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from diffusers import StableDiffusionPipeline
import torch
file_name = "/blob/main/rem_3k.ckpt"
model_url = "https://huggingface.co/waifu-research-department/Rem" + file_name
pipeline = StableDiffusionPipeline.from_single_file(
model_url,
torch_dtype=torch.float16,
)
import gradio as gr
description="""
# running stable diffusion from a ckpt file
## NOTICE ⚠️:
- this space does not work rn because it needs GPU, feel free to **clone this space** and set your own with GPU an meet your waifu **ヽ（≧□≦）ノ**
if you do not have money (just like me **(┬┬﹏┬┬)** ) you can always :
* **run the code in your PC** if you have a good GPU a good internet connection (to download the ai model only a 1 time thing)
* **run the model in the cloud** (colab, and kaggle are good alternatives and they have a pretty good internet connection )
### minimalistic code to run a ckpt model
* enable GPU (click runtime then change runtime type)
* install the following libraries
```
!pip install -q diffusers gradio omegaconf
```
* **restart your kernal** 👈 (click runtime then click restart session)
* run the following code
```python
from diffusers import StableDiffusionPipeline
import torch
pipeline = StableDiffusionPipeline.from_single_file(
"https://huggingface.co/waifu-research-department/Rem/blob/main/rem_3k.ckpt", # put your model url here
torch_dtype=torch.float16,
).to("cuda")
postive_prompt = "anime girl prompt here" # 👈 change this
negative_prompt = "3D" # 👈 things you hate here
image = pipeline(postive_prompt,negative_prompt=negative_prompt).images[0]
image # your image is saved in this PIL variable
```
"""
try :
pipeline.to("cuda")
except:
log = "no GPU available"
def text2img(positive_prompt,negative_prompt):
try :
image = pipeline(positive_prompt,negative_prompt=negative_prompt).images[0]
log = {"postive_prompt":positive_prompt,"negative_prompt":negative_prompt}
except Exception as e:
log = f"ERROR: {e}"
image = None
return log,image
gr.Interface(text2img,["text","text"],["text","image"],examples=[["rem","3D"]],description=description).launch() |