mirror of
https://github.com/modelscope/DiffSynth-Studio.git
synced 2026-03-19 06:48:12 +00:00
149 lines
6.2 KiB
Python
149 lines
6.2 KiB
Python
from ..models import ModelManager, SDTextEncoder, SDUNet, SDVAEDecoder, SDVAEEncoder
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from ..controlnets import MultiControlNetManager, ControlNetUnit, ControlNetConfigUnit, Annotator
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from ..prompts import SDPrompter
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from ..schedulers import EnhancedDDIMScheduler
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from .dancer import lets_dance
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from typing import List
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import torch
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from tqdm import tqdm
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from PIL import Image
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import numpy as np
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class SDImagePipeline(torch.nn.Module):
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def __init__(self, device="cuda", torch_dtype=torch.float16):
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super().__init__()
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self.scheduler = EnhancedDDIMScheduler()
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self.prompter = SDPrompter()
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self.device = device
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self.torch_dtype = torch_dtype
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# models
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self.text_encoder: SDTextEncoder = None
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self.unet: SDUNet = None
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self.vae_decoder: SDVAEDecoder = None
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self.vae_encoder: SDVAEEncoder = None
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self.controlnet: MultiControlNetManager = None
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def fetch_main_models(self, model_manager: ModelManager):
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self.text_encoder = model_manager.text_encoder
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self.unet = model_manager.unet
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self.vae_decoder = model_manager.vae_decoder
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self.vae_encoder = model_manager.vae_encoder
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# load textual inversion
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self.prompter.load_textual_inversion(model_manager.textual_inversion_dict)
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def fetch_controlnet_models(self, model_manager: ModelManager, controlnet_config_units: List[ControlNetConfigUnit]=[]):
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controlnet_units = []
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for config in controlnet_config_units:
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controlnet_unit = ControlNetUnit(
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Annotator(config.processor_id),
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model_manager.get_model_with_model_path(config.model_path),
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config.scale
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)
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controlnet_units.append(controlnet_unit)
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self.controlnet = MultiControlNetManager(controlnet_units)
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def fetch_beautiful_prompt(self, model_manager: ModelManager):
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if "beautiful_prompt" in model_manager.model:
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self.prompter.load_beautiful_prompt(model_manager.model["beautiful_prompt"], model_manager.model_path["beautiful_prompt"])
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@staticmethod
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def from_model_manager(model_manager: ModelManager, controlnet_config_units: List[ControlNetConfigUnit]=[]):
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pipe = SDImagePipeline(
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device=model_manager.device,
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torch_dtype=model_manager.torch_dtype,
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)
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pipe.fetch_main_models(model_manager)
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pipe.fetch_beautiful_prompt(model_manager)
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pipe.fetch_controlnet_models(model_manager, controlnet_config_units)
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return pipe
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def preprocess_image(self, image):
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image = torch.Tensor(np.array(image, dtype=np.float32) * (2 / 255) - 1).permute(2, 0, 1).unsqueeze(0)
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return image
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def decode_image(self, latent, tiled=False, tile_size=64, tile_stride=32):
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image = self.vae_decoder(latent.to(self.device), tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)[0]
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image = image.cpu().permute(1, 2, 0).numpy()
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image = Image.fromarray(((image / 2 + 0.5).clip(0, 1) * 255).astype("uint8"))
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return image
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@torch.no_grad()
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def __call__(
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self,
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prompt,
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negative_prompt="",
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cfg_scale=7.5,
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clip_skip=1,
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input_image=None,
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controlnet_image=None,
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denoising_strength=1.0,
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height=512,
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width=512,
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num_inference_steps=20,
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tiled=False,
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tile_size=64,
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tile_stride=32,
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progress_bar_cmd=tqdm,
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progress_bar_st=None,
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):
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# Prepare scheduler
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self.scheduler.set_timesteps(num_inference_steps, denoising_strength)
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# Prepare latent tensors
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if input_image is not None:
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image = self.preprocess_image(input_image).to(device=self.device, dtype=self.torch_dtype)
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latents = self.vae_encoder(image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)
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noise = torch.randn((1, 4, height//8, width//8), device=self.device, dtype=self.torch_dtype)
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latents = self.scheduler.add_noise(latents, noise, timestep=self.scheduler.timesteps[0])
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else:
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latents = torch.randn((1, 4, height//8, width//8), device=self.device, dtype=self.torch_dtype)
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# Encode prompts
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prompt_emb_posi = self.prompter.encode_prompt(self.text_encoder, prompt, clip_skip=clip_skip, device=self.device, positive=True)
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prompt_emb_nega = self.prompter.encode_prompt(self.text_encoder, negative_prompt, clip_skip=clip_skip, device=self.device, positive=False)
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# Prepare ControlNets
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if controlnet_image is not None:
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controlnet_image = self.controlnet.process_image(controlnet_image).to(device=self.device, dtype=self.torch_dtype)
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controlnet_image = controlnet_image.unsqueeze(1)
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# Denoise
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for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)):
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timestep = torch.IntTensor((timestep,))[0].to(self.device)
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# Classifier-free guidance
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noise_pred_posi = lets_dance(
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self.unet, motion_modules=None, controlnet=self.controlnet,
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sample=latents, timestep=timestep, encoder_hidden_states=prompt_emb_posi, controlnet_frames=controlnet_image,
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tiled=tiled, tile_size=tile_size, tile_stride=tile_stride,
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device=self.device, vram_limit_level=0
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)
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noise_pred_nega = lets_dance(
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self.unet, motion_modules=None, controlnet=self.controlnet,
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sample=latents, timestep=timestep, encoder_hidden_states=prompt_emb_nega, controlnet_frames=controlnet_image,
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tiled=tiled, tile_size=tile_size, tile_stride=tile_stride,
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device=self.device, vram_limit_level=0
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)
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noise_pred = noise_pred_nega + cfg_scale * (noise_pred_posi - noise_pred_nega)
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# DDIM
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latents = self.scheduler.step(noise_pred, timestep, latents)
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# UI
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if progress_bar_st is not None:
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progress_bar_st.progress(progress_id / len(self.scheduler.timesteps))
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# Decode image
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image = self.decode_image(latents, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)
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return image
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