mirror of
https://github.com/believethehype/nostrdvm.git
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203 lines
8.1 KiB
Python
203 lines
8.1 KiB
Python
import json
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import os
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from multiprocessing.pool import ThreadPool
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from nostr_sdk import Kind
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from nostr_dvm.backends.discover.utils import check_server_status, send_request_to_server
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from nostr_dvm.interfaces.dvmtaskinterface import DVMTaskInterface, process_venv
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from nostr_dvm.utils.admin_utils import AdminConfig
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from nostr_dvm.utils.definitions import EventDefinitions
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from nostr_dvm.utils.dvmconfig import DVMConfig, build_default_config
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from nostr_dvm.utils.nip88_utils import NIP88Config
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from nostr_dvm.utils.nip89_utils import NIP89Config, check_and_set_d_tag
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"""
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This File contains a module to transform Text input on n-server and receive results back.
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Accepted Inputs: Prompt (text)
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Outputs: An url to an Image
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Params: -model # models: juggernaut, dynavision, colossusProject, newreality, unstable
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-lora # loras (weights on top of models) voxel,
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"""
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class ImageGenerationSDXL(DVMTaskInterface):
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KIND: Kind = EventDefinitions.KIND_NIP90_GENERATE_IMAGE
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TASK: str = "text-to-image"
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FIX_COST: float = 50
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async def init_dvm(self, name, dvm_config: DVMConfig, nip89config: NIP89Config, nip88config: NIP88Config = None,
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admin_config: AdminConfig = None, options=None):
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dvm_config.SCRIPT = os.path.abspath(__file__)
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async def is_input_supported(self, tags, client=None, dvm_config=None):
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for tag in tags:
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if tag.as_vec()[0] == 'i':
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input_value = tag.as_vec()[1]
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input_type = tag.as_vec()[2]
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if input_type != "text":
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return False
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elif tag.as_vec()[0] == 'output':
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output = tag.as_vec()[1]
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if (output == "" or
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not (output == "image/png" or "image/jpg"
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or output == "image/png;format=url" or output == "image/jpg;format=url")):
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print("Output format not supported, skipping..")
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return False
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return True
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async def create_request_from_nostr_event(self, event, client=None, dvm_config=None):
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request_form = {"jobID": event.id().to_hex() + "_" + self.NAME.replace(" ", "")}
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request_form["trainerFilePath"] = r'modules\stablediffusionxl\stablediffusionxl.trainer'
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prompt = ""
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negative_prompt = ""
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if self.options.get("default_model") and self.options.get("default_model") != "":
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model = self.options['default_model']
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else:
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model = "stabilityai/stable-diffusion-xl-base-1.0"
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ratio_width = "1"
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ratio_height = "1"
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width = ""
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height = ""
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if self.options.get("default_lora") and self.options.get("default_lora") != "":
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lora = self.options['default_lora']
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else:
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lora = ""
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lora_weight = ""
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strength = ""
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guidance_scale = ""
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for tag in event.tags().to_vec():
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if tag.as_vec()[0] == 'i':
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input_type = tag.as_vec()[2]
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if input_type == "text":
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prompt = tag.as_vec()[1]
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elif tag.as_vec()[0] == 'param':
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print("Param: " + tag.as_vec()[1] + ": " + tag.as_vec()[2])
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if tag.as_vec()[1] == "negative_prompt":
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negative_prompt = tag.as_vec()[2]
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elif tag.as_vec()[1] == "lora":
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lora = tag.as_vec()[2]
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elif tag.as_vec()[1] == "lora_weight":
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lora_weight = tag.as_vec()[2]
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elif tag.as_vec()[1] == "strength":
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strength = float(tag.as_vec()[2])
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elif tag.as_vec()[1] == "guidance_scale":
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guidance_scale = float(tag.as_vec()[2])
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elif tag.as_vec()[1] == "ratio":
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if len(tag.as_vec()) > 3:
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ratio_width = (tag.as_vec()[2])
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ratio_height = (tag.as_vec()[3])
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elif len(tag.as_vec()) == 3:
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split = tag.as_vec()[2].split(":")
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ratio_width = split[0]
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ratio_height = split[1]
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# if size is set it will overwrite ratio.
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elif tag.as_vec()[1] == "size":
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if len(tag.as_vec()) > 3:
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width = (tag.as_vec()[2])
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height = (tag.as_vec()[3])
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elif len(tag.as_vec()) == 3:
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split = tag.as_vec()[2].split("x")
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if len(split) > 1:
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width = split[0]
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height = split[1]
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elif tag.as_vec()[1] == "model":
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model = tag.as_vec()[2]
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io_input = {
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"id": "input_prompt",
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"type": "input",
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"src": "request:text",
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"data": prompt
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}
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io_negative = {
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"id": "negative_prompt",
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"type": "input",
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"src": "request:text",
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"data": negative_prompt
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}
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io_output = {
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"id": "output_image",
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"type": "output",
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"src": "request:image"
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}
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request_form['data'] = json.dumps([io_input, io_negative, io_output])
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options = {
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"model": model,
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"ratio": ratio_width + '-' + ratio_height,
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"width": width,
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"height": height,
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"strength": strength,
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"guidance_scale": guidance_scale,
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"lora": lora,
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"lora_weight": lora_weight
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}
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request_form['options'] = json.dumps(options)
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return request_form
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async def process(self, request_form):
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try:
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# Call the process route of n-server with our request form.
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response = send_request_to_server(request_form, self.options['server'])
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if bool(json.loads(response)['success']):
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print("Job " + request_form['jobID'] + " sent to server")
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pool = ThreadPool(processes=1)
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thread = pool.apply_async(check_server_status, (request_form['jobID'], self.options['server']))
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print("Wait for results of server...")
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result = thread.get()
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return result
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except Exception as e:
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raise Exception(e)
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# We build an example here that we can call by either calling this file directly from the main directory,
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# or by adding it to our playground. You can call the example and adjust it to your needs or redefine it in the
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# playground or elsewhere
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def build_example(name, identifier, admin_config, server_address, default_model="stabilityai/stable-diffusion-xl"
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"-base-1.0", default_lora=""):
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dvm_config = build_default_config(identifier)
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dvm_config.USE_OWN_VENV = False
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admin_config.LUD16 = dvm_config.LN_ADDRESS
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# A module might have options it can be initialized with, here we set a default model, and the server
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# address it should use. These parameters can be freely defined in the task component
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options = {'default_model': default_model, 'default_lora': default_lora, 'server': server_address}
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nip89info = {
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"name": name,
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"picture": "https://image.nostr.build/c33ca6fc4cc038ca4adb46fdfdfda34951656f87ee364ef59095bae1495ce669.jpg",
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"about": "I draw images based on a prompt with a Model called unstable diffusion",
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"supportsEncryption": True,
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"acceptsNutZaps": dvm_config.ENABLE_NUTZAP,
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"nip90Params": {
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"negative_prompt": {
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"required": False,
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"values": []
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},
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"ratio": {
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"required": False,
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"values": ["1:1", "4:3", "16:9", "3:4", "9:16", "10:16"]
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}
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}
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}
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nip89config = NIP89Config()
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nip89config.DTAG = check_and_set_d_tag(identifier, name, dvm_config.PRIVATE_KEY, nip89info["picture"])
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nip89config.CONTENT = json.dumps(nip89info)
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return ImageGenerationSDXL(name=name, dvm_config=dvm_config, nip89config=nip89config,
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admin_config=admin_config, options=options)
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if __name__ == '__main__':
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process_venv(ImageGenerationSDXL)
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