Merge pull request #3519 from vladmandic/dev

refresh master

modules/ipadapter.py

@@ -113,9 +113,6 @@ def unapply(pipe): # pylint: disable=arguments-differ
 
 def apply(pipe, p: processing.StableDiffusionProcessing, adapter_names=[], adapter_scales=[1.0], adapter_crops=[False], adapter_starts=[0.0], adapter_ends=[1.0], adapter_images=[]):
     global clip_loaded # pylint: disable=global-statement
-    if shared.sd_model_type != 'sd' and shared.sd_model_type != 'sdxl':
-        shared.log.error(f'IP adapter: model={shared.sd_model_type} class={pipe.__class__.__name__} not supported')
-        return False
     # overrides
     if hasattr(p, 'ip_adapter_names'):
         if isinstance(p.ip_adapter_names, str):
@@ -132,6 +129,9 @@ def apply(pipe, p: processing.StableDiffusionProcessing, adapter_names=[], adapt
         if hasattr(p, 'ip_adapter_images'):
             del p.ip_adapter_images
         return False
+    if shared.sd_model_type != 'sd' and shared.sd_model_type != 'sdxl':
+        shared.log.error(f'IP adapter: model={shared.sd_model_type} class={pipe.__class__.__name__} not supported')
+        return False
     if hasattr(p, 'ip_adapter_scales'):
         adapter_scales = p.ip_adapter_scales
     if hasattr(p, 'ip_adapter_crops'):

modules/model_sd3.py

@@ -1,15 +1,21 @@
 import os
 import diffusers
 import transformers
-from modules import shared, devices, sd_models, sd_unet
+from modules import shared, devices, sd_models, sd_unet, model_te
 
 
 def load_overrides(kwargs, cache_dir):
     if shared.opts.sd_unet != 'None':
         try:
             fn = sd_unet.unet_dict[shared.opts.sd_unet]
-            kwargs['transformer'] = diffusers.SD3Transformer2DModel.from_single_file(fn, cache_dir=cache_dir, torch_dtype=devices.dtype)
-            shared.log.debug(f'Load model: type=SD3 unet="{shared.opts.sd_unet}"')
+            if fn.endswith('.safetensors'):
+                kwargs['transformer'] = diffusers.SD3Transformer2DModel.from_single_file(fn, cache_dir=cache_dir, torch_dtype=devices.dtype)
+                sd_unet.loaded_unet = shared.opts.sd_unet
+                shared.log.debug(f'Load model: type=SD3 unet="{shared.opts.sd_unet}" fmt=safetensors')
+            elif fn.endswith('.gguf'):
+                kwargs = load_gguf(kwargs, fn)
+                sd_unet.loaded_unet = shared.opts.sd_unet
+                shared.log.debug(f'Load model: type=SD3 unet="{shared.opts.sd_unet}" fmt=gguf')
         except Exception as e:
             shared.log.error(f"Load model: type=SD3 failed to load UNet: {e}")
             shared.opts.sd_unet = 'None'
@@ -84,6 +90,30 @@ def load_missing(kwargs, fn, cache_dir):
     return kwargs
 
 
+def load_gguf(kwargs, fn):
+    model_te.install_gguf()
+    from accelerate import init_empty_weights
+    from diffusers.loaders.single_file_utils import convert_sd3_transformer_checkpoint_to_diffusers
+    from modules import ggml, sd_hijack_accelerate
+    with init_empty_weights():
+        config = diffusers.SD3Transformer2DModel.load_config(os.path.join('configs', 'flux'), subfolder="transformer")
+        transformer = diffusers.SD3Transformer2DModel.from_config(config).to(devices.dtype)
+        expected_state_dict_keys = list(transformer.state_dict().keys())
+    state_dict, stats = ggml.load_gguf_state_dict(fn, devices.dtype)
+    state_dict = convert_sd3_transformer_checkpoint_to_diffusers(state_dict)
+    applied, skipped = 0, 0
+    for param_name, param in state_dict.items():
+        if param_name not in expected_state_dict_keys:
+            skipped += 1
+            continue
+        applied += 1
+        sd_hijack_accelerate.hijack_set_module_tensor_simple(transformer, tensor_name=param_name, value=param, device=0)
+        state_dict[param_name] = None
+    shared.log.debug(f'Load model: type=Unet/Transformer applied={applied} skipped={skipped} stats={stats} compute={devices.dtype}')
+    kwargs['transformer'] = transformer
+    return kwargs
+
+
 def load_sd3(checkpoint_info, cache_dir=None, config=None):
     repo_id = sd_models.path_to_repo(checkpoint_info.name)
     fn = checkpoint_info.path
@@ -92,15 +122,20 @@ def load_sd3(checkpoint_info, cache_dir=None, config=None):
     kwargs = load_overrides(kwargs, cache_dir)
     kwargs = load_quants(kwargs, repo_id, cache_dir)
 
-    if fn is not None and fn.endswith('.safetensors') and os.path.exists(fn):
-        kwargs = load_missing(kwargs, fn, cache_dir)
-        loader = diffusers.StableDiffusion3Pipeline.from_single_file
-        repo_id = fn
+    loader = diffusers.StableDiffusion3Pipeline.from_pretrained
+    if fn is not None and os.path.exists(fn):
+        if fn.endswith('.safetensors'):
+            loader = diffusers.StableDiffusion3Pipeline.from_single_file
+            kwargs = load_missing(kwargs, fn, cache_dir)
+            repo_id = fn
+        elif fn.endswith('.gguf'):
+            kwargs = load_gguf(kwargs, fn)
+            kwargs = load_missing(kwargs, fn, cache_dir)
+            kwargs['variant'] = 'fp16'
     else:
-        loader = diffusers.StableDiffusion3Pipeline.from_pretrained
         kwargs['variant'] = 'fp16'
 
-    shared.log.debug(f'Load model: type=FLUX preloaded={list(kwargs)}')
+    shared.log.debug(f'Load model: type=SD3 preloaded={list(kwargs)}')
 
     pipe = loader(
         repo_id,

modules/processing_diffusers.py

@@ -130,6 +130,7 @@ def process_base(p: processing.StableDiffusionProcessing):
             errors.display(e, 'Processing')
     except RuntimeError as e:
         shared.state.interrupted = True
+        err_args = base_args.copy()
         for k, v in base_args.items():
             if isinstance(v, torch.Tensor):
                 err_args[k] = f'{v.device}:{v.dtype}:{v.shape}'

modules/sd_unet.py

@@ -34,8 +34,9 @@ def load_unet(model):
             if prior_text_encoder is not None:
                 model.prior_pipe.text_encoder = None # Prevent OOM
                 model.prior_pipe.text_encoder = prior_text_encoder.to(devices.device, dtype=devices.dtype)
-        elif "Flux" in model.__class__.__name__:
-            sd_models.load_diffuser() # TODO forcing reloading entire flux as loading transformers only leads to massive memory usage
+        elif "Flux" in model.__class__.__name__ or "StableDiffusion3" in model.__class__.__name__:
+            loaded_unet = shared.opts.sd_unet
+            sd_models.load_diffuser() # TODO forcing reloading entire model as loading transformers only leads to massive memory usage
             """
             from modules.model_flux import load_transformer
             transformer = load_transformer(unet_dict[shared.opts.sd_unet])