support SDXL inference & update SD2/SDXL inference docstrings (#71)

* support SDXL inference & update SD2/SDXL inference docstrings * Update diffusion/inference/inference_model.py Co-authored-by: Landan Seguin <[email protected]> --------- Co-authored-by: Landan Seguin <[email protected]>
mosaicml · Oct 6, 2023 · c3ff584 · c3ff584
1 parent 80e2af5
commit c3ff584
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diff --git a/diffusion/inference/inference_model.py b/diffusion/inference/inference_model.py
@@ -5,13 +5,13 @@
 
 import base64
 import io
-from typing import Any, Dict, List
+from typing import Any, Dict, List, Optional
 
 import torch
 from composer.utils.file_helpers import get_file
 from PIL import Image
 
-from diffusion.models import stable_diffusion_2
+from diffusion.models import stable_diffusion_2, stable_diffusion_xl
 
 # Local checkpoint params
 LOCAL_CHECKPOINT_PATH = '/tmp/model.pt'
@@ -27,12 +27,13 @@ def download_checkpoint(chkpt_path: str):
 
 
 class StableDiffusionInference():
-    """Inference endpoint class for Stable Diffusion.
+    """Inference endpoint class for Stable Diffusion 2.
 
     Args:
-        chkpt_path (str, optional): The path to the local folder, URL or object score that contains the checkpoint.
-            If not specified, pulls the pretrained Stable Diffusion 2.0 base weights from HuggingFace.
-            Default: ``None``.
+        model_name (str, optional): Name of the model to load. Default: 'stabilityai/stable-diffusion-2-base'.
+        pretrained (bool): Whether to load pretrained weights. Default: True.
+        prediction_type (str): The type of prediction to use. Must be one of 'sample',
+            'epsilon', or 'v_prediction'. Default: `epsilon`.
     """
 
     def __init__(self,
@@ -107,3 +108,99 @@ def predict(self, model_requests: List[Dict[str, Any]]):
             base64_encoded_image = base64.b64encode(img_byte_arr.getvalue()).decode('utf-8')
             png_images.append(base64_encoded_image)
         return png_images
+
+
+class StableDiffusionXLInference():
+    """Inference endpoint class for Stable Diffusion XL.
+
+    Args:
+        model_name (str): Name of the model to load. Default: 'stabilityai/stable-diffusion-xl-base-1.0'.
+        unet_model_name (str): Name of the UNet model to load. Default: 'stabilityai/stable-diffusion-xl-base-1.0'.
+        vae_model_name (str): Name of the VAE model to load. Defaults to
+            'madebyollin/sdxl-vae-fp16-fix' as the official VAE checkpoint (from
+            'stabilityai/stable-diffusion-xl-base-1.0') is not compatible with fp16.
+        clip_qkv (float, optional): If not None, clip the qkv values to this value. Defaults to 6.0. Improves stability
+            of training.
+        pretrained (bool): Whether to load pretrained weights. Default: True.
+        prediction_type (str): The type of prediction to use. Must be one of 'sample',
+            'epsilon', or 'v_prediction'. Default: `epsilon`.
+    """
+
+    def __init__(self,
+                 model_name: str = 'stabilityai/stable-diffusion-xl-base-1.0',
+                 unet_model_name: str = 'stabilityai/stable-diffusion-xl-base-1.0',
+                 vae_model_name: str = 'madebyollin/sdxl-vae-fp16-fix',
+                 clip_qkv: Optional[float] = None,
+                 pretrained: bool = False,
+                 prediction_type: str = 'epsilon'):
+        self.device = torch.cuda.current_device()
+
+        model = stable_diffusion_xl(
+            model_name=model_name,
+            unet_model_name=unet_model_name,
+            vae_model_name=vae_model_name,
+            clip_qkv=clip_qkv,
+            pretrained=pretrained,
+            prediction_type=prediction_type,
+            encode_latents_in_fp16=True,
+            fsdp=False,
+        )
+
+        if not pretrained:
+            state_dict = torch.load(LOCAL_CHECKPOINT_PATH)
+            for key in list(state_dict['state']['model'].keys()):
+                if 'val_metrics.' in key:
+                    del state_dict['state']['model'][key]
+            model.load_state_dict(state_dict['state']['model'], strict=False)
+        model.to(self.device)
+        self.model = model.eval()
+
+    def predict(self, model_requests: List[Dict[str, Any]]):
+        prompts = []
+        negative_prompts = []
+        generate_kwargs = {}
+
+        # assumes the same generate_kwargs across all samples
+        for req in model_requests:
+            if 'input' not in req:
+                raise RuntimeError('"input" must be provided to generate call')
+            inputs = req['input']
+
+            # Prompts and negative prompts if available
+            if isinstance(inputs, str):
+                prompts.append(inputs)
+            elif isinstance(inputs, Dict):
+                if 'prompt' not in inputs:
+                    raise RuntimeError('"prompt" must be provided to generate call if using a dict as input')
+                prompts.append(inputs['prompt'])
+                if 'negative_prompt' in inputs:
+                    negative_prompts.append(inputs['negative_prompt'])
+            else:
+                raise RuntimeError(f'Input must be of type string or dict, but it is type: {type(inputs)}')
+
+            generate_kwargs = req['parameters']
+
+        # Check for prompts
+        if len(prompts) == 0:
+            raise RuntimeError('No prompts provided, must be either a string or dictionary with "prompt"')
+
+        # Check negative prompt length
+        if len(negative_prompts) == 0:
+            negative_prompts = None
+        elif len(prompts) != len(negative_prompts):
+            raise RuntimeError('There must be the same number of negative prompts as prompts.')
+
+        # Generate images
+        with torch.cuda.amp.autocast(True):
+            imgs = self.model.generate(prompt=prompts, negative_prompt=negative_prompts, **generate_kwargs).cpu()
+
+        # Send as bytes
+        png_images = []
+        for i in range(imgs.shape[0]):
+            img = (imgs[i].permute(1, 2, 0).numpy() * 255).round().astype('uint8')
+            pil_image = Image.fromarray(img, 'RGB')
+            img_byte_arr = io.BytesIO()
+            pil_image.save(img_byte_arr, format='PNG')
+            base64_encoded_image = base64.b64encode(img_byte_arr.getvalue()).decode('utf-8')
+            png_images.append(base64_encoded_image)
+        return png_images
diff --git a/diffusion/models/__init__.py b/diffusion/models/__init__.py
@@ -3,7 +3,8 @@
 
 """Diffusion models."""
 
-from diffusion.models.models import continuous_pixel_diffusion, discrete_pixel_diffusion, stable_diffusion_2
+from diffusion.models.models import (continuous_pixel_diffusion, discrete_pixel_diffusion, stable_diffusion_2,
+                                     stable_diffusion_xl)
 from diffusion.models.pixel_diffusion import PixelDiffusion
 from diffusion.models.stable_diffusion import StableDiffusion
 
@@ -12,5 +13,6 @@
     'discrete_pixel_diffusion',
     'PixelDiffusion',
     'stable_diffusion_2',
+    'stable_diffusion_xl',
     'StableDiffusion',
 ]