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Fooocus GitHub Bot Commit

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lllyasviel
2023-11-12 13:12:03 -08:00
parent ac8002d2a4
commit 7e222cf3e1
8 changed files with 28 additions and 36 deletions
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backend/headless/fcbh/samplers.py
+11 -10
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@@ -11,7 +11,7 @@ import fcbh.conds
#The main sampling function shared by all the samplers
#Returns denoised
def sampling_function(model_function, x, timestep, uncond, cond, cond_scale, model_options={}, seed=None):
def sampling_function(model, x, timestep, uncond, cond, cond_scale, model_options={}, seed=None):
def get_area_and_mult(conds, x_in, timestep_in):
area = (x_in.shape[2], x_in.shape[3], 0, 0)
strength = 1.0
@@ -134,7 +134,7 @@ def sampling_function(model_function, x, timestep, uncond, cond, cond_scale, mod
return out
def calc_cond_uncond_batch(model_function, cond, uncond, x_in, timestep, max_total_area, model_options):
def calc_cond_uncond_batch(model, cond, uncond, x_in, timestep, model_options):
out_cond = torch.zeros_like(x_in)
out_count = torch.ones_like(x_in) * 1e-37
@@ -170,9 +170,11 @@ def sampling_function(model_function, x, timestep, uncond, cond, cond_scale, mod
to_batch_temp.reverse()
to_batch = to_batch_temp[:1]
free_memory = model_management.get_free_memory(x_in.device)
for i in range(1, len(to_batch_temp) + 1):
batch_amount = to_batch_temp[:len(to_batch_temp)//i]
if (len(batch_amount) * first_shape[0] * first_shape[2] * first_shape[3] < max_total_area):
input_shape = [len(batch_amount) * first_shape[0]] + list(first_shape)[1:]
if model.memory_required(input_shape) < free_memory:
to_batch = batch_amount
break
@@ -221,9 +223,9 @@ def sampling_function(model_function, x, timestep, uncond, cond, cond_scale, mod
c['transformer_options'] = transformer_options
if 'model_function_wrapper' in model_options:
output = model_options['model_function_wrapper'](model_function, {"input": input_x, "timestep": timestep_, "c": c, "cond_or_uncond": cond_or_uncond}).chunk(batch_chunks)
output = model_options['model_function_wrapper'](model.apply_model, {"input": input_x, "timestep": timestep_, "c": c, "cond_or_uncond": cond_or_uncond}).chunk(batch_chunks)
else:
output = model_function(input_x, timestep_, **c).chunk(batch_chunks)
output = model.apply_model(input_x, timestep_, **c).chunk(batch_chunks)
del input_x
for o in range(batch_chunks):
@@ -242,11 +244,10 @@ def sampling_function(model_function, x, timestep, uncond, cond, cond_scale, mod
return out_cond, out_uncond
max_total_area = model_management.maximum_batch_area()
if math.isclose(cond_scale, 1.0):
uncond = None
cond, uncond = calc_cond_uncond_batch(model_function, cond, uncond, x, timestep, max_total_area, model_options)
cond, uncond = calc_cond_uncond_batch(model, cond, uncond, x, timestep, model_options)
if "sampler_cfg_function" in model_options:
args = {"cond": x - cond, "uncond": x - uncond, "cond_scale": cond_scale, "timestep": timestep, "input": x, "sigma": timestep}
return x - model_options["sampler_cfg_function"](args)
@@ -258,7 +259,7 @@ class CFGNoisePredictor(torch.nn.Module):
super().__init__()
self.inner_model = model
def apply_model(self, x, timestep, cond, uncond, cond_scale, model_options={}, seed=None):
out = sampling_function(self.inner_model.apply_model, x, timestep, uncond, cond, cond_scale, model_options=model_options, seed=seed)
out = sampling_function(self.inner_model, x, timestep, uncond, cond, cond_scale, model_options=model_options, seed=seed)
return out
def forward(self, *args, **kwargs):
return self.apply_model(*args, **kwargs)
@@ -511,11 +512,11 @@ class Sampler:
class UNIPC(Sampler):
def sample(self, model_wrap, sigmas, extra_args, callback, noise, latent_image=None, denoise_mask=None, disable_pbar=False):
return uni_pc.sample_unipc(model_wrap, noise, latent_image, sigmas, sampling_function=sampling_function, max_denoise=self.max_denoise(model_wrap, sigmas), extra_args=extra_args, noise_mask=denoise_mask, callback=callback, disable=disable_pbar)
return uni_pc.sample_unipc(model_wrap, noise, latent_image, sigmas, max_denoise=self.max_denoise(model_wrap, sigmas), extra_args=extra_args, noise_mask=denoise_mask, callback=callback, disable=disable_pbar)
class UNIPCBH2(Sampler):
def sample(self, model_wrap, sigmas, extra_args, callback, noise, latent_image=None, denoise_mask=None, disable_pbar=False):
return uni_pc.sample_unipc(model_wrap, noise, latent_image, sigmas, sampling_function=sampling_function, max_denoise=self.max_denoise(model_wrap, sigmas), extra_args=extra_args, noise_mask=denoise_mask, callback=callback, variant='bh2', disable=disable_pbar)
return uni_pc.sample_unipc(model_wrap, noise, latent_image, sigmas, max_denoise=self.max_denoise(model_wrap, sigmas), extra_args=extra_args, noise_mask=denoise_mask, callback=callback, variant='bh2', disable=disable_pbar)
KSAMPLER_NAMES = ["euler", "euler_ancestral", "heun", "dpm_2", "dpm_2_ancestral",
"lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_sde", "dpmpp_sde_gpu",