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Complete Z-Image support

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Disty0
2025-11-27 17:25:36 +03:00
parent e56dfc6a95
commit f7b0bbb6e1
9 changed files with 40 additions and 10 deletions
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modules/modeldata.py
+2
View File
@@ -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:
modules/sd_samplers_common.py
+1 -1
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@@ -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()
modules/sd_vae_remote.py
+3 -1
View File
@@ -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:
modules/sd_vae_taesd.py
+2 -2
View File
@@ -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'