Instructions to use ShashwatStable/my_merged_models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ShashwatStable/my_merged_models with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ShashwatStable/my_merged_models")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("ShashwatStable/my_merged_models") model = AutoModel.from_pretrained("ShashwatStable/my_merged_models", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload model_index.json
Browse files- model_index.json +32 -0
model_index.json
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{
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"_class_name": "StableDiffusionPipeline",
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"_diffusers_version": "0.6.0",
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"feature_extractor": [
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"transformers",
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"CLIPImageProcessor"
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],
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"safety_checker": [
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"stable_diffusion",
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"StableDiffusionSafetyChecker"
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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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"tokenizer": [
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"transformers",
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"CLIPTokenizer"
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],
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"unet": [
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"diffusers",
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"UNet2DConditionModel"
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],
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"vae": [
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"diffusers",
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"AutoencoderKL"
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]
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}
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