MLA 025 AI Image Generation: Midjourney vs Stable Diffusion, GPT-4o, Imagen & Firefly
Release Date: 07/09/2025
MLA 027 AI Video End-to-End Workflow
Machine Learning Guide
How to maintain character consistency, style consistency, etc in an AI video. Prosumers can use Google Veo 3’s "High-Quality Chaining" for fast social media content. Indie filmmakers can achieve narrative consistency by combining Midjourney V7 for style, Kling for lip-synced dialogue, and Runway Gen-4 for camera control, while professional studios gain full control with a layered ComfyUI pipeline to output multi-layer EXR files for standard VFX compositing. Links Notes and resources at - stay healthy & sharp while you learn & code - my favorite AI audio/video editor ...
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MLA 026 AI Video Generation: Veo 3 vs Sora, Kling, Runway, Stable Video Diffusion
Machine Learning Guide
Google Veo leads the generative video market with superior 4K photorealism and integrated audio, an advantage derived from its YouTube training data. OpenAI Sora is the top tool for narrative storytelling, while Kuaishou Kling excels at animating static images with realistic, high-speed motion. Links Notes and resources at - stay healthy & sharp while you learn & code Build the future of multi-agent software with . S-Tier: Google Veo The market leader due to superior visual quality, physics simulation, 4K resolution, and , which removes post-production steps....
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MLA 025 AI Image Generation: Midjourney vs Stable Diffusion, GPT-4o, Imagen & Firefly
Machine Learning Guide
The AI image market has split: Midjourney creates the highest quality artistic images but fails at text and precision. For business use, OpenAI's GPT-4o offers the best conversational control, while Adobe Firefly provides the strongest commercial safety from its exclusively licensed training data. Links Notes and resources at - stay healthy & sharp while you learn & code Build the future of multi-agent software with . The 2025 generative AI image market is defined by a split between two types of tools. "Artists" like Midjourney excel at creating beautiful,...
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MLG 036 Autoencoders
Machine Learning Guide
Auto encoders are neural networks that compress data into a smaller "code," enabling dimensionality reduction, data cleaning, and lossy compression by reconstructing original inputs from this code. Advanced auto encoder types, such as denoising, sparse, and variational auto encoders, extend these concepts for applications in generative modeling, interpretability, and synthetic data generation. Links Notes and resources at - stay healthy & sharp while you learn & code Build the future of multi-agent software with . Thanks to from for recording...
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MLG 035 Large Language Models 2
Machine Learning Guide
At inference, large language models use in-context learning with zero-, one-, or few-shot examples to perform new tasks without weight updates, and can be grounded with Retrieval Augmented Generation (RAG) by embedding documents into vector databases for real-time factual lookup using cosine similarity. LLM agents autonomously plan, act, and use external tools via orchestrated loops with persistent memory, while recent benchmarks like GPQA (STEM reasoning), SWE Bench (agentic coding), and MMMU (multimodal college-level tasks) test performance alongside prompt engineering techniques such as...
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MLG 034 Large Language Models 1
Machine Learning Guide
Explains language models (LLMs) advancements. Scaling laws - the relationships among model size, data size, and compute - and how emergent abilities such as in-context learning, multi-step reasoning, and instruction following arise once certain scaling thresholds are crossed. The evolution of the transformer architecture with Mixture of Experts (MoE), describes the three-phase training process culminating in Reinforcement Learning from Human Feedback (RLHF) for model alignment, and explores advanced reasoning techniques such as chain-of-thought prompting which significantly improve complex...
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MLA 024 Code AI MCP Servers, ML Engineering
Machine Learning Guide
Tool use in code AI agents allows for both in-editor code completion and agent-driven file and command actions, while the Model Context Protocol (MCP) standardizes how these agents communicate with external and internal tools. MCP integration broadens the automation capabilities for developers and machine learning engineers by enabling access to a wide variety of local and cloud-based tools directly within their coding environments. Links Notes and resources at stay healthy & sharp while you learn & code Tool Use in Code AI Agents Code AI agents offer two primary modes of...
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MLA 023 Code AI Models & Modes
Machine Learning Guide
Gemini 2.5 Pro currently leads in both accuracy and cost-effectiveness among code-focused large language models, with Claude 3.7 and a DeepSeek R1/Claude 3.5 combination also performing well in specific modes. Using local open source models via tools like Ollama offers enhanced privacy but trades off model performance, and advanced workflows like custom modes and fine-tuning can further optimize development processes. Links Notes and resources at stay healthy & sharp while you learn & code Model Current Leaders According to the (as of April 12, 2025), leading...
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MLA 022 Code AI: Cursor, Cline, Roo, Aider, Copilot, Windsurf
Machine Learning Guide
Vibe coding is using large language models within IDEs or plugins to generate, edit, and review code, and has recently become a prominent and evolving technique in software and machine learning engineering. The episode outlines a comparison of current code AI tools - such as Cursor, Copilot, Windsurf, Cline, Roo Code, and Aider - explaining their architectures, capabilities, agentic features, pricing, and practical recommendations for integrating them into development workflows. Links Notes and resources at stay healthy & sharp while you learn & code Definition and...
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MLG 033 Transformers
Machine Learning Guide
Links: Notes and resources at 3Blue1Brown videos: stay healthy & sharp while you learn & code audio/video editing with AI power-tools Background & Motivation RNN Limitations: Sequential processing prevents full parallelization—even with attention tweaks—making them inefficient on modern hardware. Breakthrough: “Attention Is All You Need” replaced recurrence with self-attention, unlocking massive parallelism and scalability. Core Architecture Layer Stack: Consists of alternating self-attention and feed-forward (MLP) layers, each wrapped...
info_outlineThe AI image market has split: Midjourney creates the highest quality artistic images but fails at text and precision. For business use, OpenAI's GPT-4o offers the best conversational control, while Adobe Firefly provides the strongest commercial safety from its exclusively licensed training data.
Links
- Notes and resources at ocdevel.com/mlg/mla-25
- Try a walking desk - stay healthy & sharp while you learn & code
- Build the future of multi-agent software with AGNTCY.
The 2025 generative AI image market is defined by a split between two types of tools. "Artists" like Midjourney excel at creating beautiful, high-quality images but lack precise control. "Collaborators" like OpenAI's GPT-4o and Google's Imagen 4 are integrated into language models, excelling at following complex instructions and accurately rendering text. Standing apart are the open-source "Sovereign Toolkit" Stable Diffusion, which offers users total control, and Adobe Firefly, a "Professional's Walled Garden" focused on commercial safety.
The Five Main Platforms
The market is dominated by five platforms with distinct strengths and weaknesses.
| Tool | Parent Company | Core Strength | Best For |
|---|---|---|---|
| Midjourney v7 | Midjourney, Inc. | Artistic Aesthetics & Photorealism | Fine Art, Concept Design, Stylized Visuals |
| GPT-4o | OpenAI | Conversational Control & Instruction Following | Marketing Materials, UI/UX Mockups, Logos |
| Google Imagen 4 | Ecosystem Integration & Speed | Business Presentations, Educational Content | |
| Stable Diffusion 3 | Stability AI | Ultimate Customization & Control | Developers, Power Users, Bespoke Workflows |
| Adobe Firefly | Adobe | Commercial Safety & Workflow Integration | Professional Designers, Agencies, Enterprise Use |
Platform Analysis
- Midjourney v7: Delivers the best aesthetic and photorealistic quality via a new web UI. Its "Draft Mode" allows for rapid, low-cost ideation. However, it cannot reliably render text, struggles to follow precise instructions (like counting objects), makes all images public on cheaper plans, and strictly prohibits API access or automation.
- GPT-4o: Its strength is conversational refinement within ChatGPT, allowing users to edit images through dialogue (e.g., "change the shirt to red"). It has excellent instruction-following and text-rendering capabilities. Weaknesses include being slower than competitors and generating only one image at a time.
- Google Imagen 4: A practical tool integrated directly into Google Workspace and Gemini. It produces high-quality, high-resolution (2K) photorealistic images quickly and renders text well. Its primary advantage is letting users generate images without leaving their documents or presentations.
- Stable Diffusion 3 (SD3): An open-source model that provides users with total control and privacy. The new SD3 architecture significantly improves prompt understanding and text generation. It can run on consumer hardware, and its quality is free after the initial hardware cost. Its power comes from a vast ecosystem of community tools (see below), but it has a steep learning curve.
- Adobe Firefly: Embedded within Adobe Creative Cloud (e.g., Photoshop's Generative Fill). Its key differentiator is commercial safety; it is trained only on licensed Adobe Stock and public domain content to indemnify users from copyright claims. It excels at editing existing images rather than generating from scratch.
Techniques & Tools
- In-painting/Out-painting: Core editing functions. In-painting modifies a specific area within an image. Out-painting expands an image beyond its original borders.
- Stable Diffusion Power Tools:
- LoRAs (Low-Rank Adaptations): Small files that apply a specific style, character, or concept to the main model.
- ControlNet: A framework that uses a reference image (e.g., a sketch or a stick-figure pose) as a "blueprint" to enforce a specific composition or pose.
- Stable Diffusion Interfaces: Users choose a UI to run the model. Automatic1111 is a beginner-friendly, tab-based dashboard. ComfyUI is a more complex but powerful node-based interface for building custom, automated workflows.
Feature Comparison & Exclusion Rules
The choice of tool often depends on a single required feature.
| Model | Text-in-Image Accuracy | Photorealism Quality | Complex Prompt Adherence |
|---|---|---|---|
| Midjourney v7 | Poor. A major weakness. | Best-in-Class | Fair |
| GPT-4o | Excellent. A key strength. | Very Good | Best-in-Class |
| Google Imagen 4 | Excellent | Excellent | Very Good |
| Stable Diffusion 3 | Good to Excellent | Good to Excellent | Good to Excellent |
This leads to several hard rules for choosing a tool:
- If you need accurate in-image text: Exclude Midjourney. Use GPT-4o, Google Imagen 4, or specialist tool Ideogram.
- If you require absolute privacy or must run locally: Stable Diffusion is your only option.
- If you require a guarantee of commercial safety: Adobe Firefly is the most prudent choice.
- If you need to automate generation via an API: Use OpenAI or Google's official APIs. Midjourney bans automation and will close your account.