Update README.md

README.md

@@ -1,199 +1,135 @@
 ---
-library_name: diffusers
 pipeline_tag: text-to-image
+widget:
+- text: >-
+    black fluffy gorgeous dangerous cat animal creature, large orange eyes, big
+    fluffy ears, piercing gaze, full moon, dark ambiance, best quality,
+    extremely detailed
+- text: >-
+    (impressionistic realism by csybgh), a 50 something male, working in
+    banking, very short dyed dark curly balding hair, Afro-Asiatic ancestry,
+    talks a lot but listens poorly, stuck in the past, wearing a suit, he has a
+    certain charm, bronze skintone, sitting in a bar at night, he is smoking and
+    feeling cool, drunk on plum wine, masterpiece, 8k, hyper detailed, smokey
+    ambiance, perfect hands AND fingers
+- text: >-
+    high quality pixel art, a pixel art silhouette of an anime space-themed girl
+    in a space-punk steampunk style, lying in her bed by the window of a
+    spaceship, smoking, with a rustic feel. The image should embody epic
+    portraiture and double exposure, featuring an isolated landscape visible
+    through the window. The colors should primarily be dynamic and
+    action-packed, with a strong use of negative space. The entire artwork
+    should be in pixel art style, emphasizing the characters shape and set
+    against a white background. Silhouette
+- text: >-
+    The image features an older man, a long white beard and mustache,  He has a
+    stern expression, giving the impression of a wise and experienced
+    individual. The mans beard and mustache are prominent, adding to his
+    distinguished appearance. The close-up shot of the mans face emphasizes his
+    facial features and the intensity of his gaze.
+- text: >-
+    Super Closeup Portrait, action shot, Profoundly dark whitish meadow, glass
+    flowers, Stains, space grunge style, Jeanne d'Arc wearing White Olive green
+    used styled Cotton frock, Wielding thin silver sword, Sci-fi vibe, dirty,
+    noisy, Vintage monk style, very detailed, hd
+- text: >-
+    cinematic film still of Kodak Motion Picture Film: (Sharp Detailed Image) An
+    Oscar winning movie for Best Cinematography a woman in a kimono standing on
+    a subway train in Japan Kodak Motion Picture Film Style, shallow depth of
+    field, vignette, highly detailed, high budget, bokeh, cinemascope, moody,
+    epic, gorgeous, film grain, grainy
+- text: >-
+    in the style of artgerm, comic style,3D model, mythical seascape, negative
+    space, space quixotic dreams, temporal hallucination, psychedelic, mystical,
+    intricate details, very bright neon colors, (vantablack background:1.5),
+    pointillism, pareidolia, melting, symbolism, very high contrast, chiaroscuro
+  parameters:
+    negative_prompt: >-
+      bad quality, bad anatomy, worst quality, low quality, low resolutions,
+      extra fingers, blur, blurry, ugly, wrongs proportions, watermark, image
+      artifacts, lowres, ugly, jpeg artifacts, deformed, noisy image
+- text: >-
+    1980s anime portrait of a character glitching. His face is separated from
+    his body by heavy static. His face is deformed by pain. Dream-like, analog
+    horror, glitch, terrifying
+- text: (("Proteus"):text_logo:1)
+- text: >-
+    dan seagrave, dante, Abandon All Hope, Ye Who Enter Here, hell religious art
+    purgatory zdzislaw Beksinski, abyss inferno, lost, wanderer
+license: gpl-3.0
 ---
+<Gallery />
+## [unofficial fp16 fork] ProteusV0.2
 
-# Model Card for Model ID
+merged with RealCartoonXL to fix issues with inability to understand tags related to anime or cartoon styles at just a weight of 0.5% out of 100% using custom scripts with slerp like methods.
 
-<!-- Provide a quick summary of what the model is/does. -->
+Version 0.2 shows subtle yet significant improvements over Version 0.1. It demonstrates enhanced prompt understanding that surpasses MJ6, while also approaching its stylistic capabilities.
 
+## Proteus
 
+Proteus serves as a sophisticated enhancement over OpenDalleV1.1, leveraging its core functionalities to deliver superior outcomes. Key areas of advancement include heightened responsiveness to prompts and augmented creative capacities. To achieve this, it was fine-tuned using approximately 220,000 GPTV captioned images from copyright-free stock images (with some anime included), which were then normalized. Additionally, DPO (Direct Preference Optimization) was employed through a collection of 10,000 carefully selected high-quality, AI-generated image pairs.
 
-## Model Details
+In pursuit of optimal performance, numerous LORA (Low-Rank Adaptation) models are trained independently before being selectively incorporated into the principal model via dynamic application methods. These techniques involve targeting particular segments within the model while avoiding interference with other areas during the learning phase. Consequently, Proteus exhibits marked improvements in portraying intricate facial characteristics and lifelike skin textures, all while sustaining commendable proficiency across various aesthetic domains, notably surrealism, anime, and cartoon-style visualizations.
 
-### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
+## Settings for ProteusV0.2
 
-This is the model card of a 🧨 diffusers model that has been pushed on the Hub. This model card has been automatically generated.
+Use these settings for the best results with ProteusV0.2:
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+CFG Scale: Use a CFG scale of 8 to 7
 
-### Model Sources [optional]
+Steps: 20 to 60 steps for more detail, 20 steps for faster results.
 
-<!-- Provide the basic links for the model. -->
+Sampler: DPM++ 2M SDE
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+Scheduler: Karras
 
-## Uses
+Resolution: 1280x1280 or 1024x1024
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+please also consider using these keep words to improve your prompts:
+best quality, HD, `~*~aesthetic~*~`. 
 
-### Direct Use
+if you are having trouble coming up with prompts you can use this GPT I put together to help you refine the prompt. https://chat.openai.com/g/g-RziQNoydR-diffusion-master
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+## Use it with 🧨 diffusers
+```python
+import torch
+from diffusers import (
+    StableDiffusionXLPipeline, 
+    KDPM2AncestralDiscreteScheduler,
+    AutoencoderKL
+)
 
-[More Information Needed]
+# Load VAE component
+vae = AutoencoderKL.from_pretrained(
+    "madebyollin/sdxl-vae-fp16-fix", 
+    torch_dtype=torch.float16
+)
 
-### Downstream Use [optional]
+# Configure the pipeline
+pipe = StableDiffusionXLPipeline.from_pretrained(
+    "dataautogpt3/ProteusV0.2", 
+    vae=vae,
+    torch_dtype=torch.float16
+)
+pipe.scheduler = KDPM2AncestralDiscreteScheduler.from_config(pipe.scheduler.config)
+pipe.to('cuda')
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+# Define prompts and generate image
+prompt = "black fluffy gorgeous dangerous cat animal creature, large orange eyes, big fluffy ears, piercing gaze, full moon, dark ambiance, best quality, extremely detailed"
+negative_prompt = "nsfw, bad quality, bad anatomy, worst quality, low quality, low resolutions, extra fingers, blur, blurry, ugly, wrongs proportions, watermark, image artifacts, lowres, ugly, jpeg artifacts, deformed, noisy image"
 
-[More Information Needed]
+image = pipe(
+    prompt, 
+    negative_prompt=negative_prompt, 
+    width=1024,
+    height=1024,
+    guidance_scale=7,
+    num_inference_steps=20
+).images[0]
+```
 
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure 
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
+please support the work I do through donating to me on: 
+https://www.buymeacoffee.com/DataVoid
+or following me on
+https://twitter.com/DataPlusEngine