Leading Models: An Expert’s Vision, Ia image generation
6 min read
Introduction
The revolution in image generation through Artificial Intelligence is radically transforming digital creativity in 2025. As designer and AI expert Diego Sanchez Cadavid demonstrates in his projects, this technology has transcended initial expectations, enabling the creation of everything from hyperrealistic compositions to conceptual works that previously required hours of manual labor. Digital creatives and agencies like ASA Creative are leveraging these tools to elevate their visual proposals to previously unattainable levels. In this definitive guide, we’ll analyze the models that are defining the future of visual design, their distinctive characteristics, and how to select the most suitable one for your specific creative projects.
1. What are Stable Diffusion, SDXL, and Flux?
As regularly analyzed in cutting-edge projects on diegocadavid.com, choosing the right model makes the difference between average and extraordinary results:
Stable Diffusion (SD):
Definition: A latent diffusion model that transforms textual descriptions into surprisingly precise images.
Key features: The perfect balance between visual quality, processing speed, and accessibility for users of all levels.
Community: Vibrant ecosystem with comprehensive documentation and continuous support.
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SDXL:
Definition: The refined evolution of Stable Diffusion that is redefining visual standards in 2025.
Advantages: Remarkably superior image quality, extraordinary level of detail, and compatibility with an expanding universe of creative extensions.
Professional impact: Adopted by leading creative studios for high-end productions, as showcased by ASA Creative.
Flux:
Definition: Revolutionary alternative gaining recognition among the most demanding professionals.
Strengths: Image quality that exceeds expectations, lighting that emulates photographic perfection, and realism that challenges the distinction between generated and captured.
Advanced considerations: Its extraordinary power comes with a learning curve that demands greater expertise in complex situations.
2. Quality vs Control: The Great Dilemma
Quality
Flux:
Excels in level of detail and “polished” finish in photorealistic scenarios.
Impressive results in close-ups and textures.
SD/SDXL:
Although considered more “basic” by some users, they can achieve high-level professional results.
With the right extensions, they generate highly competitive images.
Control and Predictability
SD/SDXL:
Robust ecosystem: ControlNet, IP Adapter, Inpainting, LoRAs.
Abundant documentation and community support, facilitating precise adjustments.
Greater consistency between consecutive generations.
Flux:
Has similar tools but may show greater variability.
Potentially more unpredictable when combining complex factors (multiple LoRAs, overlapping styles).
May require more iterations to achieve specific results.
Advanced Tools and Techniques
3. LoRAs: The Art of Personalized Training
What are LoRAs?
Low-Rank Adaptations: revolutionary technology that allows fine-tuning models with specific image sets or styles.
AI “learns” precise characteristics: from characters and objects to complete artistic styles.
Real training experience according to Diego Sanchez:
“I’ve trained LoRAs on different hardware configurations, and I can confirm it’s an intensive process to obtain truly professional results. For high-precision training, I choose the NVIDIA H100, which with a modest but optimized dataset for deep learning can capture every nuance and subtle detail of the reference images. These premium processes can extend up to 10 hours of intensive computation, but the resulting quality justifies the investment when you need absolute fidelity.”
Training methods comparison:
ApproachHardwareTimeResultIdeal use casePremiumNVIDIA H1008–10 hoursUltra-specific, captures minute detailsLuxury campaigns, brand identity charactersProfessionalNVIDIA RTX 40903–5 hoursExcellent fidelity with efficiency balanceStandard commercial productionOnline PlatformsCloud services30–60 minGood but with variations in subtle detailsProjects with time constraints
Implementation by base model:
In SD/SDXL:
Mature ecosystem with comprehensive guides and optimization tools.
Possibility of creating custom styles with notable precision and control.
Techniques developed at www.asacreative.us allow extracting maximum performance even from less intensive LoRAs.
In Flux:
Spectacular results in realism, especially with deeply trained LoRAs.
Greater sensitivity to training quality when combining multiple styles or poses.
Specific LoRAs trained with the premium method are almost indistinguishable from real photographs.
4. Control and Refinement Tools
ToolFunctionImplementation in SD/SDXLImplementation in FluxControlNetFixes poses, contours, or depth mapsVery robust, widely documentedAvailable but with less predictable resultsIP AdapterDirects the image toward specific traits or stylesHigh precision, easy integrationFunctional but may require more adjustmentsInpaintingCorrects specific areas without modifying the restWell integrated into workflowsAvailable with variable effectivenessLoRAsFine adjustments for styles or charactersMature and extensive ecosystemCompatible but less documented
Practical Considerations
5. Economic and Hardware Aspects
Model costs:
No license fee for the models themselves; both SD/SDXL and Flux are usually available for free.
The real cost is in:
Local hardware: Powerful GPUs (minimum 8GB VRAM, recommended 12GB+)
Cloud services: RunPod, AWS, Google Colab (from $0.10 to $2.00 per hour depending on power)
Recommended hardware requirements:
Casual use: NVIDIA RTX 3060 (8GB) or higher
Professional use: NVIDIA RTX 3080/4070 (12GB+) or higher
Intensive production: NVIDIA RTX 4090 (24GB) or professional solutions
6. Cloud Cost Comparison
ProviderBasic PlanIntermediate PlanAdvanced PlanAdvantagesRunPod$0.12/hour (RTX 3080)$0.39/hour (RTX 4080)$0.89/hour (RTX 4090)Easy to use, AI-focusedAWS$0.60/hour (g4dn.xlarge)$1.53/hour (g5.xlarge)$3.09/hour (g5.2xlarge)Stability, scalabilityGoogle ColabFree (limited)$9.99/month$49.99/monthAccessibility, no setup
Professional Applications: Real Success Cases
7. Revolutionizing Commercial Workflows
Transformed creative iteration:
SD/SDXL in action: As demonstrated in the visual innovation portfolio at www.asacreative.us, these models allow generating multiple iterations and refinements with revolutionary efficiency.
Competitive advantage: This approach has become essential for creative studios competing in markets where deadlines are shorter and expectations higher.
The art of expert post-processing:
Expert designers like Diego Sanchez have perfected methodologies that combine AI generation with specialized software for:
Precise refinement of key visual elements
Elimination of imperfections that reveal artificial origin
Application of professional finishes that elevate perceived quality
Perfect integration with existing brand elements
Reimagined brand consistency:
Contemporary advertising campaigns demand impeccable visual coherence between pieces.
SD/SDXL in real projects: Success cases documented in specialized portfolios show how these models maintain visual identity with millimetric precision.
Proven methodology: Developing and documenting “creative recipes” of prompts and configurations has become an industry standard to ensure consistent results.
8. Specific Use Cases by Sector
SectorRecommended ModelReasonApplication ExampleAdvertisingSD/SDXLConsistency and speedCampaigns with multiple variantsArtistic PortraitFluxSuperior photographic qualityHigh-end portraitsConcept DesignSD/SDXLRapid idea iterationVisual exploration for video gamesArchitectureBoth*Depends on approachInterior visualizations (Flux), conceptual iterations (SD)EditorialSD/SDXLAgile workflowIllustrations for deadline-driven articles
*The choice depends on the balance between available time and required level of detail.
Future and Considerations
9. Evolution and Trends
Expected improvements in Flux:
Constant evolution: Active community developing extensions.
Optimization and patches to improve prompt fidelity.
Possible new versions with improved control.
Competition and innovation:
Multiple leading models drive innovation in the field.
Each community introduces functions that serve as reference for the rest.
The speed of development benefits all users.
10. Ethical and Legal Considerations
Use of generated images:
Verify terms of service if using cloud platforms.
Transparency when using AI-generated images in commercial contexts.
Avoid reproduction of styles protected by copyright without authorization.
Consent and representation:
Special considerations when generating images of real or similar people.
Verify local policies on AI use in advertising or commercial content.
Conclusion: The Expert’s Decision
As demonstrated by the experience of the most innovative digital creatives in 2025, the selection between Stable Diffusion/SDXL and Flux must strategically align with your specific objectives:
The pragmatic vision (SD/SDXL) — Recommended by ASA Creative teams for:
Projects requiring consistent and predictable results
Campaigns with tight deadlines and multiple deliverables
Teams valuing the support of a vibrant community
Workflows demanding seamless integration with established processes
Production needs requiring coherent and controlled variations
The pursuit of visual excellence (Flux) — As Diego Sanchez explores in his most ambitious projects:
Creations where extraordinary visual quality is non-negotiable
Compositions requiring photographic realism that challenges perception
Visual concepts where lighting plays a fundamental role
Projects where available time allows iterative exploration
Cases where the visual impact of the final result justifies a more intensive process
Glossary of Terms
Prompt: Textual description that guides image generation.
ControlNet: Tool that allows controlling specific aspects such as poses or contours.
Inpainting: Technique for modifying or correcting specific areas of an image.
Sampling Steps: Number of iterations the model performs to generate the final image.
CFG Scale: Classifier Free Guidance, determines how much the model adheres to the prompt.
LoRA: Low-Rank Adaptation, allows training the model to recognize specific styles or subjects.
Checkpoint: Specific version of a model with its trained weights.
IP Adapter: Image Prompt Adapter, allows using images as reference to guide generation.
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