feat: Add inference support for MiniT2I model.

[KenForever1]

Summary

Add inference support for MiniT2I in stable-diffusion.cpp.

This PR adds a MiniT2I diffusion runner, T5/flan-t5 text conditioning integration, model detection/loading support, and MiniT2I-specific sampling flow. It also caches step-invariant positional embeddings/RoPE tensors and removes an unused conditioning branch after validating output consistency.

Changes

• Add MiniT2I model type detection and loading path.
• Add MiniT2I::MiniT2IRunner implementation for MMJiT-style diffusion inference.
• Add MiniT2I conditioner path using google/flan-t5-large.
• Add MiniT2I sampling path with conditional/unconditional forward and CFG update.
• Add backend support needed by MiniT2I graph execution.
• Cache MiniT2I positional embeddings, text RoPE, and vision/joint RoPE in runner-level backend buffers.
• Remove unused t_vec + pooled_text conditioning branch that is not consumed by the current MiniT2I graph.

Commits

• b9493fa Add MiniT2I inference support
• 8de8f95 Optimize MiniT2I position cache
• dfb6ca2 Remove unused MiniT2I conditioning branch

Models Used

MiniT2I diffusion model:

• Model: MiniT2I/minit2i-b-16
• Weight: transformer/diffusion_pytorch_model.safetensors

Text encoder:

• Model: google/flan-t5-large
• Weight: model.safetensors

Test Commands

Mac Metal test:

cd stable-diffusion.cpp

./build/bin/sd-cli \
  --backend metal \
  --model MiniT2I/MiniT2I/minit2i-b-16/transformer/diffusion_pytorch_model.safetensors \
  --t5xxl google/flan-t5-large/model.safetensors \
  --prompt "a cat" \
  --steps 100 \
  --cfg-scale 6 \
  --width 512 \
  --height 512 \
  --seed 42 \
  --sampling-method euler \
  --rng cpu \
  --output /private/tmp/minit2i_metal.png \
  --threads 8

CUDA with diffusion flash attention:

cd stable-diffusion.cpp

./build-cuda/bin/sd-cli \
  --backend cuda \
  --diffusion-fa \
  --model MiniT2I/MiniT2I/minit2i-b-16/transformer/diffusion_pytorch_model.safetensors \
  --t5xxl google/flan-t5-large/model.safetensors \
  --prompt "a cat" \
  --steps 100 \
  --cfg-scale 6 \
  --width 512 \
  --height 512 \
  --seed 42 \
  --sampling-method euler \
  --rng cpu \
  --output /tmp/minit2i_cuda_diffusion_fa.png \
  --threads 8

Validation Notes

• MiniT2I generation succeeds on CUDA and Metal.
• Position/RoPE cache optimization preserves model batch semantics.
• Removing the unused conditioning branch produced identical output in local validation.
• CUDA --diffusion-fa works with MiniT2I and reduces stable diffusion forward time significantly.

[leejet]

Please follow the existing model docs and examples to add documentation and examples for MiniT2I.

[leejet]

Please use --diffusion-model to specify the model path instead of handling the prefix here.

[leejet]

Checking for one representative weight name should be enough; there’s no need to check too many.

[leejet]

The generic sampling flow should be used, rather than adding a separate sampling branch.