Instructions to use ayushtues/blipdiffusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ayushtues/blipdiffusion with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ayushtues/blipdiffusion", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
File size: 484 Bytes
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"_name_or_path": "E:/diffusers/cache/vit",
"architectures": [
"Blip2VisionModel"
],
"attention_dropout": 0.0,
"hidden_act": "quick_gelu",
"hidden_size": 1024,
"image_size": 224,
"initializer_range": 1e-10,
"intermediate_size": 4096,
"layer_norm_eps": 1e-05,
"model_type": "blip_2_vision_model",
"num_attention_heads": 16,
"num_hidden_layers": 23,
"patch_size": 14,
"qkv_bias": true,
"torch_dtype": "float32",
"transformers_version": "4.31.0"
}
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