cat horse winning :exclamation:
@@ -583,14 +583,18 @@ if __name__ == "__main__":
e_t = unconditional_latent + unconditional_guidance_scale * (latent - unconditional_latent)
return e_t
- alphas = [0.9983, 0.6722, 0.2750, 0.0557]
- alphas_prev = [0.9991499781608582, 0.9982960224151611, 0.6721514463424683, 0.27499905228614807]
- sigmas = [0,0,0,0]
- sqrt_one_minus_alphas = [0.0413, 0.5726, 0.8515, 0.9717]
+ #alphas = [0.9983, 0.6722, 0.2750, 0.0557]
+ #alphas_prev = [0.9991499781608582, 0.9982960224151611, 0.6721514463424683, 0.27499905228614807]
+
+ alphas = [0.9983, 0.8930, 0.7521, 0.5888, 0.4229, 0.2750, 0.1598, 0.0819, 0.0365, 0.0140]
+ alphas_prev = [1.0, 0.9983, 0.8930, 0.7521, 0.5888, 0.4229, 0.2750, 0.1598, 0.0819, 0.0365]
def get_x_prev_and_pred_x0(x, e_t, index):
temperature = 1
- a_t, a_prev, sigma_t, sqrt_one_minus_at = alphas[index], alphas_prev[index], sigmas[index], sqrt_one_minus_alphas[index]
+ a_t, a_prev = alphas[index], alphas_prev[index]
+ sigma_t = 0
+ sqrt_one_minus_at = math.sqrt(1-a_t)
+
pred_x0 = (x - sqrt_one_minus_at * e_t) / math.sqrt(a_t)
# direction pointing to x_t
@@ -604,7 +608,8 @@ if __name__ == "__main__":
latent = Tensor.randn(1,4,64,64)
# is this the diffusion?
- for index, timestep in tqdm(list(enumerate([1, 251, 501, 751]))[::-1]):
+ #for index, timestep in tqdm(list(enumerate([1, 251, 501, 751]))[::-1]):
+ for index, timestep in tqdm(list(enumerate([1, 101, 201, 301, 401, 501, 601, 701, 801, 901]))[::-1]):
print(index, timestep)
e_t = get_model_output(latent, timestep)
#print(e_t.numpy())
