update pipelines and xyzgrid

CHANGELOG.md

@@ -2,16 +2,24 @@
 
 ## Update for 2023-11-08
 
+- **Diffusers**  
+  - **LCM** support for any *SD 1.5* or *SD-XL* model!
+    - download [lcm-lora-sd15](https://huggingface.co/latent-consistency/lcm-lora-sdv1-5/tree/main) and/or [lcm-lora-sdxl](https://huggingface.co/latent-consistency/lcm-lora-sdxl/tree/main)
+    - load for favorite *SD 1.5* or *SD-XL* model
+    - load **lcm lora**
+    - set **sampler** to **LCM**
+    - set number of steps to some low number, for SD-XL 6-7 steps is normally sufficient  
+      note: LCM scheduler does not support steps higher than 50  
+  - Add additional pipeline types for manual model loads when loading from `safetensors`  
+  - Updated logic for calculating **steps** when using base/hires/refiner workflows  
+  - Safe model offloading for non-standard models
+  - Fix **DPM SDE** scheduler  
 - **Extra networks**  
   - Use multi-threading for 5x load speedup  
 - **General**:
   - Reworked parser when pasting previously generated images/prompts  
     includes all `txt2img`, `img2img` and `override` params  
-- **Diffusers**  
-  - Add additional pipeline types for manual model loads when loading from `safetensors`  
-  - Updated logic for calculating steps when using base/hires/refiner workflows  
-  - Safe model offloading for non-standard models
-  - Fix DPM SDE scheduler  
+  - Add refiner options to XYZ Grid  
 - **Fixes**
   - Fix inpaint  
   - Fix manual grid image save

modules/sd_models.py

@@ -646,6 +646,14 @@ def detect_pipeline(f: str, op: str = 'model'):
                 guess = 'Stable Diffusion XL Instruct'
             else:
                 guess = 'Stable Diffusion'
+            if 'LCM_' in f or 'LCM-' in f:
+                if shared.backend == shared.Backend.ORIGINAL:
+                    shared.log.warning(f'Model detected as LCM model, but attempting to load using backend=original: {op}={f} size={size} MB')
+                guess = 'Latent Consistency Model'
+            if 'PixArt' in f:
+                if shared.backend == shared.Backend.ORIGINAL:
+                    shared.log.warning(f'Model detected as PixArt Alpha model, but attempting to load using backend=original: {op}={f} size={size} MB')
+                guess = 'PixArt Alpha'
             pipeline = shared_items.get_pipelines().get(guess, None)
             shared.log.info(f'Autodetect: {op}="{guess}" class={pipeline.__name__} file="{f}" size={size}MB')
         except Exception as e:

modules/sd_samplers_diffusers.py

@@ -60,7 +60,7 @@
     sd_samplers_common.SamplerData('Euler', lambda model: DiffusionSampler('Euler', EulerDiscreteScheduler, model), [], {}),
     sd_samplers_common.SamplerData('Euler a', lambda model: DiffusionSampler('Euler a', EulerAncestralDiscreteScheduler, model), [], {}),
     sd_samplers_common.SamplerData('Heun', lambda model: DiffusionSampler('Heun', HeunDiscreteScheduler, model), [], {}),
-    sd_samplers_common.SamplerData('LCM', lambda model: DiffusionSampler('Heun', LCMScheduler, model), [], {}),
+    sd_samplers_common.SamplerData('LCM', lambda model: DiffusionSampler('LCM', LCMScheduler, model), [], {}),
 ]
 
 class DiffusionSampler:
@@ -73,18 +73,22 @@ def __init__(self, name, constructor, model, **kwargs):
             return
         for key, value in config.get('All', {}).items(): # apply global defaults
             self.config[key] = value
+        shared.log.debug(f'Sampler: name={name} type=all config={self.config}')
         for key, value in config.get(name, {}).items(): # apply diffusers per-scheduler defaults
             self.config[key] = value
+        shared.log.debug(f'Sampler: name={name} type=scheduler config={self.config}')
         if hasattr(model.scheduler, 'scheduler_config'): # find model defaults
             orig_config = model.scheduler.scheduler_config
         else:
             orig_config = model.scheduler.config
         for key, value in orig_config.items(): # apply model defaults
             if key in self.config:
                 self.config[key] = value
+        shared.log.debug(f'Sampler: name={name} type=model config={self.config}')
         for key, value in kwargs.items(): # apply user args, if any
             if key in self.config:
                 self.config[key] = value
+        shared.log.debug(f'Sampler: name={name} type=user config={self.config}')
         # finally apply user preferences
         if shared.opts.schedulers_prediction_type != 'default':
             self.config['prediction_type'] = shared.opts.schedulers_prediction_type

scripts/xyz_grid.py

@@ -86,6 +86,17 @@ def apply_checkpoint(p, x, xs):
         p.override_settings['sd_model_checkpoint'] = info.name
 
 
+def apply_refiner(p, x, xs):
+    if x == shared.opts.sd_model_refiner:
+        return
+    info = sd_models.get_closet_checkpoint_match(x)
+    if info is None:
+        shared.log.warning(f"XYZ grid: apply refiner unknown checkpoint: {x}")
+    else:
+        sd_models.reload_model_weights(shared.sd_refiner, info)
+        p.override_settings['sd_model_refiner'] = info.name
+
+
 def apply_dict(p, x, xs):
     if x == shared.opts.sd_model_dict:
         return
@@ -240,11 +251,12 @@ def __init__(self, *args, **kwargs):
     AxisOption("[Second pass] upscaler", str, apply_field("hr_upscaler"), choices=lambda: [*shared.latent_upscale_modes, *[x.name for x in shared.sd_upscalers]]),
     AxisOption("[Second pass] sampler", str, apply_latent_sampler, fmt=format_value, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers]),
     AxisOption("[Second pass] denoising Strength", float, apply_field("denoising_strength")),
-    AxisOption("[Second pass] steps", int, apply_field("hr_second_pass_steps")),
+    AxisOption("[Second pass] hires steps", int, apply_field("hr_second_pass_steps")),
     AxisOption("[Second pass] CFG scale", float, apply_field("image_cfg_scale")),
     AxisOption("[Second pass] guidance rescale", float, apply_field("diffusers_guidance_rescale")),
-    AxisOption("[Second pass] refiner start", float, apply_field("refiner_start")),
-    AxisOption("[Second pass] refiner start", float, apply_field("refiner_start")),
+    AxisOption("[Refiner] model", str, apply_refiner, fmt=format_value, cost=1.0, choices=lambda: sorted(sd_models.checkpoints_list)),
+    AxisOption("[Refiner] refiner start", float, apply_field("refiner_start")),
+    AxisOption("[Refiner] refiner steps", float, apply_field("refiner_steps")),
     AxisOption("[TOME] Token merging ratio (txt2img)", float, apply_override('token_merging_ratio')),
     AxisOption("[TOME] Token merging ratio (hires)", float, apply_override('token_merging_ratio_hr')),
     AxisOption("[FreeU] 1st stage backbone factor", float, apply_setting('freeu_b1')),