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Modified ControlNet/T2IAdapter get_control function to receive transformer_options as additional parameter, made the model_options stored in extra_args in inner_sample be a clone of the original model_options instead of same ref

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kosinkadink1@gmail.com 2024-09-25 19:46:33 +09:00
parent d3229cbba7
commit fd2d572447
2 changed files with 8 additions and 8 deletions
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8
comfy/controlnet.py
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@ -201,10 +201,10 @@ class ControlNet(ControlBase):
self.strength_type = strength_type self.strength_type = strength_type
self.concat_mask = concat_mask self.concat_mask = concat_mask
def get_control(self, x_noisy, t, cond, batched_number): def get_control(self, x_noisy, t, cond, batched_number, transformer_options):
control_prev = None control_prev = None
if self.previous_controlnet is not None: if self.previous_controlnet is not None:
control_prev = self.previous_controlnet.get_control(x_noisy, t, cond, batched_number) control_prev = self.previous_controlnet.get_control(x_noisy, t, cond, batched_number, transformer_options)
if self.timestep_range is not None: if self.timestep_range is not None:
if t[0] > self.timestep_range[0] or t[0] < self.timestep_range[1]: if t[0] > self.timestep_range[0] or t[0] < self.timestep_range[1]:
@ -674,10 +674,10 @@ class T2IAdapter(ControlBase):
height = math.ceil(height / unshuffle_amount) * unshuffle_amount height = math.ceil(height / unshuffle_amount) * unshuffle_amount
return width, height return width, height
def get_control(self, x_noisy, t, cond, batched_number): def get_control(self, x_noisy, t, cond, batched_number, transformer_options):
control_prev = None control_prev = None
if self.previous_controlnet is not None: if self.previous_controlnet is not None:
control_prev = self.previous_controlnet.get_control(x_noisy, t, cond, batched_number) control_prev = self.previous_controlnet.get_control(x_noisy, t, cond, batched_number, transformer_options)
if self.timestep_range is not None: if self.timestep_range is not None:
if t[0] > self.timestep_range[0] or t[0] < self.timestep_range[1]: if t[0] > self.timestep_range[0] or t[0] < self.timestep_range[1]:

8
comfy/samplers.py
View File

@ -271,9 +271,6 @@ def outer_calc_cond_batch(model: 'BaseModel', conds: List[List[Dict]], x_in: tor
c = cond_cat(c) c = cond_cat(c)
timestep_ = torch.cat([timestep] * batch_chunks) timestep_ = torch.cat([timestep] * batch_chunks)
if control is not None:
c['control'] = control.get_control(input_x, timestep_, c, len(cond_or_uncond))
transformer_options = {} transformer_options = {}
if 'transformer_options' in model_options: if 'transformer_options' in model_options:
transformer_options = model_options['transformer_options'].copy() transformer_options = model_options['transformer_options'].copy()
@ -295,6 +292,9 @@ def outer_calc_cond_batch(model: 'BaseModel', conds: List[List[Dict]], x_in: tor
c['transformer_options'] = transformer_options c['transformer_options'] = transformer_options
if control is not None:
c['control'] = control.get_control(input_x, timestep_, c, len(cond_or_uncond), transformer_options)
if 'model_function_wrapper' in model_options: if 'model_function_wrapper' in model_options:
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) 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: else:
@ -769,7 +769,7 @@ class CFGGuider:
self.conds = process_conds(self.inner_model, noise, self.conds, device, latent_image, denoise_mask, seed) self.conds = process_conds(self.inner_model, noise, self.conds, device, latent_image, denoise_mask, seed)
extra_args = {"model_options": self.model_options, "seed":seed} extra_args = {"model_options": comfy.model_patcher.create_model_options_clone(self.model_options), "seed": seed}
samples = sampler.sample(self, sigmas, extra_args, callback, noise, latent_image, denoise_mask, disable_pbar) samples = sampler.sample(self, sigmas, extra_args, callback, noise, latent_image, denoise_mask, disable_pbar)
return self.inner_model.process_latent_out(samples.to(torch.float32)) return self.inner_model.process_latent_out(samples.to(torch.float32))