sdxl turbo flag
This commit is contained in:
Johannes Stelzer
parent
a7eda4a87d
commit
b1d9519a10
|
|
@ -56,17 +56,17 @@ class DiffusersHolder():
|
||||||
assert hasattr(self.pipe.__class__, "__name__"), "No valid diffusers pipeline found."
|
assert hasattr(self.pipe.__class__, "__name__"), "No valid diffusers pipeline found."
|
||||||
if self.pipe.__class__.__name__ == 'StableDiffusionXLPipeline':
|
if self.pipe.__class__.__name__ == 'StableDiffusionXLPipeline':
|
||||||
self.pipe.scheduler.set_timesteps(self.num_inference_steps, device=self.device)
|
self.pipe.scheduler.set_timesteps(self.num_inference_steps, device=self.device)
|
||||||
self.use_sd_xl = True
|
|
||||||
prompt_embeds, _, _, _ = self.pipe.encode_prompt("test")
|
prompt_embeds, _, _, _ = self.pipe.encode_prompt("test")
|
||||||
else:
|
else:
|
||||||
self.use_sd_xl = False
|
|
||||||
prompt_embeds = self.pipe._encode_prompt("test", self.device, 1, True)
|
prompt_embeds = self.pipe._encode_prompt("test", self.device, 1, True)
|
||||||
self.dtype = prompt_embeds.dtype
|
self.dtype = prompt_embeds.dtype
|
||||||
|
|
||||||
|
self.is_sdxl_turbo = 'turbo' in self.pipe._name_or_path
|
||||||
|
|
||||||
|
|
||||||
def set_num_inference_steps(self, num_inference_steps):
|
def set_num_inference_steps(self, num_inference_steps):
|
||||||
self.num_inference_steps = num_inference_steps
|
self.num_inference_steps = num_inference_steps
|
||||||
if self.use_sd_xl:
|
self.pipe.scheduler.set_timesteps(self.num_inference_steps, device=self.device)
|
||||||
self.pipe.scheduler.set_timesteps(self.num_inference_steps, device=self.device)
|
|
||||||
|
|
||||||
def set_dimensions(self, size_output):
|
def set_dimensions(self, size_output):
|
||||||
s = self.pipe.vae_scale_factor
|
s = self.pipe.vae_scale_factor
|
||||||
|
|
@ -181,400 +181,7 @@ class DiffusersHolder():
|
||||||
mixing_coeffs=0.0,
|
mixing_coeffs=0.0,
|
||||||
return_image: Optional[bool] = False):
|
return_image: Optional[bool] = False):
|
||||||
|
|
||||||
if self.pipe.__class__.__name__ == 'StableDiffusionXLPipeline':
|
return self.run_diffusion_sd_xl(text_embeddings, latents_start, idx_start, list_latents_mixing, mixing_coeffs, return_image)
|
||||||
return self.run_diffusion_sd_xl(text_embeddings, latents_start, idx_start, list_latents_mixing, mixing_coeffs, return_image)
|
|
||||||
elif self.pipe.__class__.__name__ == 'StableDiffusionPipeline':
|
|
||||||
return self.run_diffusion_sd12x(text_embeddings, latents_start, idx_start, list_latents_mixing, mixing_coeffs, return_image)
|
|
||||||
elif self.pipe.__class__.__name__ == 'StableDiffusionControlNetPipeline':
|
|
||||||
pass
|
|
||||||
|
|
||||||
@torch.no_grad()
|
|
||||||
def run_diffusion_sd12x(
|
|
||||||
self,
|
|
||||||
text_embeddings: torch.FloatTensor,
|
|
||||||
latents_start: torch.FloatTensor,
|
|
||||||
idx_start: int = 0,
|
|
||||||
list_latents_mixing=None,
|
|
||||||
mixing_coeffs=0.0,
|
|
||||||
return_image: Optional[bool] = False):
|
|
||||||
|
|
||||||
list_mixing_coeffs = self.prepare_mixing()
|
|
||||||
|
|
||||||
do_classifier_free_guidance = self.guidance_scale > 1.0
|
|
||||||
|
|
||||||
# accomodate different sd model types
|
|
||||||
self.pipe.scheduler.set_timesteps(self.num_inference_steps - 1, device=self.device)
|
|
||||||
timesteps = self.pipe.scheduler.timesteps
|
|
||||||
|
|
||||||
if len(timesteps) != self.num_inference_steps:
|
|
||||||
self.pipe.scheduler.set_timesteps(self.num_inference_steps, device=self.device)
|
|
||||||
timesteps = self.pipe.scheduler.timesteps
|
|
||||||
|
|
||||||
latents = latents_start.clone()
|
|
||||||
list_latents_out = []
|
|
||||||
|
|
||||||
for i, t in enumerate(timesteps):
|
|
||||||
# Set the right starting latents
|
|
||||||
if i == idx_start:
|
|
||||||
latents = latents_start.clone()
|
|
||||||
# Mix latents
|
|
||||||
if i > 0 and list_mixing_coeffs[i] > 0:
|
|
||||||
latents_mixtarget = list_latents_mixing[i - 1].clone()
|
|
||||||
latents = interpolate_spherical(latents, latents_mixtarget, list_mixing_coeffs[i])
|
|
||||||
|
|
||||||
if i < idx_start:
|
|
||||||
list_latents_out.append(latents)
|
|
||||||
|
|
||||||
# expand the latents if we are doing classifier free guidance
|
|
||||||
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
|
|
||||||
latent_model_input = self.pipe.scheduler.scale_model_input(latent_model_input, t)
|
|
||||||
|
|
||||||
# predict the noise residual
|
|
||||||
noise_pred = self.pipe.unet(
|
|
||||||
latent_model_input,
|
|
||||||
t,
|
|
||||||
encoder_hidden_states=text_embeddings,
|
|
||||||
return_dict=False,
|
|
||||||
)[0]
|
|
||||||
|
|
||||||
if do_classifier_free_guidance:
|
|
||||||
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
|
|
||||||
noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
|
|
||||||
|
|
||||||
# compute the previous noisy sample x_t -> x_t-1
|
|
||||||
latents = self.pipe.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
|
|
||||||
list_latents_out.append(latents.clone())
|
|
||||||
|
|
||||||
if return_image:
|
|
||||||
return self.latent2image(latents)
|
|
||||||
else:
|
|
||||||
return list_latents_out
|
|
||||||
|
|
||||||
|
|
||||||
@torch.no_grad()
|
|
||||||
def run_diffusion_controlnet(
|
|
||||||
self,
|
|
||||||
conditioning: list,
|
|
||||||
latents_start: torch.FloatTensor,
|
|
||||||
idx_start: int = 0,
|
|
||||||
list_latents_mixing=None,
|
|
||||||
mixing_coeffs=0.0,
|
|
||||||
return_image: Optional[bool] = False):
|
|
||||||
|
|
||||||
prompt_embeds = conditioning[0]
|
|
||||||
image = conditioning[1]
|
|
||||||
list_mixing_coeffs = self.prepare_mixing()
|
|
||||||
|
|
||||||
controlnet = self.pipe.controlnet
|
|
||||||
control_guidance_start = [0.0]
|
|
||||||
control_guidance_end = [1.0]
|
|
||||||
guess_mode = False
|
|
||||||
num_images_per_prompt = 1
|
|
||||||
batch_size = 1
|
|
||||||
eta = 0.0
|
|
||||||
controlnet_conditioning_scale = 1.0
|
|
||||||
|
|
||||||
# align format for control guidance
|
|
||||||
if not isinstance(control_guidance_start, list) and isinstance(control_guidance_end, list):
|
|
||||||
control_guidance_start = len(control_guidance_end) * [control_guidance_start]
|
|
||||||
elif not isinstance(control_guidance_end, list) and isinstance(control_guidance_start, list):
|
|
||||||
control_guidance_end = len(control_guidance_start) * [control_guidance_end]
|
|
||||||
|
|
||||||
# 2. Define call parameters
|
|
||||||
device = self.pipe._execution_device
|
|
||||||
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
|
|
||||||
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
|
|
||||||
# corresponds to doing no classifier free guidance.
|
|
||||||
do_classifier_free_guidance = self.guidance_scale > 1.0
|
|
||||||
|
|
||||||
# 4. Prepare image
|
|
||||||
image = self.pipe.prepare_image(
|
|
||||||
image=image,
|
|
||||||
width=None,
|
|
||||||
height=None,
|
|
||||||
batch_size=batch_size * num_images_per_prompt,
|
|
||||||
num_images_per_prompt=num_images_per_prompt,
|
|
||||||
device=self.device,
|
|
||||||
dtype=controlnet.dtype,
|
|
||||||
do_classifier_free_guidance=do_classifier_free_guidance,
|
|
||||||
guess_mode=guess_mode,
|
|
||||||
)
|
|
||||||
height, width = image.shape[-2:]
|
|
||||||
|
|
||||||
# 5. Prepare timesteps
|
|
||||||
self.pipe.scheduler.set_timesteps(self.num_inference_steps, device=self.device)
|
|
||||||
timesteps = self.pipe.scheduler.timesteps
|
|
||||||
|
|
||||||
# 6. Prepare latent variables
|
|
||||||
generator = torch.Generator(device=self.device).manual_seed(int(420))
|
|
||||||
latents = latents_start.clone()
|
|
||||||
list_latents_out = []
|
|
||||||
|
|
||||||
# 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
|
|
||||||
extra_step_kwargs = self.pipe.prepare_extra_step_kwargs(generator, eta)
|
|
||||||
|
|
||||||
# 7.1 Create tensor stating which controlnets to keep
|
|
||||||
controlnet_keep = []
|
|
||||||
for i in range(len(timesteps)):
|
|
||||||
keeps = [
|
|
||||||
1.0 - float(i / len(timesteps) < s or (i + 1) / len(timesteps) > e)
|
|
||||||
for s, e in zip(control_guidance_start, control_guidance_end)
|
|
||||||
]
|
|
||||||
controlnet_keep.append(keeps[0] if len(keeps) == 1 else keeps)
|
|
||||||
|
|
||||||
# 8. Denoising loop
|
|
||||||
for i, t in enumerate(timesteps):
|
|
||||||
if i < idx_start:
|
|
||||||
list_latents_out.append(None)
|
|
||||||
continue
|
|
||||||
elif i == idx_start:
|
|
||||||
latents = latents_start.clone()
|
|
||||||
|
|
||||||
# Mix latents for crossfeeding
|
|
||||||
if i > 0 and list_mixing_coeffs[i] > 0:
|
|
||||||
latents_mixtarget = list_latents_mixing[i - 1].clone()
|
|
||||||
latents = interpolate_spherical(latents, latents_mixtarget, list_mixing_coeffs[i])
|
|
||||||
|
|
||||||
# expand the latents if we are doing classifier free guidance
|
|
||||||
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
|
|
||||||
latent_model_input = self.pipe.scheduler.scale_model_input(latent_model_input, t)
|
|
||||||
|
|
||||||
control_model_input = latent_model_input
|
|
||||||
controlnet_prompt_embeds = prompt_embeds
|
|
||||||
|
|
||||||
if isinstance(controlnet_keep[i], list):
|
|
||||||
cond_scale = [c * s for c, s in zip(controlnet_conditioning_scale, controlnet_keep[i])]
|
|
||||||
else:
|
|
||||||
cond_scale = controlnet_conditioning_scale * controlnet_keep[i]
|
|
||||||
|
|
||||||
down_block_res_samples, mid_block_res_sample = self.pipe.controlnet(
|
|
||||||
control_model_input,
|
|
||||||
t,
|
|
||||||
encoder_hidden_states=controlnet_prompt_embeds,
|
|
||||||
controlnet_cond=image,
|
|
||||||
conditioning_scale=cond_scale,
|
|
||||||
guess_mode=guess_mode,
|
|
||||||
return_dict=False,
|
|
||||||
)
|
|
||||||
|
|
||||||
if guess_mode and do_classifier_free_guidance:
|
|
||||||
# Infered ControlNet only for the conditional batch.
|
|
||||||
# To apply the output of ControlNet to both the unconditional and conditional batches,
|
|
||||||
# add 0 to the unconditional batch to keep it unchanged.
|
|
||||||
down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples]
|
|
||||||
mid_block_res_sample = torch.cat([torch.zeros_like(mid_block_res_sample), mid_block_res_sample])
|
|
||||||
|
|
||||||
# predict the noise residual
|
|
||||||
noise_pred = self.pipe.unet(
|
|
||||||
latent_model_input,
|
|
||||||
t,
|
|
||||||
encoder_hidden_states=prompt_embeds,
|
|
||||||
cross_attention_kwargs=None,
|
|
||||||
down_block_additional_residuals=down_block_res_samples,
|
|
||||||
mid_block_additional_residual=mid_block_res_sample,
|
|
||||||
return_dict=False,
|
|
||||||
)[0]
|
|
||||||
|
|
||||||
# perform guidance
|
|
||||||
if do_classifier_free_guidance:
|
|
||||||
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
|
|
||||||
noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
|
|
||||||
|
|
||||||
# compute the previous noisy sample x_t -> x_t-1
|
|
||||||
latents = self.pipe.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
|
|
||||||
|
|
||||||
# Append latents
|
|
||||||
list_latents_out.append(latents.clone())
|
|
||||||
|
|
||||||
if return_image:
|
|
||||||
return self.latent2image(latents)
|
|
||||||
else:
|
|
||||||
return list_latents_out
|
|
||||||
|
|
||||||
|
|
||||||
@torch.no_grad()
|
|
||||||
def run_diffusion_sd_xl_turbo(
|
|
||||||
self,
|
|
||||||
text_embeddings: list,
|
|
||||||
latents_start: torch.FloatTensor,
|
|
||||||
idx_start: int = 0,
|
|
||||||
list_latents_mixing=None,
|
|
||||||
mixing_coeffs=0.0,
|
|
||||||
return_image: Optional[bool] = False,
|
|
||||||
seed=420,
|
|
||||||
**kwargs,
|
|
||||||
):
|
|
||||||
|
|
||||||
timesteps = None
|
|
||||||
denoising_end = None
|
|
||||||
guidance_scale = 0.0
|
|
||||||
negative_prompt = None
|
|
||||||
negative_prompt_2 = None
|
|
||||||
num_images_per_prompt = 1
|
|
||||||
eta = 0.0
|
|
||||||
latents = None
|
|
||||||
prompt_embeds = None
|
|
||||||
negative_prompt_embeds = None
|
|
||||||
pooled_prompt_embeds = None
|
|
||||||
negative_pooled_prompt_embeds = None
|
|
||||||
ip_adapter_image = None
|
|
||||||
return_dict = True
|
|
||||||
cross_attention_kwargs = None
|
|
||||||
guidance_rescale = 0.0
|
|
||||||
original_size = None
|
|
||||||
crops_coords_top_left = (0, 0)
|
|
||||||
target_size = None
|
|
||||||
negative_original_size = None
|
|
||||||
negative_crops_coords_top_left = (0, 0)
|
|
||||||
negative_target_size = None
|
|
||||||
clip_skip = None
|
|
||||||
|
|
||||||
|
|
||||||
# 0. Default height and width to unet
|
|
||||||
height = self.pipe.default_sample_size * self.pipe.vae_scale_factor
|
|
||||||
width = self.pipe.default_sample_size * self.pipe.vae_scale_factor
|
|
||||||
list_mixing_coeffs = self.prepare_mixing(mixing_coeffs, list_latents_mixing)
|
|
||||||
|
|
||||||
original_size = (height, width)
|
|
||||||
target_size = (height, width)
|
|
||||||
|
|
||||||
# 1. (skipped) Check inputs. Raise error if not correct
|
|
||||||
self.pipe._guidance_scale = guidance_scale
|
|
||||||
self.pipe._guidance_rescale = guidance_rescale
|
|
||||||
self.pipe._clip_skip = clip_skip
|
|
||||||
self.pipe._cross_attention_kwargs = cross_attention_kwargs
|
|
||||||
self.pipe._denoising_end = denoising_end
|
|
||||||
self.pipe._interrupt = False
|
|
||||||
|
|
||||||
# 2. Define call parameters
|
|
||||||
batch_size = 1
|
|
||||||
|
|
||||||
device = self.pipe._execution_device
|
|
||||||
|
|
||||||
# 3. Encode input prompt
|
|
||||||
prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds = text_embeddings
|
|
||||||
|
|
||||||
# 4. Prepare timesteps
|
|
||||||
timesteps, self.num_inference_steps = retrieve_timesteps(self.pipe.scheduler, self.num_inference_steps, device, timesteps)
|
|
||||||
|
|
||||||
# 5. Prepare latent variables
|
|
||||||
latents = latents_start.clone()
|
|
||||||
list_latents_out = []
|
|
||||||
|
|
||||||
# 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
|
|
||||||
|
|
||||||
extra_step_kwargs = self.pipe.prepare_extra_step_kwargs(torch.Generator(device=self.device).manual_seed(int(0)), eta)
|
|
||||||
|
|
||||||
# 7. Prepare added time ids & embeddings
|
|
||||||
add_text_embeds = pooled_prompt_embeds
|
|
||||||
if self.pipe.text_encoder_2 is None:
|
|
||||||
text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1])
|
|
||||||
else:
|
|
||||||
text_encoder_projection_dim = self.pipe.text_encoder_2.config.projection_dim
|
|
||||||
|
|
||||||
add_time_ids = self.pipe._get_add_time_ids(
|
|
||||||
original_size,
|
|
||||||
crops_coords_top_left,
|
|
||||||
target_size,
|
|
||||||
dtype=prompt_embeds.dtype,
|
|
||||||
text_encoder_projection_dim=text_encoder_projection_dim,
|
|
||||||
)
|
|
||||||
if negative_original_size is not None and negative_target_size is not None:
|
|
||||||
negative_add_time_ids = self.pipe._get_add_time_ids(
|
|
||||||
negative_original_size,
|
|
||||||
negative_crops_coords_top_left,
|
|
||||||
negative_target_size,
|
|
||||||
dtype=prompt_embeds.dtype,
|
|
||||||
text_encoder_projection_dim=text_encoder_projection_dim,
|
|
||||||
)
|
|
||||||
else:
|
|
||||||
negative_add_time_ids = add_time_ids
|
|
||||||
|
|
||||||
if self.pipe.do_classifier_free_guidance:
|
|
||||||
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
|
|
||||||
add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)
|
|
||||||
add_time_ids = torch.cat([negative_add_time_ids, add_time_ids], dim=0)
|
|
||||||
|
|
||||||
prompt_embeds = prompt_embeds.to(device)
|
|
||||||
add_text_embeds = add_text_embeds.to(device)
|
|
||||||
add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1)
|
|
||||||
|
|
||||||
if ip_adapter_image is not None:
|
|
||||||
output_hidden_state = False if isinstance(self.pipe.unet.encoder_hid_proj, ImageProjection) else True
|
|
||||||
image_embeds, negative_image_embeds = self.pipe.encode_image(
|
|
||||||
ip_adapter_image, device, num_images_per_prompt, output_hidden_state
|
|
||||||
)
|
|
||||||
if self.pipe.do_classifier_free_guidance:
|
|
||||||
image_embeds = torch.cat([negative_image_embeds, image_embeds])
|
|
||||||
image_embeds = image_embeds.to(device)
|
|
||||||
|
|
||||||
# 8. Denoising loop
|
|
||||||
num_warmup_steps = max(len(timesteps) - self.num_inference_steps * self.pipe.scheduler.order, 0)
|
|
||||||
|
|
||||||
|
|
||||||
# 9. Optionally get Guidance Scale Embedding
|
|
||||||
timestep_cond = None
|
|
||||||
if self.pipe.unet.config.time_cond_proj_dim is not None:
|
|
||||||
guidance_scale_tensor = torch.tensor(self.pipe.guidance_scale - 1).repeat(batch_size * num_images_per_prompt)
|
|
||||||
timestep_cond = self.pipe.get_guidance_scale_embedding(
|
|
||||||
guidance_scale_tensor, embedding_dim=self.pipe.unet.config.time_cond_proj_dim
|
|
||||||
).to(device=device, dtype=latents.dtype)
|
|
||||||
|
|
||||||
self.pipe._num_timesteps = len(timesteps)
|
|
||||||
|
|
||||||
|
|
||||||
for i, t in enumerate(timesteps):
|
|
||||||
# Set the right starting latents
|
|
||||||
# Write latents out and skip
|
|
||||||
if i < idx_start:
|
|
||||||
list_latents_out.append(None)
|
|
||||||
continue
|
|
||||||
elif i == idx_start:
|
|
||||||
latents = latents_start.clone()
|
|
||||||
|
|
||||||
# Mix latents for crossfeeding
|
|
||||||
if i > 0 and list_mixing_coeffs[i] > 0:
|
|
||||||
latents_mixtarget = list_latents_mixing[i - 1].clone()
|
|
||||||
latents = interpolate_spherical(latents, latents_mixtarget, list_mixing_coeffs[i])
|
|
||||||
|
|
||||||
# expand the latents if we are doing classifier free guidance
|
|
||||||
latent_model_input = torch.cat([latents] * 2) if self.pipe.do_classifier_free_guidance else latents
|
|
||||||
latent_model_input = self.pipe.scheduler.scale_model_input(latent_model_input, t)
|
|
||||||
|
|
||||||
# predict the noise residual
|
|
||||||
added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}
|
|
||||||
if ip_adapter_image is not None:
|
|
||||||
added_cond_kwargs["image_embeds"] = image_embeds
|
|
||||||
|
|
||||||
noise_pred = self.pipe.unet(
|
|
||||||
latent_model_input,
|
|
||||||
t,
|
|
||||||
encoder_hidden_states=prompt_embeds,
|
|
||||||
timestep_cond=timestep_cond,
|
|
||||||
cross_attention_kwargs=self.pipe.cross_attention_kwargs,
|
|
||||||
added_cond_kwargs=added_cond_kwargs,
|
|
||||||
return_dict=False,
|
|
||||||
)[0]
|
|
||||||
|
|
||||||
# perform guidance
|
|
||||||
if self.pipe.do_classifier_free_guidance:
|
|
||||||
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
|
|
||||||
noise_pred = noise_pred_uncond + self.pipe.guidance_scale * (noise_pred_text - noise_pred_uncond)
|
|
||||||
|
|
||||||
if self.pipe.do_classifier_free_guidance and self.pipe.guidance_rescale > 0.0:
|
|
||||||
# Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
|
|
||||||
noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=self.pipe.guidance_rescale)
|
|
||||||
|
|
||||||
# compute the previous noisy sample x_t -> x_t-1
|
|
||||||
latents = self.pipe.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
|
|
||||||
|
|
||||||
# Append latents
|
|
||||||
list_latents_out.append(latents.clone())
|
|
||||||
|
|
||||||
if return_image:
|
|
||||||
return self.latent2image(latents)
|
|
||||||
else:
|
|
||||||
return list_latents_out
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -262,7 +262,7 @@ class LatentBlending():
|
||||||
self.seed2 = fixed_seeds[1]
|
self.seed2 = fixed_seeds[1]
|
||||||
|
|
||||||
# Ensure correct num_inference_steps in holder
|
# Ensure correct num_inference_steps in holder
|
||||||
if 'turbo' in self.dh.pipe._name_or_path:
|
if self.dh.is_sdxl_turbo:
|
||||||
num_inference_steps = 4 #ideal results
|
num_inference_steps = 4 #ideal results
|
||||||
self.num_inference_steps = num_inference_steps
|
self.num_inference_steps = num_inference_steps
|
||||||
self.dh.set_num_inference_steps(num_inference_steps)
|
self.dh.set_num_inference_steps(num_inference_steps)
|
||||||
|
|
@ -286,16 +286,14 @@ class LatentBlending():
|
||||||
self.tree_idx_injection = [0, 0]
|
self.tree_idx_injection = [0, 0]
|
||||||
|
|
||||||
# Set up branching scheme (dependent on provided compute time)
|
# Set up branching scheme (dependent on provided compute time)
|
||||||
if 'turbo' in self.dh.pipe._name_or_path:
|
if self.dh.is_sdxl_turbo:
|
||||||
self.guidance_scale = 0.0
|
self.guidance_scale = 0.0
|
||||||
|
|
||||||
self.parental_crossfeed_power = 1.0
|
self.parental_crossfeed_power = 1.0
|
||||||
self.parental_crossfeed_power_decay = 1.0
|
self.parental_crossfeed_power_decay = 1.0
|
||||||
self.parental_crossfeed_range = 1.0
|
self.parental_crossfeed_range = 1.0
|
||||||
list_idx_injection = [2]
|
list_idx_injection = [2]
|
||||||
list_nmb_stems = [10]
|
list_nmb_stems = [10]
|
||||||
else:
|
else:
|
||||||
|
|
||||||
list_idx_injection, list_nmb_stems = self.get_time_based_branching(depth_strength, t_compute_max_allowed, nmb_max_branches)
|
list_idx_injection, list_nmb_stems = self.get_time_based_branching(depth_strength, t_compute_max_allowed, nmb_max_branches)
|
||||||
|
|
||||||
# Run iteratively, starting with the longest trajectory.
|
# Run iteratively, starting with the longest trajectory.
|
||||||
|
|
|
||||||
Loading…
Reference in New Issue