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
This commit is contained in:
Hitmare 2023-12-23 15:10:33 +00:00
commit b5dbfc2bca
6 changed files with 3370 additions and 3353 deletions

View File

@ -17,19 +17,17 @@ from fastapi.encoders import jsonable_encoder
from secrets import compare_digest
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.shared import opts
from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
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 PIL import PngImagePlugin,Image
from modules.sd_models import unload_model_weights, reload_model_weights, checkpoint_aliases
from PIL import PngImagePlugin, Image
from modules.sd_models_config import find_checkpoint_config_near_filename
from modules.realesrgan_model import get_realesrgan_models
from modules import devices
from typing import Dict, List, Any
from typing import Any
import piexif
import piexif.helper
from contextlib import closing
@ -103,7 +101,8 @@ def decode_base64_to_image(encoding):
def encode_pil_to_base64(image):
with io.BytesIO() as output_bytes:
if isinstance(image, str):
return image
if opts.samples_format.lower() == 'png':
use_metadata = False
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.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/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/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-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/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/prompt-styles", self.get_prompt_styles, methods=["GET"], response_model=List[models.PromptStyleItem])
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/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-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/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/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/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/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/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/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/unload-checkpoint", self.unloadapi, 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/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:
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])
def pnginfoapi(self, req: models.PNGInfoRequest):
if(not req.image.strip()):
return models.PNGInfoResponse(info="")
image = decode_base64_to_image(req.image.strip())
if image is None:
return models.PNGInfoResponse(info="")
@ -484,9 +480,10 @@ class Api:
if geninfo is None:
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()):
# copy from check_progress_call of ui.py
@ -541,12 +538,12 @@ class Api:
return {}
def unloadapi(self):
unload_model_weights()
sd_models.unload_model_weights()
return {}
def reloadapi(self):
reload_model_weights()
sd_models.send_model_to_device(shared.sd_model)
return {}
@ -564,9 +561,9 @@ class Api:
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)
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")
for k, v in req.items():
@ -676,19 +673,6 @@ class Api:
finally:
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):
try:
shared.state.begin(job="train_embedding")
@ -770,6 +754,25 @@ class Api:
cuda = {'error': f'{err}'}
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):
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)

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@ -1,92 +1,94 @@
import torch.nn
import ldm.modules.diffusionmodules.openaimodel
from modules import script_callbacks, shared, devices
unet_options = []
current_unet_option = None
current_unet = None
def list_unets():
new_unets = script_callbacks.list_unets_callback()
unet_options.clear()
unet_options.extend(new_unets)
def get_unet_option(option=None):
option = option or shared.opts.sd_unet
if option == "None":
return None
if option == "Automatic":
name = shared.sd_model.sd_checkpoint_info.model_name
options = [x for x in unet_options if x.model_name == name]
option = options[0].label if options else "None"
return next(iter([x for x in unet_options if x.label == option]), None)
def apply_unet(option=None):
global current_unet_option
global current_unet
new_option = get_unet_option(option)
if new_option == current_unet_option:
return
if current_unet is not None:
print(f"Dectivating unet: {current_unet.option.label}")
current_unet.deactivate()
current_unet_option = new_option
if current_unet_option is None:
current_unet = None
if not shared.sd_model.lowvram:
shared.sd_model.model.diffusion_model.to(devices.device)
return
shared.sd_model.model.diffusion_model.to(devices.cpu)
devices.torch_gc()
current_unet = current_unet_option.create_unet()
current_unet.option = current_unet_option
print(f"Activating unet: {current_unet.option.label}")
current_unet.activate()
class SdUnetOption:
model_name = None
"""name of related checkpoint - this option will be selected automatically for unet if the name of checkpoint matches this"""
label = None
"""name of the unet in UI"""
def create_unet(self):
"""returns SdUnet object to be used as a Unet instead of built-in unet when making pictures"""
raise NotImplementedError()
class SdUnet(torch.nn.Module):
def forward(self, x, timesteps, context, *args, **kwargs):
raise NotImplementedError()
def activate(self):
pass
def deactivate(self):
pass
def UNetModel_forward(self, x, timesteps=None, context=None, *args, **kwargs):
if current_unet is not None:
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)
import torch.nn
from modules import script_callbacks, shared, devices
unet_options = []
current_unet_option = None
current_unet = None
original_forward = None # not used, only left temporarily for compatibility
def list_unets():
new_unets = script_callbacks.list_unets_callback()
unet_options.clear()
unet_options.extend(new_unets)
def get_unet_option(option=None):
option = option or shared.opts.sd_unet
if option == "None":
return None
if option == "Automatic":
name = shared.sd_model.sd_checkpoint_info.model_name
options = [x for x in unet_options if x.model_name == name]
option = options[0].label if options else "None"
return next(iter([x for x in unet_options if x.label == option]), None)
def apply_unet(option=None):
global current_unet_option
global current_unet
new_option = get_unet_option(option)
if new_option == current_unet_option:
return
if current_unet is not None:
print(f"Dectivating unet: {current_unet.option.label}")
current_unet.deactivate()
current_unet_option = new_option
if current_unet_option is None:
current_unet = None
if not shared.sd_model.lowvram:
shared.sd_model.model.diffusion_model.to(devices.device)
return
shared.sd_model.model.diffusion_model.to(devices.cpu)
devices.torch_gc()
current_unet = current_unet_option.create_unet()
current_unet.option = current_unet_option
print(f"Activating unet: {current_unet.option.label}")
current_unet.activate()
class SdUnetOption:
model_name = None
"""name of related checkpoint - this option will be selected automatically for unet if the name of checkpoint matches this"""
label = None
"""name of the unet in UI"""
def create_unet(self):
"""returns SdUnet object to be used as a Unet instead of built-in unet when making pictures"""
raise NotImplementedError()
class SdUnet(torch.nn.Module):
def forward(self, x, timesteps, context, *args, **kwargs):
raise NotImplementedError()
def activate(self):
pass
def deactivate(self):
pass
def create_unet_forward(original_forward):
def UNetModel_forward(self, x, timesteps=None, context=None, *args, **kwargs):
if current_unet is not None:
return current_unet.forward(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 ldm.modules.diffusionmodules.openaimodel
import time
from modules import script_callbacks, shared, devices
unet_options = []
current_unet_option = None
current_unet = None
def list_unets():
new_unets = script_callbacks.list_unets_callback()
unet_options.clear()
unet_options.extend(new_unets)
def get_unet_option(option=None):
option = option or shared.opts.sd_unet
if option == "None":
return None
if option == "Automatic":
name = shared.sd_model.sd_checkpoint_info.model_name
options = [x for x in unet_options if x.model_name == name]
option = options[0].label if options else "None"
return next(iter([x for x in unet_options if x.label == option]), None)
def apply_unet(option=None):
global current_unet_option
global current_unet
new_option = get_unet_option(option)
if new_option == current_unet_option:
return
if current_unet is not None:
print(f"Dectivating unet: {current_unet.option.label}")
current_unet.deactivate()
current_unet_option = new_option
if current_unet_option is None:
current_unet = None
if not shared.sd_model.lowvram:
shared.sd_model.model.diffusion_model.to(devices.device)
return
shared.sd_model.model.diffusion_model.to(devices.cpu)
devices.torch_gc()
current_unet = current_unet_option.create_unet()
current_unet.option = current_unet_option
print(f"Activating unet: {current_unet.option.label}")
current_unet.activate()
class SdUnetOption:
model_name = None
"""name of related checkpoint - this option will be selected automatically for unet if the name of checkpoint matches this"""
label = None
"""name of the unet in UI"""
def create_unet(self):
"""returns SdUnet object to be used as a Unet instead of built-in unet when making pictures"""
raise NotImplementedError()
class SdUnet(torch.nn.Module):
def forward(self, x, timesteps, context, *args, **kwargs):
raise NotImplementedError()
def activate(self):
pass
def deactivate(self):
pass
def UNetModel_forward(self, x, timesteps=None, context=None, *args, **kwargs):
try:
if current_unet is not None and shared.current_prompt != shared.skip_unet_prompt:
if '[TRT]' in shared.opts.sd_unet and '<lora:' in shared.current_prompt:
raise Exception('LoRA unsupported in TRT UNet')
f = current_unet.forward(x, timesteps, context, *args, **kwargs)
return f
except Exception as e:
start = time.time()
print('[UNet] Skipping TRT UNet for this request:', e, '-', shared.current_prompt)
shared.sd_model.model.diffusion_model.to(devices.device)
shared.skip_unet_prompt = shared.current_prompt
print('[UNet] Used', time.time() - start, 'seconds')
return ldm.modules.diffusionmodules.openaimodel.copy_of_UNetModel_forward_for_webui(self, x, timesteps, context, *args, **kwargs)
import torch.nn
import time
from modules import script_callbacks, shared, devices
unet_options = []
current_unet_option = None
current_unet = None
original_forward = None # not used, only left temporarily for compatibility
def list_unets():
new_unets = script_callbacks.list_unets_callback()
unet_options.clear()
unet_options.extend(new_unets)
def get_unet_option(option=None):
option = option or shared.opts.sd_unet
if option == "None":
return None
if option == "Automatic":
name = shared.sd_model.sd_checkpoint_info.model_name
options = [x for x in unet_options if x.model_name == name]
option = options[0].label if options else "None"
return next(iter([x for x in unet_options if x.label == option]), None)
def apply_unet(option=None):
global current_unet_option
global current_unet
new_option = get_unet_option(option)
if new_option == current_unet_option:
return
if current_unet is not None:
print(f"Dectivating unet: {current_unet.option.label}")
current_unet.deactivate()
current_unet_option = new_option
if current_unet_option is None:
current_unet = None
if not shared.sd_model.lowvram:
shared.sd_model.model.diffusion_model.to(devices.device)
return
shared.sd_model.model.diffusion_model.to(devices.cpu)
devices.torch_gc()
current_unet = current_unet_option.create_unet()
current_unet.option = current_unet_option
print(f"Activating unet: {current_unet.option.label}")
current_unet.activate()
class SdUnetOption:
model_name = None
"""name of related checkpoint - this option will be selected automatically for unet if the name of checkpoint matches this"""
label = None
"""name of the unet in UI"""
def create_unet(self):
"""returns SdUnet object to be used as a Unet instead of built-in unet when making pictures"""
raise NotImplementedError()
class SdUnet(torch.nn.Module):
def forward(self, x, timesteps, context, *args, **kwargs):
raise NotImplementedError()
def activate(self):
pass
def deactivate(self):
pass
def create_unet_forward(original_forward):
def UNetModel_forward(self, x, timesteps=None, context=None, *args, **kwargs):
if current_unet is not None:
return current_unet.forward(x, timesteps, context, *args, **kwargs)
try:
if current_unet is not None and shared.current_prompt != shared.skip_unet_prompt:
if '[TRT]' in shared.opts.sd_unet and '<lora:' in shared.current_prompt:
raise Exception('LoRA unsupported in TRT UNet')
f = current_unet.forward(x, timesteps, context, *args, **kwargs)
return f
except Exception as e:
start = time.time()
print('[UNet] Skipping TRT UNet for this request:', e, '-', shared.current_prompt)
shared.sd_model.model.diffusion_model.to(devices.device)
shared.skip_unet_prompt = shared.current_prompt
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
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.shared import opts
from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
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 PIL import PngImagePlugin,Image
from modules.sd_models import unload_model_weights, reload_model_weights, checkpoint_aliases
from PIL import PngImagePlugin, Image
from modules.sd_models_config import find_checkpoint_config_near_filename
from modules.realesrgan_model import get_realesrgan_models
from modules import devices
from typing import Dict, List, Any
from typing import Any
import piexif
import piexif.helper
from contextlib import closing
@ -146,7 +144,8 @@ def decode_base64_to_image(encoding):
def encode_pil_to_base64(image):
with io.BytesIO() as output_bytes:
if isinstance(image, str):
return image
if opts.samples_format.lower() == 'png':
use_metadata = False
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.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/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/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-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/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/prompt-styles", self.get_prompt_styles, methods=["GET"], response_model=List[models.PromptStyleItem])
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/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-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/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/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/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/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/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/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/unload-checkpoint", self.unloadapi, 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/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:
self.add_api_route("/sdapi/v1/server-kill", self.kill_webui, methods=["POST"])
@ -462,6 +461,10 @@ class Api:
if eris_consolelog:
print('[t2i]', txt2imgreq.width, 'x', txt2imgreq.height, '|', txt2imgreq.prompt)
# Eris ______
script_runner = scripts.scripts_txt2img
if not script_runner.scripts:
script_runner.initialize_scripts(False)
@ -598,7 +601,6 @@ class Api:
if eris_consolelog:
print('[i2i]', img2imgreq.width, 'x', img2imgreq.height, '|', img2imgreq.prompt)
# Eris ______
init_images = img2imgreq.init_images
if init_images is None:
raise HTTPException(status_code=404, detail="Init image not found")
@ -618,6 +620,7 @@ class Api:
if eris_imagelog:
img2imgreq.save_images = True
# Eris ______
populate = img2imgreq.copy(update={ # Override __init__ params
"sampler_name": validate_sampler_name(img2imgreq.sampler_name or img2imgreq.sampler_index),
"do_not_save_samples": not img2imgreq.save_images,
@ -648,7 +651,7 @@ class Api:
apilogimg2imgtext.replace("\n", " ").replace("\r", " ")
apilogimg2imgfile.write(f"{apilogimg2imgtext}\n")
# Eris ______
with self.queue_lock:
with closing(StableDiffusionProcessingImg2Img(sd_model=shared.sd_model, **args)) as p:
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])
def pnginfoapi(self, req: models.PNGInfoRequest):
if(not req.image.strip()):
return models.PNGInfoResponse(info="")
image = decode_base64_to_image(req.image.strip())
if image is None:
return models.PNGInfoResponse(info="")
@ -710,9 +710,10 @@ class Api:
if geninfo is None:
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()):
# copy from check_progress_call of ui.py
@ -767,12 +768,12 @@ class Api:
return {}
def unloadapi(self):
unload_model_weights()
sd_models.unload_model_weights()
return {}
def reloadapi(self):
reload_model_weights()
sd_models.send_model_to_device(shared.sd_model)
return {}
@ -790,9 +791,9 @@ class Api:
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)
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")
for k, v in req.items():
@ -902,19 +903,6 @@ class Api:
finally:
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):
try:
shared.state.begin(job="train_embedding")
@ -996,6 +984,25 @@ class Api:
cuda = {'error': f'{err}'}
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):
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)