End of training

README.md

@@ -0,0 +1,73 @@
+---
+base_model: krea/Krea-2-Raw
+library_name: diffusers
+license: apache-2.0
+instance_prompt: TF0 woman
+widget: []
+tags:
+- text-to-image
+- diffusers-training
+- diffusers
+- lora
+- krea2
+- krea2-diffusers
+- template:sd-lora
+---
+
+<!-- This model card has been generated automatically according to the information the training script had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+
+# Krea 2 DreamBooth LoRA - aimalias/tf0
+
+<Gallery />
+
+## Model description
+
+These are aimalias/tf0 DreamBooth LoRA weights, trained on krea/Krea-2-Raw.
+
+The weights were trained using [DreamBooth](https://dreambooth.github.io/) with the [Krea 2 diffusers trainer](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/README_krea2.md).
+
+Krea 2 ships as two checkpoints: **RAW** (the non-distilled base you fine-tune on) and **Turbo** (an 8-step distilled checkpoint for fast, high-quality inference). Train your LoRA on RAW and run it on Turbo — LoRAs trained on RAW express strongly on Turbo.
+
+## Trigger words
+
+You should use `TF0 woman` to trigger the image generation.
+
+## Download model
+
+[Download the *.safetensors LoRA](aimalias/tf0/tree/main) in the Files & versions tab.
+
+## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
+
+```py
+>>> import torch
+>>> from diffusers import Krea2Pipeline
+
+>>> # Load the LoRA onto Krea 2 Turbo (the distilled inference model)
+>>> pipe = Krea2Pipeline.from_pretrained("krea/Krea-2-Turbo", torch_dtype=torch.bfloat16).to("cuda")
+>>> pipe.load_lora_weights("aimalias/tf0")
+
+>>> # Turbo recipe: 8 steps, no classifier-free guidance
+>>> image = pipe("TF0 woman", num_inference_steps=8, guidance_scale=0.0).images[0]
+>>> image.save("output.png")
+```
+
+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)
+
+
+## Intended uses & limitations
+
+#### How to use
+
+```python
+# TODO: add an example code snippet for running this diffusion pipeline
+```
+
+#### Limitations and bias
+
+[TODO: provide examples of latent issues and potential remediations]
+
+## Training details
+
+[TODO: describe the data used to train the model]

checkpoint-1000/optimizer.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d587f5ffcac436f26847e26a0fdb5233a7e5d192a803c55c96b67ccc91e07336
+size 195389021

checkpoint-1000/pytorch_lora_weights.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:488f7fcbda807dc2c9130933e1ec0dafbd8aeb4d7e46f217203be9ceb7faca2b
+size 191879256

checkpoint-1000/random_states_0.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:240bbd924e209149a543ac041ac4dc608da83be7836a29a8ca0b3c3697ba607c
+size 14757

checkpoint-1000/scheduler.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:95d5da8195335d4ea322bbf2d1a3e2e6db077cf77404469c5cb95a451f7e56fa
+size 1401

checkpoint-500/optimizer.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:211a1827ea28a11a6f247f13159ea61546cc2814c3893d30e93c5cd0bec7dd54
+size 195389021

checkpoint-500/pytorch_lora_weights.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:68fc3502a4bf97925f19b2154d2ecc238de58cbe29dd1ecdcdc435b2dcc3d5b5
+size 191879256

checkpoint-500/random_states_0.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:482cbcc6fc325a2993e2957f15d6dfc9d0988a039e29ccfab5d6288c27d8b268
+size 14757

checkpoint-500/scheduler.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f6b09a32f9bd8c3811caf53e7088f9d0094eade3af68c322a05eb696814b1a19
+size 1401

pytorch_lora_weights.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:488f7fcbda807dc2c9130933e1ec0dafbd8aeb4d7e46f217203be9ceb7faca2b
+size 191879256