Update README

README.md

@@ -48,15 +48,22 @@ Please note: This model is released under the [Stability Community License](http
 
 ### License
 
-- **Community License:**  Free for research, non-commercial, and commercial use for organizations or individuals with less than $1M in total annual revenue. More details can be found in the [Community License Agreement](https://stability.ai/community-license-agreement). Read more at https://stability.ai/license.
-- **For individuals and organizations with annual revenue above $1M**: please [contact us](https://stability.ai/enterprise) to get an Enterprise License.
+Here are the key components of the license:
+* Free for non-commercial use: Individuals and organizations can use the model free of charge for non-commercial use, including scientific research.  
+* Free for commercial use (up to $1M in annual revenue): Startups, small to medium-sized businesses, and creators can use the model for commercial purposes at no cost, as long as their total annual revenue is less than $1M.
+* Ownership of outputs: Retain ownership of the media generated without restrictive licensing implications.
+
+For organizations with annual revenue more than $1M, please contact us [here](https://stability.ai/enterprise) to inquire about an Enterprise License.
 
 ## Usage
 
 For now, we recommend using the [standalone SD3.5 repo](https://github.com/Stability-AI/sd3.5) to use the ControlNets.
 
+**Support in [ComfyUI](https://github.com/comfyanonymous/ComfyUI) and [🧨 Diffusers](https://github.com/huggingface/diffusers) is planned, and will be available very soon.**
+
 A full technical report on Stable Diffusion 3.5, with details on the ControlNet training, will be released soon as well.
 
+
 ### Using Controlnets in SD3.5 Standalone Repo
 Install the repo:
 ```
@@ -97,42 +104,34 @@ blurred_image = gaussian_blur(image)
 ```
 
 ### Tips
-- Euler sampler and a slightly higher step count (50-60) gives best results.
+- We recommend starting with a ControlNet strength of 0.9, and adjusting as needed.
+- Euler sampler and a slightly higher step count (50-60) gives best results, especially with Canny.
 - Pass `--text_encoder_device <device_name>` to load the text encoders directly to VRAM, which can speed up the full inference loop at the cost of extra VRAM usage.
 
 ## Uses
 
-### Intended Uses
+All uses of the model must be in accordance with our [Acceptable Use Policy](https://stability.ai/use-policy).
 
-Intended uses include the following:
-* Generation of artworks and use in design and other artistic processes.
-* Applications in educational or creative tools.
-* Research on generative models, including understanding the limitations of generative models.
+### Out-of-Scope Uses
 
-All uses of the model must be in accordance with our [Acceptable Use Policy](https://stability.ai/use-policy).
+The model was not trained to be factual or true representations of people or events.  As such, using the model to generate such content is out-of-scope of the abilities of this model.
 
 ### Training Data and Strategy
     
 These models were trained on a wide variety of data, including synthetic data and filtered publicly available data.
-The data used is a subset of the Stable Diffusion 3.5 post-training dataset, and satisfies the same legal and safety requirements.
-
-### Out-of-Scope Uses
-
-The model was not trained to be factual or true representations of people or events.  As such, using the model to generate such content is out-of-scope of the abilities of this model.
 
 ## Safety
 
-As part of our safety-by-design and responsible AI deployment approach, we take deliberate measures to ensure Integrity starts at the early stages of development. We implement safety measures throughout the development of our models. We have implemented safety mitigations that are intended to reduce the risk of certain harms, however we recommend that developers conduct their own testing and apply additional mitigations based on their specific use cases.  
-For more about our approach to Safety, please visit our [Safety page](https://stability.ai/safety).
+We believe in safe, responsible AI practices and take deliberate measures to ensure Integrity starts at the early stages of development. This means we have taken and continue to take reasonable steps to prevent the misuse of Stable Diffusion 3.5 by bad actors. For more information about our approach to Safety please visit our [Safety page](https://stability.ai/safety).
 
 ### Integrity Evaluation
 
-Our integrity evaluation methods include structured evaluations and red-teaming testing for certain harms.  Testing was conducted primarily in English and may not cover all possible harms.  
+Our integrity evaluation methods include structured evaluations and red-teaming testing for certain harms. Testing was conducted primarily in English and may not cover all possible harms.  
 
 ### Risks identified and mitigations:
 
-* Harmful content:  We have used filtered data sets when training our models and implemented safeguards that attempt to strike the right balance between usefulness and preventing harm. However, this does not guarantee that all possible harmful content has been removed. All developers and deployers should exercise caution and implement content safety guardrails based on their specific product policies and application use cases.
-* Misuse: Technical limitations and developer and end-user education can help mitigate against malicious applications of models. All users are required to adhere to our [Acceptable Use Policy](https://stability.ai/use-policy), including when applying fine-tuning and prompt engineering mechanisms. Please reference the Stability AI Acceptable Use Policy for information on violative uses of our products.
+* Harmful content: We have used filtered data sets when training our models and implemented safeguards that attempt to strike the right balance between usefulness and preventing harm. However, this does not guarantee that all possible harmful content has been removed. All developers and deployers should exercise caution and implement content safety guardrails based on their specific product policies and application use cases.
+* Misuse: Technical limitations and developer and end-user education can help mitigate against malicious applications of models. All users are required to adhere to our Acceptable Use Policy, including when applying fine-tuning and prompt engineering mechanisms. Please reference the Stability AI Acceptable Use Policy for information on violative uses of our products.
 * Privacy violations: Developers and deployers are encouraged to adhere to privacy regulations with techniques that respect data privacy.
 
 ### Acknowledgements