Instructions to use esencb/text-image with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use esencb/text-image with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("esencb/text-image", 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
- Local Apps
- Draw Things
- DiffusionBee
Update model_index.json
Browse files- model_index.json +0 -8
model_index.json
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{
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"_class_name": "StableDiffusionPipeline",
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"_diffusers_version": "0.7.2",
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"feature_extractor": [
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"transformers",
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"CLIPFeatureExtractor"
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],
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"scheduler": [
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"diffusers",
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"PNDMScheduler"
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],
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"text_encoder": [
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"transformers",
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"CLIPTextModel"
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{
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"_class_name": "StableDiffusionPipeline",
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"_diffusers_version": "0.7.2",
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"text_encoder": [
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"transformers",
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"CLIPTextModel"
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