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Running
on
Zero
| from diffusers.utils.peft_utils import set_weights_and_activate_adapters | |
| from S2I.modules.models import PrimaryModel | |
| import gc | |
| import torch | |
| import warnings | |
| warnings.filterwarnings("ignore") | |
| class Sketch2ImagePipeline(PrimaryModel): | |
| def __init__(self): | |
| super().__init__() | |
| self.timestep = torch.tensor([999], device="cuda").long() | |
| def generate(self, c_t, prompt=None, prompt_tokens=None, r=1.0, noise_map=None, half_model=None, model_name=None): | |
| self.from_pretrained(model_name=model_name, r=r) | |
| assert (prompt is None) != (prompt_tokens is None), "Either prompt or prompt_tokens should be provided" | |
| if half_model == 'float16': | |
| output_image = self._generate_fp16(c_t, prompt, prompt_tokens, r, noise_map) | |
| else: | |
| output_image = self._generate_full_precision(c_t, prompt, prompt_tokens, r, noise_map) | |
| return output_image | |
| def _generate_fp16(self, c_t, prompt, prompt_tokens, r, noise_map): | |
| with torch.autocast(device_type='cuda', dtype=torch.float16): | |
| caption_enc = self._get_caption_enc(prompt, prompt_tokens) | |
| self._set_weights_and_activate_adapters(r) | |
| encoded_control = self.global_vae.encode(c_t).latent_dist.sample() * self.global_vae.config.scaling_factor | |
| unet_input = encoded_control * r + noise_map * (1 - r) | |
| unet_output = self.global_unet(unet_input, self.timestep, encoder_hidden_states=caption_enc).sample | |
| x_denoise = self.global_scheduler.step(unet_output, self.timestep, unet_input, return_dict=True).prev_sample | |
| self.global_vae.decoder.incoming_skip_acts = self.global_vae.encoder.current_down_blocks | |
| self.global_vae.decoder.gamma = r | |
| output_image = self.global_vae.decode(x_denoise / self.global_vae.config.scaling_factor).sample.clamp(-1, 1) | |
| return output_image | |
| def _generate_full_precision(self, c_t, prompt, prompt_tokens, r, noise_map): | |
| caption_enc = self._get_caption_enc(prompt, prompt_tokens) | |
| self._set_weights_and_activate_adapters(r) | |
| encoded_control = self.global_vae.encode(c_t).latent_dist.sample() * self.global_vae.config.scaling_factor | |
| unet_input = encoded_control * r + noise_map * (1 - r) | |
| unet_output = self.global_unet(unet_input, self.timestep, encoder_hidden_states=caption_enc).sample | |
| x_denoise = self.global_scheduler.step(unet_output, self.timestep, unet_input, return_dict=True).prev_sample | |
| self.global_vae.decoder.incoming_skip_acts = self.global_vae.encoder.current_down_blocks | |
| self.global_vae.decoder.gamma = r | |
| output_image = self.global_vae.decode(x_denoise / self.global_vae.config.scaling_factor).sample.clamp(-1, 1) | |
| return output_image | |
| def _get_caption_enc(self, prompt, prompt_tokens): | |
| if prompt is not None: | |
| caption_tokens = self.global_tokenizer(prompt, max_length=self.global_tokenizer.model_max_length, | |
| padding="max_length", truncation=True, | |
| return_tensors="pt").input_ids.cuda() | |
| else: | |
| caption_tokens = prompt_tokens.cuda() | |
| return self.global_text_encoder(caption_tokens)[0] | |
| def _set_weights_and_activate_adapters(self, r): | |
| self.global_unet.set_adapters(["default"], weights=[r]) | |
| set_weights_and_activate_adapters(self.global_vae, ["vae_skip"], [r]) | |
| def _move_to_cpu(self, module): | |
| module.to("cpu") | |
| def _move_to_gpu(self, module): | |
| module.to("cuda") |