DALL-E, Midjourney, Flux, Imagen — What's Actually Different
Same diffusion mechanism, very different outputs. The three things that actually differ between image generation models: the text encoder, the training data, and the fine-tuning strategy.
Last post: the mechanism. Diffusion, latent space, text conditioning, denoising. Every image generation model uses some variant of this.
So why do DALL-E 3 and Midjourney produce such different images from the same prompt? If the mechanism is the same, what's actually different?
Three things: the text encoder, the training data, and the fine-tuning strategy.
The Models
DALL-E 3 (OpenAI)
The prompt rewriter. DALL-E 3's signature innovation is that it rewrites your prompt before generating.
You type: "a cat"
DALL-E 3 internally expands this to something like: "A domestic short-hair tabby cat sitting in a sunlit window, soft focus background, warm natural lighting, photorealistic, detailed fur texture."
This happens via GPT-4, which takes your prompt and generates a detailed, optimized version. The expanded prompt goes to the diffusion model. The result: your "a cat" image has composition, lighting, and detail that a raw 2-word prompt would never produce.
Text encoder: T5. DALL-E 3 uses T5 (a large language model) as its text encoder instead of CLIP. T5 understands language semantics much better than CLIP. This is why DALL-E 3 handles complex prompts, spatial relationships, and text-in-images better than CLIP-based models.
Architecture: Diffusion transformer (DiT). Not a U-Net.
Access: API only. No self-hosting. Integrated into ChatGPT.
Strength: Prompt understanding. It "gets" what you want even from vague prompts. Best-in-class text rendering in images.
Weakness: Style control. You can't fine-tune it. You can't control the aesthetic as precisely as Midjourney. The prompt rewriter sometimes adds details you didn't want.
Cost: $0.04 (1024x1024 standard) to $0.12 (1792x1024 HD) per image.
Midjourney
The aesthetic engine. Midjourney is not technically superior. It's aesthetically superior. Their images look better — more cinematic, more polished, more "art directed" — because that's what they optimized for.
Training data: Midjourney was fine-tuned heavily on high-quality, aesthetically pleasing images. Not the broad internet scrape that others use. This gives it a consistent, recognizable "look" that users associate with quality.
Architecture: Proprietary. They don't publish architectural details. Likely a DiT variant.
Access: Discord bot (original) or web interface. No API. No self-hosting. Closed ecosystem.
Strength: Aesthetics. If you want an image that looks like a concept artist made it, Midjourney wins. Their default style is polished, cinematic, and immediately eye-catching.
Weakness: No API (limits programmatic use). Prompt control is less precise than DALL-E 3. Can't run locally. Style is somewhat uniform — everything has that "Midjourney look."
Cost: $10-120/month subscription. Roughly $0.01-0.03 per image depending on plan.
Flux (Black Forest Labs)
The open-weights champion. Built by the team that originally created Stable Diffusion (after they left Stability AI and founded Black Forest Labs).
Three tiers:
- Flux Schnell: Fast, distilled (4 steps), free for personal use. Quality comparable to SDXL.
- Flux Dev: Full quality, 50-step generation. Research/non-commercial license.
- Flux Pro: Best quality. API only. Commercial.
Architecture: DiT (diffusion transformer). State-of-the-art at release.
Text encoder: Dual encoder — CLIP + T5. Gets the semantic understanding of T5 with the visual alignment of CLIP.
Access: Open weights (Schnell and Dev). Run locally on a consumer GPU (24GB VRAM for full quality, 8GB with quantization). API via fal.ai, Replicate, Together AI for Pro.
Strength: Open weights. You can fine-tune it. You can run it locally. You own the output. No API dependency. Community ecosystem of extensions.
Weakness: Requires GPU hardware for local use. Pro (best quality) is API-only. Not as "pretty" out of the box as Midjourney (but more controllable).
Cost: Free (local, Schnell/Dev) or $0.03-0.055 per image (Pro API).
Imagen 3 (Google)
Google's flagship. Part of the Gemini ecosystem.
Architecture: Cascaded diffusion. Generates a low-resolution image first, then upscales with a separate super-resolution model. This produces very high-resolution outputs (up to 2048x2048) with fine detail.
Text encoder: Their own variant. Deep integration with Gemini means the text understanding benefits from Google's LLM research.
Access: API via Google Cloud (Vertex AI) and through Gemini.
Strength: Photorealism. Especially strong on people, faces, and natural scenes. Google's massive dataset gives it broad coverage. Gemini integration means you can generate images from conversation context.
Weakness: Less community ecosystem than open models. Style control is limited compared to fine-tunable models.
Cost: ~$0.03-0.04 per image via API.
Stable Diffusion (Stability AI)
The original open model. Stable Diffusion democratized image generation by releasing open weights that ran on consumer GPUs.
Current state: SD 1.5, SD 2.1, SDXL, and SD3.5 are all available. SDXL is still widely used. SD3.5 underperformed expectations. Stability AI as a company has struggled financially.
Architecture: U-Net (SD 1.5, 2.1, SDXL) and DiT (SD3.5).
Why it still matters: The ecosystem. Thousands of fine-tuned models, LoRAs, ControlNets, and tools built on Stable Diffusion. ComfyUI, Automatic1111, and other interfaces. A massive community producing specialized models for every niche.
Strength: Ecosystem. More customization options than any other model family.
Weakness: Base model quality has fallen behind DALL-E 3, Midjourney, and Flux. Company instability.
What Actually Makes Images Look Different
Same prompt, five models, five different images. Why?
Training Data
The single biggest differentiator. A model trained on professional photography looks different from one trained on internet images.
- Midjourney: Curated, high-aesthetic training data. Result: everything looks "designed."
- DALL-E 3: Broad training with GPT-4 recaptioning. OpenAI recaptioned training images with GPT-4 to create more detailed, accurate labels. Result: better prompt-to-image alignment.
- Flux: Large-scale internet data + proprietary curation. Result: balanced quality.
- Stable Diffusion: LAION dataset (large-scale internet scrape). Result: broad coverage but variable quality.
Text Encoder
How well the model understands your prompt depends on the text encoder.
| Model | Text Encoder | Language Understanding |
|---|---|---|
| SD 1.5 | CLIP ViT-L/14 | Basic. 77 token limit. |
| SDXL | CLIP ViT-L + OpenCLIP ViT-bigG | Better. Two encoders. |
| DALL-E 3 | T5-XXL | Excellent. Full LLM understanding. |
| Flux | CLIP + T5-XXL | Excellent. Dual approach. |
| Imagen 3 | Proprietary (Gemini-integrated) | Excellent. |
| Midjourney | Proprietary | Good (exact architecture unknown). |
Fine-Tuning and RLHF
After initial training, models are fine-tuned to improve quality:
- DALL-E 3: Fine-tuned with human feedback. Humans rated generated images and the model was optimized to produce images that score higher.
- Midjourney: Heavy aesthetic fine-tuning. The team has strong design backgrounds and the curation reflects this.
- Flux Pro: Additional fine-tuning on high-quality data beyond the open Dev/Schnell versions.
This is why DALL-E 3 and Midjourney produce "nicer" images than base Stable Diffusion — they've been polished with human feedback.
Choosing a Model: Decision Framework
| Priority | Choose |
|---|---|
| Need an API for production | DALL-E 3, Flux Pro, Imagen 3 |
| Best aesthetics out of the box | Midjourney |
| Need to run locally / own infra | Flux (Schnell/Dev), Stable Diffusion |
| Need fine-tuning (custom style/subject) | Flux Dev, Stable Diffusion |
| Best prompt understanding | DALL-E 3 |
| Best photorealism | Imagen 3, DALL-E 3 |
| Cheapest at scale | Self-hosted Flux/SD |
| Need text in images | DALL-E 3, Flux, Ideogram |
| Need to build a product on top | Flux Pro API or self-hosted Flux |
The Business Layer: How Companies Actually Use Image Gen
In every case, the model is a tool. The human brings the creative direction, the domain knowledge, the judgment about what works. The model brings speed and volume.
This is post 10 of the AI Engineering Explained series.
Next post: Fine-tuning — LoRA, ControlNet, and IP-Adapter. How to make AI generate YOUR face, YOUR style, YOUR product consistently.