controlnet upd
This commit is contained in:
Johannes Stelzer
parent
bc5713241f
commit
704433e267
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@ -163,9 +163,20 @@ 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])
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return np.asarray(image[0])
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def prepare_mixing(self, mixing_coeffs, list_latents_mixing):
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if type(mixing_coeffs) == float:
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list_mixing_coeffs = (1 + self.num_inference_steps) * [mixing_coeffs]
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elif type(mixing_coeffs) == list:
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assert len(mixing_coeffs) == self.num_inference_steps, f"len(mixing_coeffs) {len(mixing_coeffs)} != self.num_inference_steps {self.num_inference_steps}"
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list_mixing_coeffs = mixing_coeffs
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else:
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raise ValueError("mixing_coeffs should be float or list with len=num_inference_steps")
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if np.sum(list_mixing_coeffs) > 0:
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assert len(list_latents_mixing) == self.num_inference_steps, f"len(list_latents_mixing) {len(list_latents_mixing)} != self.num_inference_steps {self.num_inference_steps}"
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return list_mixing_coeffs
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@torch.no_grad()
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def run_diffusion(
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self,
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@ -183,7 +194,6 @@ class DiffusersHolder():
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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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@ -194,20 +204,11 @@ class DiffusersHolder():
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mixing_coeffs=0.0,
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return_image: Optional[bool] = False):
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if type(mixing_coeffs) == float:
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list_mixing_coeffs = (1+self.num_inference_steps) * [mixing_coeffs]
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elif type(mixing_coeffs) == list:
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assert len(mixing_coeffs) == self.num_inference_steps, f"len(mixing_coeffs) {len(mixing_coeffs)} != self.num_inference_steps {self.num_inference_steps}"
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list_mixing_coeffs = mixing_coeffs
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else:
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raise ValueError("mixing_coeffs should be float or list with len=num_inference_steps")
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if np.sum(list_mixing_coeffs) > 0:
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assert len(list_latents_mixing) == self.num_inference_steps, f"len(list_latents_mixing) {len(list_latents_mixing)} != self.num_inference_steps {self.num_inference_steps}"
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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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# diffusers bit wiggly
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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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@ -276,16 +277,7 @@ class DiffusersHolder():
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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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if type(mixing_coeffs) == float:
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list_mixing_coeffs = (1+self.num_inference_steps) * [mixing_coeffs]
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elif type(mixing_coeffs) == list:
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assert len(mixing_coeffs) == self.num_inference_steps, f"len(mixing_coeffs) {len(mixing_coeffs)} != self.num_inference_steps {self.num_inference_steps}"
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list_mixing_coeffs = mixing_coeffs
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else:
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raise ValueError("mixing_coeffs should be float or list with len=num_inference_steps")
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if np.sum(list_mixing_coeffs) > 0:
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assert len(list_latents_mixing) == self.num_inference_steps, f"len(list_latents_mixing) {len(list_latents_mixing)} != self.num_inference_steps {self.num_inference_steps}"
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list_mixing_coeffs = self.prepare_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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@ -375,8 +367,9 @@ class DiffusersHolder():
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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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@ -386,17 +379,14 @@ class DiffusersHolder():
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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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image = Image.open("/home/lugo/glif/lora_models/pretrained_model_name_or_path/value_runwayml_stable-diffusion-v1-5_fabian/fabian_in_the_desert/img_001.jpg")
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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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@ -424,6 +414,7 @@ class DiffusersHolder():
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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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@ -439,6 +430,17 @@ class DiffusersHolder():
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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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@ -487,10 +489,13 @@ class DiffusersHolder():
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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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image = self.pipe.vae.decode(latents / self.pipe.vae.config.scaling_factor, return_dict=False)[0]
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image, has_nsfw_concept = self.pipe.run_safety_checker(image, device, prompt_embeds.dtype)
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image = self.pipe.image_processor.postprocess(image, output_type="pil")
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return image
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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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#%%
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