poc for sdxl turbo
This commit is contained in:
Johannes Stelzer
parent
25a6cff6b6
commit
26cb67f1d5
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@ -45,6 +45,8 @@ class DiffusersHolder():
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self.width_latent = self.pipe.unet.config.sample_size
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self.height_latent = self.pipe.unet.config.sample_size
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self.width_image = self.width_latent * self.pipe.vae_scale_factor
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self.height_image = self.height_latent * self.pipe.vae_scale_factor
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def init_types(self):
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assert hasattr(self.pipe, "__class__"), "No valid diffusers pipeline found."
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@ -95,38 +97,60 @@ class DiffusersHolder():
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prompt_embeds = pr_encoder(
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prompt=prompt,
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prompt_2=prompt,
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device=self.device,
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num_images_per_prompt=1,
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do_classifier_free_guidance=do_classifier_free_guidance,
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negative_prompt=self.negative_prompt,
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negative_prompt_2=self.negative_prompt,
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prompt_embeds=None,
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negative_prompt_embeds=None,
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pooled_prompt_embeds=None,
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lora_scale=None,
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clip_skip=False,
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)
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return prompt_embeds
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def get_noise(self, seed=420):
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H = self.height_latent
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W = self.width_latent
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C = self.pipe.unet.config.in_channels
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generator = torch.Generator(device=self.device).manual_seed(int(seed))
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latents = torch.randn((1, C, H, W), generator=generator, dtype=self.dtype, device=self.device)
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if self.use_sd_xl:
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latents = latents * self.pipe.scheduler.init_noise_sigma
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latents = self.pipe.prepare_latents(
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1,
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self.pipe.unet.config.in_channels,
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self.height_image,
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self.width_image,
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torch.float16,
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self.pipe._execution_device,
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generator,
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None,
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)
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return latents
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# H = self.height_latent
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# W = self.width_latent
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# C = self.pipe.unet.config.in_channels
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# generator = torch.Generator(device=self.device).manual_seed(int(seed))
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# latents = torch.randn((1, C, H, W), generator=generator, dtype=self.dtype, device=self.device)
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# if self.use_sd_xl:
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# latents = latents * self.pipe.scheduler.init_noise_sigma
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return latents
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@torch.no_grad()
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def latent2image(
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self,
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latents: torch.FloatTensor,
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convert_numpy=True):
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output_type="pil"):
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r"""
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Returns an image provided a latent representation from diffusion.
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Args:
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latents: torch.FloatTensor
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Result of the diffusion process.
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convert_numpy: if converting to numpy
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output_type: "pil" or "np"
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"""
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assert output_type in ["pil", "np"]
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if self.use_sd_xl:
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# make sure the VAE is in float32 mode, as it overflows in float16
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self.pipe.vae.to(dtype=torch.float32)
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@ -151,7 +175,7 @@ class DiffusersHolder():
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image = self.pipe.vae.decode(latents / self.pipe.vae.config.scaling_factor, return_dict=False)[0]
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image = self.pipe.image_processor.postprocess(image, output_type="pil", do_denormalize=[True] * image.shape[0])[0]
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if convert_numpy:
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if output_type == "np":
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return np.asarray(image)
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else:
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return image
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@ -233,6 +257,7 @@ class DiffusersHolder():
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encoder_hidden_states=text_embeddings,
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return_dict=False,
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)[0]
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if do_classifier_free_guidance:
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noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
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noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
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@ -246,114 +271,6 @@ class DiffusersHolder():
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else:
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return list_latents_out
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@torch.no_grad()
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def run_diffusion_sd_xl(
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self,
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text_embeddings: list,
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latents_start: torch.FloatTensor,
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idx_start: int = 0,
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list_latents_mixing=None,
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mixing_coeffs=0.0,
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return_image: Optional[bool] = False):
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# 0. Default height and width to unet
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original_size = (self.width_img, self.height_img)
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crops_coords_top_left = (0, 0)
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target_size = original_size
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batch_size = 1
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eta = 0.0
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num_images_per_prompt = 1
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cross_attention_kwargs = None
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generator = torch.Generator(device=self.device) # dummy generator
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do_classifier_free_guidance = self.guidance_scale > 1.0
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# 1. Check inputs. Raise error if not correct & 2. Define call parameters
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list_mixing_coeffs = self.prepare_mixing(mixing_coeffs, list_latents_mixing)
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# 3. Encode input prompt (already encoded outside bc of mixing, just split here)
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prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds = text_embeddings
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# 4. Prepare timesteps
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self.pipe.scheduler.set_timesteps(self.num_inference_steps, device=self.device)
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timesteps = self.pipe.scheduler.timesteps
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# 5. Prepare latent variables
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latents = latents_start.clone()
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list_latents_out = []
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# 6. Prepare extra step kwargs. usedummy generator
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extra_step_kwargs = self.pipe.prepare_extra_step_kwargs(generator, eta) # dummy
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# 7. Prepare added time ids & embeddings
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add_text_embeds = pooled_prompt_embeds
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if self.pipe.text_encoder_2 is None:
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text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1])
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else:
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text_encoder_projection_dim = self.pipe.text_encoder_2.config.projection_dim
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add_time_ids = self.pipe._get_add_time_ids(
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original_size,
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crops_coords_top_left,
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target_size,
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dtype=prompt_embeds.dtype,
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text_encoder_projection_dim=text_encoder_projection_dim,
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)
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negative_add_time_ids = add_time_ids
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prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
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add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)
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add_time_ids = torch.cat([negative_add_time_ids, add_time_ids], dim=0)
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prompt_embeds = prompt_embeds.to(self.device)
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add_text_embeds = add_text_embeds.to(self.device)
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add_time_ids = add_time_ids.to(self.device).repeat(batch_size * num_images_per_prompt, 1)
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# 8. Denoising loop
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for i, t in enumerate(timesteps):
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# Set the right starting latents
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if i < idx_start:
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list_latents_out.append(None)
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continue
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elif i == idx_start:
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latents = latents_start.clone()
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# Mix latents for crossfeeding
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if i > 0 and list_mixing_coeffs[i] > 0:
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latents_mixtarget = list_latents_mixing[i - 1].clone()
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latents = interpolate_spherical(latents, latents_mixtarget, list_mixing_coeffs[i])
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# expand the latents if we are doing classifier free guidance
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latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
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# Always scale latents
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latent_model_input = self.pipe.scheduler.scale_model_input(latent_model_input, t)
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# predict the noise residual
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added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}
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noise_pred = self.pipe.unet(
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latent_model_input,
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t,
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encoder_hidden_states=prompt_embeds,
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cross_attention_kwargs=cross_attention_kwargs,
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added_cond_kwargs=added_cond_kwargs,
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return_dict=False,
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)[0]
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# perform guidance
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if do_classifier_free_guidance:
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noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
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noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
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# compute the previous noisy sample x_t -> x_t-1
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latents = self.pipe.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
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# Append latents
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list_latents_out.append(latents.clone())
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if return_image:
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return self.latent2image(latents)
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else:
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return list_latents_out
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@torch.no_grad()
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def run_diffusion_controlnet(
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@ -496,27 +413,404 @@ class DiffusersHolder():
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return list_latents_out
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@torch.no_grad()
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def run_diffusion_sd_xl(
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self,
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text_embeddings: list,
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latents_start: torch.FloatTensor,
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idx_start: int = 0,
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list_latents_mixing=None,
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mixing_coeffs=0.0,
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return_image: Optional[bool] = False,
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**kwargs,
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):
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timesteps = None
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denoising_end = None
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guidance_scale = 0.0
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negative_prompt = None
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negative_prompt_2 = None
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num_images_per_prompt = 1
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eta = 0.0
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generator = None
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latents = None
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prompt_embeds = None
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negative_prompt_embeds = None
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pooled_prompt_embeds = None
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negative_pooled_prompt_embeds = None
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ip_adapter_image = None
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output_type = "pil"
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return_dict = True
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cross_attention_kwargs = None
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guidance_rescale = 0.0
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original_size = None
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crops_coords_top_left = (0, 0)
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target_size = None
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negative_original_size = None
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negative_crops_coords_top_left = (0, 0)
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negative_target_size = None
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clip_skip = None
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callback_on_step_end = None
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callback_on_step_end_tensor_inputs = ["latents"]
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# 0. Default height and width to unet
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height = self.pipe.default_sample_size * self.pipe.vae_scale_factor
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width = self.pipe.default_sample_size * self.pipe.vae_scale_factor
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list_mixing_coeffs = self.prepare_mixing(mixing_coeffs, list_latents_mixing)
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original_size = (height, width)
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target_size = (height, width)
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# 1. (skipped) Check inputs. Raise error if not correct
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self.pipe._guidance_scale = guidance_scale
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self.pipe._guidance_rescale = guidance_rescale
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self.pipe._clip_skip = clip_skip
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self.pipe._cross_attention_kwargs = cross_attention_kwargs
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self.pipe._denoising_end = denoising_end
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self.pipe._interrupt = False
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# 2. Define call parameters
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batch_size = 1
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device = self.pipe._execution_device
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# 3. Encode input prompt
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prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds = text_embeddings
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# 4. Prepare timesteps
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timesteps, self.num_inference_steps = retrieve_timesteps(self.pipe.scheduler, self.num_inference_steps, device, timesteps)
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# 5. Prepare latent variables
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latents = latents_start.clone()
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list_latents_out = []
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# 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
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extra_step_kwargs = self.pipe.prepare_extra_step_kwargs(generator, eta)
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# 7. Prepare added time ids & embeddings
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add_text_embeds = pooled_prompt_embeds
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if self.pipe.text_encoder_2 is None:
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text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1])
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else:
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text_encoder_projection_dim = self.pipe.text_encoder_2.config.projection_dim
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add_time_ids = self.pipe._get_add_time_ids(
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original_size,
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crops_coords_top_left,
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target_size,
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dtype=prompt_embeds.dtype,
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text_encoder_projection_dim=text_encoder_projection_dim,
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)
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if negative_original_size is not None and negative_target_size is not None:
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negative_add_time_ids = self.pipe._get_add_time_ids(
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negative_original_size,
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negative_crops_coords_top_left,
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negative_target_size,
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dtype=prompt_embeds.dtype,
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text_encoder_projection_dim=text_encoder_projection_dim,
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)
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else:
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negative_add_time_ids = add_time_ids
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if self.pipe.do_classifier_free_guidance:
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prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
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add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)
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add_time_ids = torch.cat([negative_add_time_ids, add_time_ids], dim=0)
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prompt_embeds = prompt_embeds.to(device)
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add_text_embeds = add_text_embeds.to(device)
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add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1)
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if ip_adapter_image is not None:
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output_hidden_state = False if isinstance(self.pipe.unet.encoder_hid_proj, ImageProjection) else True
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image_embeds, negative_image_embeds = self.pipe.encode_image(
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ip_adapter_image, device, num_images_per_prompt, output_hidden_state
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)
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if self.pipe.do_classifier_free_guidance:
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image_embeds = torch.cat([negative_image_embeds, image_embeds])
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image_embeds = image_embeds.to(device)
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# 8. Denoising loop
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num_warmup_steps = max(len(timesteps) - self.num_inference_steps * self.pipe.scheduler.order, 0)
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# 9. Optionally get Guidance Scale Embedding
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timestep_cond = None
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if self.pipe.unet.config.time_cond_proj_dim is not None:
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guidance_scale_tensor = torch.tensor(self.pipe.guidance_scale - 1).repeat(batch_size * num_images_per_prompt)
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timestep_cond = self.pipe.get_guidance_scale_embedding(
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guidance_scale_tensor, embedding_dim=self.pipe.unet.config.time_cond_proj_dim
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).to(device=device, dtype=latents.dtype)
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self.pipe._num_timesteps = len(timesteps)
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for i, t in enumerate(timesteps):
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# Set the right starting latents
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if i < idx_start:
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list_latents_out.append(None)
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continue
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elif i == idx_start:
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latents = latents_start.clone()
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# Mix latents for crossfeeding
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if i > 0 and list_mixing_coeffs[i] > 0:
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latents_mixtarget = list_latents_mixing[i - 1].clone()
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latents = interpolate_spherical(latents, latents_mixtarget, list_mixing_coeffs[i])
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# expand the latents if we are doing classifier free guidance
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latent_model_input = torch.cat([latents] * 2) if self.pipe.do_classifier_free_guidance else latents
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latent_model_input = self.pipe.scheduler.scale_model_input(latent_model_input, t)
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# predict the noise residual
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added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}
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if ip_adapter_image is not None:
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added_cond_kwargs["image_embeds"] = image_embeds
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noise_pred = self.pipe.unet(
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latent_model_input,
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t,
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encoder_hidden_states=prompt_embeds,
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timestep_cond=timestep_cond,
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cross_attention_kwargs=self.pipe.cross_attention_kwargs,
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added_cond_kwargs=added_cond_kwargs,
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return_dict=False,
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)[0]
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# perform guidance
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if self.pipe.do_classifier_free_guidance:
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noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
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noise_pred = noise_pred_uncond + self.pipe.guidance_scale * (noise_pred_text - noise_pred_uncond)
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if self.pipe.do_classifier_free_guidance and self.pipe.guidance_rescale > 0.0:
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# Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
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noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=self.pipe.guidance_rescale)
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# compute the previous noisy sample x_t -> x_t-1
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latents = self.pipe.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
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# Append latents
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list_latents_out.append(latents.clone())
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if return_image:
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return self.latent2image(latents)
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else:
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return list_latents_out
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@torch.no_grad()
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def run_diffusion_sd_xl_old(
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self,
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text_embeddings: list,
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latents_start: torch.FloatTensor,
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idx_start: int = 0,
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list_latents_mixing=None,
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mixing_coeffs=0.0,
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return_image: Optional[bool] = False,
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**kwargs,
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):
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# 0. Default height and width to unet
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original_size = (self.width_img, self.height_img)
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crops_coords_top_left = (0, 0)
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target_size = original_size
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batch_size = 1
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eta = 0.0
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num_images_per_prompt = 1
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cross_attention_kwargs = None
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generator = torch.Generator(device=self.device) # dummy generator
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do_classifier_free_guidance = self.guidance_scale > 1.0
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# 1. Check inputs. Raise error if not correct & 2. Define call parameters
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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)
|
||||
|
||||
"""
|
||||
|
|
|
|||
Loading…
Reference in New Issue