readme: ---
  license: creativeml-openrail-m
  language: 
    - en
  thumbnail: "https://huggingface.co/Norod78/sd2-dreambooth-ClaymationXmas/resolve/main/collage_1.jpeg"
  tags:
  - stable-diffusion
  - stable-diffusion-diffusers
  - text-to-image
  datasets:
  - Norod78/ChristmasClaymation-blip-captions
  inference: true
  widget:
  - text: Whilly Wonka, ClaymationXmas
  - text: Pikachu, ClaymationXmas, very detailed, clean, high quality, sharp image, Naoto Hattori
  ---
  # sd2-dreambooth-ClaymationXmas
  ## Use ClaymationXmas in your prompt
  
  ### WebUI Examples
  See [examples](https://huggingface.co/Norod78/sd2-dreambooth-ClaymationXmas/tree/main/examples) folder for images generated with this model using a1111's WebUI
  
  ### WebUI Example Collage
  ![Collage 1](https://huggingface.co/Norod78/sd2-dreambooth-ClaymationXmas/resolve/main/collage_1.jpeg)
  ![Collage 2](https://huggingface.co/Norod78/sd2-dreambooth-ClaymationXmas/resolve/main/collage_2.jpeg)
  ![Collage 3](https://huggingface.co/Norod78/sd2-dreambooth-ClaymationXmas/resolve/main/collage_3.jpeg)
  ![Collage 4](https://huggingface.co/Norod78/sd2-dreambooth-ClaymationXmas/resolve/main/collage_4.jpeg)
  
  ```py
  
  from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler
  import torch
  
  def main():
      #////////////////////////////////////////////
      seed = 42
      model = "Norod78/sd2-dreambooth-ClaymationXmas"
      #////////////////////////////////////////////
  
      torch.manual_seed(seed)
      generator = torch.Generator()
      generator.manual_seed(seed)
  
      scheduler = DPMSolverMultistepScheduler(
          beta_start=0.00085,
          beta_end=0.012,
          beta_schedule="scaled_linear",
          num_train_timesteps=1000,
          trained_betas=None,
          predict_epsilon=True,
          thresholding=False,
          algorithm_type="dpmsolver++",
          solver_type="midpoint",
          lower_order_final=True,
  )
  
      device = "cuda" if torch.cuda.is_available() else "cpu"
      dtype = torch.float16 if device == "cuda" else torch.float32
      pipe = StableDiffusionPipeline.from_pretrained(model, scheduler=scheduler,torch_dtype=dtype, generator=generator,use_auth_token=True).to(device)
  
      #////////////////////////////////////////////
      num_inference_steps = 20
      width=512
      height=512
      samples=4
      #////////////////////////////////////////////
  
      prompt = "Willy Wonka, ClaymationXmas"
      result = pipe([prompt] * samples, num_inference_steps=num_inference_steps, height=height, width=width)
      images = result["images"]
      for i, image in enumerate(images):       
          prompt_to_print = str(i) + "-" + prompt
          output_file = prompt_to_print.replace(" ", "_") + "-" + str(width) + "x" +str(height)+ "_" + str(num_inference_steps) + "steps" + "_seed" + str(seed) + ".jpg"
          image.save(output_file)
          print("Saved: " + str(output_file))
  
  if __name__ == '__main__':
      main()
  ```
  
  Fine Tuned by [@Norod78](https://twitter.com/Norod78)
  