Instructions to use Dikshan1234/ImageGen with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Dikshan1234/ImageGen with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("Dikshan1234/ImageGen") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
README @ step 6200
Browse files
README.md
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for improved prompt adherence and image quality over stock SDXL.
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## Training status
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- **Step:** 6,
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- **Epoch:** 2.00
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- **Validation loss:** 0.1364
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- **LoRA:** rank 32, alpha 64, targets `to_q, to_k, to_v, to_out.0`
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- `latest/` — most recent adapter weights
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- `best/` — lowest validation-loss adapter weights
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for improved prompt adherence and image quality over stock SDXL.
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## Training status
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- **Step:** 6,200
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- **Epoch:** 2.00
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- **Validation loss:** 0.1364
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- **LoRA:** rank 32, alpha 64, targets `to_q, to_k, to_v, to_out.0`
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- `latest/` — most recent adapter weights
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- `best/` — lowest validation-loss adapter weights
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## Dataset sources
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- `Spawning/PD12M` (target ~30,000)
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