Text-to-Image
Diffusers
Safetensors
StableDiffusionPipeline
stable-diffusion
stable-diffusion-diffusers
diffusers-training
import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("MohamedAcadys/PointConImageModelV2", dtype=torch.bfloat16, device_map="cuda")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]Text-to-image finetuning - MohamedAcadys/PointConImageModelV2
This pipeline was finetuned from CompVis/stable-diffusion-v1-4 on the Acadys/PointConImagesV2 dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ["Un patron donne un dossier à un employé dans le style 'Edition point Con'"]:
Pipeline usage
You can use the pipeline like so:
from diffusers import DiffusionPipeline
import torch
pipeline = DiffusionPipeline.from_pretrained("MohamedAcadys/PointConImageModelV2", torch_dtype=torch.float16)
prompt = "Un patron donne un dossier à un employé dans le style 'Edition point Con'"
image = pipeline(prompt).images[0]
image.save("my_image.png")
Training info
These are the key hyperparameters used during training:
- Epochs: 20
- 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.
Intended uses & limitations
How to use
# 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]
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Model tree for MohamedAcadys/PointConImageModelV2
Base model
CompVis/stable-diffusion-v1-4