poc for sdxl turbo

This commit is contained in:
Johannes Stelzer 2024-01-06 17:08:35 +01:00
parent 25a6cff6b6
commit 26cb67f1d5
1 changed files with 423 additions and 129 deletions

View File

@ -45,6 +45,8 @@ class DiffusersHolder():
self.width_latent = self.pipe.unet.config.sample_size self.width_latent = self.pipe.unet.config.sample_size
self.height_latent = self.pipe.unet.config.sample_size self.height_latent = self.pipe.unet.config.sample_size
self.width_image = self.width_latent * self.pipe.vae_scale_factor
self.height_image = self.height_latent * self.pipe.vae_scale_factor
def init_types(self): def init_types(self):
assert hasattr(self.pipe, "__class__"), "No valid diffusers pipeline found." assert hasattr(self.pipe, "__class__"), "No valid diffusers pipeline found."
@ -95,38 +97,60 @@ class DiffusersHolder():
prompt_embeds = pr_encoder( prompt_embeds = pr_encoder(
prompt=prompt, prompt=prompt,
prompt_2=prompt,
device=self.device, device=self.device,
num_images_per_prompt=1, num_images_per_prompt=1,
do_classifier_free_guidance=do_classifier_free_guidance, do_classifier_free_guidance=do_classifier_free_guidance,
negative_prompt=self.negative_prompt, negative_prompt=self.negative_prompt,
negative_prompt_2=self.negative_prompt,
prompt_embeds=None, prompt_embeds=None,
negative_prompt_embeds=None, negative_prompt_embeds=None,
pooled_prompt_embeds=None,
lora_scale=None, lora_scale=None,
clip_skip=False,
) )
return prompt_embeds return prompt_embeds
def get_noise(self, seed=420): def get_noise(self, seed=420):
H = self.height_latent
W = self.width_latent
C = self.pipe.unet.config.in_channels
generator = torch.Generator(device=self.device).manual_seed(int(seed)) generator = torch.Generator(device=self.device).manual_seed(int(seed))
latents = torch.randn((1, C, H, W), generator=generator, dtype=self.dtype, device=self.device)
if self.use_sd_xl: latents = self.pipe.prepare_latents(
latents = latents * self.pipe.scheduler.init_noise_sigma 1,
self.pipe.unet.config.in_channels,
self.height_image,
self.width_image,
torch.float16,
self.pipe._execution_device,
generator,
None,
)
return latents
# H = self.height_latent
# W = self.width_latent
# C = self.pipe.unet.config.in_channels
# generator = torch.Generator(device=self.device).manual_seed(int(seed))
# latents = torch.randn((1, C, H, W), generator=generator, dtype=self.dtype, device=self.device)
# if self.use_sd_xl:
# latents = latents * self.pipe.scheduler.init_noise_sigma
return latents return latents
@torch.no_grad() @torch.no_grad()
def latent2image( def latent2image(
self, self,
latents: torch.FloatTensor, latents: torch.FloatTensor,
convert_numpy=True): output_type="pil"):
r""" r"""
Returns an image provided a latent representation from diffusion. Returns an image provided a latent representation from diffusion.
Args: Args:
latents: torch.FloatTensor latents: torch.FloatTensor
Result of the diffusion process. Result of the diffusion process.
convert_numpy: if converting to numpy output_type: "pil" or "np"
""" """
assert output_type in ["pil", "np"]
if self.use_sd_xl: if self.use_sd_xl:
# make sure the VAE is in float32 mode, as it overflows in float16 # make sure the VAE is in float32 mode, as it overflows in float16
self.pipe.vae.to(dtype=torch.float32) self.pipe.vae.to(dtype=torch.float32)
@ -151,7 +175,7 @@ class DiffusersHolder():
image = self.pipe.vae.decode(latents / self.pipe.vae.config.scaling_factor, return_dict=False)[0] image = self.pipe.vae.decode(latents / self.pipe.vae.config.scaling_factor, return_dict=False)[0]
image = self.pipe.image_processor.postprocess(image, output_type="pil", do_denormalize=[True] * image.shape[0])[0] image = self.pipe.image_processor.postprocess(image, output_type="pil", do_denormalize=[True] * image.shape[0])[0]
if convert_numpy: if output_type == "np":
return np.asarray(image) return np.asarray(image)
else: else:
return image return image
@ -233,6 +257,7 @@ class DiffusersHolder():
encoder_hidden_states=text_embeddings, encoder_hidden_states=text_embeddings,
return_dict=False, return_dict=False,
)[0] )[0]
if do_classifier_free_guidance: if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond) noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
@ -245,115 +270,7 @@ class DiffusersHolder():
return self.latent2image(latents) return self.latent2image(latents)
else: else:
return list_latents_out return list_latents_out
@torch.no_grad()
def run_diffusion_sd_xl(
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):
# 0. Default height and width to unet
original_size = (self.width_img, self.height_img)
crops_coords_top_left = (0, 0)
target_size = original_size
batch_size = 1
eta = 0.0
num_images_per_prompt = 1
cross_attention_kwargs = None
generator = torch.Generator(device=self.device) # dummy generator
do_classifier_free_guidance = self.guidance_scale > 1.0
# 1. Check inputs. Raise error if not correct & 2. Define call parameters
list_mixing_coeffs = self.prepare_mixing(mixing_coeffs, list_latents_mixing)
# 3. Encode input prompt (already encoded outside bc of mixing, just split here)
prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds = text_embeddings
# 4. Prepare timesteps
self.pipe.scheduler.set_timesteps(self.num_inference_steps, device=self.device)
timesteps = self.pipe.scheduler.timesteps
# 5. Prepare latent variables
latents = latents_start.clone()
list_latents_out = []
# 6. Prepare extra step kwargs. usedummy generator
extra_step_kwargs = self.pipe.prepare_extra_step_kwargs(generator, eta) # dummy
# 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,
)
negative_add_time_ids = add_time_ids
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(self.device)
add_text_embeds = add_text_embeds.to(self.device)
add_time_ids = add_time_ids.to(self.device).repeat(batch_size * num_images_per_prompt, 1)
# 8. Denoising loop
for i, t in enumerate(timesteps):
# Set the right starting latents
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
# Always scale 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}
noise_pred = self.pipe.unet(
latent_model_input,
t,
encoder_hidden_states=prompt_embeds,
cross_attention_kwargs=cross_attention_kwargs,
added_cond_kwargs=added_cond_kwargs,
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() @torch.no_grad()
def run_diffusion_controlnet( def run_diffusion_controlnet(
@ -494,29 +411,406 @@ class DiffusersHolder():
return self.latent2image(latents) return self.latent2image(latents)
else: else:
return list_latents_out return list_latents_out
@torch.no_grad()
def run_diffusion_sd_xl(
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,
**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
generator = None
latents = None
prompt_embeds = None
negative_prompt_embeds = None
pooled_prompt_embeds = None
negative_pooled_prompt_embeds = None
ip_adapter_image = None
output_type = "pil"
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
callback_on_step_end = None
callback_on_step_end_tensor_inputs = ["latents"]
# 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(generator, 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
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
@torch.no_grad()
def run_diffusion_sd_xl_old(
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,
**kwargs,
):
# 0. Default height and width to unet
original_size = (self.width_img, self.height_img)
crops_coords_top_left = (0, 0)
target_size = original_size
batch_size = 1
eta = 0.0
num_images_per_prompt = 1
cross_attention_kwargs = None
generator = torch.Generator(device=self.device) # dummy generator
do_classifier_free_guidance = self.guidance_scale > 1.0
# 1. Check inputs. Raise error if not correct & 2. Define call parameters
list_mixing_coeffs = self.prepare_mixing(mixing_coeffs, list_latents_mixing)
# 3. Encode input prompt (already encoded outside bc of mixing, just split here)
prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds = text_embeddings
# 4. Prepare timesteps
self.pipe.scheduler.set_timesteps(self.num_inference_steps, device=self.device)
timesteps = self.pipe.scheduler.timesteps
# 5. Prepare latent variables
latents = latents_start.clone()
list_latents_out = []
# 6. Prepare extra step kwargs. usedummy generator
extra_step_kwargs = self.pipe.prepare_extra_step_kwargs(generator, eta) # dummy
# 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,
)
negative_add_time_ids = add_time_ids
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(self.device)
add_text_embeds = add_text_embeds.to(self.device)
add_time_ids = add_time_ids.to(self.device).repeat(batch_size * num_images_per_prompt, 1)
# 8. Denoising loop
for i, t in enumerate(timesteps):
# Set the right starting latents
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
# Always scale 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}
noise_pred = self.pipe.unet(
latent_model_input,
t,
encoder_hidden_states=prompt_embeds,
cross_attention_kwargs=cross_attention_kwargs,
added_cond_kwargs=added_cond_kwargs,
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
#%% #%%
if __name__ == "__main__": if __name__ == "__main__":
from PIL import Image from PIL import Image
#%% #%%
pretrained_model_name_or_path = "stabilityai/stable-diffusion-xl-base-1.0" # pretrained_model_name_or_path = "stabilityai/stable-diffusion-xl-base-1.0"
pretrained_model_name_or_path = "stabilityai/sdxl-turbo"
pipe = DiffusionPipeline.from_pretrained(pretrained_model_name_or_path, torch_dtype=torch.float16) pipe = DiffusionPipeline.from_pretrained(pretrained_model_name_or_path, torch_dtype=torch.float16)
pipe.to('cuda') # xxx pipe.to('cuda') # xxx
#%% #%%
# # xxx
# self.set_dimensions((512, 512))
# self.set_num_inference_steps(4)
# self.guidance_scale = 2
# # self.set_dimensions(1536, 1024)
# latents_start = torch.randn((1,4,64//1,64)).half().cuda()
# # latents_start = self.get_noise()
# list_latents_1 = self.run_diffusion_sd_xl(text_embeddings, latents_start)
# img_orig = self.latent2image(list_latents_1[-1])
#%%
self = DiffusersHolder(pipe) self = DiffusersHolder(pipe)
# xxx num_inference_steps = 4
self.set_dimensions((1024, 704)) self.set_num_inference_steps(num_inference_steps)
self.set_num_inference_steps(40)
# self.set_dimensions(1536, 1024)
prompt = "Surreal painting of eerie, nebulous glow of an indigo moon, a spine-chilling spectacle unfolds; a baroque, marbled hand reaches out from a viscous, purple lake clutching a melting clock, its face distorted in a never-ending scream of hysteria, while a cluster of laughing orchids, their petals morphed into grotesque human lips, festoon a crimson tree weeping blood instead of sap, a psychedelic cat with an unnaturally playful grin and mismatched eyes lounges atop a floating vintage television showing static, an albino peacock with iridescent, crystalline feathers dances around a towering, inverted pyramid on top of which a humanoid figure with an octopus head lounges seductively, all against the backdrop of a sprawling cityscape where buildings are inverted and writhing as if alive, and the sky is punctuated by floating aquatic creatures glowing neon, adding a touch of haunting beauty to this otherwise deeply unsettling tableau"
text_embeddings = self.get_text_embedding(prompt)
generator = torch.Generator(device=self.device).manual_seed(int(420))
latents_start = self.get_noise() latents_start = self.get_noise()
list_latents_1 = self.run_diffusion(text_embeddings, latents_start) guidance_scale = 0
img_orig = self.latent2image(list_latents_1[-1])
#% get embeddings1
prompt1 = "Photo of a colorful landscape with a blue sky with clouds"
text_embeddings1 = self.get_text_embedding(prompt1)
prompt_embeds1, negative_prompt_embeds1, pooled_prompt_embeds1, negative_pooled_prompt_embeds1 = text_embeddings1
#% get embeddings2
prompt2 = "Photo of a tree"
text_embeddings2 = self.get_text_embedding(prompt2)
prompt_embeds2, negative_prompt_embeds2, pooled_prompt_embeds2, negative_pooled_prompt_embeds2 = text_embeddings2
latents1 = self.run_diffusion_sd_xl(text_embeddings1, latents_start, idx_start=0, return_image=False)
latents2 = self.run_diffusion_sd_xl(text_embeddings2, latents_start, idx_start=0, return_image=False)
# check if brings same image if restarted
img1_return = self.run_diffusion_sd_xl(text_embeddings1, latents1[idx_mix-1], idx_start=idx_start, return_image=True)
# mix latents
#%%
idx_mix = 2
fract=0.8
latents_start_mixed = interpolate_spherical(latents1[idx_mix-1], latents2[idx_mix-1], fract)
prompt_embeds = interpolate_spherical(prompt_embeds1, prompt_embeds2, fract)
pooled_prompt_embeds = interpolate_spherical(pooled_prompt_embeds1, pooled_prompt_embeds2, fract)
negative_prompt_embeds = negative_prompt_embeds1
negative_pooled_prompt_embeds = negative_pooled_prompt_embeds1
text_embeddings_mix = [prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds]
self.run_diffusion_sd_xl(text_embeddings_mix, latents_start_mixed, idx_start=idx_start, return_image=True)
#%%
"""
xxxxx
# step1: first latents
latents1_step1 = pipe(latents=latents_start, guidance_scale=guidance_scale, prompt_embeds=prompt_embeds1, negative_prompt_embeds=negative_prompt_embeds1, pooled_prompt_embeds=pooled_prompt_embeds1, negative_pooled_prompt_embeds=negative_pooled_prompt_embeds1, output_type='latent', timesteps=timesteps_step1)
# step2: second latents
img_diffusion1 = pipe(latents=latents1_step1[0], guidance_scale=guidance_scale, prompt_embeds=prompt_embeds1, negative_prompt_embeds=negative_prompt_embeds1, pooled_prompt_embeds=pooled_prompt_embeds1, negative_pooled_prompt_embeds=negative_pooled_prompt_embeds1, timesteps=timesteps_step2)
#%% img2
latents_start = torch.randn((1,4,64//1,64)).half().cuda()
# step1: first latents
latents2_step1 = pipe(latents=latents_start, guidance_scale=guidance_scale, prompt_embeds=prompt_embeds2, negative_prompt_embeds=negative_prompt_embeds2, pooled_prompt_embeds=pooled_prompt_embeds2, negative_pooled_prompt_embeds=negative_pooled_prompt_embeds2, output_type='latent', timesteps=timesteps_step1)
# step2: second latents
img_diffusion2 = pipe(latents=latents2_step1[0], guidance_scale=guidance_scale, prompt_embeds=prompt_embeds2, negative_prompt_embeds=negative_prompt_embeds2, pooled_prompt_embeds=pooled_prompt_embeds2, negative_pooled_prompt_embeds=negative_pooled_prompt_embeds2, timesteps=timesteps_step2)
xxx
#%% find the middle
prompt_embeds = prompt_embeds1 #interpolate_spherical(prompt_embeds1, prompt_embeds2, 0.5)
pooled_prompt_embeds = pooled_prompt_embeds1# interpolate_spherical(pooled_prompt_embeds1, pooled_prompt_embeds2, 0.5)
negative_prompt_embeds = negative_prompt_embeds1
negative_pooled_prompt_embeds = negative_pooled_prompt_embeds1
latents1_stepM = interpolate_spherical(latents1_step1[0], latents2_step1[0], 0.5)
img_diffusionM = pipe(latents=latents1_stepM, guidance_scale=guidance_scale, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, pooled_prompt_embeds=pooled_prompt_embeds, negative_pooled_prompt_embeds=negative_pooled_prompt_embeds, timesteps=timesteps_step2)
"""