End of training
Browse files- .gitattributes +1 -0
- README.md +82 -0
- checkpoint-1000/optimizer.bin +3 -0
- checkpoint-1000/pytorch_lora_weights.safetensors +3 -0
- checkpoint-1000/random_states_0.pkl +3 -0
- checkpoint-1000/scheduler.bin +3 -0
- checkpoint-200/optimizer.bin +3 -0
- checkpoint-200/pytorch_lora_weights.safetensors +3 -0
- checkpoint-200/random_states_0.pkl +3 -0
- checkpoint-200/scheduler.bin +3 -0
- checkpoint-400/optimizer.bin +3 -0
- checkpoint-400/pytorch_lora_weights.safetensors +3 -0
- checkpoint-400/random_states_0.pkl +3 -0
- checkpoint-400/scheduler.bin +3 -0
- checkpoint-600/optimizer.bin +3 -0
- checkpoint-600/pytorch_lora_weights.safetensors +3 -0
- checkpoint-600/random_states_0.pkl +3 -0
- checkpoint-600/scheduler.bin +3 -0
- checkpoint-800/optimizer.bin +3 -0
- checkpoint-800/pytorch_lora_weights.safetensors +3 -0
- checkpoint-800/random_states_0.pkl +3 -0
- checkpoint-800/scheduler.bin +3 -0
- image_0.png +3 -0
- pytorch_lora_weights.safetensors +3 -0
- socialmedia-std-xl-base-1-0.safetensors +3 -0
- socialmedia-std-xl-base-1-0_emb.safetensors +3 -0
.gitattributes
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README.md
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---
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tags:
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- stable-diffusion-xl
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- stable-diffusion-xl-diffusers
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- diffusers-training
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- text-to-image
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- diffusers
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- lora
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- template:sd-lora
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widget:
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- text: 'Design a promotional poster for AI chatbot integration services. The image should feature the text WARNER & SPENCER with a chatbot icon. Include bold text for AI CHATBOT and Integration Services, service details with checkmarks, and a Contact Us button on the left. A friendly chatbot graphic should be placed on the right side.'
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output:
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url:
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"image_0.png"
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base_model: stabilityai/stable-diffusion-xl-base-1.0
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instance_prompt: Create a promotional image for a SHOE SALE with bold black text near the bottom on a white and gray background. Include a red 50% OFF badge, a SHOP NOW button, sneakers, socks, and shoeboxes overlaying a gray diagonal strip. The design should have red cursive text and a liceria & co. logo in the top-left corner.
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license: openrail++
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---
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# SDXL LoRA DreamBooth - javeriahassan/socialmedia-std-xl-base-1-0
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<Gallery />
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## Model description
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### These are javeriahassan/socialmedia-std-xl-base-1-0 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
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## Download model
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### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke
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- **LoRA**: download **[`socialmedia-std-xl-base-1-0.safetensors` here 💾](/javeriahassan/socialmedia-std-xl-base-1-0/blob/main/socialmedia-std-xl-base-1-0.safetensors)**.
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- Place it on your `models/Lora` folder.
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- On AUTOMATIC1111, load the LoRA by adding `<lora:socialmedia-std-xl-base-1-0:1>` 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 **[`socialmedia-std-xl-base-1-0_emb.safetensors` here 💾](/javeriahassan/socialmedia-std-xl-base-1-0/blob/main/socialmedia-std-xl-base-1-0_emb.safetensors)**.
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- Place it on it on your `embeddings` folder
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- Use it by adding `socialmedia-std-xl-base-1-0_emb` to your prompt. For example, `Create a promotional image for a SHOE SALE with bold black text near the bottom on a white and gray background. Include a red 50% OFF badge, a SHOP NOW button, sneakers, socks, and shoeboxes overlaying a gray diagonal strip. The design should have red cursive text and a liceria & co. logo in the top-left corner.`
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(you need both the LoRA and the embeddings as they were trained together for this LoRA)
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## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
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```py
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from diffusers import AutoPipelineForText2Image
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import torch
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from huggingface_hub import hf_hub_download
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from safetensors.torch import load_file
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pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16).to('cuda')
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pipeline.load_lora_weights('javeriahassan/socialmedia-std-xl-base-1-0', weight_name='pytorch_lora_weights.safetensors')
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embedding_path = hf_hub_download(repo_id='javeriahassan/socialmedia-std-xl-base-1-0', filename='socialmedia-std-xl-base-1-0_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=[], text_encoder=pipeline.text_encoder, tokenizer=pipeline.tokenizer)
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pipeline.load_textual_inversion(state_dict["clip_g"], token=[], text_encoder=pipeline.text_encoder_2, tokenizer=pipeline.tokenizer_2)
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image = pipeline('Design a promotional poster for AI chatbot integration services. The image should feature the text WARNER & SPENCER with a chatbot icon. Include bold text for AI CHATBOT and Integration Services, service details with checkmarks, and a Contact Us button on the left. A friendly chatbot graphic should be placed on the right side.').images[0]
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```
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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)
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## Trigger words
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To trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens:
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to trigger concept `TOK` → use `<s0><s1>` in your prompt
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## Details
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All [Files & versions](/javeriahassan/socialmedia-std-xl-base-1-0/tree/main).
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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).
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LoRA for the text encoder was enabled. False.
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Pivotal tuning was enabled: True.
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Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
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