Public/automatic
1
0
Fork 0
mirror of https://github.com/vladmandic/automatic synced 2026-09-10 14:58:44 +02:00
Code Issues Packages Projects Releases Wiki Activity

uniform hf auth check

Browse Source 
Signed-off-by: Vladimir Mandic <mandic00@live.com>
This commit is contained in:
Vladimir Mandic
2025-08-29 10:34:40 -04:00
parent 928b8838b9
commit 032bf6972a
9 changed files with 67 additions and 66 deletions
Download Patch File Download Diff File Expand all files Collapse all files
modules/sd_models.py
+1 -1
View File
@@ -339,7 +339,7 @@ def load_diffuser_force(model_type, checkpoint_info, diffusers_load_config, op='
sd_model = load_meissonic(checkpoint_info, diffusers_load_config)
allow_post_quant = True
elif model_type in ['OmniGen2']: # forced pipeline
from pipelines.model_omnigen2 import load_omnigen2
from pipelines.model_omnigen import load_omnigen2
sd_model = load_omnigen2(checkpoint_info, diffusers_load_config)
allow_post_quant = False
elif model_type in ['OmniGen']: # forced pipeline
pipelines/model_hdm.py
+1
View File
@@ -6,6 +6,7 @@ from modules import shared, devices, sd_models, errors
def load_hdm(checkpoint_info, diffusers_load_config={}): # pylint: disable=unused-argument
repo_id = sd_models.path_to_repo(checkpoint_info)
sd_models.hf_auth_check(checkpoint_info)
try:
devices.dtype = torch.float16
pipelines/model_kolors.py
+5 -3
View File
@@ -1,14 +1,16 @@
import torch
import diffusers
from modules import shared, devices, sd_hijack_te
from modules import shared, devices, sd_models, sd_hijack_te
def load_kolors(_checkpoint_info, diffusers_load_config={}):
def load_kolors(checkpoint_info, diffusers_load_config={}):
repo_id = sd_models.path_to_repo(checkpoint_info)
sd_models.hf_auth_check(checkpoint_info)
diffusers_load_config['variant'] = "fp16"
if 'torch_dtype' not in diffusers_load_config:
diffusers_load_config['torch_dtype'] = torch.float16
repo_id = 'Kwai-Kolors/Kolors-diffusers'
shared.log.debug(f'Load model: type=Kolors repo="{repo_id}" config={diffusers_load_config} offload={shared.opts.diffusers_offload_mode} dtype={devices.dtype} args={diffusers_load_config}')
pipe = diffusers.KolorsPipeline.from_pretrained(
repo_id,
pipelines/model_lumina.py
+4 -2
View File
@@ -1,12 +1,13 @@
import transformers
import diffusers
from modules import shared, sd_models, sd_hijack_te, devices, modelloader, model_quant
from modules import shared, sd_models, sd_hijack_te, devices, model_quant
from pipelines import generic
def load_lumina(checkpoint_info, diffusers_load_config={}):
repo_id = sd_models.path_to_repo(checkpoint_info)
modelloader.hf_login()
sd_models.hf_auth_check(checkpoint_info)
load_config, _quant_config = model_quant.get_dit_args(diffusers_load_config, allow_quant=False)
shared.log.debug(f'Load model: type=LuminaSFT repo="{repo_id}" config={diffusers_load_config} offload={shared.opts.diffusers_offload_mode} dtype={devices.dtype} args={diffusers_load_config}')
pipe = diffusers.LuminaText2ImgPipeline.from_pretrained(
@@ -21,6 +22,7 @@ def load_lumina(checkpoint_info, diffusers_load_config={}):
def load_lumina2(checkpoint_info, diffusers_load_config={}):
repo_id = sd_models.path_to_repo(checkpoint_info)
sd_models.hf_auth_check(checkpoint_info)
if shared.opts.teacache_enabled:
from modules import teacache
pipelines/model_meissonic.py
+6 -7
View File
@@ -11,9 +11,8 @@ def load_meissonic(checkpoint_info, diffusers_load_config={}):
from pipelines.meissonic.pipeline_inpaint import MeissonicInpaintPipeline
shared_items.pipelines['Meissonic'] = MeissonicPipeline
modelloader.hf_login()
repo_id = sd_models.path_to_repo(checkpoint_info)
cache_dir = shared.opts.diffusers_dir
sd_models.hf_auth_check(checkpoint_info)
diffusers_load_config['variant'] = 'fp16'
diffusers_load_config['trust_remote_code'] = True
@@ -22,29 +21,29 @@ def load_meissonic(checkpoint_info, diffusers_load_config={}):
model = TransformerMeissonic.from_pretrained(
repo_id,
subfolder="transformer",
cache_dir=cache_dir,
cache_dir=shared.opts.diffusers_dir,
**diffusers_load_config,
)
vqvae = diffusers.VQModel.from_pretrained(
repo_id,
subfolder="vqvae",
cache_dir=cache_dir,
cache_dir=shared.opts.diffusers_dir,
**diffusers_load_config,
)
text_encoder = transformers.CLIPTextModelWithProjection.from_pretrained(
repo_id,
subfolder="text_encoder",
cache_dir=cache_dir,
cache_dir=shared.opts.diffusers_dir,
)
tokenizer = transformers.CLIPTokenizer.from_pretrained(
repo_id,
subfolder="tokenizer",
cache_dir=cache_dir,
cache_dir=shared.opts.diffusers_dir,
)
scheduler = MeissonicScheduler.from_pretrained(
repo_id,
subfolder="scheduler",
cache_dir=cache_dir,
cache_dir=shared.opts.diffusers_dir,
)
pipe = MeissonicPipeline(
vqvae=vqvae.to(devices.dtype),
pipelines/model_omnigen.py
+48 -3
View File
@@ -4,7 +4,7 @@ from modules import shared, devices, sd_models, model_quant, sd_hijack_te
def load_omnigen(checkpoint_info, diffusers_load_config={}): # pylint: disable=unused-argument
repo_id = sd_models.path_to_repo(checkpoint_info)
vae = None
sd_models.hf_auth_check(checkpoint_info)
load_config, quant_config = model_quant.get_dit_args(diffusers_load_config, module='Model')
shared.log.debug(f'Load model: type=OmniGen repo="{repo_id}" config={diffusers_load_config} offload={shared.opts.diffusers_offload_mode} dtype={devices.dtype} args={diffusers_load_config}')
@@ -17,8 +17,6 @@ def load_omnigen(checkpoint_info, diffusers_load_config={}): # pylint: disable=u
)
load_config, quant_config = model_quant.get_dit_args(diffusers_load_config, allow_quant=False)
if vae is not None:
load_config['vae'] = vae
pipe = diffusers.OmniGenPipeline.from_pretrained(
repo_id,
transformer=transformer,
@@ -30,3 +28,50 @@ def load_omnigen(checkpoint_info, diffusers_load_config={}): # pylint: disable=u
devices.torch_gc(force=True, reason='load')
return pipe
def load_omnigen2(checkpoint_info, diffusers_load_config={}): # pylint: disable=unused-argument
repo_id = sd_models.path_to_repo(checkpoint_info)
sd_models.hf_auth_check(checkpoint_info)
from pipelines.omnigen2 import OmniGen2Pipeline, OmniGen2Transformer2DModel, Qwen2_5_VLForConditionalGeneration
diffusers.OmniGen2Pipeline = OmniGen2Pipeline # monkey-pathch
diffusers.pipelines.auto_pipeline.AUTO_TEXT2IMAGE_PIPELINES_MAPPING["omnigen2"] = diffusers.OmniGen2Pipeline
diffusers.pipelines.auto_pipeline.AUTO_IMAGE2IMAGE_PIPELINES_MAPPING["omnigen2"] = diffusers.OmniGen2Pipeline
diffusers.pipelines.auto_pipeline.AUTO_INPAINT_PIPELINES_MAPPING["omnigen2"] = diffusers.OmniGen2Pipeline
load_config, quant_config = model_quant.get_dit_args(diffusers_load_config, module='Model')
shared.log.debug(f'Load model: type=OmniGen2 repo="{repo_id}" config={diffusers_load_config} offload={shared.opts.diffusers_offload_mode} dtype={devices.dtype} args={diffusers_load_config}')
transformer = OmniGen2Transformer2DModel.from_pretrained(
repo_id,
subfolder="transformer",
cache_dir=shared.opts.diffusers_dir,
trust_remote_code=True,
**load_config,
**quant_config,
)
load_config, quant_config = model_quant.get_dit_args(diffusers_load_config, module='TE')
mllm = Qwen2_5_VLForConditionalGeneration.from_pretrained(
repo_id,
subfolder="mllm",
cache_dir=shared.opts.diffusers_dir,
trust_remote_code=True,
**load_config,
**quant_config,
)
pipe = OmniGen2Pipeline.from_pretrained(
repo_id,
# transformer=transformer,
mllm=mllm,
cache_dir=shared.opts.diffusers_dir,
trust_remote_code=True,
**load_config,
)
pipe.transformer = transformer # for omnigen2 transformer must be loaded after pipeline
sd_hijack_te.init_hijack(pipe)
devices.torch_gc(force=True, reason='load')
return pipe
pipelines/model_omnigen2.py
-48
View File
@@ -1,48 +0,0 @@
import diffusers
from modules import shared, devices, sd_models, model_quant, sd_hijack_te
def load_omnigen2(checkpoint_info, diffusers_load_config={}): # pylint: disable=unused-argument
repo_id = sd_models.path_to_repo(checkpoint_info)
from pipelines.omnigen2 import OmniGen2Pipeline, OmniGen2Transformer2DModel, Qwen2_5_VLForConditionalGeneration
diffusers.OmniGen2Pipeline = OmniGen2Pipeline # monkey-pathch
diffusers.pipelines.auto_pipeline.AUTO_TEXT2IMAGE_PIPELINES_MAPPING["omnigen2"] = diffusers.OmniGen2Pipeline
diffusers.pipelines.auto_pipeline.AUTO_IMAGE2IMAGE_PIPELINES_MAPPING["omnigen2"] = diffusers.OmniGen2Pipeline
diffusers.pipelines.auto_pipeline.AUTO_INPAINT_PIPELINES_MAPPING["omnigen2"] = diffusers.OmniGen2Pipeline
load_config, quant_config = model_quant.get_dit_args(diffusers_load_config, module='Model')
shared.log.debug(f'Load model: type=OmniGen2 repo="{repo_id}" config={diffusers_load_config} offload={shared.opts.diffusers_offload_mode} dtype={devices.dtype} args={diffusers_load_config}')
transformer = OmniGen2Transformer2DModel.from_pretrained(
repo_id,
subfolder="transformer",
cache_dir=shared.opts.diffusers_dir,
trust_remote_code=True,
**load_config,
**quant_config,
)
load_config, quant_config = model_quant.get_dit_args(diffusers_load_config, module='TE')
mllm = Qwen2_5_VLForConditionalGeneration.from_pretrained(
repo_id,
subfolder="mllm",
cache_dir=shared.opts.diffusers_dir,
trust_remote_code=True,
**load_config,
**quant_config,
)
pipe = OmniGen2Pipeline.from_pretrained(
repo_id,
# transformer=transformer,
mllm=mllm,
cache_dir=shared.opts.diffusers_dir,
trust_remote_code=True,
**load_config,
)
pipe.transformer = transformer # for omnigen2 transformer must be loaded after pipeline
sd_hijack_te.init_hijack(pipe)
devices.torch_gc(force=True, reason='load')
return pipe
pipelines/model_pixart.py
+1 -1
View File
@@ -33,7 +33,7 @@ def load_pixart(checkpoint_info, diffusers_load_config={}):
del text_encoder
del transformer
# sd_hijack_te.init_hijack(pipe)
sd_hijack_te.init_hijack(pipe)
devices.torch_gc(force=True, reason='load')
return pipe
pipelines/model_sana.py
+1 -1
View File
@@ -22,8 +22,8 @@ def load_quants(kwargs, repo_id, cache_dir):
def load_sana(checkpoint_info, kwargs={}):
modelloader.hf_login()
repo_id = sd_models.path_to_repo(checkpoint_info)
sd_models.hf_auth_check(checkpoint_info)
kwargs.pop('load_connected_pipeline', None)
kwargs.pop('safety_checker', None)