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Implement Civitai data population methods for LoRA and checkpoint entries

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- Added `populate_lora_from_civitai` and `populate_checkpoint_from_civitai` methods to enhance the extraction of model information from Civitai API responses.
- These methods populate LoRA and checkpoint entries with relevant data such as model name, version, thumbnail URL, base model, download URL, and file details.
- Improved error handling and logging for scenarios where models are not found or data retrieval fails.
- Refactored existing code to utilize the new methods, streamlining the process of fetching and updating LoRA and checkpoint metadata.
This commit is contained in:
Will Miao
2025-03-28 02:16:53 +08:00
parent 7cae0ee169
commit 450592b0d4
2 changed files with 178 additions and 242 deletions
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408
py/utils/recipe_parsers.py
View File

@@ -45,6 +45,138 @@ class RecipeMetadataParser(ABC):
""" """
pass pass
async def populate_lora_from_civitai(self, lora_entry: Dict[str, Any], civitai_info: Dict[str, Any],
recipe_scanner=None, base_model_counts=None, hash_value=None) -> Dict[str, Any]:
"""
Populate a lora entry with information from Civitai API response
Args:
lora_entry: The lora entry to populate
civitai_info: The response from Civitai API
recipe_scanner: Optional recipe scanner for local file lookup
base_model_counts: Optional dict to track base model counts
hash_value: Optional hash value to use if not available in civitai_info
Returns:
The populated lora_entry dict
"""
try:
if civitai_info and civitai_info.get("error") != "Model not found":
# Check if this is an early access lora
if civitai_info.get('earlyAccessEndsAt'):
# Convert earlyAccessEndsAt to a human-readable date
early_access_date = civitai_info.get('earlyAccessEndsAt', '')
lora_entry['isEarlyAccess'] = True
lora_entry['earlyAccessEndsAt'] = early_access_date
# Update model name if available
if 'model' in civitai_info and 'name' in civitai_info['model']:
lora_entry['name'] = civitai_info['model']['name']
# Update version if available
if 'name' in civitai_info:
lora_entry['version'] = civitai_info.get('name', '')
# Get thumbnail URL from first image
if 'images' in civitai_info and civitai_info['images']:
lora_entry['thumbnailUrl'] = civitai_info['images'][0].get('url', '')
# Get base model
current_base_model = civitai_info.get('baseModel', '')
lora_entry['baseModel'] = current_base_model
# Update base model counts if tracking them
if base_model_counts is not None and current_base_model:
base_model_counts[current_base_model] = base_model_counts.get(current_base_model, 0) + 1
# Get download URL
lora_entry['downloadUrl'] = civitai_info.get('downloadUrl', '')
# Process file information if available
if 'files' in civitai_info:
model_file = next((file for file in civitai_info.get('files', [])
if file.get('type') == 'Model'), None)
if model_file:
# Get size
lora_entry['size'] = model_file.get('sizeKB', 0) * 1024
# Get SHA256 hash
sha256 = model_file.get('hashes', {}).get('SHA256', hash_value)
if sha256:
lora_entry['hash'] = sha256.lower()
# Check if exists locally
if recipe_scanner and lora_entry['hash']:
lora_scanner = recipe_scanner._lora_scanner
exists_locally = lora_scanner.has_lora_hash(lora_entry['hash'])
if exists_locally:
try:
local_path = lora_scanner.get_lora_path_by_hash(lora_entry['hash'])
lora_entry['existsLocally'] = True
lora_entry['localPath'] = local_path
lora_entry['file_name'] = os.path.splitext(os.path.basename(local_path))[0]
# Get thumbnail from local preview if available
lora_cache = await lora_scanner.get_cached_data()
lora_item = next((item for item in lora_cache.raw_data
if item['sha256'].lower() == lora_entry['hash'].lower()), None)
if lora_item and 'preview_url' in lora_item:
lora_entry['thumbnailUrl'] = config.get_preview_static_url(lora_item['preview_url'])
except Exception as e:
logger.error(f"Error getting local lora path: {e}")
else:
# For missing LoRAs, get file_name from model_file.name
file_name = model_file.get('name', '')
lora_entry['file_name'] = os.path.splitext(file_name)[0] if file_name else ''
else:
# Model not found or deleted
lora_entry['isDeleted'] = True
lora_entry['thumbnailUrl'] = '/loras_static/images/no-preview.png'
except Exception as e:
logger.error(f"Error populating lora from Civitai info: {e}")
return lora_entry
async def populate_checkpoint_from_civitai(self, checkpoint: Dict[str, Any], civitai_info: Dict[str, Any]) -> Dict[str, Any]:
"""
Populate checkpoint information from Civitai API response
Args:
checkpoint: The checkpoint entry to populate
civitai_info: The response from Civitai API
Returns:
The populated checkpoint dict
"""
try:
if civitai_info and civitai_info.get("error") != "Model not found":
# Update model name if available
if 'model' in civitai_info and 'name' in civitai_info['model']:
checkpoint['name'] = civitai_info['model']['name']
# Update version if available
if 'name' in civitai_info:
checkpoint['version'] = civitai_info.get('name', '')
# Get thumbnail URL from first image
if 'images' in civitai_info and civitai_info['images']:
checkpoint['thumbnailUrl'] = civitai_info['images'][0].get('url', '')
# Get base model
checkpoint['baseModel'] = civitai_info.get('baseModel', '')
# Get download URL
checkpoint['downloadUrl'] = civitai_info.get('downloadUrl', '')
else:
# Model not found or deleted
checkpoint['isDeleted'] = True
except Exception as e:
logger.error(f"Error populating checkpoint from Civitai info: {e}")
return checkpoint
class RecipeFormatParser(RecipeMetadataParser): class RecipeFormatParser(RecipeMetadataParser):
"""Parser for images with dedicated recipe metadata format""" """Parser for images with dedicated recipe metadata format"""
@@ -110,33 +242,14 @@ class RecipeFormatParser(RecipeMetadataParser):
if lora.get('modelVersionId') and civitai_client: if lora.get('modelVersionId') and civitai_client:
try: try:
civitai_info = await civitai_client.get_model_version_info(lora['modelVersionId']) civitai_info = await civitai_client.get_model_version_info(lora['modelVersionId'])
if civitai_info and civitai_info.get("error") != "Model not found": # Populate lora entry with Civitai info
# Check if this is an early access lora lora_entry = await self.populate_lora_from_civitai(
if civitai_info.get('earlyAccessEndsAt'): lora_entry,
# Convert earlyAccessEndsAt to a human-readable date civitai_info,
early_access_date = civitai_info.get('earlyAccessEndsAt', '') recipe_scanner,
lora_entry['isEarlyAccess'] = True None, # No need to track base model counts
lora_entry['earlyAccessEndsAt'] = early_access_date lora['hash']
)
# Get thumbnail URL from first image
if 'images' in civitai_info and civitai_info['images']:
lora_entry['thumbnailUrl'] = civitai_info['images'][0].get('url', '')
# Get base model
lora_entry['baseModel'] = civitai_info.get('baseModel', '')
# Get download URL
lora_entry['downloadUrl'] = civitai_info.get('downloadUrl', '')
# Get size from files if available
if 'files' in civitai_info:
model_file = next((file for file in civitai_info.get('files', [])
if file.get('type') == 'Model'), None)
if model_file:
lora_entry['size'] = model_file.get('sizeKB', 0) * 1024
else:
lora_entry['isDeleted'] = True
lora_entry['thumbnailUrl'] = '/loras_static/images/no-preview.png'
except Exception as e: except Exception as e:
logger.error(f"Error fetching Civitai info for LoRA: {e}") logger.error(f"Error fetching Civitai info for LoRA: {e}")
lora_entry['thumbnailUrl'] = '/loras_static/images/no-preview.png' lora_entry['thumbnailUrl'] = '/loras_static/images/no-preview.png'
@@ -222,57 +335,16 @@ class StandardMetadataParser(RecipeMetadataParser):
# Get additional info from Civitai if client is available # Get additional info from Civitai if client is available
if civitai_client: if civitai_client:
civitai_info = await civitai_client.get_model_version_info(model_version_id) try:
civitai_info = await civitai_client.get_model_version_info(model_version_id)
# Check if this LoRA exists locally by SHA256 hash # Populate lora entry with Civitai info
if civitai_info and civitai_info.get("error") != "Model not found": lora_entry = await self.populate_lora_from_civitai(
# Check if this is an early access lora lora_entry,
if civitai_info.get('earlyAccessEndsAt'): civitai_info,
# Convert earlyAccessEndsAt to a human-readable date recipe_scanner
early_access_date = civitai_info.get('earlyAccessEndsAt', '') )
lora_entry['isEarlyAccess'] = True except Exception as e:
lora_entry['earlyAccessEndsAt'] = early_access_date logger.error(f"Error fetching Civitai info for LoRA: {e}")
# LoRA exists on Civitai, process its information
if 'files' in civitai_info:
# Find the model file (type="Model") in the files list
model_file = next((file for file in civitai_info.get('files', [])
if file.get('type') == 'Model'), None)
if model_file and recipe_scanner:
sha256 = model_file.get('hashes', {}).get('SHA256', '')
if sha256:
lora_scanner = recipe_scanner._lora_scanner
exists_locally = lora_scanner.has_lora_hash(sha256)
if exists_locally:
local_path = lora_scanner.get_lora_path_by_hash(sha256)
lora_entry['existsLocally'] = True
lora_entry['localPath'] = local_path
lora_entry['file_name'] = os.path.splitext(os.path.basename(local_path))[0]
else:
# For missing LoRAs, get file_name from model_file.name
file_name = model_file.get('name', '')
lora_entry['file_name'] = os.path.splitext(file_name)[0] if file_name else ''
lora_entry['hash'] = sha256
lora_entry['size'] = model_file.get('sizeKB', 0) * 1024
# Get thumbnail URL from first image
if 'images' in civitai_info and civitai_info['images']:
lora_entry['thumbnailUrl'] = civitai_info['images'][0].get('url', '')
# Get base model and update counts
current_base_model = civitai_info.get('baseModel', '')
lora_entry['baseModel'] = current_base_model
if current_base_model:
base_model_counts[current_base_model] = base_model_counts.get(current_base_model, 0) + 1
# Get download URL
lora_entry['downloadUrl'] = civitai_info.get('downloadUrl', '')
else:
# LoRA is deleted from Civitai or not found
lora_entry['isDeleted'] = True
lora_entry['thumbnailUrl'] = '/loras_static/images/no-preview.png'
loras.append(lora_entry) loras.append(lora_entry)
@@ -436,61 +508,18 @@ class A1111MetadataParser(RecipeMetadataParser):
'isDeleted': False 'isDeleted': False
} }
# Get info from Civitai by hash # Get info from Civitai by hash if available
if civitai_client: if civitai_client and hash_value:
try: try:
civitai_info = await civitai_client.get_model_by_hash(hash_value) civitai_info = await civitai_client.get_model_by_hash(hash_value)
if civitai_info and civitai_info.get("error") != "Model not found": # Populate lora entry with Civitai info
# Check if this is an early access lora lora_entry = await self.populate_lora_from_civitai(
if civitai_info.get('earlyAccessEndsAt'): lora_entry,
# Convert earlyAccessEndsAt to a human-readable date civitai_info,
early_access_date = civitai_info.get('earlyAccessEndsAt', '') recipe_scanner,
lora_entry['isEarlyAccess'] = True base_model_counts,
lora_entry['earlyAccessEndsAt'] = early_access_date hash_value
)
# Get model version ID
lora_entry['id'] = civitai_info.get('id', '')
# Get model name and version
lora_entry['name'] = civitai_info.get('model', {}).get('name', lora_name)
lora_entry['version'] = civitai_info.get('name', '')
# Get thumbnail URL
if 'images' in civitai_info and civitai_info['images']:
lora_entry['thumbnailUrl'] = civitai_info['images'][0].get('url', '')
# Get base model and update counts
current_base_model = civitai_info.get('baseModel', '')
lora_entry['baseModel'] = current_base_model
if current_base_model:
base_model_counts[current_base_model] = base_model_counts.get(current_base_model, 0) + 1
# Get download URL
lora_entry['downloadUrl'] = civitai_info.get('downloadUrl', '')
# Get file name and size from Civitai
if 'files' in civitai_info:
model_file = next((file for file in civitai_info.get('files', [])
if file.get('type') == 'Model'), None)
if model_file:
file_name = model_file.get('name', '')
lora_entry['file_name'] = os.path.splitext(file_name)[0] if file_name else lora_name
lora_entry['size'] = model_file.get('sizeKB', 0) * 1024
# Update hash to sha256
lora_entry['hash'] = model_file.get('hashes', {}).get('SHA256', hash_value).lower()
# Check if exists locally with sha256 hash
if recipe_scanner and lora_entry['hash']:
lora_scanner = recipe_scanner._lora_scanner
exists_locally = lora_scanner.has_lora_hash(lora_entry['hash'])
if exists_locally:
lora_cache = await lora_scanner.get_cached_data()
lora_item = next((item for item in lora_cache.raw_data if item['sha256'] == lora_entry['hash']), None)
if lora_item:
lora_entry['existsLocally'] = True
lora_entry['localPath'] = lora_item['file_path']
lora_entry['thumbnailUrl'] = config.get_preview_static_url(lora_item['preview_url'])
except Exception as e: except Exception as e:
logger.error(f"Error fetching Civitai info for LoRA hash {hash_value}: {e}") logger.error(f"Error fetching Civitai info for LoRA hash {hash_value}: {e}")
@@ -591,51 +620,12 @@ class ComfyMetadataParser(RecipeMetadataParser):
if civitai_client: if civitai_client:
try: try:
civitai_info = await civitai_client.get_model_version_info(model_version_id) civitai_info = await civitai_client.get_model_version_info(model_version_id)
if civitai_info and civitai_info.get("error") != "Model not found": # Populate lora entry with Civitai info
# Update lora entry with model name and version lora_entry = await self.populate_lora_from_civitai(
if 'model' in civitai_info and 'name' in civitai_info['model']: lora_entry,
lora_entry['name'] = civitai_info['model']['name'] civitai_info,
lora_entry['version'] = civitai_info.get('name', '') recipe_scanner
)
# Check if this is an early access lora
if civitai_info.get('earlyAccessEndsAt'):
early_access_date = civitai_info.get('earlyAccessEndsAt', '')
lora_entry['isEarlyAccess'] = True
lora_entry['earlyAccessEndsAt'] = early_access_date
# Get thumbnail URL from first image
if 'images' in civitai_info and civitai_info['images']:
lora_entry['thumbnailUrl'] = civitai_info['images'][0].get('url', '')
# Get base model
lora_entry['baseModel'] = civitai_info.get('baseModel', '')
# Get download URL
lora_entry['downloadUrl'] = civitai_info.get('downloadUrl', '')
# Check if this LoRA exists locally by SHA256 hash
if 'files' in civitai_info:
model_file = next((file for file in civitai_info.get('files', [])
if file.get('type') == 'Model'), None)
if model_file and recipe_scanner:
sha256 = model_file.get('hashes', {}).get('SHA256', '')
if sha256:
lora_scanner = recipe_scanner._lora_scanner
exists_locally = lora_scanner.has_lora_hash(sha256)
if exists_locally:
local_path = lora_scanner.get_lora_path_by_hash(sha256)
lora_entry['existsLocally'] = True
lora_entry['localPath'] = local_path
lora_entry['file_name'] = os.path.splitext(os.path.basename(local_path))[0]
else:
# For missing LoRAs, get file_name from model_file.name
file_name = model_file.get('name', '')
lora_entry['file_name'] = os.path.splitext(file_name)[0] if file_name else ''
lora_entry['hash'] = sha256
lora_entry['size'] = model_file.get('sizeKB', 0) * 1024
else:
lora_entry['isDeleted'] = True
except Exception as e: except Exception as e:
logger.error(f"Error fetching Civitai info for LoRA: {e}") logger.error(f"Error fetching Civitai info for LoRA: {e}")
@@ -669,10 +659,8 @@ class ComfyMetadataParser(RecipeMetadataParser):
if civitai_client: if civitai_client:
try: try:
civitai_info = await civitai_client.get_model_version_info(checkpoint_version_id) civitai_info = await civitai_client.get_model_version_info(checkpoint_version_id)
if civitai_info and civitai_info.get("error") != "Model not found": # Populate checkpoint with Civitai info
if 'model' in civitai_info and 'name' in civitai_info['model']: checkpoint = await self.populate_checkpoint_from_civitai(checkpoint, civitai_info)
checkpoint['name'] = civitai_info['model']['name']
checkpoint['version'] = civitai_info.get('name', '')
except Exception as e: except Exception as e:
logger.error(f"Error fetching Civitai info for checkpoint: {e}") logger.error(f"Error fetching Civitai info for checkpoint: {e}")
@@ -884,58 +872,14 @@ class MetaFormatParser(RecipeMetadataParser):
if civitai_client and hash_value: if civitai_client and hash_value:
try: try:
civitai_info = await civitai_client.get_model_by_hash(hash_value) civitai_info = await civitai_client.get_model_by_hash(hash_value)
if civitai_info and civitai_info.get("error") != "Model not found": # Populate lora entry with Civitai info
# Check if this is an early access lora lora_entry = await self.populate_lora_from_civitai(
if civitai_info.get('earlyAccessEndsAt'): lora_entry,
early_access_date = civitai_info.get('earlyAccessEndsAt', '') civitai_info,
lora_entry['isEarlyAccess'] = True recipe_scanner,
lora_entry['earlyAccessEndsAt'] = early_access_date base_model_counts,
hash_value
# Get model version ID )
lora_entry['id'] = civitai_info.get('id', '')
# Get model name and version
lora_entry['name'] = civitai_info.get('model', {}).get('name', name)
lora_entry['version'] = civitai_info.get('name', '')
# Get thumbnail URL
if 'images' in civitai_info and civitai_info['images']:
lora_entry['thumbnailUrl'] = civitai_info['images'][0].get('url', '')
# Get base model and update counts
current_base_model = civitai_info.get('baseModel', '')
lora_entry['baseModel'] = current_base_model
if current_base_model:
base_model_counts[current_base_model] = base_model_counts.get(current_base_model, 0) + 1
# Get download URL
lora_entry['downloadUrl'] = civitai_info.get('downloadUrl', '')
# Get file name and size from Civitai
if 'files' in civitai_info:
model_file = next((file for file in civitai_info.get('files', [])
if file.get('type') == 'Model'), None)
if model_file:
file_name = model_file.get('name', '')
lora_entry['file_name'] = os.path.splitext(file_name)[0] if file_name else name
lora_entry['size'] = model_file.get('sizeKB', 0) * 1024
# Update hash to sha256
new_hash = model_file.get('hashes', {}).get('SHA256', hash_value).lower()
lora_entry['hash'] = new_hash
# Check if exists locally with sha256 hash
if recipe_scanner and lora_entry['hash']:
lora_scanner = recipe_scanner._lora_scanner
exists_locally = lora_scanner.has_lora_hash(lora_entry['hash'])
if exists_locally:
lora_cache = await lora_scanner.get_cached_data()
lora_item = next((item for item in lora_cache.raw_data if item['sha256'].lower() == lora_entry['hash'].lower()), None)
if lora_item:
lora_entry['existsLocally'] = True
lora_entry['localPath'] = lora_item['file_path']
lora_entry['thumbnailUrl'] = config.get_preview_static_url(lora_item['preview_url'])
else:
lora_entry['isDeleted'] = True
except Exception as e: except Exception as e:
logger.error(f"Error fetching Civitai info for LoRA hash {hash_value}: {e}") logger.error(f"Error fetching Civitai info for LoRA hash {hash_value}: {e}")

12
refs/jpeg_civitai_exif_userComment_example
View File

@@ -12,18 +12,10 @@ holographic skin, holofoil glitter, faint, glowing, ethereal, neon hair, glowing
Negative prompt: score_6, score_5, score_4, bad quality, worst quality, worst detail, sketch, censorship, furry, window, headphones, Negative prompt: score_6, score_5, score_4, bad quality, worst quality, worst detail, sketch, censorship, furry, window, headphones,
Steps: 30, Sampler: Euler a, Schedule type: Simple, CFG scale: 7, Seed: 1405717592, Size: 832x1216, Model hash: 1ad6ca7f70, Model: waiNSFWIllustrious_v100, Denoising strength: 0.35, Hires CFG Scale: 5, Hires upscale: 1.3, Hires steps: 20, Hires upscaler: 4x-AnimeSharp, Lora hashes: "ck-shadow-circuit-IL: 88e247aa8c3d, ck-nc-cyberpunk-IL-000011: 935e6755554c, ck-neon-retrowave-IL: edafb9df7da1, ck-yoneyama-mai-IL-000014: 1b9305692a2e", Version: f2.0.1v1.10.1-1.10.1, Diffusion in Low Bits: Automatic (fp16 LoRA) Steps: 30, Sampler: Euler a, Schedule type: Simple, CFG scale: 7, Seed: 1405717592, Size: 832x1216, Model hash: 1ad6ca7f70, Model: waiNSFWIllustrious_v100, Denoising strength: 0.35, Hires CFG Scale: 5, Hires upscale: 1.3, Hires steps: 20, Hires upscaler: 4x-AnimeSharp, Lora hashes: "ck-shadow-circuit-IL: 88e247aa8c3d, ck-nc-cyberpunk-IL-000011: 935e6755554c, ck-neon-retrowave-IL: edafb9df7da1, ck-yoneyama-mai-IL-000014: 1b9305692a2e", Version: f2.0.1v1.10.1-1.10.1, Diffusion in Low Bits: Automatic (fp16 LoRA)
masterpiece, best quality,high quality, newest, highres,8K,HDR,absurdres, 1girl, solo, steampunk aesthetic, mechanical monocle, long trench coat, leather gloves, brass accessories, intricate clockwork rifle, aiming at viewer, wind-blown scarf, high boots, fingerless gloves, pocket watch, corset, brown and gold color scheme, industrial cityscape, smoke and gears, atmospheric lighting, depth of field, dynamic pose, dramatic composition, detailed background, foreshortening, detailed background, dynamic pose, dynamic composition,dutch angle, detailed backgroud,foreshortening,blurry edges <lora:iLLMythAn1m3Style:1> MythAn1m3
Negative prompt: worst quality, normal quality, anatomical nonsense, bad anatomy,interlocked fingers, extra fingers,watermark,simple background, loli,
Steps: 35, Sampler: DPM++ 2M SDE, Schedule type: Karras, CFG scale: 4, Seed: 3537159932, Size: 1072x1376, Model hash: c364bbdae9, Model: waiNSFWIllustrious_v110, Clip skip: 2, ADetailer model: face_yolov8n.pt, ADetailer confidence: 0.3, ADetailer dilate erode: 4, ADetailer mask blur: 4, ADetailer denoising strength: 0.4, ADetailer inpaint only masked: True, ADetailer inpaint padding: 32, ADetailer version: 24.8.0, Lora hashes: "iLLMythAn1m3Style: d3480076057b", Version: f2.0.1v1.10.1-previous-519-g44eb4ea8, Module 1: sdxl.vae
Masterpiece, best quality, high quality, newest, highres, 8K, HDR, absurdres, 1girl, solo, futuristic warrior, sleek exosuit with glowing energy cores, long braided hair flowing behind, gripping a high-tech bow with an energy arrow drawn, standing on a floating platform overlooking a massive space station, planets and nebulae in the distance, soft glow from distant stars, cinematic depth, foreshortening, dynamic pose, dramatic sci-fi lighting. Masterpiece, best quality, high quality, newest, highres, 8K, HDR, absurdres, 1girl, solo, futuristic warrior, sleek exosuit with glowing energy cores, long braided hair flowing behind, gripping a high-tech bow with an energy arrow drawn, standing on a floating platform overlooking a massive space station, planets and nebulae in the distance, soft glow from distant stars, cinematic depth, foreshortening, dynamic pose, dramatic sci-fi lighting.
Negative prompt: worst quality, normal quality, anatomical nonsense, bad anatomy,interlocked fingers, extra fingers,watermark,simple background, loli, Negative prompt: worst quality, normal quality, anatomical nonsense, bad anatomy,interlocked fingers, extra fingers,watermark,simple background, loli,
Steps: 20, Sampler: euler_ancestral_karras, CFG scale: 8.0, Seed: 691121152183439, Model: il\waiNSFWIllustrious_v110.safetensors, Model hash: c3688ee04c, Lora_0 Model name: iLLMythAn1m3Style.safetensors, Lora_0 Model hash: ba7a040786, Lora_0 Strength model: 1.0, Lora_0 Strength clip: 1.0, Hashes: {"model": "c3688ee04c", "lora:iLLMythAn1m3Style": "ba7a040786"} Steps: 20, Sampler: euler_ancestral_karras, CFG scale: 8.0, Seed: 691121152183439, Model: il\waiNSFWIllustrious_v110.safetensors, Model hash: c3688ee04c, Lora_0 Model name: iLLMythAn1m3Style.safetensors, Lora_0 Model hash: ba7a040786, Lora_0 Strength model: 1.0, Lora_0 Strength clip: 1.0, Hashes: {"model": "c3688ee04c", "lora:iLLMythAn1m3Style": "ba7a040786"}
Masterpiece, best quality, high quality, newest, highres, 8K, HDR, absurdres, 1boy, solo, gothic horror, pale vampire lord in regal, intricately detailed robes, crimson eyes glowing under the dim candlelight of a grand but decayed castle hall, holding a silver goblet filled with an unknown substance, a massive stained-glass window shattered behind him, cold mist rolling in, dramatic lighting, dark yet elegant aesthetic, foreshortening, cinematic perspective. Immerse yourself in the enchanting journey, where harmonious transmutation of Bauhaus art unites photographic precision and contemporary illustration, capturing an enthralling blend between vivid abstract nature and urban landscapes. Let your eyes be captivated by a kaleidoscope of rich, deep reds and yellows, entwined with intriguing shades that beckon a somber atmosphere. As your spirit ventures along this haunting path, witness the mysterious, high-angle perspective dominated by scattered clouds – granting you a mesmerizing glimpse into the ever-transforming realm of metamorphosing environments. ,<lora:flux/fav/ck-charcoal-drawing-000014.safetensors:1.0:1.0>
Negative prompt: worst quality, normal quality, anatomical nonsense, bad anatomy,interlocked fingers, extra fingers,watermark,simple background, loli,
Steps: 20, Sampler: euler_ancestral_karras, CFG scale: 8.0, Seed: 290117945770094, Model: il\waiNSFWIllustrious_v110.safetensors, Model hash: c3688ee04c, Lora_0 Model name: iLLMythAn1m3Style.safetensors, Lora_0 Model hash: ba7a040786, Lora_0 Strength model: 0.6, Lora_0 Strength clip: 0.7000000000000001, Hashes: {"model": "c3688ee04c", "lora:iLLMythAn1m3Style": "ba7a040786"}
bo-exposure, An impressionistic oil painting in the style of J.M.W. Turner, depicting a ghostly ship sailing through a sea of swirling golden mist. The waves crash and dissolve into abstract, fiery strokes of orange and deep indigo, blurring the line between ocean and sky. The ship appears almost ethereal, as if drifting between worlds, lost in the ever-changing tides of memory and myth. The dynamic brushstrokes capture the relentless power of nature and the fleeting essence of time.
Negative prompt: Negative prompt:
Steps: 25, Sampler: DPM++ 2M, CFG scale: 3.5, Seed: 1024252061321625, Size: 832x1216, Clip skip: 1, Model hash: , Model: flux_dev, Hashes: {"model": ""}, Version: ComfyUI Steps: 20, Sampler: Euler, CFG scale: 3.5, Seed: 885491426361006, Size: 832x1216, Model hash: 4610115bb0, Model: flux_dev, Hashes: {"LORA:flux/fav/ck-charcoal-drawing-000014.safetensors": "34d36c17c1", "model": "4610115bb0"}, Version: ComfyUI