clean up

run_local_xl.py

@@ -1,56 +0,0 @@
-#!/usr/bin/env python3
-from diffusers import DiffusionPipeline, EulerDiscreteScheduler, StableDiffusionPipeline, KDPM2DiscreteScheduler, StableDiffusionImg2ImgPipeline, HeunDiscreteScheduler, KDPM2AncestralDiscreteScheduler, DDIMScheduler
-import time
-import os
-from huggingface_hub import HfApi
-# from compel import Compel
-import torch
-import sys
-from pathlib import Path
-import requests
-from PIL import Image
-from io import BytesIO
-
-path = sys.argv[1]
-
-api = HfApi()
-start_time = time.time()
-pipe = DiffusionPipeline.from_pretrained(path, torch_dtype=torch.float16)
-pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config)
-# pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
-# pipe = StableDiffusionImg2ImgPipeline.from_pretrained(path, torch_dtype=torch.float16, safety_checker=None
-
-# compel = Compel(tokenizer=pipe.tokenizer, text_encoder=pipe.text_encoder)
-
-
-pipe = pipe.to("cuda")
-
-prompt = "An astronaut riding a green horse on Mars"
-
-# rompts = ["a cat playing with a ball++ in the forest", "a cat playing with a ball++ in the forest", "a cat playing with a ball-- in the forest"]
-
-# prompt_embeds = torch.cat([compel.build_conditioning_tensor(prompt) for prompt in prompts])
-
-# generator = [torch.Generator(device="cuda").manual_seed(0) for _ in range(prompt_embeds.shape[0])]
-#
-# url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"
-# 
-# response = requests.get(url)
-# image = Image.open(BytesIO(response.content)).convert("RGB")
-# image.thumbnail((768, 768))
-#
-
-# pipe.unet.set_default_attn_processor()
-image = pipe(prompt=prompt).images[0]
-
-file_name = f"aaa"
-path = os.path.join(Path.home(), "images", f"{file_name}.png")
-image.save(path)
-
-api.upload_file(
-    path_or_fileobj=path,
-    path_in_repo=path.split("/")[-1],
-    repo_id="patrickvonplaten/images",
-    repo_type="dataset",
-)
-print(f"https://huggingface.co/datasets/patrickvonplaten/images/blob/main/{file_name}.png")

src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl.py

@@ -14,8 +14,7 @@
 
 import inspect
 import warnings
-from typing import Any, Callable, Dict, List, Optional, Union
-from pytorch_lightning import seed_everything
+from typing import Any, Callable, Dict, List, Optional, Union, Tuple
 
 import torch
 from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPTextModelWithProjection
@@ -511,10 +510,7 @@ class StableDiffusionXLPipeline(DiffusionPipeline):
             )
 
         if latents is None:
-            seed_everything(0)
-            # latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
-            latents = randn_tensor(shape, generator=generator, device="cpu", dtype=torch.float32)
-            latents = latents.to(dtype=dtype, device=device)
+            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
         else:
             latents = latents.to(device)
 
@@ -544,6 +540,9 @@ class StableDiffusionXLPipeline(DiffusionPipeline):
         callback_steps: int = 1,
         cross_attention_kwargs: Optional[Dict[str, Any]] = None,
         guidance_rescale: float = 0.0,
+        original_size: Tuple[int, int] = (1024, 1024),
+        crops_coords_top_left: Tuple[int, int] = (0, 0),
+        target_size: Tuple[int, int] = (1024, 1024),
     ):
         r"""
         Function invoked when calling the pipeline for generation.
@@ -609,6 +608,12 @@ class StableDiffusionXLPipeline(DiffusionPipeline):
                 Flawed](https://arxiv.org/pdf/2305.08891.pdf) `guidance_scale` is defined as `φ` in equation 16. of
                 [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf).
                 Guidance rescale factor should fix overexposure when using zero terminal SNR.
+            original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
+                TODO
+            crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):
+                TODO
+            target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
+                TODO
 
         Examples:
 
@@ -681,15 +686,18 @@ class StableDiffusionXLPipeline(DiffusionPipeline):
 
         # 7. Denoising loop
         num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
-        prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
-        add_text_embeds = torch.cat([negative_pooled_prompt_embeds, pooled_prompt_embeds], dim=0)
+
+        add_text_embeds = pooled_prompt_embeds
+        add_time_ids = torch.tensor([list(original_size + crops_coords_top_left + target_size)], dtype=torch.long)
+
+        if do_classifier_free_guidance:
+            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
+            add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)
+            add_time_ids = torch.cat([add_time_ids, add_time_ids], dim=0)
 
         prompt_embeds = prompt_embeds.to(device)
         add_text_embeds = add_text_embeds.to(device)
-
-        # TODO - find better explanations where they come from
-        # original_size_as_tuple x crops_coords_top_left x target_size_as_tuple
-        add_time_ids = torch.tensor(2 * [[1024, 1024, 0, 0, 1024, 1024]], dtype=torch.long, device=add_text_embeds.device)
+        add_time_ids = add_time_ids.to(device)
 
         with self.progress_bar(total=num_inference_steps) as progress_bar:
             for i, t in enumerate(timesteps):