eer

README.md

@@ -2,7 +2,7 @@
 license: openrail++
 tags:
 - stable-diffusion
-- image-to-image
+- text-to-image
 ---
 # SD-XL 1.0-refiner Model Card
 ![row01](01.png)
@@ -58,22 +58,20 @@ In addition make sure to install `transformers`, `safetensors`, `accelerate` as
 pip install invisible_watermark transformers accelerate safetensors
 ```
 
-Yon can then use the refiner to improve images.
-
+You can use the model then as follows
 ```py
+from diffusers import DiffusionPipeline
 import torch
-from diffusers import StableDiffusionXLImg2ImgPipeline
-from diffusers.utils import load_image
-
-pipe = StableDiffusionXLImg2ImgPipeline.from_pretrained(
-    "stabilityai/stable-diffusion-xl-refiner-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
-)
-pipe = pipe.to("cuda")
-url = "https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/aa_xl/000000009.png"
-
-init_image = load_image(url).convert("RGB")
-prompt = "a photo of an astronaut riding a horse on mars"
-image = pipe(prompt, image=init_image).images
+
+pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1-0", torch_dtype=torch.float16, use_safetensors=True, variant="fp16")
+pipe.to("cuda")
+
+# if using torch < 2.0
+# pipe.enable_xformers_memory_efficient_attention()
+
+prompt = "An astronaut riding a green horse"
+
+images = pipe(prompt=prompt).images[0]
 ```
 
 When using `torch >= 2.0`, you can improve the inference speed by 20-30% with torch.compile. Simple wrap the unet with torch compile before running the pipeline:
@@ -89,7 +87,6 @@ instead of `.to("cuda")`:
 + pipe.enable_model_cpu_offload()
 ```
 
-For more advanced use cases, please have a look at [the docs](https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion/stable_diffusion_xl).
 
 ## Uses
 

model_index.json

@@ -2,7 +2,6 @@
   "_class_name": "StableDiffusionXLImg2ImgPipeline",
   "_diffusers_version": "0.19.0.dev0",
   "force_zeros_for_empty_prompt": false,
-  "add_watermarker": null,
   "requires_aesthetics_score": true,
   "scheduler": [
     "diffusers",

vae/config.json

@@ -21,7 +21,7 @@
   "layers_per_block": 2,
   "norm_num_groups": 32,
   "out_channels": 3,
-  "sample_size": 1024,
+  "sample_size": 512,
   "scaling_factor": 0.13025,
   "up_block_types": [
     "UpDecoderBlock2D",