End of training 430,000 steps

README.md

@@ -24,24 +24,13 @@ This pipeline was finetuned from **CompVis/stable-diffusion-v1-4** with replacem
 You can use the pipeline like so:
 
 ```python
-from diffusers import StableDiffusionPipeline
+from diffusers import DiffusionPipeline
 import torch
 
-# Set device
-device = (
-    "mps"
-    if torch.backends.mps.is_available()
-    else "cuda"
-    if torch.cuda.is_available()
-    else "cpu"
-)
-torch_dtype = torch.float16 if device == "cuda" else torch.float32
-pipeline = StableDiffusionPipeline.from_pretrained("jangmin/foodai-pipeline-ko", torch_dtype=torch_dtype)
-pipeline.to(device)
-
-prompt = "짜장면, 정면에서 본 사진, 그릇에 담긴"
-image = pipeline(prompt, guidance_scale=8, num_inference_steps=35).images[0]
-image
+pipeline = DiffusionPipeline.from_pretrained("jangmin/foodai-pipeline-ko", torch_dtype=torch.float16)
+prompt = "해물 짬뽕"
+image = pipeline(prompt).images[0]
+image.save("my_image.png")
 ```
 
 ## Training info
@@ -56,3 +45,19 @@ These are the key hyperparameters used during training:
 * Mixed-precision: bf16
 
 
+
+## 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]

unet/config.json

@@ -1,7 +1,7 @@
 {
   "_class_name": "UNet2DConditionModel",
   "_diffusers_version": "0.29.2",
-  "_name_or_path": "/Jupyter/models/sd-foodai-model-ko/checkpoint-180000/unet",
+  "_name_or_path": "/Jupyter/models/sd-foodai-model-ko/checkpoint-430000/unet",
   "act_fn": "silu",
   "addition_embed_type": null,
   "addition_embed_type_num_heads": 64,

unet/diffusion_pytorch_model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:56e6a0366a8089698201fd149394bcbaa015f548e52d0b5d53ecbb3fa900c429
+oid sha256:aadea00bd97600850c8c26374da0ae20c189a468cfeb6a87e7b9d49ac0373a07
 size 1719125304