Instructions to use CiroN2022/ldr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use CiroN2022/ldr with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("CiroN2022/ldr", 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 Settings
- Draw Things
- DiffusionBee
Commit History
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Fix deprecation warning by changing `CLIPFeatureExtractor` to `CLIPImageProcessor`. (#3)
- Fix deprecation warning by changing `CLIPFeatureExtractor` to `CLIPImageProcessor`. (915d0663238f02b7d18312719c0ada1504a64bcf)50a1b9a
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Add `clip_sample=False` to scheduler to make model compatible with DDIM. (#1)
- Add `clip_sample=False` to scheduler to make model compatible with DDIM. (de2657db160d46f4aaad68416405ade8db5338fe) Co-authored-by: Patrick von Platen <patrickvonplaten@users.noreply.huggingface.co>2377779 -
Add `scale_factor` to vae config. (#2)
- Add `scale_factor` to vae config. (b4159927d8989ef478eec18658af6dfea24724ff) Co-authored-by: Suraj Patil <valhalla@users.noreply.huggingface.co>c1ae338
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Update scheduler/scheduler_config.json
e1da345
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Upload concept_images with huggingface_hub
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Upload model_index.json with huggingface_hub
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Upload vae with huggingface_hub
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Upload unet with huggingface_hub
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Upload tokenizer with huggingface_hub
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Upload text_encoder with huggingface_hub
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Upload scheduler with huggingface_hub
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Upload safety_checker with huggingface_hub
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Upload feature_extractor with huggingface_hub
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Upload the concept ldr embeds and token
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initial commit
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