Trained for 2000 steps on: #|fire%{random_prompt}, fire:-0.1|fire++:guidance=2 animations use lora strength 0.0 to 1.1 with the trigger word fire Here's how: Use the runpod at <a target="_blank" rel="ugc" href="https://runpod.io/gsc?template=8y3jhbola2&amp;ref=xf9c949d">https://runpod.io/gsc?template=8y3jhbola2&amp;ref=xf9c949d</a> 1. Create a pod I chose a 3090. It needs &gt; 20 GB ram. Don't encrypt your volume, container disk defaults to 5 gb and volume disk to 50 gb, these are fine click Continue It will take a few minutes to download conceptmod, once Connect becomes enabled, you are ready to continue. 2. Login to the web console "Connect", SSH or "Start web console" and connect to it. Once you log in, it will install dependencies (take a minute) then output a welcome message. 3. Send over your the base model checkpoint that you want to train on:Note: be sure to use a safetensors checkpoint. Using <a target="_blank" rel="ugc" href="https://github.com/runpod/runpodctl">https://github.com/runpod/runpodctl</a> on local: runpodctl send mycheckpoint.safetensors on pod: cd /workspace/stable-diffusion-webui/models/Stable-diffusion/ runpodctl receive &lt;code from send&gt; Note: You can also use scp, wget or a cloud storage attachment to transfer your model 4. Train on your phrase (takes 3 hours for 1000 steps) cd /workspace/sd-scriptspython3 train-scripts/train-esd.py --prompt "#|fire%{random_prompt}, fire:-0.1|fire++:guidance=2" --train_method selfattn --ckpt_path /workspace/stable-diffusion-webui/models/Stable-diffusion/mycheckpoint.safetensors It saves every 300 steps, which is about an hour. Selecting a phrase Look at the models here and find one to modify: <a target="_blank" rel="ugc" href="https://civarchive.com/tag/conceptmod?model=58873&amp;sort=Newest">https://civarchive.com/tag/conceptmod?model=58873&amp;sort=Newest</a> 5. Extract the lora bash /workspace/conceptmod/docker/extract_lora.sh /workspace/stable-diffusion-webui/models/Stable-diffusion/&lt;mycheckpoint&gt;.safetensors The argument is your base checkpoint from (3). 6. (optional) Test the lora in webui Your model (and the training intermediates) will now be available in webui as a lora. Select your base model from (3) and apply the lora to figure out what the strength should be. Freeze the seed to manually see how lora strength changes the model. For ease of use: cd /workspace/stable-diffusion-webui/Lora mv compvis-word_firefire%\{random_prompt\}-0.1-metho.safetensors fire.safetensors 7. (optional) Create animations to show how your lora changes the images choice a) To create an animation on your prompt: python3 lora_anim.py -s 0.0 -e 0.7 -l "fire" -p "fire prompt" -s is starting lora strength-e is ending lora strength-l "fire" is your lora-p "fire prompt" is your prompt choice b) To create one animation using a top 80k prompt appended with your trigger (like these previews): python3 lora_anim.py -s 0.0 -e 0.7 -l "fire" -lp ", fire" prompt defaults to <a target="_blank" rel="ugc" href="https://huggingface.co/datasets/Gustavosta/Stable-Diffusion-Prompts">https://huggingface.co/datasets/Gustavosta/Stable-Diffusion-Prompts</a>-lp ", fire" is your trigger. Run continuously for many videos while true; do python3 lora_anim.py -s 0.0 -e 0.7 -l "fire" -lp ", fire"; done Break with ctrl-c. The animations are in the v4 directory as mpv files. Transfer your videos:on pod:runpodctl send v4 on local: runpodctl receive &lt;code from send&gt; 8. Download the lora on pod:runpodctl send /workspace/stable-diffusion-webui/models/Lora/ on local:runpodctl receive &lt;code from send&gt; 9. Stop and terminate the pod to stop paying money on <a target="_blank" rel="ugc" href="https://runpod.io/console.pods">https://runpod.io/console.pods</a> , stop and terminate the running pod 10. Post the lora to civitai with the tag conceptmod. Include your training phrase for a 5 star review. <a target="_blank" rel="ugc" href="https://civarchive.com/tag/conceptmod?model=58873&amp;sort=Newest">https://civarchive.com/tag/conceptmod?model=58873&amp;sort=Newest</a>

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