add sd3_model.py

model_index.json

@@ -30,7 +30,7 @@
     "T5Tokenizer"
   ],
   "transformer": [
-    "pipelines.sd3_model",
+    "sd3_model",
     "SD3Transformer2DModel"
   ],
   "vae": [

pipelines/sd3_teefusion_pipeline.py

@@ -1,264 +0,0 @@
-# Copyright (C) 2025 AIDC-AI
-# This project is licensed under the Attribution-NonCommercial 4.0 International 
-# License (SPDX-License-Identifier: CC-BY-NC-4.0). 
-
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import os
-import torch
-import torch.nn as nn
-
-from typing import Union, List, Any, Optional
-from diffusers.configuration_utils import ConfigMixin, register_to_config
-from PIL import Image
-from diffusers import DiffusionPipeline, AutoencoderKL
-from transformers import CLIPTextModelWithProjection, T5EncoderModel, CLIPTokenizer, T5Tokenizer
-
-def get_noise(
-    num_samples: int,
-    channel: int,
-    height: int,
-    width: int,
-    device: torch.device,
-    dtype: torch.dtype,
-    seed: int,
-):
-    return torch.randn(
-        num_samples,
-        channel,
-        height // 8,
-        width // 8,
-        device=device,
-        dtype=dtype,
-        generator=torch.Generator(device=device).manual_seed(seed),
-    )
-
-def get_clip_prompt_embeds(
-    clip_tokenizers, 
-    clip_text_encoders,
-    prompt: Union[str, List[str]],
-    num_images_per_prompt: int = 1,
-    device: Optional[torch.device] = None,
-    clip_skip: Optional[int] = None,
-    clip_model_index: int = 0,
-):
-
-    tokenizer_max_length = 77
-    tokenizer = clip_tokenizers[clip_model_index]
-    text_encoder = clip_text_encoders[clip_model_index]
-
-    batch_size = len(prompt)
-
-    text_inputs = tokenizer(
-        prompt,
-        padding="max_length",
-        max_length=tokenizer_max_length,
-        truncation=True,
-        return_tensors="pt",
-    )
-
-    text_input_ids = text_inputs.input_ids
-    untruncated_ids = tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
-    if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):
-        removed_text = tokenizer.batch_decode(untruncated_ids[:, tokenizer_max_length - 1 : -1])
-
-    prompt_embeds = text_encoder(text_input_ids.to(device), output_hidden_states=True)
-    pooled_prompt_embeds = prompt_embeds[0]
-
-    if clip_skip is None:
-        prompt_embeds = prompt_embeds.hidden_states[-2]
-    else:
-        prompt_embeds = prompt_embeds.hidden_states[-(clip_skip + 2)]
-
-    prompt_embeds = prompt_embeds.to(dtype=text_encoder.dtype, device=device)
-
-    _, seq_len, _ = prompt_embeds.shape
-    prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
-    prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
-
-    pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt, 1)
-    pooled_prompt_embeds = pooled_prompt_embeds.view(batch_size * num_images_per_prompt, -1)
-
-    return prompt_embeds, pooled_prompt_embeds
-    
-def get_t5_prompt_embeds(
-    tokenizer_3,
-    text_encoder_3,
-    prompt: Union[str, List[str]] = None,
-    num_images_per_prompt: int = 1,
-    max_sequence_length: int = 256,
-    device: Optional[torch.device] = None,
-    dtype: Optional[torch.dtype] = None,
-):
-
-    tokenizer_max_length = 77
-    batch_size = len(prompt)
-
-    text_inputs = tokenizer_3(
-        prompt,
-        padding="max_length",
-        max_length=max_sequence_length,
-        truncation=True,
-        add_special_tokens=True,
-        return_tensors="pt",
-    )
-    text_input_ids = text_inputs.input_ids
-    untruncated_ids = tokenizer_3(prompt, padding="longest", return_tensors="pt").input_ids
-
-    if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):
-        removed_text = tokenizer_3.batch_decode(untruncated_ids[:, tokenizer_max_length - 1 : -1])
-
-    prompt_embeds = text_encoder_3(text_input_ids.to(device))[0]
-
-    dtype = text_encoder_3.dtype
-    prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
-
-    _, seq_len, _ = prompt_embeds.shape
-
-    # duplicate text embeddings and attention mask for each generation per prompt, using mps friendly method
-    prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
-    prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
-
-    return prompt_embeds
-
-    
-@torch.no_grad()
-def encode_text(clip_tokenizers, clip_text_encoders, tokenizer_3, text_encoder_3, prompt, device, max_sequence_length=256):
-    
-    prompt_embed, pooled_prompt_embed = get_clip_prompt_embeds(clip_tokenizers, clip_text_encoders, prompt=prompt, device=device, clip_model_index=0)
-    prompt_2_embed, pooled_prompt_2_embed = get_clip_prompt_embeds(clip_tokenizers, clip_text_encoders, prompt=prompt, device=device, clip_model_index=1)
-    clip_prompt_embeds = torch.cat([prompt_embed, prompt_2_embed], dim=-1)
-
-    t5_prompt_embed = get_t5_prompt_embeds(tokenizer_3, text_encoder_3, prompt=prompt, max_sequence_length=max_sequence_length, device=device)
-
-    clip_prompt_embeds = torch.nn.functional.pad(clip_prompt_embeds, (0, t5_prompt_embed.shape[-1] - clip_prompt_embeds.shape[-1]))
-
-    prompt_embeds = torch.cat([clip_prompt_embeds, t5_prompt_embed], dim=-2)
-    pooled_prompt_embeds = torch.cat([pooled_prompt_embed, pooled_prompt_2_embed], dim=-1)
-
-    return prompt_embeds, pooled_prompt_embeds
-
-
-class TeEFusionSD3Pipeline(DiffusionPipeline, ConfigMixin):
-
-    @register_to_config
-    def __init__(
-        self,
-        transformer: nn.Module,
-        text_encoder: CLIPTextModelWithProjection,
-        text_encoder_2: CLIPTextModelWithProjection,
-        text_encoder_3: T5EncoderModel,
-        tokenizer: CLIPTokenizer,
-        tokenizer_2: CLIPTokenizer,
-        tokenizer_3: T5Tokenizer,
-        vae: AutoencoderKL,
-        scheduler: Any
-    ):
-        super().__init__()
-
-        self.register_modules(
-            transformer=transformer,
-            text_encoder=text_encoder,
-            text_encoder_2=text_encoder_2,
-            text_encoder_3=text_encoder_3,
-            tokenizer=tokenizer,
-            tokenizer_2=tokenizer_2,
-            tokenizer_3=tokenizer_3,
-            vae=vae,
-            scheduler=scheduler
-        )
-
-    
-    @classmethod
-    def from_pretrained(
-        cls,
-        pretrained_model_name_or_path: Union[str, os.PathLike],
-        **kwargs,
-    ) -> "TeEFusionSD3Pipeline":
-
-        return super().from_pretrained(pretrained_model_name_or_path, **kwargs)
-
-    def save_pretrained(self, save_directory: Union[str, os.PathLike]):
-        super().save_pretrained(save_directory)
-
-    @torch.no_grad()
-    def __call__(
-        self,
-        prompt: Union[str, List[str]],
-        num_inference_steps: int = 50,
-        guidance_scale: float = 7.5,
-        latents: torch.FloatTensor = None,
-        height: int = 1024,
-        width: int = 1024,
-        seed: int = 0,
-    ):  
-        if isinstance(prompt, str):
-            prompt = [prompt]
-
-        device = self.transformer.device
-
-        clip_tokenizers = [self.tokenizer, self.tokenizer_2]
-        clip_text_encoders = [self.text_encoder, self.text_encoder_2]
-
-        prompt_embeds, pooled_prompt_embeds = encode_text(clip_tokenizers, clip_text_encoders, self.tokenizer_3, self.text_encoder_3, prompt, device)
-
-        _, negative_pooled_prompt_embeds = encode_text(clip_tokenizers, clip_text_encoders, self.tokenizer_3, self.text_encoder_3, [''], device)
-
-
-        self.scheduler.set_timesteps(num_inference_steps, device=device)
-        timesteps = self.scheduler.timesteps
-
-        bs = len(prompt)
-        channels = self.transformer.config.in_channels
-        height = 16 * (height // 16)
-        width = 16 * (width // 16)
-
-        # prepare input
-        if latents is None:
-            latents = get_noise(
-                bs,
-                channels,
-                height,
-                width,
-                device=device,
-                dtype=self.transformer.dtype,
-                seed=seed,
-            )
-
-        for i, t in enumerate(timesteps):
-            noise_pred = self.transformer(
-                hidden_states=latents,
-                timestep=t.reshape(1),
-                encoder_hidden_states=prompt_embeds,
-                pooled_projections=pooled_prompt_embeds,
-                return_dict=False,
-                txt_align_guidance=torch.tensor(data=(guidance_scale,), dtype=self.transformer.dtype, device=self.transformer.device) * 1000.,
-                txt_align_vec=pooled_prompt_embeds - negative_pooled_prompt_embeds 
-            )[0]
-
-            latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
-
-        x = latents.float()
-
-        with torch.no_grad():
-            with torch.autocast(device_type=device.type, dtype=torch.float32):
-                if hasattr(self.vae.config, 'scaling_factor') and self.vae.config.scaling_factor is not None:
-                    x = x / self.vae.config.scaling_factor
-                if hasattr(self.vae.config, 'shift_factor') and self.vae.config.shift_factor is not None:
-                    x = x + self.vae.config.shift_factor
-                x = self.vae.decode(x, return_dict=False)[0]
-
-        # bring into PIL format and save
-        x = (x / 2 + 0.5).clamp(0, 1)
-        x = x.cpu().permute(0, 2, 3, 1).float().numpy()
-        images = (x * 255).round().astype("uint8")
-        pil_images = [Image.fromarray(image) for image in images]
-
-        return pil_images
-
-
-