Text To Image Finetune

--- license: openrail++ base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - lora - text-to-image - multi-domain --- # Multi-domain LoRA — Text-to-Image Fine-tuned từ `runwayml/stable-diffusion-v1-5` bằng LoRA rank 32. ## Domains được train - ✅ Realistic (COCO, Flickr30K) - ✅ Anime / Illustration (Pokemon captions) - ✅ Art styles (ArtBench) - ✅ Portrait / Human faces - ✅ Vietnamese culture ## Training config ```json { "model_id": "runwayml/stable-diffusion-v1-5", "output_dir": "/kaggle/working/lora_output", "hf_repo_id": "huydev0000/text_to_image_finetune", "lora_rank": 32, "lora_alpha": 64, "lora_dropout": 0.05, "target_modules": [ "to_k", "to_q", "to_v", "to_out.0", "ff.net.0.proj", "ff.net.2" ], "resolution": 512, "train_batch_size": 4, "gradient_accum": 2, "learning_rate": 0.0002, "max_train_steps": 4000, "save_steps": 2000, "lr_scheduler": "cosine", "warmup_steps": 500, "mixed_precision": "fp16", "seed": 42, "snr_gamma": 5.0, "cfg_drop_prob": 0.1, "resume_from": "" } ``` ## Usage ```python from diffusers import StableDiffusionPipeline from peft import PeftModel import torch pipe = StableDiffusionPipeline.from_pretrained( "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16 ) pipe.unet = PeftModel.from_pretrained(pipe.unet, "your-username/your-lora") pipe.to("cuda") image = pipe("your prompt here").images[0] ```

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