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
Upload model_index.json
Browse files- model_index.json +7 -2
model_index.json
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{
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"_class_name": "BlipDiffusionPipeline",
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"_diffusers_version": "0.18.0.dev0",
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"qformer": [
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"
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"Blip2QFormerModel"
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],
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"scheduler": [
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"PNDMScheduler"
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],
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"text_encoder": [
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"
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"CtxCLIPTextModel"
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],
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"tokenizer": [
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{
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"_class_name": "BlipDiffusionPipeline",
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"_diffusers_version": "0.18.0.dev0",
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"controlnet": [
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"diffusers",
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"ControlNetModel"
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],
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"ctx_begin_pos": 2,
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"qformer": [
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"blip_diffusion",
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"Blip2QFormerModel"
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],
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"scheduler": [
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"PNDMScheduler"
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],
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"text_encoder": [
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"blip_diffusion",
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"CtxCLIPTextModel"
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],
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"tokenizer": [
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