lcm scheduler

fooocus_version.py

@@ -1 +1 @@
-version = '2.1.797'
+version = '2.1.798'

modules/async_worker.py

@@ -575,6 +575,23 @@ def worker():
         preparation_time = time.perf_counter() - execution_start_time
         print(f'Preparation time: {preparation_time:.2f} seconds')
 
+        final_sampler_name = sampler_name
+        final_scheduler_name = scheduler_name
+
+        if scheduler_name == 'lcm':
+            final_scheduler_name = 'sgm_uniform'
+            if pipeline.final_unet is not None:
+                pipeline.final_unet = core.opModelSamplingDiscrete.patch(
+                    pipeline.final_unet,
+                    sampling='lcm',
+                    zsnr=False)[0]
+            if pipeline.final_refiner_unet is not None:
+                pipeline.final_refiner_unet = core.opModelSamplingDiscrete.patch(
+                    pipeline.final_refiner_unet,
+                    sampling='lcm',
+                    zsnr=False)[0]
+            print('Using lcm scheduler.')
+
         outputs.append(['preview', (13, 'Moving model to GPU ...', None)])
 
         def callback(step, x0, x, total_steps, y):
@@ -609,8 +626,8 @@ def worker():
                     height=height,
                     image_seed=task['task_seed'],
                     callback=callback,
-                    sampler_name=sampler_name,
-                    scheduler_name=scheduler_name,
+                    sampler_name=final_sampler_name,
+                    scheduler_name=final_scheduler_name,
                     latent=initial_latent,
                     denoise=denoising_strength,
                     tiled=tiled,

modules/core.py

@@ -26,6 +26,7 @@ from modules.patch import patched_sampler_cfg_function
 from fcbh.lora import model_lora_keys_unet, model_lora_keys_clip, load_lora
 from modules.config import path_embeddings
 from modules.lora import load_dangerous_lora
+from fcbh_extras.nodes_model_advanced import ModelSamplingDiscrete
 
 
 opEmptyLatentImage = EmptyLatentImage()
@@ -35,6 +36,7 @@ opVAEDecodeTiled = VAEDecodeTiled()
 opVAEEncodeTiled = VAEEncodeTiled()
 opControlNetApplyAdvanced = ControlNetApplyAdvanced()
 opFreeU = FreeU_V2()
+opModelSamplingDiscrete = ModelSamplingDiscrete()
 
 
 class StableDiffusionModel:

modules/flags.py

@@ -14,7 +14,7 @@ KSAMPLER_NAMES = ["euler", "euler_ancestral", "heun", "dpm_2", "dpm_2_ancestral"
                   "lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_sde", "dpmpp_sde_gpu",
                   "dpmpp_2m", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "lcm"]
 
-SCHEDULER_NAMES = ["normal", "karras", "exponential", "sgm_uniform", "simple", "ddim_uniform"]
+SCHEDULER_NAMES = ["normal", "karras", "exponential", "sgm_uniform", "simple", "ddim_uniform", "lcm"]
 SAMPLER_NAMES = KSAMPLER_NAMES + ["ddim", "uni_pc", "uni_pc_bh2"]
 
 sampler_list = SAMPLER_NAMES

update_log.md

@@ -1,3 +1,7 @@
+# 2.1.798
+
+* added lcm scheduler - LCM may need to set both sampler and scheduler to "lcm". Other than that, see the description in 2.1.782 logs.
+
 # 2.1.797
 
 * fixed some dependency problems with facexlib and filterpy.
@@ -35,7 +39,7 @@ Now when you load a lora, the following things will happen:
 
 In this way, Fooocus 2.1.782 can benefit from all models and loras from CivitAI with both SDXL and SD1.5 ecosystem, using the unique Fooocus swap algorithm, to achieve extremely high quality results (although the default setting is already very high quality), especially in some anime use cases, if users really want to play with all these things.
 
-Recently the community also developed LCM loras. Users can use it by setting the sampler as 'LCM', sceduler as 'sgm_uniform', the forced overwrite of sampling step as 4 to 8, and CFG guidance as 1.0, in dev tools. Do not forget to change the LCM lora weight to 1.0 (many people forget this and report failure cases). Also, set refiner to None. If LCM's feedback in the artists community is good (not the feedback in the programmer community of Stable Diffusion), Fooocus may add some other shortcuts in the future.
+Recently the community also developed LCM loras. Users can use it by setting the sampler as 'LCM', scheduler as 'sgm_uniform' (Update in 2.1.798: scheduler should also be "lcm"), the forced overwrite of sampling step as 4 to 8, and CFG guidance as 1.0, in dev tools. Do not forget to change the LCM lora weight to 1.0 (many people forget this and report failure cases). Also, set refiner to None. If LCM's feedback in the artists community is good (not the feedback in the programmer community of Stable Diffusion), Fooocus may add some other shortcuts in the future.
 
 # 2.1.781