAN_x4plus': # x4 RRDBNet model
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model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
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netscale = 4
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file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth']
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elif model_name == 'RealESRNet_x4plus': # x4 RRDBNet model
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model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
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netscale = 4
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file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/RealESRNet_x4plus.pth']
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elif model_name == 'RealESRGAN_x4plus_anime_6B': # x4 RRDBNet model with 6 blocks
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model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4)
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netscale = 4
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file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth']
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elif model_name == 'RealESRGAN_x2plus': # x2 RRDBNet model
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model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2)
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netscale = 2
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file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth']
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elif model_name == 'realesr-animevideov3': # x4 VGG-style model (XS size)
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model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu')
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netscale = 4
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file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-animevideov3.pth']
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elif model_name == 'realesr-general-x4v3': # x4 VGG-style model (S size)
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model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
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netscale = 4
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file_url = [
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'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-wdn-x4v3.pth',
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'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth'
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]
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return model, netscale, file_url
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<?xml version="1.0" ?>
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<trainer ssi-v="5">
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<info trained="true" seed="1234"/>
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<meta backend="nova-server" category="ImageGeneration" description="Upscales an Image" is_iterable="False">
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<io type="input" id="input_image" data="image" default_value=""/>
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<io type="output" id="output_image" data="image" default_value=""/>
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</meta>
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<model create="RealESRGan" script="image_upscale_realesrgan.py" optstr="{model:LIST:RealESRGAN_x4plus,RealESRNet_x4plus,RealESRGAN_x4plus_anime_6B,RealESRGAN_x2plus,realesr-animevideov3,realesr-general-x4v3};{outscale:STRING:4};{denoise_strength:STRING:0.5};{tile:STRING:0};{tile_pad:STRING:10};{pre_pad:STRING:0};{compute_type:STRING:fp32};{face_enhance:BOOL:False}"/>
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</trainer>
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import argparse
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import cv2
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import glob
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import os
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from basicsr.archs.rrdbnet_arch import RRDBNet
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from basicsr.utils.download_util import load_file_from_url
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from realesrgan import RealESRGANer
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from realesrgan.archs.srvgg_arch import SRVGGNetCompact
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def main():
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"""Inference demo for Real-ESRGAN.
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"""
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parser = argparse.ArgumentParser()
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parser.add_argument('-i', '--input', type=str, default='inputs', help='Input image or folder')
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parser.add_argument(
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'-n',
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'--model_name',
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type=str,
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default='RealESRGAN_x4plus',
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help=('Model names: RealESRGAN_x4plus | RealESRNet_x4plus | RealESRGAN_x4plus_anime_6B | RealESRGAN_x2plus | '
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'realesr-animevideov3 | realesr-general-x4v3'))
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parser.add_argument('-o', '--output', type=str, default='results', help='Output folder')
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parser.add_argument(
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'-dn',
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'--denoise_strength',
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type=float,
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default=0.5,
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help=('Denoise strength. 0 for weak denoise (keep noise), 1 for strong denoise ability. '
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'Only used for the realesr-general-x4v3 model'))
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parser.add_argument('-s', '--outscale', type=float, default=4, help='The final upsampling scale of the image')
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parser.add_argument(
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'--model_path', type=str, default=None, help='[Option] Model path. Usually, you do not need to specify it')
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parser.add_argument('--suffix', type=str, default='out', help='Suffix of the restored image')
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parser.add_argument('-t', '--tile', type=int, default=0, help='Tile size, 0 for no tile during testing')
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parser.add_argument('--tile_pad', type=int, default=10, help='Tile padding')
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parser.add_argument('--pre_pad', type=int, default=0, help='Pre padding size at each border')
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parser.add_argument('--face_enhance', action='store_true', help='Use GFPGAN to enhance face')
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parser.add_argument(
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'--fp32', action='store_true', help='Use fp32 precision during inference. Default: fp16 (half precision).')
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parser.add_argument(
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'--alpha_upsampler',
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type=str,
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default='realesrgan',
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help='The upsampler for the alpha channels. Options: realesrgan | bicubic')
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parser.add_argument(
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'--ext',
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type=str,
|
||||
default='auto',
|
||||
help='Image extension. Options: auto | jpg | png, auto means using the same extension as inputs')
|
||||
parser.add_argument(
|
||||
'-g', '--gpu-id', type=int, default=None, help='gpu device to use (default=None) can be 0,1,2 for multi-gpu')
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
# determine models according to model names
|
||||
args.model_name = args.model_name.split('.')[0]
|
||||
if args.model_name == 'RealESRGAN_x4plus': # x4 RRDBNet model
|
||||
model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
|
||||
netscale = 4
|
||||
file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth']
|
||||
elif args.model_name == 'RealESRNet_x4plus': # x4 RRDBNet model
|
||||
model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
|
||||
netscale = 4
|
||||
file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/RealESRNet_x4plus.pth']
|
||||
elif args.model_name == 'RealESRGAN_x4plus_anime_6B': # x4 RRDBNet model with 6 blocks
|
||||
model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4)
|
||||
netscale = 4
|
||||
file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth']
|
||||
elif args.model_name == 'RealESRGAN_x2plus': # x2 RRDBNet model
|
||||
model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2)
|
||||
netscale = 2
|
||||
file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth']
|
||||
elif args.model_name == 'realesr-animevideov3': # x4 VGG-style model (XS size)
|
||||
model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu')
|
||||
netscale = 4
|
||||
file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-animevideov3.pth']
|
||||
elif args.model_name == 'realesr-general-x4v3': # x4 VGG-style model (S size)
|
||||
model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
|
||||
netscale = 4
|
||||
file_url = [
|
||||
'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-wdn-x4v3.pth',
|
||||
'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth'
|
||||
]
|
||||
|
||||
# determine model paths
|
||||
if args.model_path is not None:
|
||||
model_path = args.model_path
|
||||
else:
|
||||
model_path = os.path.join('weights', args.model_name + '.pth')
|
||||
if not os.path.isfile(model_path):
|
||||
ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
|
||||
for url in file_url:
|
||||
# model_path will be updated
|
||||
model_path = load_file_from_url(
|
||||
url=url, model_dir=os.path.join(ROOT_DIR, 'weights'), progress=True, file_name=None)
|
||||
|
||||
# use dni to control the denoise strength
|
||||
dni_weight = None
|
||||
if args.model_name == 'realesr-general-x4v3' and args.denoise_strength != 1:
|
||||
wdn_model_path = model_path.replace('realesr-general-x4v3', 'realesr-general-wdn-x4v3')
|
||||
model_path = [model_path, wdn_model_path]
|
||||
dni_weight = [args.denoise_strength, 1 - args.denoise_strength]
|
||||
|
||||
# restorer
|
||||
upsampler = RealESRGANer(
|
||||
scale=netscale,
|
||||
model_path=model_path,
|
||||
dni_weight=dni_weight,
|
||||
model=model,
|
||||
tile=args.tile,
|
||||
tile_pad=args.tile_pad,
|
||||
pre_pad=args.pre_pad,
|
||||
half=not args.fp32,
|
||||
gpu_id=args.gpu_id)
|
||||
|
||||
if args.face_enhance: # Use GFPGAN for face enhancement
|
||||
from gfpgan import GFPGANer
|
||||
face_enhancer = GFPGANer(
|
||||
model_path='https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth',
|
||||
upscale=args.outscale,
|
||||
arch='clean',
|
||||
channel_multiplier=2,
|
||||
bg_upsampler=upsampler)
|
||||
os.makedirs(args.output, exist_ok=True)
|
||||
|
||||
if os.path.isfile(args.input):
|
||||
paths = [args.input]
|
||||
else:
|
||||
paths = sorted(glob.glob(os.path.join(args.input, '*')))
|
||||
|
||||
for idx, path in enumerate(paths):
|
||||
imgname, extension = os.path.splitext(os.path.basename(path))
|
||||
print('Testing', idx, imgname)
|
||||
|
||||
img = cv2.imread(path, cv2.IMREAD_UNCHANGED)
|
||||
if len(img.shape) == 3 and img.shape[2] == 4:
|
||||
img_mode = 'RGBA'
|
||||
else:
|
||||
img_mode = None
|
||||
|
||||
try:
|
||||
if args.face_enhance:
|
||||
_, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True)
|
||||
else:
|
||||
output, _ = upsampler.enhance(img, outscale=args.outscale)
|
||||
except RuntimeError as error:
|
||||
print('Error', error)
|
||||
print('If you encounter CUDA out of memory, try to set --tile with a smaller number.')
|
||||
else:
|
||||
if args.ext == 'auto':
|
||||
extension = extension[1:]
|
||||
else:
|
||||
extension = args.ext
|
||||
if img_mode == 'RGBA': # RGBA images should be saved in png format
|
||||
extension = 'png'
|
||||
if args.suffix == '':
|
||||
save_path = os.path.join(args.output, f'{imgname}.{extension}')
|
||||
else:
|
||||
save_path = os.path.join(args.output, f'{imgname}_{args.suffix}.{extension}')
|
||||
cv2.imwrite(save_path, output)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
13
backends/nova-server/modules/image_upscale/requirements.txt
Normal file
13
backends/nova-server/modules/image_upscale/requirements.txt
Normal file
@ -0,0 +1,13 @@
|
||||
realesrgan @git+https://github.com/xinntao/Real-ESRGAN.git
|
||||
hcai-nova-utils>=1.5.5
|
||||
--extra-index-url https://download.pytorch.org/whl/cu118
|
||||
torch==2.1.0
|
||||
torchvision
|
||||
basicsr>=1.4.2
|
||||
facexlib>=0.2.5
|
||||
gfpgan>=1.3.5
|
||||
numpy
|
||||
opencv-python
|
||||
Pillow
|
||||
tqdm
|
||||
git+https://github.com/huggingface/diffusers.git
|
||||
12
backends/nova-server/modules/image_upscale/version.py
Normal file
12
backends/nova-server/modules/image_upscale/version.py
Normal file
@ -0,0 +1,12 @@
|
||||
""" RealESRGan
|
||||
"""
|
||||
# We follow Semantic Versioning (https://semver.org/)
|
||||
_MAJOR_VERSION = '1'
|
||||
_MINOR_VERSION = '0'
|
||||
_PATCH_VERSION = '0'
|
||||
|
||||
__version__ = '.'.join([
|
||||
_MAJOR_VERSION,
|
||||
_MINOR_VERSION,
|
||||
_PATCH_VERSION,
|
||||
])
|
||||
Binary file not shown.
File diff suppressed because it is too large
Load Diff
Binary file not shown.
100
backends/nova-server/modules/stablediffusionxl/lora.py
Normal file
100
backends/nova-server/modules/stablediffusionxl/lora.py
Normal file
@ -0,0 +1,100 @@
|
||||
def build_lora_xl(lora, prompt, lora_weight):
|
||||
existing_lora = False
|
||||
if lora == "3drenderstyle":
|
||||
if lora_weight == "":
|
||||
lora_weight = "1"
|
||||
prompt = "3d style, 3d render, " + prompt + " <lora:3d_render_style_xl:"+lora_weight+">"
|
||||
existing_lora = True
|
||||
|
||||
if lora == "psychedelicnoir":
|
||||
if lora_weight == "":
|
||||
lora_weight = "1"
|
||||
prompt = prompt + " <lora:Psychedelic_Noir__sdxl:"+lora_weight+">>"
|
||||
existing_lora = True
|
||||
|
||||
if lora == "wojak":
|
||||
if lora_weight == "":
|
||||
lora_weight = "1"
|
||||
prompt = "<lora:wojak_big:"+lora_weight+">, " + prompt + ", wojak"
|
||||
existing_lora = True
|
||||
|
||||
if lora == "dreamarts":
|
||||
if lora_weight == "":
|
||||
lora_weight = "1"
|
||||
prompt = "<lora:DreamARTSDXL:"+lora_weight+">, " + prompt
|
||||
existing_lora = True
|
||||
|
||||
if lora == "voxel":
|
||||
if lora_weight == "":
|
||||
lora_weight = "1"
|
||||
prompt = "voxel style, " + prompt + " <lora:last:"+lora_weight+">"
|
||||
existing_lora = True
|
||||
|
||||
if lora == "kru3ger":
|
||||
if lora_weight == "":
|
||||
lora_weight = "1"
|
||||
prompt = "kru3ger_style, " + prompt + "<lora:sebastiankrueger-kru3ger_style-000007:"+lora_weight+">"
|
||||
existing_lora = True
|
||||
|
||||
if lora == "inkpunk":
|
||||
if lora_weight == "":
|
||||
lora_weight = "0.5"
|
||||
prompt = "inkpunk style, " + prompt + " <lora:IPXL_v2:"+lora_weight+">"
|
||||
existing_lora = True
|
||||
|
||||
if lora == "inkscenery":
|
||||
if lora_weight == "":
|
||||
lora_weight = "1"
|
||||
prompt = " ink scenery, " + prompt + " <lora:ink_scenery_xl:"+lora_weight+">"
|
||||
existing_lora = True
|
||||
|
||||
if lora == "inkpainting":
|
||||
if lora_weight == "":
|
||||
lora_weight = "0.7"
|
||||
prompt = "painting style, " + prompt + " <lora:Ink_Painting-000006::"+lora_weight+">,"
|
||||
existing_lora = True
|
||||
|
||||
if lora == "timburton":
|
||||
if lora_weight == "":
|
||||
lora_weight = "1.27"
|
||||
pencil_weight = "1.15"
|
||||
prompt = prompt + " (hand drawn with pencil"+pencil_weight+"), (tim burton style:"+lora_weight+")"
|
||||
existing_lora = True
|
||||
|
||||
if lora == "pixelart":
|
||||
if lora_weight == "":
|
||||
lora_weight = "1"
|
||||
prompt = prompt + " (flat shading:1.2), (minimalist:1.4), <lora:pixelbuildings128-v2:"+lora_weight+"> "
|
||||
existing_lora = True
|
||||
|
||||
if lora == "pepe":
|
||||
if lora_weight == "":
|
||||
lora_weight = "0.8"
|
||||
prompt = prompt + " ,<lora:DD-pepe-v2:"+lora_weight+"> pepe"
|
||||
existing_lora = True
|
||||
|
||||
if lora == "bettertext":
|
||||
if lora_weight == "":
|
||||
lora_weight = "1"
|
||||
prompt = prompt + " ,<lora:BetterTextRedmond:"+lora_weight+">"
|
||||
existing_lora = True
|
||||
|
||||
if lora == "mspaint":
|
||||
if lora_weight == "":
|
||||
lora_weight = "1"
|
||||
prompt = "MSPaint drawing " + prompt +">"
|
||||
existing_lora = True
|
||||
|
||||
if lora == "woodfigure":
|
||||
if lora_weight == "":
|
||||
lora_weight = "0.7"
|
||||
prompt = prompt + ",woodfigurez,artistic style <lora:woodfigurez-sdxl:"+lora_weight+">"
|
||||
existing_lora = True
|
||||
|
||||
if lora == "fireelement":
|
||||
prompt = prompt + ",composed of fire elements, fire element"
|
||||
existing_lora = True
|
||||
|
||||
|
||||
|
||||
return lora, prompt, existing_lora
|
||||
35
backends/nova-server/modules/stablediffusionxl/readme.md
Normal file
35
backends/nova-server/modules/stablediffusionxl/readme.md
Normal file
@ -0,0 +1,35 @@
|
||||
# Stable Diffusion XL
|
||||
|
||||
This modules provides image generation based on prompts
|
||||
|
||||
* https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0
|
||||
|
||||
## Options
|
||||
|
||||
- `model`: string, identifier of the model to choose
|
||||
- `stabilityai/stable-diffusion-xl-base-1.0`: Default Stable Diffusion XL model
|
||||
|
||||
|
||||
- `ratio`: Ratio of the output image
|
||||
- `1-1` ,`4-3`, `16-9`, `16-10`, `3-4`,`9-16`,`10-16`
|
||||
|
||||
- `high_noise_frac`: Denoising factor
|
||||
|
||||
- `n_steps`: how many iterations should be performed
|
||||
|
||||
## Example payload
|
||||
|
||||
```python
|
||||
payload = {
|
||||
'trainerFilePath': 'modules\\stablediffusionxl\\stablediffusionxl.trainer',
|
||||
'server': '127.0.0.1',
|
||||
'data' = '[{"id":"input_prompt","type":"input","src":"user:text","prompt":"' + prompt +'","active":"True"},{"id":"negative_prompt","type":"input","src":"user:text","prompt":"' + negative_prompt +'","active":"True"},{"id":"output_image","type":"output","src":"file:image","uri":"' + outputfile+'","active":"True"}]'
|
||||
'optStr': 'model=stabilityai/stable-diffusion-xl-base-1.0;ratio=4-3'
|
||||
}
|
||||
|
||||
import requests
|
||||
|
||||
url = 'http://127.0.0.1:53770/predict'
|
||||
headers = {'Content-type': 'application/x-www-form-urlencoded'}
|
||||
requests.post(url, headers=headers, data=payload)
|
||||
```
|
||||
@ -0,0 +1,9 @@
|
||||
hcai-nova-utils>=1.5.5
|
||||
--extra-index-url https://download.pytorch.org/whl/cu118
|
||||
torch==2.1.0
|
||||
compel~=2.0.2
|
||||
git+https://github.com/huggingface/diffusers.git
|
||||
transformers
|
||||
accelerate
|
||||
numpy
|
||||
omegaconf
|
||||
@ -0,0 +1,176 @@
|
||||
"""StableDiffusionXL Module
|
||||
"""
|
||||
|
||||
import gc
|
||||
import sys
|
||||
import os
|
||||
|
||||
# Add local dir to path for relative imports
|
||||
sys.path.insert(0, os.path.dirname(__file__))
|
||||
|
||||
from nova_utils.interfaces.server_module import Processor
|
||||
from nova_utils.utils.cache_utils import get_file
|
||||
from diffusers import StableDiffusionXLImg2ImgPipeline, StableDiffusionInstructPix2PixPipeline, EulerAncestralDiscreteScheduler
|
||||
from diffusers.utils import load_image
|
||||
import numpy as np
|
||||
from PIL import Image as PILImage
|
||||
from lora import build_lora_xl
|
||||
|
||||
|
||||
|
||||
# Setting defaults
|
||||
_default_options = {"model": "stabilityai/stable-diffusion-xl-refiner-1.0", "strength" : "0.58", "guidance_scale" : "11.0", "n_steps" : "30", "lora": "","lora_weight": "0.5" }
|
||||
|
||||
# TODO: add log infos,
|
||||
class StableDiffusionXL(Processor):
|
||||
def __init__(self, *args, **kwargs):
|
||||
super().__init__(*args, **kwargs)
|
||||
self.options = _default_options | self.options
|
||||
self.device = None
|
||||
self.ds_iter = None
|
||||
self.current_session = None
|
||||
|
||||
|
||||
# IO shortcuts
|
||||
self.input = [x for x in self.model_io if x.io_type == "input"]
|
||||
self.output = [x for x in self.model_io if x.io_type == "output"]
|
||||
self.input = self.input[0]
|
||||
self.output = self.output[0]
|
||||
|
||||
def process_data(self, ds_iter) -> dict:
|
||||
import torch
|
||||
self.device = "cuda" if torch.cuda.is_available() else "cpu"
|
||||
self.ds_iter = ds_iter
|
||||
current_session_name = self.ds_iter.session_names[0]
|
||||
self.current_session = self.ds_iter.sessions[current_session_name]['manager']
|
||||
#input_image_url = self.current_session.input_data['input_image_url'].data
|
||||
#input_image_url = ' '.join(input_image_url)
|
||||
input_image = self.current_session.input_data['input_image'].data
|
||||
input_prompt = self.current_session.input_data['input_prompt'].data
|
||||
input_prompt = ' '.join(input_prompt)
|
||||
negative_prompt = self.current_session.input_data['negative_prompt'].data
|
||||
negative_prompt = ' '.join(negative_prompt)
|
||||
# print("Input Image: " + input_image_url)
|
||||
print("Input prompt: " + input_prompt)
|
||||
print("Negative prompt: " + negative_prompt)
|
||||
|
||||
try:
|
||||
|
||||
model = self.options['model']
|
||||
lora = self.options['lora']
|
||||
#init_image = load_image(input_image_url).convert("RGB")
|
||||
init_image = PILImage.fromarray(input_image)
|
||||
|
||||
mwidth = 1024
|
||||
mheight = 1024
|
||||
w = mwidth
|
||||
h = mheight
|
||||
if init_image.width > init_image.height:
|
||||
scale = float(init_image.height / init_image.width)
|
||||
w = mwidth
|
||||
h = int(mheight * scale)
|
||||
elif init_image.width < init_image.height:
|
||||
scale = float(init_image.width / init_image.height)
|
||||
w = int(mwidth * scale)
|
||||
h = mheight
|
||||
else:
|
||||
w = mwidth
|
||||
h = mheight
|
||||
|
||||
init_image = init_image.resize((w, h))
|
||||
|
||||
if lora != "" and lora != "None":
|
||||
print("Loading lora...")
|
||||
|
||||
lora, input_prompt, existing_lora = build_lora_xl(lora, input_prompt, "" )
|
||||
|
||||
from diffusers import AutoPipelineForImage2Image
|
||||
import torch
|
||||
|
||||
|
||||
|
||||
#init_image = init_image.resize((int(w/2), int(h/2)))
|
||||
|
||||
pipe = AutoPipelineForImage2Image.from_pretrained(
|
||||
"stabilityai/stable-diffusion-xl-base-1.0",
|
||||
torch_dtype=torch.float16).to("cuda")
|
||||
|
||||
if existing_lora:
|
||||
lora_uri = [ x for x in self.trainer.meta_uri if x.uri_id == lora][0]
|
||||
if str(lora_uri) == "":
|
||||
return "Lora not found"
|
||||
lora_path = get_file(
|
||||
fname=str(lora_uri.uri_id) + ".safetensors",
|
||||
origin=lora_uri.uri_url,
|
||||
file_hash=lora_uri.uri_hash,
|
||||
cache_dir=os.getenv("CACHE_DIR"),
|
||||
tmp_dir=os.getenv("TMP_DIR"),
|
||||
)
|
||||
pipe.load_lora_weights(str(lora_path))
|
||||
print("Loaded Lora: " + str(lora_path))
|
||||
|
||||
seed = 20000
|
||||
generator = torch.manual_seed(seed)
|
||||
|
||||
#os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "max_split_size_mb:512"
|
||||
|
||||
image = pipe(
|
||||
prompt=input_prompt,
|
||||
negative_prompt=negative_prompt,
|
||||
image=init_image,
|
||||
generator=generator,
|
||||
num_inference_steps=int(self.options['n_steps']),
|
||||
image_guidance_scale=float(self.options['guidance_scale']),
|
||||
strength=float(str(self.options['strength']))).images[0]
|
||||
|
||||
|
||||
elif model == "stabilityai/stable-diffusion-xl-refiner-1.0":
|
||||
|
||||
pipe = StableDiffusionXLImg2ImgPipeline.from_pretrained(
|
||||
model, torch_dtype=torch.float16, variant="fp16",
|
||||
use_safetensors=True
|
||||
)
|
||||
|
||||
n_steps = int(self.options['n_steps'])
|
||||
transformation_strength = float(self.options['strength'])
|
||||
cfg_scale = float(self.options['guidance_scale'])
|
||||
|
||||
pipe = pipe.to(self.device)
|
||||
image = pipe(input_prompt, image=init_image,
|
||||
negative_prompt=negative_prompt, num_inference_steps=n_steps, strength=transformation_strength, guidance_scale=cfg_scale).images[0]
|
||||
|
||||
elif model == "timbrooks/instruct-pix2pix":
|
||||
pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(model, torch_dtype=torch.float16,
|
||||
safety_checker=None)
|
||||
|
||||
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
|
||||
|
||||
pipe.to(self.device)
|
||||
n_steps = int(self.options['n_steps'])
|
||||
cfg_scale = float(self.options['guidance_scale'])
|
||||
image = pipe(input_prompt, negative_prompt=negative_prompt, image=init_image, num_inference_steps=n_steps, image_guidance_scale=cfg_scale).images[0]
|
||||
|
||||
|
||||
if torch.cuda.is_available():
|
||||
del pipe
|
||||
gc.collect()
|
||||
torch.cuda.empty_cache()
|
||||
torch.cuda.ipc_collect()
|
||||
|
||||
|
||||
numpy_array = np.array(image)
|
||||
return numpy_array
|
||||
|
||||
|
||||
except Exception as e:
|
||||
print(e)
|
||||
sys.stdout.flush()
|
||||
return "Error"
|
||||
|
||||
|
||||
def to_output(self, data: dict):
|
||||
self.current_session.output_data_templates['output_image'].data = data
|
||||
return self.current_session.output_data_templates
|
||||
|
||||
|
||||
|
||||
@ -0,0 +1,26 @@
|
||||
<?xml version="1.0" ?>
|
||||
<trainer ssi-v="5">
|
||||
<info trained="true" seed="1234"/>
|
||||
<meta backend="nova-server" category="ImageGeneration" description="Generates Image from existing image based on a prompt" is_iterable="False">
|
||||
<io type="input" id="input_image" data="Image" default_value=""/>
|
||||
<io type="input" id="input_prompt" data="prompt" default_value=""/>
|
||||
<io type="input" id="negative_prompt" data="prompt" default_value=""/>
|
||||
<io type="output" id="output_image" data="image" default_value=""/>
|
||||
|
||||
<uri id="voxel" url="https://civitai.com/api/download/models/128609" hash='7D9A5F11E1B38D97F75D2B84BFB5BB3BF95CD0E5F2500B002D13374EB4F88B5C'/>
|
||||
<uri id="inkpunk" url="https://civitai.com/api/download/models/201552" hash='6BD1A90A93AE288D959B6A90738EB2DB79EC26936F460750D8379C78554A8D53'/>
|
||||
<uri id="3drenderstyle" url="https://civitai.com/api/download/models/218206" hash='C4AD16F1B116F10BBB4070D3ABD0249F799B609DAD8BC8CF92A0AC94A8DE8133'/>
|
||||
<uri id="psychedelicnoir" url="https://civitai.com/api/download/models/140194" hash='896B6B4B6DDC4A28C1CB69359944F04AEBF5954B7A5909FD9629E5549FFC2BDF'/>
|
||||
<uri id="dreamarts" url="https://civitai.com/api/download/models/137124" hash='6A8A5968FB31FB6D83E8E0FE390CF2F3693A35FC4CF247A794B0B261E166B19B'/>
|
||||
<uri id="wojak" url="https://civitai.com/api/download/models/140160" hash='0BD68F0199197CD9D8377A30E9F288479721D1838228A4484272EFF09A479209'/>
|
||||
<uri id="kru3ger" url="https://civitai.com/api/download/models/142129" hash='AE92E349446A74D44ABDB1441AF648B2078E4FBB8F46C7158AD18120553DDC3D'/>
|
||||
<uri id="timburton" url="https://civitai.com/api/download/models/207862" hash='62C229B13622B19928B2D5B9FA5988E612C6DC3060D3AACFE720F43D034D9870'/>
|
||||
<uri id="pixelart" url="https://civitai.com/api/download/models/135931" hash='BBF3D8DEFBFB3FB71331545225C0CF50C74A748D2525F7C19EBB8F74445DE274'/>
|
||||
<uri id="pepe" url="https://civitai.com/api/download/models/181917" hash='CBE1E1C746301801613CB331F2051AD16FF724DDA764A54135AA89D909067B97'/>
|
||||
<uri id="bettertext" url="https://civitai.com/api/download/models/163224" hash='AB1EE501387633DFBFD05970D7BBC0921D23CA804FFC0E717828A8796E8D63CF'/>
|
||||
<uri id="mspaint" url="https://civitai.com/api/download/models/205793" hash='C9503F84E12F2B016FFB8BA689220BA38BBC511573C64AC9BD0ADC853780DA5D'/>
|
||||
<uri id="woodfigure" url="https://civitai.com/api/download/models/207919" hash='9E8D768E0D707867717EBF0CB93EBF65431CC5A105982FA5FFD162D78E20B8C1'/>
|
||||
<uri id="fireelement" url="https://civitai.com/api/download/models/175257" hash='CB04B04F2D90B0A168AFFB26CC7C6F76834FEB8C2F0F30ABE35784084D1FFFBE'/>
|
||||
</meta>
|
||||
<model create="StableDiffusionXL" script="stablediffusionxl-img2img.py" optstr="{model:LIST:stabilityai/stable-diffusion-xl-refiner-1.0,timbrooks/instruct-pix2pix};{lora:LIST:None,voxel,inkpunk,3drenderstyle,psychedelicnoir,dreamarts,kru3ger,wojak,timburton,pixelart,pepe,bettertext,mspaint,woodfigure};{strength:STRING:0.8};{guidance_scale:STRING:11.0};{n_steps:STRING:30}"/>
|
||||
</trainer>
|
||||
@ -0,0 +1,242 @@
|
||||
"""StableDiffusionXL Module
|
||||
"""
|
||||
import gc
|
||||
import sys
|
||||
import os
|
||||
|
||||
sys.path.insert(0, os.path.dirname(__file__))
|
||||
|
||||
from ssl import Options
|
||||
from nova_utils.interfaces.server_module import Processor
|
||||
from diffusers import StableDiffusionXLImg2ImgPipeline, StableDiffusionXLPipeline, logging
|
||||
from compel import Compel, ReturnedEmbeddingsType
|
||||
from nova_utils.utils.cache_utils import get_file
|
||||
import numpy as np
|
||||
PYTORCH_ENABLE_MPS_FALLBACK = 1
|
||||
|
||||
import torch
|
||||
from PIL import Image
|
||||
from lora import build_lora_xl
|
||||
logging.disable_progress_bar()
|
||||
logging.enable_explicit_format()
|
||||
#logging.set_verbosity_info()
|
||||
|
||||
|
||||
# Setting defaults
|
||||
_default_options = {"model": "stabilityai/stable-diffusion-xl-base-1.0", "ratio": "1-1", "width": "", "height":"", "high_noise_frac" : "0.8", "n_steps" : "35", "lora" : "" }
|
||||
|
||||
# TODO: add log infos,
|
||||
class StableDiffusionXL(Processor):
|
||||
def __init__(self, *args, **kwargs):
|
||||
super().__init__(*args, **kwargs)
|
||||
self.options = _default_options | self.options
|
||||
self.device = None
|
||||
self.ds_iter = None
|
||||
self.current_session = None
|
||||
|
||||
|
||||
# IO shortcuts
|
||||
self.input = [x for x in self.model_io if x.io_type == "input"]
|
||||
self.output = [x for x in self.model_io if x.io_type == "output"]
|
||||
self.input = self.input[0]
|
||||
self.output = self.output[0]
|
||||
|
||||
def process_data(self, ds_iter) -> dict:
|
||||
self._device = ("cuda" if torch.cuda.is_available() else ("mps" if torch.backends.mps.is_built() else "cpu"))
|
||||
self.variant = "fp16"
|
||||
self.torch_d_type = torch.float16
|
||||
self.ds_iter = ds_iter
|
||||
current_session_name = self.ds_iter.session_names[0]
|
||||
self.current_session = self.ds_iter.sessions[current_session_name]['manager']
|
||||
input_prompt = self.current_session.input_data['input_prompt'].data
|
||||
input_prompt = ' '.join(input_prompt)
|
||||
negative_prompt = self.current_session.input_data['negative_prompt'].data
|
||||
negative_prompt = ' '.join(negative_prompt)
|
||||
new_width = 0
|
||||
new_height = 0
|
||||
print("Input prompt: " + input_prompt)
|
||||
print("Negative prompt: " + negative_prompt)
|
||||
|
||||
try:
|
||||
if self.options['width'] != "" and self.options['height'] != "":
|
||||
new_width = int(self.options['width'])
|
||||
new_height = int(self.options['height'])
|
||||
ratiow, ratioh = self.calculate_aspect(new_width, new_height)
|
||||
print("Ratio:" + str(ratiow) + ":" + str(ratioh))
|
||||
|
||||
else:
|
||||
ratiow = str(self.options['ratio']).split('-')[0]
|
||||
ratioh =str(self.options['ratio']).split('-')[1]
|
||||
|
||||
model = self.options["model"]
|
||||
lora = self.options["lora"]
|
||||
mwidth = 1024
|
||||
mheight = 1024
|
||||
|
||||
height = mheight
|
||||
width = mwidth
|
||||
|
||||
ratiown = int(ratiow)
|
||||
ratiohn= int(ratioh)
|
||||
|
||||
if ratiown > ratiohn:
|
||||
height = int((ratiohn/ratiown) * float(width))
|
||||
elif ratiown < ratiohn:
|
||||
width = int((ratiown/ratiohn) * float(height))
|
||||
elif ratiown == ratiohn:
|
||||
width = height
|
||||
|
||||
|
||||
print("Processing Output width: " + str(width) + " Output height: " + str(height))
|
||||
|
||||
|
||||
|
||||
|
||||
if model == "stabilityai/stable-diffusion-xl-base-1.0":
|
||||
base = StableDiffusionXLPipeline.from_pretrained(model, torch_dtype=self.torch_d_type, variant=self.variant, use_safetensors=True).to(self.device)
|
||||
print("Loaded model: " + model)
|
||||
|
||||
else:
|
||||
|
||||
model_uri = [ x for x in self.trainer.meta_uri if x.uri_id == model][0]
|
||||
if str(model_uri) == "":
|
||||
return "Model not found"
|
||||
|
||||
model_path = get_file(
|
||||
fname=str(model_uri.uri_id) + ".safetensors",
|
||||
origin=model_uri.uri_url,
|
||||
file_hash=model_uri.uri_hash,
|
||||
cache_dir=os.getenv("CACHE_DIR"),
|
||||
tmp_dir=os.getenv("TMP_DIR"),
|
||||
)
|
||||
|
||||
print(str(model_path))
|
||||
|
||||
|
||||
base = StableDiffusionXLPipeline.from_single_file(str(model_path), torch_dtype=self.torch_d_type, variant=self.variant, use_safetensors=True).to(self.device)
|
||||
print("Loaded model: " + model)
|
||||
|
||||
if lora != "" and lora != "None":
|
||||
print("Loading lora...")
|
||||
lora, input_prompt, existing_lora = build_lora_xl(lora, input_prompt, "")
|
||||
|
||||
if existing_lora:
|
||||
lora_uri = [ x for x in self.trainer.meta_uri if x.uri_id == lora][0]
|
||||
if str(lora_uri) == "":
|
||||
return "Lora not found"
|
||||
lora_path = get_file(
|
||||
fname=str(lora_uri.uri_id) + ".safetensors",
|
||||
origin=lora_uri.uri_url,
|
||||
file_hash=lora_uri.uri_hash,
|
||||
cache_dir=os.getenv("CACHE_DIR"),
|
||||
tmp_dir=os.getenv("TMP_DIR"),
|
||||
)
|
||||
|
||||
base.load_lora_weights(str(lora_path))
|
||||
print("Loaded Lora: " + str(lora_path))
|
||||
|
||||
refiner = StableDiffusionXLImg2ImgPipeline.from_pretrained(
|
||||
"stabilityai/stable-diffusion-xl-refiner-1.0",
|
||||
text_encoder_2=base.text_encoder_2,
|
||||
vae=base.vae,
|
||||
torch_dtype=self.torch_d_type,
|
||||
use_safetensors=True,
|
||||
variant=self.variant,
|
||||
)
|
||||
|
||||
|
||||
compel_base = Compel(
|
||||
tokenizer=[base.tokenizer, base.tokenizer_2],
|
||||
text_encoder=[base.text_encoder, base.text_encoder_2],
|
||||
returned_embeddings_type=ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NON_NORMALIZED,
|
||||
requires_pooled=[False, True],
|
||||
)
|
||||
|
||||
compel_refiner = Compel(
|
||||
tokenizer=[refiner.tokenizer_2],
|
||||
text_encoder=[refiner.text_encoder_2],
|
||||
returned_embeddings_type=ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NON_NORMALIZED,
|
||||
requires_pooled=[True])
|
||||
|
||||
conditioning, pooled = compel_base(input_prompt)
|
||||
negative_conditioning, negative_pooled = compel_base(negative_prompt)
|
||||
|
||||
conditioning_refiner, pooled_refiner = compel_refiner(input_prompt)
|
||||
negative_conditioning_refiner, negative_pooled_refiner = compel_refiner(
|
||||
negative_prompt)
|
||||
|
||||
|
||||
n_steps = int(self.options['n_steps'])
|
||||
high_noise_frac = float(self.options['high_noise_frac'])
|
||||
|
||||
|
||||
#base.unet = torch.compile(base.unet, mode="reduce-overhead", fullgraph=True)
|
||||
|
||||
|
||||
|
||||
img = base(
|
||||
prompt_embeds=conditioning,
|
||||
pooled_prompt_embeds=pooled,
|
||||
negative_prompt_embeds=negative_conditioning,
|
||||
negative_pooled_prompt_embeds=negative_pooled,
|
||||
width=width,
|
||||
height=height,
|
||||
num_inference_steps=n_steps,
|
||||
denoising_end=high_noise_frac,
|
||||
output_type="latent",
|
||||
).images
|
||||
|
||||
if torch.cuda.is_available():
|
||||
del base
|
||||
gc.collect()
|
||||
torch.cuda.empty_cache()
|
||||
torch.cuda.ipc_collect()
|
||||
|
||||
refiner.to(self.device)
|
||||
# refiner.enable_model_cpu_offload()
|
||||
image = refiner(
|
||||
prompt_embeds=conditioning_refiner,
|
||||
pooled_prompt_embeds=pooled_refiner,
|
||||
negative_prompt_embeds=negative_conditioning_refiner,
|
||||
negative_pooled_prompt_embeds=negative_pooled_refiner,
|
||||
num_inference_steps=n_steps,
|
||||
denoising_start=high_noise_frac,
|
||||
num_images_per_prompt=1,
|
||||
image=img,
|
||||
).images[0]
|
||||
|
||||
if torch.cuda.is_available():
|
||||
del refiner
|
||||
gc.collect()
|
||||
torch.cuda.empty_cache()
|
||||
torch.cuda.ipc_collect()
|
||||
|
||||
if new_height != 0 or new_width != 0 and (new_width != mwidth or new_height != mheight) :
|
||||
print("Resizing to width: " + str(new_width) + " height: " + str(new_height))
|
||||
image = image.resize((new_width, new_height), Image.LANCZOS)
|
||||
|
||||
numpy_array = np.array(image)
|
||||
return numpy_array
|
||||
|
||||
|
||||
except Exception as e:
|
||||
print(e)
|
||||
sys.stdout.flush()
|
||||
return "Error"
|
||||
|
||||
def calculate_aspect(self, width: int, height: int):
|
||||
def gcd(a, b):
|
||||
"""The GCD (greatest common divisor) is the highest number that evenly divides both width and height."""
|
||||
return a if b == 0 else gcd(b, a % b)
|
||||
|
||||
r = gcd(width, height)
|
||||
x = int(width / r)
|
||||
y = int(height / r)
|
||||
|
||||
return x, y
|
||||
|
||||
|
||||
|
||||
def to_output(self, data: dict):
|
||||
self.current_session.output_data_templates['output_image'].data = data
|
||||
return self.current_session.output_data_templates
|
||||
@ -0,0 +1,41 @@
|
||||
<?xml version="1.0" ?>
|
||||
<trainer ssi-v="5">
|
||||
<info trained="true" seed="1234"/>
|
||||
<meta backend="nova-server" category="ImageGeneration" description="Generates Image from prompt" is_iterable="False">
|
||||
<io type="input" id="input_prompt" data="prompt" default_value=""/>
|
||||
<io type="input" id="negative_prompt" data="prompt" default_value=""/>
|
||||
<io type="output" id="output_image" data="image" default_value="sd.jpg"/>
|
||||
|
||||
<uri id="juggernaut" url="https://civitai.com/api/download/models/198530" hash='1FE6C7EC54C786040CDABC7B4E89720069D97096922E20D01F13E7764412B47F'/>
|
||||
<uri id="dynavision" url="https://civitai.com/api/download/models/198962" hash='FD9CDC26C3B6D1F30BACBC435E455E925E35622E4873CCFC55FD1C88E980585E'/>
|
||||
<uri id="colossus" url="https://civitai.com/api/download/models/213982" hash='5A7E9DD581B3A9EDF2ED0D9FB2036C389325CD7BA13A754CE19BEEDBB69CEB73'/>
|
||||
<uri id="newreality" url="https://civitai.com/api/download/models/232565" hash='06A85616411135F8CAF161F71CB0948F79E85750E4AF36A885C75485A9B68E2F'/>
|
||||
<uri id="unstable" url="https://civitai.com/api/download/models/209647" hash='05C9E2274A74AE6957B986C92E5699FDFACFFD7EE24CED0D33CB696DE1A6C98B'/>
|
||||
<uri id="fantastic" url="https://civitai.com/api/download/models/143722" hash='B0C590726969EF93BC4136C167D339A277946787223BFAD7B1DC9A68A4F183FC'/>
|
||||
<uri id="mohawk" url="https://civitai.com/api/download/models/207419" hash='0248CA08AA5D5B342355173677C77ADD42E41ECEC3B6B6E52E9C9C471C30C508'/>
|
||||
<uri id="dreamshaper" url="https://civitai.com/api/download/models/126688" hash='0F1B80CFE81B9C3BDE7FDCBF6898897B2811B27BE1DF684583C3D85CBC9B1FA4'/>
|
||||
<uri id="timeless" url="https://civitai.com/api/download/models/198246" hash='A771B2B5E8D2A3C23A3A65F9A51E675F253F101C34BE7DC06FD18D534579D8F8'/>
|
||||
<uri id="crystalclear" url="https://civitai.com/api/download/models/133832" hash='0B76532E03A1BAC388CBF559AF00384ABCBD2B5B3F8834158AE4B1B9146A3843'/>
|
||||
<uri id="chroma" url="https://civitai.com/api/download/models/169740" hash='D2B9E5240C4BC74BB98063CEE16671FDC08D5B7BF197074A0C896E5DBB25BD24'/>
|
||||
<uri id="bluepencil" url="https://civitai.com/api/download/models/212090" hash='C4D7E01814F0EED57A7120629D3017AC018AD7CDECB48F7FBE6B12F9C9C4D6B9'/>
|
||||
|
||||
<uri id="voxel" url="https://civitai.com/api/download/models/128609" hash='7D9A5F11E1B38D97F75D2B84BFB5BB3BF95CD0E5F2500B002D13374EB4F88B5C'/>
|
||||
<uri id="inkpunk" url="https://civitai.com/api/download/models/201552" hash='6BD1A90A93AE288D959B6A90738EB2DB79EC26936F460750D8379C78554A8D53'/>
|
||||
<uri id="3drenderstyle" url="https://civitai.com/api/download/models/218206" hash='C4AD16F1B116F10BBB4070D3ABD0249F799B609DAD8BC8CF92A0AC94A8DE8133'/>
|
||||
<uri id="psychedelicnoir" url="https://civitai.com/api/download/models/140194" hash='896B6B4B6DDC4A28C1CB69359944F04AEBF5954B7A5909FD9629E5549FFC2BDF'/>
|
||||
<uri id="dreamarts" url="https://civitai.com/api/download/models/137124" hash='6A8A5968FB31FB6D83E8E0FE390CF2F3693A35FC4CF247A794B0B261E166B19B'/>
|
||||
<uri id="wojak" url="https://civitai.com/api/download/models/140160" hash='0BD68F0199197CD9D8377A30E9F288479721D1838228A4484272EFF09A479209'/>
|
||||
<uri id="kru3ger" url="https://civitai.com/api/download/models/142129" hash='AE92E349446A74D44ABDB1441AF648B2078E4FBB8F46C7158AD18120553DDC3D'/>
|
||||
<uri id="timburton" url="https://civitai.com/api/download/models/207862" hash='62C229B13622B19928B2D5B9FA5988E612C6DC3060D3AACFE720F43D034D9870'/>
|
||||
<uri id="pixelart" url="https://civitai.com/api/download/models/135931" hash='BBF3D8DEFBFB3FB71331545225C0CF50C74A748D2525F7C19EBB8F74445DE274'/>
|
||||
<uri id="pepe" url="https://civitai.com/api/download/models/181917" hash='CBE1E1C746301801613CB331F2051AD16FF724DDA764A54135AA89D909067B97'/>
|
||||
<uri id="bettertext" url="https://civitai.com/api/download/models/163224" hash='AB1EE501387633DFBFD05970D7BBC0921D23CA804FFC0E717828A8796E8D63CF'/>
|
||||
<uri id="mspaint" url="https://civitai.com/api/download/models/205793" hash='C9503F84E12F2B016FFB8BA689220BA38BBC511573C64AC9BD0ADC853780DA5D'/>
|
||||
<uri id="woodfigure" url="https://civitai.com/api/download/models/207919" hash='9E8D768E0D707867717EBF0CB93EBF65431CC5A105982FA5FFD162D78E20B8C1'/>
|
||||
<uri id="fireelement" url="https://civitai.com/api/download/models/175257" hash='CB04B04F2D90B0A168AFFB26CC7C6F76834FEB8C2F0F30ABE35784084D1FFFBE'/>
|
||||
|
||||
|
||||
|
||||
</meta>
|
||||
<model create="StableDiffusionXL" script="stablediffusionxl.py" optstr="{model:LIST:stabilityai/stable-diffusion-xl-base-1.0,juggernaut,dynavision,colossus,newreality,unstable,fantastic,mohawk,dreamshaper,timeless,crystalclear,chroma,bluepencil};{lora:LIST:None,voxel,inkpunk,3drenderstyle,psychedelicnoir,dreamarts,kru3ger,wojak,timburton,pixelart,pepe,bettertext,mspaint,woodfigure,fireelement};{width:STRING:1024};{height:STRING:1024};{high_noise_frac:STRING:0.8};{n_steps:STRING:35}"/>
|
||||
</trainer>
|
||||
12
backends/nova-server/modules/stablediffusionxl/version.py
Normal file
12
backends/nova-server/modules/stablediffusionxl/version.py
Normal file
@ -0,0 +1,12 @@
|
||||
""" Stable Diffusion XL
|
||||
"""
|
||||
# We follow Semantic Versioning (https://semver.org/)
|
||||
_MAJOR_VERSION = '1'
|
||||
_MINOR_VERSION = '0'
|
||||
_PATCH_VERSION = '0'
|
||||
|
||||
__version__ = '.'.join([
|
||||
_MAJOR_VERSION,
|
||||
_MINOR_VERSION,
|
||||
_PATCH_VERSION,
|
||||
])
|
||||
52
backends/nova-server/modules/whisperx/readme.md
Normal file
52
backends/nova-server/modules/whisperx/readme.md
Normal file
@ -0,0 +1,52 @@
|
||||
# WhisperX
|
||||
|
||||
This modules provides fast automatic speech recognition (70x realtime with large-v2) with word-level timestamps and
|
||||
speaker diarization.
|
||||
|
||||
* https://github.com/m-bain/whisperX
|
||||
|
||||
## Options
|
||||
|
||||
- `model`: string, identifier of the model to choose, sorted ascending in required (V)RAM:
|
||||
- `tiny`, `tiny.en`
|
||||
- `base`, `base.en`
|
||||
- `small`, `small.en`
|
||||
- `medium`, `medium.en`
|
||||
- `large-v1`
|
||||
- `large-v2`
|
||||
|
||||
- `alignment_mode`: string, alignment method to use
|
||||
- `raw` Segments as identified by Whisper
|
||||
- `segment` Improved segmentation using separate alignment model. Roughly equivalent to sentence alignment.
|
||||
- `word` Improved segmentation using separate alignment model. Equivalent to word alignment.
|
||||
|
||||
- `language`: language code for transcription and alignment models. Supported languages:
|
||||
- `ar`, `cs`, `da`, `de`, `el`, `en`, `es`, `fa`, `fi`, `fr`, `he`, `hu`, `it`, `ja`, `ko`, `nl`, `pl`, `pt`, `ru`, `te`, `tr`, `uk`, `ur`, `vi`, `zh`
|
||||
- `None`: auto-detect language from first 30 seconds of audio
|
||||
|
||||
- `batch_size`: how many samples to process at once, increases speed but also (V)RAM consumption
|
||||
|
||||
## Examples
|
||||
|
||||
### Request
|
||||
|
||||
```python
|
||||
import requests
|
||||
import json
|
||||
|
||||
payload = {
|
||||
"jobID" : "whisper_transcript",
|
||||
"data": json.dumps([
|
||||
{"src":"file:stream:audio", "type":"input", "id":"audio", "uri":"path/to/my/file.wav"},
|
||||
{"src":"file:annotation:free", "type":"output", "id":"transcript", "uri":"path/to/my/transcript.annotation"}
|
||||
]),
|
||||
"trainerFilePath": "modules\\whisperx\\whisperx_transcript.trainer",
|
||||
}
|
||||
|
||||
|
||||
url = 'http://127.0.0.1:8080/process'
|
||||
headers = {'Content-type': 'application/x-www-form-urlencoded'}
|
||||
x = requests.post(url, headers=headers, data=payload)
|
||||
print(x.text)
|
||||
|
||||
```
|
||||
7
backends/nova-server/modules/whisperx/requirements.txt
Normal file
7
backends/nova-server/modules/whisperx/requirements.txt
Normal file
@ -0,0 +1,7 @@
|
||||
hcai-nova-utils>=1.5.5
|
||||
--extra-index-url https://download.pytorch.org/whl/cu118
|
||||
torch==2.1.0+cu118
|
||||
torchvision>= 0.15.1+cu118
|
||||
torchaudio >= 2.0.0+cu118
|
||||
pyannote-audio @ git+https://github.com/shelm/pyannote-audio.git@d7b4de3
|
||||
whisperx @ git+https://github.com/m-bain/whisperx.git@49e0130
|
||||
12
backends/nova-server/modules/whisperx/version.py
Normal file
12
backends/nova-server/modules/whisperx/version.py
Normal file
@ -0,0 +1,12 @@
|
||||
""" WhisperX
|
||||
"""
|
||||
# We follow Semantic Versioning (https://semver.org/)
|
||||
_MAJOR_VERSION = '1'
|
||||
_MINOR_VERSION = '0'
|
||||
_PATCH_VERSION = '1'
|
||||
|
||||
__version__ = '.'.join([
|
||||
_MAJOR_VERSION,
|
||||
_MINOR_VERSION,
|
||||
_PATCH_VERSION,
|
||||
])
|
||||
124
backends/nova-server/modules/whisperx/whisperx_transcript.py
Normal file
124
backends/nova-server/modules/whisperx/whisperx_transcript.py
Normal file
@ -0,0 +1,124 @@
|
||||
"""WhisperX Module
|
||||
"""
|
||||
from nova_utils.interfaces.server_module import Processor
|
||||
import sys
|
||||
|
||||
# Setting defaults
|
||||
_default_options = {"model": "tiny", "alignment_mode": "segment", "batch_size": "16", 'language': None, 'compute_type': 'float16'}
|
||||
|
||||
# supported language codes, cf. whisperx/alignment.py
|
||||
# DEFAULT_ALIGN_MODELS_TORCH.keys() | DEFAULT_ALIGN_MODELS_HF.keys() | {None}
|
||||
# {'vi', 'uk', 'pl', 'ur', 'ru', 'ko', 'en', 'zh', 'es', 'it', 'el', 'te', 'da', 'he', 'fa', 'pt', 'de',
|
||||
# 'fr', 'tr', 'nl', 'cs', 'hu', 'fi', 'ar', 'ja', None}
|
||||
|
||||
class WhisperX(Processor):
|
||||
def __init__(self, *args, **kwargs):
|
||||
super().__init__(*args, **kwargs)
|
||||
self.options = _default_options | self.options
|
||||
self.device = None
|
||||
self.ds_iter = None
|
||||
self.session_manager = None
|
||||
|
||||
# IO shortcuts
|
||||
self.input = [x for x in self.model_io if x.io_type == "input"]
|
||||
self.output = [x for x in self.model_io if x.io_type == "output"]
|
||||
assert len(self.input) == 1 and len(self.output) == 1
|
||||
self.input = self.input[0]
|
||||
self.output = self.output[0]
|
||||
|
||||
def process_data(self, ds_manager) -> dict:
|
||||
import whisperx
|
||||
import torch
|
||||
self.device = "cuda" if torch.cuda.is_available() else "cpu"
|
||||
self.session_manager = self.get_session_manager(ds_manager)
|
||||
input_audio = self.session_manager.input_data['audio']
|
||||
|
||||
# sliding window will be applied by WhisperX
|
||||
audio = whisperx.load_audio(input_audio.meta_data.file_path)
|
||||
|
||||
# transcribe with original whisper
|
||||
try:
|
||||
model = whisperx.load_model(self.options["model"], self.device, compute_type=self.options['compute_type'],
|
||||
language=self.options['language'])
|
||||
except ValueError:
|
||||
print(f'Your hardware does not support {self.options["compute_type"]} - fallback to float32')
|
||||
sys.stdout.flush()
|
||||
model = whisperx.load_model(self.options["model"], self.device, compute_type='float32',
|
||||
language=self.options['language'])
|
||||
|
||||
result = model.transcribe(audio, batch_size=int(self.options["batch_size"]))
|
||||
|
||||
# delete model if low on GPU resources
|
||||
import gc; gc.collect(); torch.cuda.empty_cache(); del model
|
||||
|
||||
if not self.options["alignment_mode"] == "raw":
|
||||
# load alignment model and metadata
|
||||
model_a, metadata = whisperx.load_align_model(
|
||||
language_code=result["language"], device=self.device
|
||||
)
|
||||
|
||||
# align whisper output
|
||||
result_aligned = whisperx.align(
|
||||
result["segments"], model_a, metadata, audio, self.device
|
||||
)
|
||||
result = result_aligned
|
||||
|
||||
# delete model if low on GPU resources
|
||||
import gc; gc.collect(); torch.cuda.empty_cache(); del model_a
|
||||
|
||||
return result
|
||||
|
||||
def to_output(self, data: dict):
|
||||
def _fix_missing_timestamps(data):
|
||||
"""
|
||||
https://github.com/m-bain/whisperX/issues/253
|
||||
Some characters might miss timestamps and recognition scores. This function adds estimated time stamps assuming a fixed time per character of 65ms.
|
||||
Confidence for each added timestamp will be 0.
|
||||
Args:
|
||||
data (dictionary): output dictionary as returned by process_data
|
||||
"""
|
||||
last_end = 0
|
||||
for s in data["segments"]:
|
||||
for w in s["words"]:
|
||||
if "end" in w.keys():
|
||||
last_end = w["end"]
|
||||
else:
|
||||
#TODO: rethink lower bound for confidence; place word centred instead of left aligned
|
||||
w["start"] = last_end
|
||||
last_end += 0.065
|
||||
w["end"] = last_end
|
||||
#w["score"] = 0.000
|
||||
w['score'] = _hmean([x['score'] for x in s['words'] if len(x) == 4])
|
||||
|
||||
def _hmean(scores):
|
||||
if len(scores) > 0:
|
||||
prod = scores[0]
|
||||
for s in scores[1:]:
|
||||
prod *= s
|
||||
prod = prod**(1/len(scores))
|
||||
else:
|
||||
prod = 0
|
||||
return prod
|
||||
|
||||
if (
|
||||
self.options["alignment_mode"] == "word"
|
||||
or self.options["alignment_mode"] == "segment"
|
||||
):
|
||||
_fix_missing_timestamps(data)
|
||||
|
||||
if self.options["alignment_mode"] == "word":
|
||||
anno_data = [
|
||||
(w["start"], w["end"], w["word"], w["score"])
|
||||
for w in data["word_segments"]
|
||||
]
|
||||
else:
|
||||
anno_data = [
|
||||
#(w["start"], w["end"], w["text"], _hmean([x['score'] for x in w['words']])) for w in data["segments"]
|
||||
(w["start"], w["end"], w["text"], 1) for w in data["segments"] # alignment 'raw' no longer contains a score(?)
|
||||
]
|
||||
|
||||
# convert to milliseconds
|
||||
anno_data = [(x[0]*1000, x[1]*1000, x[2], x[3]) for x in anno_data]
|
||||
out = self.session_manager.output_data_templates[self.output.io_id]
|
||||
out.data = anno_data
|
||||
return self.session_manager.output_data_templates
|
||||
@ -0,0 +1,9 @@
|
||||
<?xml version="1.0" ?>
|
||||
<trainer ssi-v="5">
|
||||
<info trained="true" seed="1234"/>
|
||||
<meta backend="nova-server" category="Transcript" description="Transcribes audio" is_iterable="False">
|
||||
<io type="input" id="audio" data="stream:Audio" default_value="audio"/>
|
||||
<io type="output" id="transcript" data="annotation:Free" default_value="transcript"/>
|
||||
</meta>
|
||||
<model create="WhisperX" script="whisperx_transcript.py" optstr="{model:LIST:base,tiny,small,medium,large-v1,large-v2,tiny.en,base.en,small.en,medium.en};{alignment_mode:LIST:segment,word,raw};{language:LIST:None,en,de,ar,cs,da,el,es,fa,fi,fr,he,hu,it,ja,ko,nl,pl,pt,ru,te,tr,uk,ur,vi,zh};{batch_size:STRING:16};{compute_type:LIST:float16,float32,int8}"/>
|
||||
</trainer>
|
||||
2
backends/nova-server/run_windows.cmd
Normal file
2
backends/nova-server/run_windows.cmd
Normal file
@ -0,0 +1,2 @@
|
||||
call venv/Scripts/activate
|
||||
nova-server
|
||||
3
backends/nova-server/setup_windows.cmd
Normal file
3
backends/nova-server/setup_windows.cmd
Normal file
@ -0,0 +1,3 @@
|
||||
python -m venv venv
|
||||
call venv/Scripts/activate
|
||||
pip install hcai-nova-server
|
||||
226
nostr_dvm/tasks/imagegeneration_sdxl.py
Normal file
226
nostr_dvm/tasks/imagegeneration_sdxl.py
Normal file
@ -0,0 +1,226 @@
|
||||
import json
|
||||
import os
|
||||
from multiprocessing.pool import ThreadPool
|
||||
from pathlib import Path
|
||||
|
||||
import dotenv
|
||||
|
||||
from nostr_dvm.backends.nova_server.utils import check_server_status, send_request_to_server
|
||||
from nostr_dvm.interfaces.dvmtaskinterface import DVMTaskInterface
|
||||
from nostr_dvm.utils.admin_utils import AdminConfig
|
||||
from nostr_dvm.utils.backend_utils import keep_alive
|
||||
from nostr_dvm.utils.dvmconfig import DVMConfig
|
||||
from nostr_dvm.utils.nip89_utils import NIP89Config, check_and_set_d_tag
|
||||
from nostr_dvm.utils.definitions import EventDefinitions
|
||||
from nostr_dvm.utils.nostr_utils import check_and_set_private_key
|
||||
from nostr_sdk import Keys
|
||||
|
||||
from nostr_dvm.utils.zap_utils import check_and_set_ln_bits_keys
|
||||
|
||||
"""
|
||||
This File contains a module to transform Text input on n-server and receive results back.
|
||||
|
||||
Accepted Inputs: Prompt (text)
|
||||
Outputs: An url to an Image
|
||||
Params: -model # models: juggernaut, dynavision, colossusProject, newreality, unstable
|
||||
-lora # loras (weights on top of models) voxel,
|
||||
"""
|
||||
|
||||
|
||||
class ImageGenerationSDXL(DVMTaskInterface):
|
||||
KIND: int = EventDefinitions.KIND_NIP90_GENERATE_IMAGE
|
||||
TASK: str = "text-to-image"
|
||||
FIX_COST: float = 50
|
||||
|
||||
def __init__(self, name, dvm_config: DVMConfig, nip89config: NIP89Config,
|
||||
admin_config: AdminConfig = None, options=None):
|
||||
super().__init__(name, dvm_config, nip89config, admin_config, options)
|
||||
|
||||
def is_input_supported(self, tags):
|
||||
for tag in tags:
|
||||
if tag.as_vec()[0] == 'i':
|
||||
input_value = tag.as_vec()[1]
|
||||
input_type = tag.as_vec()[2]
|
||||
if input_type != "text":
|
||||
return False
|
||||
|
||||
elif tag.as_vec()[0] == 'output':
|
||||
output = tag.as_vec()[1]
|
||||
if (output == "" or
|
||||
not (output == "image/png" or "image/jpg"
|
||||
or output == "image/png;format=url" or output == "image/jpg;format=url")):
|
||||
print("Output format not supported, skipping..")
|
||||
return False
|
||||
|
||||
return True
|
||||
|
||||
def create_request_from_nostr_event(self, event, client=None, dvm_config=None):
|
||||
request_form = {"jobID": event.id().to_hex() + "_" + self.NAME.replace(" ", "")}
|
||||
request_form["trainerFilePath"] = r'modules\stablediffusionxl\stablediffusionxl.trainer'
|
||||
|
||||
prompt = ""
|
||||
negative_prompt = ""
|
||||
if self.options.get("default_model") and self.options.get("default_model") != "":
|
||||
model = self.options['default_model']
|
||||
else:
|
||||
model = "stabilityai/stable-diffusion-xl-base-1.0"
|
||||
|
||||
ratio_width = "1"
|
||||
ratio_height = "1"
|
||||
width = ""
|
||||
height = ""
|
||||
if self.options.get("default_lora") and self.options.get("default_lora") != "":
|
||||
lora = self.options['default_lora']
|
||||
else:
|
||||
lora = ""
|
||||
lora_weight = ""
|
||||
strength = ""
|
||||
guidance_scale = ""
|
||||
for tag in event.tags():
|
||||
if tag.as_vec()[0] == 'i':
|
||||
input_type = tag.as_vec()[2]
|
||||
if input_type == "text":
|
||||
prompt = tag.as_vec()[1]
|
||||
|
||||
elif tag.as_vec()[0] == 'param':
|
||||
print("Param: " + tag.as_vec()[1] + ": " + tag.as_vec()[2])
|
||||
if tag.as_vec()[1] == "negative_prompt":
|
||||
negative_prompt = tag.as_vec()[2]
|
||||
elif tag.as_vec()[1] == "lora":
|
||||
lora = tag.as_vec()[2]
|
||||
elif tag.as_vec()[1] == "lora_weight":
|
||||
lora_weight = tag.as_vec()[2]
|
||||
elif tag.as_vec()[1] == "strength":
|
||||
strength = float(tag.as_vec()[2])
|
||||
elif tag.as_vec()[1] == "guidance_scale":
|
||||
guidance_scale = float(tag.as_vec()[2])
|
||||
elif tag.as_vec()[1] == "ratio":
|
||||
if len(tag.as_vec()) > 3:
|
||||
ratio_width = (tag.as_vec()[2])
|
||||
ratio_height = (tag.as_vec()[3])
|
||||
elif len(tag.as_vec()) == 3:
|
||||
split = tag.as_vec()[2].split(":")
|
||||
ratio_width = split[0]
|
||||
ratio_height = split[1]
|
||||
# if size is set it will overwrite ratio.
|
||||
elif tag.as_vec()[1] == "size":
|
||||
if len(tag.as_vec()) > 3:
|
||||
width = (tag.as_vec()[2])
|
||||
height = (tag.as_vec()[3])
|
||||
elif len(tag.as_vec()) == 3:
|
||||
split = tag.as_vec()[2].split("x")
|
||||
if len(split) > 1:
|
||||
width = split[0]
|
||||
height = split[1]
|
||||
elif tag.as_vec()[1] == "model":
|
||||
model = tag.as_vec()[2]
|
||||
|
||||
io_input = {
|
||||
"id": "input_prompt",
|
||||
"type": "input",
|
||||
"src": "request:text",
|
||||
"data": prompt
|
||||
}
|
||||
io_negative = {
|
||||
"id": "negative_prompt",
|
||||
"type": "input",
|
||||
"src": "request:text",
|
||||
"data": negative_prompt
|
||||
}
|
||||
io_output = {
|
||||
"id": "output_image",
|
||||
"type": "output",
|
||||
"src": "request:image"
|
||||
}
|
||||
|
||||
request_form['data'] = json.dumps([io_input, io_negative, io_output])
|
||||
|
||||
options = {
|
||||
"model": model,
|
||||
"ratio": ratio_width + '-' + ratio_height,
|
||||
"width": width,
|
||||
"height": height,
|
||||
"strength": strength,
|
||||
"guidance_scale": guidance_scale,
|
||||
"lora": lora,
|
||||
"lora_weight": lora_weight
|
||||
}
|
||||
request_form['options'] = json.dumps(options)
|
||||
|
||||
return request_form
|
||||
|
||||
def process(self, request_form):
|
||||
try:
|
||||
# Call the process route of n-server with our request form.
|
||||
response = send_request_to_server(request_form, self.options['server'])
|
||||
if bool(json.loads(response)['success']):
|
||||
print("Job " + request_form['jobID'] + " sent to server")
|
||||
|
||||
pool = ThreadPool(processes=1)
|
||||
thread = pool.apply_async(check_server_status, (request_form['jobID'], self.options['server']))
|
||||
print("Wait for results of server...")
|
||||
result = thread.get()
|
||||
return result
|
||||
|
||||
except Exception as e:
|
||||
raise Exception(e)
|
||||
|
||||
# We build an example here that we can call by either calling this file directly from the main directory,
|
||||
# or by adding it to our playground. You can call the example and adjust it to your needs or redefine it in the
|
||||
# playground or elsewhere
|
||||
def build_example(name, identifier, admin_config, server_address, default_model="stabilityai/stable-diffusion-xl"
|
||||
"-base-1.0", default_lora=""):
|
||||
dvm_config = DVMConfig()
|
||||
dvm_config.PRIVATE_KEY = check_and_set_private_key(identifier)
|
||||
npub = Keys.from_sk_str(dvm_config.PRIVATE_KEY).public_key().to_bech32()
|
||||
invoice_key, admin_key, wallet_id, user_id, lnaddress = check_and_set_ln_bits_keys(identifier, npub)
|
||||
dvm_config.LNBITS_INVOICE_KEY = invoice_key
|
||||
dvm_config.LNBITS_ADMIN_KEY = admin_key # The dvm might pay failed jobs back
|
||||
dvm_config.LNBITS_URL = os.getenv("LNBITS_HOST")
|
||||
admin_config.LUD16 = lnaddress
|
||||
|
||||
# A module might have options it can be initialized with, here we set a default model, and the server
|
||||
# address it should use. These parameters can be freely defined in the task component
|
||||
options = {'default_model': default_model, 'default_lora': default_lora, 'server': server_address}
|
||||
|
||||
nip90params = {
|
||||
"negative_prompt": {
|
||||
"required": False,
|
||||
"values": []
|
||||
},
|
||||
"ratio": {
|
||||
"required": False,
|
||||
"values": ["1:1", "4:3", "16:9", "3:4", "9:16", "10:16"]
|
||||
}
|
||||
}
|
||||
nip89info = {
|
||||
"name": name,
|
||||
"image": "https://image.nostr.build/c33ca6fc4cc038ca4adb46fdfdfda34951656f87ee364ef59095bae1495ce669.jpg",
|
||||
"about": "I draw images based on a prompt with a Model called unstable diffusion",
|
||||
"encryptionSupported": True,
|
||||
"cashuAccepted": True,
|
||||
"nip90Params": nip90params
|
||||
}
|
||||
nip89config = NIP89Config()
|
||||
nip89config.DTAG = check_and_set_d_tag(identifier, name, dvm_config.PRIVATE_KEY,
|
||||
nip89info["image"])
|
||||
nip89config.CONTENT = json.dumps(nip89info)
|
||||
return ImageGenerationSDXL(name=name, dvm_config=dvm_config, nip89config=nip89config,
|
||||
admin_config=admin_config, options=options)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
env_path = Path('.env')
|
||||
if env_path.is_file():
|
||||
print(f'loading environment from {env_path.resolve()}')
|
||||
dotenv.load_dotenv(env_path, verbose=True, override=True)
|
||||
else:
|
||||
raise FileNotFoundError(f'.env file not found at {env_path} ')
|
||||
|
||||
admin_config = AdminConfig()
|
||||
admin_config.REBROADCAST_NIP89 = False
|
||||
admin_config.UPDATE_PROFILE = False
|
||||
dvm = build_example("Unstable Diffusion", "unstable_diffusion", admin_config, os.getenv("N_SERVER"), "stabilityai/stable-diffusion-xl", "")
|
||||
dvm.run()
|
||||
|
||||
keep_alive()
|
||||
260
nostr_dvm/tasks/imagegeneration_sdxlimg2img.py
Normal file
260
nostr_dvm/tasks/imagegeneration_sdxlimg2img.py
Normal file
@ -0,0 +1,260 @@
|
||||
import json
|
||||
import os
|
||||
from multiprocessing.pool import ThreadPool
|
||||
from pathlib import Path
|
||||
|
||||
import dotenv
|
||||
|
||||
from nostr_dvm.backends.nova_server.utils import check_server_status, send_request_to_server
|
||||
from nostr_dvm.interfaces.dvmtaskinterface import DVMTaskInterface
|
||||
from nostr_dvm.utils.admin_utils import AdminConfig
|
||||
from nostr_dvm.utils.backend_utils import keep_alive
|
||||
from nostr_dvm.utils.dvmconfig import DVMConfig
|
||||
from nostr_dvm.utils.nip89_utils import NIP89Config, check_and_set_d_tag
|
||||
from nostr_dvm.utils.definitions import EventDefinitions
|
||||
from nostr_dvm.utils.nostr_utils import check_and_set_private_key
|
||||
from nostr_dvm.utils.zap_utils import check_and_set_ln_bits_keys
|
||||
from nostr_sdk import Keys
|
||||
|
||||
"""
|
||||
This File contains a Module to transform Text input on N-server and receive results back.
|
||||
|
||||
Accepted Inputs: Prompt (text)
|
||||
Outputs: An url to an Image
|
||||
Params: -model # models: juggernaut, dynavision, colossusProject, newreality, unstable
|
||||
-lora # loras (weights on top of models) voxel,
|
||||
"""
|
||||
|
||||
|
||||
class ImageGenerationSDXLIMG2IMG(DVMTaskInterface):
|
||||
KIND: int = EventDefinitions.KIND_NIP90_GENERATE_IMAGE
|
||||
TASK: str = "image-to-image"
|
||||
FIX_COST: float = 50
|
||||
|
||||
def __init__(self, name, dvm_config: DVMConfig, nip89config: NIP89Config,
|
||||
admin_config: AdminConfig = None, options=None):
|
||||
super().__init__(name, dvm_config, nip89config, admin_config, options)
|
||||
|
||||
def is_input_supported(self, tags):
|
||||
hasurl = False
|
||||
hasprompt = False
|
||||
for tag in tags:
|
||||
if tag.as_vec()[0] == 'i':
|
||||
input_value = tag.as_vec()[1]
|
||||
input_type = tag.as_vec()[2]
|
||||
if input_type == "url":
|
||||
hasurl = True
|
||||
elif input_type == "text":
|
||||
hasprompt = True #Little optional when lora is set
|
||||
|
||||
elif tag.as_vec()[0] == 'output':
|
||||
output = tag.as_vec()[1]
|
||||
if (output == "" or
|
||||
not (output == "image/png" or "image/jpg"
|
||||
or output == "image/png;format=url" or output == "image/jpg;format=url")):
|
||||
print("Output format not supported, skipping..")
|
||||
return False
|
||||
|
||||
if not hasurl:
|
||||
return False
|
||||
|
||||
return True
|
||||
|
||||
def create_request_from_nostr_event(self, event, client=None, dvm_config=None):
|
||||
request_form = {"jobID": event.id().to_hex() + "_" + self.NAME.replace(" ", "")}
|
||||
request_form["trainerFilePath"] = r'modules\stablediffusionxl\stablediffusionxl-img2img.trainer'
|
||||
|
||||
prompt = ""
|
||||
negative_prompt = ""
|
||||
url = ""
|
||||
if self.options.get("default_model"):
|
||||
model = self.options['default_model']
|
||||
else:
|
||||
model = "stabilityai/stable-diffusion-xl-refiner-1.0"
|
||||
|
||||
ratio_width = "1"
|
||||
ratio_height = "1"
|
||||
width = ""
|
||||
height = ""
|
||||
|
||||
if self.options.get("default_lora") and self.options.get("default_lora") != "":
|
||||
lora = self.options['default_lora']
|
||||
else:
|
||||
lora = ""
|
||||
|
||||
lora_weight = ""
|
||||
if self.options.get("strength"):
|
||||
strength = float(self.options['strength'])
|
||||
else:
|
||||
strength = 0.8
|
||||
if self.options.get("guidance_scale"):
|
||||
guidance_scale = float(self.options['guidance_scale'])
|
||||
else:
|
||||
guidance_scale = 11.0
|
||||
for tag in event.tags():
|
||||
if tag.as_vec()[0] == 'i':
|
||||
input_type = tag.as_vec()[2]
|
||||
if input_type == "text":
|
||||
prompt = tag.as_vec()[1]
|
||||
elif input_type == "url":
|
||||
url = tag.as_vec()[1]
|
||||
|
||||
elif tag.as_vec()[0] == 'param':
|
||||
print("Param: " + tag.as_vec()[1] + ": " + tag.as_vec()[2])
|
||||
if tag.as_vec()[1] == "negative_prompt":
|
||||
negative_prompt = tag.as_vec()[2]
|
||||
elif tag.as_vec()[1] == "lora":
|
||||
lora = tag.as_vec()[2]
|
||||
elif tag.as_vec()[1] == "lora_weight":
|
||||
lora_weight = tag.as_vec()[2]
|
||||
elif tag.as_vec()[1] == "strength":
|
||||
strength = float(tag.as_vec()[2])
|
||||
elif tag.as_vec()[1] == "guidance_scale":
|
||||
guidance_scale = float(tag.as_vec()[2])
|
||||
elif tag.as_vec()[1] == "ratio":
|
||||
if len(tag.as_vec()) > 3:
|
||||
ratio_width = (tag.as_vec()[2])
|
||||
ratio_height = (tag.as_vec()[3])
|
||||
elif len(tag.as_vec()) == 3:
|
||||
split = tag.as_vec()[2].split(":")
|
||||
ratio_width = split[0]
|
||||
ratio_height = split[1]
|
||||
# if size is set it will overwrite ratio.
|
||||
elif tag.as_vec()[1] == "size":
|
||||
if len(tag.as_vec()) > 3:
|
||||
width = (tag.as_vec()[2])
|
||||
height = (tag.as_vec()[3])
|
||||
elif len(tag.as_vec()) == 3:
|
||||
split = tag.as_vec()[2].split("x")
|
||||
if len(split) > 1:
|
||||
width = split[0]
|
||||
height = split[1]
|
||||
elif tag.as_vec()[1] == "model":
|
||||
model = tag.as_vec()[2]
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
io_input_image = {
|
||||
"id": "input_image",
|
||||
"type": "input",
|
||||
"src": "url:Image",
|
||||
"uri": url
|
||||
}
|
||||
io_input = {
|
||||
"id": "input_prompt",
|
||||
"type": "input",
|
||||
"src": "request:text",
|
||||
"data": prompt
|
||||
}
|
||||
io_negative = {
|
||||
"id": "negative_prompt",
|
||||
"type": "input",
|
||||
"src": "request:text",
|
||||
"data": negative_prompt
|
||||
}
|
||||
io_output = {
|
||||
"id": "output_image",
|
||||
"type": "output",
|
||||
"src": "request:image"
|
||||
}
|
||||
|
||||
request_form['data'] = json.dumps([io_input_image, io_input, io_negative, io_output])
|
||||
|
||||
options = {
|
||||
"model": model,
|
||||
"ratio": ratio_width + '-' + ratio_height,
|
||||
"width": width,
|
||||
"height": height,
|
||||
"strength": strength,
|
||||
"guidance_scale": guidance_scale,
|
||||
"lora": lora,
|
||||
"lora_weight": lora_weight,
|
||||
"n_steps": 30
|
||||
}
|
||||
request_form['options'] = json.dumps(options)
|
||||
|
||||
return request_form
|
||||
|
||||
def process(self, request_form):
|
||||
try:
|
||||
# Call the process route of NOVA-Server with our request form.
|
||||
response = send_request_to_server(request_form, self.options['server'])
|
||||
if bool(json.loads(response)['success']):
|
||||
print("Job " + request_form['jobID'] + " sent to server")
|
||||
|
||||
pool = ThreadPool(processes=1)
|
||||
thread = pool.apply_async(check_server_status, (request_form['jobID'], self.options['server']))
|
||||
print("Wait for results of server...")
|
||||
result = thread.get()
|
||||
return result
|
||||
|
||||
except Exception as e:
|
||||
raise Exception(e)
|
||||
|
||||
# We build an example here that we can call by either calling this file directly from the main directory,
|
||||
# or by adding it to our playground. You can call the example and adjust it to your needs or redefine it in the
|
||||
# playground or elsewhere
|
||||
def build_example(name, identifier, admin_config, server_address, default_lora="", strength=0.6):
|
||||
dvm_config = DVMConfig()
|
||||
dvm_config.PRIVATE_KEY = check_and_set_private_key(identifier)
|
||||
npub = Keys.from_sk_str(dvm_config.PRIVATE_KEY).public_key().to_bech32()
|
||||
invoice_key, admin_key, wallet_id, user_id, lnaddress = check_and_set_ln_bits_keys(identifier, npub)
|
||||
dvm_config.LNBITS_INVOICE_KEY = invoice_key
|
||||
dvm_config.LNBITS_ADMIN_KEY = admin_key # The dvm might pay failed jobs back
|
||||
dvm_config.LNBITS_URL = os.getenv("LNBITS_HOST")
|
||||
admin_config.LUD16 = lnaddress
|
||||
|
||||
nip90params = {
|
||||
"negative_prompt": {
|
||||
"required": False,
|
||||
"values": []
|
||||
},
|
||||
"lora": {
|
||||
"required": False,
|
||||
"values": ["inkpunk", "timburton", "voxel"]
|
||||
},
|
||||
|
||||
"strength": {
|
||||
"required": False,
|
||||
"values": []
|
||||
}
|
||||
}
|
||||
nip89info = {
|
||||
"name": name,
|
||||
"image": "https://image.nostr.build/229c14e440895da30de77b3ca145d66d4b04efb4027ba3c44ca147eecde891f1.jpg",
|
||||
"about": "I convert an image to another image, kinda random for now. ",
|
||||
"encryptionSupported": True,
|
||||
"cashuAccepted": True,
|
||||
"nip90Params": nip90params
|
||||
}
|
||||
|
||||
# A module might have options it can be initialized with, here we set a default model, lora and the server
|
||||
options = {'default_lora': default_lora, 'strength': strength, 'server': server_address}
|
||||
|
||||
nip89config = NIP89Config()
|
||||
|
||||
nip89config.DTAG = check_and_set_d_tag(identifier, name, dvm_config.PRIVATE_KEY,
|
||||
nip89info["image"])
|
||||
nip89config.CONTENT = json.dumps(nip89info)
|
||||
# We add an optional AdminConfig for this one, and tell the dvm to rebroadcast its NIP89
|
||||
return ImageGenerationSDXLIMG2IMG(name=name, dvm_config=dvm_config, nip89config=nip89config,
|
||||
admin_config=admin_config, options=options)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
env_path = Path('.env')
|
||||
if env_path.is_file():
|
||||
print(f'loading environment from {env_path.resolve()}')
|
||||
dotenv.load_dotenv(env_path, verbose=True, override=True)
|
||||
else:
|
||||
raise FileNotFoundError(f'.env file not found at {env_path} ')
|
||||
|
||||
admin_config = AdminConfig()
|
||||
admin_config.REBROADCAST_NIP89 = False
|
||||
admin_config.UPDATE_PROFILE = False
|
||||
dvm = build_example("Image Converter Inkpunk", "image2image", admin_config, os.getenv("N_SERVER"), "", 0.6)
|
||||
dvm.run()
|
||||
|
||||
keep_alive()
|
||||
169
nostr_dvm/tasks/imageinterrogator.py
Normal file
169
nostr_dvm/tasks/imageinterrogator.py
Normal file
@ -0,0 +1,169 @@
|
||||
import json
|
||||
import os
|
||||
from multiprocessing.pool import ThreadPool
|
||||
from pathlib import Path
|
||||
|
||||
import dotenv
|
||||
|
||||
from nostr_dvm.backends.nova_server.utils import check_server_status, send_request_to_server
|
||||
from nostr_dvm.interfaces.dvmtaskinterface import DVMTaskInterface
|
||||
from nostr_dvm.utils.admin_utils import AdminConfig
|
||||
from nostr_dvm.utils.backend_utils import keep_alive
|
||||
from nostr_dvm.utils.dvmconfig import DVMConfig
|
||||
from nostr_dvm.utils.nip89_utils import NIP89Config, check_and_set_d_tag
|
||||
from nostr_dvm.utils.definitions import EventDefinitions
|
||||
from nostr_dvm.utils.nostr_utils import check_and_set_private_key
|
||||
from nostr_dvm.utils.zap_utils import check_and_set_ln_bits_keys
|
||||
from nostr_sdk import Keys
|
||||
|
||||
"""
|
||||
This File contains a Module to extract a prompt from an image from an url.
|
||||
|
||||
Accepted Inputs: link to image (url)
|
||||
Outputs: An textual description of the image
|
||||
|
||||
"""
|
||||
|
||||
|
||||
class ImageInterrogator(DVMTaskInterface):
|
||||
KIND: int = EventDefinitions.KIND_NIP90_EXTRACT_TEXT
|
||||
TASK: str = "image-to-text"
|
||||
FIX_COST: float = 80
|
||||
|
||||
def __init__(self, name, dvm_config: DVMConfig, nip89config: NIP89Config,
|
||||
admin_config: AdminConfig = None, options=None):
|
||||
super().__init__(name, dvm_config, nip89config, admin_config, options)
|
||||
|
||||
def is_input_supported(self, tags):
|
||||
hasurl = False
|
||||
for tag in tags:
|
||||
if tag.as_vec()[0] == 'i':
|
||||
input_value = tag.as_vec()[1]
|
||||
input_type = tag.as_vec()[2]
|
||||
if input_type == "url":
|
||||
hasurl = True
|
||||
|
||||
if not hasurl:
|
||||
return False
|
||||
|
||||
return True
|
||||
|
||||
def create_request_from_nostr_event(self, event, client=None, dvm_config=None):
|
||||
request_form = {"jobID": event.id().to_hex() + "_" + self.NAME.replace(" ", "")}
|
||||
request_form["trainerFilePath"] = r'modules\image_interrogator\image_interrogator.trainer'
|
||||
url = ""
|
||||
method = "prompt"
|
||||
mode = "best"
|
||||
|
||||
|
||||
for tag in event.tags():
|
||||
if tag.as_vec()[0] == 'i':
|
||||
input_type = tag.as_vec()[2]
|
||||
if input_type == "url":
|
||||
url = tag.as_vec()[1]
|
||||
elif tag.as_vec()[0] == 'param':
|
||||
print("Param: " + tag.as_vec()[1] + ": " + tag.as_vec()[2])
|
||||
if tag.as_vec()[1] == "method":
|
||||
method = tag.as_vec()[2]
|
||||
elif tag.as_vec()[1] == "mode":
|
||||
mode = tag.as_vec()[2]
|
||||
|
||||
io_input_image = {
|
||||
"id": "input_image",
|
||||
"type": "input",
|
||||
"src": "url:Image",
|
||||
"uri": url
|
||||
}
|
||||
|
||||
io_output = {
|
||||
"id": "output",
|
||||
"type": "output",
|
||||
"src": "request:text"
|
||||
}
|
||||
|
||||
request_form['data'] = json.dumps([io_input_image, io_output])
|
||||
|
||||
options = {
|
||||
"kind": method,
|
||||
"mode": mode
|
||||
|
||||
}
|
||||
request_form['options'] = json.dumps(options)
|
||||
|
||||
return request_form
|
||||
|
||||
def process(self, request_form):
|
||||
try:
|
||||
# Call the process route of NOVA-Server with our request form.
|
||||
response = send_request_to_server(request_form, self.options['server'])
|
||||
if bool(json.loads(response)['success']):
|
||||
print("Job " + request_form['jobID'] + " sent to server")
|
||||
|
||||
pool = ThreadPool(processes=1)
|
||||
thread = pool.apply_async(check_server_status, (request_form['jobID'], self.options['server']))
|
||||
print("Wait for results of server...")
|
||||
result = thread.get()
|
||||
return result
|
||||
|
||||
except Exception as e:
|
||||
raise Exception(e)
|
||||
|
||||
# We build an example here that we can call by either calling this file directly from the main directory,
|
||||
# or by adding it to our playground. You can call the example and adjust it to your needs or redefine it in the
|
||||
# playground or elsewhere
|
||||
def build_example(name, identifier, admin_config, server_address):
|
||||
dvm_config = DVMConfig()
|
||||
dvm_config.PRIVATE_KEY = check_and_set_private_key(identifier)
|
||||
npub = Keys.from_sk_str(dvm_config.PRIVATE_KEY).public_key().to_bech32()
|
||||
invoice_key, admin_key, wallet_id, user_id, lnaddress = check_and_set_ln_bits_keys(identifier, npub)
|
||||
dvm_config.LNBITS_INVOICE_KEY = invoice_key
|
||||
dvm_config.LNBITS_ADMIN_KEY = admin_key # The dvm might pay failed jobs back
|
||||
dvm_config.LNBITS_URL = os.getenv("LNBITS_HOST")
|
||||
admin_config.LUD16 = lnaddress
|
||||
|
||||
nip90params = {
|
||||
"method": {
|
||||
"required": False,
|
||||
"values": ["prompt", "analysis"]
|
||||
},
|
||||
"mode": {
|
||||
"required": False,
|
||||
"values": ["best", "classic", "fast", "negative"]
|
||||
}
|
||||
}
|
||||
nip89info = {
|
||||
"name": name,
|
||||
"image": "https://image.nostr.build/229c14e440895da30de77b3ca145d66d4b04efb4027ba3c44ca147eecde891f1.jpg",
|
||||
"about": "I analyse Images an return a prompt or a prompt analysis",
|
||||
"encryptionSupported": True,
|
||||
"cashuAccepted": True,
|
||||
"nip90Params": nip90params
|
||||
}
|
||||
|
||||
# A module might have options it can be initialized with, here we set a default model, lora and the server
|
||||
options = {'server': server_address}
|
||||
|
||||
nip89config = NIP89Config()
|
||||
nip89config.DTAG = check_and_set_d_tag(identifier, name, dvm_config.PRIVATE_KEY,
|
||||
nip89info["image"])
|
||||
nip89config.CONTENT = json.dumps(nip89info)
|
||||
# We add an optional AdminConfig for this one, and tell the dvm to rebroadcast its NIP89
|
||||
return ImageInterrogator(name=name, dvm_config=dvm_config, nip89config=nip89config,
|
||||
admin_config=admin_config, options=options)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
env_path = Path('.env')
|
||||
if env_path.is_file():
|
||||
print(f'loading environment from {env_path.resolve()}')
|
||||
dotenv.load_dotenv(env_path, verbose=True, override=True)
|
||||
else:
|
||||
raise FileNotFoundError(f'.env file not found at {env_path} ')
|
||||
|
||||
admin_config = AdminConfig()
|
||||
admin_config.REBROADCAST_NIP89 = False
|
||||
admin_config.UPDATE_PROFILE = False
|
||||
dvm = build_example("Image Interrogator", "imageinterrogator", admin_config, os.getenv("N_SERVER"))
|
||||
dvm.run()
|
||||
|
||||
keep_alive()
|
||||
163
nostr_dvm/tasks/imageupscale.py
Normal file
163
nostr_dvm/tasks/imageupscale.py
Normal file
@ -0,0 +1,163 @@
|
||||
import json
|
||||
import os
|
||||
from multiprocessing.pool import ThreadPool
|
||||
from pathlib import Path
|
||||
|
||||
import dotenv
|
||||
|
||||
from nostr_dvm.backends.nova_server.utils import check_server_status, send_request_to_server
|
||||
from nostr_dvm.interfaces.dvmtaskinterface import DVMTaskInterface
|
||||
from nostr_dvm.utils.admin_utils import AdminConfig
|
||||
from nostr_dvm.utils.backend_utils import keep_alive
|
||||
from nostr_dvm.utils.dvmconfig import DVMConfig
|
||||
from nostr_dvm.utils.nip89_utils import NIP89Config, check_and_set_d_tag
|
||||
from nostr_dvm.utils.definitions import EventDefinitions
|
||||
from nostr_dvm.utils.nostr_utils import check_and_set_private_key
|
||||
from nostr_dvm.utils.zap_utils import check_and_set_ln_bits_keys
|
||||
from nostr_sdk import Keys
|
||||
|
||||
"""
|
||||
This File contains a Module to upscale an image from an url by factor 2-4
|
||||
|
||||
Accepted Inputs: link to image (url)
|
||||
Outputs: An url to an Image
|
||||
Params: -upscale 2,3,4
|
||||
"""
|
||||
|
||||
|
||||
class ImageUpscale(DVMTaskInterface):
|
||||
KIND: int = EventDefinitions.KIND_NIP90_GENERATE_IMAGE
|
||||
TASK: str = "image-to-image"
|
||||
FIX_COST: float = 20
|
||||
|
||||
def __init__(self, name, dvm_config: DVMConfig, nip89config: NIP89Config,
|
||||
admin_config: AdminConfig = None, options=None):
|
||||
super().__init__(name, dvm_config, nip89config, admin_config, options)
|
||||
|
||||
def is_input_supported(self, tags):
|
||||
hasurl = False
|
||||
for tag in tags:
|
||||
if tag.as_vec()[0] == 'i':
|
||||
input_value = tag.as_vec()[1]
|
||||
input_type = tag.as_vec()[2]
|
||||
if input_type == "url":
|
||||
hasurl = True
|
||||
|
||||
if not hasurl:
|
||||
return False
|
||||
|
||||
return True
|
||||
|
||||
def create_request_from_nostr_event(self, event, client=None, dvm_config=None):
|
||||
request_form = {"jobID": event.id().to_hex() + "_" + self.NAME.replace(" ", "")}
|
||||
request_form["trainerFilePath"] = r'modules\image_upscale\image_upscale_realesrgan.trainer'
|
||||
url = ""
|
||||
out_scale = 4
|
||||
|
||||
for tag in event.tags():
|
||||
if tag.as_vec()[0] == 'i':
|
||||
input_type = tag.as_vec()[2]
|
||||
if input_type == "url":
|
||||
url = tag.as_vec()[1]
|
||||
|
||||
elif tag.as_vec()[0] == 'param':
|
||||
print("Param: " + tag.as_vec()[1] + ": " + tag.as_vec()[2])
|
||||
if tag.as_vec()[1] == "upscale":
|
||||
out_scale = tag.as_vec()[2]
|
||||
|
||||
|
||||
|
||||
io_input_image = {
|
||||
"id": "input_image",
|
||||
"type": "input",
|
||||
"src": "url:Image",
|
||||
"uri": url
|
||||
}
|
||||
|
||||
io_output = {
|
||||
"id": "output_image",
|
||||
"type": "output",
|
||||
"src": "request:image"
|
||||
}
|
||||
|
||||
request_form['data'] = json.dumps([io_input_image, io_output])
|
||||
|
||||
options = {
|
||||
"outscale": out_scale,
|
||||
|
||||
}
|
||||
request_form['options'] = json.dumps(options)
|
||||
|
||||
return request_form
|
||||
|
||||
def process(self, request_form):
|
||||
try:
|
||||
# Call the process route of NOVA-Server with our request form.
|
||||
response = send_request_to_server(request_form, self.options['server'])
|
||||
if bool(json.loads(response)['success']):
|
||||
print("Job " + request_form['jobID'] + " sent to server")
|
||||
|
||||
pool = ThreadPool(processes=1)
|
||||
thread = pool.apply_async(check_server_status, (request_form['jobID'], self.options['server']))
|
||||
print("Wait for results of server...")
|
||||
result = thread.get()
|
||||
return result
|
||||
|
||||
except Exception as e:
|
||||
raise Exception(e)
|
||||
|
||||
# We build an example here that we can call by either calling this file directly from the main directory,
|
||||
# or by adding it to our playground. You can call the example and adjust it to your needs or redefine it in the
|
||||
# playground or elsewhere
|
||||
def build_example(name, identifier, admin_config, server_address):
|
||||
dvm_config = DVMConfig()
|
||||
dvm_config.PRIVATE_KEY = check_and_set_private_key(identifier)
|
||||
npub = Keys.from_sk_str(dvm_config.PRIVATE_KEY).public_key().to_bech32()
|
||||
invoice_key, admin_key, wallet_id, user_id, lnaddress = check_and_set_ln_bits_keys(identifier, npub)
|
||||
dvm_config.LNBITS_INVOICE_KEY = invoice_key
|
||||
dvm_config.LNBITS_ADMIN_KEY = admin_key # The dvm might pay failed jobs back
|
||||
dvm_config.LNBITS_URL = os.getenv("LNBITS_HOST")
|
||||
admin_config.LUD16 = lnaddress
|
||||
|
||||
nip90params = {
|
||||
"upscale": {
|
||||
"required": False,
|
||||
"values": ["2", "3", "4"]
|
||||
}
|
||||
}
|
||||
nip89info = {
|
||||
"name": name,
|
||||
"image": "https://image.nostr.build/229c14e440895da30de77b3ca145d66d4b04efb4027ba3c44ca147eecde891f1.jpg",
|
||||
"about": "I upscale an image using realESRGan up to factor 4 (default is factor 4)",
|
||||
"encryptionSupported": True,
|
||||
"cashuAccepted": True,
|
||||
"nip90Params": nip90params
|
||||
}
|
||||
|
||||
# A module might have options it can be initialized with, here we set a default model, lora and the server
|
||||
options = {'server': server_address}
|
||||
|
||||
nip89config = NIP89Config()
|
||||
nip89config.DTAG = check_and_set_d_tag(identifier, name, dvm_config.PRIVATE_KEY,
|
||||
nip89info["image"])
|
||||
nip89config.CONTENT = json.dumps(nip89info)
|
||||
# We add an optional AdminConfig for this one, and tell the dvm to rebroadcast its NIP89
|
||||
return ImageUpscale(name=name, dvm_config=dvm_config, nip89config=nip89config,
|
||||
admin_config=admin_config, options=options)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
env_path = Path('.env')
|
||||
if env_path.is_file():
|
||||
print(f'loading environment from {env_path.resolve()}')
|
||||
dotenv.load_dotenv(env_path, verbose=True, override=True)
|
||||
else:
|
||||
raise FileNotFoundError(f'.env file not found at {env_path} ')
|
||||
|
||||
admin_config = AdminConfig()
|
||||
admin_config.REBROADCAST_NIP89 = False
|
||||
admin_config.UPDATE_PROFILE = False
|
||||
dvm = build_example("Image Upscaler", "imageupscale", admin_config, os.getenv("N_SERVER"))
|
||||
dvm.run()
|
||||
|
||||
keep_alive()
|
||||
210
nostr_dvm/tasks/textextraction_whisperx.py
Normal file
210
nostr_dvm/tasks/textextraction_whisperx.py
Normal file
@ -0,0 +1,210 @@
|
||||
import json
|
||||
import os
|
||||
import time
|
||||
from multiprocessing.pool import ThreadPool
|
||||
from pathlib import Path
|
||||
|
||||
import dotenv
|
||||
|
||||
from nostr_dvm.backends.nova_server.utils import check_server_status, send_request_to_server, send_file_to_server
|
||||
from nostr_dvm.interfaces.dvmtaskinterface import DVMTaskInterface
|
||||
from nostr_dvm.utils.admin_utils import AdminConfig
|
||||
from nostr_dvm.utils.backend_utils import keep_alive
|
||||
from nostr_dvm.utils.dvmconfig import DVMConfig
|
||||
from nostr_dvm.utils.mediasource_utils import organize_input_media_data
|
||||
from nostr_dvm.utils.nip89_utils import NIP89Config, check_and_set_d_tag
|
||||
from nostr_dvm.utils.definitions import EventDefinitions
|
||||
from nostr_dvm.utils.nostr_utils import check_and_set_private_key
|
||||
from nostr_dvm.utils.zap_utils import check_and_set_ln_bits_keys
|
||||
from nostr_sdk import Keys
|
||||
|
||||
"""
|
||||
This File contains a Module to transform A media file input on n-server and receive results back.
|
||||
|
||||
Accepted Inputs: Url to media file (url)
|
||||
Outputs: Transcribed text
|
||||
|
||||
"""
|
||||
|
||||
|
||||
class SpeechToTextWhisperX(DVMTaskInterface):
|
||||
KIND: int = EventDefinitions.KIND_NIP90_EXTRACT_TEXT
|
||||
TASK: str = "speech-to-text"
|
||||
FIX_COST: float = 10
|
||||
PER_UNIT_COST: float = 0.1
|
||||
|
||||
def __init__(self, name, dvm_config: DVMConfig, nip89config: NIP89Config,
|
||||
admin_config: AdminConfig = None, options=None):
|
||||
super().__init__(name, dvm_config, nip89config, admin_config, options)
|
||||
|
||||
def is_input_supported(self, tags):
|
||||
for tag in tags:
|
||||
if tag.as_vec()[0] == 'i':
|
||||
input_value = tag.as_vec()[1]
|
||||
input_type = tag.as_vec()[2]
|
||||
if input_type != "url":
|
||||
return False
|
||||
|
||||
elif tag.as_vec()[0] == 'output':
|
||||
output = tag.as_vec()[1]
|
||||
if output == "" or not (output == "text/plain"):
|
||||
print("Output format not supported, skipping..")
|
||||
return False
|
||||
|
||||
return True
|
||||
|
||||
def create_request_from_nostr_event(self, event, client=None, dvm_config=None):
|
||||
request_form = {"jobID": event.id().to_hex() + "_" + self.NAME.replace(" ", ""),
|
||||
"trainerFilePath": r'modules\whisperx\whisperx_transcript.trainer'}
|
||||
|
||||
if self.options.get("default_model"):
|
||||
model = self.options['default_model']
|
||||
else:
|
||||
model = "base"
|
||||
if self.options.get("alignment"):
|
||||
alignment = self.options['alignment']
|
||||
else:
|
||||
alignment = "raw"
|
||||
|
||||
url = ""
|
||||
input_type = "url"
|
||||
start_time = 0
|
||||
end_time = 0
|
||||
media_format = "audio/mp3"
|
||||
|
||||
for tag in event.tags():
|
||||
if tag.as_vec()[0] == 'i':
|
||||
input_type = tag.as_vec()[2]
|
||||
if input_type == "url":
|
||||
url = tag.as_vec()[1]
|
||||
|
||||
elif tag.as_vec()[0] == 'param':
|
||||
print("Param: " + tag.as_vec()[1] + ": " + tag.as_vec()[2])
|
||||
if tag.as_vec()[1] == "alignment":
|
||||
alignment = tag.as_vec()[2]
|
||||
elif tag.as_vec()[1] == "model":
|
||||
model = tag.as_vec()[2]
|
||||
elif tag.as_vec()[1] == "range":
|
||||
try:
|
||||
t = time.strptime(tag.as_vec()[2], "%H:%M:%S")
|
||||
seconds = t.tm_hour * 60 * 60 + t.tm_min * 60 + t.tm_sec
|
||||
start_time = float(seconds)
|
||||
except:
|
||||
try:
|
||||
t = time.strptime(tag.as_vec()[2], "%M:%S")
|
||||
seconds = t.tm_min * 60 + t.tm_sec
|
||||
start_time = float(seconds)
|
||||
except:
|
||||
start_time = tag.as_vec()[2]
|
||||
try:
|
||||
t = time.strptime(tag.as_vec()[3], "%H:%M:%S")
|
||||
seconds = t.tm_hour * 60 * 60 + t.tm_min * 60 + t.tm_sec
|
||||
end_time = float(seconds)
|
||||
except:
|
||||
try:
|
||||
t = time.strptime(tag.as_vec()[3], "%M:%S")
|
||||
seconds = t.tm_min * 60 + t.tm_sec
|
||||
end_time = float(seconds)
|
||||
except:
|
||||
end_time = float(tag.as_vec()[3])
|
||||
|
||||
filepath = organize_input_media_data(url, input_type, start_time, end_time, dvm_config, client, True, media_format)
|
||||
path_on_server = send_file_to_server(os.path.realpath(filepath), self.options['server'])
|
||||
|
||||
io_input = {
|
||||
"id": "audio",
|
||||
"type": "input",
|
||||
"src": "file:stream",
|
||||
"uri": path_on_server
|
||||
}
|
||||
|
||||
io_output = {
|
||||
"id": "transcript",
|
||||
"type": "output",
|
||||
"src": "request:annotation:free"
|
||||
}
|
||||
|
||||
request_form['data'] = json.dumps([io_input, io_output])
|
||||
|
||||
options = {
|
||||
"model": model,
|
||||
"alignment_mode": alignment,
|
||||
}
|
||||
request_form['options'] = json.dumps(options)
|
||||
return request_form
|
||||
|
||||
def process(self, request_form):
|
||||
try:
|
||||
# Call the process route of NOVA-Server with our request form.
|
||||
response = send_request_to_server(request_form, self.options['server'])
|
||||
if bool(json.loads(response)['success']):
|
||||
print("Job " + request_form['jobID'] + " sent to server")
|
||||
|
||||
pool = ThreadPool(processes=1)
|
||||
thread = pool.apply_async(check_server_status, (request_form['jobID'], self.options['server']))
|
||||
print("Wait for results of server...")
|
||||
result = thread.get()
|
||||
return result
|
||||
|
||||
except Exception as e:
|
||||
raise Exception(e)
|
||||
|
||||
# We build an example here that we can call by either calling this file directly from the main directory,
|
||||
# or by adding it to our playground. You can call the example and adjust it to your needs or redefine it in the
|
||||
# playground or elsewhere
|
||||
def build_example(name, identifier, admin_config, server_address):
|
||||
dvm_config = DVMConfig()
|
||||
dvm_config.PRIVATE_KEY = check_and_set_private_key(identifier)
|
||||
npub = Keys.from_sk_str(dvm_config.PRIVATE_KEY).public_key().to_bech32()
|
||||
invoice_key, admin_key, wallet_id, user_id, lnaddress = check_and_set_ln_bits_keys(identifier, npub)
|
||||
dvm_config.LNBITS_INVOICE_KEY = invoice_key
|
||||
dvm_config.LNBITS_ADMIN_KEY = admin_key # The dvm might pay failed jobs back
|
||||
dvm_config.LNBITS_URL = os.getenv("LNBITS_HOST")
|
||||
admin_config.LUD16 = lnaddress
|
||||
|
||||
# A module might have options it can be initialized with, here we set a default model, and the server
|
||||
# address it should use. These parameters can be freely defined in the task component
|
||||
options = {'default_model': "base", 'server': server_address}
|
||||
|
||||
nip90params = {
|
||||
"model": {
|
||||
"required": False,
|
||||
"values": ["base", "tiny", "small", "medium", "large-v1", "large-v2", "tiny.en", "base.en", "small.en",
|
||||
"medium.en"]
|
||||
},
|
||||
"alignment": {
|
||||
"required": False,
|
||||
"values": ["raw", "segment", "word"]
|
||||
}
|
||||
}
|
||||
nip89info = {
|
||||
"name": name,
|
||||
"image": "https://image.nostr.build/c33ca6fc4cc038ca4adb46fdfdfda34951656f87ee364ef59095bae1495ce669.jpg",
|
||||
"about": "I extract text from media files with WhisperX",
|
||||
"encryptionSupported": True,
|
||||
"cashuAccepted": True,
|
||||
"nip90Params": nip90params
|
||||
}
|
||||
nip89config = NIP89Config()
|
||||
nip89config.DTAG = check_and_set_d_tag(identifier, name, dvm_config.PRIVATE_KEY,
|
||||
nip89info["image"])
|
||||
nip89config.CONTENT = json.dumps(nip89info)
|
||||
return SpeechToTextWhisperX(name=name, dvm_config=dvm_config, nip89config=nip89config,
|
||||
admin_config=admin_config, options=options)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
env_path = Path('.env')
|
||||
if env_path.is_file():
|
||||
print(f'loading environment from {env_path.resolve()}')
|
||||
dotenv.load_dotenv(env_path, verbose=True, override=True)
|
||||
else:
|
||||
raise FileNotFoundError(f'.env file not found at {env_path} ')
|
||||
|
||||
admin_config = AdminConfig()
|
||||
admin_config.REBROADCAST_NIP89 = False
|
||||
admin_config.UPDATE_PROFILE = False
|
||||
dvm = build_example("Whisperer", "whisperx", admin_config, os.getenv("N_SERVER"))
|
||||
dvm.run()
|
||||
|
||||
keep_alive()
|
||||
Loading…
x
Reference in New Issue
Block a user