add playground models

CHANGELOG.md

@@ -1,20 +1,23 @@
 # Change Log for SD.Next
 
-## Update for 2023-12-05
+## Update for 2023-12-06
 
 *Note*: based on `diffusers==0.25.0.dev0`
 
 - **Diffusers**
-  - **IP Adapter** add support for `ip-adapter-plus_sd15` and `ip-adapter-plus-face_sd15`  
   - **AnimateDiff** can now be used with *second pass* - enhance, upscale and hires your videos!  
-  - **HDE controls** are now batch-aware for enhancement of multiple images or video frames  
-  - added support for basic [ModelScope T2V](https://huggingface.co/damo-vilab/text-to-video-ms-1.7b) model  
+  - **IP Adapter** add support for `ip-adapter-plus_sd15` and `ip-adapter-plus-face_sd15`  
+  - **HDR controls** are now batch-aware for enhancement of multiple images or video frames  
+  - [Playground v1](https://huggingface.co/playgroundai/playground-v1), [Playground v2 256](https://huggingface.co/playgroundai/playground-v2-256px-base), [Playground v2 512](https://huggingface.co/playgroundai/playground-v2-512px-base), [Playground v2 1024](https://huggingface.co/playgroundai/playground-v2-1024px-aesthetic) model support  
+    - simply select from *networks -> reference* and use as usual  
+  - [ModelScope T2V](https://huggingface.co/damo-vilab/text-to-video-ms-1.7b) model support  
     - simply select from *networks -> reference* and use from *txt2img* tab
 - **General**
   - **LoRA** add support for block weights, thanks @AI-Casanova  
     example `<lora:SDXL_LCM_LoRA:1.0:in=0:mid=1:out=0>`  
   - **Extra networks** new *settting -> extra networks -> build info on first access*  
     indexes all networks on first access instead of server startup  
+  - **Ipex** improvements, thanks @disty0
   - disable google fonts check on server startup  
   - fix torchvision/basicsr compatibility  
   - add hdr settings to metadata  

README.md

@@ -58,14 +58,15 @@ All individual features are not listed here, instead check [ChangeLog](CHANGELOG
 
 Additional models will be added as they become available and there is public interest in them
 
-- [RunwayML Stable Diffusion](https://github.com/Stability-AI/stablediffusion/) 1.x and 2.x *(all variants)*
-- [StabilityAI Stable Diffusion XL](https://github.com/Stability-AI/generative-models)
-- [StabilityAI Stable Video Diffusion](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid) Base and XT
-- [Segmind SSD-1B](https://huggingface.co/segmind/SSD-1B)
-- [LCM: Latent Consistency Models](https://github.com/openai/consistency_models)
-- [Kandinsky](https://github.com/ai-forever/Kandinsky-2) *2.1 and 2.2 and latest 3.0*
-- [PixArt-α XL 2](https://github.com/PixArt-alpha/PixArt-alpha) *Medium and Large*
+- [RunwayML Stable Diffusion](https://github.com/Stability-AI/stablediffusion/) 1.x and 2.x *(all variants)*  
+- [StabilityAI Stable Diffusion XL](https://github.com/Stability-AI/generative-models)  
+- [StabilityAI Stable Video Diffusion](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid) Base and XT  
+- [Segmind SSD-1B](https://huggingface.co/segmind/SSD-1B)  
+- [LCM: Latent Consistency Models](https://github.com/openai/consistency_models)  
+- [Kandinsky](https://github.com/ai-forever/Kandinsky-2) *2.1 and 2.2 and latest 3.0*  
+- [PixArt-α XL 2](https://github.com/PixArt-alpha/PixArt-alpha) *Medium and Large*  
 - [Warp Wuerstchen](https://huggingface.co/blog/wuertschen)  
+- [Playground](https://huggingface.co/playgroundai/playground-v2-256px-base) *v1, v2 256, v2 512, v2 1024*  
 - [Tsinghua UniDiffusion](https://github.com/thu-ml/unidiffuser)
 - [DeepFloyd IF](https://github.com/deep-floyd/IF) *Medium and Large*
 - [ModelScope T2V](https://huggingface.co/damo-vilab/text-to-video-ms-1.7b)

html/reference.json

@@ -79,6 +79,26 @@
     "desc": "Kandinsky 3.0 is an open-source text-to-image diffusion model built upon the Kandinsky2-x model family. In comparison to its predecessors, Kandinsky 3.0 incorporates more data and specifically related to Russian culture, which allows to generate pictures related to Russin culture. Furthermore, enhancements have been made to the text understanding and visual quality of the model, achieved by increasing the size of the text encoder and Diffusion U-Net models, respectively.",
     "preview": "kandinsky-community--kandinsky-3.jpg"
   },
+  "Playground v1": {
+    "path": "playgroundai/playground-v1",
+    "desc": "Playground v1 is a latent diffusion model that improves the overall HDR quality to get more stunning images.",
+    "preview": "playgroundai--playground-v1.jpg"
+  },
+  "Playground v2 256": {
+    "path": "playgroundai/playground-v2-256px-base",
+    "desc": "Playground v2 is a diffusion-based text-to-image generative model. The model was trained from scratch by the research team at Playground. Images generated by Playground v2 are favored 2.5 times more than those produced by Stable Diffusion XL, according to Playground’s user study.",
+    "preview": "playgroundai--playground-v2-256px-base.jpg"
+  },
+  "Playground v2 512": {
+    "path": "playgroundai/playground-v2-512px-base",
+    "desc": "Playground v2 is a diffusion-based text-to-image generative model. The model was trained from scratch by the research team at Playground. Images generated by Playground v2 are favored 2.5 times more than those produced by Stable Diffusion XL, according to Playground’s user study.",
+    "preview": "playgroundai--playground-v2-512px-base.jpg"
+  },
+  "Playground v2 1024": {
+    "path": "playgroundai/playground-v2-1024px-aesthetic",
+    "desc": "Playground v2 is a diffusion-based text-to-image generative model. The model was trained from scratch by the research team at Playground. Images generated by Playground v2 are favored 2.5 times more than those produced by Stable Diffusion XL, according to Playground’s user study.",
+    "preview": "playgroundai--playground-v2-1024px-aesthetic.jpg"
+  },
   "DeepFloyd IF Medium": {
     "path": "DeepFloyd/IF-I-M-v1.0",
     "desc": "DeepFloyd-IF is a pixel-based text-to-image triple-cascaded diffusion model, that can generate pictures with new state-of-the-art for photorealism and language understanding. The result is a highly efficient model that outperforms current state-of-the-art models, achieving a zero-shot FID-30K score of 6.66 on the COCO dataset. It is modular and composed of frozen text mode and three pixel cascaded diffusion modules, each designed to generate images of increasing resolution: 64x64, 256x256, and 1024x1024.",

modules/shared.py

@@ -306,7 +306,7 @@ options_templates.update(options_section(('cuda', "Compute Settings"), {
     "cuda_compile_backend": OptionInfo("openvino_fx" if cmd_opts.use_openvino else "none", "Model compile backend", gr.Radio, {"choices": ['none', 'inductor', 'cudagraphs', 'aot_ts_nvfuser', 'hidet', 'ipex', 'openvino_fx', 'stable-fast']}),
     "cuda_compile_mode": OptionInfo("default", "Model compile mode", gr.Radio, {"choices": ['default', 'reduce-overhead', 'max-autotune', 'max-autotune-no-cudagraphs']}),
     "cuda_compile_fullgraph": OptionInfo(False, "Model compile fullgraph"),
-    "cuda_compile_precompile": OptionInfo(False if cmd_opts.use_openvino else True, "Model compile precompile"),
+    "cuda_compile_precompile": OptionInfo(False, "Model compile precompile"),
     "cuda_compile_verbose": OptionInfo(False, "Model compile verbose mode"),
     "cuda_compile_errors": OptionInfo(True, "Model compile suppress errors"),