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boomerang.py

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+import inspect
+from PIL import Image
+import torch
+from torch import autocast
+from torchvision import transforms as T
+from types import MethodType
+from typing import List, Optional, Tuple, Union
+
+from diffusers import StableDiffusionPipeline
+from diffusers.models.unet_2d_condition import UNet2DConditionOutput
+from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
+
+pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", revision="fp16", torch_dtype=torch.float16, use_auth_token=True)  
+#pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", use_auth_token=True)  
+
+pipe = pipe.to('cuda')
+
+
+
+
+# Overriding the U-Net forward pass
+def forward(
+    self,
+    sample: torch.FloatTensor,
+    timestep: Union[torch.Tensor, float, int],
+    encoder_hidden_states: torch.Tensor,
+    return_dict: bool = True,
+) -> Union[UNet2DConditionOutput, Tuple]:
+    """r
+    Args:
+        sample (`torch.FloatTensor`): (batch, channel, height, width) noisy inputs tensor
+        timestep (`torch.FloatTensor` or `float` or `int`): (batch) timesteps
+        encoder_hidden_states (`torch.FloatTensor`): (batch, channel, height, width) encoder hidden states
+        return_dict (`bool`, *optional*, defaults to `True`):
+            Whether or not to return a [`models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain tuple.
+
+    Returns:
+        [`~models.unet_2d_condition.UNet2DConditionOutput`] or `tuple`:
+        [`~models.unet_2d_condition.UNet2DConditionOutput`] if `return_dict` is True, otherwise a `tuple`. When
+        returning a tuple, the first element is the sample tensor.
+    """
+    # 0. center input if necessary
+    if self.config.center_input_sample:
+        sample = 2 * sample - 1.0
+
+    # 1. time
+    timesteps = timestep
+    if not torch.is_tensor(timesteps):
+        timesteps = torch.tensor([timesteps], dtype=torch.long, device=sample.device)
+    elif torch.is_tensor(timesteps) and len(timesteps.shape) == 0:
+        timesteps = timesteps.to(dtype=torch.float32)
+        timesteps = timesteps[None].to(device=sample.device)
+
+    # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
+    timesteps = timesteps.expand(sample.shape[0])
+
+    t_emb = self.time_proj(timesteps)
+    #emb = self.time_embedding(t_emb)
+    emb = self.time_embedding(t_emb.to(sample.dtype))
+
+    # 2. pre-process
+    sample = self.conv_in(sample)
+
+    # 3. down
+    down_block_res_samples = (sample,)
+    for downsample_block in self.down_blocks:
+        if hasattr(downsample_block, "attentions") and downsample_block.attentions is not None:
+            sample, res_samples = downsample_block(
+                hidden_states=sample, temb=emb, encoder_hidden_states=encoder_hidden_states
+            )
+        else:
+            sample, res_samples = downsample_block(hidden_states=sample, temb=emb)
+
+        down_block_res_samples += res_samples
+
+    # 4. mid
+    sample = self.mid_block(sample, emb, encoder_hidden_states=encoder_hidden_states)
+
+    # 5. up
+    for upsample_block in self.up_blocks:
+        res_samples = down_block_res_samples[-len(upsample_block.resnets) :]
+        down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]
+
+        if hasattr(upsample_block, "attentions") and upsample_block.attentions is not None:
+            sample = upsample_block(
+                hidden_states=sample,
+                temb=emb,
+                res_hidden_states_tuple=res_samples,
+                encoder_hidden_states=encoder_hidden_states,
+            )
+        else:
+            sample = upsample_block(hidden_states=sample, temb=emb, res_hidden_states_tuple=res_samples)
+
+    # 6. post-process
+    # make sure hidden states is in float32
+    # when running in half-precision
+    #sample = self.conv_norm_out(sample.float()).type(sample.dtype)
+    sample = self.conv_norm_out(sample)
+    sample = self.conv_act(sample)
+    sample = self.conv_out(sample)
+
+    if not return_dict:
+        return (sample,)
+
+    return UNet2DConditionOutput(sample=sample)
+
+
+def safety_forward(self, clip_input, images):
+  return images, False
+
+
+# Overriding the Stable Diffusion call method
+@torch.no_grad()
+def call(
+    self,
+    prompt: Union[str, List[str]],
+    height: Optional[int] = 512,
+    width: Optional[int] = 512,
+    num_inference_steps: Optional[int] = 50,
+    guidance_scale: Optional[float] = 7.5,
+    eta: Optional[float] = 0.0,
+    generator: Optional[torch.Generator] = None,
+    latents: Optional[torch.FloatTensor] = None,
+    output_type: Optional[str] = "pil",
+    return_dict: bool = True,
+    percent_noise: float = 0.7,
+    **kwargs,
+):
+    if isinstance(prompt, str):
+        batch_size = 1
+    elif isinstance(prompt, list):
+        batch_size = len(prompt)
+    else:
+        raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+
+    if height % 8 != 0 or width % 8 != 0:
+        raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
+
+    # get prompt text embeddings
+    text_input = self.tokenizer(
+        prompt,
+        padding="max_length",
+        max_length=self.tokenizer.model_max_length,
+        truncation=True,
+        return_tensors="pt",
+    )
+    text_embeddings = self.text_encoder(text_input.input_ids.to(self.device))[0]
+
+    # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
+    # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
+    # corresponds to doing no classifier free guidance.
+    do_classifier_free_guidance = guidance_scale > 1.0
+    # get unconditional embeddings for classifier free guidance
+    if do_classifier_free_guidance:
+        max_length = text_input.input_ids.shape[-1]
+        uncond_input = self.tokenizer(
+            [""] * batch_size, padding="max_length", max_length=max_length, return_tensors="pt"
+        )
+        uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
+
+        # For classifier free guidance, we need to do two forward passes.
+        # Here we concatenate the unconditional and text embeddings into a single batch
+        # to avoid doing two forward passes
+        text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
+
+    # get the initial random noise unless the user supplied it
+
+    # Unlike in other pipelines, latents need to be generated in the target device
+    # for 1-to-1 results reproducibility with the CompVis implementation.
+    # However this currently doesn't work in `mps`.
+    latents_device = "cpu" if self.device.type == "mps" else self.device
+    latents_shape = (batch_size, self.unet.in_channels, height // 8, width // 8)
+    if latents is None:
+        latents = torch.randn(
+            latents_shape,
+            generator=generator,
+            device=latents_device,
+        )
+    else:
+        if latents.shape != latents_shape:
+            raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
+    latents = latents.to(self.device)
+
+    # set timesteps
+    self.scheduler.set_timesteps(num_inference_steps)
+
+    # if we use LMSDiscreteScheduler, let's make sure latents are multiplied by sigmas
+    #if isinstance(self.scheduler, LMSDiscreteScheduler):
+    #    latents = latents * self.scheduler.sigmas[0]
+
+    # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
+    # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
+    # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
+    # and should be between [0, 1]
+    accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
+    extra_step_kwargs = {}
+    if accepts_eta:
+        extra_step_kwargs["eta"] = eta
+
+    
+    for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
+
+        if t - 1 > 1000 * percent_noise:
+            continue
+
+        #print(t)
+
+        # expand the latents if we are doing classifier free guidance
+        latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
+        #if isinstance(self.scheduler, LMSDiscreteScheduler):
+        #    sigma = self.scheduler.sigmas[i]
+        #    # the model input needs to be scaled to match the continuous ODE formulation in K-LMS
+        #    latent_model_input = latent_model_input / ((sigma**2 + 1) ** 0.5)
+
+        # predict the noise residual
+        noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
+
+        # perform guidance
+        if do_classifier_free_guidance:
+            noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+            noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+        # compute the previous noisy sample x_t -> x_t-1
+        #if isinstance(self.scheduler, LMSDiscreteScheduler):
+        #    latents = self.scheduler.step(noise_pred, i, latents, **extra_step_kwargs).prev_sample
+        #else:
+        latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
+    
+
+    # scale and decode the image latents with vae
+    latents = 1 / 0.18215 * latents
+    image = self.vae.decode(latents).sample
+
+    image = (image / 2 + 0.5).clamp(0, 1)
+    image = image.cpu().permute(0, 2, 3, 1).numpy()
+
+    # run safety checker
+    safety_cheker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(self.device)
+    image, has_nsfw_concept = self.safety_checker(images=image, clip_input=safety_cheker_input.pixel_values)
+
+    if output_type == "pil":
+        image = self.numpy_to_pil(image)
+
+    if not return_dict:
+        return (image, has_nsfw_concept)
+
+    return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
+
+
+pipe.unet.forward = MethodType(forward, pipe.unet)
+pipe.safety_checker.forward = MethodType(safety_forward, pipe.safety_checker)
+type(pipe).__call__ = call