sdxl turbo flag
This commit is contained in:
Johannes Stelzer
parent
a7eda4a87d
commit
b1d9519a10
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@ -56,17 +56,17 @@ class DiffusersHolder():
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assert hasattr(self.pipe.__class__, "__name__"), "No valid diffusers pipeline found."
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assert hasattr(self.pipe.__class__, "__name__"), "No valid diffusers pipeline found."
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if self.pipe.__class__.__name__ == 'StableDiffusionXLPipeline':
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if self.pipe.__class__.__name__ == 'StableDiffusionXLPipeline':
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self.pipe.scheduler.set_timesteps(self.num_inference_steps, device=self.device)
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self.pipe.scheduler.set_timesteps(self.num_inference_steps, device=self.device)
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self.use_sd_xl = True
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prompt_embeds, _, _, _ = self.pipe.encode_prompt("test")
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prompt_embeds, _, _, _ = self.pipe.encode_prompt("test")
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else:
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else:
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self.use_sd_xl = False
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prompt_embeds = self.pipe._encode_prompt("test", self.device, 1, True)
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prompt_embeds = self.pipe._encode_prompt("test", self.device, 1, True)
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self.dtype = prompt_embeds.dtype
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self.dtype = prompt_embeds.dtype
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self.is_sdxl_turbo = 'turbo' in self.pipe._name_or_path
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def set_num_inference_steps(self, num_inference_steps):
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def set_num_inference_steps(self, num_inference_steps):
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self.num_inference_steps = num_inference_steps
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self.num_inference_steps = num_inference_steps
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if self.use_sd_xl:
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self.pipe.scheduler.set_timesteps(self.num_inference_steps, device=self.device)
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self.pipe.scheduler.set_timesteps(self.num_inference_steps, device=self.device)
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def set_dimensions(self, size_output):
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def set_dimensions(self, size_output):
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s = self.pipe.vae_scale_factor
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s = self.pipe.vae_scale_factor
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@ -181,401 +181,8 @@ class DiffusersHolder():
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mixing_coeffs=0.0,
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mixing_coeffs=0.0,
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return_image: Optional[bool] = False):
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return_image: Optional[bool] = False):
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if self.pipe.__class__.__name__ == 'StableDiffusionXLPipeline':
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return self.run_diffusion_sd_xl(text_embeddings, latents_start, idx_start, list_latents_mixing, mixing_coeffs, return_image)
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return self.run_diffusion_sd_xl(text_embeddings, latents_start, idx_start, list_latents_mixing, mixing_coeffs, return_image)
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elif self.pipe.__class__.__name__ == 'StableDiffusionPipeline':
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return self.run_diffusion_sd12x(text_embeddings, latents_start, idx_start, list_latents_mixing, mixing_coeffs, return_image)
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elif self.pipe.__class__.__name__ == 'StableDiffusionControlNetPipeline':
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pass
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@torch.no_grad()
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def run_diffusion_sd12x(
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self,
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text_embeddings: torch.FloatTensor,
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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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list_mixing_coeffs = self.prepare_mixing()
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do_classifier_free_guidance = self.guidance_scale > 1.0
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# accomodate different sd model types
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self.pipe.scheduler.set_timesteps(self.num_inference_steps - 1, device=self.device)
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timesteps = self.pipe.scheduler.timesteps
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if len(timesteps) != self.num_inference_steps:
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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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latents = latents_start.clone()
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list_latents_out = []
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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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latents = latents_start.clone()
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# Mix latents
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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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if i < idx_start:
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list_latents_out.append(latents)
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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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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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noise_pred = self.pipe.unet(
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latent_model_input,
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t,
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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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# compute the previous noisy sample x_t -> x_t-1
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latents = self.pipe.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
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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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self,
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conditioning: 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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prompt_embeds = conditioning[0]
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image = conditioning[1]
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list_mixing_coeffs = self.prepare_mixing()
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controlnet = self.pipe.controlnet
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control_guidance_start = [0.0]
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control_guidance_end = [1.0]
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guess_mode = False
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num_images_per_prompt = 1
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batch_size = 1
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eta = 0.0
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controlnet_conditioning_scale = 1.0
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# align format for control guidance
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if not isinstance(control_guidance_start, list) and isinstance(control_guidance_end, list):
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control_guidance_start = len(control_guidance_end) * [control_guidance_start]
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elif not isinstance(control_guidance_end, list) and isinstance(control_guidance_start, list):
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control_guidance_end = len(control_guidance_start) * [control_guidance_end]
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# 2. Define call parameters
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device = self.pipe._execution_device
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# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
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# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
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# corresponds to doing no classifier free guidance.
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do_classifier_free_guidance = self.guidance_scale > 1.0
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# 4. Prepare image
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image = self.pipe.prepare_image(
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image=image,
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width=None,
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height=None,
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batch_size=batch_size * num_images_per_prompt,
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num_images_per_prompt=num_images_per_prompt,
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device=self.device,
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dtype=controlnet.dtype,
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do_classifier_free_guidance=do_classifier_free_guidance,
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guess_mode=guess_mode,
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)
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height, width = image.shape[-2:]
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# 5. 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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# 6. Prepare latent variables
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generator = torch.Generator(device=self.device).manual_seed(int(420))
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latents = latents_start.clone()
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list_latents_out = []
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# 7. 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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# 7.1 Create tensor stating which controlnets to keep
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controlnet_keep = []
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for i in range(len(timesteps)):
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keeps = [
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1.0 - float(i / len(timesteps) < s or (i + 1) / len(timesteps) > e)
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for s, e in zip(control_guidance_start, control_guidance_end)
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]
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controlnet_keep.append(keeps[0] if len(keeps) == 1 else keeps)
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# 8. Denoising loop
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for i, t in enumerate(timesteps):
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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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latent_model_input = self.pipe.scheduler.scale_model_input(latent_model_input, t)
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control_model_input = latent_model_input
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controlnet_prompt_embeds = prompt_embeds
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if isinstance(controlnet_keep[i], list):
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cond_scale = [c * s for c, s in zip(controlnet_conditioning_scale, controlnet_keep[i])]
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else:
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cond_scale = controlnet_conditioning_scale * controlnet_keep[i]
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down_block_res_samples, mid_block_res_sample = self.pipe.controlnet(
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control_model_input,
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t,
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encoder_hidden_states=controlnet_prompt_embeds,
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controlnet_cond=image,
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conditioning_scale=cond_scale,
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guess_mode=guess_mode,
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return_dict=False,
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)
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if guess_mode and do_classifier_free_guidance:
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# Infered ControlNet only for the conditional batch.
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# To apply the output of ControlNet to both the unconditional and conditional batches,
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# add 0 to the unconditional batch to keep it unchanged.
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down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples]
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mid_block_res_sample = torch.cat([torch.zeros_like(mid_block_res_sample), mid_block_res_sample])
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# predict the noise residual
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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=None,
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down_block_additional_residuals=down_block_res_samples,
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mid_block_additional_residual=mid_block_res_sample,
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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_sd_xl_turbo(
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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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seed=420,
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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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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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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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# 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(torch.Generator(device=self.device).manual_seed(int(0)), 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)
|
|
||||||
|
|
||||||
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
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
@torch.no_grad()
|
@torch.no_grad()
|
||||||
|
|
|
||||||
|
|
@ -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