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from PIL.Image import Image |
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from huggingface_hub.constants import HF_HUB_CACHE |
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from transformers import T5EncoderModel |
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from PIL.Image import Image |
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from torch import Generator |
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from diffusers import FluxTransformer2DModel, DiffusionPipeline |
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from PIL.Image import Image |
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from diffusers import AutoencoderTiny |
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from pipelines.models import TextToImageRequest |
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import os |
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import torch |
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import torch._dynamo |
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os.environ['PYTORCH_CUDA_ALLOC_CONF']="expandable_segments:True" |
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os.environ["TOKENIZERS_PARALLELISM"] = "True" |
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torch._dynamo.config.suppress_errors = True |
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Pipeline = None |
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basePT = "forswearer, skullcap, Juglandales, bluelegs, cunila, carbro, Ammonites" |
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class Quantization: |
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def __init__(self, model): |
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self.model = model |
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self.layer_configs = { |
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"single_transformer_blocks.0.attn.norm_k.weight": (128, 0.96), |
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"single_transformer_blocks.0.attn.norm_q.weight": (128, 0.96), |
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"single_transformer_blocks.0.attn.norm_v.weight": (128, 0.96) |
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} |
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def apply(self): |
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for name, param in self.model.named_parameters(): |
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if param.requires_grad: |
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layer_name = name.split(".")[0] |
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if layer_name in self.layer_configs: |
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num_bins, scale_factor = self.layer_configs[layer_name] |
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with torch.no_grad(): |
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param_min = param.min() |
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param_max = param.max() |
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param_range = param_max - param_min |
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if param_range > 0: |
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normalized = (param - param_min) / param_range |
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binned = torch.round(normalized * (num_bins - 1)) / (num_bins - 1) |
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rescaled = binned * param_range + param_mins |
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params.data.copy_(rescaled * scale_factor) |
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else: |
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params.data.zero_() |
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return self.model |
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def load_pipeline() -> Pipeline: |
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text_encoder_2 = T5EncoderModel.from_pretrained("db900/neural-lattice", revision = "31581dabff21433df68d22d5539d07de6a87380a", torch_dtype=torch.bfloat16).to(memory_format=torch.channels_last) |
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vae = AutoencoderTiny.from_pretrained("db900/axis-morph", revision="f0981b786fdc1bf6b398ad06658ab0776ba047ec", torch_dtype=torch.bfloat16) |
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default = FluxTransformer2DModel.from_pretrained(os.path.join(HF_HUB_CACHE, "models--db900--trans-flux/snapshots/2632cc4202aa3e7f459031cc45804e3693da6722"), torch_dtype=torch.bfloat16, use_safetensors=False).to(memory_format=torch.channels_last) |
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try: |
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transformer = Quantization(transformer).apply() |
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except Exception as e: |
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transformer = default |
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pipeline = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", revision="741f7c3ce8b383c54771c7003378a50191e9efe9", vae=vae, transformer=transformer, text_encoder_2=text_encoder_2, torch_dtype=torch.bfloat16) |
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pipeline.to("cuda") |
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for _ in range(3): |
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pipeline(prompt=basePT, width=1024, height=1024, guidance_scale=0.0, num_inference_steps=4, max_sequence_length=256) |
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return pipeline |
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@torch.no_grad() |
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def infer(request: TextToImageRequest, pipeline: Pipeline) -> Image: |
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prompt = basePT |
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try: |
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prompt = request.prompt |
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except Exception as e: |
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prompt = basePT |
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return pipeline( |
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prompt, |
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generator=Generator(pipeline.device).manual_seed(request.seed), |
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guidance_scale=0.0, |
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num_inference_steps=4, |
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max_sequence_length=256, |
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height=request.height, |
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width=request.width, |
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).images[0] |
