End of training

README.md

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+
+---
+license: creativeml-openrail-m
+base_model: CompVis/stable-diffusion-v1-4
+datasets:
+- IUseAMouse/PointConImages
+tags:
+- stable-diffusion
+- stable-diffusion-diffusers
+- text-to-image
+- diffusers
+inference: true
+---
+    
+# Text-to-image finetuning - IUseAMouse/PointConImageModel
+
+This pipeline was finetuned from **CompVis/stable-diffusion-v1-4** on the **IUseAMouse/PointConImages** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['Un patron donne un dossier à un employé']: 
+
+![val_imgs_grid](./val_imgs_grid.png)
+
+
+## Pipeline usage
+
+You can use the pipeline like so:
+
+```python
+from diffusers import DiffusionPipeline
+import torch
+
+pipeline = DiffusionPipeline.from_pretrained("IUseAMouse/PointConImageModel", torch_dtype=torch.float16)
+prompt = "Un patron donne un dossier à un employé"
+image = pipeline(prompt).images[0]
+image.save("my_image.png")
+```
+
+## Training info
+
+These are the key hyperparameters used during training:
+
+* Epochs: 60
+* Learning rate: 1e-05
+* Batch size: 1
+* Gradient accumulation steps: 4
+* 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/acadys/text2image-fine-tune/runs/cpxo8qcw).

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+  "_diffusers_version": "0.22.0.dev0",
+  "_name_or_path": "CompVis/stable-diffusion-v1-4",
+  "act_fn": "silu",
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+  "down_block_types": [
+    "CrossAttnDownBlock2D",
+    "CrossAttnDownBlock2D",
+    "CrossAttnDownBlock2D",
+    "DownBlock2D"
+  ],
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feature_extractor/preprocessor_config.json

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+  "vae": [
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