End of training

README.md

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+
+---
+license: creativeml-openrail-m
+base_model: runwayml/stable-diffusion-v1-5
+datasets:
+- None
+tags:
+- stable-diffusion
+- stable-diffusion-diffusers
+- text-to-image
+- diffusers
+inference: true
+---
+    
+# Text-to-image finetuning - juliajoanna/sd-runwayml-flintstones-model
+
+This pipeline was finetuned from **runwayml/stable-diffusion-v1-5** on the **None** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['Fred is driving a car']: 
+
+![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("juliajoanna/sd-runwayml-flintstones-model", torch_dtype=torch.float16)
+prompt = "Fred is driving a car"
+image = pipeline(prompt).images[0]
+image.save("my_image.png")
+```
+
+## Training info
+
+These are the key hyperparameters used during training:
+
+* Epochs: 0
+* Learning rate: 1e-05
+* Batch size: 1
+* Gradient accumulation steps: 4
+* Image resolution: 512
+* Mixed-precision: None
+
+
+More information on all the CLI arguments and the environment are available on your [`wandb` run page](https://wandb.ai/dream0team/text2image-fine-tune/runs/h76tuji9).

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checkpoint-12500/unet_ema/config.json

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+  "_diffusers_version": "0.22.0.dev0",
+  "_name_or_path": "runwayml/stable-diffusion-v1-5",
+  "act_fn": "silu",
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+  "optimization_step": 12500,
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+  "power": 0.6666666666666666,
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+  "resnet_out_scale_factor": 1.0,
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+  "resnet_time_scale_shift": "default",
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+  "sample_size": 64,
+  "time_cond_proj_dim": null,
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+  "time_embedding_type": "positional",
+  "timestep_post_act": null,
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+  "up_block_types": [
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+    "CrossAttnUpBlock2D",
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+  "upcast_attention": false,
+  "update_after_step": 0,
+  "use_ema_warmup": false,
+  "use_linear_projection": false
+}

checkpoint-12500/unet_ema/diffusion_pytorch_model.safetensors

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