Create README.md

README.md

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+---
+license: creativeml-openrail-m
+tags:
+  - stable-diffusion
+  - stable-diffusion-diffusers
+  - text-to-image
+  - multires_noise
+inference: true
+---
+
+
+A model trained with Pyramid Noise - see https://wandb.ai/johnowhitaker/multires_noise/reports/Multi-Resolution-Noise-for-Diffusion-Model-Training--VmlldzozNjYyOTU2 for details
+
+```python
+from torch import nn
+import random
+
+def pyramid_noise_like(x, discount=0.8):
+  b, c, w, h = x.shape
+  u = nn.Upsample(size=(w, h), mode='bilinear')
+  noise = torch.randn_like(x)
+  for i in range(6):
+    r = random.random()*2+2 # Rather than always going 2x, 
+    w, h = max(1, int(w/(r**i))), max(1, int(h/(r**i)))
+    noise += u(torch.randn(b, c, w, h).to(x)) * discount**i
+    if w==1 or h==1: break 
+  return noise / noise.std() # Scale back to unit variance
+```
+
+To use the mode for inference, just load it like a normal stable diffusion pipeline:
+
+```python
+from diffusers import StableDiffusionPipeline
+
+model_path = "johnowhitaker/pyramid_noise_test_600steps_08discount"
+pipe = StableDiffusionPipeline.from_pretrained(model_path, torch_dtype=torch.float16)
+pipe.to("cuda")
+
+image = pipe(prompt="A black image").images[0]
+image
+```