Update README.md

README.md

@@ -1,25 +1,25 @@
 ---
-tasks:
-- text-to-image-synthesis
-model-type:
-- stable_diffusion
-domain:
-- mm
-frameworks:
-- pytorch
-customized-quickstart: False
-finetune-support: False
 license: creativeml-openrail-m
-language: 
-- cn
-- en
 tags:
 - stable-diffusion
 - stable-diffusion-diffusers
 - text-to-image
-- zh
-- Chinese
-
+inference: false
+library_name: diffusers
+extra_gated_prompt: |-
+  One more step before getting this model.
+  This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
+  The CreativeML OpenRAIL License specifies: 
+
+  1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content 
+  2. CompVis claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
+  3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
+  Please read the full license here: https://huggingface.co/spaces/CompVis/stable-diffusion-license
+  
+  By clicking on "Access repository" below, you accept that your *contact information* (email address and username) can be shared with the model authors as well.
+    
+extra_gated_fields:
+ I have read the License and agree with its terms: checkbox
 ---
 
 # Stable Diffusion v1-5 Model Card
@@ -32,33 +32,16 @@ checkpoint and subsequently fine-tuned on 595k steps at resolution 512x512 on "l
 
 You can use this both with the [🧨Diffusers library](https://github.com/huggingface/diffusers) and the [RunwayML GitHub repository](https://github.com/runwayml/stable-diffusion).
 
-
-### modelscope usage
-
-```python
-from modelscope.utils.constant import Tasks
-from modelscope.pipelines import pipeline
-import cv2
-
-pipe = pipeline(task=Tasks.text_to_image_synthesis, 
-                model='AI-ModelScope/stable-diffusion-v1-5',
-                model_revision='v1.0.0')
-
-prompt = '飞流直下三千尺，油画'
-output = pipe({'text': prompt})
-cv2.imwrite('result.png', output['output_imgs'][0])
-
-```
-
-
-
 ### Diffusers usage
 ```py
 from diffusers import StableDiffusionPipeline
 import torch
 
-model_id = "runwayml/stable-diffusion-v1-5"
-pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
+pipe = StableDiffusionPipeline.from_pretrained(
+    "benjamin-paine/stable-diffusion-v1-5",
+    variant="fp16",
+    torch_dtype=torch.float16
+)
 pipe = pipe.to("cuda")
 
 prompt = "a photo of an astronaut riding a horse on mars"
@@ -66,15 +49,9 @@ image = pipe(prompt).images[0]
     
 image.save("astronaut_rides_horse.png")
 ```
-For more detailed instructions, use-cases and examples in JAX follow the instructions [here](https://github.com/huggingface/diffusers#text-to-image-generation-with-stable-diffusion)
 
-### Original GitHub Repository
-
-1. Download the weights 
-   - [v1-5-pruned-emaonly.ckpt](https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt) - 4.27GB, ema-only weight. uses less VRAM - suitable for inference
-   - [v1-5-pruned.ckpt](https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned.ckpt) - 7.7GB, ema+non-ema weights. uses more VRAM - suitable for fine-tuning
+For more detailed instructions, use-cases and examples in JAX follow the instructions [here](https://github.com/huggingface/diffusers#text-to-image-generation-with-stable-diffusion)
 
-2. Follow instructions [here](https://github.com/runwayml/stable-diffusion).
 
 ## Model Details
 - **Developed by:** Robin Rombach, Patrick Esser
@@ -163,7 +140,6 @@ The concepts are intentionally hidden to reduce the likelihood of reverse-engine
 Specifically, the checker compares the class probability of harmful concepts in the embedding space of the `CLIPTextModel` *after generation* of the images. 
 The concepts are passed into the model with the generated image and compared to a hand-engineered weight for each NSFW concept.
 
-
 ## Training
 
 **Training Data**