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Refactor of sampler code to deal more easily with different model types.

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comfyanonymous
2023-07-17 01:22:12 -04:00
parent ac9c038ac2
commit 3ded1a3a04
8 changed files with 68 additions and 53 deletions
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9
comfy/ldm/models/diffusion/ddim.py
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@@ -14,6 +14,7 @@ class DDIMSampler(object):
self.ddpm_num_timesteps = model.num_timesteps
self.schedule = schedule
self.device = device
self.parameterization = kwargs.get("parameterization", "eps")
def register_buffer(self, name, attr):
if type(attr) == torch.Tensor:
@@ -261,7 +262,7 @@ class DDIMSampler(object):
b, *_, device = *x.shape, x.device
if denoise_function is not None:
model_output = denoise_function(self.model.apply_model, x, t, **extra_args)
model_output = denoise_function(x, t, **extra_args)
elif unconditional_conditioning is None or unconditional_guidance_scale == 1.:
model_output = self.model.apply_model(x, t, c)
else:
@@ -289,13 +290,13 @@ class DDIMSampler(object):
model_uncond, model_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)
model_output = model_uncond + unconditional_guidance_scale * (model_t - model_uncond)
if self.model.parameterization == "v":
if self.parameterization == "v":
e_t = extract_into_tensor(self.sqrt_alphas_cumprod, t, x.shape) * model_output + extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x.shape) * x
else:
e_t = model_output
if score_corrector is not None:
assert self.model.parameterization == "eps", 'not implemented'
assert self.parameterization == "eps", 'not implemented'
e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
@@ -309,7 +310,7 @@ class DDIMSampler(object):
sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)
# current prediction for x_0
if self.model.parameterization != "v":
if self.parameterization != "v":
pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
else:
pred_x0 = extract_into_tensor(self.sqrt_alphas_cumprod, t, x.shape) * x - extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x.shape) * model_output