End of training

.gitattributes

@@ -33,3 +33,7 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+image_0.png filter=lfs diff=lfs merge=lfs -text
+image_1.png filter=lfs diff=lfs merge=lfs -text
+image_2.png filter=lfs diff=lfs merge=lfs -text
+image_3.png filter=lfs diff=lfs merge=lfs -text

.ipynb_checkpoints/metadata-checkpoint.jsonl

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+{"file_name": "y2k_ad_04.jpeg", "prompt": "A TOK ad, got milk? ad campaign"}
+{"file_name": "y2k_ad_07.jpeg", "prompt": "A TOK ad, an advertisement for the honda s2000 roadster"}
+{"file_name": "y2k_ad_05.jpeg", "prompt": "A TOK ad, an ipod advertisement with a woman holding up her ipod"}
+{"file_name": "y2k_ad_01.jpeg", "prompt": "A TOK ad, skechers shoes - volume 1"}
+{"file_name": "y2k_ad_03.jpeg", "prompt": "A TOK ad, got milk? ad campaign"}
+{"file_name": "y2k_ad_06.jpeg", "prompt": "A TOK ad, the fantanas wallpaper"}
+{"file_name": "y2k_ad_09.jpeg", "prompt": "A TOK ad, mustang fragrance"}
+{"file_name": "y2k_ad_08.jpeg", "prompt": "A TOK ad, samsung digital expressions"}
+{"file_name": "y2k_ad_02.jpeg", "prompt": "A TOK ad, mcdonalds i'm lovin' it"}

2000_ads.safetensors

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+oid sha256:1a374e7ad4b74f60777fc39f9b65a2620e5eaa7fbebb5f8b491614efa14b4784
+size 186046568

2000_ads_emb.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:cacafa17051c386b2e79bf1521140c18ed122913bc314096ac9ab60c99f28498
+size 8344

README.md

@@ -0,0 +1,95 @@
+---
+tags:
+- stable-diffusion-xl
+- stable-diffusion-xl-diffusers
+- text-to-image
+- diffusers
+- lora
+- template:sd-lora
+widget:
+
+        - text: '<s0><s1> ad of a llama wearing headphones'
+          output:
+            url:
+                "image_0.png"
+        
+        - text: '<s0><s1> ad of a llama wearing headphones'
+          output:
+            url:
+                "image_1.png"
+        
+        - text: '<s0><s1> ad of a llama wearing headphones'
+          output:
+            url:
+                "image_2.png"
+        
+        - text: '<s0><s1> ad of a llama wearing headphones'
+          output:
+            url:
+                "image_3.png"
+        
+base_model: stabilityai/stable-diffusion-xl-base-1.0
+instance_prompt: an ad in the style of <s0><s1>
+license: openrail++
+---
+
+# SDXL LoRA DreamBooth - LinoyTsaban/2000_ads
+
+<Gallery />
+
+## Model description
+
+### These are LinoyTsaban/2000_ads LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
+
+## Download model
+
+### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke
+
+- **LoRA**: download **[`2000_ads.safetensors` here 💾](/LinoyTsaban/2000_ads/blob/main/2000_ads.safetensors)**.
+    - Place it on your `models/Lora` folder.
+    - On AUTOMATIC1111, load the LoRA by adding `<lora:2000_ads:1>` to your prompt. On ComfyUI just [load it as a regular LoRA](https://comfyanonymous.github.io/ComfyUI_examples/lora/).
+- *Embeddings*: download **[`2000_ads_emb.safetensors` here 💾](/LinoyTsaban/2000_ads/blob/main/2000_ads_emb.safetensors)**.
+    - Place it on it on your `embeddings` folder
+    - Use it by adding `2000_ads_emb` to your prompt. For example, `an ad in the style of 2000_ads_emb`
+    (you need both the LoRA and the embeddings as they were trained together for this LoRA)
+    
+
+## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
+
+```py
+from diffusers import AutoPipelineForText2Image
+import torch
+from huggingface_hub import hf_hub_download
+from safetensors.torch import load_file
+        
+pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16).to('cuda')
+pipeline.load_lora_weights('LinoyTsaban/2000_ads', weight_name='pytorch_lora_weights.safetensors')
+embedding_path = hf_hub_download(repo_id='LinoyTsaban/2000_ads', filename='2000_ads_emb.safetensors' repo_type="model")
+state_dict = load_file(embedding_path)
+pipeline.load_textual_inversion(state_dict["clip_l"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder, tokenizer=pipeline.tokenizer)
+pipeline.load_textual_inversion(state_dict["clip_g"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder_2, tokenizer=pipeline.tokenizer_2)
+        
+image = pipeline('<s0><s1> ad of a llama wearing headphones').images[0]
+```
+
+For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
+
+## Trigger words
+
+To trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens:
+
+to trigger concept `TOK` → use `<s0><s1>` in your prompt 
+
+
+
+## Details
+All [Files & versions](/LinoyTsaban/2000_ads/tree/main).
+
+The weights were trained using [🧨 diffusers Advanced Dreambooth Training Script](https://github.com/huggingface/diffusers/blob/main/examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py).
+
+LoRA for the text encoder was enabled. False.
+
+Pivotal tuning was enabled: True.
+
+Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
+

metadata.jsonl

@@ -0,0 +1,9 @@
+{"file_name": "y2k_ad_04.jpeg", "prompt": "A TOK ad, got milk? ad campaign"}
+{"file_name": "y2k_ad_07.jpeg", "prompt": "A TOK ad, an advertisement for the honda s2000 roadster"}
+{"file_name": "y2k_ad_05.jpeg", "prompt": "A TOK ad, an ipod advertisement with a woman holding up her ipod"}
+{"file_name": "y2k_ad_01.jpeg", "prompt": "A TOK ad, skechers shoes - volume 1"}
+{"file_name": "y2k_ad_03.jpeg", "prompt": "A TOK ad, got milk? ad campaign"}
+{"file_name": "y2k_ad_06.jpeg", "prompt": "A TOK ad, the fantanas wallpaper"}
+{"file_name": "y2k_ad_09.jpeg", "prompt": "A TOK ad, mustang fragrance"}
+{"file_name": "y2k_ad_08.jpeg", "prompt": "A TOK ad, samsung digital expressions"}
+{"file_name": "y2k_ad_02.jpeg", "prompt": "A TOK ad, mcdonalds i'm lovin' it"}

pytorch_lora_weights.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7ffc7468a807cad384211d296fbeceba95ff52f8ab2b4e1671670ec4cfaddc1a
+size 185963768