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Merge pull request #4634 from CalamitousFelicitousness/nunchaku-reference 

Nunchaku reference
pull/4638/head
Vladimir Mandic 2026-02-09 18:06:34 +01:00 committed by GitHub
parent 480b58e994 34a18c9098
commit d0f9e25906
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GPG Key ID: B5690EEEBB952194
16 changed files with 318 additions and 44 deletions
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209
data/reference-nunchaku.json Normal file
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@ -0,0 +1,209 @@
{
"FLUX.1-Dev Nunchaku SVDQuant": {
"path": "black-forest-labs/FLUX.1-dev",
"subfolder": "nunchaku",
"preview": "black-forest-labs--FLUX.1-dev.jpg",
"desc": "Nunchaku SVDQuant quantization of FLUX.1-dev transformer with INT4 and SVD rank 32",
"skip": true,
"nunchaku": ["Model", "TE"],
"tags": "nunchaku",
"size": 0,
"date": "2025 June"
},
"FLUX.1-Schnell Nunchaku SVDQuant": {
"path": "black-forest-labs/FLUX.1-schnell",
"subfolder": "nunchaku",
"preview": "black-forest-labs--FLUX.1-schnell.jpg",
"desc": "Nunchaku SVDQuant quantization of FLUX.1-schnell transformer with INT4 and SVD rank 32",
"skip": true,
"nunchaku": ["Model", "TE"],
"tags": "nunchaku",
"extras": "sampler: Default, cfg_scale: 1.0, steps: 4",
"size": 0,
"date": "2025 June"
},
"FLUX.1-Kontext Nunchaku SVDQuant": {
"path": "black-forest-labs/FLUX.1-Kontext-dev",
"subfolder": "nunchaku",
"preview": "black-forest-labs--FLUX.1-Kontext-dev.jpg",
"desc": "Nunchaku SVDQuant quantization of FLUX.1-Kontext-dev transformer with INT4 and SVD rank 32",
"skip": true,
"nunchaku": ["Model", "TE"],
"tags": "nunchaku",
"size": 0,
"date": "2025 June"
},
"FLUX.1-Krea Nunchaku SVDQuant": {
"path": "black-forest-labs/FLUX.1-Krea-dev",
"subfolder": "nunchaku",
"preview": "black-forest-labs--FLUX.1-Krea-dev.jpg",
"desc": "Nunchaku SVDQuant quantization of FLUX.1-Krea-dev transformer with INT4 and SVD rank 32",
"skip": true,
"nunchaku": ["Model", "TE"],
"tags": "nunchaku",
"size": 0,
"date": "2025 June"
},
"FLUX.1-Fill Nunchaku SVDQuant": {
"path": "black-forest-labs/FLUX.1-Fill-dev",
"subfolder": "nunchaku",
"preview": "black-forest-labs--FLUX.1-Fill-dev.jpg",
"desc": "Nunchaku SVDQuant quantization of FLUX.1-Fill-dev transformer for inpainting",
"skip": true,
"hidden": true,
"nunchaku": ["Model", "TE"],
"tags": "nunchaku",
"size": 0,
"date": "2025 June"
},
"FLUX.1-Depth Nunchaku SVDQuant": {
"path": "black-forest-labs/FLUX.1-Depth-dev",
"subfolder": "nunchaku",
"preview": "black-forest-labs--FLUX.1-Depth-dev.jpg",
"desc": "Nunchaku SVDQuant quantization of FLUX.1-Depth-dev transformer for depth-conditioned generation",
"skip": true,
"hidden": true,
"nunchaku": ["Model", "TE"],
"tags": "nunchaku",
"size": 0,
"date": "2025 June"
},
"Shuttle Jaguar Nunchaku SVDQuant": {
"path": "shuttleai/shuttle-jaguar",
"subfolder": "nunchaku",
"preview": "shuttleai--shuttle-jaguar.jpg",
"desc": "Nunchaku SVDQuant quantization of Shuttle Jaguar transformer",
"skip": true,
"nunchaku": ["Model", "TE"],
"tags": "nunchaku",
"size": 0,
"date": "2025 June"
},
"Qwen-Image Nunchaku SVDQuant": {
"path": "Qwen/Qwen-Image",
"subfolder": "nunchaku",
"preview": "Qwen--Qwen-Image.jpg",
"desc": "Nunchaku SVDQuant quantization of Qwen-Image transformer with INT4 and SVD rank 128",
"skip": true,
"nunchaku": ["Model"],
"tags": "nunchaku",
"size": 0,
"date": "2025 June"
},
"Qwen-Lightning (8-step) Nunchaku SVDQuant": {
"path": "vladmandic/Qwen-Lightning",
"subfolder": "nunchaku",
"preview": "vladmandic--Qwen-Lightning.jpg",
"desc": "Nunchaku SVDQuant quantization of Qwen-Lightning (8-step distilled) transformer with INT4 and SVD rank 128",
"skip": true,
"nunchaku": ["Model"],
"tags": "nunchaku",
"extras": "steps: 8",
"size": 0,
"date": "2025 June"
},
"Qwen-Lightning (4-step) Nunchaku SVDQuant": {
"path": "vladmandic/Qwen-Lightning",
"subfolder": "nunchaku-4step",
"preview": "vladmandic--Qwen-Lightning.jpg",
"desc": "Nunchaku SVDQuant quantization of Qwen-Lightning (4-step distilled) transformer with INT4 and SVD rank 128",
"skip": true,
"nunchaku": ["Model"],
"tags": "nunchaku",
"extras": "steps: 4",
"size": 0,
"date": "2025 June"
},
"Qwen-Image-Edit Nunchaku SVDQuant": {
"path": "Qwen/Qwen-Image-Edit",
"subfolder": "nunchaku",
"preview": "Qwen--Qwen-Image-Edit.jpg",
"desc": "Nunchaku SVDQuant quantization of Qwen-Image-Edit transformer with INT4 and SVD rank 128",
"skip": true,
"nunchaku": ["Model"],
"tags": "nunchaku",
"size": 0,
"date": "2025 June"
},
"Qwen-Lightning-Edit (8-step) Nunchaku SVDQuant": {
"path": "vladmandic/Qwen-Lightning-Edit",
"subfolder": "nunchaku",
"preview": "vladmandic--Qwen-Lightning-Edit.jpg",
"desc": "Nunchaku SVDQuant quantization of Qwen-Lightning-Edit (8-step distilled editing) transformer with INT4 and SVD rank 128",
"skip": true,
"nunchaku": ["Model"],
"tags": "nunchaku",
"extras": "steps: 8",
"size": 0,
"date": "2025 June"
},
"Qwen-Lightning-Edit (4-step) Nunchaku SVDQuant": {
"path": "vladmandic/Qwen-Lightning-Edit",
"subfolder": "nunchaku-4step",
"preview": "vladmandic--Qwen-Lightning-Edit.jpg",
"desc": "Nunchaku SVDQuant quantization of Qwen-Lightning-Edit (4-step distilled editing) transformer with INT4 and SVD rank 128",
"skip": true,
"nunchaku": ["Model"],
"tags": "nunchaku",
"extras": "steps: 4",
"size": 0,
"date": "2025 June"
},
"Qwen-Image-Edit-2509 Nunchaku SVDQuant": {
"path": "Qwen/Qwen-Image-Edit-2509",
"subfolder": "nunchaku",
"preview": "Qwen--Qwen-Image-Edit-2509.jpg",
"desc": "Nunchaku SVDQuant quantization of Qwen-Image-Edit-2509 transformer with INT4 and SVD rank 128",
"skip": true,
"nunchaku": ["Model"],
"tags": "nunchaku",
"size": 0,
"date": "2025 September"
},
"Sana 1.6B 1k Nunchaku SVDQuant": {
"path": "Efficient-Large-Model/Sana_1600M_1024px_BF16_diffusers",
"subfolder": "nunchaku",
"preview": "Efficient-Large-Model--Sana_1600M_1024px_BF16_diffusers.jpg",
"desc": "Nunchaku SVDQuant quantization of Sana 1.6B 1024px transformer with INT4 and SVD rank 32",
"skip": true,
"nunchaku": ["Model"],
"tags": "nunchaku",
"size": 0,
"date": "2025 June"
},
"Z-Image-Turbo Nunchaku SVDQuant": {
"path": "Tongyi-MAI/Z-Image-Turbo",
"subfolder": "nunchaku",
"preview": "Tongyi-MAI--Z-Image-Turbo.jpg",
"desc": "Nunchaku SVDQuant quantization of Z-Image-Turbo transformer with INT4 and SVD rank 128",
"skip": true,
"nunchaku": ["Model"],
"tags": "nunchaku",
"extras": "sampler: Default, cfg_scale: 1.0, steps: 9",
"size": 0,
"date": "2025 June"
},
"SDXL Base Nunchaku SVDQuant": {
"path": "stabilityai/stable-diffusion-xl-base-1.0",
"subfolder": "nunchaku",
"preview": "stabilityai--stable-diffusion-xl-base-1.0.jpg",
"desc": "Nunchaku SVDQuant quantization of SDXL Base 1.0 UNet with INT4 and SVD rank 32",
"skip": true,
"nunchaku": ["Model"],
"tags": "nunchaku",
"size": 0,
"date": "2025 June"
},
"SDXL Turbo Nunchaku SVDQuant": {
"path": "stabilityai/sdxl-turbo",
"subfolder": "nunchaku",
"preview": "stabilityai--sdxl-turbo.jpg",
"desc": "Nunchaku SVDQuant quantization of SDXL Turbo UNet with INT4 and SVD rank 32",
"skip": true,
"nunchaku": ["Model"],
"tags": "nunchaku",
"extras": "sampler: Default, cfg_scale: 1.0, steps: 4",
"size": 0,
"date": "2025 June"
}
}

2
html/locale_en.json
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@ -337,6 +337,8 @@
{"id":"","label":"Model Options","localized":"","reload":"","hint":"Settings related to behavior of specific models"},
{"id":"","label":"Model Offloading","localized":"","reload":"","hint":"Settings related to model offloading and memory management"},
{"id":"","label":"Model Quantization","localized":"","reload":"","hint":"Settings related to model quantization which is used to reduce memory usage"},
{"id":"","label":"Nunchaku attention","localized":"","reload":"","hint":"Replaces default attention with Nunchaku's custom FP16 attention kernel for faster inference on consumer NVIDIA GPUs.<br>Might provide performance improvement on GPUs which have higher FP16 tensor cores throughput than BF16.<br><br>Currently only affects Flux-based models (Dev, Schnell, Kontext, Fill, Depth, etc.). Has no effect on Qwen, SDXL, Sana, or other architectures.<br><br>Disabled by default."},
{"id":"","label":"Nunchaku offloading","localized":"","reload":"","hint":"Enables Nunchaku's own per-block CPU offloading with asynchronous CUDA streams to reduce VRAM usage.<br>Uses a ping-pong buffer strategy: while one transformer block computes on GPU, the next block preloads from CPU in the background, hiding most of the transfer latency.<br><br>Can reduce VRAM usage at the cost of slower inference.<br>This replaces SD.Next's pipeline offloading for the transformer component.<br><br>Only useful on low-VRAM GPUs. If your GPU has enough memory to hold the quantized model (16+ GB), keep this disabled for maximum speed.<br>Supports Flux and Qwen models. Not supported for SDXL where this setting is ignored.<br>Disabled by default."},
{"id":"","label":"Image Metadata","localized":"","reload":"","hint":"Settings related to handling of metadata that is created with generated images"},
{"id":"","label":"Legacy Options","localized":"","reload":"","hint":"Settings related to legacy options - should not be used"},
{"id":"","label":"Restart server","localized":"","reload":"","hint":"Restart server"},

6
javascript/extraNetworks.js
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@ -171,6 +171,12 @@ async function filterExtraNetworksForTab(searchTerm) {
.toLowerCase()
.includes('quantized') ? '' : 'none';
});
} else if (searchTerm === 'nunchaku/') {
cards.forEach((elem) => {
elem.style.display = elem.dataset.tags
.toLowerCase()
.includes('nunchaku') ? '' : 'none';
});
} else if (searchTerm === 'local/') {
cards.forEach((elem) => {
elem.style.display = elem.dataset.name

30
modules/mit_nunchaku.py
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@ -4,10 +4,27 @@ from installer import log, pip
from modules import devices
nunchaku_ver = '1.1.0'
nunchaku_versions = {
'2.5': '1.0.1',
'2.6': '1.0.1',
'2.7': '1.1.0',
'2.8': '1.1.0',
'2.9': '1.1.0',
'2.10': '1.0.2',
'2.11': '1.1.0',
}
ok = False
def _expected_ver():
try:
import torch
torch_ver = '.'.join(torch.__version__.split('+')[0].split('.')[:2])
return nunchaku_versions.get(torch_ver)
except Exception:
return None
def check():
global ok # pylint: disable=global-statement
if ok:
@ -16,8 +33,9 @@ def check():
import nunchaku
import nunchaku.utils
from nunchaku import __version__
expected = _expected_ver()
log.info(f'Nunchaku: path={nunchaku.__path__} version={__version__.__version__} precision={nunchaku.utils.get_precision()}')
if __version__.__version__ != nunchaku_ver:
if expected is not None and __version__.__version__ != expected:
ok = False
return False
ok = True
@ -49,14 +67,16 @@ def install_nunchaku():
if devices.backend not in ['cuda']:
log.error(f'Nunchaku: backend={devices.backend} unsupported')
return False
torch_ver = torch.__version__[:3]
if torch_ver not in ['2.5', '2.6', '2.7', '2.8', '2.9', '2.10']:
torch_ver = '.'.join(torch.__version__.split('+')[0].split('.')[:2])
nunchaku_ver = nunchaku_versions.get(torch_ver)
if nunchaku_ver is None:
log.error(f'Nunchaku: torch={torch.__version__} unsupported')
return False
suffix = 'x86_64' if arch == 'linux' else 'win_amd64'
url = os.environ.get('NUNCHAKU_COMMAND', None)
if url is None:
arch = f'{arch}_' if arch == 'linux' else ''
url = f'https://huggingface.co/nunchaku-tech/nunchaku/resolve/main/nunchaku-{nunchaku_ver}'
url = f'https://huggingface.co/nunchaku-ai/nunchaku/resolve/main/nunchaku-{nunchaku_ver}'
url += f'+torch{torch_ver}-cp{python_ver}-cp{python_ver}-{arch}{suffix}.whl'
cmd = f'install --upgrade {url}'
log.debug(f'Nunchaku: install="{url}"')

24
modules/model_quant.py
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@ -255,13 +255,25 @@ def check_quant(module: str = ''):
def check_nunchaku(module: str = ''):
from modules import shared
if module not in shared.opts.nunchaku_quantization:
model_name = getattr(shared.opts, 'sd_model_checkpoint', '')
if '+nunchaku' not in model_name:
return False
from modules import mit_nunchaku
mit_nunchaku.install_nunchaku()
if not mit_nunchaku.ok:
return False
return True
base_path = model_name.split('+')[0]
for v in shared.reference_models.values():
if v.get('path', '') != base_path:
continue
nunchaku_modules = v.get('nunchaku', None)
if nunchaku_modules is None:
continue
if isinstance(nunchaku_modules, bool) and nunchaku_modules:
nunchaku_modules = ['Model', 'TE']
if not isinstance(nunchaku_modules, list):
continue
if module in nunchaku_modules:
from modules import mit_nunchaku
mit_nunchaku.install_nunchaku()
return mit_nunchaku.ok
return False
def create_config(kwargs = None, allow: bool = True, module: str = 'Model', modules_to_not_convert: list = None, modules_dtype_dict: dict = None):

9
modules/sd_unet.py
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@ -18,14 +18,15 @@ def load_unet_sdxl_nunchaku(repo_id):
shared.log.error(f'Load module: quant=Nunchaku module=unet repo="{repo_id}" low nunchaku version')
return None
if 'turbo' in repo_id.lower():
nunchaku_repo = 'nunchaku-tech/nunchaku-sdxl-turbo/svdq-int4_r32-sdxl-turbo.safetensors'
nunchaku_repo = 'nunchaku-ai/nunchaku-sdxl-turbo/svdq-int4_r32-sdxl-turbo.safetensors'
else:
nunchaku_repo = 'nunchaku-tech/nunchaku-sdxl/svdq-int4_r32-sdxl.safetensors'
nunchaku_repo = 'nunchaku-ai/nunchaku-sdxl/svdq-int4_r32-sdxl.safetensors'
shared.log.debug(f'Load module: quant=Nunchaku module=unet repo="{nunchaku_repo}" offload={shared.opts.nunchaku_offload}')
if shared.opts.nunchaku_offload:
shared.log.warning('Load module: quant=Nunchaku module=unet offload not supported for SDXL, ignoring')
shared.log.debug(f'Load module: quant=Nunchaku module=unet repo="{nunchaku_repo}"')
unet = NunchakuSDXLUNet2DConditionModel.from_pretrained(
nunchaku_repo,
offload=shared.opts.nunchaku_offload,
torch_dtype=devices.dtype,
cache_dir=shared.opts.hfcache_dir,
)

2
modules/shared.py
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@ -281,7 +281,6 @@ options_templates.update(options_section(("quantization", "Model Quantization"),
"sdnq_quantize_shuffle_weights": OptionInfo(False, "Shuffle weights in post mode", gr.Checkbox),
"nunchaku_sep": OptionInfo("<h2>Nunchaku Engine</h2>", "", gr.HTML),
"nunchaku_quantization": OptionInfo([], "SVDQuant enabled", gr.CheckboxGroup, {"choices": ["Model", "TE"]}),
"nunchaku_attention": OptionInfo(False, "Nunchaku attention", gr.Checkbox),
"nunchaku_offload": OptionInfo(False, "Nunchaku offloading", gr.Checkbox),
@ -881,6 +880,7 @@ profiler = None
import modules.styles
prompt_styles = modules.styles.StyleDatabase(opts)
reference_models = readfile(os.path.join('data', 'reference.json'), as_type="dict") if opts.extra_network_reference_enable else {}
reference_models.update(readfile(os.path.join('data', 'reference-nunchaku.json'), as_type="dict") if opts.extra_network_reference_enable else {})
cmd_opts.disable_extension_access = (cmd_opts.share or cmd_opts.listen or (cmd_opts.server_name or False)) and not cmd_opts.insecure
log.debug('Initializing: devices')

5
modules/ui_extra_networks.py
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@ -305,6 +305,7 @@ class ExtraNetworksPage:
subdirs['Reference'] = 1
subdirs['Distilled'] = 1
subdirs['Quantized'] = 1
subdirs['Nunchaku'] = 1
subdirs['Community'] = 1
subdirs['Cloud'] = 1
subdirs[diffusers_base] = 1
@ -324,6 +325,8 @@ class ExtraNetworksPage:
subdirs.move_to_end('Distilled', last=True)
if 'Quantized' in subdirs:
subdirs.move_to_end('Quantized', last=True)
if 'Nunchaku' in subdirs:
subdirs.move_to_end('Nunchaku', last=True)
if 'Community' in subdirs:
subdirs.move_to_end('Community', last=True)
if 'Cloud' in subdirs:
@ -332,7 +335,7 @@ class ExtraNetworksPage:
for subdir in subdirs:
if len(subdir) == 0:
continue
if subdir in ['All', 'Local', 'Diffusers', 'Reference', 'Distilled', 'Quantized', 'Community', 'Cloud']:
if subdir in ['All', 'Local', 'Diffusers', 'Reference', 'Distilled', 'Quantized', 'Nunchaku', 'Community', 'Cloud']:
style = 'network-reference'
else:
style = 'network-folder'

10
modules/ui_extra_networks_checkpoints.py
View File

@ -3,7 +3,7 @@ import html
import json
import concurrent
from datetime import datetime
from modules import shared, ui_extra_networks, sd_models, modelstats, paths
from modules import shared, ui_extra_networks, sd_models, modelstats, paths, devices
from modules.json_helpers import readfile
@ -48,16 +48,21 @@ class ExtraNetworksPageCheckpoints(ui_extra_networks.ExtraNetworksPage):
reference_distilled = readfile(os.path.join('data', 'reference-distilled.json'), as_type="dict")
reference_community = readfile(os.path.join('data', 'reference-community.json'), as_type="dict")
reference_cloud = readfile(os.path.join('data', 'reference-cloud.json'), as_type="dict")
reference_nunchaku = readfile(os.path.join('data', 'reference-nunchaku.json'), as_type="dict")
shared.reference_models = {}
shared.reference_models.update(reference_base)
shared.reference_models.update(reference_quant)
shared.reference_models.update(reference_community)
shared.reference_models.update(reference_distilled)
shared.reference_models.update(reference_cloud)
shared.reference_models.update(reference_nunchaku)
for k, v in shared.reference_models.items():
count['total'] += 1
url = v['path']
if v.get('hidden', False):
count['hidden'] += 1
continue
experimental = v.get('experimental', False)
if experimental:
if shared.cmd_opts.experimental:
@ -83,6 +88,9 @@ class ExtraNetworksPageCheckpoints(ui_extra_networks.ExtraNetworksPage):
path = f'{v.get("path", "")}'
tag = v.get('tags', '')
if tag == 'nunchaku' and devices.backend != 'cuda':
count['hidden'] += 1
continue
if tag in count:
count[tag] += 1
elif tag != '':

14
pipelines/flux/flux_nunchaku.py
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@ -9,19 +9,19 @@ def load_flux_nunchaku(repo_id):
if 'srpo' in repo_id.lower():
pass
elif 'flux.1-dev' in repo_id.lower():
nunchaku_repo = f"nunchaku-tech/nunchaku-flux.1-dev/svdq-{nunchaku_precision}_r32-flux.1-dev.safetensors"
nunchaku_repo = f"nunchaku-ai/nunchaku-flux.1-dev/svdq-{nunchaku_precision}_r32-flux.1-dev.safetensors"
elif 'flux.1-schnell' in repo_id.lower():
nunchaku_repo = f"nunchaku-tech/nunchaku-flux.1-schnell/svdq-{nunchaku_precision}_r32-flux.1-schnell.safetensors"
nunchaku_repo = f"nunchaku-ai/nunchaku-flux.1-schnell/svdq-{nunchaku_precision}_r32-flux.1-schnell.safetensors"
elif 'flux.1-kontext' in repo_id.lower():
nunchaku_repo = f"nunchaku-tech/nunchaku-flux.1-kontext-dev/svdq-{nunchaku_precision}_r32-flux.1-kontext-dev.safetensors"
nunchaku_repo = f"nunchaku-ai/nunchaku-flux.1-kontext-dev/svdq-{nunchaku_precision}_r32-flux.1-kontext-dev.safetensors"
elif 'flux.1-krea' in repo_id.lower():
nunchaku_repo = f"nunchaku-tech/nunchaku-flux.1-krea-dev/svdq-{nunchaku_precision}_r32-flux.1-krea-dev.safetensors"
nunchaku_repo = f"nunchaku-ai/nunchaku-flux.1-krea-dev/svdq-{nunchaku_precision}_r32-flux.1-krea-dev.safetensors"
elif 'flux.1-fill' in repo_id.lower():
nunchaku_repo = f"nunchaku-tech/nunchaku-flux.1-fill-dev/svdq-{nunchaku_precision}-flux.1-fill-dev.safetensors"
nunchaku_repo = f"nunchaku-ai/nunchaku-flux.1-fill-dev/svdq-{nunchaku_precision}-flux.1-fill-dev.safetensors"
elif 'flux.1-depth' in repo_id.lower():
nunchaku_repo = f"nunchaku-tech/nunchaku-flux.1-depth-dev/svdq-{nunchaku_precision}-flux.1-depth-dev.safetensors"
nunchaku_repo = f"nunchaku-ai/nunchaku-flux.1-depth-dev/svdq-{nunchaku_precision}-flux.1-depth-dev.safetensors"
elif 'shuttle' in repo_id.lower():
nunchaku_repo = f"nunchaku-tech/nunchaku-shuttle-jaguar/svdq-{nunchaku_precision}-shuttle-jaguar.safetensors"
nunchaku_repo = f"nunchaku-ai/nunchaku-shuttle-jaguar/svdq-{nunchaku_precision}-shuttle-jaguar.safetensors"
else:
shared.log.error(f'Load module: quant=Nunchaku module=transformer repo="{repo_id}" unsupported')
if nunchaku_repo is not None:

2
pipelines/generic.py
View File

@ -152,7 +152,7 @@ def load_text_encoder(repo_id, cls_name, load_config=None, subfolder="text_encod
elif cls_name == transformers.T5EncoderModel and allow_shared and shared.opts.te_shared_t5:
if model_quant.check_nunchaku('TE'):
import nunchaku
repo_id = 'nunchaku-tech/nunchaku-t5/awq-int4-flux.1-t5xxl.safetensors'
repo_id = 'nunchaku-ai/nunchaku-t5/awq-int4-flux.1-t5xxl.safetensors'
cls_name = nunchaku.NunchakuT5EncoderModel
shared.log.debug(f'Load model: text_encoder="{repo_id}" cls={cls_name.__name__} quant="SVDQuant" loader={_loader("transformers")}')
text_encoder = nunchaku.NunchakuT5EncoderModel.from_pretrained(

4
pipelines/model_qwen.py
View File

@ -37,7 +37,7 @@ def load_qwen(checkpoint_info, diffusers_load_config=None):
diffusers.pipelines.auto_pipeline.AUTO_INPAINT_PIPELINES_MAPPING["qwen-image"] = diffusers.QwenImageInpaintPipeline
if model_quant.check_nunchaku('Model'):
transformer = qwen.load_qwen_nunchaku(repo_id)
transformer = qwen.load_qwen_nunchaku(repo_id, subfolder=repo_subfolder)
if 'Qwen-Image-Distill-Full' in repo_id:
repo_transformer = repo_id
@ -63,6 +63,8 @@ def load_qwen(checkpoint_info, diffusers_load_config=None):
text_encoder = generic.load_text_encoder(repo_te, cls_name=transformers.Qwen2_5_VLForConditionalGeneration, load_config=diffusers_load_config)
repo_id, repo_subfolder = qwen.check_qwen_pruning(repo_id, repo_subfolder)
if repo_subfolder is not None and repo_subfolder.startswith('nunchaku'):
repo_subfolder = None
pipe = cls_name.from_pretrained(
repo_id,
transformer=transformer,

2
pipelines/model_sana.py
View File

@ -9,7 +9,7 @@ def load_quants(kwargs, repo_id, cache_dir):
if 'Sana_1600M_1024px' in repo_id and model_quant.check_nunchaku('Model'): # only available model
import nunchaku
nunchaku_precision = nunchaku.utils.get_precision()
nunchaku_repo = "nunchaku-tech/nunchaku-sana/svdq-int4_r32-sana1.6b.safetensors"
nunchaku_repo = "nunchaku-ai/nunchaku-sana/svdq-int4_r32-sana1.6b.safetensors"
shared.log.debug(f'Load module: quant=Nunchaku module=transformer repo="{nunchaku_repo}" precision={nunchaku_precision} attention={shared.opts.nunchaku_attention}')
kwargs['transformer'] = nunchaku.NunchakuSanaTransformer2DModel.from_pretrained(nunchaku_repo, torch_dtype=devices.dtype, cache_dir=cache_dir)
elif model_quant.check_quant('Model'):

2
pipelines/model_z_image.py
View File

@ -8,7 +8,7 @@ def load_nunchaku():
import nunchaku
nunchaku_precision = nunchaku.utils.get_precision()
nunchaku_rank = 128
nunchaku_repo = f"nunchaku-tech/nunchaku-z-image-turbo/svdq-{nunchaku_precision}_r{nunchaku_rank}-z-image-turbo.safetensors"
nunchaku_repo = f"nunchaku-ai/nunchaku-z-image-turbo/svdq-{nunchaku_precision}_r{nunchaku_rank}-z-image-turbo.safetensors"
shared.log.debug(f'Load module: quant=Nunchaku module=transformer repo="{nunchaku_repo}" attention={shared.opts.nunchaku_attention}')
transformer = nunchaku.NunchakuZImageTransformer2DModel.from_pretrained(
nunchaku_repo,

19
pipelines/qwen/qwen_nunchaku.py
View File

@ -1,11 +1,12 @@
from modules import shared, devices
def load_qwen_nunchaku(repo_id):
def load_qwen_nunchaku(repo_id, subfolder=None):
import nunchaku
nunchaku_precision = nunchaku.utils.get_precision()
nunchaku_repo = None
transformer = None
four_step = subfolder is not None and '4step' in subfolder
try:
from nunchaku.models.transformers.transformer_qwenimage import NunchakuQwenImageTransformer2DModel
except Exception:
@ -14,15 +15,21 @@ def load_qwen_nunchaku(repo_id):
if 'pruning' in repo_id.lower() or 'distill' in repo_id.lower():
return None
elif repo_id.lower().endswith('qwen-image'):
nunchaku_repo = f"nunchaku-tech/nunchaku-qwen-image/svdq-{nunchaku_precision}_r128-qwen-image.safetensors" # r32 vs r128
nunchaku_repo = f"nunchaku-ai/nunchaku-qwen-image/svdq-{nunchaku_precision}_r128-qwen-image.safetensors"
elif repo_id.lower().endswith('qwen-lightning'):
nunchaku_repo = f"nunchaku-tech/nunchaku-qwen-image/svdq-{nunchaku_precision}_r128-qwen-image-lightningv1.1-8steps.safetensors" # 8-step variant
if four_step:
nunchaku_repo = f"nunchaku-ai/nunchaku-qwen-image/svdq-{nunchaku_precision}_r128-qwen-image-lightningv1.0-4steps.safetensors"
else:
nunchaku_repo = f"nunchaku-ai/nunchaku-qwen-image/svdq-{nunchaku_precision}_r128-qwen-image-lightningv1.1-8steps.safetensors"
elif repo_id.lower().endswith('qwen-image-edit-2509'):
nunchaku_repo = f"nunchaku-tech/nunchaku-qwen-image-edit-2509/svdq-{nunchaku_precision}_r128-qwen-image-edit-2509.safetensors" # 8-step variant
nunchaku_repo = f"nunchaku-ai/nunchaku-qwen-image-edit-2509/svdq-{nunchaku_precision}_r128-qwen-image-edit-2509.safetensors"
elif repo_id.lower().endswith('qwen-image-edit'):
nunchaku_repo = f"nunchaku-tech/nunchaku-qwen-image-edit/svdq-{nunchaku_precision}_r128-qwen-image-edit.safetensors" # 8-step variant
nunchaku_repo = f"nunchaku-ai/nunchaku-qwen-image-edit/svdq-{nunchaku_precision}_r128-qwen-image-edit.safetensors"
elif repo_id.lower().endswith('qwen-lightning-edit'):
nunchaku_repo = f"nunchaku-tech/nunchaku-qwen-image-edit/svdq-{nunchaku_precision}_r128-qwen-image-edit-lightningv1.0-8steps.safetensors" # 8-step variant
if four_step:
nunchaku_repo = f"nunchaku-ai/nunchaku-qwen-image-edit/svdq-{nunchaku_precision}_r128-qwen-image-edit-lightningv1.0-4steps.safetensors"
else:
nunchaku_repo = f"nunchaku-ai/nunchaku-qwen-image-edit/svdq-{nunchaku_precision}_r128-qwen-image-edit-lightningv1.0-8steps.safetensors"
else:
shared.log.error(f'Load module: quant=Nunchaku module=transformer repo="{repo_id}" unsupported')
if nunchaku_repo is not None:

22
scripts/flux_tools.py
View File

@ -25,7 +25,7 @@ class Script(scripts_manager.Script):
with gr.Row():
gr.HTML('<a href="https://blackforestlabs.ai/flux-1-tools/">&nbsp Flux.1 Redux</a><br>')
with gr.Row():
tool = gr.Dropdown(label='Tool', choices=['None', 'Redux', 'Fill', 'Canny', 'Depth'], value='None')
tool = gr.Dropdown(label='Tool', choices=['None', 'Redux', 'Fill', 'Fill (Nunchaku)', 'Canny', 'Depth', 'Depth (Nunchaku)'], value='None')
with gr.Row():
prompt = gr.Slider(label='Redux prompt strength', minimum=0, maximum=2, step=0.01, value=0, visible=False)
process = gr.Checkbox(label='Control preprocess input images', value=True, visible=False)
@ -34,8 +34,8 @@ class Script(scripts_manager.Script):
def display(tool):
return [
gr.update(visible=tool in ['Redux']),
gr.update(visible=tool in ['Canny', 'Depth']),
gr.update(visible=tool in ['Canny', 'Depth']),
gr.update(visible=tool in ['Canny', 'Depth', 'Depth (Nunchaku)']),
gr.update(visible=tool in ['Canny', 'Depth', 'Depth (Nunchaku)']),
]
tool.change(fn=display, inputs=[tool], outputs=[prompt, process, strength])
@ -91,13 +91,15 @@ class Script(scripts_manager.Script):
shared.log.debug(f'{title}: tool=Redux unload')
redux_pipe = None
if tool == 'Fill':
if tool in ['Fill', 'Fill (Nunchaku)']:
# pipe = FluxFillPipeline.from_pretrained("black-forest-labs/FLUX.1-Fill-dev", torch_dtype=torch.bfloat16, revision="refs/pr/4").to("cuda")
if p.image_mask is None:
shared.log.error(f'{title}: tool={tool} no image_mask')
return None
if shared.sd_model.__class__.__name__ != 'FluxFillPipeline':
shared.opts.data["sd_model_checkpoint"] = "black-forest-labs/FLUX.1-Fill-dev"
nunchaku_suffix = '+nunchaku' if tool == 'Fill (Nunchaku)' else ''
checkpoint = f"black-forest-labs/FLUX.1-Fill-dev{nunchaku_suffix}"
if shared.sd_model.__class__.__name__ != 'FluxFillPipeline' or shared.opts.sd_model_checkpoint != checkpoint:
shared.opts.data["sd_model_checkpoint"] = checkpoint
sd_models.reload_model_weights(op='model', revision="refs/pr/4")
p.task_args['image'] = image
p.task_args['mask_image'] = p.image_mask
@ -124,11 +126,13 @@ class Script(scripts_manager.Script):
shared.log.debug(f'{title}: tool=Canny unload processor')
processor_canny = None
if tool == 'Depth':
if tool in ['Depth', 'Depth (Nunchaku)']:
# pipe = diffusers.FluxControlPipeline.from_pretrained("black-forest-labs/FLUX.1-Depth-dev", torch_dtype=torch.bfloat16, revision="refs/pr/1").to("cuda")
install('git+https://github.com/huggingface/image_gen_aux.git', 'image_gen_aux')
if shared.sd_model.__class__.__name__ != 'FluxControlPipeline' or 'Depth' not in shared.opts.sd_model_checkpoint:
shared.opts.data["sd_model_checkpoint"] = "black-forest-labs/FLUX.1-Depth-dev"
nunchaku_suffix = '+nunchaku' if tool == 'Depth (Nunchaku)' else ''
checkpoint = f"black-forest-labs/FLUX.1-Depth-dev{nunchaku_suffix}"
if shared.sd_model.__class__.__name__ != 'FluxControlPipeline' or shared.opts.sd_model_checkpoint != checkpoint:
shared.opts.data["sd_model_checkpoint"] = checkpoint
sd_models.reload_model_weights(op='model', revision="refs/pr/1")
if processor_depth is None:
from image_gen_aux import DepthPreprocessor