nodes/CosmicLaca-ComfyUI_Primere_Nodes
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

V 2.0.0 - Auto config #1 - preplan

Browse Source
This commit is contained in:
DESKTOP-TVBJISQ\Primere
2026-03-10 19:47:25 +01:00
parent 81f435b382
commit 6e6b280ebb
3 changed files with 104 additions and 141 deletions
Download Patch File Download Diff File Expand all files Collapse all files
Nodes/Dashboard.py
+86 -138
View File
@@ -1,6 +1,7 @@
import math
from ..components.tree import TREE_DASHBOARD
from ..components.tree import PRIMERE_ROOT
from server import PromptServer
import comfy.samplers
import folder_paths
import nodes
@@ -645,11 +646,12 @@ class PrimereModelConceptSelector:
zimage_model, zimage_clip, zimage_vae
)
class PrimereControlledSamplersSteps:
class PrimereAutoSamplerSettings:
CATEGORY = TREE_DASHBOARD
RETURN_TYPES = ("STRING", comfy.samplers.KSampler.SAMPLERS, comfy.samplers.KSampler.SCHEDULERS, "INT", "FLOAT")
RETURN_NAMES = ("MODEL_CONCEPT", "SAMPLER_NAME", "SCHEDULER_NAME", "STEPS", "CFG")
FUNCTION = "get_controlledsampler_step"
RETURN_TYPES = ("TUPLE",)
RETURN_NAMES = ("DATA",)
FUNCTION = "get_controlledsampler"
OUTPUT_NODE = True
kolors_schedulers = ["EulerDiscreteScheduler", "EulerAncestralDiscreteScheduler", "DPMSolverMultistepScheduler", "DPMSolverMultistepScheduler_SDE_karras", "UniPCMultistepScheduler", "DEISMultistepScheduler"]
sana_schedulers = ['flow_dpm-solver']
@@ -662,9 +664,21 @@ class PrimereControlledSamplersSteps:
CLIPLIST = PrimereModelConceptSelector.CLIPLIST
MODELLIST = PrimereModelConceptSelector.MODELLIST
TEXT_ENCODERS_PATHS = PrimereModelConceptSelector.TEXT_ENCODERS_PATHS
CONCEPT_LIST = PrimereModelConceptSelector.CONCEPT_LIST
CUSTOMLORA_DIR = os.path.join(PRIMERE_ROOT, 'Nodes', 'Downloads')
folder_paths.add_model_folder_path("customlora", CUSTOMLORA_DIR)
CustomLoras = folder_paths.get_filename_list("customlora")
CustomLorasList = folder_paths.filter_files_extensions(CustomLoras, ['.safetensors'])
LCM_LORAS = [n for n in CustomLorasList if "lcm" in n.lower()]
SPEED_LORAS = [n for n in CustomLorasList if any(s in n.lower() for s in ("lightning", "hyper", "turbo"))]
SRPO_LORAS = [n for n in CustomLorasList if "srpo" in n.lower() and "svdq" not in n.lower()]
SRPO_SVDQ_LORAS = [n for n in CustomLorasList if "srpo" in n.lower() and "svdq" in n.lower()]
NUNCHAKU_LORAS = [n for n in CustomLorasList if "nunchaku" in n.lower()]
REFINER_MODELS = [n for n in PrimereModelConceptSelector.MODELLIST if "refiner" in os.path.basename(n).lower() or "refiner" in os.path.dirname(n).lower()]
@classmethod
def INPUT_TYPES(cls):
return {
@@ -674,31 +688,41 @@ class PrimereControlledSamplersSteps:
"sampler_name": (comfy.samplers.KSampler.SAMPLERS,),
"scheduler_name": (cls.sana_schedulers + cls.kolors_schedulers + comfy.samplers.KSampler.SCHEDULERS,),
"steps": ("INT", {"default": 12, "min": 1, "max": 1000, "step": 1}),
"override_steps": ("BOOLEAN", {"default": False, "label_off": "Set by sampler settings", "label_on": "Set by model filename"}),
"cfg": ("FLOAT", {"default": 7, "min": 0.1, "max": 100, "step": 0.01}),
"vae": (["None"] + cls.VAELIST,),
"encoder_1": (["None"] + cls.TEXT_ENCODERS + cls.CLIPLIST + cls.TEXT_ENCODERS_PATHS,),
"encoder_2": (["None"] + cls.TEXT_ENCODERS + cls.CLIPLIST + cls.TEXT_ENCODERS_PATHS,),
"encoder_3": (["None"] + cls.TEXT_ENCODERS + cls.CLIPLIST + cls.TEXT_ENCODERS_PATHS,),
"sampler": (["None"] + ["custom_advanced", "ksampler"], {"default": "ksampler"}),
"vae": (cls.VAELIST,),
"vae_selection": ("BOOLEAN", {"default": True, "label_on": "Use baked if exist", "label_off": "Always use custom"}),
"clip_selection": ("BOOLEAN", {"default": True, "label_on": "Use baked if exist", "label_off": "Always use custom"}),
"encoder_1": (["None"] + cls.TEXT_ENCODERS + cls.CLIPLIST + cls.UNETLIST + cls.TEXT_ENCODERS_PATHS,),
"encoder_2": (["None"] + cls.TEXT_ENCODERS + cls.CLIPLIST + cls.UNETLIST + cls.TEXT_ENCODERS_PATHS,),
"encoder_3": (["None"] + cls.TEXT_ENCODERS + cls.CLIPLIST + cls.UNETLIST + cls.TEXT_ENCODERS_PATHS,),
"sampler": (["custom_advanced", "ksampler"], {"default": "ksampler"}),
"guidance": ('FLOAT', {"default": 3.5, "min": 0.0, "max": 100.0, "step": 0.1}),
"weight_dtype": (["None"] + ["Auto", "default", "fp16", "bf16", "fp32", "fp8_e4m3fn", "fp8_e5m2"], {"default": "default"}),
"precision": (["None"] + ['fp32', 'fp16', 'quant8', 'quant4'], {"default": "fp16"}),
"use_speed_lora": ("BOOLEAN", {"default": False, "label_on": "Seed lora ON", "label_off": "Seed lora OFF"}),
"speed_lora": (["None"],),
"speed_lora_version": ([1.0, 1.1, 2.0], {"default": 2.0}),
"speed_lora_precision": ("BOOLEAN", {"default": True, "label_on": "FP32", "label_off": "BF16"}),
"speed_lora_step": ([4, 6, 8, 10, 12, 16], {"default": 8}),
"lcm_lora": ("BOOLEAN", {"default": False, "label_on": "LCM lora ON", "label_off": "LCM lora OFF"}),
# "lcm_lora_name": (cls.LCM_LORAS,),
"lcm_lora_strength": ("FLOAT", {"default": 1.000, "min": -20.000, "max": 20.000, "step": 0.001}),
"speed_lora": ("BOOLEAN", {"default": False, "label_on": "Speed lora ON", "label_off": "Speed lora OFF"}),
"speed_lora_name": (cls.SPEED_LORAS,),
# "speed_lora_version": ([1.0, 1.1, 2.0], {"default": 2.0}),
# "speed_lora_precision": ("BOOLEAN", {"default": True, "label_on": "FP32", "label_off": "BF16"}),
# "speed_lora_step": ([4, 6, 8, 10, 12, 16], {"default": 8}),
"speed_lora_strength": ("FLOAT", {"default": 1.00, "min": -20.00, "max": 20.00, "step": 0.01}),
"use_srpo_lora": ("BOOLEAN", {"default": False, "label_on": "Use SRPO Lora", "label_off": "Ignore SRPO Lora"}),
"use_srpo_svdq_lora": ("BOOLEAN", {"default": False, "label_on": "Use SRPO-NUNCHAKU Lora", "label_off": "Ignore SRPO-NUNCHAKU Lora"}),
"srpo_lora_type": (["R&Q", "RockerBOO", "oficial", "adaptive"], {"default": "oficial"}),
"srpo_lora_rank": ([8, 16, 32, 64, 128, 256], {"default": 8}),
"srpo_lora": ("BOOLEAN", {"default": False, "label_on": "Use SRPO Lora", "label_off": "Ignore SRPO Lora"}),
"srpo_lora_name": (cls.SRPO_LORAS,),
# "srpo_lora_type": (["R&Q", "RockerBOO", "oficial", "adaptive"], {"default": "oficial"}),
# "srpo_lora_rank": ([8, 16, 32, 64, 128, 256], {"default": 8}),
"srpo_lora_strength": ("FLOAT", {"default": 1, "min": -20.000, "max": 20.000, "step": 0.001}),
"use_nunchaku_lora": ("BOOLEAN", {"default": False, "label_on": "Use nunchaku Lora", "label_off": "Ignore nunchaku Lora"}),
"nunchaku_lora_type": (["kontext_deblur", "kontext_face_detailer", "anything_extracted"], {"default": "anything_extracted"}),
"nunchaku_lora_rank": ([64, 256], {"default": 64}),
"srpo_svdq_lora": ("BOOLEAN", {"default": False, "label_on": "Use SRPO SVDQ Lora", "label_off": "Ignore SRPO SVDQ Lora"}),
"srpo_svdq_lora_name": (cls.SRPO_SVDQ_LORAS,),
"nunchaku_lora": ("BOOLEAN", {"default": False, "label_on": "Use nunchaku Lora", "label_off": "Ignore nunchaku Lora"}),
"nunchaku_lora_name": (cls.NUNCHAKU_LORAS,),
# "nunchaku_lora_type": (["kontext_deblur", "kontext_face_detailer", "anything_extracted"], {"default": "anything_extracted"}),
# "nunchaku_lora_rank": ([64, 256], {"default": 64}),
"nunchaku_lora_strength": ("FLOAT", {"default": 1, "min": -20.000, "max": 20.000, "step": 0.001}),
"pixart_refiner_model": (["None"] + cls.MODELLIST,),
"refiner": ("BOOLEAN", {"default": False, "label_on": "Refiner ON", "label_off": "Refiner OFF"}),
"refiner_model": (cls.REFINER_MODELS,),
"refiner_sampler": (comfy.samplers.KSampler.SAMPLERS, {"default": "dpmpp_2m"}),
"refiner_scheduler": (comfy.samplers.KSampler.SCHEDULERS, {"default": "normal"}),
"refiner_cfg": ("FLOAT", {"default": 2.0, "min": 0.1, "max": 100, "step": 0.01}),
@@ -709,12 +733,40 @@ class PrimereControlledSamplersSteps:
}
}
def get_controlledsampler_step(self, model_concept, sampler_name, scheduler_name, steps=12, cfg=7, **kwargs):
return model_concept, sampler_name, scheduler_name, steps, round(cfg, 2)
def get_controlledsampler(self, **kwargs):
model_concept = kwargs.pop('model_concept', 'SD1')
concepts = kwargs.pop('concepts', 'Auto')
sampler_name = kwargs.pop('sampler_name', comfy.samplers.KSampler.SAMPLERS[0])
scheduler_name = kwargs.pop('scheduler_name', comfy.samplers.KSampler.SCHEDULERS[0])
steps = kwargs.pop('steps', 12)
cfg = kwargs.pop('cfg', 7.0)
active_concept = model_concept if concepts == "Auto" else concepts
json_path = os.path.join(PRIMERE_ROOT, 'front_end', 'model_concept.json')
concept_data = utility.json2tuple(json_path)
if not concept_data or active_concept not in concept_data:
PromptServer.instance.send_sync("primere.concept_setting", {"status": "missing", "concept": active_concept})
else:
saved = concept_data[active_concept]
sampler_name = saved.get('sampler_name', sampler_name)
scheduler_name = saved.get('scheduler_name', scheduler_name)
steps = saved.get('steps', steps)
cfg = saved.get('cfg', cfg)
for k, v in saved.items():
if k in kwargs:
kwargs[k] = v
suppressed = [k + "_" for k, v in kwargs.items() if v == "None" or v is False]
kwargs = {k: v for k, v in kwargs.items() if v != "None" and not any(k.startswith(p) for p in suppressed)}
kwargs['encoders'] = [kwargs[k] for k in ('encoder_1', 'encoder_2', 'encoder_3') if kwargs.get(k) not in (None, 'None')]
kwargs['model_concept'] = active_concept
kwargs['sampler_name'] = sampler_name
kwargs['scheduler_name'] = scheduler_name
kwargs['steps'] = steps
kwargs['cfg'] = round(cfg, 2)
return {"ui": {"active_concept": [active_concept]}, "result": (kwargs,)}
class PrimereConceptDataTuple:
RETURN_TYPES = ("TUPLE",)
RETURN_NAMES = ("CONCEPT_DATA",)
RETURN_TYPES = (comfy.samplers.KSampler.SAMPLERS, comfy.samplers.KSampler.SCHEDULERS, "INT", "FLOAT", "TUPLE",)
RETURN_NAMES = ("SAMPLER_NAME", "SCHEDULER_NAME", "STEPS", "CFG", "DATA",)
FUNCTION = "load_concept_collector"
CATEGORY = TREE_DASHBOARD
@@ -722,120 +774,16 @@ class PrimereConceptDataTuple:
def INPUT_TYPES(cls):
return {
"required": {
"sampler_name": (comfy.samplers.KSampler.SAMPLERS, {"forceInput": True}),
"scheduler_name": (comfy.samplers.KSampler.SCHEDULERS, {"forceInput": True}),
"steps": ("INT", {"forceInput": True}),
"cfg": ("FLOAT", {"forceInput": True}),
"override_steps": ("OVERRIDE_STEPS", {"default": False, "forceInput": True}),
"clip_selection": ("CLIP_SELECTION", {"default": True, "forceInput": True}),
"vae_selection": ("VAE_SELECTION", {"default": True, "forceInput": True}),
"vae_name": ("VAE_NAME", {"default": "Baked", "forceInput": True}),
"strength_lcm_lora_model": ("FLOAT", {"default": 1, "forceInput": True}),
"lightning_selector": ("STRING", {"default": "SAFETENSOR", "forceInput": True}),
"lightning_model_step": ("INT", {"default": 8, "forceInput": True}),
"strength_lightning_lora_model": ("FLOAT", {"default": 1, "forceInput": True}),
"cascade_stage_a": ("STRING", {"forceInput": True}),
"cascade_stage_b": ("STRING", {"forceInput": True}),
"cascade_stage_c": ("STRING", {"forceInput": True}),
"cascade_clip": ("STRING", {"forceInput": True}),
"hypersd_selector": ("STRING", {"default": "LORA", "forceInput": True}),
"hypersd_model_step": ("INT", {"default": 8, "forceInput": True}),
"strength_hypersd_lora_model": ("FLOAT", {"default": 1, "forceInput": True}),
"flux_selector": ("STRING", {"default": "DIFFUSION", "forceInput": True}),
"flux_diffusion": ("STRING", {"forceInput": True}),
"flux_weight_dtype": ("STRING", {"forceInput": True}),
"flux_gguf": ("STRING", {"forceInput": True}),
"flux_clip_t5xxl": ("STRING", {"forceInput": True}),
"flux_clip_l": ("STRING", {"forceInput": True}),
"flux_clip_guidance": ("FLOAT", {"default": 3.5, "forceInput": True}),
"flux_vae": ("STRING", {"forceInput": True}),
"flux_sampler": ("STRING", {"forceInput": True}),
"use_flux_hyper_lora": ("FLUX_HYPER_LORA", {"forceInput": True}),
"flux_hyper_lora_type": ("STRING", {"forceInput": True}),
"flux_hyper_lora_step": ("INT", {"forceInput": True}),
"flux_hyper_lora_strength": ("FLOAT", {"default": 0.125, "forceInput": True}),
"use_flux_turbo_lora": ("FLUX_TURBO_LORA", {"forceInput": True}),
"flux_turbo_lora_type": ("STRING", {"forceInput": True}),
"flux_turbo_lora_step": ("INT", {"forceInput": True}),
"flux_turbo_lora_strength": ("FLOAT", {"default": 0.125, "forceInput": True}),
"use_flux_srpo_lora": ("FLUX_SRPO_LORA", {"forceInput": True}),
"use_flux_srpo_svdq_lora": ("FLUX_SRPO_SVDQ_LORA", {"forceInput": True}),
"flux_srpo_lora_type": ("STRING", {"default": "oficial", "forceInput": True}),
"flux_srpo_lora_rank": ("INT", {"default": 8, "forceInput": True}),
"flux_srpo_lora_strength": ("FLOAT", {"default": 1.000, "forceInput": True}),
"use_flux_nunchaku_lora": ("FLUX_NUNCHAKU_LORA", {"forceInput": True}),
"flux_nunchaku_lora_type": ("STRING", {"default": "anything_extracted", "forceInput": True}),
"flux_nunchaku_lora_rank": ("INT", {"default": 64, "forceInput": True}),
"flux_nunchaku_lora_strength": ("FLOAT", {"default": 1.000, "forceInput": True}),
"hunyuan_clip_t5xxl": ("STRING", {"forceInput": True}),
"hunyuan_clip_l": ("STRING", {"forceInput": True}),
"hunyuan_vae": ("STRING", {"forceInput": True}),
"sd3_clip_g": ("STRING", {"forceInput": True}),
"sd3_clip_l": ("STRING", {"forceInput": True}),
"sd3_clip_t5xxl": ("STRING", {"forceInput": True}),
"sd3_unet_vae": ("STRING", {"forceInput": True}),
"use_sd3_hyper_lora": ("SD3_HYPER_LORA", {"forceInput": True}),
"sd3_hyper_lora_step": ("INT", {"default": 8, "forceInput": True}),
"sd3_hyper_lora_strength": ("FLOAT", {"default": 0.125, "forceInput": True}),
"kolors_precision": ("STRING", {"forceInput": True}),
"pixart_model_type": ("STRING", {"forceInput": True}),
"pixart_T5_encoder": ("STRING", {"forceInput": True}),
"pixart_vae": ("STRING", {"forceInput": True}),
"pixart_denoise": ("FLOAT", {"forceInput": True}),
"pixart_refiner_model": ("STRING", {"forceInput": True}),
"pixart_refiner_sampler": ("STRING", {"forceInput": True}),
"pixart_refiner_scheduler": ("STRING", {"forceInput": True}),
"pixart_refiner_cfg": ("FLOAT", {"forceInput": True}),
"pixart_refiner_steps": ("INT", {"forceInput": True}),
"pixart_refiner_start": ("INT", {"forceInput": True}),
"pixart_refiner_denoise": ("FLOAT", {"forceInput": True}),
"pixart_refiner_ignore_prompt": ("BOOLEAN", {"forceInput": True}),
"sana_model": ("STRING", {"forceInput": True}),
"sana_encoder": ("STRING", {"forceInput": True}),
"sana_vae": ("STRING", {"forceInput": True}),
"sana_weight_dtype": ("STRING", {"forceInput": True}),
"sana_precision": ("STRING", {"forceInput": True}),
"qwen_gen_model": ("STRING", {"forceInput": True}),
"qwen_gen_clip": ("STRING", {"forceInput": True}),
"qwen_gen_vae":("STRING", {"forceInput": True}),
"use_qwen_gen_lightning_lora": ("QWEN_GEN_LIGHTNING_LORA", {"forceInput": True}),
"qwen_gen_lightning_lora_version": ("FLOAT", {"forceInput": True}),
"qwen_gen_lightning_precision": ("QWEN_GEN_LORA_PRECISION", {"forceInput": True}),
"qwen_gen_lightning_lora_step": ("INT", {"default": 8, "forceInput": True}),
"qwen_gen_lightning_lora_strength": ("FLOAT", {"default": 1.00, "forceInput": True}),
"qwen_edit_model": ("STRING", {"forceInput": True}),
"qwen_edit_clip": ("STRING", {"forceInput": True}),
"qwen_edit_vae": ("STRING", {"forceInput": True}),
"use_qwen_edit_lightning_lora": ("QWEN_EDIT_LIGHTNING_LORA", {"forceInput": True}),
"qwen_edit_lightning_lora_version": ("FLOAT", {"forceInput": True}),
"qwen_edit_lightning_precision": ("QWEN_EDIT_LORA_PRECISION", {"forceInput": True}),
"qwen_edit_lightning_lora_step": ("INT", {"default": 8, "forceInput": True}),
"qwen_edit_lightning_lora_strength": ("FLOAT", {"default": 1.00, "forceInput": True}),
"auraflow_clip": ("STRING", {"forceInput": True}),
"auraflow_vae": ("STRING", {"forceInput": True}),
"zimage_model": ("STRING", {"forceInput": True}),
"zimage_clip": ("STRING", {"forceInput": True}),
"zimage_vae": ("STRING", {"forceInput": True})
"data": ("TUPLE", {"forceInput": True}),
},
}
def load_concept_collector(self, **kwargs):
return (kwargs,)
def load_concept_collector(self, data):
sampler_name = data.get("sampler_name", comfy.samplers.KSampler.SAMPLERS[0])
scheduler_name = data.get("scheduler_name", comfy.samplers.KSampler.SCHEDULERS[0])
steps = data.get("steps", 20)
cfg = data.get("cfg", 7.0)
return (sampler_name, scheduler_name, steps, cfg, data,)
class PrimereCKPTLoader:
RETURN_TYPES = ("MODEL", "CLIP", "VAE", "STRING",)
__init__.py
+2 -2
View File
@@ -70,7 +70,7 @@ for subdirs in valid_FElist:
NODE_CLASS_MAPPINGS = {
"PrimereSamplersSteps": Dashboard.PrimereSamplersSteps,
"PrimereControlledSamplersSteps": Dashboard.PrimereControlledSamplersSteps,
"PrimereAutoSamplerSettings": Dashboard.PrimereAutoSamplerSettings,
"PrimereVAE": Dashboard.PrimereVAE,
"PrimereCKPT": Dashboard.PrimereCKPT,
"PrimereVAELoader": Dashboard.PrimereVAELoader,
@@ -149,7 +149,7 @@ NODE_CLASS_MAPPINGS = {
NODE_DISPLAY_NAME_MAPPINGS = {
"PrimereSamplersSteps": "Primere Samplers & Steps & Cfg",
"PrimereControlledSamplersSteps": "Primere Controlled Sampler Setting",
"PrimereAutoSamplerSettings": "Primere Auto Sampler Settings",
"PrimereVAE": "Primere VAE Selector",
"PrimereCKPT": "Primere CKPT Selector",
"PrimereVAELoader": "Primere VAE Loader",
components/primereserver.py
+16 -1
View File
@@ -527,4 +527,19 @@ routes17 = PromptServer.instance.routes
@routes17.get('/primere_apiconfig_check')
async def primere_apiconfig_check(request):
config_path = os.path.join(PRIMERE_ROOT, 'json', 'apiconfig.json')
return web.json_response({"exists": os.path.isfile(config_path)})
return web.json_response({"exists": os.path.isfile(config_path)})
routes18 = PromptServer.instance.routes
@routes18.post('/primere_model_concept_save')
async def primere_model_concept_save(request):
post = await request.json()
concept = post.get('concept')
data = post.get('data')
if not concept or data is None:
return web.json_response({"success": False, "error": "Missing concept or data"}, status=400)
json_path = os.path.join(PRIMERE_ROOT, 'front_end', 'model_concept.json')
existing = utility.json2tuple(json_path) or {}
existing[concept] = data
with open(json_path, 'w', encoding='utf-8') as f:
json.dump(existing, f, indent=2)
return web.json_response({"success": True})