readme: # Diffusers Tools
  
  This is a collection of scripts that can be useful for various tasks related to the [diffusers library](https://github.com/huggingface/diffusers)
  
  ## 1. Test against original checkpoints
  
  **It's very important to have visually the exact same results as the original code bases.!**
  
  E.g. to make use `diffusers` is identical to the original [CompVis codebase](https://github.com/CompVis/stable-diffusion), you can run the following script in the original CompVis codebase:
  
  1. Download the original [SD-1-4 checkpoint](https://huggingface.co/CompVis/stable-diffusion-v1-4) and put it in the correct folder following the instructions on: https://github.com/CompVis/stable-diffusion
  
  2. Run the following command
  ```
  python scripts/txt2img.py --prompt "a photograph of an astronaut riding a horse" --seed 0 --n_samples 1 --n_rows 1 --n_iter 1
  ```
  
  and compare this to the same command in diffusers:
  
  ```python
  from diffusers import DiffusionPipeline, StableDiffusionPipeline, DDIMScheduler
  import torch
  
  #  python scripts/txt2img.py --prompt "a photograph of an astronaut riding a horse" --seed 0 --n_samples 1 --n_rows 1 --n_iter 1
  seed = 0
  
  prompt = "a photograph of an astronaut riding a horse"
  pipe = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", torch_dtype=torch.float16)
  
  pipe = pipe.to("cuda")
  pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
  torch.manual_seed(0)
  image = pipe(prompt, num_inference_steps=50).images[0]
  
  image.save("/home/patrick_huggingface_co/images/aa_comp.png")
  ```
  
  Both commands should give the following image on a V100:
  
  
  ## 2. Test against [k-diffusion](https://github.com/crowsonkb/k-diffusion):
  
  You can run the following script to compare against k-diffusion.
  
  See results [here](https://huggingface.co/datasets/patrickvonplaten/images)
  
  ```python
  from diffusers import StableDiffusionKDiffusionPipeline, HeunDiscreteScheduler, StableDiffusionPipeline, DPMSolverMultistepScheduler, EulerDiscreteScheduler, LMSDiscreteScheduler
  import torch
  import os
  
  seed = 13
  inference_steps = 25
  #checkpoint = "CompVis/stable-diffusion-v1-4"
  checkpoint = "stabilityai/stable-diffusion-2-1"
  prompts = ["astronaut riding horse", "whale falling from sky", "magical forest", "highly photorealistic picture of johnny depp"]
  prompts = 8 * ["highly photorealistic picture of johnny depp"]
  #prompts = prompts[:1]
  samplers = ["sample_dpmpp_2m", "sample_euler", "sample_heun", "sample_dpm_2", "sample_lms"]
  #samplers = samplers[:1]
  
  pipe = StableDiffusionKDiffusionPipeline.from_pretrained(checkpoint, torch_dtype=torch.float16, safety_checker=None)
  pipe = pipe.to("cuda")
  
  for i, prompt in enumerate(prompts):
      prompt_f = f"{'_'.join(prompt.split())}_{i}"
      for sampler in samplers:
          pipe.set_scheduler(sampler)
          torch.manual_seed(seed + i)
          image = pipe(prompt, num_inference_steps=inference_steps).images[0]
          checkpoint_f = f"{'--'.join(checkpoint.split('/'))}"
          os.makedirs(f"/home/patrick_huggingface_co/images/{checkpoint_f}", exist_ok=True)
          os.makedirs(f"/home/patrick_huggingface_co/images/{checkpoint_f}/{sampler}", exist_ok=True)
          image.save(f"/home/patrick_huggingface_co/images/{checkpoint_f}/{sampler}/{prompt_f}.png")
  
  
  pipe = StableDiffusionPipeline(**pipe.components)
  pipe = pipe.to("cuda")
  
  for i, prompt in enumerate(prompts):
      prompt_f = f"{'_'.join(prompt.split())}_{i}"
      for sampler in samplers:
          if sampler == "sample_euler":
              pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config)
          elif sampler == "sample_heun":
              pipe.scheduler = HeunDiscreteScheduler.from_config(pipe.scheduler.config)
          elif sampler == "sample_dpmpp_2m":
              pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
          elif sampler == "sample_lms":
              pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config)
   
          torch.manual_seed(seed + i)
          image = pipe(prompt, num_inference_steps=inference_steps).images[0]
          checkpoint_f = f"{'--'.join(checkpoint.split('/'))}"
          os.makedirs("/home/patrick_huggingface_co/images/{checkpoint_f}", exist_ok=True)
          os.makedirs(f"/home/patrick_huggingface_co/images/{checkpoint_f}/{sampler}", exist_ok=True)
          image.save(f"/home/patrick_huggingface_co/images/{checkpoint_f}/{sampler}/{prompt_f}_hf.png")
  ```
  