End of training

README.md

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+
+---
+license: mit
+base_model: warp-ai/wuerstchen-prior
+datasets:
+- dongOi071102/meme-pretreatment-dataset
+tags:
+- wuerstchen
+- text-to-image
+- diffusers
+- diffusers-training
+- lora
+inference: true
+---
+    
+# LoRA Finetuning - dongOi071102/wuerstchen-prior-meme-lora-epochs-10
+
+This pipeline was finetuned from **warp-ai/wuerstchen-prior** on the **dongOi071102/meme-pretreatment-dataset** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['a catton cat with angry face']: 
+
+![val_imgs_grid](./val_imgs_grid.png)
+
+
+## Pipeline usage
+
+You can use the pipeline like so:
+
+```python
+from diffusers import DiffusionPipeline
+import torch
+
+pipeline = AutoPipelineForText2Image.from_pretrained(
+                "warp-ai/wuerstchen", torch_dtype=float32
+            )
+# load lora weights from folder:
+pipeline.prior_pipe.load_lora_weights("dongOi071102/wuerstchen-prior-meme-lora-epochs-10", torch_dtype=float32)
+
+image = pipeline(prompt=prompt).images[0]
+image.save("my_image.png")
+```
+
+## Training info
+
+These are the key hyperparameters used during training:
+
+* LoRA rank: 4
+* Epochs: 10
+* Learning rate: 0.0001
+* Batch size: 8
+* Gradient accumulation steps: 1
+* Image resolution: 512
+* Mixed-precision: fp16
+
+
+More information on all the CLI arguments and the environment are available on your [`wandb` run page](https://wandb.ai/2111818-no/text2image-fine-tune/runs/64ixewdo).

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