add sd35 medium

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

CHANGELOG.md

@@ -1,6 +1,6 @@
 # Change Log for SD.Next
 
-## Update for 2024-10-28
+## Update for 2024-10-29
 
 improvements:
 - model selector:
@@ -19,6 +19,7 @@ improvements:
   - handle missing model components during load  
   - handle component preloading  
   - native lora handler  
+  - support for all sd35 variants: *medium/large/large-turbo*
   - gguf transformer loader (prototype)  
 - samplers:
   - support for original k-diffusion samplers  
@@ -37,6 +38,11 @@ improvements:
   - OpenVINO: add accuracy option  
   - ZLUDA: guess GPU arch  
 - major model load refactor
+- wiki: new articles
+  - [Gated Access Wiki](https://github.com/vladmandic/automatic/wiki/Gated)  
+  - [Quantization Wiki](https://github.com/vladmandic/automatic/wiki/Quantization)  
+  - [Offloading Wiki](https://github.com/vladmandic/automatic/wiki/Offload)  
+
 
 fixes:  
 - fix send-to-control  

html/reference.json

@@ -119,11 +119,19 @@
     "preview": "stabilityai--stable-diffusion-3.jpg",
     "extras": "sampler: Default, cfg_scale: 7.0"
   },
+  "StabilityAI Stable Diffusion 3.5 Medium": {
+    "path": "stabilityai/stable-diffusion-3.5-medium",
+    "skip": true,
+    "variant": "fp16",
+    "desc": "Stable Diffusion 3.5 Medium is a Multimodal Diffusion Transformer with improvements (MMDiT-X) text-to-image model that features improved performance in image quality, typography, complex prompt understanding, and resource-efficiency.",
+    "preview": "stabilityai--stable-diffusion-3_5.jpg",
+    "extras": "sampler: Default, cfg_scale: 7.0"
+  },
   "StabilityAI Stable Diffusion 3.5 Large": {
     "path": "stabilityai/stable-diffusion-3.5-large",
     "skip": true,
     "variant": "fp16",
-    "desc": "Stable Diffusion 3 Medium is a Multimodal Diffusion Transformer (MMDiT) text-to-image model that features greatly improved performance in image quality, typography, complex prompt understanding, and resource-efficiency",
+    "desc": "Stable Diffusion 3.5 Large is a Multimodal Diffusion Transformer (MMDiT) text-to-image model that features improved performance in image quality, typography, complex prompt understanding, and resource-efficiency.",
     "preview": "stabilityai--stable-diffusion-3_5.jpg",
     "extras": "sampler: Default, cfg_scale: 7.0"
   },
@@ -131,7 +139,7 @@
     "path": "stabilityai/stable-diffusion-3.5-large-turbo",
     "skip": true,
     "variant": "fp16",
-    "desc": "Stable Diffusion 3 Medium is a Multimodal Diffusion Transformer (MMDiT) text-to-image model that features greatly improved performance in image quality, typography, complex prompt understanding, and resource-efficiency",
+    "desc": "Stable Diffusion 3.5 Large Turbo is a Multimodal Diffusion Transformer (MMDiT) text-to-image model with Adversarial Diffusion Distillation (ADD) that features improved performance in image quality, typography, complex prompt understanding, and resource-efficiency, with a focus on fewer inference steps.",
     "preview": "stabilityai--stable-diffusion-3_5.jpg",
     "extras": "sampler: Default, cfg_scale: 7.0"
   },

installer.py

@@ -455,7 +455,7 @@ def check_python(supported_minors=[9, 10, 11, 12], reason=None):
 
 # check diffusers version
 def check_diffusers():
-    sha = '435f6b7e47c031f98b8374b1689e1abeb17bfdb6'
+    sha = '0d1d267b12e47b40b0e8f265339c76e0f45f8c49'
     pkg = pkg_resources.working_set.by_key.get('diffusers', None)
     minor = int(pkg.version.split('.')[1] if pkg is not None else 0)
     cur = opts.get('diffusers_version', '') if minor > 0 else ''

modules/model_sd3.py

@@ -49,6 +49,24 @@ def load_overrides(kwargs, cache_dir):
     return kwargs
 
 
+def create_bnb_config(kwargs):
+    if len(shared.opts.bnb_quantization) > 0:
+        if 'Model' in shared.opts.bnb_quantization and 'transformer' not in kwargs:
+            from modules.model_quant import load_bnb
+            load_bnb('Load model: type=SD3')
+            bnb_config = diffusers.BitsAndBytesConfig(
+                load_in_8bit=shared.opts.bnb_quantization_type in ['fp8'],
+                load_in_4bit=shared.opts.bnb_quantization_type in ['nf4', 'fp4'],
+                bnb_4bit_quant_storage=shared.opts.bnb_quantization_storage,
+                bnb_4bit_quant_type=shared.opts.bnb_quantization_type,
+                bnb_4bit_compute_dtype=devices.dtype
+            )
+            kwargs['quantization_config'] = bnb_config
+            shared.log.debug(f'Quantization: module=all type=bnb dtype={shared.opts.bnb_quantization_type} storage={shared.opts.bnb_quantization_storage}')
+
+    return kwargs
+
+
 def load_quants(kwargs, repo_id, cache_dir):
     if len(shared.opts.bnb_quantization) > 0:
         from modules.model_quant import load_bnb
@@ -127,7 +145,7 @@ def load_sd3(checkpoint_info, cache_dir=None, config=None):
         kwargs = load_quants(kwargs, repo_id, cache_dir)
 
     loader = diffusers.StableDiffusion3Pipeline.from_pretrained
-    if fn is not None and os.path.exists(fn):
+    if fn is not None and os.path.exists(fn) and os.path.isfile(fn):
         if fn.endswith('.safetensors'):
             loader = diffusers.StableDiffusion3Pipeline.from_single_file
             kwargs = load_missing(kwargs, fn, cache_dir)
@@ -139,8 +157,10 @@ def load_sd3(checkpoint_info, cache_dir=None, config=None):
     else:
         kwargs['variant'] = 'fp16'
 
-    shared.log.debug(f'Load model: type=SD3 preloaded={list(kwargs)}')
+    shared.log.debug(f'Load model: type=SD3 kwargs={list(kwargs)}')
 
+    kwargs = create_bnb_config(kwargs)
+    print('HERE', repo_id, kwargs)
     pipe = loader(
         repo_id,
         torch_dtype=devices.dtype,
@@ -148,5 +168,5 @@ def load_sd3(checkpoint_info, cache_dir=None, config=None):
         config=config,
         **kwargs,
     )
-    devices.torch_gc()
+    devices.torch_gc(force=True)
     return pipe

modules/modelloader.py

@@ -318,7 +318,7 @@ def load_diffusers_models(clear=True):
 def find_diffuser(name: str, full=False):
     repo = [r for r in diffuser_repos if name == r['name'] or name == r['friendly'] or name == r['path']]
     if len(repo) > 0:
-        return repo['name']
+        return [repo[0]['name']]
     hf_api = hf.HfApi()
     models = list(hf_api.list_models(model_name=name, library=['diffusers'], full=True, limit=20, sort="downloads", direction=-1))
     shared.log.debug(f'Searching diffusers models: {name} {len(models) > 0}')