Google Imagen 2
Google’s announcement of its Imagen 2 image-generation model on Vertex AI is drawing criticism for confusing access policies: it is marketed as “generally available,” yet in practice appears limited to allowlisted or “trusted tester” cloud customers with opaque onboarding. Commenters question whether the model meaningfully surpasses existing systems like DALL‑E 3, Midjourney, and Stable Diffusion, noting a lack of benchmarks, mixed early results, and heavily curated examples. The launch is widely seen as emblematic of Google’s broader AI and cloud issues, including fragmented documentation, cumbersome workflows, regional restrictions, and a perception that open-source tools now offer more practical value and flexibility.
Access & Documentation Confusion
- Many commenters can’t figure out how to actually use Imagen 2 despite the blog claiming “general availability.”
- Docs and console screens mix “GA,” “restricted GA,” “allowlist,” and “Trusted Tester Program,” creating ambiguity about who truly has access.
- Some users report the API working without special permissions using the
imagegeneration@00xendpoints; others hit “request access” forms, 404s, or “limited customers only” messages. - Navigation through the Google Cloud console (Vertex AI → Model Garden → Imagen) is described as convoluted and poorly documented.
- No simple public playground (like a hosted demo page) is available; many see this as a major release failure.
- In Canada, Imagen-related services are currently unavailable due to “regulatory uncertainty,” adding to frustration.
Model Quality & Comparisons
- Several people say this would have been impressive two years ago, but now it competes with DALL·E 3, Midjourney, SDXL, etc.
- Some early testers find Imagen 2 clearly worse than DALL·E 3 and only “okay” on simple prompts, with visible artifacts and “years-old” quality.
- Others think it outperforms Stable Diffusion specifically on text rendering and logos.
- Skepticism is high due to Google’s history of heavily cherry‑picked demos and the recent Gemini controversy; lack of benchmarks or a paper amplifies doubts.
- Example-based tests (e.g., complex hugging scenes, specific animals like flying squirrels) suggest that compositional and multi-entity reasoning remains a general weakness, not uniquely solved here.
Open-Source vs Cloud Services
- One camp argues Stable Diffusion (plus community tooling like AUTOMATIC1111, ControlNet, LoRA) has effectively “won” for serious or professional workflows: more control, no hard content filters, and cheap or local inference.
- Another camp counters that closed APIs (OpenAI, Google, Adobe, Canva) dominate on usability, integrations, indemnification, and non-expert accessibility, even if open models are more flexible.
- Cost is debated: ~$0.02/image on cloud is seen as expensive by some relative to self-hosting; others prefer paying per image to avoid GPU purchases and setup.
Google’s Product Strategy & Org Issues
- Many see this as another example of Google becoming a slow, bureaucratic, marketing-heavy “IBM-like” company: flashy AI announcements, confusing access, and hard-to-use platforms.
- There’s extensive meta‑discussion about internal bloat, coasting engineers vs. dysfunctional middle management, and how this culture may explain sluggish, half‑finished AI products.
- Some argue Google is prioritizing PR and stock market optics over shipping robust, clearly accessible tools.