diff --git a/html/reference.json b/html/reference.json
index 170d8e7ef..cd10f5626 100644
--- a/html/reference.json
+++ b/html/reference.json
@@ -162,6 +162,17 @@
"extras": "sampler: Default, cfg_scale: 4.5"
},
+ "Z-Image-Turbo": {
+ "path": "Tongyi-MAI/Z-Image-Turbo",
+ "preview": "Tongyi-MAI--Z-Image-Turbo.jpg",
+ "desc": "Z-Image-Turbo, a distilled version of Z-Image that matches or exceeds leading competitors with only 8 NFEs (Number of Function Evaluations). It offers sub-second inference latency on enterprise-grade H800 GPUs and fits comfortably within 16G VRAM consumer devices. It excels in photorealistic image generation, bilingual text rendering (English & Chinese), and robust instruction adherence.",
+ "tags": "distilled",
+ "skip": true,
+ "extras": "sampler: Default, cfg_scale: 1.0, steps: 9",
+ "size": 20.3,
+ "date": "2025 November"
+ },
+
"Qwen-Image": {
"path": "Qwen/Qwen-Image",
"preview": "Qwen--Qwen-Image.jpg",
@@ -1008,6 +1019,16 @@
"size": 23.55,
"extras": ""
},
+ "Z-Image-Turbo sdnq-svd-uint4": {
+ "path": "Disty0/Z-Image-Turbo-SDNQ-uint4-svd-r32",
+ "preview": "Tongyi-MAI--Z-Image-Turbo.jpg",
+ "desc": "Quantization of Tongyi-MAI/Z-Image-Turbo using SDNQ: sdnq-svd 4-bit uint with svd rank 32",
+ "skip": true,
+ "tags": "quantized",
+ "extras": "sampler: Default, cfg_scale: 1.0, steps: 9",
+ "size": 6.5,
+ "date": "2025 November"
+ },
"Qwen-Image sdnq-svd-uint4": {
"path": "Disty0/Qwen-Image-SDNQ-uint4-svd-r32",
"preview": "Qwen--Qwen-Image.jpg",
diff --git a/installer.py b/installer.py
index d725d118d..3edb2e2e7 100644
--- a/installer.py
+++ b/installer.py
@@ -619,7 +619,7 @@ def check_diffusers():
t_start = time.time()
if args.skip_all:
return
- sha = 'c8656ed73c638e51fc2e777a5fd355d69fa5220f' # diffusers commit hash
+ sha = '6bf668c4d217ebc96065e673d8a257fd79950d34' # diffusers commit hash
# if args.use_rocm or args.use_zluda or args.use_directml:
# sha = '043ab2520f6a19fce78e6e060a68dbc947edb9f9' # lock diffusers versions for now
pkg = pkg_resources.working_set.by_key.get('diffusers', None)
diff --git a/models/Reference/Tongyi-MAI--Z-Image-Turbo.jpg b/models/Reference/Tongyi-MAI--Z-Image-Turbo.jpg
new file mode 100644
index 000000000..fe55a0e3b
Binary files /dev/null and b/models/Reference/Tongyi-MAI--Z-Image-Turbo.jpg differ
diff --git a/modules/modeldata.py b/modules/modeldata.py
index a7c14c02c..466d7f53b 100644
--- a/modules/modeldata.py
+++ b/modules/modeldata.py
@@ -36,6 +36,8 @@ def get_model_type(pipe):
model_type = 'f2'
elif "Flux" in name or "Flex1" in name or "Flex2" in name:
model_type = 'f1'
+ elif "ZImage" in name or "Z-Image" in name:
+ model_type = 'z_image'
elif "Lumina2" in name:
model_type = 'lumina2'
elif "Lumina" in name:
diff --git a/modules/sd_samplers_common.py b/modules/sd_samplers_common.py
index 0cea700a6..dfebbedc9 100644
--- a/modules/sd_samplers_common.py
+++ b/modules/sd_samplers_common.py
@@ -9,7 +9,7 @@ from modules import shared, devices, processing, images, sd_vae_approx, sd_vae_t
SamplerData = namedtuple('SamplerData', ['name', 'constructor', 'aliases', 'options'])
approximation_indexes = { "Simple": 0, "Approximate": 1, "TAESD": 2, "Full VAE": 3 }
-flow_models = ['f1', 'f2', 'sd3', 'lumina', 'auraflow', 'sana', 'lumina2', 'cogview4', 'h1', 'cosmos', 'chroma', 'omnigen', 'omnigen2']
+flow_models = ['f1', 'f2', 'sd3', 'lumina', 'auraflow', 'sana', 'z_image', 'lumina2', 'cogview4', 'h1', 'cosmos', 'chroma', 'omnigen', 'omnigen2']
warned = False
queue_lock = threading.Lock()
diff --git a/modules/sd_vae_remote.py b/modules/sd_vae_remote.py
index bb645e528..e7a179ec4 100644
--- a/modules/sd_vae_remote.py
+++ b/modules/sd_vae_remote.py
@@ -21,6 +21,7 @@ hf_decode_endpoints['auraflow'] = hf_decode_endpoints['sdxl']
hf_decode_endpoints['omnigen'] = hf_decode_endpoints['sdxl']
hf_decode_endpoints['h1'] = hf_decode_endpoints['f1']
hf_decode_endpoints['chroma'] = hf_decode_endpoints['f1']
+hf_decode_endpoints['z_image'] = hf_decode_endpoints['f1']
hf_decode_endpoints['lumina2'] = hf_decode_endpoints['f1']
hf_encode_endpoints = {
@@ -35,6 +36,7 @@ hf_encode_endpoints['hunyuandit'] = hf_encode_endpoints['sdxl']
hf_encode_endpoints['auraflow'] = hf_encode_endpoints['sdxl']
hf_encode_endpoints['omnigen'] = hf_encode_endpoints['sdxl']
hf_encode_endpoints['h1'] = hf_encode_endpoints['f1']
+hf_encode_endpoints['z_image'] = hf_encode_endpoints['f1']
hf_encode_endpoints['lumina2'] = hf_encode_endpoints['f1']
dtypes = {
@@ -91,7 +93,7 @@ def remote_decode(latents: torch.Tensor, width: int = 0, height: int = 0, model_
params["output_type"] = "pt"
params["output_tensor_type"] = "binary"
headers["Accept"] = "tensor/binary"
- if model_type in {'f1', 'h1', 'lumina2', 'chroma'} and (width > 0) and (height > 0):
+ if model_type in {'f1', 'h1', 'z_image', 'lumina2', 'chroma'} and (width > 0) and (height > 0):
params['width'] = width
params['height'] = height
if shared.sd_model.vae is not None and shared.sd_model.vae.config is not None:
diff --git a/modules/sd_vae_taesd.py b/modules/sd_vae_taesd.py
index 08c3facc3..e599fe70e 100644
--- a/modules/sd_vae_taesd.py
+++ b/modules/sd_vae_taesd.py
@@ -38,7 +38,7 @@ prev_cls = ''
prev_type = ''
prev_model = ''
lock = threading.Lock()
-supported = ['sd', 'sdxl', 'sd3', 'f1', 'h1', 'lumina2', 'hunyuanvideo', 'wanai', 'chrono', 'mochivideo', 'pixartsigma', 'pixartalpha', 'hunyuandit', 'omnigen', 'qwen']
+supported = ['sd', 'sdxl', 'sd3', 'f1', 'h1', 'z_image', 'lumina2', 'hunyuanvideo', 'wanai', 'chrono', 'mochivideo', 'pixartsigma', 'pixartalpha', 'hunyuandit', 'omnigen', 'qwen']
def warn_once(msg, variant=None):
@@ -59,7 +59,7 @@ def get_model(model_type = 'decoder', variant = None):
model_cls = 'sd'
elif model_cls in {'pixartsigma', 'hunyuandit', 'omnigen', 'auraflow'}:
model_cls = 'sdxl'
- elif model_cls in {'h1', 'lumina2', 'chroma'}:
+ elif model_cls in {'h1', 'z_image', 'lumina2', 'chroma'}:
model_cls = 'f1'
elif model_cls in {'wanai', 'qwen', 'chrono'}:
variant = variant or 'TAE WanVideo'
diff --git a/pipelines/model_flux2.py b/pipelines/model_flux2.py
index 388ecd6f6..5119f5588 100644
--- a/pipelines/model_flux2.py
+++ b/pipelines/model_flux2.py
@@ -35,5 +35,5 @@ def load_flux2(checkpoint_info, diffusers_load_config=None):
sd_hijack_te.init_hijack(pipe)
sd_hijack_vae.init_hijack(pipe)
- devices.torch_gc()
+ devices.torch_gc(force=True, reason='load')
return pipe
diff --git a/pipelines/model_z_image.py b/pipelines/model_z_image.py
index 74ae9cfcf..1f2a18bfd 100644
--- a/pipelines/model_z_image.py
+++ b/pipelines/model_z_image.py
@@ -1,5 +1,6 @@
+import transformers
import diffusers
-from modules import shared, sd_models, sd_hijack_te, devices, model_quant
+from modules import shared, devices, sd_models, model_quant, sd_hijack_te
from pipelines import generic
@@ -9,18 +10,22 @@ def load_z_image(checkpoint_info, diffusers_load_config=None):
repo_id = sd_models.path_to_repo(checkpoint_info)
sd_models.hf_auth_check(checkpoint_info)
+ load_args, _quant_args = model_quant.get_dit_args(diffusers_load_config, allow_quant=False)
shared.log.debug(f'Load model: type=Z-Image repo="{repo_id}" config={diffusers_load_config} offload={shared.opts.diffusers_offload_mode} dtype={devices.dtype} args={diffusers_load_config}')
- transformer = generic.load_transformer(repo_id, cls_name=diffusers.ZImageTransformer2DModel, load_config=diffusers_load_config)
- load_config, _quant_args = model_quant.get_dit_args(diffusers_load_config, allow_quant=False)
+ transformer = generic.load_transformer(repo_id, cls_name=diffusers.ZImageTransformer2DModel, load_config=diffusers_load_config)
+ text_encoder = generic.load_text_encoder(repo_id, cls_name=transformers.Qwen3ForCausalLM, load_config=diffusers_load_config)
+
pipe = diffusers.ZImagePipeline.from_pretrained(
repo_id,
cache_dir=shared.opts.diffusers_dir,
transformer=transformer,
- **load_config,
+ text_encoder=text_encoder,
+ **load_args,
)
del transformer
+ del text_encoder
sd_hijack_te.init_hijack(pipe)
devices.torch_gc(force=True, reason='load')