Update README.md

README.md

@@ -5,20 +5,20 @@ license_link: https://bria.ai/legal-lobby
 ---
 
 
-# BRIA 3.0 ControlNet Union Model Card
+# BRIA-4B-Adapt ControlNet Union Model Card
 
-BRIA-3.0 ControlNet-Union, trained on the foundation of [BRIA-4B-Adapt Text-to-Image](https://huggingface.co/briaai/BRIA-4B-Adapt), supports 6 control modes, including depth (0), canny (1), colorgrid (2), recolor (3), tile (4), pose (5). This model can be jointly used with other ControlNets.
+BRIA-4B-Adapt ControlNet-Union, trained on the foundation of [BRIA-4B-Adapt Text-to-Image](https://huggingface.co/briaai/BRIA-4B-Adapt), supports 6 control modes, including depth (0), canny (1), colorgrid (2), recolor (3), tile (4), pose (5). This model can be jointly used with other ControlNets.
 
 This model combines technological innovation with ethical responsibility and legal security, setting a new standard in the AI industry. Bria AI licenses the foundation model with full legal liability coverage. Our dataset does not contain copyrighted materials, such as fictional characters, logos, trademarks, public figures, harmful content, or privacy-infringing content.
 
-[CLICK HERE FOR A DEMO](https://huggingface.co/spaces/briaai/BRIA-3.0-ControlNet-Union)
+[CLICK HERE FOR A DEMO](https://huggingface.co/spaces/briaai/BRIA-4B-Adapt-ControlNet-Union)
 
 For more information, please visit our [website](https://bria.ai/).
 
 Join our [Discord community](https://discord.gg/Nxe9YW9zHS) for more information, tutorials, tools, and to connect with other users!
 
 ### Get Access
-Interested in BRIA-3.0 ControlNet-Union? Purchase is required to license and access BRIA-3.0 ControlNet-Union, ensuring royalty management with our data partners and full liability coverage for commercial use. 
+Interested in BRIA-4B-Adapt ControlNet-Union? Purchase is required to license and access BRIA-4B-Adapt ControlNet-Union, ensuring royalty management with our data partners and full liability coverage for commercial use. 
 
 Are you a startup or a student? We encourage you to apply for our [Startup Program](https://pages.bria.ai/the-visual-generative-ai-platform-for-builders-startups-plan?_gl=1*cqrl81*_ga*MTIxMDI2NzI5OC4xNjk5NTQ3MDAz*_ga_WRN60H46X4*MTcwOTM5OTMzNC4yNzguMC4xNzA5Mzk5MzM0LjYwLjAuMA..) to request access. This program are designed to support emerging businesses and academic pursuits with our cutting-edge technology.
 
@@ -75,8 +75,8 @@ except:
 hf_hub_download(repo_id="briaai/BRIA-4B-Adapt", filename='pipeline_bria.py', local_dir=local_dir)
 hf_hub_download(repo_id="briaai/BRIA-4B-Adapt", filename='transformer_bria.py', local_dir=local_dir)
 hf_hub_download(repo_id="briaai/BRIA-4B-Adapt", filename='bria_utils.py', local_dir=local_dir)
-hf_hub_download(repo_id="briaai/BRIA-3.0-ControlNet-Union", filename='pipeline_bria_controlnet.py', local_dir=local_dir)
-hf_hub_download(repo_id="briaai/BRIA-3.0-ControlNet-Union", filename='controlnet_bria.py', local_dir=local_dir)
+hf_hub_download(repo_id="briaai/BRIA-4B-Adapt-ControlNet-Union", filename='pipeline_bria_controlnet.py', local_dir=local_dir)
+hf_hub_download(repo_id="briaai/BRIA-4B-Adapt-ControlNet-Union", filename='controlnet_bria.py', local_dir=local_dir)
 
 import torch
 from diffusers.utils import load_image
@@ -85,13 +85,13 @@ from pipeline_bria_controlnet import BriaControlNetPipeline
 import PIL.Image as Image
 
 base_model = 'briaai/BRIA-4B-Adapt'
-controlnet_model = 'briaai/BRIA-3.0-ControlNet-Union'
+controlnet_model = 'briaai/BRIA-4B-Adapt-ControlNet-Union'
 
 controlnet = BriaControlNetModel.from_pretrained(controlnet_model, torch_dtype=torch.bfloat16)
 pipe = BriaControlNetPipeline.from_pretrained(base_model, controlnet=controlnet, torch_dtype=torch.bfloat16, trust_remote_code=True)
 pipe.to("cuda")
 
-control_image_canny = load_image("https://huggingface.co/briaai/BRIA-3.0-ControlNet-Union/resolve/main/images/canny.jpg")
+control_image_canny = load_image("https://huggingface.co/briaai/BRIA-4B-Adapt-ControlNet-Union/resolve/main/images/canny.jpg")
 controlnet_conditioning_scale = 1.0
 control_mode = 1
 
@@ -127,8 +127,8 @@ except:
 hf_hub_download(repo_id="briaai/BRIA-4B-Adapt", filename='pipeline_bria.py', local_dir=local_dir)
 hf_hub_download(repo_id="briaai/BRIA-4B-Adapt", filename='transformer_bria.py', local_dir=local_dir)
 hf_hub_download(repo_id="briaai/BRIA-4B-Adapt", filename='bria_utils.py', local_dir=local_dir)
-hf_hub_download(repo_id="briaai/BRIA-3.0-ControlNet-Union", filename='pipeline_bria_controlnet.py', local_dir=local_dir)
-hf_hub_download(repo_id="briaai/BRIA-3.0-ControlNet-Union", filename='controlnet_bria.py', local_dir=local_dir)
+hf_hub_download(repo_id="briaai/BRIA-4B-Adapt-ControlNet-Union", filename='pipeline_bria_controlnet.py', local_dir=local_dir)
+hf_hub_download(repo_id="briaai/BRIA-4B-Adapt-ControlNet-Union", filename='controlnet_bria.py', local_dir=local_dir)
 
 
 import torch
@@ -138,7 +138,7 @@ from pipeline_bria_controlnet import BriaControlNetPipeline
 import PIL.Image as Image
 
 base_model = 'briaai/BRIA-4B-Adapt'
-controlnet_model = 'briaai/BRIA-3.0-ControlNet-Union'
+controlnet_model = 'briaai/BRIA-4B-Adapt-ControlNet-Union'
 
 controlnet = BriaControlNetModel.from_pretrained(controlnet_model, torch_dtype=torch.bfloat16)
 controlnet = BriaMultiControlNetModel([controlnet])
@@ -146,8 +146,8 @@ controlnet = BriaMultiControlNetModel([controlnet])
 pipe = BriaControlNetPipeline.from_pretrained(base_model, controlnet=controlnet, torch_dtype=torch.bfloat16, trust_remote_code=True)
 pipe.to("cuda")
 
-control_image_colorgrid = load_image("https://huggingface.co/briaai/BRIA-3.0-ControlNet-Union/resolve/main/images/colorgrid.jpg")
-control_image_pose = load_image("https://huggingface.co/briaai/BRIA-3.0-ControlNet-Union/resolve/main/images/pose.jpg")
+control_image_colorgrid = load_image("https://huggingface.co/briaai/BRIA-4B-Adapt-ControlNet-Union/resolve/main/images/colorgrid.jpg")
+control_image_pose = load_image("https://huggingface.co/briaai/BRIA-4B-Adapt-ControlNet-Union/resolve/main/images/pose.jpg")
 
 control_image = [control_image_colorgrid, control_image_pose]
 controlnet_conditioning_scale = [0.5, 0.5]