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

V 1.0.0 - Auto concept check 1

Browse Source
This commit is contained in:
DESKTOP-CNFQ7PM\Primere
2024-10-16 20:24:06 +02:00
parent 1577efc582
commit 3a5ac7564d
3 changed files with 186 additions and 115 deletions
Download Patch File Download Diff File Expand all files Collapse all files
Nodes/Dashboard.py
+69 -11
View File
@@ -379,6 +379,12 @@ class PrimereCKPTLoader:
playground_sigma_max = 120
playground_sigma_min = 0.002
print('----------- CONCEPT CHECK ---------------------')
print(model_concept)
print('unload')
comfy.model_management.unload_all_models()
comfy.model_management.soft_empty_cache()
if concept_data is not None:
if 'lightning_selector' in concept_data:
lightning_selector = concept_data['lightning_selector']
@@ -420,10 +426,14 @@ class PrimereCKPTLoader:
modelname_only = Path(ckpt_name).stem
if model_concept == "LCM" or (model_concept == 'Lightning' and lightning_selector == 'LORA') or (model_concept == 'Hyper' and hypersd_selector == 'LORA'):
print('1')
MODEL_VERSION = utility.get_value_from_cache('model_version', modelname_only)
print('2')
if MODEL_VERSION is None:
MODEL_VERSION = utility.getModelType(ckpt_name, 'checkpoints')
print('3')
utility.add_value_to_cache('model_version', ckpt_name, MODEL_VERSION)
print('4')
else:
if model_concept is not None:
MODEL_VERSION = model_concept
@@ -434,6 +444,7 @@ class PrimereCKPTLoader:
utility.add_value_to_cache('model_version', ckpt_name, MODEL_VERSION)
if model_concept == "StableCascade" and cascade_stage_a is not None and cascade_stage_b is not None and cascade_stage_c is not None and cascade_clip is not None:
print('StableCascade')
OUTPUT_CLIP_CAS = nodes.CLIPLoader.load_clip(self, cascade_clip, 'stable_cascade')[0]
OUTPUT_VAE_CAS = nodes.VAELoader.load_vae(self, cascade_stage_a)[0]
MODEL_C_CAS = nodes.UNETLoader.load_unet(self, cascade_stage_c, 'default')[0]
@@ -443,6 +454,8 @@ class PrimereCKPTLoader:
return (OUTPUT_MODEL_CAS,) + (OUTPUT_CLIP_CAS,) + (OUTPUT_VAE_CAS,) + (MODEL_VERSION,)
if model_concept == "Flux" and flux_selector is not None and flux_diffusion is not None and flux_weight_dtype is not None and flux_gguf is not None and flux_clip_t5xxl is not None and flux_clip_l is not None and flux_clip_guidance is not None and flux_vae is not None:
print('Flux')
print(flux_selector)
match flux_selector:
case 'DIFFUSION':
MODEL_DIFFUSION = nodes.UNETLoader.load_unet(self, flux_diffusion, flux_weight_dtype)[0]
@@ -457,6 +470,7 @@ class PrimereCKPTLoader:
# case 'SAFETENSOR':
if model_concept == "Hyper" and hypersd_selector == 'UNET':
print('hyper-unet')
ModelConceptChanges = utility.ModelConceptNames(ckpt_name, model_concept, lightning_selector, lightning_model_step, hypersd_selector, hypersd_model_step, 'SDXL')
lora_name = ModelConceptChanges['lora_name']
unet_name = ModelConceptChanges['unet_name']
@@ -464,7 +478,9 @@ class PrimereCKPTLoader:
OUTPUT_MODEL = utility.LightningConceptModel(self, model_concept, hyperModeValid, hypersd_selector, hypersd_model_step, None, lora_name, unet_name)
return (OUTPUT_MODEL[0],) + (OUTPUT_MODEL[1],) + (OUTPUT_MODEL[2],) + (MODEL_VERSION,)
print('5')
ModelConceptChanges = utility.ModelConceptNames(ckpt_name, model_concept, lightning_selector, lightning_model_step, hypersd_selector, hypersd_model_step)
print('6')
ckpt_name = ModelConceptChanges['ckpt_name']
lora_name = ModelConceptChanges['lora_name']
unet_name = ModelConceptChanges['unet_name']
@@ -474,36 +490,56 @@ class PrimereCKPTLoader:
ModelName = path.stem
ModelConfigPath = path.parent.joinpath(ModelName + '.yaml')
ModelConfigFullPath = Path(folder_paths.models_dir).joinpath('checkpoints').joinpath(ModelConfigPath)
print('7')
print(loaded_model)
print(loaded_clip)
print(loaded_vae)
LOADED_CHECKPOINT = []
if loaded_model is not None and loaded_clip is not None and loaded_vae is not None:
LOADED_CHECKPOINT = []
print('7.0')
LOADED_CHECKPOINT.insert(0, loaded_model)
LOADED_CHECKPOINT.insert(1, loaded_clip)
LOADED_CHECKPOINT.insert(2, loaded_vae)
else:
print(ckpt_name)
if os.path.isfile(ModelConfigFullPath) and use_yaml == True:
print('7')
print(ModelConfigFullPath)
print(ckpt_name)
ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
print(ckpt_path)
print('7.1')
print(ModelName + '.yaml file found and loading...')
try:
LOADED_CHECKPOINT = comfy.sd.load_checkpoint(ModelConfigFullPath, ckpt_path, True, True, None, None, None)
except Exception:
LOADED_CHECKPOINT = nodes.CheckpointLoaderSimple.load_checkpoint(self, ckpt_name)
else:
print('7.2')
print(ckpt_name)
LOADED_CHECKPOINT = nodes.CheckpointLoaderSimple.load_checkpoint(self, ckpt_name)
print('7.2.1')
print('8')
OUTPUT_MODEL = LOADED_CHECKPOINT[0]
OUTPUT_CLIP = LOADED_CHECKPOINT[1]
print('9')
hyperModeValid = False
if model_concept == "Hyper":
print('hyper')
ModelConceptChanges = utility.ModelConceptNames(ckpt_name, model_concept, lightning_selector, lightning_model_step, hypersd_selector, hypersd_model_step, MODEL_VERSION)
print(ModelConceptChanges)
# ckpt_name = ModelConceptChanges['ckpt_name']
lora_name = ModelConceptChanges['lora_name']
unet_name = ModelConceptChanges['unet_name']
hyperModeValid = ModelConceptChanges['hyperModeValid']
def lcm(self, model, zsnr=False):
print('def lcm ok')
m = model.clone()
print(ckpt_name)
# sampling_base = comfy.model_sampling.ModelSamplingDiscrete
sampling_type = nodes_model_advanced.LCM
@@ -519,7 +555,8 @@ class PrimereCKPTLoader:
m.add_object_patch("model_sampling", model_sampling)
return m
if model_concept == "LCM":
print(model_concept)
if model_concept == "LCM" and (MODEL_VERSION == 'SD1' or MODEL_VERSION == 'SDXL'):
SDXL_LORA = 'https://huggingface.co/latent-consistency/lcm-lora-sdxl/resolve/main/pytorch_lora_weights.safetensors?download=true'
SD_LORA = 'https://huggingface.co/latent-consistency/lcm-lora-sdv1-5/resolve/main/pytorch_lora_weights.safetensors?download=true'
DOWNLOADED_SD_LORA = os.path.join(PRIMERE_ROOT, 'Nodes', 'Downloads', 'lcm_lora_sd.safetensors')
@@ -541,12 +578,16 @@ class PrimereCKPTLoader:
else:
print('ERROR: Cannot dowload SDXL LCM Lora')
print(MODEL_VERSION)
LORA_PATH = None
if MODEL_VERSION == 'SDXL':
LORA_PATH = DOWNLOADED_SDXL_LORA
else:
elif MODEL_VERSION == 'SD1':
LORA_PATH = DOWNLOADED_SD_LORA
print(LORA_PATH)
if os.path.exists(LORA_PATH) == True:
if LORA_PATH is not None and os.path.exists(LORA_PATH) == True:
print('Lora path ok')
if strength_lcm_model > 0 or strength_lcm_clip > 0:
lora = None
@@ -562,18 +603,22 @@ class PrimereCKPTLoader:
lora = comfy.utils.load_torch_file(LORA_PATH, safe_load=True)
self.loaded_lora = (LORA_PATH, lora)
print(LORA_PATH)
MODEL_LORA, CLIP_LORA = comfy.sd.load_lora_for_models(OUTPUT_MODEL, OUTPUT_CLIP, lora, strength_lcm_model, strength_lcm_clip)
OUTPUT_MODEL = lcm(self, MODEL_LORA, False)
OUTPUT_CLIP = CLIP_LORA
if model_concept == "Lightning" and lightningModeValid == True and loaded_model is None:
print('Lightning end')
OUTPUT_MODEL = utility.LightningConceptModel(self, model_concept, lightningModeValid, lightning_selector, lightning_model_step, OUTPUT_MODEL, lora_name, unet_name)
if model_concept == "Hyper" and hyperModeValid == True and loaded_model is None:
print('Hyper end')
OUTPUT_MODEL = utility.LightningConceptModel(self, model_concept, hyperModeValid, hypersd_selector, hypersd_model_step, OUTPUT_MODEL, lora_name, unet_name)
if model_concept == "Playground":
print('Playground end')
OUTPUT_MODEL = nodes_model_advanced.ModelSamplingContinuousEDM.patch(self, OUTPUT_MODEL, 'edm_playground_v2.5', playground_sigma_max, playground_sigma_min)[0]
return (OUTPUT_MODEL,) + (OUTPUT_CLIP,) + (LOADED_CHECKPOINT[2],) + (MODEL_VERSION,)
@@ -816,6 +861,8 @@ class PrimereCLIP:
}
def clip_encode(self, clip, clip_mode, last_layer, negative_strength, int_style_pos_strength, int_style_neg_strength, opt_pos_strength, opt_neg_strength, style_pos_strength, style_neg_strength, int_style_pos, int_style_neg, adv_encode, token_normalization, weight_interpretation, sdxl_l_strength, extra_pnginfo, prompt, copy_prompt_to_l = True, width = 1024, height = 1024, positive_prompt = "", negative_prompt = "", clip_model = 'Default', longclip_model = 'Default', model_keywords = None, lora_keywords = None, lycoris_keywords = None, embedding_pos = None, embedding_neg = None, opt_pos_prompt = "", opt_neg_prompt = "", style_position = False, style_neg_prompt = "", style_pos_prompt = "", sdxl_positive_l = "", sdxl_negative_l = "", use_int_style = False, model_version = "SD1", model_concept = "Normal", workflow_tuple = None):
print('--------- CLIP CONCEPT TEST ---------------------')
if workflow_tuple is not None and len(workflow_tuple) > 0 and 'exif_status' in workflow_tuple and workflow_tuple['exif_status'] == 'SUCCEED':
if 'prompt_encoder' in workflow_tuple and len(workflow_tuple['prompt_encoder']) > 0 and 'setup_states' in workflow_tuple and 'clip_encoder_setup' in workflow_tuple['setup_states']:
if workflow_tuple['setup_states']['clip_encoder_setup'] == True:
@@ -860,7 +907,8 @@ class PrimereCLIP:
if workflow_tuple is None:
workflow_tuple = {}
if model_concept == 'Cascade' or model_concept == 'Turbo' or model_concept == 'Flux':
print(model_concept)
if model_concept == 'SD3' or model_concept == 'Playground' or model_concept == 'StableCascade' or model_concept == 'Turbo' or model_concept == 'Flux' or model_concept == 'Lightning':
model_version = 'SDXL'
clip_model = 'Default'
@@ -869,6 +917,9 @@ class PrimereCLIP:
case 'SDXL':
is_sdxl = 1
print(is_sdxl)
print(model_version)
additional_positive = int_style_pos
additional_negative = int_style_neg
if int_style_pos == 'None' or use_int_style == False:
@@ -977,7 +1028,7 @@ class PrimereCLIP:
else:
negative_text = negative_text + ', ' + embn_keyword
if (model_version == 'BaseModel_1024'):
if model_version == 'SD1' or model_concept == 'StableCascade':
adv_encode = False
if model_concept == 'Flux':
@@ -990,13 +1041,17 @@ class PrimereCLIP:
WORKFLOWDATA = extra_pnginfo['workflow']['nodes']
# CONCEPT_SELECTOR = utility.getDataFromWorkflow(WORKFLOWDATA, 'PrimereModelConceptSelector', 4)
CONCEPT_SELECTOR = utility.getDataFromWorkflowByName(WORKFLOWDATA, 'PrimereModelConceptSelector', 'model_concept', prompt)
print(CONCEPT_SELECTOR)
print(model_concept)
if CONCEPT_SELECTOR == 'Flux' and (model_concept == 'Flux' and model_concept is None):
if CONCEPT_SELECTOR == 'Flux' and (model_concept == 'Flux' and model_concept is not None):
adv_encode = False
clip_model = 'Default'
# clip_mode = True
last_layer = 0
print('*************')
print(clip_mode)
if clip_mode == False:
if longclip_model == 'Default':
longclip_model = 'longclip-L.pt'
@@ -1069,10 +1124,12 @@ class PrimereCLIP:
workflow_tuple['prompt_encoder']['copy_prompt_to_l'] = copy_prompt_to_l
workflow_tuple['prompt_encoder']['sdxl_l_strength'] = sdxl_l_strength
if model_concept == 'Cascade':
if model_concept == 'StableCascade':
print('---Cascade')
positive_text = utility.clear_cascade(positive_text)
negative_text = utility.clear_cascade(negative_text)
print(clip_model)
if clip_model != 'Default' and clip_mode == True:
if is_sdxl == 1:
# adv_encode = False
@@ -1087,12 +1144,13 @@ class PrimereCLIP:
else:
clip_path = folder_paths.get_full_path("clip", clip_model)
if clip_path is not None:
if model_concept == 'Cascade':
if model_concept == 'StableCascade':
concept_type = 'stable_cascade'
else:
concept_type = 'stable_diffusion'
clip = nodes.CLIPLoader.load_clip(self, clip_model, concept_type)[0]
print(adv_encode)
if adv_encode == True:
tokens_p = clip.tokenize(positive_text)
tokens_n = clip.tokenize(negative_text)
@@ -1146,7 +1204,7 @@ class PrimereCLIP:
CONDITIONING_NEG = nodes_flux.CLIPTextEncodeFlux.encode(self, clip, negative_text, negative_text, FLUX_GUIDANCE)[0]
return (CONDITIONING_POS, CONDITIONING_NEG, positive_text, negative_text, "", "", workflow_tuple)
if model_concept == 'Cascade':
if model_concept == 'StableCascade':
positive_text = utility.clear_cascade(positive_text)
negative_text = utility.clear_cascade(negative_text)
@@ -1563,7 +1621,7 @@ class PrimereClearPrompt:
positive_prompt = utility.clear_prompt(NETWORK_START, NETWORK_END, positive_prompt)
negative_prompt = utility.clear_prompt(NETWORK_START, NETWORK_END, negative_prompt)
if model_concept == 'Cascade':
if model_concept == 'StableCascade':
positive_prompt = utility.clear_cascade(positive_prompt)
negative_prompt = utility.clear_cascade(negative_prompt)
Nodes/Outputs.py
+7 -3
View File
@@ -178,7 +178,7 @@ class PrimereMetaSave:
image_metadata['model'] = image_metadata['concept_data']['flux_gguf']
else:
image_metadata['model'] = image_metadata['concept_data']['flux_diffusion']
case 'Cascade':
case 'StableCascade':
image_metadata['model'] = image_metadata['concept_data']['cascade_stage_c']
ModelPath = Path(image_metadata['model'])
@@ -630,15 +630,19 @@ class PrimereKSampler:
noise_constant = noise_extender_ksampler
print('sampling step:')
print(steps)
print(model_concept)
match model_concept:
case "Turbo":
samples_out = primeresamplers.PTurboSampler(model, seed, cfg, positive, negative, latent_image, steps, denoise, sampler_name)[0]
case "Cascade":
case "StableCascade":
noise_constant = noise_extender_cascade
samples_out = primeresamplers.PCascadeSampler(self, model, seed, steps, cfg, sampler_name, scheduler_name, positive, negative, latent_image, denoise, device, variation_level, variation_limit, variation_extender_original, variation_batch_step_original, variation_extender, variation_batch_step, batch_counter, noise_extender_cascade)[0]
case "Hyper-SD":
case "Hyper":
samples_out = primeresamplers.PSamplerHyper(self, extra_pnginfo, model, seed, steps, cfg, positive, negative, sampler_name, scheduler_name, latent_image, denoise, prompt)[0]
case 'Flux':
Workflow/Primere_basic_workflow.json
+110 -101
View File
@@ -1,6 +1,6 @@
{
"last_node_id": 132,
"last_link_id": 836,
"last_node_id": 133,
"last_link_id": 838,
"nodes": [
{
"id": 61,
@@ -233,7 +233,7 @@
"Node name for S&R": "PrimereSeed"
},
"widgets_values": [
420856771046412,
-1,
null,
null,
null
@@ -498,9 +498,9 @@
},
"widgets_values": [
"lcm",
"normal",
"simple",
8,
1.1500000000000001
1.6
],
"color": "#941414",
"bgcolor": "#800000"
@@ -631,8 +631,8 @@
"widgets_values": [
"dpmpp_sde",
"karras",
30,
4.5
25,
3.2
],
"color": "#082323",
"bgcolor": "#1c3737"
@@ -1235,7 +1235,7 @@
"Node name for S&R": "PrimereVisualStyle"
},
"widgets_values": [
"Sci-fi UFO",
"Preview woman",
true,
true,
true,
@@ -1348,8 +1348,8 @@
"id": 3,
"type": "PrimereVisualCKPT",
"pos": {
"0": 688,
"1": 91
"0": 689,
"1": 89
},
"size": {
"0": 463.38775634765625,
@@ -1395,7 +1395,7 @@
"Node name for S&R": "PrimereVisualCKPT"
},
"widgets_values": [
"LCM\\classicBYSTABLEYOGI_v2LCM.safetensors",
"LCM\\cyberrealisticLCM_cyberrealistic33.safetensors",
true,
true,
true,
@@ -1540,7 +1540,7 @@
"Node name for S&R": "PrimerePromptSwitch"
},
"widgets_values": [
4
2
],
"color": "#549494",
"bgcolor": "#408080"
@@ -1585,7 +1585,7 @@
"First",
1,
1,
"None"
"stubble"
],
"color": "#1414ff",
"bgcolor": "#0000ff"
@@ -2163,8 +2163,9 @@
783,
784,
820,
822,
826
826,
837,
838
],
"slot_index": 4,
"shape": 3
@@ -2359,24 +2360,24 @@
7,
12,
"Auto",
"LORA",
"CUSTOM",
8,
false,
"LORA",
8,
false,
"None",
"None",
"None",
"None",
"Stable-Cascade\\stage_a.safetensors",
"Stable-Cascade\\stable_cascade_stage_b.safetensors",
"Stable-Cascade\\stable_cascade_stage_c.safetensors",
"cascade_clip.safetensors",
"GGUF",
"None",
"Flux\\flux1-dev-fp8.safetensors",
"fp8_e4m3fn",
"None",
"None",
"None",
"FLUX1\\flux1-dev-Q4_1.gguf",
"Flux\\t5xxl_fp8_e4m3fn.safetensors",
"clip_l.safetensors",
4.5,
"None",
"flux-ae.sft",
"ksampler",
"None",
"None",
@@ -2491,7 +2492,7 @@
},
"widgets_values": [
"",
129907382027018,
223454168915314,
"randomize",
true
],
@@ -2547,7 +2548,7 @@
},
"widgets_values": [
"",
220818132191176,
332485028055199,
"randomize",
true
],
@@ -2882,7 +2883,7 @@
{
"name": "model_concept",
"type": "STRING",
"link": null,
"link": 837,
"widget": {
"name": "model_concept"
}
@@ -3131,12 +3132,12 @@
1024,
512,
false,
"Horizontal",
"Vertical",
true,
false,
1.6,
2.8,
843583184237617,
1000984967572092,
"randomize",
"SD1",
"Auto"
@@ -3322,7 +3323,7 @@
1,
0.8,
1.4,
837880561550265,
99178769860329,
"randomize",
true,
"cpu",
@@ -3414,7 +3415,7 @@
{
"name": "model_concept",
"type": "STRING",
"link": 822,
"link": 838,
"widget": {
"name": "model_concept"
}
@@ -3551,12 +3552,12 @@
"BaseModel_1024",
"",
"",
true,
"Default",
"Default",
false,
"ViT-L-14-GmP-ft-TE-only-HF-format.safetensors",
"Long-ViT-L-14-GmP-ft.safetensors",
0,
1.2,
true,
false,
"None",
1,
"BETTER_IMAGES",
@@ -3576,7 +3577,7 @@
1,
"",
"",
true,
false,
1,
1024,
1024
@@ -3724,7 +3725,7 @@
"Node name for S&R": "PrimereKSampler"
},
"widgets_values": [
557134671699716,
1019487594587993,
"randomize",
20,
8,
@@ -3733,9 +3734,9 @@
1,
0,
0,
true,
"DEFAULT",
false,
"DEFAULT",
true,
"Normal"
],
"color": "#941414",
@@ -3792,7 +3793,7 @@
"id": 79,
"type": "PrimerePreviewImage",
"pos": {
"0": 2961,
"0": 2965,
"1": 52
},
"size": {
@@ -3814,13 +3815,13 @@
"Node name for S&R": "PrimerePreviewImage"
},
"widgets_values": [
false,
true,
"jpeg",
0,
90,
"Checkpoint",
"Overwrite",
"LCM\\classicBYSTABLEYOGI_v2LCM.safetensors",
"LCM\\cyberrealisticLCM_cyberrealistic33.safetensors",
null
],
"color": "#549494",
@@ -3868,10 +3869,10 @@
"Node name for S&R": "PrimereAestheticCKPTScorer"
},
"widgets_values": [
false,
true,
true,
"*** Aesthetic scorer off ***"
true,
"612"
],
"color": "#5d005d",
"bgcolor": "#710071"
@@ -4072,7 +4073,7 @@
"widgets_values": [
"Red sportcar racing",
"Cute cat, nsfw, nude, nudity, porn",
234,
442,
"randomize",
"",
"",
@@ -5334,14 +5335,6 @@
2,
"STRING"
],
[
822,
127,
4,
106,
10,
"STRING"
],
[
823,
71,
@@ -5453,57 +5446,37 @@
104,
4,
"INT"
],
[
837,
127,
4,
71,
5,
"STRING"
],
[
838,
127,
4,
106,
10,
"STRING"
]
],
"groups": [
{
"title": "Concept sampler group",
"title": "Dashboard settings",
"bounding": [
679,
999,
1569,
1902
20,
1226,
791
],
"color": "#3f789e",
"font_size": 24,
"flags": {}
},
{
"title": "File save",
"bounding": [
2962,
1137,
1268,
1257
],
"color": "#008040",
"font_size": 24,
"flags": {}
},
{
"title": "Network loader",
"bounding": [
-367,
1137,
726,
657
],
"color": "#804000",
"font_size": 24,
"flags": {}
},
{
"title": "Encoder, sampler, decoder",
"bounding": [
1916,
21,
1036,
961
],
"color": "#A88",
"font_size": 24,
"flags": {}
},
{
"title": "Prompt",
"bounding": [
@@ -5517,12 +5490,48 @@
"flags": {}
},
{
"title": "Dashboard settings",
"title": "Encoder, sampler, decoder",
"bounding": [
1916,
21,
1036,
961
],
"color": "#A88",
"font_size": 24,
"flags": {}
},
{
"title": "Network loader",
"bounding": [
-367,
1137,
726,
657
],
"color": "#804000",
"font_size": 24,
"flags": {}
},
{
"title": "File save",
"bounding": [
2962,
1137,
1268,
1257
],
"color": "#008040",
"font_size": 24,
"flags": {}
},
{
"title": "Concept sampler group",
"bounding": [
679,
20,
1226,
791
999,
1569,
1902
],
"color": "#3f789e",
"font_size": 24,
@@ -5532,10 +5541,10 @@
"config": {},
"extra": {
"ds": {
"scale": 0.8264462809917354,
"scale": 1,
"offset": [
284.2850274220734,
-848.6282424439238
-1413.4188381987294,
130.25453853906293
]
}
},