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Author SHA1 Message Date
Hitmare b5dbfc2bca Merge pull request 'hitmare-patch-1' (#2) from hitmare-patch-1 into main
Reviewed-on: #2
code was checked and changed parts were copied. SD was able to start without errors
2023-12-23 15:10:33 +00:00
Hitmare 1d22d29b55 Update for 1.7.0
New clean file of the 1.7.0 Version
2023-12-23 15:08:07 +00:00
Hitmare f919025ac6 Update for 1.7.0
New clean file of the 1.7.0 Version
2023-12-23 15:07:33 +00:00
Hitmare e0000cab58 Update for 1.7.0
New clean file of the 1.7.0 Version
2023-12-23 15:07:03 +00:00
Hitmare 1d24924c3a Update for 1.7.0
Update of the TRT Patch of the original 1.7.0 version of the file
2023-12-23 15:04:20 +00:00
Hitmare cc119297ba Update for 1.7.0
Update of the TRT Patch for the 1.7.0 version of the original file
2023-12-23 15:03:35 +00:00
Hitmare 28b331f4e8 Update for 1.7.0
Update for the 1.7.0 version of the original file
2023-12-23 15:02:50 +00:00
Hitmare eb16857005 Updated api.py for version 1.7.0
Updated api.py for stable-diffusion 1.7.0
2023-12-23 09:50:10 +00:00
6 changed files with 3370 additions and 3353 deletions

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@ -17,19 +17,17 @@ from fastapi.encoders import jsonable_encoder
from secrets import compare_digest from secrets import compare_digest
import modules.shared as shared import modules.shared as shared
from modules import sd_samplers, deepbooru, sd_hijack, images, scripts, ui, postprocessing, errors, restart, shared_items from modules import sd_samplers, deepbooru, sd_hijack, images, scripts, ui, postprocessing, errors, restart, shared_items, script_callbacks, generation_parameters_copypaste, sd_models
from modules.api import models from modules.api import models
from modules.shared import opts from modules.shared import opts
from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
from modules.textual_inversion.textual_inversion import create_embedding, train_embedding from modules.textual_inversion.textual_inversion import create_embedding, train_embedding
from modules.textual_inversion.preprocess import preprocess
from modules.hypernetworks.hypernetwork import create_hypernetwork, train_hypernetwork from modules.hypernetworks.hypernetwork import create_hypernetwork, train_hypernetwork
from PIL import PngImagePlugin,Image from PIL import PngImagePlugin, Image
from modules.sd_models import unload_model_weights, reload_model_weights, checkpoint_aliases
from modules.sd_models_config import find_checkpoint_config_near_filename from modules.sd_models_config import find_checkpoint_config_near_filename
from modules.realesrgan_model import get_realesrgan_models from modules.realesrgan_model import get_realesrgan_models
from modules import devices from modules import devices
from typing import Dict, List, Any from typing import Any
import piexif import piexif
import piexif.helper import piexif.helper
from contextlib import closing from contextlib import closing
@ -103,7 +101,8 @@ def decode_base64_to_image(encoding):
def encode_pil_to_base64(image): def encode_pil_to_base64(image):
with io.BytesIO() as output_bytes: with io.BytesIO() as output_bytes:
if isinstance(image, str):
return image
if opts.samples_format.lower() == 'png': if opts.samples_format.lower() == 'png':
use_metadata = False use_metadata = False
metadata = PngImagePlugin.PngInfo() metadata = PngImagePlugin.PngInfo()
@ -221,28 +220,28 @@ class Api:
self.add_api_route("/sdapi/v1/options", self.get_config, methods=["GET"], response_model=models.OptionsModel) self.add_api_route("/sdapi/v1/options", self.get_config, methods=["GET"], response_model=models.OptionsModel)
self.add_api_route("/sdapi/v1/options", self.set_config, methods=["POST"]) self.add_api_route("/sdapi/v1/options", self.set_config, methods=["POST"])
self.add_api_route("/sdapi/v1/cmd-flags", self.get_cmd_flags, methods=["GET"], response_model=models.FlagsModel) self.add_api_route("/sdapi/v1/cmd-flags", self.get_cmd_flags, methods=["GET"], response_model=models.FlagsModel)
self.add_api_route("/sdapi/v1/samplers", self.get_samplers, methods=["GET"], response_model=List[models.SamplerItem]) self.add_api_route("/sdapi/v1/samplers", self.get_samplers, methods=["GET"], response_model=list[models.SamplerItem])
self.add_api_route("/sdapi/v1/upscalers", self.get_upscalers, methods=["GET"], response_model=List[models.UpscalerItem]) self.add_api_route("/sdapi/v1/upscalers", self.get_upscalers, methods=["GET"], response_model=list[models.UpscalerItem])
self.add_api_route("/sdapi/v1/latent-upscale-modes", self.get_latent_upscale_modes, methods=["GET"], response_model=List[models.LatentUpscalerModeItem]) self.add_api_route("/sdapi/v1/latent-upscale-modes", self.get_latent_upscale_modes, methods=["GET"], response_model=list[models.LatentUpscalerModeItem])
self.add_api_route("/sdapi/v1/sd-models", self.get_sd_models, methods=["GET"], response_model=List[models.SDModelItem]) self.add_api_route("/sdapi/v1/sd-models", self.get_sd_models, methods=["GET"], response_model=list[models.SDModelItem])
self.add_api_route("/sdapi/v1/sd-vae", self.get_sd_vaes, methods=["GET"], response_model=List[models.SDVaeItem]) self.add_api_route("/sdapi/v1/sd-vae", self.get_sd_vaes, methods=["GET"], response_model=list[models.SDVaeItem])
self.add_api_route("/sdapi/v1/hypernetworks", self.get_hypernetworks, methods=["GET"], response_model=List[models.HypernetworkItem]) self.add_api_route("/sdapi/v1/hypernetworks", self.get_hypernetworks, methods=["GET"], response_model=list[models.HypernetworkItem])
self.add_api_route("/sdapi/v1/face-restorers", self.get_face_restorers, methods=["GET"], response_model=List[models.FaceRestorerItem]) self.add_api_route("/sdapi/v1/face-restorers", self.get_face_restorers, methods=["GET"], response_model=list[models.FaceRestorerItem])
self.add_api_route("/sdapi/v1/realesrgan-models", self.get_realesrgan_models, methods=["GET"], response_model=List[models.RealesrganItem]) self.add_api_route("/sdapi/v1/realesrgan-models", self.get_realesrgan_models, methods=["GET"], response_model=list[models.RealesrganItem])
self.add_api_route("/sdapi/v1/prompt-styles", self.get_prompt_styles, methods=["GET"], response_model=List[models.PromptStyleItem]) self.add_api_route("/sdapi/v1/prompt-styles", self.get_prompt_styles, methods=["GET"], response_model=list[models.PromptStyleItem])
self.add_api_route("/sdapi/v1/embeddings", self.get_embeddings, methods=["GET"], response_model=models.EmbeddingsResponse) self.add_api_route("/sdapi/v1/embeddings", self.get_embeddings, methods=["GET"], response_model=models.EmbeddingsResponse)
self.add_api_route("/sdapi/v1/refresh-checkpoints", self.refresh_checkpoints, methods=["POST"]) self.add_api_route("/sdapi/v1/refresh-checkpoints", self.refresh_checkpoints, methods=["POST"])
self.add_api_route("/sdapi/v1/refresh-vae", self.refresh_vae, methods=["POST"]) self.add_api_route("/sdapi/v1/refresh-vae", self.refresh_vae, methods=["POST"])
self.add_api_route("/sdapi/v1/create/embedding", self.create_embedding, methods=["POST"], response_model=models.CreateResponse) self.add_api_route("/sdapi/v1/create/embedding", self.create_embedding, methods=["POST"], response_model=models.CreateResponse)
self.add_api_route("/sdapi/v1/create/hypernetwork", self.create_hypernetwork, methods=["POST"], response_model=models.CreateResponse) self.add_api_route("/sdapi/v1/create/hypernetwork", self.create_hypernetwork, methods=["POST"], response_model=models.CreateResponse)
self.add_api_route("/sdapi/v1/preprocess", self.preprocess, methods=["POST"], response_model=models.PreprocessResponse)
self.add_api_route("/sdapi/v1/train/embedding", self.train_embedding, methods=["POST"], response_model=models.TrainResponse) self.add_api_route("/sdapi/v1/train/embedding", self.train_embedding, methods=["POST"], response_model=models.TrainResponse)
self.add_api_route("/sdapi/v1/train/hypernetwork", self.train_hypernetwork, methods=["POST"], response_model=models.TrainResponse) self.add_api_route("/sdapi/v1/train/hypernetwork", self.train_hypernetwork, methods=["POST"], response_model=models.TrainResponse)
self.add_api_route("/sdapi/v1/memory", self.get_memory, methods=["GET"], response_model=models.MemoryResponse) self.add_api_route("/sdapi/v1/memory", self.get_memory, methods=["GET"], response_model=models.MemoryResponse)
self.add_api_route("/sdapi/v1/unload-checkpoint", self.unloadapi, methods=["POST"]) self.add_api_route("/sdapi/v1/unload-checkpoint", self.unloadapi, methods=["POST"])
self.add_api_route("/sdapi/v1/reload-checkpoint", self.reloadapi, methods=["POST"]) self.add_api_route("/sdapi/v1/reload-checkpoint", self.reloadapi, methods=["POST"])
self.add_api_route("/sdapi/v1/scripts", self.get_scripts_list, methods=["GET"], response_model=models.ScriptsList) self.add_api_route("/sdapi/v1/scripts", self.get_scripts_list, methods=["GET"], response_model=models.ScriptsList)
self.add_api_route("/sdapi/v1/script-info", self.get_script_info, methods=["GET"], response_model=List[models.ScriptInfo]) self.add_api_route("/sdapi/v1/script-info", self.get_script_info, methods=["GET"], response_model=list[models.ScriptInfo])
self.add_api_route("/sdapi/v1/extensions", self.get_extensions_list, methods=["GET"], response_model=list[models.ExtensionItem])
if shared.cmd_opts.api_server_stop: if shared.cmd_opts.api_server_stop:
self.add_api_route("/sdapi/v1/server-kill", self.kill_webui, methods=["POST"]) self.add_api_route("/sdapi/v1/server-kill", self.kill_webui, methods=["POST"])
@ -473,9 +472,6 @@ class Api:
return models.ExtrasBatchImagesResponse(images=list(map(encode_pil_to_base64, result[0])), html_info=result[1]) return models.ExtrasBatchImagesResponse(images=list(map(encode_pil_to_base64, result[0])), html_info=result[1])
def pnginfoapi(self, req: models.PNGInfoRequest): def pnginfoapi(self, req: models.PNGInfoRequest):
if(not req.image.strip()):
return models.PNGInfoResponse(info="")
image = decode_base64_to_image(req.image.strip()) image = decode_base64_to_image(req.image.strip())
if image is None: if image is None:
return models.PNGInfoResponse(info="") return models.PNGInfoResponse(info="")
@ -484,9 +480,10 @@ class Api:
if geninfo is None: if geninfo is None:
geninfo = "" geninfo = ""
items = {**{'parameters': geninfo}, **items} params = generation_parameters_copypaste.parse_generation_parameters(geninfo)
script_callbacks.infotext_pasted_callback(geninfo, params)
return models.PNGInfoResponse(info=geninfo, items=items) return models.PNGInfoResponse(info=geninfo, items=items, parameters=params)
def progressapi(self, req: models.ProgressRequest = Depends()): def progressapi(self, req: models.ProgressRequest = Depends()):
# copy from check_progress_call of ui.py # copy from check_progress_call of ui.py
@ -541,12 +538,12 @@ class Api:
return {} return {}
def unloadapi(self): def unloadapi(self):
unload_model_weights() sd_models.unload_model_weights()
return {} return {}
def reloadapi(self): def reloadapi(self):
reload_model_weights() sd_models.send_model_to_device(shared.sd_model)
return {} return {}
@ -564,9 +561,9 @@ class Api:
return options return options
def set_config(self, req: Dict[str, Any]): def set_config(self, req: dict[str, Any]):
checkpoint_name = req.get("sd_model_checkpoint", None) checkpoint_name = req.get("sd_model_checkpoint", None)
if checkpoint_name is not None and checkpoint_name not in checkpoint_aliases: if checkpoint_name is not None and checkpoint_name not in sd_models.checkpoint_aliases:
raise RuntimeError(f"model {checkpoint_name!r} not found") raise RuntimeError(f"model {checkpoint_name!r} not found")
for k, v in req.items(): for k, v in req.items():
@ -676,19 +673,6 @@ class Api:
finally: finally:
shared.state.end() shared.state.end()
def preprocess(self, args: dict):
try:
shared.state.begin(job="preprocess")
preprocess(**args) # quick operation unless blip/booru interrogation is enabled
shared.state.end()
return models.PreprocessResponse(info='preprocess complete')
except KeyError as e:
return models.PreprocessResponse(info=f"preprocess error: invalid token: {e}")
except Exception as e:
return models.PreprocessResponse(info=f"preprocess error: {e}")
finally:
shared.state.end()
def train_embedding(self, args: dict): def train_embedding(self, args: dict):
try: try:
shared.state.begin(job="train_embedding") shared.state.begin(job="train_embedding")
@ -770,6 +754,25 @@ class Api:
cuda = {'error': f'{err}'} cuda = {'error': f'{err}'}
return models.MemoryResponse(ram=ram, cuda=cuda) return models.MemoryResponse(ram=ram, cuda=cuda)
def get_extensions_list(self):
from modules import extensions
extensions.list_extensions()
ext_list = []
for ext in extensions.extensions:
ext: extensions.Extension
ext.read_info_from_repo()
if ext.remote is not None:
ext_list.append({
"name": ext.name,
"remote": ext.remote,
"branch": ext.branch,
"commit_hash":ext.commit_hash,
"commit_date":ext.commit_date,
"version":ext.version,
"enabled":ext.enabled
})
return ext_list
def launch(self, server_name, port, root_path): def launch(self, server_name, port, root_path):
self.app.include_router(self.router) self.app.include_router(self.router)
uvicorn.run(self.app, host=server_name, port=port, timeout_keep_alive=shared.cmd_opts.timeout_keep_alive, root_path=root_path) uvicorn.run(self.app, host=server_name, port=port, timeout_keep_alive=shared.cmd_opts.timeout_keep_alive, root_path=root_path)

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@ -1,92 +1,94 @@
import torch.nn import torch.nn
import ldm.modules.diffusionmodules.openaimodel
from modules import script_callbacks, shared, devices
from modules import script_callbacks, shared, devices
unet_options = []
unet_options = [] current_unet_option = None
current_unet_option = None current_unet = None
current_unet = None original_forward = None # not used, only left temporarily for compatibility
def list_unets():
def list_unets(): new_unets = script_callbacks.list_unets_callback()
new_unets = script_callbacks.list_unets_callback()
unet_options.clear()
unet_options.clear() unet_options.extend(new_unets)
unet_options.extend(new_unets)
def get_unet_option(option=None):
def get_unet_option(option=None): option = option or shared.opts.sd_unet
option = option or shared.opts.sd_unet
if option == "None":
if option == "None": return None
return None
if option == "Automatic":
if option == "Automatic": name = shared.sd_model.sd_checkpoint_info.model_name
name = shared.sd_model.sd_checkpoint_info.model_name
options = [x for x in unet_options if x.model_name == name]
options = [x for x in unet_options if x.model_name == name]
option = options[0].label if options else "None"
option = options[0].label if options else "None"
return next(iter([x for x in unet_options if x.label == option]), None)
return next(iter([x for x in unet_options if x.label == option]), None)
def apply_unet(option=None):
def apply_unet(option=None): global current_unet_option
global current_unet_option global current_unet
global current_unet
new_option = get_unet_option(option)
new_option = get_unet_option(option) if new_option == current_unet_option:
if new_option == current_unet_option: return
return
if current_unet is not None:
if current_unet is not None: print(f"Dectivating unet: {current_unet.option.label}")
print(f"Dectivating unet: {current_unet.option.label}") current_unet.deactivate()
current_unet.deactivate()
current_unet_option = new_option
current_unet_option = new_option if current_unet_option is None:
if current_unet_option is None: current_unet = None
current_unet = None
if not shared.sd_model.lowvram:
if not shared.sd_model.lowvram: shared.sd_model.model.diffusion_model.to(devices.device)
shared.sd_model.model.diffusion_model.to(devices.device)
return
return
shared.sd_model.model.diffusion_model.to(devices.cpu)
shared.sd_model.model.diffusion_model.to(devices.cpu) devices.torch_gc()
devices.torch_gc()
current_unet = current_unet_option.create_unet()
current_unet = current_unet_option.create_unet() current_unet.option = current_unet_option
current_unet.option = current_unet_option print(f"Activating unet: {current_unet.option.label}")
print(f"Activating unet: {current_unet.option.label}") current_unet.activate()
current_unet.activate()
class SdUnetOption:
class SdUnetOption: model_name = None
model_name = None """name of related checkpoint - this option will be selected automatically for unet if the name of checkpoint matches this"""
"""name of related checkpoint - this option will be selected automatically for unet if the name of checkpoint matches this"""
label = None
label = None """name of the unet in UI"""
"""name of the unet in UI"""
def create_unet(self):
def create_unet(self): """returns SdUnet object to be used as a Unet instead of built-in unet when making pictures"""
"""returns SdUnet object to be used as a Unet instead of built-in unet when making pictures""" raise NotImplementedError()
raise NotImplementedError()
class SdUnet(torch.nn.Module):
class SdUnet(torch.nn.Module): def forward(self, x, timesteps, context, *args, **kwargs):
def forward(self, x, timesteps, context, *args, **kwargs): raise NotImplementedError()
raise NotImplementedError()
def activate(self):
def activate(self): pass
pass
def deactivate(self):
def deactivate(self): pass
pass
def create_unet_forward(original_forward):
def UNetModel_forward(self, x, timesteps=None, context=None, *args, **kwargs): def UNetModel_forward(self, x, timesteps=None, context=None, *args, **kwargs):
if current_unet is not None: if current_unet is not None:
return current_unet.forward(x, timesteps, context, *args, **kwargs) return current_unet.forward(x, timesteps, context, *args, **kwargs)
return ldm.modules.diffusionmodules.openaimodel.copy_of_UNetModel_forward_for_webui(self, x, timesteps, context, *args, **kwargs) return original_forward(self, x, timesteps, context, *args, **kwargs)
return UNetModel_forward

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@ -1,101 +1,107 @@
import torch.nn import torch.nn
import ldm.modules.diffusionmodules.openaimodel import time
from modules import script_callbacks, shared, devices
import time
from modules import script_callbacks, shared, devices unet_options = []
unet_options = [] current_unet_option = None
current_unet_option = None current_unet = None
current_unet = None original_forward = None # not used, only left temporarily for compatibility
def list_unets():
def list_unets(): new_unets = script_callbacks.list_unets_callback()
new_unets = script_callbacks.list_unets_callback()
unet_options.clear()
unet_options.clear() unet_options.extend(new_unets)
unet_options.extend(new_unets)
def get_unet_option(option=None):
def get_unet_option(option=None): option = option or shared.opts.sd_unet
option = option or shared.opts.sd_unet
if option == "None":
if option == "None": return None
return None
if option == "Automatic":
if option == "Automatic": name = shared.sd_model.sd_checkpoint_info.model_name
name = shared.sd_model.sd_checkpoint_info.model_name
options = [x for x in unet_options if x.model_name == name]
options = [x for x in unet_options if x.model_name == name]
option = options[0].label if options else "None"
option = options[0].label if options else "None"
return next(iter([x for x in unet_options if x.label == option]), None)
return next(iter([x for x in unet_options if x.label == option]), None)
def apply_unet(option=None):
def apply_unet(option=None): global current_unet_option
global current_unet_option global current_unet
global current_unet
new_option = get_unet_option(option)
new_option = get_unet_option(option) if new_option == current_unet_option:
if new_option == current_unet_option: return
return
if current_unet is not None:
if current_unet is not None: print(f"Dectivating unet: {current_unet.option.label}")
print(f"Dectivating unet: {current_unet.option.label}") current_unet.deactivate()
current_unet.deactivate()
current_unet_option = new_option
current_unet_option = new_option if current_unet_option is None:
if current_unet_option is None: current_unet = None
current_unet = None
if not shared.sd_model.lowvram:
if not shared.sd_model.lowvram: shared.sd_model.model.diffusion_model.to(devices.device)
shared.sd_model.model.diffusion_model.to(devices.device)
return
return
shared.sd_model.model.diffusion_model.to(devices.cpu)
shared.sd_model.model.diffusion_model.to(devices.cpu) devices.torch_gc()
devices.torch_gc()
current_unet = current_unet_option.create_unet()
current_unet = current_unet_option.create_unet() current_unet.option = current_unet_option
current_unet.option = current_unet_option print(f"Activating unet: {current_unet.option.label}")
print(f"Activating unet: {current_unet.option.label}") current_unet.activate()
current_unet.activate()
class SdUnetOption:
class SdUnetOption: model_name = None
model_name = None """name of related checkpoint - this option will be selected automatically for unet if the name of checkpoint matches this"""
"""name of related checkpoint - this option will be selected automatically for unet if the name of checkpoint matches this"""
label = None
label = None """name of the unet in UI"""
"""name of the unet in UI"""
def create_unet(self):
def create_unet(self): """returns SdUnet object to be used as a Unet instead of built-in unet when making pictures"""
"""returns SdUnet object to be used as a Unet instead of built-in unet when making pictures""" raise NotImplementedError()
raise NotImplementedError()
class SdUnet(torch.nn.Module):
class SdUnet(torch.nn.Module): def forward(self, x, timesteps, context, *args, **kwargs):
def forward(self, x, timesteps, context, *args, **kwargs): raise NotImplementedError()
raise NotImplementedError()
def activate(self):
def activate(self): pass
pass
def deactivate(self):
def deactivate(self): pass
pass
def create_unet_forward(original_forward):
def UNetModel_forward(self, x, timesteps=None, context=None, *args, **kwargs): def UNetModel_forward(self, x, timesteps=None, context=None, *args, **kwargs):
try: if current_unet is not None:
if current_unet is not None and shared.current_prompt != shared.skip_unet_prompt: return current_unet.forward(x, timesteps, context, *args, **kwargs)
if '[TRT]' in shared.opts.sd_unet and '<lora:' in shared.current_prompt: try:
raise Exception('LoRA unsupported in TRT UNet') if current_unet is not None and shared.current_prompt != shared.skip_unet_prompt:
f = current_unet.forward(x, timesteps, context, *args, **kwargs) if '[TRT]' in shared.opts.sd_unet and '<lora:' in shared.current_prompt:
return f raise Exception('LoRA unsupported in TRT UNet')
except Exception as e: f = current_unet.forward(x, timesteps, context, *args, **kwargs)
start = time.time() return f
print('[UNet] Skipping TRT UNet for this request:', e, '-', shared.current_prompt) except Exception as e:
shared.sd_model.model.diffusion_model.to(devices.device) start = time.time()
shared.skip_unet_prompt = shared.current_prompt print('[UNet] Skipping TRT UNet for this request:', e, '-', shared.current_prompt)
print('[UNet] Used', time.time() - start, 'seconds') shared.sd_model.model.diffusion_model.to(devices.device)
shared.skip_unet_prompt = shared.current_prompt
return ldm.modules.diffusionmodules.openaimodel.copy_of_UNetModel_forward_for_webui(self, x, timesteps, context, *args, **kwargs) print('[UNet] Used', time.time() - start, 'seconds')
return original_forward(self, x, timesteps, context, *args, **kwargs)
return UNetModel_forward

89
api.py
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@ -17,19 +17,17 @@ from fastapi.encoders import jsonable_encoder
from secrets import compare_digest from secrets import compare_digest
import modules.shared as shared import modules.shared as shared
from modules import sd_samplers, deepbooru, sd_hijack, images, scripts, ui, postprocessing, errors, restart, shared_items from modules import sd_samplers, deepbooru, sd_hijack, images, scripts, ui, postprocessing, errors, restart, shared_items, script_callbacks, generation_parameters_copypaste, sd_models
from modules.api import models from modules.api import models
from modules.shared import opts from modules.shared import opts
from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
from modules.textual_inversion.textual_inversion import create_embedding, train_embedding from modules.textual_inversion.textual_inversion import create_embedding, train_embedding
from modules.textual_inversion.preprocess import preprocess
from modules.hypernetworks.hypernetwork import create_hypernetwork, train_hypernetwork from modules.hypernetworks.hypernetwork import create_hypernetwork, train_hypernetwork
from PIL import PngImagePlugin,Image from PIL import PngImagePlugin, Image
from modules.sd_models import unload_model_weights, reload_model_weights, checkpoint_aliases
from modules.sd_models_config import find_checkpoint_config_near_filename from modules.sd_models_config import find_checkpoint_config_near_filename
from modules.realesrgan_model import get_realesrgan_models from modules.realesrgan_model import get_realesrgan_models
from modules import devices from modules import devices
from typing import Dict, List, Any from typing import Any
import piexif import piexif
import piexif.helper import piexif.helper
from contextlib import closing from contextlib import closing
@ -146,7 +144,8 @@ def decode_base64_to_image(encoding):
def encode_pil_to_base64(image): def encode_pil_to_base64(image):
with io.BytesIO() as output_bytes: with io.BytesIO() as output_bytes:
if isinstance(image, str):
return image
if opts.samples_format.lower() == 'png': if opts.samples_format.lower() == 'png':
use_metadata = False use_metadata = False
metadata = PngImagePlugin.PngInfo() metadata = PngImagePlugin.PngInfo()
@ -264,28 +263,28 @@ class Api:
self.add_api_route("/sdapi/v1/options", self.get_config, methods=["GET"], response_model=models.OptionsModel) self.add_api_route("/sdapi/v1/options", self.get_config, methods=["GET"], response_model=models.OptionsModel)
self.add_api_route("/sdapi/v1/options", self.set_config, methods=["POST"]) self.add_api_route("/sdapi/v1/options", self.set_config, methods=["POST"])
self.add_api_route("/sdapi/v1/cmd-flags", self.get_cmd_flags, methods=["GET"], response_model=models.FlagsModel) self.add_api_route("/sdapi/v1/cmd-flags", self.get_cmd_flags, methods=["GET"], response_model=models.FlagsModel)
self.add_api_route("/sdapi/v1/samplers", self.get_samplers, methods=["GET"], response_model=List[models.SamplerItem]) self.add_api_route("/sdapi/v1/samplers", self.get_samplers, methods=["GET"], response_model=list[models.SamplerItem])
self.add_api_route("/sdapi/v1/upscalers", self.get_upscalers, methods=["GET"], response_model=List[models.UpscalerItem]) self.add_api_route("/sdapi/v1/upscalers", self.get_upscalers, methods=["GET"], response_model=list[models.UpscalerItem])
self.add_api_route("/sdapi/v1/latent-upscale-modes", self.get_latent_upscale_modes, methods=["GET"], response_model=List[models.LatentUpscalerModeItem]) self.add_api_route("/sdapi/v1/latent-upscale-modes", self.get_latent_upscale_modes, methods=["GET"], response_model=list[models.LatentUpscalerModeItem])
self.add_api_route("/sdapi/v1/sd-models", self.get_sd_models, methods=["GET"], response_model=List[models.SDModelItem]) self.add_api_route("/sdapi/v1/sd-models", self.get_sd_models, methods=["GET"], response_model=list[models.SDModelItem])
self.add_api_route("/sdapi/v1/sd-vae", self.get_sd_vaes, methods=["GET"], response_model=List[models.SDVaeItem]) self.add_api_route("/sdapi/v1/sd-vae", self.get_sd_vaes, methods=["GET"], response_model=list[models.SDVaeItem])
self.add_api_route("/sdapi/v1/hypernetworks", self.get_hypernetworks, methods=["GET"], response_model=List[models.HypernetworkItem]) self.add_api_route("/sdapi/v1/hypernetworks", self.get_hypernetworks, methods=["GET"], response_model=list[models.HypernetworkItem])
self.add_api_route("/sdapi/v1/face-restorers", self.get_face_restorers, methods=["GET"], response_model=List[models.FaceRestorerItem]) self.add_api_route("/sdapi/v1/face-restorers", self.get_face_restorers, methods=["GET"], response_model=list[models.FaceRestorerItem])
self.add_api_route("/sdapi/v1/realesrgan-models", self.get_realesrgan_models, methods=["GET"], response_model=List[models.RealesrganItem]) self.add_api_route("/sdapi/v1/realesrgan-models", self.get_realesrgan_models, methods=["GET"], response_model=list[models.RealesrganItem])
self.add_api_route("/sdapi/v1/prompt-styles", self.get_prompt_styles, methods=["GET"], response_model=List[models.PromptStyleItem]) self.add_api_route("/sdapi/v1/prompt-styles", self.get_prompt_styles, methods=["GET"], response_model=list[models.PromptStyleItem])
self.add_api_route("/sdapi/v1/embeddings", self.get_embeddings, methods=["GET"], response_model=models.EmbeddingsResponse) self.add_api_route("/sdapi/v1/embeddings", self.get_embeddings, methods=["GET"], response_model=models.EmbeddingsResponse)
self.add_api_route("/sdapi/v1/refresh-checkpoints", self.refresh_checkpoints, methods=["POST"]) self.add_api_route("/sdapi/v1/refresh-checkpoints", self.refresh_checkpoints, methods=["POST"])
self.add_api_route("/sdapi/v1/refresh-vae", self.refresh_vae, methods=["POST"]) self.add_api_route("/sdapi/v1/refresh-vae", self.refresh_vae, methods=["POST"])
self.add_api_route("/sdapi/v1/create/embedding", self.create_embedding, methods=["POST"], response_model=models.CreateResponse) self.add_api_route("/sdapi/v1/create/embedding", self.create_embedding, methods=["POST"], response_model=models.CreateResponse)
self.add_api_route("/sdapi/v1/create/hypernetwork", self.create_hypernetwork, methods=["POST"], response_model=models.CreateResponse) self.add_api_route("/sdapi/v1/create/hypernetwork", self.create_hypernetwork, methods=["POST"], response_model=models.CreateResponse)
self.add_api_route("/sdapi/v1/preprocess", self.preprocess, methods=["POST"], response_model=models.PreprocessResponse)
self.add_api_route("/sdapi/v1/train/embedding", self.train_embedding, methods=["POST"], response_model=models.TrainResponse) self.add_api_route("/sdapi/v1/train/embedding", self.train_embedding, methods=["POST"], response_model=models.TrainResponse)
self.add_api_route("/sdapi/v1/train/hypernetwork", self.train_hypernetwork, methods=["POST"], response_model=models.TrainResponse) self.add_api_route("/sdapi/v1/train/hypernetwork", self.train_hypernetwork, methods=["POST"], response_model=models.TrainResponse)
self.add_api_route("/sdapi/v1/memory", self.get_memory, methods=["GET"], response_model=models.MemoryResponse) self.add_api_route("/sdapi/v1/memory", self.get_memory, methods=["GET"], response_model=models.MemoryResponse)
self.add_api_route("/sdapi/v1/unload-checkpoint", self.unloadapi, methods=["POST"]) self.add_api_route("/sdapi/v1/unload-checkpoint", self.unloadapi, methods=["POST"])
self.add_api_route("/sdapi/v1/reload-checkpoint", self.reloadapi, methods=["POST"]) self.add_api_route("/sdapi/v1/reload-checkpoint", self.reloadapi, methods=["POST"])
self.add_api_route("/sdapi/v1/scripts", self.get_scripts_list, methods=["GET"], response_model=models.ScriptsList) self.add_api_route("/sdapi/v1/scripts", self.get_scripts_list, methods=["GET"], response_model=models.ScriptsList)
self.add_api_route("/sdapi/v1/script-info", self.get_script_info, methods=["GET"], response_model=List[models.ScriptInfo]) self.add_api_route("/sdapi/v1/script-info", self.get_script_info, methods=["GET"], response_model=list[models.ScriptInfo])
self.add_api_route("/sdapi/v1/extensions", self.get_extensions_list, methods=["GET"], response_model=list[models.ExtensionItem])
if shared.cmd_opts.api_server_stop: if shared.cmd_opts.api_server_stop:
self.add_api_route("/sdapi/v1/server-kill", self.kill_webui, methods=["POST"]) self.add_api_route("/sdapi/v1/server-kill", self.kill_webui, methods=["POST"])
@ -462,6 +461,10 @@ class Api:
if eris_consolelog: if eris_consolelog:
print('[t2i]', txt2imgreq.width, 'x', txt2imgreq.height, '|', txt2imgreq.prompt) print('[t2i]', txt2imgreq.width, 'x', txt2imgreq.height, '|', txt2imgreq.prompt)
# Eris ______ # Eris ______
script_runner = scripts.scripts_txt2img script_runner = scripts.scripts_txt2img
if not script_runner.scripts: if not script_runner.scripts:
script_runner.initialize_scripts(False) script_runner.initialize_scripts(False)
@ -598,7 +601,6 @@ class Api:
if eris_consolelog: if eris_consolelog:
print('[i2i]', img2imgreq.width, 'x', img2imgreq.height, '|', img2imgreq.prompt) print('[i2i]', img2imgreq.width, 'x', img2imgreq.height, '|', img2imgreq.prompt)
# Eris ______ # Eris ______
init_images = img2imgreq.init_images init_images = img2imgreq.init_images
if init_images is None: if init_images is None:
raise HTTPException(status_code=404, detail="Init image not found") raise HTTPException(status_code=404, detail="Init image not found")
@ -618,6 +620,7 @@ class Api:
if eris_imagelog: if eris_imagelog:
img2imgreq.save_images = True img2imgreq.save_images = True
# Eris ______ # Eris ______
populate = img2imgreq.copy(update={ # Override __init__ params populate = img2imgreq.copy(update={ # Override __init__ params
"sampler_name": validate_sampler_name(img2imgreq.sampler_name or img2imgreq.sampler_index), "sampler_name": validate_sampler_name(img2imgreq.sampler_name or img2imgreq.sampler_index),
"do_not_save_samples": not img2imgreq.save_images, "do_not_save_samples": not img2imgreq.save_images,
@ -648,7 +651,7 @@ class Api:
apilogimg2imgtext.replace("\n", " ").replace("\r", " ") apilogimg2imgtext.replace("\n", " ").replace("\r", " ")
apilogimg2imgfile.write(f"{apilogimg2imgtext}\n") apilogimg2imgfile.write(f"{apilogimg2imgtext}\n")
# Eris ______ # Eris ______
with self.queue_lock: with self.queue_lock:
with closing(StableDiffusionProcessingImg2Img(sd_model=shared.sd_model, **args)) as p: with closing(StableDiffusionProcessingImg2Img(sd_model=shared.sd_model, **args)) as p:
p.init_images = [decode_base64_to_image(x) for x in init_images] p.init_images = [decode_base64_to_image(x) for x in init_images]
@ -699,9 +702,6 @@ class Api:
return models.ExtrasBatchImagesResponse(images=list(map(encode_pil_to_base64, result[0])), html_info=result[1]) return models.ExtrasBatchImagesResponse(images=list(map(encode_pil_to_base64, result[0])), html_info=result[1])
def pnginfoapi(self, req: models.PNGInfoRequest): def pnginfoapi(self, req: models.PNGInfoRequest):
if(not req.image.strip()):
return models.PNGInfoResponse(info="")
image = decode_base64_to_image(req.image.strip()) image = decode_base64_to_image(req.image.strip())
if image is None: if image is None:
return models.PNGInfoResponse(info="") return models.PNGInfoResponse(info="")
@ -710,9 +710,10 @@ class Api:
if geninfo is None: if geninfo is None:
geninfo = "" geninfo = ""
items = {**{'parameters': geninfo}, **items} params = generation_parameters_copypaste.parse_generation_parameters(geninfo)
script_callbacks.infotext_pasted_callback(geninfo, params)
return models.PNGInfoResponse(info=geninfo, items=items) return models.PNGInfoResponse(info=geninfo, items=items, parameters=params)
def progressapi(self, req: models.ProgressRequest = Depends()): def progressapi(self, req: models.ProgressRequest = Depends()):
# copy from check_progress_call of ui.py # copy from check_progress_call of ui.py
@ -767,12 +768,12 @@ class Api:
return {} return {}
def unloadapi(self): def unloadapi(self):
unload_model_weights() sd_models.unload_model_weights()
return {} return {}
def reloadapi(self): def reloadapi(self):
reload_model_weights() sd_models.send_model_to_device(shared.sd_model)
return {} return {}
@ -790,9 +791,9 @@ class Api:
return options return options
def set_config(self, req: Dict[str, Any]): def set_config(self, req: dict[str, Any]):
checkpoint_name = req.get("sd_model_checkpoint", None) checkpoint_name = req.get("sd_model_checkpoint", None)
if checkpoint_name is not None and checkpoint_name not in checkpoint_aliases: if checkpoint_name is not None and checkpoint_name not in sd_models.checkpoint_aliases:
raise RuntimeError(f"model {checkpoint_name!r} not found") raise RuntimeError(f"model {checkpoint_name!r} not found")
for k, v in req.items(): for k, v in req.items():
@ -902,19 +903,6 @@ class Api:
finally: finally:
shared.state.end() shared.state.end()
def preprocess(self, args: dict):
try:
shared.state.begin(job="preprocess")
preprocess(**args) # quick operation unless blip/booru interrogation is enabled
shared.state.end()
return models.PreprocessResponse(info='preprocess complete')
except KeyError as e:
return models.PreprocessResponse(info=f"preprocess error: invalid token: {e}")
except Exception as e:
return models.PreprocessResponse(info=f"preprocess error: {e}")
finally:
shared.state.end()
def train_embedding(self, args: dict): def train_embedding(self, args: dict):
try: try:
shared.state.begin(job="train_embedding") shared.state.begin(job="train_embedding")
@ -996,6 +984,25 @@ class Api:
cuda = {'error': f'{err}'} cuda = {'error': f'{err}'}
return models.MemoryResponse(ram=ram, cuda=cuda) return models.MemoryResponse(ram=ram, cuda=cuda)
def get_extensions_list(self):
from modules import extensions
extensions.list_extensions()
ext_list = []
for ext in extensions.extensions:
ext: extensions.Extension
ext.read_info_from_repo()
if ext.remote is not None:
ext_list.append({
"name": ext.name,
"remote": ext.remote,
"branch": ext.branch,
"commit_hash":ext.commit_hash,
"commit_date":ext.commit_date,
"version":ext.version,
"enabled":ext.enabled
})
return ext_list
def launch(self, server_name, port, root_path): def launch(self, server_name, port, root_path):
self.app.include_router(self.router) self.app.include_router(self.router)
uvicorn.run(self.app, host=server_name, port=port, timeout_keep_alive=shared.cmd_opts.timeout_keep_alive, root_path=root_path) uvicorn.run(self.app, host=server_name, port=port, timeout_keep_alive=shared.cmd_opts.timeout_keep_alive, root_path=root_path)