End of training
Browse files- 2000_ads_linoy_multi.safetensors +1 -1
- 2000_ads_linoy_multi_emb.safetensors +1 -1
- README.md +4 -4
- image_0.png +2 -2
- image_1.png +2 -2
- image_2.png +2 -2
- image_3.png +2 -2
- pytorch_lora_weights.safetensors +1 -1
2000_ads_linoy_multi.safetensors
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2000_ads_linoy_multi_emb.safetensors
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README.md
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"image_3.png"
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base_model: stabilityai/stable-diffusion-xl-base-1.0
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instance_prompt: an ad in the style of <s0><s1>
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license: openrail++
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---
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- On AUTOMATIC1111, load the LoRA by adding `<lora:2000_ads_linoy_multi:1>` to your prompt. On ComfyUI just [load it as a regular LoRA](https://comfyanonymous.github.io/ComfyUI_examples/lora/).
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- *Embeddings*: download **[`2000_ads_linoy_multi_emb.safetensors` here 💾](/linoyts/2000_ads_linoy_multi/blob/main/2000_ads_linoy_multi_emb.safetensors)**.
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- Place it on it on your `embeddings` folder
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- Use it by adding `2000_ads_linoy_multi_emb` to your prompt. For example, `an ad in the style of 2000_ads_linoy_multi_emb`
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(you need both the LoRA and the embeddings as they were trained together for this LoRA)
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pipeline.load_lora_weights('linoyts/2000_ads_linoy_multi', weight_name='pytorch_lora_weights.safetensors')
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embedding_path = hf_hub_download(repo_id='linoyts/2000_ads_linoy_multi', filename='2000_ads_linoy_multi_emb.safetensors' repo_type="model")
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state_dict = load_file(embedding_path)
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pipeline.load_textual_inversion(state_dict["clip_l"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder, tokenizer=pipeline.tokenizer)
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pipeline.load_textual_inversion(state_dict["clip_g"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder_2, tokenizer=pipeline.tokenizer_2)
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image = pipeline('<s0><s1> ad of a <s2><s3> woman wearing headphones').images[0]
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```
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"image_3.png"
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base_model: stabilityai/stable-diffusion-xl-base-1.0
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instance_prompt: an ad in the style of <s0><s1> of a <s2><s3> woman
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license: openrail++
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---
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- On AUTOMATIC1111, load the LoRA by adding `<lora:2000_ads_linoy_multi:1>` to your prompt. On ComfyUI just [load it as a regular LoRA](https://comfyanonymous.github.io/ComfyUI_examples/lora/).
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- *Embeddings*: download **[`2000_ads_linoy_multi_emb.safetensors` here 💾](/linoyts/2000_ads_linoy_multi/blob/main/2000_ads_linoy_multi_emb.safetensors)**.
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- Place it on it on your `embeddings` folder
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- Use it by adding `2000_ads_linoy_multi_emb` to your prompt. For example, `an ad in the style of 2000_ads_linoy_multi_emb of a woman`
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(you need both the LoRA and the embeddings as they were trained together for this LoRA)
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pipeline.load_lora_weights('linoyts/2000_ads_linoy_multi', weight_name='pytorch_lora_weights.safetensors')
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embedding_path = hf_hub_download(repo_id='linoyts/2000_ads_linoy_multi', filename='2000_ads_linoy_multi_emb.safetensors' repo_type="model")
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state_dict = load_file(embedding_path)
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pipeline.load_textual_inversion(state_dict["clip_l"], token=["<s0>", "<s1>", "<s2>", "<s3>"], text_encoder=pipeline.text_encoder, tokenizer=pipeline.tokenizer)
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pipeline.load_textual_inversion(state_dict["clip_g"], token=["<s0>", "<s1>", "<s2>", "<s3>"], text_encoder=pipeline.text_encoder_2, tokenizer=pipeline.tokenizer_2)
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image = pipeline('<s0><s1> ad of a <s2><s3> woman wearing headphones').images[0]
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```
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image_0.png
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image_3.png
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pytorch_lora_weights.safetensors
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