add lumina2

Signed-off-by: Vladimir Mandic <mandic00@live.com>

modules/model_lumina.py

@@ -1,3 +1,4 @@
+import transformers
 import diffusers
 
 
@@ -22,3 +23,23 @@ def load_lumina(_checkpoint_info, diffusers_load_config={}):
     )
     devices.torch_gc()
     return pipe
+
+
+def load_lumina2(checkpoint_info, diffusers_load_config={}):
+    from modules import shared, devices, sd_models, model_quant
+    quant_args = {}
+    quant_args = model_quant.create_bnb_config(quant_args)
+    if quant_args:
+        model_quant.load_bnb(f'Load model: type=Lumina quant={quant_args}')
+    if not quant_args:
+        quant_args = model_quant.create_ao_config(quant_args)
+        if quant_args:
+            model_quant.load_torchao(f'Load model: type=Lumina quant={quant_args}')
+    kwargs = {}
+    repo_id = sd_models.path_to_repo(checkpoint_info.name)
+    if ('Model' in shared.opts.bnb_quantization or 'Model' in shared.opts.torchao_quantization):
+        kwargs['transformer'] = diffusers.Lumina2Transformer2DModel.from_pretrained(repo_id, subfolder="transformer", cache_dir=shared.opts.diffusers_dir, torch_dtype=devices.dtype, **quant_args)
+    if ('Text Encoder' in shared.opts.bnb_quantization or 'Text Encoder' in shared.opts.torchao_quantization):
+        kwargs['text_encoder'] = transformers.AutoModel.from_pretrained(repo_id, subfolder="text_encoder", cache_dir=shared.opts.diffusers_dir, torch_dtype=devices.dtype, **quant_args)
+    sd_model = diffusers.Lumina2Text2ImgPipeline.from_pretrained(checkpoint_info.path, cache_dir=shared.opts.diffusers_dir, **diffusers_load_config, **quant_args, **kwargs)
+    return sd_model

modules/model_quant.py

@@ -30,8 +30,6 @@ def get_quant(name):
     return 'none'
 
 
-
-
 def create_bnb_config(kwargs = None, allow_bnb: bool = True):
     from modules import shared, devices
     if len(shared.opts.bnb_quantization) > 0 and allow_bnb:

modules/modeldata.py

@@ -31,6 +31,8 @@ def get_model_type(pipe):
         model_type = 'f1'
     elif "Mochi" in name:
         model_type = 'mochi'
+    elif "Lumina2" in name:
+        model_type = 'lumina2'
     elif "Lumina" in name:
         model_type = 'lumina'
     elif "OmniGen" in name:

modules/sd_detect.py

@@ -69,6 +69,8 @@ def detect_pipeline(f: str, op: str = 'model', warning=True, quiet=False):
                 guess = 'Sana'
             if 'lumina-next' in f.lower():
                 guess = 'Lumina-Next'
+            if 'lumina-image-2' in f.lower():
+                guess = 'Lumina2'
             if 'kolors' in f.lower():
                 guess = 'Kolors'
             if 'auraflow' in f.lower():
@@ -101,6 +103,8 @@ def detect_pipeline(f: str, op: str = 'model', warning=True, quiet=False):
                     guess = 'FLUX'
                 if 'StableDiffusion3' in pipeline.__name__:
                     guess = 'Stable Diffusion 3'
+                if 'Lumina2' in pipeline.__name__:
+                    guess = 'Lumina 2'
             # switch for specific variant
             if guess == 'Stable Diffusion' and 'inpaint' in f.lower():
                 guess = 'Stable Diffusion Inpaint'

modules/sd_models.py

@@ -290,6 +290,9 @@ def load_diffuser_force(model_type, checkpoint_info, diffusers_load_config, op='
         elif model_type in ['FLUX']:
             from modules.model_flux import load_flux
             sd_model = load_flux(checkpoint_info, diffusers_load_config)
+        elif model_type in ['Lumina 2']:
+            from modules.model_lumina import load_lumina2
+            sd_model = load_lumina2(checkpoint_info, diffusers_load_config)
         elif model_type in ['Stable Diffusion 3']:
             from modules.model_sd3 import load_sd3
             shared.log.debug(f'Load {op}: model="Stable Diffusion 3"')
@@ -314,7 +317,7 @@ def load_diffuser_folder(model_type, pipeline, checkpoint_info, diffusers_load_c
     files = shared.walk_files(checkpoint_info.path, ['.safetensors', '.bin', '.ckpt'])
     if 'variant' not in diffusers_load_config and any('diffusion_pytorch_model.fp16' in f for f in files): # deal with diffusers lack of variant fallback when loading
         diffusers_load_config['variant'] = 'fp16'
-    if model_type is not None and pipeline is not None and 'ONNX' in model_type: # forced pipeline
+    if (model_type is not None) and (pipeline is not None) and ('ONNX' in model_type): # forced pipeline
         try:
             sd_model = pipeline.from_pretrained(checkpoint_info.path)
         except Exception as e:

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', 'sd3', 'lumina', 'auraflow', 'sana']
+flow_models = ['f1', 'sd3', 'lumina', 'auraflow', 'sana', 'lumina2']
 warned = False
 queue_lock = threading.Lock()
 

modules/sd_samplers_diffusers.py

@@ -306,6 +306,5 @@ class DiffusionSampler:
         if name == 'DC Solver':
             if not hasattr(self.sampler, 'dc_ratios'):
                 pass
-                # self.sampler.dc_ratios = self.sampler.cascade_polynomial_regression(test_CFG=6.0, test_NFE=10, cpr_path='tmp/sd2.1.npy')
         # shared.log.debug_log(f'Sampler: class="{self.sampler.__class__.__name__}" config={self.sampler.config}')
         self.sampler.name = name