Hitmare
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Eris_api_tensor_patch
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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
parent a4394df014 1d22d29b55
commit b5dbfc2bca
6 changed files with 3370 additions and 3353 deletions
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81
#Original/api.py
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)

51
TRT-Patch/#Original/processing.py
View File

@ -142,7 +142,7 @@ class StableDiffusionProcessing:
overlay_images: list = None
eta: float = None
do_not_reload_embeddings: bool = False
denoising_strength: float = 0
denoising_strength: float = None
ddim_discretize: str = None
s_min_uncond: float = None
s_churn: float = None
@ -296,7 +296,7 @@ class StableDiffusionProcessing:
return conditioning
def edit_image_conditioning(self, source_image):
conditioning_image = images_tensor_to_samples(source_image*0.5+0.5, approximation_indexes.get(opts.sd_vae_encode_method))
conditioning_image = shared.sd_model.encode_first_stage(source_image).mode()
return conditioning_image
@ -533,6 +533,7 @@ class Processed:
self.all_seeds = all_seeds or p.all_seeds or [self.seed]
self.all_subseeds = all_subseeds or p.all_subseeds or [self.subseed]
self.infotexts = infotexts or [info]
self.version = program_version()
def js(self):
obj = {
@ -567,6 +568,7 @@ class Processed:
"job_timestamp": self.job_timestamp,
"clip_skip": self.clip_skip,
"is_using_inpainting_conditioning": self.is_using_inpainting_conditioning,
"version": self.version,
}
return json.dumps(obj)
@ -677,8 +679,8 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
"Size": f"{p.width}x{p.height}",
"Model hash": p.sd_model_hash if opts.add_model_hash_to_info else None,
"Model": p.sd_model_name if opts.add_model_name_to_info else None,
"VAE hash": p.sd_vae_hash if opts.add_model_hash_to_info else None,
"VAE": p.sd_vae_name if opts.add_model_name_to_info else None,
"VAE hash": p.sd_vae_hash if opts.add_vae_hash_to_info else None,
"VAE": p.sd_vae_name if opts.add_vae_name_to_info else None,
"Variation seed": (None if p.subseed_strength == 0 else (p.all_subseeds[0] if use_main_prompt else all_subseeds[index])),
"Variation seed strength": (None if p.subseed_strength == 0 else p.subseed_strength),
"Seed resize from": (None if p.seed_resize_from_w <= 0 or p.seed_resize_from_h <= 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"),
@ -709,7 +711,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
if p.scripts is not None:
p.scripts.before_process(p)
stored_opts = {k: opts.data[k] for k in p.override_settings.keys()}
stored_opts = {k: opts.data[k] if k in opts.data else opts.get_default(k) for k in p.override_settings.keys() if k in opts.data}
try:
# if no checkpoint override or the override checkpoint can't be found, remove override entry and load opts checkpoint
@ -797,7 +799,6 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
infotexts = []
output_images = []
with torch.no_grad(), p.sd_model.ema_scope():
with devices.autocast():
p.init(p.all_prompts, p.all_seeds, p.all_subseeds)
@ -871,7 +872,6 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
else:
if opts.sd_vae_decode_method != 'Full':
p.extra_generation_params['VAE Decoder'] = opts.sd_vae_decode_method
x_samples_ddim = decode_latent_batch(p.sd_model, samples_ddim, target_device=devices.cpu, check_for_nans=True)
x_samples_ddim = torch.stack(x_samples_ddim).float()
@ -884,6 +884,8 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
devices.torch_gc()
state.nextjob()
if p.scripts is not None:
p.scripts.postprocess_batch(p, x_samples_ddim, batch_number=n)
@ -936,27 +938,27 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
if opts.enable_pnginfo:
image.info["parameters"] = text
output_images.append(image)
if save_samples and hasattr(p, 'mask_for_overlay') and p.mask_for_overlay and any([opts.save_mask, opts.save_mask_composite, opts.return_mask, opts.return_mask_composite]):
image_mask = p.mask_for_overlay.convert('RGB')
image_mask_composite = Image.composite(image.convert('RGBA').convert('RGBa'), Image.new('RGBa', image.size), images.resize_image(2, p.mask_for_overlay, image.width, image.height).convert('L')).convert('RGBA')
if hasattr(p, 'mask_for_overlay') and p.mask_for_overlay:
if opts.return_mask or opts.save_mask:
image_mask = p.mask_for_overlay.convert('RGB')
if save_samples and opts.save_mask:
images.save_image(image_mask, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p, suffix="-mask")
if opts.return_mask:
output_images.append(image_mask)
if opts.save_mask:
images.save_image(image_mask, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p, suffix="-mask")
if opts.save_mask_composite:
images.save_image(image_mask_composite, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p, suffix="-mask-composite")
if opts.return_mask:
output_images.append(image_mask)
if opts.return_mask_composite:
output_images.append(image_mask_composite)
if opts.return_mask_composite or opts.save_mask_composite:
image_mask_composite = Image.composite(image.convert('RGBA').convert('RGBa'), Image.new('RGBa', image.size), images.resize_image(2, p.mask_for_overlay, image.width, image.height).convert('L')).convert('RGBA')
if save_samples and opts.save_mask_composite:
images.save_image(image_mask_composite, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p, suffix="-mask-composite")
if opts.return_mask_composite:
output_images.append(image_mask_composite)
del x_samples_ddim
devices.torch_gc()
state.nextjob()
if not infotexts:
infotexts.append(Processed(p, []).infotext(p, 0))
p.color_corrections = None
@ -1142,6 +1144,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
if not self.enable_hr:
return samples
devices.torch_gc()
if self.latent_scale_mode is None:
decoded_samples = torch.stack(decode_latent_batch(self.sd_model, samples, target_device=devices.cpu, check_for_nans=True)).to(dtype=torch.float32)
@ -1151,8 +1154,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
with sd_models.SkipWritingToConfig():
sd_models.reload_model_weights(info=self.hr_checkpoint_info)
devices.torch_gc()
return self.sample_hr_pass(samples, decoded_samples, seeds, subseeds, subseed_strength, prompts)
def sample_hr_pass(self, samples, decoded_samples, seeds, subseeds, subseed_strength, prompts):
@ -1160,7 +1161,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
return samples
self.is_hr_pass = True
target_width = self.hr_upscale_to_x
target_height = self.hr_upscale_to_y
@ -1249,7 +1249,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
decoded_samples = decode_latent_batch(self.sd_model, samples, target_device=devices.cpu, check_for_nans=True)
self.is_hr_pass = False
return decoded_samples
def close(self):

14
TRT-Patch/#Original/sd_unet.py
View File

@ -1,12 +1,11 @@
import torch.nn
import ldm.modules.diffusionmodules.openaimodel
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()
@ -84,9 +83,12 @@ class SdUnet(torch.nn.Module):
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)
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 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

52
TRT-Patch/modules/processing.py
View File

@ -88,6 +88,7 @@ def create_binary_mask(image):
image = image.convert('L')
return image
def txt2img_image_conditioning(sd_model, x, width, height):
if sd_model.model.conditioning_key in {'hybrid', 'concat'}: # Inpainting models
@ -142,7 +143,7 @@ class StableDiffusionProcessing:
overlay_images: list = None
eta: float = None
do_not_reload_embeddings: bool = False
denoising_strength: float = 0
denoising_strength: float = None
ddim_discretize: str = None
s_min_uncond: float = None
s_churn: float = None
@ -298,7 +299,7 @@ class StableDiffusionProcessing:
return conditioning
def edit_image_conditioning(self, source_image):
conditioning_image = images_tensor_to_samples(source_image*0.5+0.5, approximation_indexes.get(opts.sd_vae_encode_method))
conditioning_image = shared.sd_model.encode_first_stage(source_image).mode()
return conditioning_image
@ -535,6 +536,7 @@ class Processed:
self.all_seeds = all_seeds or p.all_seeds or [self.seed]
self.all_subseeds = all_subseeds or p.all_subseeds or [self.subseed]
self.infotexts = infotexts or [info]
self.version = program_version()
def js(self):
obj = {
@ -569,6 +571,7 @@ class Processed:
"job_timestamp": self.job_timestamp,
"clip_skip": self.clip_skip,
"is_using_inpainting_conditioning": self.is_using_inpainting_conditioning,
"version": self.version,
}
return json.dumps(obj)
@ -679,8 +682,8 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
"Size": f"{p.width}x{p.height}",
"Model hash": p.sd_model_hash if opts.add_model_hash_to_info else None,
"Model": p.sd_model_name if opts.add_model_name_to_info else None,
"VAE hash": p.sd_vae_hash if opts.add_model_hash_to_info else None,
"VAE": p.sd_vae_name if opts.add_model_name_to_info else None,
"VAE hash": p.sd_vae_hash if opts.add_vae_hash_to_info else None,
"VAE": p.sd_vae_name if opts.add_vae_name_to_info else None,
"Variation seed": (None if p.subseed_strength == 0 else (p.all_subseeds[0] if use_main_prompt else all_subseeds[index])),
"Variation seed strength": (None if p.subseed_strength == 0 else p.subseed_strength),
"Seed resize from": (None if p.seed_resize_from_w <= 0 or p.seed_resize_from_h <= 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"),
@ -711,7 +714,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
if p.scripts is not None:
p.scripts.before_process(p)
stored_opts = {k: opts.data[k] for k in p.override_settings.keys()}
stored_opts = {k: opts.data[k] if k in opts.data else opts.get_default(k) for k in p.override_settings.keys() if k in opts.data}
try:
# if no checkpoint override or the override checkpoint can't be found, remove override entry and load opts checkpoint
@ -799,7 +802,6 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
infotexts = []
output_images = []
with torch.no_grad(), p.sd_model.ema_scope():
with devices.autocast():
p.init(p.all_prompts, p.all_seeds, p.all_subseeds)
@ -873,7 +875,6 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
else:
if opts.sd_vae_decode_method != 'Full':
p.extra_generation_params['VAE Decoder'] = opts.sd_vae_decode_method
x_samples_ddim = decode_latent_batch(p.sd_model, samples_ddim, target_device=devices.cpu, check_for_nans=True)
x_samples_ddim = torch.stack(x_samples_ddim).float()
@ -886,6 +887,8 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
devices.torch_gc()
state.nextjob()
if p.scripts is not None:
p.scripts.postprocess_batch(p, x_samples_ddim, batch_number=n)
@ -938,27 +941,27 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
if opts.enable_pnginfo:
image.info["parameters"] = text
output_images.append(image)
if save_samples and hasattr(p, 'mask_for_overlay') and p.mask_for_overlay and any([opts.save_mask, opts.save_mask_composite, opts.return_mask, opts.return_mask_composite]):
image_mask = p.mask_for_overlay.convert('RGB')
image_mask_composite = Image.composite(image.convert('RGBA').convert('RGBa'), Image.new('RGBa', image.size), images.resize_image(2, p.mask_for_overlay, image.width, image.height).convert('L')).convert('RGBA')
if hasattr(p, 'mask_for_overlay') and p.mask_for_overlay:
if opts.return_mask or opts.save_mask:
image_mask = p.mask_for_overlay.convert('RGB')
if save_samples and opts.save_mask:
images.save_image(image_mask, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p, suffix="-mask")
if opts.return_mask:
output_images.append(image_mask)
if opts.save_mask:
images.save_image(image_mask, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p, suffix="-mask")
if opts.save_mask_composite:
images.save_image(image_mask_composite, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p, suffix="-mask-composite")
if opts.return_mask:
output_images.append(image_mask)
if opts.return_mask_composite:
output_images.append(image_mask_composite)
if opts.return_mask_composite or opts.save_mask_composite:
image_mask_composite = Image.composite(image.convert('RGBA').convert('RGBa'), Image.new('RGBa', image.size), images.resize_image(2, p.mask_for_overlay, image.width, image.height).convert('L')).convert('RGBA')
if save_samples and opts.save_mask_composite:
images.save_image(image_mask_composite, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p, suffix="-mask-composite")
if opts.return_mask_composite:
output_images.append(image_mask_composite)
del x_samples_ddim
devices.torch_gc()
state.nextjob()
if not infotexts:
infotexts.append(Processed(p, []).infotext(p, 0))
p.color_corrections = None
@ -1144,6 +1147,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
if not self.enable_hr:
return samples
devices.torch_gc()
if self.latent_scale_mode is None:
decoded_samples = torch.stack(decode_latent_batch(self.sd_model, samples, target_device=devices.cpu, check_for_nans=True)).to(dtype=torch.float32)
@ -1153,8 +1157,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
with sd_models.SkipWritingToConfig():
sd_models.reload_model_weights(info=self.hr_checkpoint_info)
devices.torch_gc()
return self.sample_hr_pass(samples, decoded_samples, seeds, subseeds, subseed_strength, prompts)
def sample_hr_pass(self, samples, decoded_samples, seeds, subseeds, subseed_strength, prompts):
@ -1162,7 +1164,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
return samples
self.is_hr_pass = True
target_width = self.hr_upscale_to_x
target_height = self.hr_upscale_to_y
@ -1251,7 +1252,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
decoded_samples = decode_latent_batch(self.sd_model, samples, target_device=devices.cpu, check_for_nans=True)
self.is_hr_pass = False
return decoded_samples
def close(self):

40
TRT-Patch/modules/sd_unet.py
View File

@ -1,12 +1,11 @@
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
original_forward = None # not used, only left temporarily for compatibility
def list_unets():
new_unets = script_callbacks.list_unets_callback()
@ -84,18 +83,25 @@ class SdUnet(torch.nn.Module):
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')
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
return ldm.modules.diffusionmodules.openaimodel.copy_of_UNetModel_forward_for_webui(self, x, timesteps, context, *args, **kwargs)

87
api.py
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
@ -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,
@ -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)