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IGNY8 VPS (Salman)
2026-01-10 11:54:31 +00:00
parent 1246f8ac5d
commit 6fb0411f56
10 changed files with 1230 additions and 25 deletions
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10
backend/igny8_core/ai/ai_core.py
View File

@@ -1026,11 +1026,19 @@ class AICore:
del inference_task['height']
inference_task['resolution'] = '1k' # Use 1K tier for optimal speed/quality
print(f"[AI][{function_name}] Using Nano Banana config: resolution=1k (no width/height)")
else:
elif runware_model.startswith('bytedance:'):
# Seedream 4.5 (bytedance:seedream@4.5) - High quality ByteDance model
# Uses basic format with just width/height - no steps, CFGScale, or special providerSettings needed
# Just use the base inference_task as-is
print(f"[AI][{function_name}] Using Seedream 4.5 config: basic format, width={width}, height={height}")
elif runware_model.startswith('runware:'):
# Hi Dream Full (runware:97@1) - General diffusion, steps 20, CFGScale 7
inference_task['steps'] = 20
inference_task['CFGScale'] = 7
print(f"[AI][{function_name}] Using Hi Dream Full config: steps=20, CFGScale=7")
else:
# Unknown model - use basic format without extra parameters
print(f"[AI][{function_name}] Using basic format for unknown model: {runware_model}")
payload = [
{

29
backend/igny8_core/ai/functions/generate_images.py
View File

@@ -70,22 +70,37 @@ class GenerateImagesFunction(BaseAIFunction):
# Get image generation settings from AISettings (with account overrides)
from igny8_core.modules.system.ai_settings import AISettings
from igny8_core.ai.model_registry import ModelRegistry
from igny8_core.business.billing.models import AIModelConfig
# Get effective settings (AISettings + AccountSettings overrides)
image_style = AISettings.get_effective_image_style(account)
max_images = AISettings.get_effective_max_images(account)
quality_tier = AISettings.get_effective_quality_tier(account)
# Get default image model and provider from database
default_model = ModelRegistry.get_default_model('image')
if default_model:
model_config = ModelRegistry.get_model(default_model)
provider = model_config.provider if model_config else 'openai'
model = default_model
# Get image model based on user's selected quality tier
selected_model = None
if quality_tier:
selected_model = AIModelConfig.objects.filter(
model_type='image',
quality_tier=quality_tier,
is_active=True
).first()
# Fall back to default model if no tier match
if not selected_model:
default_model_name = ModelRegistry.get_default_model('image')
if default_model_name:
selected_model = ModelRegistry.get_model(default_model_name)
# Set provider and model from selected model
if selected_model:
provider = selected_model.provider if selected_model.provider else 'openai'
model = selected_model.model_name
else:
provider = 'openai'
model = 'dall-e-3'
logger.info(f"Using image settings: provider={provider}, model={model}, style={image_style}, max={max_images}")
logger.info(f"Using image settings: provider={provider}, model={model}, tier={quality_tier}, style={image_style}, max={max_images}")
return {
'tasks': tasks,

1
backend/igny8_core/business/billing/models.py
View File

@@ -736,6 +736,7 @@ class AIModelConfig(models.Model):
QUALITY_TIER_CHOICES = [
('basic', 'Basic'),
('quality', 'Quality'),
('quality_option2', 'Quality-Option2'),
('premium', 'Premium'),
]

40
backend/igny8_core/modules/billing/migrations/0030_add_aimodel_image_sizes.py
View File

@@ -10,6 +10,42 @@ class Migration(migrations.Migration):
]
operations = [
# Fields already added via direct SQL, just mark as noop
# This ensures the model matches the database schema
# Declare the fields in Django schema so migrations can use them
# These fields already exist in DB, so state_operations syncs Django's understanding
migrations.SeparateDatabaseAndState(
state_operations=[
migrations.AddField(
model_name='aimodelconfig',
name='landscape_size',
field=models.CharField(
blank=True,
help_text="Landscape image size for this model (e.g., '1792x1024', '1280x768')",
max_length=20,
null=True,
),
),
migrations.AddField(
model_name='aimodelconfig',
name='square_size',
field=models.CharField(
blank=True,
default='1024x1024',
help_text="Square image size for this model (e.g., '1024x1024')",
max_length=20,
),
),
migrations.AddField(
model_name='aimodelconfig',
name='valid_sizes',
field=models.JSONField(
blank=True,
default=list,
help_text="List of valid sizes for this model (e.g., ['1024x1024', '1792x1024'])",
),
),
],
database_operations=[
# No DB operations - fields already exist via direct SQL
],
),
]

80
backend/igny8_core/modules/billing/migrations/0031_add_seedream_model.py Normal file
View File

@@ -0,0 +1,80 @@
# Generated manually - Add Seedream 4.5 image model and update quality_tier choices
from decimal import Decimal
from django.db import migrations, models
def add_seedream_model(apps, schema_editor):
"""
Add ByteDance Seedream 4.5 image model via Runware.
Model specs:
- model: bytedance:seedream@4.5
- Square size: 2048x2048
- Landscape size: 2304x1728
- Quality tier: quality_option2
- Credits per image: 5
"""
AIModelConfig = apps.get_model('billing', 'AIModelConfig')
AIModelConfig.objects.update_or_create(
model_name='bytedance:seedream@4.5',
defaults={
'display_name': 'Seedream 4.5 - High Quality',
'model_type': 'image',
'provider': 'runware',
'is_default': False,
'is_active': True,
'credits_per_image': 5,
'quality_tier': 'quality_option2',
'landscape_size': '2304x1728',
'square_size': '2048x2048',
'valid_sizes': ['2048x2048', '2304x1728', '2560x1440', '1728x2304', '1440x2560'],
'capabilities': {
'max_sequential_images': 4,
'high_resolution': True,
'provider_settings': {
'bytedance': {
'maxSequentialImages': 4
}
}
},
}
)
print("✅ Added Seedream 4.5 image model")
def reverse_migration(apps, schema_editor):
"""Remove Seedream model"""
AIModelConfig = apps.get_model('billing', 'AIModelConfig')
AIModelConfig.objects.filter(model_name='bytedance:seedream@4.5').delete()
print("❌ Removed Seedream 4.5 image model")
class Migration(migrations.Migration):
"""Add Seedream 4.5 image model and update quality_tier choices."""
dependencies = [
('billing', '0030_add_aimodel_image_sizes'),
]
operations = [
# Update quality_tier field choices to include quality_option2
migrations.AlterField(
model_name='aimodelconfig',
name='quality_tier',
field=models.CharField(
blank=True,
choices=[
('basic', 'Basic'),
('quality', 'Quality'),
('quality_option2', 'Quality-Option2'),
('premium', 'Premium'),
],
help_text='basic / quality / quality_option2 / premium - for image models',
max_length=20,
null=True,
),
),
# Add the Seedream model
migrations.RunPython(add_seedream_model, reverse_migration),
]

25
backend/igny8_core/modules/system/ai_settings.py
View File

@@ -50,6 +50,7 @@ class SystemAISettings(models.Model):
QUALITY_TIER_CHOICES = [
('basic', 'Basic'),
('quality', 'Quality'),
('quality_option2', 'Quality-Option2'),
('premium', 'Premium'),
]
@@ -191,6 +192,30 @@ class SystemAISettings(models.Model):
return str(override)
return cls.get_instance().image_size
@classmethod
def get_effective_quality_tier(cls, account=None) -> str:
"""Get quality_tier, checking account override first (from ai_settings key)"""
if account:
# Check consolidated ai_settings first
try:
from igny8_core.modules.system.settings_models import AccountSettings
setting = AccountSettings.objects.filter(
account=account,
key='ai_settings'
).first()
if setting and setting.value:
tier = setting.value.get('quality_tier')
if tier:
return str(tier)
except Exception as e:
logger.debug(f"Could not get quality_tier from ai_settings: {e}")
# Fall back to individual key
override = cls._get_account_override(account, 'ai.quality_tier')
if override is not None:
return str(override)
return cls.get_instance().default_quality_tier
@staticmethod
def _get_account_override(account, key: str):
"""Get account-specific override from AccountSettings"""

4
backend/igny8_core/modules/system/settings_views.py
View File

@@ -594,10 +594,12 @@ class ContentGenerationSettingsViewSet(viewsets.ViewSet):
tier = model.quality_tier or 'basic'
# Avoid duplicates
if not any(t['tier'] == tier for t in quality_tiers):
# Format label: quality_option2 -> "Quality-Option2"
tier_label = tier.replace('_', '-').title() if tier else 'Basic'
quality_tiers.append({
'tier': tier,
'credits': model.credits_per_image or 1,
'label': tier.title(),
'label': tier_label,
'description': f"{model.display_name} quality",
'model': model.model_name,
})

48
backend/igny8_core/utils/ai_processor.py
View File

@@ -845,24 +845,46 @@ Make sure each prompt is detailed enough for image generation, describing the vi
# Reference: image-generation.php lines 79-97
import uuid
logger.info(f"[AIProcessor.generate_image] Runware API key check: has_key={bool(api_key)}, key_length={len(api_key) if api_key else 0}, key_preview={api_key[:10] + '...' + api_key[-4:] if api_key and len(api_key) > 14 else 'N/A'}")
# Build base inference task
inference_task = {
'taskType': 'imageInference',
'taskUUID': str(uuid.uuid4()),
'positivePrompt': prompt,
'negativePrompt': negative_prompt,
'model': runware_model,
'width': width,
'height': height,
'numberResults': 1,
'outputFormat': kwargs.get('format', 'webp')
}
# Model-specific parameter configuration
if runware_model.startswith('bria:'):
# Bria models need steps
inference_task['steps'] = 20
elif runware_model.startswith('google:'):
# Google models use resolution instead of width/height
del inference_task['width']
del inference_task['height']
inference_task['resolution'] = '1k'
elif runware_model.startswith('bytedance:'):
# Seedream models use basic format - no steps, CFGScale needed
pass
elif runware_model.startswith('runware:'):
# Hi Dream Full - needs steps and CFGScale
inference_task['steps'] = 30
inference_task['CFGScale'] = 7.5
else:
# Unknown model - use basic format
pass
payload = [
{
'taskType': 'authentication',
'apiKey': api_key
},
{
'taskType': 'imageInference',
'taskUUID': str(uuid.uuid4()),
'positivePrompt': prompt,
'negativePrompt': negative_prompt,
'model': runware_model,
'width': width,
'height': height,
'steps': 30,
'CFGScale': 7.5,
'numberResults': 1,
'outputFormat': kwargs.get('format', 'webp')
}
inference_task
]
logger.info(f"[AIProcessor.generate_image] Runware request payload: model={runware_model}, width={width}, height={height}, prompt_length={len(prompt)}")