From 26cb67f1d5462b4c1f3a84fb1a1dde3401c2f265 Mon Sep 17 00:00:00 2001 From: Johannes Stelzer Date: Sat, 6 Jan 2024 17:08:35 +0100 Subject: [PATCH] poc for sdxl turbo --- diffusers_holder.py | 552 +++++++++++++++++++++++++++++++++----------- 1 file changed, 423 insertions(+), 129 deletions(-) diff --git a/diffusers_holder.py b/diffusers_holder.py index 59a2824..b9df12c 100644 --- a/diffusers_holder.py +++ b/diffusers_holder.py @@ -45,6 +45,8 @@ class DiffusersHolder(): self.width_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): assert hasattr(self.pipe, "__class__"), "No valid diffusers pipeline found." @@ -95,38 +97,60 @@ class DiffusersHolder(): prompt_embeds = pr_encoder( prompt=prompt, + prompt_2=prompt, device=self.device, num_images_per_prompt=1, do_classifier_free_guidance=do_classifier_free_guidance, negative_prompt=self.negative_prompt, + negative_prompt_2=self.negative_prompt, prompt_embeds=None, negative_prompt_embeds=None, + pooled_prompt_embeds=None, lora_scale=None, + clip_skip=False, ) return prompt_embeds 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)) - 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 + + latents = self.pipe.prepare_latents( + 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 @torch.no_grad() def latent2image( self, latents: torch.FloatTensor, - convert_numpy=True): + output_type="pil"): r""" Returns an image provided a latent representation from diffusion. Args: latents: torch.FloatTensor 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: # make sure the VAE is in float32 mode, as it overflows in float16 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.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) else: return image @@ -233,6 +257,7 @@ class DiffusersHolder(): 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) @@ -245,115 +270,7 @@ class DiffusersHolder(): return self.latent2image(latents) else: 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() def run_diffusion_controlnet( @@ -494,29 +411,406 @@ class DiffusersHolder(): return self.latent2image(latents) else: 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__": 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.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) - # xxx - self.set_dimensions((1024, 704)) - 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)) + num_inference_steps = 4 + self.set_num_inference_steps(num_inference_steps) latents_start = self.get_noise() - list_latents_1 = self.run_diffusion(text_embeddings, latents_start) - img_orig = self.latent2image(list_latents_1[-1]) + guidance_scale = 0 + + #% 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) + +"""